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Research Report AP-581-18

Connected and Automated Vehicles (CAV) Open Data Recommendations


Prepared by

Andrew Somers

Publisher

Austroads Ltd. Level 9, 287 Elizabeth Street Sydney NSW 2000 Australia Phone: +61 2 8265 3300 [email protected] www.austroads.com.au

Project Manager

Chris Jones

Abstract

This report sets out the strategic context for the supply of road operator data for use by Connected and Automated Vehicles (CAVs) in Australia and includes recommended next steps.

CAVs are anticipated to rely heavily on data from their sensors for vehicle control actions such as acceleration and braking. External data complements this by assisting dispatch, route and path planning as well as providing a horizon of expected conditions. Road operator data that may be of interest to CAVs includes data from traffic signals and Managed Motorways systems, data on roadworks, incidents and special events and data on traffic restrictions such as speed limits.

The report includes as appendices the results of the contributing development activities: background research, engagement with road operators and industry as well as summaries of relevant Open Data policies.

About Austroads

Austroads is the peak organisation of Australasian road transport and traffic agencies.

Austroads’ purpose is to support our member organisations to deliver an improved Australasian road transport network. To succeed in this task, we undertake leading-edge road and transport research which underpins our input to policy development and published guidance on the design, construction and management of the road network and its associated infrastructure.

Austroads provides a collective approach that delivers value for money, encourages shared knowledge and drives consistency for road users.

Austroads is governed by a Board consisting of senior executive representatives from each of its eleven member organisations: • Roads and Maritime Services New South Wales • Roads Corporation Victoria • Queensland Department of Transport and Main Roads • Main Roads Western Australia • Department of Planning, Transport and Infrastructure

South Australia • Department of State Growth Tasmania • Department of Infrastructure, Planning and Logistics

Northern Territory • Transport Canberra and City Services Directorate,

Australian Capital Territory • Australian Government Department of Infrastructure,

Regional Development and Cities • Australian Local Government Association • New Zealand Transport Agency.

Keywords

Connected and Automated Vehicles (CAVs), Open Data

ISBN 978-1-925671-73-5

Austroads Publication No. AP-R581-18

Publication date August 2018

Pages 67

© Austroads 2018

This work is copyright. Apart from any use as permitted under the Copyright Act 1968, no part may be reproduced by any process without the prior written permission of Austroads.

Acknowledgements

This project included interviews with a range of industry and road operator stakeholders within the CAV data ecosystem. The input from each of these stakeholders is appreciated and has played an important role in shaping this report. The contribution of Stuart Ballingall (Transport for Victoria) and Richard Zhou (VicRoads) to shaping the project is also appreciated.

This report has been prepared for Austroads as part of its work to promote improved Australian and New Zealand transport outcomes by providing expert technical input on road and road transport issues. As this report responds to an Australian Government policy framework, it does not include coverage of New Zealand and New Zealand stakeholders were not consulted in the preparation of the report.

Individual road agencies will determine their response to this report following consideration of their legislative or administrative arrangements, available funding, as well as local circumstances and priorities.

Austroads believes this publication to be correct at the time of printing and does not accept responsibility for any consequences arising from the use of information herein. Readers should rely on their own skill and judgement to apply information to particular issues.


Austroads 2018 | page i

Summary

The 2016 Transport and Infrastructure Council National Land Transport Technology Policy Framework (TIC, 2016) included as action 8:

Improve the availability of open data in the transport sector

Governments can assist industry, researchers and the public to develop innovative solutions to transport problems by providing open access to transport data. Australian governments are committed to an open-by-default approach to transport data and through this action will improve the availability of open access transport data.

This CAV Open Data Recommendations Report contributes to Action 8 of the policy framework. The action applies to open data across the transport sector generally, of which Connected and Automated Vehicles (CAVs) are only one component. In this report CAV Open Data refers to data made available by road operators using Open Data methods for potential use by CAVs.

There are a broad range of possible CAV use-cases for road authority data some of which are apparent now. Others may not have been envisioned yet and are therefore difficult to predict. The three use-cases most commonly considered are along of the lines of:

• Use in driver assistance systems (ADAS) which provide drivers with assistive function to enhance safety or mobility;

• Information which can extend the sensor horizon of an automated driving system (ADS) beyond the range of its on-board sensors or where a line of sight is difficult to achieve and act as a back-up information source in the interpretation of data from sensors;

• Information on the environment and conditions that an ADS can expect to encounter, including traffic restrictions, traffic signal current phase and changes to available traffic lanes.

As this report responds to an Australian Government policy framework, it does not include coverage of New Zealand and New Zealand stakeholders were not consulted in the preparation of the report.

Method

To arrive at the findings and recommendations for this project, the project team undertook:

1. Background research through a review of available literature.

2. Interviews with stakeholders across the CAV data ecosystem including public and private road operators and well as others involved in the CAV data chain including data aggregators, map makers and automotive manufacturers. Each stakeholder was supplied with a summary of the background research and a list of indicative questions to assist preparations.

3. A full day stakeholder workshop with Austroads member road operators.

The results from each of these activities are included as appendices to the report.

Findings

Road operators interviewed during this project were in strong agreement that the objective of CAV Open Data was subsidiary to the objectives for CAV deployment, with two rationales for road operator investment in this area:


Austroads 2018 | page ii

• Address areas where unavailability of data may be a barrier to CAV deployment; and

• Strengthens realisation of safety, efficiency, environmental and/or outcomes for the CAV deployment that does occur.

Data used by CAVs can come directly from the CAV’s on-board sensors, it can come from “maps” stored on the CAVs and it can come from live updates through the cloud and directly from other vehicles and infrastructure.

In addition to road operators supplying data for use by CAVs, road operators will be interested in using data sourced from CAVs. This process was largely outside the scope of this report, except insofar as it relates to the concept of feedback loops or self-healing maps.

From the stakeholders interviewed, and literature analysed, it appears that CAV prioritises data that it directly senses, through the on-board sensors such as radar, camera, LIDAR, particularly for any decisions a vehicle must make, including control of the vehicle. Externally sourced data takes priority beyond the range of the on-board sensors. Where both on-board sensor and externally sourced data is available, the externally sourced data is used to support or complement the sensor data by providing an expectation such as an electronic horizon.

In both the background research and stakeholder interviews, there was general agreement that the data ecosystem for CAVs was likely to include map providers in an intermediate role between road operators and vehicle manufacturers. In understanding the role of “map maker” (or data aggregator) in Figure 3.1, it is important to remember that road operators are only one source of data used by in creating the map. There are also a range of other map data suppliers from both the private and public sectors.

A full list of findings is included in the findings section of the report. Key findings include that:

• For the foreseeable future, for any data where the driving actions of a CAV operating in automated mode have an immediate safety impact (including speed limits, traffic signals, closed lanes), the CAV will need to accurately respond to the same stimuli as human drivers, pedestrians and cyclists. Even if road signs, lines and traffic signals are complemented by methods to assist interpretation by CAVs (including C-ITS), the signs, lines and signals would remain the authoritative regulatory devices. Road operator provided data should not and will not be treated as “true” where it conflicts with sensed data, except in limited cases where it is already so such as for restricted vehicle access permits.

• Road operator data types that were identified to be a high priority for CAVs include:

– Live feeds from traffic management systems for variable speed limits and lane closures

– Live feeds of traffic signal phase and timing data (such as SPaT messaging)

– Available data for emergency road closures (fire, flood, etc)

– Available data for temporary conditions associated with works, events and incidents

– Advance notification of new and changed roads (that may not have been mapped)

– Coordinate with actions already occurring on speed limit data and extend to cover other traffic restrictions (vehicle size and mass)

• Improved data availability and quality may be needed to achieve the desired benefit realisation from CAV deployment. The use of Open Data methods is appropriate for government road operator supply of data for CAVs but further consideration around data for private roads may be needed. Road operators may wish to consider the potential extent of additional costs to meet the CAV data needs and the business model and funding opportunities to offset such costs.

• Australian road operators are taking a clear position that they will not accept liability for any supplied data and this is not disputed by the consumers of the data. It is less clear what road operators may need to do to discharge any remaining duty of care they have in providing data.


Austroads 2018 | page iii

• A general model of the data ecosystem has emerged, including a role for map providers, but there remains significant uncertainty at the detailed level. There is broad support for a national body to act as an advocate for good practices in data for CAVs; support for a national data aggregator is notably weaker.

Recommendations

The recommendations from this project seek to map out a strategic approach to the next steps that Austroads could take in this field. The recommended approach includes a phase of active learning to assist the development of the positive guidance sought by government and industry. While some aspects of the future data ecosystem have emerged, much remains unclear and developments are occurring at fast pace. The value of the future guidance will be strengthened by using this active learning as a key input. Beyond this, the focus shifts to supporting change.

Figure 0.1: Graphical view of recommendations

Further detail on the recommendations can be found in the Recommendations section of the report.

Assist preparations for change

Monitor progress on

key data sets

Develop guidance and

roadmap

Strategic review 2017/18 This CAV Open Data Recommendations report

2018/19

2019/20

Active learning +

Develop guidance

Active participation in standards development

Leverage existing

initiatives

2020/21 Support change Release roadmap + best practice guidance + facilitate and support implementation of best practice approaches


Austroads 2018 | page i

Contents

Summary ......................................................................................................................................................... i

1. Road Operator Data and CAVs – An Introduction .............................................................................. 1 1.1.1 BITRE dissemination plan ................................................................................................... 1 1.1.2 How this project relates to other initiatives .......................................................................... 2

1.2.1 Traffic light information/assist .............................................................................................. 3 1.2.2 Speed assistance systems .................................................................................................. 3 1.2.3 GM Supercruise .................................................................................................................. 4 1.2.4 Volvo Drive-Me concept ...................................................................................................... 4

2. Digital Infrastructure – A Core Responsibility? ................................................................................ 11

3. Service Models and Open Data .......................................................................................................... 13

4. Do Current Business Processes Meet CAV Data Needs? ............................................................... 16

5. A Virtuous Circle – Using Data Generated by CAVs ........................................................................ 18

6. Planning the Way Forward .................................................................................................................. 20

7. Summarised Findings and Recommendations ................................................................................ 22

7.2.1 Leverage existing initiatives by assisting the capture and sharing of lessons learned ..... 24 7.2.2 Develop a roadmap for road operator data for CAVs, with accompanying best practice

guidance ............................................................................................................................ 25 7.2.3 Assist preparations for change .......................................................................................... 25 7.2.4 Active participation in international standards development ............................................. 26 7.2.5 Monitor progress on key data sets .................................................................................... 26 7.2.6 Support change ................................................................................................................. 26


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References ................................................................................................................................................... 27 Appendix A Background Research Working Paper .......................................................................... 28 Appendix B State Open Data Policies and Portals ........................................................................... 56 Appendix C Summarised Interview Findings .................................................................................... 58 Appendix D Summarised Notes of Stakeholder Workshop ............................................................. 64

Tables

Table 1.1: Driving tasks and primary data source ........................................................................................ 7 Table 1.2: Standard methods for priority data types .................................................................................. 10 Table 4.1: Status of different types of road operator data that may be relevant to CAVs ........................ 17 Table 7.1: Status of different types of road operator data that may be relevant to CAVs ......................... 25 Table A 1: Guidance for road operators on digital infrastructure .............................................................. 36

Figures

Figure 0.1: Graphical view of recommendations .......................................................................................... iii Figure 1.1: Audi traffic light information display ............................................................................................ 3 Figure 1.2: Map of Supercruise roads .......................................................................................................... 4 Figure 1.3: Data flow from Swedish road authority Trafikverket to cars in the Volvo Drive-Me trial ............ 5 Figure 1.4 The Local Dynamic Map representation illustrates the data a CAV may need .......................... 6 Figure 1.5: Examples of externally sourced data for a CAV ......................................................................... 6 Figure 3.1: The TN-ITS data chain .............................................................................................................. 13 Figure 7.1: Graphical view of recommendations ........................................................................................ 24


Austroads 2018 | page 1

1. Road Operator Data and CAVs – An Introduction

In 2016, the Transport and Infrastructure Council released a National Land Transport Technology Policy Framework with the objective of fostering an integrated policy approach by governments to the development and adoption of emerging transport technologies (TIC, 2016). This CAV Open Data Recommendations Report contributes to Action 8 of the policy framework. The action applies to open data across the transport sector generally, of which Connected and Automated Vehicles (CAVs) are only one component.

Action 8 reads in full as:


Governments can assist industry, researchers and the public to develop innovative solutions to transport problems by providing open access to transport data. Australian governments are committed to an open-by-default approach to transport data and through this action will improve the availability of open access transport data. In particular, existing jurisdictional data sets will be consolidated into national level information in a shared format, and made available through a common portal. New datasets will also be created, including improved information on speed zones across Australia and a national map of low-gear warning zones (which could be used to provide safety warnings to drivers).

In this report CAV Open Data refers to data made available by road operators using Open Data methods for potential use by CAVs.

This report provides recommendations for next steps in CAV Open Data. In doing this it builds upon the project work that was undertaken through three main activities, presented here in the general order they were undertaken:

• Background Research, a summary is included in Appendix A along with a link to the full report;

• 14 stakeholder interviews with government and industry stakeholders (summarised in Appendix C); and

• A stakeholder workshop reviewing some preliminary findings (summary notes are included as Appendix D).

This recommendations report uses a key issues and recommendations style approach to provide a concise manner in which to present the recommendations. Much of the supporting information for these recommendations can be found in the three appendices listed above.

As this report responds to an Australian Government policy framework, it does not include coverage of New Zealand and New Zealand stakeholders were not consulted in the preparation of the report.

BITRE dissemination plan

On 15 September 2017, the Commonwealth Minister for Urban Infrastructure, the Hon Paul Fletcher MP, released BITRE’s Draft Data Collection and Dissemination Plan (BITRE, 2017).

A key objective of the Data Collection and Dissemination Plan, as set out in the terms of reference, is to “provide improved and more timely information for infrastructure investment decisions and monitoring of the performance of Australia's infrastructure networks” (DIRD, 2017).



It was therefore not a starting point for the Data Collection and Dissemination Plan to focus on Connected and Automated Vehicles as a consumer of road operator data. Nevertheless, the relevance of ongoing advances in transport technology to improving the safety, efficiency, sustainability and accessibility of Australia’s transport systems means that enduring question 6.2 was identified to be “Open data to support the implementation of Connected and Automated Vehicles (CAVs): Identify gaps between what road operator data is provided to users (e.g. TomTom) and what is likely to be required in future for CAV operations.” Examples of data required were identified from Austroads (2017) to include speed zone changes, road closures and road works information. Issues identified by BITRE (2017) as being of relevance include road data management, standardisation/harmonisation and support for proprietary models, and road authority regulatory framework in a digital environment.

It is intended that the CAV Open Data Recommendations Report contributes to the finalisation of BITRE’s Data Collection and Dissemination Plan.

How this project relates to other initiatives

The exchange of data between road operators and CAVs can take many forms and directions. This project to develop a CAV Open Data Recommendations Report is therefore only part of a complete picture. Other relevant initiatives include:

• NTC regulatory access to C-ITS and Automated Vehicle data

– This project will identify and develop options to manage government access to AV data and the focus is therefore on data generated by the AV. The consideration of options will seek to balance road safety and network efficiency outcomes and efficient enforcement of traffic laws with sufficient privacy protections. This project is currently in planning and is due to be undertaken during 2018.

• NTC Safety Assurance System for Automated Vehicles

– This project considered four regulatory options for the safety assurance of AV functions, including self-certification approaches and pre-market approvals. This project is currently going through a regulatory impact assessment. The approach taken to safety assurance of AV functions provides important context for any data consumed by AVs will be used as part of achieving the safe operation of this AV.

• State initiatives to supply data that may be beneficial for by CAVs

– Examples include Queensland’s Connected and Automated Vehicle Initiative (CAVI), Victoria’s road data exchange platform and NSW’s CITI and Truck Signal Priority projects.

Australia will benefit from a generally consistent and joined-up approach to CAVs. This recommendations report has therefore sought to account for the developments in these other projects as part of developing appropriate recommendations.

1.1 Potential use cases for road operator data in CAVs

There are feasibly a broad range of CAV use-cases for road authority data some of which are apparent now. Others may not have been envisioned yet and are therefore difficult to predict. The three use-cases most commonly considered are along of the lines of:

• Use in driver assistance systems (ADAS) which provide drivers with assistive function to enhance safety or mobility;

• Information which can extend the sensor horizon of an automated driving system (ADS) beyond the range of its on-board sensors or where a line of sight is difficult to achieve and act as a back-up information source in the interpretation of data from sensors;

• Information on the environment and conditions that an ADS can expect to encounter, including traffic restrictions, traffic signal current phase and changes to available traffic lanes.

The following examples do not form an exhaustive list of use-cases for road authority data, but highlight some near-term examples of these use-cases.



1.1.1 Traffic light information/assist

Figure 1.1: Audi traffic light information display

Source: www.motorauthority.com

The Audi implementation of traffic light information, enables the car to communicate with the infrastructure in around 10 cities across the United States (Audi, 2016). The car receives real-time signal information from the advanced traffic management system that monitors traffic light signal timing. The link between vehicle and central traffic control systems is routed via the on-board LTE data connection and a third party data aggregator. The service can also suggest whether drivers will make it through an intersection within the upcoming phases, and the posted speed limit. As the owners of the traffic signal infrastructure and systems, there is a clear link between road authority data and CAVs. A similar service has been demonstrated in Australia by Intelematics in partnership with government.

1.1.2 Speed assistance systems

From 2018 onwards, ANCAP’s scoring protocol provides points for systems which provide speed limit information to drivers, and warnings or assistance in reducing the vehicle to a posted speed limit (ANCAP, 2018). As a result, many new vehicles are being equipped with speed assistance systems which use a combination of a spatial database of speed limits (potentially including road authority sourced data), and in some cases traffic sign recognition.

Road authority changes to speed signs could be an integral part of ensuring that users of CAVs experience information in the vehicle is consistent with the sign displayed at the roadside.



1.1.3 GM Supercruise

Figure 1.2: Map of Supercruise roads

Source: www.cadillac.com

The Cadillac CT6 (not available in Australia) is equipped with a ‘hands-off wheel’ (SAE level 2) driver assistance system. Built into the system is a spatial database of roads where the feature may be activated or may not be available. As part of the service, GM uses its ‘On-Star’ LTE connection to provide advance notice to the vehicle of potential changes to road geometry so that Supercruise can be disabled. The data to do this is compiled through a 3rd party service which monitors road authority announcements of road works events or lane closures (Davies, 2018).

1.1.4 Volvo Drive-Me concept

As part of the forthcoming Volvo Drive-Me concept in Gothenburg, Sweden, a range of data sources about the roadway is being collected. These inform vehicles within the trial of forthcoming roadway conditions which may mean that it is not optimal to operate the vehicle in an automated mode. Data for this service is aggregated from a range of sources through APIs, one of which includes motorway data from the Swedish Traffic Control Authority – Trafikverket. This data is read from the Swedish Road Authority - Trafikverket’s - open dynamic API using the DATEX II standard (Drive Sweden, 2017).



Figure 1.3: Data flow from Swedish road authority Trafikverket to cars in the Volvo Drive-Me trial

Source: www.drivesweden.net

1.2 What motivates road operators’ activities in this area?

Road operators interviewed during this project were in strong agreement that the objective of CAV Open Data was subsidiary to the objectives for CAV deployment:

1. Transport policy and strategy is seeking safety, efficiency, environmental, and mobility outcomes;

2. CAV deployment may assist achieving these objectives; and

3. data from road operators may assist CAV deployment.

There was also a desire to encourage innovation that was aligned with road operators’ objectives and a view from some that this was a part of smart cities agendas.

This motivation for road operators’ activities provides two rationales for road operator investment in this area:

• Address areas where unavailability of data may be a barrier to CAV deployment; and

• Strengthens realisation of safety, efficiency, environmental and/or outcomes for the CAV deployment that does occur.

In addition to these reasons for road operators to choose to invest, some private sector stakeholders saw an additional reason to be simply that their view was that digital infrastructure is a core responsibility for road operators (see also Section 2). It is useful to note that even where stakeholders believed that road operators should charge some fee for the provision on data, this was viewed only as a cost recovery exercise, not the monetisation of data held by road operators.

1.3 What use do CAVs make of road operator data?

To understand the world around them and to plan and action their journey through it, CAVs need detailed data models with many elements. The Local Dynamic Map concept in Figure 1.4 below provides a representation of a CAV data model.



Figure 1.4 The Local Dynamic Map representation illustrates the data a CAV may need

Source: Shimada et al, 2015

The data in this model can come directly from the CAV’s on-board sensors, it can come from “maps” stored on the CAVs and it can come from live updates through the cloud and directly from other vehicles and infrastructure. Figure 1.5 below sets out a variety of data sources other than on-board sensors that the CAV may use.

Figure 1.5: Examples of externally sourced data for a CAV

Source: provided by S. Ballingall (Austroads)



But how does a CAV reconcile these different data sources and what does it choose to action for any particular driving task? To understand this and its implications, it is meaningful to explore both those driving tasks and how a CAV determines “truth” or at least chooses what to action.

1.4 With multiple data sources, which data do CAVs act upon?

The table below (Table 1.1) sets out a range of driving tasks along with whether a CAV is relying primarily on on-board sensor data or externally sourced data for that driving task. This table reflects the project team’s understanding of the current approaches used for CAVs and these may change in future. Externally sourced data can include data from on-board sensors in other vehicles.

Table 1.1: Driving tasks and primary data source

Human driver AV operation What the task is Primary data source

Dispatch For shared vehicles (including taxis and robotaxis), assigning vehicles to person and freight movements

Navigate route Route planning Trip planning and route following – which roads to travel on, where to pick-up/set-down passengers or freight

Guidance (plan path) Path planning

Where to be on the road, including which lane and planning a safe and efficient path ahead in response to traffic conditions

Control vehicle along planned path

Speed (acceleration, braking) and steering within selected lane and around obstacles, change lanes

Generally speaking, the CAV prioritises data that it directly senses, through the on-board sensors such as radar, camera, LIDAR, etc. Externally sourced data takes priority beyond the range of the on-board sensors. Where both on-board sensor and externally sourced data is available, the externally sourced data is used to support or complement the sensor data by providing an expectation such as an electronic horizon.

This priority placed on data from on-board sensors provides some level of alignment between how humans and computers (CAVs) undertake driving tasks, which assists during any transition phase where there are both human road users and CAVs.

While there are humans making decisions in the road environment as drivers, pedestrians and cyclists, it was accepted by all stakeholders that the authorative source of data would remain geared to those humans. That means the speed limit sign remains the regulatory instrument, not a speed limit database; the existence of road works on the road likewise takes precedence over a database of road works. The prioritisation of data from on-board sensors for CAVs sits well with this.

This then comes to the question of truth – what element of directly sensed or externally sourced data should be held to be true? In considering the context, the word “truth” can perhaps be misleading. What matters more is “actionable” – what data the CAV chooses to act upon. This may be from one data source determined to be most trusted (most likely the vehicle’s on-board sensors) or it may be a conservative approach determined from the mix of data sources.

Externally sourced data

On-board sensor data



In discussions with stakeholders, in cases of conflict government stakeholders generally preferred the CAV to take a conservative action, such as actioning a road operator supplied 40km/h school speed limit over a 70km/h limit detected by a camera. In discussions with industry stakeholders, it was identified that some vehicles may prioritise the data from on-board sensors even in such cases. This divergence of views raises some interesting questions that may be of relevance to the safety assurance process for future Automated Driving Systems (ADS).

Finding 1: For the foreseeable future, for any data where the driving actions of a CAV operating in automated mode have an immediate safety impact (including speed limits, traffic signals, closed lanes), the CAV will need to accurately respond to the same stimuli as human drivers, pedestrians and cyclists

Finding 2: Even if road signs, lines and traffic signals are complemented by methods to assist interpretation by CAVs (including C-ITS), the signs, lines and signals would remain the authoritative regulatory devices

Finding 3: The approach of CAVs prioritising data from on-board sensors (where available) and using other inputs to complement and support interpretation is well aligned with this

Finding 4: Road operators should not assume that data they provide will be prioritised for action by a CAV over conflicting data from on-board sensors. There are limited exceptions to this, such as restricted vehicle access permits.

Beyond the range of a vehicle’s on-board sensors, data accuracy still matters, but often more for efficiency than direct operational safety impacts. Road operators are not the only source of data useful for tasks such as dispatch and route planning, and the expectation is that CAVs would use appropriate commercially available data for this.

Even the best available data is still likely to be imperfect. If a CAV chooses a route based on map data but then faces an unexpected turn restriction, provided the CAV correctly interprets the turn ban sign then the result is a less efficient route. In some cases, situations such as this create challenges for CAVs with highly restricted Operational Design Domains (ODDs). Such errors and/or temporary conditions may require them to call upon human intervention to continue their journey.

1.5 Describing quality and confidence as an alternative to guaranteeing accuracy

Although there is little appetite and little demand for road operators to provide absolute guaranteed “truth” in data they provide, this should not be read as weak interest in data quality. Instead what the users of data were most interested in was that data be supplied with indicators of confidence and quality of the data (accuracy, age of data, etc). These quality indicators would be relevant both for overall data sources / streams as well as for individual values.

In addition to indicators of data quality, users of data may have value in indicators of data integrity being included. These indicators of integrity may provide greater confidence that the data is authentic and has not been maliciously modified or corrupted.

Finding 5: Users of data supplied by road operators expressed strong interest in indicators of data quality and integrity being included within the supplied data to assist their decisions on use of the data. Indicators of data quality may include accuracy, age of data / recency, original source, etc.



1.6 What road operator data is most useful?

Many different types of data were discussed in the course of this project, from variable speed limits through to parking restrictions and electric vehicle charge stations. Although each of these data types have some value, some clear priorities emerged through the discussions with data users and the road operators who could supply data. Data was generally viewed as being of higher priority when it addressed aspects that CAVs would encounter in early deployment, assisted with situations that CAVs may find difficult to interpret and was likely to be available with reasonable data accuracy and timeliness (latency between changes in real world condition and changes to data). Data from road operators was viewed as less important in cases where the commercial data providers already had good quality data from other methods.

Finding 6: Road operator data types that were identified to be a high priority for CAVs include:

a. Real-time feeds from traffic management systems for variable speed limits and lane closures

b. Real-time feeds of traffic signal phase and timing data (such as SPaT messaging)

c. Available data for emergency road closures (fire, flood, etc)

d. Available data for temporary conditions associated with works, events and incidents

e. Advance notification of newly built and altered roads (major works) as mapping vehicles will not have driven these

f. Coordinate with actions already occurring on speed limit data and extend to cover other traffic restrictions (vehicle size and mass)

Finding 7: In addition to the high priority data types identified in Finding 6, most other data types that are or could be held by a road operator were of at least some interest to at least some industry participants

For data type (d) on works, events and incidents, there was discussion around three different levels of detail:

• An alert of possible works, event or incident at an approximate location (such as works approved but may or may not be active);

• An alert that a works, event or incident is definitely active now; and

• Changed road layout and speed information for a worksite, event or incident.

The most detailed form of this data is the most useful for CAVs. While it was recognised that road operators often do not have this data today (except for Managed Motorways), that this was an area of interest. Achieving better data in this area is closely linked to changed business processes, as discussed in Section 4.

An additional area of interest from road operators was data in rural areas where low traffic densities may make viability of normal commercial data methods difficult without some form of support. Although this was discussed as a topic of interest, there were no clear or specific requests for this type of data from users.

1.7 What formats should road operators use to provide data?

In establishing this project and in undertaking the background research, the identification of standard formats and methods for road operator supply of data was an area of focus. Section A6 of the background research report (Appendix A) covers this topic in some length. This focus was carried through into the interviews, but it became clear that only in one area are Australian stakeholders expressing a clear preferred approach. For real-time feeds of traffic signal data, the approach of SPAT, MAP, SRM, SSM message types as set out in the C-ITS standards is the clear preference. For all other data types, the only consistent view was that data should be well-structured and well-described and preferably available through an API type of method.



Table 1.2: Standard methods for priority data types

Priority data type Is there anything emerging internationally as the standard approach for this?

Australian stakeholder support?

a. Real-time feeds from traffic management systems for variable speed limits and lane closures

DATEX II, TPEG, IVI, DENM Not yet or partial

b. Real-time feeds of traffic signal phase and timing data (such as SPaT messaging)

SPAT, MAP, SRM, SSM message types as set out in the C-ITS standards

Yes





e. Advance notification of newly built and altered roads (major works)

TN-ITS, ROSATTE (although these are more focussed on attributes than on the underlying road), IVI, DENM

Not yet or partial


Refer to Intelligent Speed Assist initiatives (international formats from DATEX II, TN-ITS, ROSATTE may also be applicable), IVI

Not yet or partial

For the priority types of data identified in Finding 6, any clear emergent approaches have been identified in Table 1.2 above. For all but one data type, Australian stakeholder support is shown as “not yet”. This is as Australian stakeholders have not yet determined their preferred approach at this level of detail. Even in cases where some data is already being supplied, governments generally did not indicate any necessity that they stick with their current method.

Finding 8: Users of the data are seeking well-structured and well-described data and expressed a general preference for access through APIs rather than periodic updates. Standards for formats were seen as useful, but secondary to this. Although there are some clear standards emerging, the Australian market had generally not yet locked in to these.

Finding 9: Australian participation in the standardisation discussion (particularly in Europe) will assist in planning any adoption of these emerging standards in Australia.



2. Digital Infrastructure – A Core Responsibility?

Digital infrastructure is a term that has different meanings to different people and in different contexts. For this project, we considered digital infrastructure to be the complement to the traditional physical infrastructure of a road operator. This therefore includes data, databases and digital models as well as ICT and data communications infrastructure.

In stakeholder discussions a question arose as to the extent to which digital infrastructure should be considered a core function of road operators as a necessary adjunct to their physical infrastructure functions. Amongst private-sector technology stakeholders there was a general view that not only was this the case, but that road operators should make decisions that would protect the sovereignty and control of their digital infrastructure supply chains. In workshop discussions with road operator stakeholders there was a variety of views on this topic and it can be viewed as an area relevant to strategic direction considerations of road operators but with some uncertainty remaining.

Consideration of whether current business processes meet the CAV data need (Section 4) makes it clear that gaps exist between what road operators currently do and what road operators would need to do in order to maximise the value of the data they provide for use by CAVs. Some of the changes required may involve significant costs, whereas others may not. In some geographic areas and / or for some data types, the data needs of CAVs may be well serviced by the commercial sector. In areas with lower traffic levels, such as rural and remote Australia, there may be gaps in commercial provision of data for CAVs.

The likelihood of cost pressures is however clear enough to warrant consideration of the business models and/or sources of funds that may cover the costs in meeting the CAV data need. Any such business models need to reflect also the requirements of Government Open Data policies to provide data (see further discussion in Section 3.1).

Finding 10: Road operators may wish to consider the potential extent of additional costs to meet the CAV data needs and the business model and funding opportunities to offset such costs.

2.1 Liability and duty of care

The question of liability is a recurring one in this space. Fortunately, there is a rare consistency in the views of road operators locally and internationally that they wish to have no liability for data they provide. The Open Data process and the Creative Commons Attribution licence both accommodate and reflect this preference. It is too early for the effectiveness of these licenses in avoiding application of liability to road operators to have been tested, nevertheless the intent is clear.

Consumers of CAV data such as map data providers and automotive OEMs did not appear to challenge the road operator position of accepting no liability for provided data. Although they did not seek to make road operators liable for any errors in provided data, these consumers of data had a keen interest in road operators both improving the quality of the provided data and for the provided data to include fields that indicated the quality of the provided data.

In discussions with stakeholders about liability, some questions also arose around what, if any, duty of care road operators may have to provide accurate data on their infrastructure and operations. It was noted that there are some differences between states in the legal environment associated with such questions and no clear resolution was reached.



The current arrangements for the safe operation of roads rely upon road signs and lines as the regulatory devices to be adhered to by both human and robot drivers (ADS), as identified in Section 1.4. Any changes that would allow ADS to rely on electronic data as the regulatory device may significantly change the requirements upon road operators providing that data. In the interim before that occurs, it is possible there is some duty of care arising from CAVs use of road operator data, however any advice in that area this would need to be the subject of a more focussed specialised examination of that topic.

Finding 11: Road operators are taking a clear position that they will not accept liability for any supplied data and this is not disputed by the consumers of the data. It is less clear what road operators may need to do to discharge any remaining duty of care they have in providing data.

In addition to traditional dimensions in which liability is relevant to road operator activities, a question arises to which parties in the service chain are responsible (and liable) for managing cyber-security risks. All aspects of the anticipated approach (such as Open Data licencing and the NTC’s safety assurance approach) point to the end-user of the data having the responsibility to be satisfied with the integrity of the data they use, for the purposes they use it. The ultimate end-user in this data ecosystem is the Automated Driving System Entity (ADSE). Map providers and road operators may also choose to take actions that assist the ADSE as this may lower barriers to realising benefits from the use of that data.



3. Service Models and Open Data

In both the background research and stakeholder interviews, there was general agreement that the data ecosystem for CAVs was likely to include map providers in an intermediate role between road operators and auto OEMs or ADSEs. This reflects the model adopted in the European TN-ITS approach, as illustrated in Figure 3.1 below.

Figure 3.1: The TN-ITS data chain

Source: Pandazis, 2017

In understanding the role of “map maker” (or data aggregator) in the figure above, it is important to remember that road operators are only one source of data used by in creating the map. There are also a range of other map data suppliers from both the private and public sectors. The figure above does not explicitly show a feedback loop for data sourced from vehicles to update the map, however the value of such feedback is well accepted, including to assist “self-healing” maps.

While this model of road operator – map provider (or data aggregator) – vehicle (OEM/ADSE) appears to be well accepted, there remains significant uncertainty at the more detailed levels. Most recently, this uncertainty can be seen in the responses of industry and government stakeholders to the US Federal Highway Administration’s call for comments on Automated Driving Systems. This remaining uncertainty informs what recommendations are reasonable and practical from this project.

Finding 12: A general model of the data ecosystem has emerged, including a role for map providers, but there remains significant uncertainty at the detailed level.



3.1 Is Open Data a good approach for road operators providing data for CAVs?

The Commonwealth and all Australian states have policy positions requiring government entities to make their data available as Open Data. The requirement for Open Data is the supply of the data that governments possess. It does not require the collection of additional data, improvements in quality and timeliness of that data nor the provision of any guarantees in the supply of data.

These exclusions (or at least, not mandatory inclusions) led to questions amongst some stakeholders as to whether some sort of commercial data agreement would be better suited to the needs of CAVs. Some government stakeholders are strongly interested in exploring commercial data agreements, others had milder interest. Although there was some appetite from private sector interviewees to pay for improved data, this was only if that was the best commercial option for them when considering also alternative methods to secure data. The general preference of these potential customers of a commercial service was to instead receive reasonable Open Data and then to make any improvements themselves.

Similarly, although there is a general expectation that map providers will be bound by quality-based service level agreements (SLAs) to their OEM customers, there was no equivalent expectation that government data they used as an input would necessarily be similarly bound. The map provider would therefore be acting as a value-adding intermediary in this respect as well as through other means.

One alternative to the arrangements above would be a “Freemium” model, where a road operator made the Open Data generally available and then agreed with specific data consumers a paid-for premium service tailored to their needs. In that case, some SLA requirements may apply to the road operator.

Discussions of liability and duty of care are also relevant to considering the appropriateness of Open Data and these can be found in Section 2.1.

Overall, this led to the most common view amongst both government and industry stakeholders being that the Open Data approach was appropriate to use. Realising the desired benefits from CAV deployment may require better data (better and better described quality) and changed business processes to support this quality (see Section 4) but the mechanism appears appropriate in addition to being required by policy.

In addition to the points discussed above, two other themes of importance emerged related to the use of Open Data:

• Whether the promise of open data is actually being met, with the available data sets being made available through Open Data; and

• That we cannot assume that private road operators are bound by Open Data policies made by government.

In terms of how users of data wanted to access Open Data, a clear preference was evident. The recency of the data matters and this contributed to a preference for access to APIs (Application Programming Interfaces) where users can access and query for live data rather than for periodic dumps of data (including of periodic dumps of updates).

Finding 13: The use of Open Data methods is appropriate for government road operator supply of data for CAVs but further consideration around data for private roads may be needed.

Finding 14: There is a strong preference for access to APIs rather than periodic provision of data files.

Finding 15: Improved data availability and quality may be needed to achieve the desired benefit realisation from CAV deployment.



It is noted the Open Data approach is about releasing data which is available and as such covers only that data which is available, not that which may be most valuable (but which is not available). Further discussion on what data is most valuable can be found in Section 2.5 and the associated findings.

3.2 Is there a need for a national aggregator or national advocate?

In the stakeholder interviews, the level of support for a national aggregator was explored. Such an aggregator would receive data from the data owning road operator and may undertake some adjustments to that data to enable its supply in a nationally consistent data set. In exploring this question, potential alternative or additional functions for a national body were discussed, including advocacy.

The support from interviewees was different for these different potential functions for a national body.

There was general support for some sort of national advocate for harmonisation. Data users expressed strong preferences for data being available, well-structured and well-described. This led to some support for the work of a national advocate being prioritised on helping the data owning road operators to achieve this rather than seeking to establish a stricter national consistency.

With regards to an aggregator, there was some support but also significant caution regarding both the speed of the process (change in real-world conditions to change in provided data) as well as to the value proposition. Interestingly, support for an aggregator appeared stronger amongst road operator suppliers of the data than the private sector users. This in part reflects a preference for live direct access APIs, not the periodic data releases most commonly associated with a data aggregator.

In stakeholder interviews there was some discussion as to the value of an aggregator in allowing consumers of data to access a single source for data rather than needing to make arrangements with multiple data suppliers. This was seen as having some advantage, but given the small number of states in Australia, the other disadvantages cited in the aggregator process were seen as offsetting this advantage.

The higher number of local governments led to greater interest in an aggregator to simplify data access arrangements for data from this source. As with other discussions on an aggregator, the timeliness of the data through this method was a significant concern.

With regards to data quality, a clear preference was that the data owner had responsibility for data quality. A national advocate may assist in providing frameworks around to support this, but it was noting that having an aggregator responsible for quality of data as it passed through contributed to timeliness challenges.

Finding 16: There is broad support for a national body to act as an advocate for good practices in data for CAVs; support for a national data aggregator is notably weaker.



4. Do Current Business Processes Meet CAV Data Needs?

In looking to the future business responsibilities and operations of road operators, both many stakeholder discussions and previous experience with Intelligent Speed Assist (ISA) together highlighted that in many cases, current as-is business processes will not provide the data that is most useful to CAVs. In a narrow sense, this is not a barrier to Open Data (to release what is available) but does affect the efforts to maximise benefit realisation through CAV deployment and operations identified as the reason for road operator interest in this area in Section 1.2.

4.1 Achieving the data types, accuracy and timeliness that CAVs desire

Achieving the desired benefits from supplying data for use by CAVs is linked to having the right types of data available, with sufficient accuracy and timeliness (low latency) to be useful to the CAV. The Open Data approach focuses on releasing what data is available, however it was identified that for many data types of relevance for CAVs, the data available today would fall short of achieving the full desired benefits.

Examples of this occurred across most data types and more detail can be found in the interview responses as summarised in Appendix C. Timeliness came through as an area of both importance and challenge in achieving. The general desire for CAV data is for either data in advance (the speed limit will change at specifically this day / time) or with a latency in seconds or at most a few minutes between the changed conditions and a data update. Most current business processes do not aim nor get near achieving this for many data types relevant to CAVs. While timeliness and recency of data is of importance to all data types relevant to CAVs, it is appropriate to prioritise efforts to those data sets where quick wins are available and/or the changes have the greatest value.

Perhaps the best demonstration occurs with speed limit data, as there is strong recent experience through efforts to deploy Intelligent Speed Assist (ISA) functionality. This experience highlighted that not only were the road operator datasets often incomplete at start of that process, the nature of the data was not necessarily well suited such as in the manner, accuracy and precision in which speed limit sign locations were described. Additionally, the processes around changes in speed limits often lead to lags between changes in speed limits and the change being reflected in internal databases, let alone where slower update cycles are applied to external releases. The shortcomings of road operator speed limit databases for the ISA purpose reflects that the databases and the variety of associated business processes had been established and managed for rather different purposes. Significant effort and investment has been applied in multiple jurisdictions to change both databases and business processes, but with mixed results.

Whilst this ISA experience may lead to some disillusionment, it provides both hard won lessons learned and some positivity about future opportunities. Through partnering with a private sector map provider, NSW has managed to make significant leaps forward in the coverage, accuracy and timeliness of speed limit information provided to their users and a consequential growth in usage.

The ISA experience is relevant to many data sets that involve some manual processes such as sign changes by field crews, but this is not true for all road operator data sets. Notably, the completeness, accuracy and timeliness of data is very good for regulatory devices directly managed by traffic management control systems, such as traffic signals and the variable speed limits and lane use management on Managed Motorways.



Finding 17: Data in traffic management control systems for variable speed limits and lane closures (on Managed Motorways) and traffic signals was identified by data users as strongly desirable for CAVs and available with suitable accuracy and timeliness. Providing further access to this type of data provides a clear opportunity for quick wins.

Finding 18: Being able to supply the most relevant and useful data about roadworks, events and incidents with good accuracy and timeliness may in many cases require business process changes. Trial opportunities for this data type should look to trial both the road operator business processes and the beneficial use of the data by CAVs.

The extent to which road operator data meets the needs of CAVs varies both by data type and by road operator. The table below seeks to provide an indicative overall assessment of the current suitability within each data type.

• The green cells represent where current data and/or supply arrangements are highly suitable.

• The yellow cells represent where some progress has been made but a significant gap remains.

• The red cells represent where little progress has yet been made.

Table 4.1: Status of different types of road operator data that may be relevant to CAVs

Real-time traffic management system data (variable speed limits, lane closures)

Real-time traffic signal phase and timing data (such as SPaT messaging)

Emergency road closure data (fire, flood, etc)

Temporary conditions associated with works, events and incidents

Static road attributes (the example here is speed limits)

Advance notification of new and changed roads (that may not have been mapped)

Road operator supply of data implemented and active

Agreed data standards and communication method adopted

Data available with necessary coverage, quality, timeliness

4.2 Doing the best with what is available now: describing quality and confidence

In Section 1.5 it was identified that data users recognise that the available road operator data will be imperfect. What they were keen to have quality measures included with the data, not only for the data set as a whole but for individual data records. Quality measures could describe aspects such as accuracy of the data, recency of the data and the underlying source of the data. Finding 5 (in Section 1.5) captures this learning.

In addition to including quality indicators with published data, there appears to be some value in road operators also using these quality indicators to flag when improvements to data quality may be required. Improvements may include making data more complete, of better accuracy and/or more timely.



5. A Virtuous Circle – Using Data Generated by CAVs

This project drew a strong distinction between data consumed by CAVs and data generated by CAVs. There is much work required for data generated by CAVs, but this is generally best covered in other processes, such as highlighted in Section 1.

What this project did briefly explore was where data generated by CAVs could be used to update road operator data sets of the type then being supplied back to CAVs. This concept of a virtuous circle was met with much enthusiasm across the stakeholder group. This approach is something already being used by road operators for some incident and travel conditions data, for example through two-way data exchanges with Waze.

There was universal support that CAV generated data would be beneficial and desirable as a way of improving road operator data sets. This CAV generated data was seen as having many uses, both for updating data sets such as speed limits and current works/events as well as for other road operator data sets such as asset condition data.

Finding 19: There appears to be strong potential and there is much interest in using crowdsourced data such as data generated by CAVs to improve road operator datasets.

Although general support for the virtuous circle approach was uniform, there was a divergence of views amongst stakeholders in two areas:

• Whether providing some minimum set of data without charge to a road operator should be a condition of access to the road network; and

• Whether (and in what circumstances) road operators would use crowdsourced data without undertaking field verification.

5.1 Should road operators have to pay for CAV generated data?

There was general agreement that there was a role for commercial value-added data generated from CAVs to be marketed to road operators and a general acceptance from road operators that they would purchase some data of this nature. Most road operators interviewed showed strongest interest in receiving processed intelligence about observed differences (ie. data from an on-board sensor such a camera says a 60km/h limit, map data says 80km/h) rather than receiving a full set of sensor data and this was generally seen as a commercial value-adding proposition.

Although some road operators showed interest primarily in only this processed intelligence, others were keen that there should also be in parallel some minimum set of data needing to be supplied by CAVs for no charge as part of accessing the road network. There was some indication that this other data set may fit within C-ITS methods in the “Day 2” use case for AV collective perception data. Interestingly, some private sector interviewees proactively expressed support for road operators requiring some conditions in return for access to the road network, but this did not extend to discussion of specific conditions.



5.2 Using crowdsourced data – manual verification or believing the crowd?

There were different views amongst stakeholders as to whether the use of crowdsourced data (including that generated by CAVs) required manual field verification prior to accepting the update or whether accepting the statement of the crowd was appropriate. There was general agreement that the nature of the data being updated impacted the extent to which verification was or was not required, but this is only a partial explanation for the divergence of views.

In this area, road operator practices appear to be lagging behind industry practices, in part due to the differences in priorities and requirements upon government and commercial providers.

Crowdsourcing of data updates is a well-established practice within the map providers, often on the basis that 3 to 5 concurring reports of a change leads to an automated change in that data element without any manual review. The use by government of map provider data for certain purposes (including some branded apps) means that this crowdsourced update process is already influencing publicly provided data by some road operators.

A further consideration in the use of crowdsourced data is privacy. The EU’s recent General Data Protection Regulation (GDPR) has focussed attention on this topic around the world. Although the direct application of GDPR to Australia is limited (eg. companies with a European base and/or data stored in Europe), the design of services provided in Australia may be structured around what is possible in the European or global market and it may also reduce the willingness for companies to share data that is sourced from vehicles. The privacy question is one of relevance to any data generated by CAVs, and some guidance can be found in NTC projects that have considered these aspects.



6. Planning the Way Forward

6.1 Should CAV data have its own strategic roadmap?

In looking to plan a way forward beyond this project, one question explored was whether CAV data would benefit from a dedicated strategic roadmap.

Although there was general agreement that something may be useful, there was much divergences as to what should be covered and some statements cautioning the difficulty in achieving what people may desire such a roadmap to achieve. This difficulty reflects in part the uncertainty of the future CAV data environment at the detailed level. To the extent that there was agreement on content for a roadmap, data consumers were looking for leadership from road operators through clear statements on what road operators will provide, when, and with what quality and format. On the other hand, road operators were seeking leadership from users of the data as to what data they would most value.

At the workshop in early February 2018, potential directions for a roadmap were discussed with stakeholders, however the conclusion of the group was that a focus in other areas would yield better results in the near term.

Finding 20: A roadmap for CAV data will be appropriate in future, however for the next 1-2 years, a stronger focus should instead be on active learning to lead into the development on a roadmap.

6.2 Realising the opportunities for CAV field trials

Across both government and industry stakeholders, there was good support for trials to be used to continue progress in this area. It was noted that current trials may not have achieved their full potential in this area and there was a strong desire to better leverage existing trial initiatives including through better capture and sharing of lessons learned.

Specific areas of interest for trials included live data from Managed Motorways and traffic signals as well and exploring the combination of business processes and data supply for road works and event data. This second area appears to lend itself to prototyping activities for a road operator of the future as part of trials so that the business processes needed to supply the required data can be explored in a meaningful way.

Finding 21: Priority areas for trial initiatives are for Managed Motorway data (including variable speed limits), traffic signal data, emergency road closures and road works and events data (in combination with trialling updated business process). There is some opportunity to leverage existing trial initiatives by providing support for improved capture and sharing of lessons learned.

6.3 Participating in international standards development

Amongst stakeholders there was broad agreement that Australia will be a standards taker rather than standards setter in this space. This mirrors the approach normally taken in the automotive area as well as with both Connected and Automated Vehicles and is to be expected.



Many stakeholders were of the view that this does not mean that Australia should be an entirely passive player in the development of standards. There was a common (but not universal) view that Australia needed to be an active participant in efforts to develop standards in this field to both ensure that Australia’s interests and needs were accounted for and to assist readiness for beneficial CAV deployment in Australia.

Finding 22: Australia should be an active participant in international activities for standards development for CAV data, so that Australia’s needs are met and to assist Australia’s readiness for beneficial CAV deployment.



7. Summarised Findings and Recommendations

7.1 Summarised findings

1. For the foreseeable future, for any data where the driving actions of a CAV operating in automated mode have an immediate safety impact (including speed limits, traffic signals, closed lanes), the CAV will need to accurately respond to the same stimuli as human drivers, pedestrians and cyclists

2. Even if road signs, lines and traffic signals are complemented by methods to assist interpretation by CAVs (including C-ITS), the signs, lines and signals would remain the authoritative regulatory devices

3. The approach of CAVs prioritising data from on-board sensors (where available) and using other inputs to complement and support interpretation is well aligned with this

4. Road operator provided data should not and will not be treated as “true” where it conflicts with sensed data, except in limited cases where it is already so such as for restricted vehicle access permits

5. Users of data supplied by road operators expressed strong interest in indicators of data quality and integrity being included within the supplied data to assist their decisions on use of the data. Indicators of data quality may include accuracy, age of data / recency, original source, etc.

6. Road operator data types that were identified to be a high priority for CAVs include:

a. Live feeds from traffic management systems for variable speed limits and lane closures

b. Live feeds of traffic signal phase and timing data (such as SPaT messaging)



e. Advance notification of new and changed roads (that may not have been mapped)


7. In addition to the high priority data types identified in Finding 6, most other data types that are or could be held by a road operator were of at least some interest to at least some industry participants.

8. Users of the data are seeking well-structured and well-described data and expressed a general preference for access through APIs rather than periodic updates. Standards for formats were seen as useful, but secondary to this. Although there are some clear standards emerging, the Australian market had generally not yet locked in to these.

9. Australian participation in the standardisation discussion (particularly in Europe) will assist in planning any adoption of these emerging standards in Australia

10. Road operators may wish to consider the potential extent of additional costs to meet the CAV data needs and the business model and funding opportunities to offset such costs.

11. Road operators are taking a clear position that they will not accept liability for any supplied data and this is not disputed by the consumers of the data. It is less clear what road operators may need to do to discharge any remaining duty of care they have in providing data.



12. A general model of the data ecosystem has emerged, including a role for map providers, but there remains significant uncertainty at the detailed level.

13. The use of Open Data methods is appropriate for government road operator supply of data for CAVs but further consideration around data for private roads may be needed.

14. There is a strong preference for access to APIs rather than periodic provision of data files.

15. Improved data availability and quality may be needed to achieve the desired benefit realisation from CAV deployment.

16. There is broad support for a national body to act as an advocate for good practices in data for CAVs; support for a national data aggregator is notably weaker.

17. Data in traffic management control systems for variable speed limits and lane closures (on Managed Motorways) and traffic signals was identified by data users as strongly desirable for CAVs and available with suitable accuracy and timeliness. Providing further access to this type of data provides a clear opportunity for quick wins.

18. Being able to supply the most relevant and useful data about roadworks, events and incidents with good accuracy and timeliness may in many cases require business process changes. Trial opportunities for this data type should look to trial both the road operator business processes and the beneficial use of the data by CAVs.

19. There appears to be strong potential and there is much interest in using crowdsourced data such as data generated by CAVs to improve road operator datasets.

20. A roadmap for CAV data will be appropriate in future, however for the next 1-2 years, a stronger focus should instead be on active learning to lead into the development on a roadmap.

21. Priority areas for trial initiatives are for Managed Motorway data, traffic signal data and road works and events data (in combination with trialling updated business process). There is some opportunity to leverage existing trial initiatives by providing support for improved capture and sharing of lessons learned.

22. Australia should be an active participant in international activities for standards development for CAV data, so that Australia’s needs are met and to assist Australia’s readiness for beneficial CAV deployment.

7.2 Recommendations

This project’s purpose was to consider the challenges and opportunities of Open Data for CAVs and to provide recommendations for the next steps to be taken by Australian governments in this area.

The summarised findings (in Section 7.1) provide a concise assessment informed by background research of local and international practice and extensive stakeholder interviews. They provide the basis for these recommendations.

The intent for the recommendations from this project was to map out a strategic approach to the next steps that Austroads could take in this field. The approach mapped out below includes a phase of active learning to assist the development of the positive guidance sought by government and industry. While some aspects of the future data ecosystem have emerged, much remains unclear and developments are occurring at fast pace. The value of the future guidance will be strengthened by using this active learning as a key input. Beyond this, the focus shifts to supporting change.



Figure 7.1: Graphical view of recommendations

7.2.1 Leverage existing initiatives by assisting the capture and sharing of lessons learned

The first recommended activity is to undertake active learning through making use of what is already happening. Australia already has a vibrant program of C-ITS and Automated Vehicle trials and other initiatives related to Open Data, with further trials understood to be in the pipeline. This recommendation seeks to further leverage the value of these current and planned trials by maximising the capture and sharing of lessons learned.

Getting the best lessons learned from these trials requires Austroads to assist rather than burden those implementing the trials. It is therefore recommended that the capture and sharing of lessons learned occurs through a national (Austroads) project that works cooperatively with those undertaking the trials.

Example topics to be included to maximise the value of the lessons learned for future guidance on CAV Open Data are:

• Providing live traffic signal data (case examples may include NSW and/or Queensland)

• Providing live data on Managed Motorways variable speed limits and lane status (case examples may include Victoria)

• Implementing changed business processes to achieve improved road work data (case examples may include Queensland)

• Implementing changed business processes to achieve improved incident data (case examples may include NSW and / or Queensland)

• The opportunities and challenges of partnership approaches to providing data services (case examples may include NSW’s progress in ISA).

These case examples cover a range of road operator data with differing levels to which that data meets the needs of CAVs. In the table below, the colours represent an indicative overall assessment of the current suitability within each data type.

• The green cells represent where current data and/or supply arrangements are highly suitable.

• The yellow cells represent where some progress has been made but a significant gap remains.

• The red cells represent where little progress has yet been made.

Assist preparations for change

Monitor progress on

key data sets

Develop guidance and

roadmap

Strategic review 2017/18 This CAV Open Data Recommendations report

2018/19

2019/20

Active learning +

Develop guidance

Active participation in standards development

Leverage existing

initiatives

2020/21 Support change Release roadmap + best practice guidance + facilitate and support implementation of best practice approaches



Table 7.1: Status of different types of road operator data that may be relevant to CAVs

Real-time traffic management system data (variable speed limits, lane closures)

Real-time traffic signal phase and timing data (such as SPaT messaging)

Emergency road closure data (fire, flood, etc)

Temporary conditions associated with works, events and incidents

Static road attributes (the example here is speed limits)

Advance notification of new and changed roads (that may not have been mapped)

Road operator supply of data implemented and active

Agreed data standards and communication method adopted

Data available with necessary coverage, quality, timeliness

7.2.2 Develop a roadmap for road operator data for CAVs, with accompanying best practice guidance

The active learning activities assist Austroads for the development of positive guidance, including both best practice guidance and a roadmap. There was a clear appetite for a roadmap from both government and industry, along with supporting best practice guidance as to what CAV Open Data is most useful and how it is best provided.

The roadmap will assist progress by providing clarity and reduced uncertainty for both industry and government. The scope of this roadmap will need cover the broader topic of road operator data for CAVs. Open Data will be an important part of that, but the roadmap needs to be able to cover other approaches, including the use of crowdsourced data (generated by CAVs and others) to update road operator datasets. The roadmap should seek to provide the clarity that both government and industry are looking for, leading to a plan in which the most valuable road operator data sets are confirmed and the method and timing of their provision identified. The roadmap would be strengthened by also considering use by road operators of data generated by CAVs.

Given the timing of the roadmap, its focus will be on supporting operational deployment of CAVs, not only the present trials and demonstrations.

7.2.3 Assist preparations for change

For road operators to supply the most suitable data to CAVs may require business process changes. What changes would be required will differ from operator to operator and from data set to data set.

An example is that for road operators to be able to supply the most relevant and useful data about roadworks, events and incidents with good accuracy and timeliness, business process changes may be required in many cases. Experience to date with Intelligent Speed Assist (ISA) have illustrated that business process changes can also be required for more static data types, in that case speed limit data.

The case examples (see 7.2.1) will assist to illustrate some of the changes required. The roadmap (see 7.2.2) helps to identify the targets to be met and the timing to meet them. This action builds upon these by seeking to provide further relevant inputs to road operators so that they can each assess the business impacts of required change, develop business models to match any updated operational models and build business cases to secure revenue or funding for change.



7.2.4 Active participation in international standards development

This activity supports the development of best practice guidance. The standards that will both enable and shape the way in which data is shared between road operators and CAVs are currently being developed and are being put into trial deployments overseas as part of furthering their development. A significant part of the clarity at the detail level that is currently missing in this field will come through these standards along with understanding how industry and government have chosen to implement them. Australia can use active participation as part of understanding these standards while they are still in final development to ensure that they meet Australia’s needs and to allow their adoption with a minimum of cost and delay. Austroads should consider opportunities to leverage any participation by member road operators or industry as well as how any gaps in participation may be addressed.

The benefits of this active participation in international standards development may be amplified through the establishment of an Australian community of practice for CAV data. Such a group would both assist with input to the standards and assist with transferring learnings from the standards development to a broader group of Australian practitioners. In addition to this function, the group may also serve as a reference group for the broader set of CAV data activities.

7.2.5 Monitor progress on key data sets

Austroads should consider monitoring and updating both members and industry on the progress with key data sets, similarly to coordination activities for CAV trials. This action assists the road operator suppliers of data to understand the state of play nationally and assists industry to make productive use of the data sets available to them.

7.2.6 Support change

Beyond the release of the roadmap and best practice guidance, the focus of Austroads should shift to supporting road operators in their activities for the supply of data to CAVs.



References

This list of references applies to this main report section only. A separate list of references is included in Appendix A for the Background Research Working Paper.

ANCAP (2018) ANCAP Test Protocol, Speed Assist Systems v2.0, Australasian New Car Assessment Program, http://s3.amazonaws.com/cdn.ancap.com.au/app/public/assets/a41f179a92acb520e1df72e2318c8d77380e9f99/original.pdf?1513578443

Audi (2016) Audi networks with traffic lights in the USA, https://www.audi-mediacenter.com/en/press-releases/audi-networks-with-traffic-lights-in-the-usa-7147

Austroads (2017) Assessment of Key Road Operator Actions to Support Automated Vehicles, https://www.onlinepublications.austroads.com.au/items/AP-R543-17

BITRE (2017) National Infrastructure Data Collection and Dissemination Plan, Consultation Draft September 2017, Commonwealth Bureau of Infrastructure, Transport and Regional Economics, Canberra

Davies A (2018) “The Laser Maps Behind Cadillac’s Superb Self-Driving Skills”, Wired, https://www.wired.com/story/cadillac-super-cruise-ct6-lidar-laser-maps/

DIRD (2017) Terms of Reference for Data Collection and Dissemination Plan, Commonwealth Department of Infrastructure and Regional Development https://bitre.gov.au/data_dissemination/tor.aspx

Drive Sweden (2017) Autonomous Driving Aware Traffic Control – Final Report, July 2017 https://www.drivesweden.net/sites/default/files/content/ad_aware_traffic_control_-_final_report_v11_0.pdf

Pandazis JC (2017) Challenges on data necessary to serve Automated Driving, SIP-adus workshop dynamic map session, Tokyo, 14 November 2017

Shimada H, Yamaguchi A, Takada H and Sato K (2015) “Implementation and Evaluation of Local Dynamic Map in Safety Driving Systems”, Journal of Transportation Technologies, Vol.05 No.02(2015)

TIC (2016) National Land Transport Technology Policy Framework, Transport and Infrastructure Council http://transportinfrastructurecouncil.gov.au/publications/files/National_Policy_Framework_for_Land_Transport_Technology.pdf

https://www.audi-mediacenter.com/en/press-releases/audi-networks-with-traffic-lights-in-the-usa-7147

https://www.audi-mediacenter.com/en/press-releases/audi-networks-with-traffic-lights-in-the-usa-7147

https://www.onlinepublications.austroads.com.au/items/AP-R543-17

https://www.wired.com/story/cadillac-super-cruise-ct6-lidar-laser-maps/

https://bitre.gov.au/data_dissemination/tor.aspx

https://www.drivesweden.net/sites/default/files/content/ad_aware_traffic_control_-_final_report_v11_0.pdf

http://transportinfrastructurecouncil.gov.au/publications/files/National_Policy_Framework_for_Land_Transport_Technology.pdf




Appendix A Background Research Working Paper

A.1 Summary of findings from the Background Research Working Paper

The initial deliverable in this project was a review of international and Australian practice through a Background Research Working Paper. The Background Research Working Paper included a number of key findings:

• A distinction should be made by data generated by AVs and data consumed by AVs;

• This project considers that data consumed by AVs which originates from road operators;

• CAVs need much data to perform the driving task, including data from on-board sensors, through Cooperative ITS and from the cloud (including mapping type data);

• The data originating from road operators is only a small subset of the data required for automated driving;

• Even for this road operator originated data, there are available alternative sources and an important consideration is how the CAV works out what to believe;

• Australian federal and state governments have Open Data policies with an open by default principle;

• This means that attributes that road operators possess should be made available, however the standard approach to do this makes this available effectively as is, and specifically provides no guarantee of accuracy and no acceptance of liability;

• There has been notable progress in Europe in establishing frameworks and standards for supplying at least some road operator data attributes, however this appears to uniformly assume that road operators continue to maintain full geodatabases of their own as per current practice.

An update was made to the Background Research Working Paper in 2018 to account for two new US activities of relevance – submissions to a FHWA request for comments on Automated Driving Systems and the findings of a USDOT roundtable on CAV data.

The full Background Research Working Paper in included over the following pages.

A.2 About This Background Research Working Paper

This Background Research Working Paper is the first deliverable in the development of a CAV Open Data Recommendations Report that responds to part of Action 8 in the National Land Transport Technology Policy Framework.

The structure of this working paper is intended to guide the reader through an appreciation of both the needs for CAV open data and the approaches taken by others to address this and similar needs.

• This Section A1 provides context for this CAV Open Data Recommendations Report;

• Section A2 explores what data is it that CAVs need, regardless of whether open data or not;

• Section A3 identifies what “open data” means and the positions adopted by Australian governments on providing open data;



• Previous Austroads work in related areas is set out in Section A4;

• The Australian experience with Intelligent Speed Assist (ISA) is in Section A5; and

• Finally, Section A6 outlines examples of approaches taken by others to address this need.

This report was originally prepared in November 2017. An update was made in May 2018 to incorporate key themes from the US FHWA call for submissions on Automated Driving Systems and the USDOT Roundtable on Data for Automated Vehicle Safety. This was a focussed update to address some these key activities that occurred in the intervening period and other sections of the report were not updated at that time.

As this report considers CAVs, this means vehicles that are connected as well as automated. This is an important clarification as C-ITS includes an inherent element of something akin to open data in the messages exchanged between C-ITS actors (vehicles, infrastructure, pedestrians, etc). There is already extensive standardisation activity around this C-ITS data exchange, which is not yet so for the case for data consumed and generated by AVs other than through the C-ITS process. Given the extent of existing standardisation of data processes for C-ITS, this working paper will not seek to reproduce that work.

A.2.1 National Land Transport Technology Policy Framework

In 2016, the Transport and Infrastructure Council released a National Land Transport Technology Policy Framework with the objective of fostering an integrated policy approach by governments to the development and adoption of emerging transport technologies (TIC, 2016).

The Policy Framework includes an action plan with 14 actions. Action 8 reads in full as:


Governments can assist industry, researchers and the public to develop innovative solutions to transport problems by providing open access to transport data. Australian governments are committed to an open-by-default approach to transport data and through this action will improve the availability of open access transport data. In particular, existing jurisdictional data sets will be consolidated into national level information in a shared format, and made available through a common portal. New datasets will also be created, including improved information on speed zones across Australia and a national map of low-gear warning zones (which could be used to provide safety warnings to drivers).

The CAV Open Data Recommendations Report, of which this Background Research Working Paper is a part, contributes to Action 8. The action applies to open data across the transport sector generally, of which CAVs are only one component.

A.2.2 BITRE dissemination plan

On 15 September 2017, the Commonwealth Minister for Urban Infrastructure, the Hon Paul Fletcher MP, released BITRE’s Draft Data Collection and Dissemination Plan (BITRE, 2017).

A key objective of the Data Collection and Dissemination Plan, as set out in the terms of reference, is to “provide improved and more timely information for infrastructure investment decisions and monitoring of the performance of Australia's infrastructure networks” (DIRD, 2017).



It was therefore not a starting point for the Data Collection and Dissemination Plan to focus on Connected and Automated Vehicles as a consumer of road operator data. Nevertheless, the relevance of ongoing advances in transport technology to improving the safety, efficiency, sustainability and accessibility of Australia’s transport systems means that enduring question 6.2 was identified to be “Open data to support the implementation of Connected and Automated Vehicles (CAVs): Identify gaps between what road operator data is provided to users (e.g. TomTom) and what is likely to be required in future for CAV operations.” Examples of data required were identified from Austroads (2017) to include speed zone changes, road closures and road works information. Issues identified by BITRE (2017) as being of relevance include road data management, standardisation/harmonisation and support for proprietary models, and road authority regulatory framework in a digital environment.

It is intended that the CAV Open Data Recommendations Report, of which this Background Research Working Paper is a part, contributes to the finalisation of BITRE’s Data Collection and Dissemination Plan.

A.2.3 How this project relates to other projects

As there is much happening in the area of CAVs, this project to develop a CAV Open Data Recommendations Report is complemented by other initiatives:

• NTC regulatory access to C-ITS and Automated Vehicle data

– This project will identify and develop options to manage government access to AV data and the focus is therefore on data generated by the AV. The consideration of options will seek to balance road safety and network efficiency outcomes and efficient enforcement of traffic laws with sufficient privacy protections. This project is currently in planning and is due to be undertaken during 2018.

• NTC Safety Assurance System for Automated Vehicles

– This project considered four regulatory options for the safety assurance of AV functions, including self-certification approaches and pre-market approvals. This project is currently analysing the options, with an expected delivery date of late 2017. The approach taken to safety assurance of AV functions provides important context for what data generated by AVs governments may have access to and under what circumstances.

The two NTC projects are related initiatives that provide improved clarify around arrangements for data generated by CAVs. There is a need for Australia to have a generally consistent and joined-up approach to CAVs and therefore there is strong value in information sharing between projects even where there are not strict interdependencies.

A.3 What Data Do CAVs Need?

AASHTO (2001) sets out three general tasks to vehicle operation, and these hold true regardless of whether the vehicle is being driven by a human or in highly automated driving by a robot / computer (Austroads, 2017):

• Navigation: trip planning and route following.

• Guidance: following the road and maintaining a safe path in response to traffic conditions (including lane choice).

• Control: steering and speed control (including braking).

Just as for a human driver, the computing processes driving CAVs have data needs in each of these areas.

For navigation, a CAV needs to know about the road network, any restrictions applying to road use, including turn bans and height restrictions as well as the prevailing traffic conditions on the roads to allow selection of the optimal route. Further to this, a CAV needs to know about the availability of stopping and parking places. This includes permitted stopping places for picking-up and setting-down passengers as well as safe stopping places in case of a CAV malfunction.



For guidance, a CAV needs to have an appreciation of the available lanes on roads and how they can be used to create a pathway to the destination as well as traffic conditions within lanes to inform selection of the best lane.

For control, a CAV needs to know its position relative to lane lines and kerbs, the positions of other vehicles, pedestrians and cyclists and the applicable speed limit.

The CAV aggregates all of this in a single or multiple digital representation(s) of those parts of the world relevant to that CAV’s journey. Some data in this digital representation will be data that changes only slowly, such as a map of the road network. Other data in this digital representation will be changing very rapidly, such as the positions and trajectories of vehicles.

Highly Automated Driving will benefit from information that is beyond the reach of vehicle sensors. This additional information can come from other vehicles and infrastructure (C-ITS) and through cloud data services. It is anticipated that complementing vehicle sensor data with information beyond the line of sight from these other sources will extend the Operational Design Domains (ODDs) in which Highly Automated Driving is possible.

The concept of a Local Dynamic Map (LDM) as used in C-ITS is useful as a way of conceptualising this digital representation. The LDM is a dynamically updated world model representing the vehicle’s and infrastructure’s knowledge of the surrounding environment with fused static data and sensor data as inputs (Austroads, 2013). Shimada et al (2015) drew upon the EU SAFESPOT work to develop their take on the Local Dynamic Map, shown in Figure A1 below.

Figure A 1: The Local Dynamic Map shows the variety of data needed within a CAVs representation of the world around it

Source: Shimada et al, 2015

This required data can come from many different sources, including on-vehicle sensors, data received from other vehicles and infrastructure (C-ITS) and data received from the cloud. At least some of the more slowly changing data may be able to be stored on the vehicle, with updates only as required. The more dynamic data will need to be received live.



Figure A2 provides some examples of data types used by a CAV as well as how the CAV may receive this data.

Figure A 2: Examples of data used by a CAV

Source: provided by S. Ballingal

A.3.1 Data generated by CAVs vs data consumed by CAVs

As they travel around the road network, the multitude of sensors on CAVs generate enormous volumes of data, with many references describing this in terabytes. Indeed, the vast majority of discussion on CAV data has related to data generated by CAVs. In these discussions, a focus on data ownership, data protection and privacy considerations is common. For example:

• The Australian Automobile Association’s 2016 submission to the Productivity Commission identified that “the AAA has developed a set of key guiding policy principles that would improve access and monitoring of these issues:

– Open, timely access to public data sets;

– Where possible, consistent reporting of data between jurisdictions;

– Access to and control of personal private data; and

– Open access to private data, where there is a public benefit, can improve competition and transparency.”

• In its 2017 enquiry into CAV’s, the UK House of Lords consideration of data revolved around privacy, data protection and access by law enforcement to vehicle data after crashes.

• The 2017 Commonwealth parliamentary inquiry into social issues relating to land-based automated vehicles in Australia considered the data generated by CAVs, with recommendations 4 and 10 addressing issues related to data ownership, privacy and data protection.

• The 2016 NTC review of regulatory barriers considered questions around data, however this was primarily on questions around privacy and data protection for the large volume of data generated by AVs. As such, the report did not discuss the consumption of public open data by CAVs, although this does not guarantee that no regulatory barriers to this exist.



Others have focussed on the opportunities for data generated by CAVs. For example:

• The 2016 US Federal AV Policy identified value in data sharing to allow faster progress for the industry as a whole. This data sharing was particularly with regards to incidents and near-misses that AVs were involved in, to allow for further learning for the automated driving algorithms.

• Infrastructure Partnerships Australia’s 2017 report on AVs identified a number of opportunities regarding data; these were in large part the opportunities to use data generated by AVs, including as an alternative to infrastructure-based collection of that data.

Although all of these topics are interesting, this CAV Open Data Recommendations Report has a focus on the data provided by road operators to be consumed by CAVs.

Although there is some potential for open data approaches to apply to data that is generated by CAVs, that cannot be meaningfully considered separately from addressing the data ownership, data protection and privacy considerations. Other projects seek to do just that. Accordingly, this Background Research Working Paper shall also focus solely on the data provided by road operators to be consumed by CAVs.

A.4 What is Open Data?

The Open Data Institute (2017) describes it like this:

“Open data is data that anyone can access, use or share. Simple as that.”

The term open data was first used in 1995, by an American government science agency in respect to a desire for open sharing of atmosphere, ocean and biosphere data (Chignard, 2013). It was an extension of a concept dating back further that the results of research should be freely accessible to all. The first government moves on open data came during the first days of the Obama presidency, where open data was included as part of his initial presidential decrees for open government (Chignard, 2013).

Governments have been the prime suppliers of open data. This aligns with reasoning that governments act as the agents of their communities and therefore an open data approach is seen as releasing to the community something which the community already owns. The open data approach reverses the onus inherent in Freedom of Information concepts, as data is made available not only by default, but also proactively without waiting for specific requests.

The provision of open data by government’s is intended to create economic benefits and stimulate innovation. Within the transport sphere, a measurement of how this can work is available through Deloitte’s 2017 assessment of the value of Transport for London’s open data and digital partnerships:

“The release of open data by TfL is generating annual economic benefits and savings of up to £130m for travellers, London and TfL itself. TfL open data that supports 42% of travel apps and real-time alerts used by Londoners is saving £70m-£95m pa in saved time. The release of open data by TfL has supported the growth of London’s Tech economy to the value of £14m pa in GVA and over 700 jobs.”

Isaac’s 2016 guide to government agencies also espouses benefits of open data for CAVs, although this is through the use of general statements as to the promise it holds.

A.4.1 Commonwealth government position on open data

The Australian Government’s push towards open data has been running for nearly a decade. The national portal data.gov.au states that it is a response to the report of the 2009 Government 2.0 taskforce. One of the key themes in this report is that “once public sector information is liberated as a key national asset, possibilities — foreseeable and otherwise — are unlocked through the invention, creativity and hard work of citizens, business and community organisations. Open public sector information is thus an invitation to the public to engage, innovate and create new public value.”



In 2015, the Australian Government Public Data Policy Statement was issued (DPMC, 2015). This sets out a clear policy statement that:

“The Australian Government commits to optimise the use and reuse of public data; to release non-sensitive data as open by default; and to collaborate with the private and research sectors to extend the value of public data for the benefit of the Australian public.

Public data includes all data collected by government entities for any purposes including; government administration, research or service delivery. Non-sensitive data is anonymised data that does not identify an individual or breach privacy or security requirements. …

At a minimum, Australian Government entities will publish appropriately anonymised government data by default:

• on or linked through data.gov.au for discoverability and availability;

• in a machine-readable, spatially-enabled format;

• with high quality, easy to use and freely available API access;

• with descriptive metadata;

• using agreed open standards;

• kept up to date in an automated way; and

• under a Creative Commons By Attribution licence unless a clear case is made to the Department of the Prime Minister and Cabinet for another open licence.”

While the nature of our federation means that commitments of the Australian Government are generally not binding on state road operators, local governments nor private road operators, this Australian Government statement still serves to provide guidance on a national position.

Following the 2015 policy statement, the Open Government National Action Plan 2016-18 (DPMC, 2016) was published. An area of commitment in this plan is open data and digital transformation. A further area of commitment is to improve the discoverability and accessibility of government data and information. In March 2017, Australia adopted the international Open Data Charter (Taylor, 2017).

A.4.2 State open data policies

All Australian states have a whole of government Open Data policy, first adopted between 2012 (Victoria) and 2017 (Queensland). There is great similarity in approach, with all states except Tasmania making extensive use of a data.state.gov.au portal for access. All states have an “open by default” principle; some states (Vic, NSW, WA and Tas) include an explicit statement that some data may not be open. There is also a common focus on timely availability, clear structuring of data using available and preferably open standards and on free supply with open licencing.

More detailed information on each state’s open data policy can be found in Appendix B.

A.4.3 Open data, “truth” and liability

CAVs use data to navigate, guide and control motor vehicles. The consequences of data errors can therefore be serious as they may lead to crashes, injuries and deaths.

An apparent assumption of the government approaches to Open Data is that the data so provided will be used an input to processes such as business analysis and traveller information, not as a direct input to real-time control of safety-important processes such as driving. This however is an assumption, as there does not appear to be anything that specifically prevents the use of Open Data for automated vehicle control functions.

http://opendatacharter.net/principles/



This difference in purpose taken on increased importance when considered in light of road operators being seen as a possible source of “truth” for certain road attributes such as speed limits. Using a supplied data set as “truth” is not aligned with the Creative Commons Attribution licence commonly used for Open Data releases, including for regulatory attributes such as speed signs. This licence includes not only a waiver of liability to the extent permissible but also explicitly excludes any warranty for the accuracy of contents. The resistance of road operators to accepting liability for the accuracy of the supplied data is understandable and appears to be a consistent position (see Sections A.5.2 and A.7.1).

The use of the Creative Commons Attribution licence assists in making data open faster and more easily due to this exclusion of warranties for accuracy and a waiver of liability. It has however the likely effect of requiring Automated Driving Systems Entities to demonstrate how they will achieve safe highly automated driving without reliance on road operator supplied data.

A.5 Previous Austroads work

Three areas of previous Austroads work have been identified as directly relevant to this Background Research Working Paper. Road operator actions to support AVs are in Section A4.1, aspects from C-ITS are covered in Section A4.2 and traveller information in Section A4.3.

A.5.1 Road operator actions to support AVs

Earlier in 2017, Austroads published an assessment of key road operator actions to support AVs. This report included a discussion of digital infrastructure needs:

“A variety of definitions exist for digital infrastructure, although a common theme with the definitions is that it involves data and the ability to store, manage, and exchange data with information and communication technology (ICT) systems. …

An AV will rely on a range of systems to operate effectively and safely. This includes not only a range of on board systems and sensors that capture data about a vehicles immediate environment, but it also includes the use of data from other sources external to the vehicle.”

An image developed by Bosch was used to show how map-style data and sensor derived data worked together to provide the intelligence needed for automated driving.

Figure A 3: Sensor fusion and localisation

Source: Bosch



In summary, the report identified that:

“Digital infrastructure is a key area for consideration in supporting the operation of AVs. Data management, positioning services and communication technologies are important areas to be considered, and there are many issues to be addressed to support the rollout of AVs across the road network.”

Pertinent issues identified as requiring attention were:

• Road data management;

• Data ownership;

• Support for proprietary models;

• Standards and guidelines for data; and

• How the road authority regulatory framework applies in a digital environment.

The 2017 Austroads report also identified that “there is a specific need to consider road works and other special events. When the layout or access to the road network has changed accurate real time indication of the current and future availability of lane space is required as well as consistency of traffic management around work zones.”

The report’s recommended approaches on digital infrastructure are included in Table A1 below.

Table A 1: Guidance for road operators on digital infrastructure

Issue Timing of response

Asset data Make key data available (in addition to roadside dissemination): • Time based and dynamic speed limit data is

needed in real time • Accurate speed zone data (permanent signs) is

needed in a timely, well controlled manner • Road closure and lane availability data (road

works data) to be provided in real time. Seen as critical to AV operation for planning as well as guidance on route

• Information about clearways, loading zones and parking restrictions to be provided in a timely and well controlled manner

• Information about new and changed roads to be shared with industry in advance of road opening

Short term: Speed limits: Consider and adjust business processes (to provide this data in timely manner) Road Works: Seek to provide information on lane closure and alternative routes – data provided directly to the vehicle by cellular or non-cellular means Parking Signs: Work with local and state authorities to determine what meaningful information can be provided and the timeliness of this information Access for Mapping: Allow access to drive through new infrastructure as early as possible prior to opening

Road asset condition data More asset condition information needed to assist in maintaining best possible environment for all road users (incl. AVs)

Short term: Seek discussions with OEMs and Systems Providers to get access to these data sets Long term: Design of systems to support asset management and road operations informed by availability of AV and other historic and real time data sources

Privacy Ensure appropriate consideration of relevant privacy and data surveillance legislation and guidelines from the perspective of any data exchange

Short term: State/Territory and national regulations and guidelines need careful consideration by all operators in particular to consider the implications of the expanded information exchange likely to be in place Ensure compliance with relevant privacy regulations and privacy principles Medium term: Consideration of a national code of practice or guidelines for road operator capture and use of data from vehicles



Issue Timing of response

Data ownership Road operators will be an authoritative source of some information, other information will be available from 3rd party sources but potentially still owned by operators.

Short term: Consider need for ownership of data. The private sector will promote alternative models for consideration. This will ensure that operators are best positioned to use these datasets in processes which assist them to improve the operation of their networks

Business models Digital infrastructure is also a focus for road agencies for other reasons beyond AV mapping such as BIM/asset information management. As yet unclear how road agencies, industry and vendors can best work together to realise efficiencies and unlock benefits

Medium term: Consider the opportunities and challenges for the emerging digital models as well as the business processes that support and/or are transformed by these models. This will ensure operators have the flexibility to use these datasets in processes which assist them to improve the operation of their networks

Cellular communication coverage Likely minimum pre-requisite for AV operation (for most use cases) Support coverage for all carriers

Short term: Future expansion plans for cellular networks should consider needs of the road network and AV use cases. Medium term: Consider approach which ensures coverage is available from multiple suppliers

Other wireless communication Potential need for non-cellular V2I and I2V Communication

Short term: Consider availability of device in or on vehicle to deal with data will be the key element determining likely take-up. Consider need for C-ITS infrastructure (DSRC) or Bluetooth and other direct forms of communication. Consider likely adoption given potential penetration and benefits

Positioning services Need for positioning services with high accuracy and integrity to support AV operation • Across whole of road network • Consider particular positioning needs in tunnels

and built up areas (urban canyons)

Short term: Continue to work with key government agencies (e.g. Geosciences Australia) to outline need for future positioning services (which may include SBAS) Monitor international efforts to provide solutions to positioning needs for tunnels and built up areas (urban canyons)

Source: Austroads (2017)

A.5.2 C-ITS

Austroads’ 2013 review of digital mapping requirements for C-ITS identified many challenges applicable when considering the data that CAVs need. These challenges are:

• Variability in coverage for roadway data – does it cover all required roads?

• Resolution of roadway features – is it to the detail required?

• Current availability of specific roadway attributes – are all required attributes available?

• Data standards – coverage is incomplete with many gaps

• Data maintenance and updating – it is unlikely that current processes will meet the needs

• Liability – road operators the world over are concerned about the potential for them to be liable for the accuracy for the supplied data and the automated driving decisions made using that data

The 2013 review identified that “the consensus was that it would be unlikely that a government agency would ever be the single authoritative source of an enhanced map database that met the accuracy and timeliness requirements of C-ITS.” This means that:

“An appropriate data supply chain/model should be set up. Under this model, industry would be best placed to collect and maintain the high accuracy attributes (particularly 3D geometry, lane level and where-in-lane). Government would be best placed to be the authoritative source of attributes such as traffic signals, fixed and variable speed limits, road closures (both planned and unplanned) and road use hierarchy for heavy vehicle routing. Industry suppliers would then supplement their eMaps with the government-sourced attributes and provide these eMaps to third-party service providers.”



These 2013 findings applicable to C-ITS remain relevant to the 2017 situation for CAVs. If anything, the inclusion of automated driving increases the importance of having the right data available as well as the amount of data required.

These issues were further explored in Austroads’ (2015) Concept of Operations (ConOps) for C-ITS core functions. This ConOps recognised that open data may have some application to C-ITS data flows, however this was simply as a note in the report rather than a more extensive discussion.

The 2015 ConOps identified the required changes to achieve C-ITS operations in Australia. Two of these were intended to provide access to consistent authoritative and trusted road operator data for service providers:

• Road operators need to disclose data in standardised data formats; and

• Road operators and service providers need to agree on the conditions for access to this data.

In supplying this data, a key function identified within content processing is a quality or integrity check (Austroads, 2015):

“For safety-critical applications, it will be a key requirement that these applications can trust various data messages and attributes that they are using. For example, if the position of a vehicle in a basic safety message is out by 10 metres, if the speed zone data in an IVI message is 20 km/h above the correct limit, or the red light timing in a SPaT message is wrong by even a second, the lack of integrity with these messages could cause road crashes, not avoid them.”

A life-cycle process figure sourced from ISO 17427 was included in the ConOps as is reproduced below as Figure A 4.

Figure A 4: General life-cycle process description for C-ITS

Source: ISO 17427

Regarding the operation of the system, the following was included about provision on content, noting that is a separate function to the provision of applications and services and to the presentation of results to end users:

“Content may include data about road traffic conditions, speed zones, intersection geometry, signal phase and timing, etc. For some data, there may be a requirement to improve trust (e.g. safety critical data attributes). Some data will likely be provided by road agencies and other data (probe data) by traveller opted in to certain services.”



As a repeated theme, it was also noted that “there will be an increasing demand for content, including attribution, geographic coverage, accuracy, timeliness, etc. This may require integrity check processes to be established for different data sources to ensure the data can be trusted and is fit for purpose” (Austroads, 2015).

The recommended system operation improvement to address this was an “open platform, based on standards, [which] allows service providers to develop and deploy while sharing resources”. Importantly, it was also noted that “map data is not considered a core function [to be performed by a central body], because different C-ITS devices might be using different map data for the same application.”

A.5.3 Traveller information

Austroads’ previous work (2010) on models for road operators to provide data for use in traveller information services provides a good insight into changes over recent years as the open data approach has taken hold.

Based on practices and opportunities at that time, the report identified four business models:

• Contract business model: the road operator provides traffic data to data aggregators without guarantees under a data licence agreement that places all risk associated with the adequacy, sufficiency or timeliness of that data onto the aggregator, the road operator does not dictate how the data is used and charges a licence fee to cover the marginal cost of providing the data;

• Partnership business model: the road operator seeks to provide high quality data to data aggregators for a fee may receive for a fee (or offset) some data back from aggregators;

• Shared collection business model: commercial parties supply a part of the data that road operators would otherwise need to themselves collect but where the government is not the only customer for that data so that the price to government reflects data collection costs being shared across multiple customers; and

• Value added supply chain business model: the road operator acts as an aggregator for that market and seeks to sell high quality data to commercial parties at full commercial rates.

At the time of the report (2010), the dominant approach was the contract business model. Since then, there has been growth in the shared collection approach enabled by the data collection approach by commercial entities shifting from roadside equipment to vehicle and mobile phone probes. Vestiges remain of the contract business model for that data supplied by road operators, but this has been extensively supplemented by government open data portal approaches.

A.6 Australian Experiences with Intelligent Speed Assist

Intelligent Speed Assist (ISA) is a tool to assist drivers to keep to the speed limit. It checks for the current speed limit using map data and vehicle position and alerts the driver if travelling above the speed limit. In some cases, haptic feedback or reduced acceleration are also provided in addition to audible warnings. The nature of ISA makes it a case of relevance in considering CAV open data.

Wall et al (2011) noted that “one of the most significant challenges to deploying ISA technology in Australia, or anywhere in the world, is ensuring that the infrastructure required to support ISA is developed, implemented and maintained. ISA is unlikely to achieve widespread support until it can provide reliable and accurate advice on speed limits in all circumstances and throughout all states and territories.” In 2016, Wall et al found that “the availability of comprehensive and current speed zone data was a major issue for the ISA system developers during its creation and remains so.”



As at 2010 there were varying levels to which the required speed zone mapping had occurred (Doecke and Woolley, 2010). In Western Australia and Victoria, all speed zones on public roads had been mapped, albeit with some unresolved accuracy issues in Western Australia. New South Wales had 30% mapped and they were 2.5 years into the process of implementing a system to maintain the accuracy of their database. NSW’s Speedlink system now provides a single spatially referenced source of speed sign and speed zone locations across the entire state, including the local road network. The Speedlink system is integrated into the approval process for new speed zones and authorisation to change or create new speed zones in New South Wales must be generated through the Speedlink system (Wall et al, 2016).

Other states such as Tasmania, South Australia and Queensland had no digital speed zone maps produced by their respective road transport authorities at that time. A commercial enterprise (Speed Alert) had undertaken some mapping, as subsequently have other map providers. In some states, only the state road authority has the authority to change speed limits, making the coordination process easier. In other states, such authority is shared with local governments.

The costs of creating and maintaining speed zone maps are significant. Implementing the speed zone map had cost $2.6m in Victoria alone, with $400k per annum estimated as the cost of maintaining the Victorian map (Doecke and Woolley, 2010).

In February 2014 Transport for NSW made advisory ISA technology available for free by releasing its own smartphone ISA app, Speed Adviser (Wall et al, 2016). The Speedlink database previously referred to is not used directly by the Speed Adviser application as the ISA system requires the data to be in format of a routable map which Speedlink does not provide. The Speed Adviser app instead uses a routable map provided by TomTom under a licence agreement. Speed sign data from the Speedlink system is provided to TomTom on a regular basis as part of the licencing agreement. The Speed Advisor app stores a local database of speed zones on the mobile device, requiring 160Mb of storage space.

The approach taken by NSW appears to have met customers’ needs for data accuracy and currency. Wall et al (2016) reports that although there are more than 18,000 speed zones recorded in NSW’s Speedlink database, only 44 speed zone accuracy issues (0.2 per cent) were raised through the Speed Adviser support email account in its first 2.5 years of operation.

A.7 International approaches

A search was undertaken for international open data approaches relevant to CAVs. This search returned few results, and those that it did were find were almost always focussed around data generated by CAVs, as identified in Section A2.1.

As such, most discussion in this section is around European initiatives, as these are what was able to be identified as most relevant to data consumed by CAVs. These European initiatives are rarely described as “open data” actions, but rather are described in a very similar way to Intelligent Speed Assist (ISA) for which the Australian experience is covered in Section A5.

A.7.1 Europe

Three related initiatives in Europe are of particular relevance to CAV open data. These are each discussed in sub-sections below:

• The INSPIRE mandate for sharing spatial data across many fields, in Section A6.1.1;

• The ROSATTE project for road safety focussed attributes in Section A6.1.2; and

• The TN-ITS approach and its implementation in the EULF transportation pilot, in Section A6.1.3.



Following these, discussion is included of ERTICO and HERE’s SENSORIS approach (Section A6.1.4), another EU funded imitative of relevance but which is not so closely aligned to the above: Open Transport Net (Section A6.1.5), as well as a UK research initiative ATLAS (in Section A6.1.6). Finally, a section (A6.1.7) is included to discuss European initiatives still in progress.

INSPIRE

The EU’s INSPIRE initiative covers the sharing of public authority spatial information from 34 theme areas, only one of which is transport networks. The applicable EU directive that is binding on EU member governments that action first came into force in 2007, with full implementation required by 2021 (European Commission, 2017a).

INSPIRE does have some open data aspects, however its objective is to simplify cross-border data sharing. This more specific and limited remit means that it provides only partial coverage, but still offers some data standards and sharing methods that may be of potential relevance to Australian CAV open data.

INSPIRE obliged public authorities to make their road data available according to the INSPIRE Implementing Rules from December 2012 for newly created datasets, and December 2017 for all other datasets (European Commission, 2011). The INSPIRE implementing rules however do not provide the means for the publication of the detailed road data required by more advanced ITS applications.

ROSATTE

The ROSATTE project sought to provide a complete framework to facilitate access to, exchange of and maintenance of spatial data from public sources including multi-level national / regional / local aggregation and incremental updates of map data (Flament, 2011). ROSATTE focused on the hot topics of the time: speed limit data for ISA applications and traffic signs and regulations relevant to enhanced navigation, particularly for trucks.

Figure A 5: Overview of the ROSATTE framework

Source: Flament, 2011

Following completion of the project, ROSATTE has been implemented in countries with many different road database working models (Flament, 2011). Part of the success of ROSATTE may lie in its attempts to understand the data value chain connecting stakeholders as well as the requirements for technical specifications.



Figure A 6: The road data value chain identified in the ROSATTE project

Source: Flament, 2011

Liability was a strong focus for road operators in the project. This view is shown in this extract from Flament (2011), “when public road network data is used in safety-critical applications (e.g. ISA), it is essential that public authorities cannot be held liable for any possible damages resulting from errors in the road map data. This could accomplished by including a disclaimer in the common license agreement that waivers any liability of the public data source.”

Data types covered within ROSATTE are:

• Traffic regulations (e.g. speed limits)

• Traffic signs

• Road Geometry new roads

• Road Geometry, long-term roadworks

• Traffic restrictions (vehicle dimensions, weight, temporal, tolling, routing and parking)

• Topology, road surface, lane information (number, width, divider, connectivity)

• Traffic lights

• Crossings and stops (pedestrian, tram)

• Speed bumps, accident hotspots

• Slope and banking

These data types provide only a partial set of the data needed by a CAV for automated driving. They do however cover a much larger proportion of data types where a road agency may be considered to be the logical source of truth.



The EULF transportation pilot and TN-ITS

TN-ITS (Transport Network – Intelligent Transport Systems) approach ties together INSPIRE and ROSATTE. It embeds both the ROSATTE exchange specification into INSPIRE and adds process elements around maintenance of the data, quality control and location referencing (ERTICO, 2017). TN-ITS’s scope is the exchange of information on changes in static road attributes. This covers attributes that have a more or less permanent nature, but which sometimes change. An example of this is speed limits and other traffic restrictions.

The TN-ITS concept uses the road operator as the source of truth for these updates, which imposes requirements onto the road operators to have the processes in place to have the necessary data coverage, accuracy and frequency of updates.

Figure A 7: The TN-ITS data chain


The EULF transportation pilot was the initial implementation of the TN-ITS approach (ERTICO, 2017). The pilot covered two countries (Sweden and Norway), two main commercial map data providers (TomTom and HERE) and some data attributes. It was completed in 2015 and found some benefits of relevance (Borzachiello et al, 2016):

• The use of the TN-ITS revealed tangible benefits for map providers and consumers, reducing speed limit error rates from 25% to 7%;

• Participating road authorities (Sweden and Norway) successfully moved from quarterly to daily updates to map providers, improving currency of data used in vehicles;

• The objective of moving from disparate operator by operator processes to more standardised ones was achieved for the participating road operators; and

• Reduced effort in handling incremental updates compared to handle full datasets.

Progress towards a broader roll-out of the TN-ITS approach continues beyond the EULF transportation pilot but at a moderate or slow pace.

An important aspect of the EULF transportation pilot is that it created real supply chains for the data. This can be seen in the stakeholder representation shown in Figure A 8 below.



Figure A 8: Stakeholders in the EULF transportation pilot

Source: Borzachiello et al, 2016

Figure A9 sets out the attributes which were included in the EULF transportation pilot. As can be seen, these attributes have much in common with those covered in efforts such as ROSATTE. The focus is on attributes where the road operator is most likely to be the source of truth.

Figure A 9: Attributed provided in the EULF transportation pilot




The general manner in which the pilot was implemented is shown in Figure A10 below, followed by the workflow used by one of the mapping providers (HERE) in Figure A 11:. This workflow highlights that there is a process of matching and interpretation required between provision of the data by the road operator and implementation into vehicles as an integral part of geospatial information.

Figure A 10: Generic data flow from road operator to map provider


Figure A 11: High level TN-ITS implementation workflow at HERE

Source: HERE



Borzachiello et al (2016) included a key statement about how HERE viewed the role of TN-ITS in their overall business activities (my emphasis):

“Overall the TN-ITS service proved to be an important complementary update mechanism to HERE when it comes to getting updated attributes integrated into the database in a fast and effective way. It is clear that having only the changes (incremental updates) provided through the service is very beneficial. Previously complete datasets for the countries were compared with the HERE map to identify changes.”

A key lesson from the EULF transportation pilot, is that even if the road operator provides perfect data, there is still a matching an integration challenge for the mapping provider (Borzachiello et al, 2016):

“The database of the acting authority and map provider’s database will never be a mirror of each other and therefore the matching of the two will always provide some challenges. A learning that has been confirmed during the pilot, is that [road] authorities and map providers will probably never have the same specifications and therefore some of those challenges need to be accepted.

In the context of the pilot, success rates are defined as changes that flow from acting authority through the process developed within the pilot and automatically into the HERE database. For speed limits in Sweden, the success rate is 50% [for] high confidence matches.”

SENSORIS

SENSORIS is a platform to develop a global standard for vehicle-to-cloud data. SENSORIS was initiated in 2015 by mapping provider HERE; during 2016 ERTICO agreed to coordinate SENSORIS into a standardized interface specification for broader use across the automotive industry (VRA, 2016).

Much of the discussion on SENSORIS focuses on its use in providing standards for data generated by CAVs. SENSORIS does include consideration of data consumed by CAVs in the form of location-based services for mobility and automated driving, but in the context of this using standards described by NDS and TPEG. This can be seen in Figure A 12: below, whereby the vehicle (local live map) consumes data supplied in the NDS and TPEG formats and provides generated data in the SENSORIS format to a community data store (of which road operators are a part).

Figure A 12: SENSORIS in the context of other standardisation efforts as represented in June 2018

Source: SENSORIS



Information consumed from road authorities is represented through information through TN-ITS and DatexII.

Open Data hubs: Open Transport Net (OTN)

OpenTransportNet (OTN) is a European funded project that seeks to unlock value from data by creating a European network of data hubs that aggregate transport-related data and spatial information to drive the rapid creation of innovative and collaborative new ITS applications and services (Ruston et al, 2015). The focus so far appears to have been on the difficult technical tasks of getting disparate data sources into a form suitable for use and then providing some easy-to-understand visual interpretations as a demonstration of the data. The prospect of the initiative providing the level of capability required to support CAV operations is unclear.

UK ATLAS project

The UK research programs for CAVs have one project that appears to address questions around CAV open data: ATLAS. The Atlas Project will study the feasibility of and requirements of the technologies and services required to deliver autonomous navigation ‘anywhere’ in a safe, reliable and resilient manner (RCUK, 2017). It seeks to address the following challenges (UK CCAV, 2017):

• Defining the datasets required for autonomous operation;

• Examining the communication systems performance requirements in a range of operational scenarios;

• Contributing to the resilient and safe operation of connected and autonomous vehicles;

• Developing protocols for transfer of data between vehicles; and

• Improving the resilience of vehicle autonomy.

Unfortunately, no results are yet available for the project. The project was originally scheduled to report in July 2017, but appears to have been extended to mid 2018.

EU actions in progress

At the EU C-ITS Plenary on 14 June 2017, an update was presented on working group progress, including for a working group on physical and digital infrastructure (European Commission, 2017b).

One of the areas of recommendation from this working group is in the area of consistency between physical and digital infrastructure (European Commission, 2017b):

• “Physical infrastructure will increasingly be complemented by digital

• To avoid confusing and potentially dangerous situations consistency is vital

• To be investigated for which data legal implications are carried over (e.g. broadcast of speed limits)

• Increased collaboration between public & private needed to update digital infrastructure.”



Figure A 13: Relationship of physical and digital infrastructure

Source European Commission, 2017b

The report on the plenary concluded with a table showing a possible approach that takes advantage of both EU research (Horizon 2020 / H2020) and deployment (Connecting Europe Facility / CEF) funding sources. This is shown as Figure A 14: below.

Figure A 14: Possible research and deployment considerations for Europe

Source: European Commission, 2017b



A.8 United States

A.8.1 FHWA call for submissions on Automated Driving Systems

In January 2018, the US Federal Highway Administration (FHWA) called for submissions on a range of issues related to Automated Driving Systems (ADS). These included assessing the infrastructure requirements, ADS-infrastructure interface standards and operating practices that may be necessary for enabling safe and efficient operations of ADS (FHWA, 2018). Questions asked by the FHWA included:

• Q5: What is the role of digital infrastructure and data in enabling needed information exchange between ADS and roadside infrastructure? What types of data transmission between ADS and roadside infrastructure could enhance safe and efficient ADS operations? What type of infrastructure and operations data, if available, would help accelerate safe and efficient deployment of the ADS on our Nation’s public roadways? How might the interface between ADS and digital infrastructure best be defined to facilitate nationwide nteroperability while still maximizing flexibility and cost effectiveness for ADS technology developers and transportation agencies and minimizing threats to cybersecurity or privacy?

• Q9: What variable information or data would ADS benefit from obtaining and how should that data be best obtained? Examples might include information about zone locations, incidents, special event routing, bottleneck locations, weather conditions, and speed recommendations.

122 submissions were received, from across governments, industry and individuals, although there was an under-representation of leading-edge ADS developers. In analysing the submissions provided, key themes identified that are relevant to this project are:

• There was broad agreement on the need for more collaboration between road operators and ADS developers

• Respondents generally shared a similar high-level understanding of the ecosystem to that set out elsewhere in this Austroads project. As the space is still emerging, the most focus was on this high-level understanding with repeated comments about that uncertainty at the detailed level remains to be resolved.

• Map (non-sensor) data is expected to be used for planning and to establish a digital horizon (expected situation) relevant to control but with sensor data being the “truth” for that control

• A focus on C-ITS as part of the methods for exchanging data was particularly evident in government sector submissions, although there was a split between those advocating DSRC and other agnostic between DSRC and alternative methods.

• Little interest was shown by submitters for a national aggregator (or similar). Value was instead seen in standardisation of data sets and methods – generally with reference to SAE standards developed for use in C-ITS as the mechanism, although with some mention of others (Open AutoDrive, etc). Government submitters were particularly keen for the FHWA to provide national level guidance that included but also extended beyond standards.

• Government submitters expressed keen interest in using data collected by ADS to assist in maintaining road operator data sets



A.8.2 USDOT roundtable on data for AV safety

In December 2017, the United States Department of Transportation (USDOT) hosted a roundtable on data for Automated Vehicle Safety (USDOT, 2018). The roundtable brought together government, industry and academia to assist finalisation of USDOT’s Guiding Principles on Voluntary Data Exchanges to Accelerate Safe Deployment of Automated Vehicles and Framework for Voluntary Data Exchanges to Accelerate Safe Deployment of Automated Vehicles. The roundtable also identified seven high priority use cases for data exchange:

• Monitoring Planned and Unplanned Work Zones

• Providing Real-Time Road Conditions

• Diversifying Automated Vehicle Testing Scenarios

• Improving Cybersecurity for Automated Vehicles

• Improving Roadway Inventories

• Developing Automated Vehicle Inventories

• Assessing Automated Vehicle Safety Features and Performance.

The USDOT has four guiding principles for their involvement as the federal government actor in voluntary data exchanges:

1. Promote proactive, data-driven safety, cybersecurity, and privacy-protection practices

2. Act as a facilitator to inspire and enable voluntary data exchanges

3. Start small to demonstrate value, and scale what works toward a bigger vision

4. Coordinate across modes to reduce costs, reduce industry burden, and accelerate action

The framework validated through the roundtable sets out a number of categories of data exchange, noting differences between categories as to the goals, how data is generated and used and the real-world methods used to achieve this:

• Business-to-Business (B2B);

• Business-to-Government (B2G) and vice-versa;

• Infrastructure-to-Business (I2B) and vice-versa; and

• Open training data (ie. libraries of roads, signage, testing edge cases).

A.8.3 US public private cooperation agreements

The need for cooperation between public road operators and private sector mapping service providers appears to have been recognised in at least some US jurisdictions. PSC and CAR (2017) provided two examples of this occurring:

• The Michigan Department of Transportation (MDOT) has partnered with HERE since 2009 in a two-way exchange of data. HERE also is creating a precision HD map of Mcity.

• The Waze Connected Citizens Program is a two-way free data share with public partners that started in 2014. Waze, with 65 million active monthly users, gives public agencies data about system-generated traffic jams and user-reported traffic incidents, including jams, accidents, hazards, construction, potholes, roadkill, stopped vehicles, objects on the road, and missing signs. In turn, public agencies give data to Waze according to specific data formats and categories, including feeds from road sensors, real-time traffic data, and planned road work and road closures. In the United States, 72 partnerships currently exist between cities, counties, state DOTs and Waze.



A.9 Open Autodrive Forum (OADF), Navigation Data Standard (NDS) and TPEG

The representation of SENSORIS in Figure A15 includes reference to some international initiatives – the Open Autodrive Forum (OADF), the Navigation Data Standard (NDS) and the Traveller Information Services Association’s (TISA) TPEG protocol. OADF is an collaboration group for the sub-groups represented by NDS, TISA and SENSORIS, as well as for ADASIS. The purpose of each of these sub-groups is more complementary than overlapping; together they provide a more complete set of standardised approaches.

Figure A 15: Automated driving data chain


How these protocols and groups fit together is covered well in the diagram above (Figure A15). The most relevant data flow for this project is from public authorities to static and dynamic services, and this is shown to use TN-ITS, as described in Section A6.1.3. NDS, TPEG and SENSORIS all have roles to play in the data ecosystem and additionally ADASIS is shown for its role inside the vehicle.

NDS is a Navigation Data Standard used by a range of auto industry navigation stakeholders – mapping providers, navigation equipment providers and OEMs (Sasse, 2017). NDS is used widely in Europe, with some use in China, Japan and North America. The NDS map model is shown in Figure A16 below.



Figure A 16: HD Map Layers in the NDS Navigation Data Standard

Source: Sasse, 2017

TPEG stands for the Traffic Protocol Experts Group. The TPEG protocols are used to communicate traffic and traveller to in-car systems. Current in-car systems that have real-time traffic information will generally use either TPEG or a predecessor method. As it relates to real-time traffic information, TPEG is of most relevance for road works and event information as well as congestion alerts. TPEG is the protocol used from the traffic information provider to the vehicle; between traffic management centres a protocol such as DATEX II is more likely to be used to exchange similar information.

A.10 References

References in this list are those used in this Appendix A Background Research Working Paper and Appendix B State Open Data policies and portals

AASHTO (2001), A Policy on Geometric Design of Highways and Streets, American Association of State Highway and Transportation Officials, Washington DC, USA. http://nacto.org/docs/usdg/geometric_design_highways_and_streets_aashto.pdf

Australian Automobile Association (2016) Productivity Commission issues paper: data availability and use

Australian House of Representatives Standing Committee on Industry, Innovation, Science and Resources (2017) Social issues relating to land-based automated vehicles in Australia

Austroads (2010) The Commercial and Core Function Role of Road Agencies in Providing Raw Data and/or Traveller Information, AP-R352-10

Austroads (2013) AP-R432-13 Emerging Digital Mapping Requirements for C-ITS,

Austroads (2015) Concept of Operations for C-ITS Core Functions,

Austroads (2017), Assessment of Key Road Operator Actions to Support Automated Vehicles, https://www.onlinepublications.austroads.com.au/items/AP-R543-17

BITRE (2017) National Infrastructure Data Collection and Dissemination Plan, Consultation Draft September 2017, Commonwealth Bureau of Infrastructure, Transport and Regional Economics, Canberra

http://nacto.org/docs/usdg/geometric_design_highways_and_streets_aashto.pdf

https://www.onlinepublications.austroads.com.au/items/AP-R543-17



Borzachiello M, Boguslawski R and Pignatelli F (2016) Improving accuracy in road safety data exchange for navigation system: European Union Location Framework Transportation Pilots, European Commission Joint Research Centre Technical Report

Chignard S (2013) A brief history of Open Data, Paris Innovation Review, http://parisinnovationreview.com/articles-en/a-brief-history-of-open-data

Deloitte (2017) Assessing the value of TfL’s open data and digital partnerships,

DIRD (2017) Terms of Reference for Data Collection and Dissemination Plan, Commonwealth Department of Infrastructure and Regional Development https://bitre.gov.au/data_dissemination/tor.aspx

Doecke S and Woolley J (2010) Cost Benefit Analysis of Intelligent Speed Assist, Centre for Automotive Safety Research

DPMC (2015) Australian Government Public Data Policy Statement, Commonwealth Department of Prime Minister and Cabinet https://www.pmc.gov.au/sites/default/files/publications/aust_govt_public_data_policy_statement_1.pdf

DPMC (2016) Australia's first Open Government National Action Plan 2016-18, Commonwealth Department of Prime Minister and Cabinet, https://ogpau.pmc.gov.au/australias-first-open-government-national-action-plan-2016-18

ERTICO (2017) TN-ITS, http://tn-its.eu/

European Commission (2011) ITS Action Plan, D7 –Final Report, Action 1.3 - Availability of Public Data for Digital Maps

European Commission (2017a) INSPIRE knowledge base, http://inspire.ec.europa.eu/

European Commission (2017b) C-ITS Plenary, 14th June 2017

Government 2.0 Taskforce (2009) Engage Getting on with Government 2.0, Report of the Government 2.0 Taskforce, Commonwealth Department of Finance and Deregulation, Canberra

FHWA (2016) Federal Automated Vehicles Policy: accelerating the next revolution in roadway safety, US Federal Highways Administration

FHWA (2018) Docket no. FHWA–2017–0049, Automated Driving Systems, US Federal Highways Administration, https://www.transportation.gov/regulations-fr/notices/2018-00784

Infrastructure Partnerships Australia (2017) Automated vehicles: do we know which road to take?,

Isaac L (2016) Driving Towards Driverless: a guide for government agencies, WSP | Parsons Brinckerhoff

Kyrouz M (2017) What’s New in the Latest California DMV Regulations for Autonomous Vehicles?, https://medium.com/smart-cars-a-podcast-about-autonomous-vehicles/whats-new-in-the-latest-california-dmv-regulations-for-autonomous-vehicles-32608f308f05

Miller B (2015) California DMV creates first public data set of driverless car crashes,

NTC (2016) Regulatory barriers to more automated road and rail vehicles issues paper, National Transport Commission, Melbourne

NSW DFSI (2016) NSW Government Open Data Policy, NSW Department of Finance, Services and Innovation, https://www.finance.nsw.gov.au/ict/resources/nsw-government-open-data-policy

http://parisinnovationreview.com/articles-en/a-brief-history-of-open-data

https://bitre.gov.au/data_dissemination/tor.aspx

https://www.pmc.gov.au/sites/default/files/publications/aust_govt_public_data_policy_statement_1.pdf

https://ogpau.pmc.gov.au/australias-first-open-government-national-action-plan-2016-18

https://ogpau.pmc.gov.au/australias-first-open-government-national-action-plan-2016-18

http://tn-its.eu/

http://inspire.ec.europa.eu/

https://www.transportation.gov/regulations-fr/notices/2018-00784

https://medium.com/smart-cars-a-podcast-about-autonomous-vehicles/whats-new-in-the-latest-california-dmv-regulations-for-autonomous-vehicles-32608f308f05

https://medium.com/smart-cars-a-podcast-about-autonomous-vehicles/whats-new-in-the-latest-california-dmv-regulations-for-autonomous-vehicles-32608f308f05

https://www.finance.nsw.gov.au/ict/resources/nsw-government-open-data-policy



Qld DSITI (2017) Queensland Government Open Data Policy Statement, Queensland Department of Science, Information Technology and Innovation, https://www.qld.gov.au/data/qld-data-policy-statement.pdf

Open Data Institute (2017) What is open data? https://theodi.org/what-is-open-data

Ordnance Survey (2017) 5G key to success of automated vehicles, https://www.ordnancesurvey.co.uk/business-and-government/smart/5g-connected-autonomous-vehicles.html

Pandazis JC (2017) Challenges on data necessary to serve Automated Driving, SIP-adus workshop dynamic map session, Tokyo, 14 November 2017

Public Sector Consultants and Center for Automotive Research (2017) Planning for Connected and Automated Vehicles,

RCUK (2017) Gateway to Research: Atlas, Research Councils UK , http://gtr.rcuk.ac.uk/projects?ref=132271

Ruston S, Legale E, Mildorf T, Making I, Charvat K and Kozuch D (2015) “OTN data hubs: stimulating innovation by improving access to open geographic information”, Proceedings of the 22nd ITS World Congress, Bordeaux

Sasse V (2017) NDS Status and Activities, Strategic Innovation Program – Automated Driving for Universal Services, Tokyo, 14-16 November 2017

Shimada H, Yamaguchi A, Takada H and Sato K (2015) “Implementation and Evaluation of Local Dynamic Map in Safety Driving Systems”, Journal of Transportation Technologies, Vol.05 No.02(2015)

SA DPC (2013) Open Data Declaration, South Australian Department of Premier and Cabinet, https://digital.sa.gov.au/resources/topic/open-data/open-data-declaration

Taylor A (2017) Australia’s adoption of the International Open Data Charter, https://drive.google.com/file/d/0B44SovahLueTMVNYY3pKNFh6ajAyZklScThvVWdOMlRLbkxB/view

TIC (2016) National Land Transport Technology Policy Framework, Transport and Infrastructure Council http://transportinfrastructurecouncil.gov.au/publications/files/National_Policy_Framework_for_Land_Transport_Technology.pdf

UK CCAV (2017) UK Connected and Autonomous Vehicle Research and Development Projects 2017, UK Centre for Connected and Autonomous Vehicles, https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/650444/ccav-research-and-development-projects-2017.pdf

UK House of Lords (2017) Connected and Autonomous Vehicles: The future?, Science and Technology Select Committee, 2nd Report of Session 2016–17

USDOT (2018) Roundtable on data for Automated Vehicle Safety, US Department of Transportation

Vic DTF (2012) DataVic access policy, Victorian Department of Treasury and Finance, http://www.dtf.vic.gov.au/Publications/Victoria-Economy-publications/IP-and-DataVic/DataVic-Access-Policy

VRA (2016) SENSORIS, Vehicle and Road Automation http://vra-net.eu/wiki/index.php?title=SENSORIS

WA DPC (2015) Whole of Government Open Data Policy, Western Australian Department of Premier and Cabinet https://data.wa.gov.au/open-data-policy

https://www.qld.gov.au/data/qld-data-policy-statement.pdf

https://www.qld.gov.au/data/qld-data-policy-statement.pdf

https://theodi.org/what-is-open-data

https://www.ordnancesurvey.co.uk/business-and-government/smart/5g-connected-autonomous-vehicles.html

https://www.ordnancesurvey.co.uk/business-and-government/smart/5g-connected-autonomous-vehicles.html

http://gtr.rcuk.ac.uk/projects?ref=132271

https://digital.sa.gov.au/resources/topic/open-data/open-data-declaration

https://drive.google.com/file/d/0B44SovahLueTMVNYY3pKNFh6ajAyZklScThvVWdOMlRLbkxB/view



https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/650444/ccav-research-and-development-projects-2017.pdf

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/650444/ccav-research-and-development-projects-2017.pdf

http://www.dtf.vic.gov.au/Publications/Victoria-Economy-publications/IP-and-DataVic/DataVic-Access-Policy

http://www.dtf.vic.gov.au/Publications/Victoria-Economy-publications/IP-and-DataVic/DataVic-Access-Policy

http://vra-net.eu/wiki/index.php?title=SENSORIS

https://data.wa.gov.au/open-data-policy



Wall J, Boland P and Vecovski V (2016) “Rapid deployment of Intelligent Speed Adaptation through the Speed Advisor smartphone application”, Proceedings of the 23rd ITS World Congress, Melbourne

Wall J, Creef K, Boland P, Vecovski V, Prendergast M, Stow J, Fernandes R, Beck J, Doecke S and Woolley J (2011) “Road Safety Benefits of Intelligent Speed Adaptation for Australia”, Proceedings of the 2011 Australasian Road Safety Research, Policing and Education Conference



Appendix B State Open Data Policies and Portals

B.1 Victoria Victoria published its open data policy in 2012 (Vic DTF, 2012). It sets out five principles:

• Government data will be made available unless access is restricted for reasons of privacy, public safety, security and law enforcement, public health, and compliance with the law.

• Government data will be made available under flexible licences.

• With limited exceptions, government data will be made available at no or minimal cost.

• Government data will be easy to find (discoverable) and accessible in formats that promote its reuse.

• Government will follow standards and guidelines relating to release of data and agency accountability for that release.

Victorian government data is primarily published through the data.vic.gov.au portal. There are some supplementary agency portals and sites maintained for data with specific needs, these are generally at least referenced on the data.vic.gov.au portal.

B.2 New South Wales The 2016 NSW Government Open Data Policy and Action Plan replaced an earlier 2013 policy (NSW DFSI, 2016). The NSW policy identifies smart cities as a key focus area for Open Data. NSW’s Open Data principles are:

• Open by default, protected where required

• Prioritised, discoverable and usable

• Primary and timely

• Well managed, trusted and authoritative

• Free where appropriate

• Subject to public input.

NSW Open Data is primarily published through the Data NSW portal. There are some supplementary agency portals and sites maintained for data with specific needs, these are generally at least referenced on the Data NSW portal.

B.3 Queensland Queensland has an Open Data Policy and an Open Data Strategy 2017-2021 (Qld DSITI, 2017). Queensland states that it follows the International Open Data Charter, and its principles are:

• Open by default

• Timely and comprehensive

• Accessible and usable

• Comparable and interoperable

• For improved governance and citizen engagement

• For inclusive development and innovation

Queensland Open Data is published through the data.qld.gov.au portal.



B.4 South Australia

South Australia’s Declaration of Open Data was made in 2013 (SA DPC, 2013). SA’s principles are:

• Open by default

• Proactively released by government in accordance with international best practice

• Available online

• Free wherever possible

• Published using agreed open standards

• Openly licensed for commercial and other re-use

The South Australian open data portal is data.sa.gov.au.

B.5 Western Australia

Western Australia published its Whole of Government Open Data Policy in 2015 (WA DPC, 2015). WA’s principles are:

• Open by default

• Easily discoverable and subject to public input

• Usable

• Protected where required

• Timely

The Western Australian open data portal is data.wa.gov.au.

B.6 Tasmania

Tasmania published its Open Data Policy in 2016. Its principles are:

• Open by default

• Protected where required

• Free where appropriate

• Prioritised

• Discoverable

• Usable

• Primary

• Timely

• Trusted and authoritative

• Least restrictive licensing

• Engaging

Tasmania is the only state not to make primary use of a data.state.gov.au portal for publishing open data.



Appendix C Summarised Interview Findings

This appendix provides a summary of the findings of the interview component. Only generalised findings will be presented rather than detailed interview by interview comments. This is as the interviews were kept confidential and comments non-attributable to assist stakeholders to be able to share sensitive information.

Although the interviews used free-ranging discussion where appropriate, this was off a base structure of questions that were circulated in advance to interviewees. The interview summary findings here are structured using the questions and order of questions that were supplied to interviewees.

Remembering that Open Data is a means, not the end – what should we be seeking to achieve from road operator involvement in the data cycle?

Q1. Stripping this exercise right back to fundamentals, we are talking about a business activity in which road operators make available for use by CAVs certain road attributes that are useful to CAVs. This includes speed limits, turn restrictions, height restrictions, traffic signal operational data, road works activities, parking restrictions including places to set-down or pick-up passengers and other similar attributes. What do you see as the purpose statement for this activity? Is there something more specific than the general concept of supporting CAVs to achieve improved safety, congestion, accessibility and environmental outcomes?

Interviewees were in strong agreement that the objective of CAV Open Data was subsidiary to objectives for CAV deployment:

1. transport policy and strategy wants safety, efficiency, environmental, mobility outcomes;

2. AV deployment may assist that; and

3. data may assist AV deployment.

There was also a desire to encouraging innovation that was aligned with road operators’ objectives and a view from some that this was a part of smart cities agendas.

What data do you see a road operator providing to CAVs? Our preliminary research has identified the following four categories of data consumed by CAVs and originating with road operators as being of interest:

1. Static traffic regulations attributes, such as speed limits and vehicle size restrictions;

2. Temporary traffic changes, including variable speed limits as well as changes due to road works, incidents and events;

3. Traffic signal operational data (current operation); and

4. Parking regulations and availability, including set-down and pick-up locations.

Q2. Are there any additional categories of data you think should also be covered?

Various suggestions were provided, a repeated one was around safety ratings as an input to route planning, also for EV charge stations; notable input from a government that for low volume regional/rural areas, government may need to create the HD maps or map elements (virtual wire rope barriers) that otherwise wouldn't occur commercially. There was also some interest in influencing or directing route choice (not necessarily specifying route, also by placing restrictions).



Q3. For static traffic regulations attributes, such as speed limits and vehicle size:

a. What attributes do you think may be of particular value?

In addition to attributes found in the European ROSATTE/TN-ITS standards, there was some discussion on road layout type attributes (such as dual carriageway). There was also a view that road operator data was particularly useful for new roads that mappers may not have got to. Time-based restrictions (school speeds, shopping centre speeds, turn bans, special lanes) were mentioned in multiple interviews.

b. How do you see CAVs (and any intermediaries) using this data received from road operators, compared to other ways of finding out?

All interviewees accepted that sensors will have primary input status (for determining things like current speed limit) but there were differing views as to whether road operator data is a strongly trusted input or just another input.

c. What do you see to be any business process impacts to road operators of being able to provide suitable data? These business process impacts may extend to how installation and maintenance of the regulatory devices (such as speed signs) is performed.

Experience with the Intelligent Speed Assist initiatives was seen as indicative of the challenges.

d. What do you see as an acceptable latency between a change in static regulation and this being provided by road operators to others?

Most responses expressed a desire to provide in advance (in time). Where not in advance, answers were mixed, from seconds to an hour. It was noted that current processes can mean delay of weeks, particularly with respect from moving from planned to confirmed as implemented with detailed location. It was noted that map update cycles were currently weekly or fortnightly, but with a move to daily by the end of 2018 and real-time beyond that.

e. If you have formed a view as to what standard methods and standards should be used, what do you prefer and why?

There were no firm views expressed on standard methods, neither to accept a European style ROSATTE, TN-ITS approach nor to reject it. From data consumers, a clear preference emerged for real-time API access rather than periodic dumps of data.

Q4. For temporary traffic changes, such as variable speed limits and changes due to road works, incidents and events:


This was a strong area of focus in the interview discussions. There was a recognition of differences between different depths of data, such as: type 1 (event possible / maybe event), 2 (event definitely active now) and 3 (event layout) data. In cases where overhead electronic speed limit and lane use signs on Managed Motorways exist (LUMS) better data was noted as available from control system compared to in general. Strong interest emerged in better access to this live traffic management control system data (traffic signals as well as LUMS / variable speed limits).


Strongest use expected for route planning (avoid passing works / events) as well as an electronic horizon concept for path planning (expected conditions for the driving task).

c. What do you see to be any business process impacts to road operators of being able to provide suitable data? These business process impacts may extend to how approvals for works and events occur as well as notifications of actual start and end times.

Significant challenges were noted in moving from event possible / maybe event to event confirmed as active now to event layout. There was not general acceptance that road operators will get to event layout, with the exception of Managed Motorways.



Some tie in was noted with road space booking type initiatives as these help provide better works data. It was also noted that an Australian pilot project was focussing in this area, including the required business process changes.

d. What do you see as an acceptable latency between a change in temporary traffic arrangements and this being provided by road operators to others?

The desire is to provide in advance (in time); otherwise answers were mixed and ranged from seconds to an hour.


No clear standard method identified, with the exception of a single interview identifying DATEX II.

Q5. For traffic signal operational data:


The good coverage of this data type in C-ITS meant that it was noted as important but generally not discussed in depth. Interest was shown by industry in live signal phase and timing data for complex intersections and when signal visibility difficult.

b. How do you see CAVs (and any intermediaries) using this data received about traffic signals from road operators, compared to other ways of finding out?

Where interviewees discussed this, the general view was that the ADS would determine “truth” from sensors and use any road operator data as a supporting input.

c. What do you see to be any business process impacts to road operators of being able to provide suitable data?

This question was generally not addressed due to coverage within C-ITS initiatives.

d. If you have formed a view as to what standard methods and standards should be used, what do you prefer and why?

No interviewees disagreed with the notion that C-ITS methods were the most likely approach to be used for this data type.

Q6. For parking attributes, including set-down and pick-up locations:


Both parking restrictions and parking availability (for public spaces) are of interest.


This data type was seen as most valuable for route planning (where to try to pick up/set down, charge, etc).

c. What do you see to be any business process impacts to road operators of being able to provide suitable data? These business process impacts may extend to how installation and maintenance of the parking restriction signs is performed.

Coordination across a large number of local governments with varying capabilities was noted as a challenge.

d. What do you see as an acceptable latency between a change in static regulation and this being provided by road operators to others?

Challenges were noted in tracking updates of restrictions as well as the cost of getting access to live data – it is technically possible to monitor on street spaces, but not considered affordable for many local governments.


No interviewees identified a suitable standard for this data type.



How does a CAV determine truth for the relevant data types?

Q7. While the road and parking signs and line marking remain the regulatory devices, what role(s) can any database be allowed to play in assisting CAVs to navigate, guide and control their way around the road network?

The notion of "truth" or "ground truth" is perhaps not the best here, but rather the question is what data is actioned by the ADS. The predominant view was that the sensor data was take priority in being actioned (ie. Traffic Sign Recognition vs map data speed limit), but some interviewees felt that the ADS should action whatever the most conservative input was.

Q8. How should a CAV reconcile any differences between received traffic signal operational data (including SPaT information) with what it may observe on site?

This question was generally not addressed separately, but in conjunction with Q7 above.

<<Q9 was omitted from the numbering, next question is Q10>>

Reliance on data quality and Open Data

Q10. Although Open Data in a general sense refers to principles around making data available by default, it has developed in Australian governments a typical methodology that revolves around use of a data.state.gov.au portal and Creative Commons Attribution licencing. This licence type excludes any warranties for accuracy and accepts no liability for how the data is used. How relevant do you see this standard Open Data approach to be for road operator data consumed by CAVs?

There as general agreement that the Open Data approach can have value, with three areas of discussion around that being:

• Whether the promise of open data is actually being met;

• That we cannot assume that private road operators are bound by Open Data policies made by government; and

• The value (or not) of an improved service focussed on CAV needs with customised offering compared to a baseline Open Data service.

There was some appetite from private sector interviewees to pay for improved data, but only if that was the best commercial option for them when considering also alternative methods to secure data.

A key discussion point was a general expectation that map providers will be bound by quality-based service level agreements (SLAs) to their OEM customers, but that there was not an expectation that government data would necessarily be similarly bound. The map provider would therefore be acting as a value-adding intermediary in this respect as well as through other means.

Planning for and achieving progress

Q11 Is there a need for a road map for road operator data for CAVs? What should be the scope of this road map: should a road map seek to rapidly achieve clarity for this single topic or is there greater value in being part of a more comprehensive CAV readiness roadmap even if that took much longer to develop?

There was general agreement that something would be useful, but also some cautious statements and strong divergence about what should be covered. Data consumers showed interest in clarity on what data road operators will provide, when, and with what quality and format. Road operators showed interest in clarity on what data would be valued by users.



Q12. What opportunities do you see for these CAV data considerations to be progressed through Australian CAV trials and pilots?

There was good support for trials to be used, particularly for the approach of prototyping road operator of the future activities as part of trials (ie. the business processes as well as the data supply). Specific interest in this area related to Managed Motorways, traffic signals and road works and event data. Some sentiment was expressed that current approaches to trials may not maximise value in this space.

Q13. What role, if any, should Australian governments play in overseas trials and pilots that coordinate or explore this area?

There was broad agreement that Australia will be a standards taker rather than standards setter. Interviewees generally (but not universally) supported active participation in standards coordination exercises so that Australia’s needs would be considered.

Q14. What other initiatives and mechanisms could be used to advance progress in this area?

There was no consistent view in response to this question, generally the discussion returned to the areas mentioned above.

Considering the possible service models

Q15. Do you agree that it is likely there will be specialist map service providers acting as intermediaries between road operators and automotive OEMs or Automated Driving System Entities (ADSEs)? Do you see it as likely that any OEMs or ADSEs will want a direct relationship with road operators on data consumed by CAVs?

Interviewees generally accepted that there will be a role for map service providers (with most mentions being of a small number of global-scale companies) but not that there would necessarily be map providers between road operators and OEMs/ADSEs in all circumstances. It was noted that even the map providers do not yet see the ecosystem structure as being settled, including for their role in it.

Q16. Do you feel that there will be similar intermediaries between CAVs and road operators for data generated by CAVs?

Interviewees saw a probable role for map service providers but there was also some support for more direct (vehicle to road operator) interactions for CAV generated data including through C-ITS methods.

Q17. Is there a requirement for a single national road operator data aggregator or hub to minimise the number of interfaces for each party in the ecosystem? If so, what would you see as its role, who might perform this role and how might it be funded?

The responses to this question are best viewed as split between advocacy/support and aggregation.

There was general support for some sort of national advocate for harmonisation. Strong preferences were expressed for data being available, well-structured and well-described. There was some support that the work of a national advocate would be focused on those areas over a notion of national consistency.

With regards to an aggregator, there was a mix of support and caution regarding the speed and value proposition. Support for an aggregator appeared stronger amongst road operator suppliers of the data than the private sector users. These users of the data expressed a strong preference for live direct access APIs, not periodic data releases. Access to local government data was noted as an area where an aggregator may have more value due to the number of local governments, but again latency of the data through this method was a significant concern.

Q18. If there were to be a single national road operator data aggregator or hub, what role may they play in establishing “truth” for the road operator data used by CAVs?

The clear preference was that the data owner had responsibility for data quality. A national advocate may assist in providing frameworks around to support this. Data users noted interest in quality indicators being attached to data.



Q19. What role should data generated by CAVs play in the update of road operator databases and how may this occur? What opportunities exist for a service model that enables a commercial exchange of information? An example would be a CAV sensing a speed sign to have a different value to that in the database.

There was universal support that CAV generated data would be beneficial and desirable as a way of improving road operator data sets. This CAV generated data has many uses, both for updating data sets such as speed limits and current works/events as well as for other road operator data sets such as asset condition data. A divergence of views emerged however in two areas:

• Whether providing some minimum set of data without charge to a road operator should be a condition of access to the road network; and

• Whether (and in what circumstances) road operators would use crowdsourced data could be used without undertaking field verification.

With regards to the first area of divergence; there was general agreement that there was a role for commercial value-added data generated from CAVs to be marketed to road operators. Where disagreement emerged was as to whether this was all that was needed or whether the minimum (no charge) data set was also necessary. Views on this varied across stakeholders, including within stakeholder types.

With respect to crowdsourcing, an interesting point is that this crowdsourced updates without manual review are already occurring, including in some government apps. The general approach is that such automated changes are on the basis of 3-5 reports at a consistent location.

Q20. Currently road operators typically maintain a geodatabase of their road network (with various proprietary names different between operators). Do you see any opportunity to make use of the road data models used by CAV as an alternative to or complementary to the road operator’s own database? How far into the future may a changed approach occur, if it does?

Views on this topic were quite mixed. A number of road operators noted moves in this direction and even some well-established practice along these lines. Support within private sector interviewees was mixed, including a perspective that government should have control over their digital infrastructure for both sovereignty and commercial reasons.

Q21. Can you foresee a time when a database of traffic regulations (speed limits, truck restrictions) is either the authoritative regulatory device or carries at least equal weight to roadside signs? If so, what high-level changes in vehicle fleet and regulatory approach has needed to happen for this change to have occurred?

Answers to this question were often linked to SAE Level 5 automation, although it was also noted that pedestrians and cyclists remained relevant even in that circumstance. Generally this was seen as occurring beyond the time horizon for this project, if at all, however some interviewees did see this to be more of a medium-term than long-term prospect.



Appendix D Summarised Notes of Stakeholder Workshop

Austroads workshop note

CAV Open Data, Melbourne, 1 Feb 2018 – Chris Jones

D.1.1 Attendees

Andrew Somers (Transoptim), Dale Andrea (VicRoads), David Gray (TfV), Hossein Parsa (TfV/VicRoads), John Wall (TfNSW), Niko Limans (Austroads/QDTRM), Phillip Blake (DPTI), Phillip Reid (VicRoads), Scott Martin (DIRDAC), Steven Shaw (RMS), Stuart Ballingall (Austroads, TfV), Wayne Harvey (VicRoads), Marcus Burke (NTC), Peter Lee (TfNSW), Wayne Cannel (MRWA), Nick Koukoulas (Austroads), Chris Jones (Austroads), Ben Wilson (Here – Industry presentation), Mehrnoush Ghorbani (Here – Industry presentation)

D.1.2 Purpose

The workshop purposes were to:

• Provide consolidated feedback to Austroads member agencies on findings on CAV open data to date.

• Achieve a common understanding among stakeholders of the state of play, issues, barriers and benefits of CAV open data

• Agree on any outstanding points of difference between state road authorities

• Achieve agreement on any recommendation or future strategic actions identified in the project to date

D.1.3 Summary of key notes from workshop

Opening remarks

The session was opened with an overview of key issues that have led to the focus of the workshop. These include:

• We are focused on data generated by road operators and consumed by CAVs

• Privacy and data access/ownership questions are out of scope, and considerations within the NTC’s current work program

• Electric vehicles are also emerging and may present physical and digital infrastructure needs. However, it was noted that the scope of the CAV program doesn’t relate to electric vehicles. Furthermore, electric vehicle policy is subject to ongoing discussion through the National Land Transport Technology Action Plan update, handled through DIRDAC.

• To date, we have not seen significant needs for road operator data in trials, because many of trials have been based on sensor only vehicles such as shuttles, and level 2 passenger cars (ACC, LKA, SAS equipped)



• Therefore the key experience to date has been through the Intelligent Speed Assist initiatives across many jurisdictions

• Local Government Authorities have been less active that State Road Authorities in considering their role in the provision of road data to CAVs

• Policy objectives stated by government authorities to date are first to enable the CAV, and then secondly direct, and influence the travel of the CAV to optimize network outcomes.

• To date, there is only one data set that stakeholders are aware of where a data source is given higher authority than a roadside traffic control device, and that is in the area of heavy vehicle permits (for restricted access arrangements).

• Much further in the future, but with great uncertainty, many stakeholders can conceive of map data becoming a source of regulatory truth. However, it is not considered an immediate prospect.

The role of road operator data

• There was general agreement that road operator data would be an important, but not critical asset for connected and automated driving. Road operator data will be consumed by vehicles, but through enriched datasets, or as a source of additional information for vehicle on-board sensors.

• It was also generally agreed that road operator data was unlikely to be relied on by automated vehicles for vehicle control functions (steering, braking, heading, headway) in the near term, but that services that would consume road operator data will continue to appear in near term products (such as speed data for speed assistance systems, or SPAT data for GLOSA).

• These services will significantly benefit from road operator data, but industry have multiple sources for collecting data (particularly on board sensors).

• An industry presentation outlined that a key interface with road operators was likely to be around dynamic map changes from regulatory changes. Examples given were lane changes or closures, road changes, and change of turn directions. These datasets would be key for the safe navigation of automated vehicles during the trip planning and navigation phases. Their view was that the key role for road operators was around regulatory changes, as this is already the ‘natural’ role for road authorities to play. A further role could be to move to use this data strategically to influence the network optimization of roads. E.g by predicitively providing data to guide route planning of automated vehicles. Further toward a 2030 horizon, there are opportunities for road authorities to better orchestrate the network through provision of road operator data.

Barriers to the use of road operator data

• Given the above points on the role of road operator data, it was noted that the use of the creative commons license wasn’t considered a significant barrier as warrants of accuracy or reliability were unlikely to be significant when other complementary data sources were available. This was supported by the stakeholder interviews with industry where issues of road operator liability were not raised as significant concerns.

• An industry presentation made clear that the quality of road operator datasets was not a considerable barrier for their organization, as their emerging high definition mapping platform would use multiple data sources.

• Andrew noted that industry had noted during consultation that road operators’ position of not taking liability was not a barrier for them, but that higher quality data made it more likely for it to used and consumed in CAD applications.

• Notwithstanding the above, it was noted that the waiver of liability within the creative commons license did not remove road operator duty of care for ensuring that the accuracy and reliability of data was supported. It was noted that common law still applies regardless of commercial agreements. What this means was uncertain and could be dependent on road operator plans under their road management acts which establish their processes for managing liability for provision of public infrastructure.



• Another potential barrier that had previously been raised was the need for data users further down the data value chain to negotiate with multiple road authorities for integration in a final service, supplying data in a range of formats. At this stage, it wasn’t clear that barriers exist as data aggregators have built into their business models the capability to deal with multiple sources of data. Some opportunities may exist to improve harmonization, consistency and standardization of data, but this wasn’t a strong message from industry.

Policy guidance

• With regards to policy guidance, the open data policies within each Australian jurisdiction compel road authorities to provide open access to data sets in a form that is consumable by industry for development of third party services. However, the open data policy does not compel road authorities to develop new data sets or services to support emerging technologies. For example, if a dataset does not exist for traffic roadworks that may be critical to operation of a CAV, open data policies do not demand that it be developed. Rather, it just ensures that any dataset that is collect is consumable. Therefore there is a gap with a policy framework toward the development of road operator datasets to support CAV.

• Generally there was still strong support for the open data policy, and the ongoing need to make road operator datasets as freely available as possible. While commercial arrangements are less clear into the future, there was general support for policies that enabled industry to develop 3rd party services from road operators’ data.

• One state noted that they are already in the field of providing road operator data through a commercialised product. However, it was noted that these services are optional, ‘nice-to-do’ functions, rather than a core business objective for their department.

• Without compelling guidance, road operators note that they respond to support new data sets on a case-by-case basis. This could be through parliamentary inquiries which demand the provision of data sets. Or through individual policy actions such as the previous Australian Intelligent Speed Assistance work.

• The costs of collecting and servicing road operator data sets is considerable. It was noted that the development of one state’s speed zone management system was $959,000, with a $1.2 million per annum operating budget. The business case for this data remains unclear, as the demand for this data has not been high.

• A key delivery mechanism for this speed zone has been through a state branded smartphone speed assistance application.

• Another reason for uncertainty in investment in high quality road operator data was the lack of set of requirements from industry. While most road operators are aware of a general desire for higher quality road operator data, particularly for highly transient datasets (roadworks, SPAT, traffic etc..), the lack of a clear industry position on key datasets confounds investment. It was noted from the industry perspective that the vehicle industry supply vehicles to a global market which could in some cases be supplied in markets where no road operator data is available. Therefore services must be able to be built on the assumption of market available data only.

• Underlining this point, was a comment by one state that they are already working on a free data service of key motorway datasets. However, in trying to determine what are key industry requirements, there was little guidance provided other than that all data sets were of value, but where they are made available, this should be done on a consistent basis with other road authorities.

• Another reason for investing could be through a vision for support of services – for example, traffic sign recognition was noted as a case where road operators only learned of issues with camera vision systems reading road signs, post deployment in the market. This general concern drives a general desire and awareness that road operators need to continue to invest in higher quality datasets to support CAV, but without a specific goal or target to aim for.

• Confounding the logic to invest in road operator data to support CAVs was the possibility of the market being capable of servicing and supporting data sets regardless of the provision of road operator data. This could be seen as an opportunity for road operators not to invest in certain data sets.



Strategic guidance and next steps

• Broadly, there was agreement that Government datasets focused on regulatory changes were the most obvious point of difference in the CAV data value chain. Most clearly this is in the area of highly dynamic changes including SPAT, motorways speed and lane closure. However, other changes including roadworks, construction zones and speed zone changes across fixed assets are also of value, particularly where these changes could be predicted and provided ahead of implementation.

• It was noted that in many cases, the business case is not always clear for industry to map data across areas of Australia with more challenging commercial propositions such as remote and regional areas. There are service gaps across these parts of the network in major mapping company datasets. While it was not clear from the workshop, there was a concern that market failure may lead to future gaps in services in these regions where government intervention may be required.

• There was a suggestion that further work is needed to understand where market failures exist or may exist, which datasets will lead to fastest introduction, and which datasets address the most critical road transport outcomes (road safety, mobility, sustainability). This is not within the scope of this project but would be of interest in future projects. Given that industry can’t necessarily tell us which datasets are of key importance then, prioritization may need to occur on the road operator side.

• With emerging datasets from industry that may enrich road operator data sets, there is a concern that over-investment could occur in datasets that become redundant as emerging technology becomes more capable than road operators in providing data.

• With regards to strategic next steps, several opportunities were discussed. These included:

– developing a roadmap or policy statement to guide road operators’ greater investment toward CAV Open Data, and address any notable barriers

– further work with industry to understand future road operator data needs through trial deployments

– establishing a road operator/industry forum to continue to discuss the problem

– seeking further opportunities to learn from European initiatives (such as ‘twinning projects)

• Given that a significant barrier or problem beyond investment uncertainty was not identified, a roadmap was generally not supported.

• Further, it was noted, that industry’s preference for engagement with road operators is primarily through the Austroads’ Industry Reference Group and that further mechanisms were unlikely to achieve strong participation until a specific project was identified.

• There was strong appetite for further work and collaboration through trial deployments with industry. This seemed to be the mostly strongly supported next step as it would help road operators further understanding the emerging data market, and their role in it.

• There was broad or general support for learning from European projects, but a specific example or direction was not provided.

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