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What does vehicle automation mean for infrastructure, road user behaviour and safety?

Dr. ir. Haneen Farah

Delft University of Technology

April 16th 2019

h.farah@tudelft.nl

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Content• My Research & T&P Department, TU Delft

• Vehicle Automation

• Implications on Infrastructure

– Physical & Digital

• Implications on Road User Behaviour and Safety

– Control transitions

– Behavioural adaptation

• Future Research Focus

• Workshop & Discussion

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Transport & Planning

Civil Engineering andGeosciences (CiTG)

• Fundamental research

• Empirical basis

• Application-oriented perspective

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TTSLab Research Themes

The lab will address the following research themes:

Safety Models

Road Users Safety

Infrastructure Design and

Safety

Vehicle Automation and Safety

Low and Middle Income

Countries

Methods and Data

T r a f f i c a n d T r a n s p o r t a t i o n S a f e t y L a b ( T T S L a b )

Haneen Farah

Marjan Hagenzieker

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Research oriented labs

Located at the Green Village, at TU Delft;

RADD facilitates experimentation with automated vehicles in real-life conditions;

Bike Operations Research lab

• Monitoring and management of bicycle flows;

• Monitoring mobility;

• Smart Bike experiments;

• Evaluate interventions and policies to improve the safety and throughput of bicycle flows.

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Vehicle Automation

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Motivation

Source: European Transport Safety Council (ETSC), https://etsc.eu/euroadsafetydata/

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How many sensors in AV?

Source: https://www.novatel.com/industries/autonomous-vehicles/#technology

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Automation Humans

• Faster reaction • Understanding complex environment

• Accurate control • Intention prediction of other participants

• Reliable performance over time

• Observe and react to multiple vehicles

• Understanding complexenvironment

• Delayed reaction

• Prediction of other participants

• Inaccurate control

• Limited line of sight (sensors, range, blocked by other vehicles)

• Behavioural adaptation,workload, fatigue, situational awareness

Source: Meng Wang, TU Delft

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Assessment of sensor performance across driving tasks

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Crash and injury rates for conventionalvehicles and self-driving vehicles (2013)

Distance travelled by self-driving vehicles is relatively low (about 1.2 million miles by 50 vehicles, compared with about 3 trillion miles by 269 million conventional vehicles in US);

Driven in limited (and generally less challenging) conditions (e.g., snow, complex traffic);

Their exposure has not yet been representative of the exposure for conventional vehicles;

Based on 11 registered self-driving vehicles crashes (9 property damage, 2 with injuries).

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-34.

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Automated Vehicles Crash CategoriesThere are three broad potential crash causation scenarios for automated vehicles:

Faulty human-machine interactions contribute

to a crash.

The automated driving system encounters a situation unforeseen and unanticipated by

its code and algorithms.

Sensors do not detect a critical part of the

vehicle environment or incorrectly identify it.

Potential crash causation scenarios

As vehicles move up to higher levels of automation on the SAE scale, the relative importance of these sensing inputs increases as the potential for human drivers to make corrective action diminishes and, ultimately, disappears.

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SAE Levels of Automation

Shladover, S. E. (2016). The Truth about “Self-Driving” Cars. Scientific American, 314(6), 52-57.

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Relevant Aspects to Vehicle Automation

• Traffic flow efficiency • Logistics

• Safety • Ethical and social implications

• Cybersecurity • Driving education

• Equity • Legislations

• Infrastructure • Public acceptance

• Travel mode choice and travel behaviour

• Urban planning & built up environment

• Regulations • …

• Energy and emissions

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What does vehicle automation mean for infrastructure?

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Physical & Digital infrastructure

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‘Generations’ of Roads

4,000 B.C.

Stone paved roads

8th century

Early tar-paved road

19th/20th century

Modern roads

From 21st century

Smart road

The road network has taken thousands of years to develop…

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‘Generations’ of Vehicles

19th century

Powered automobile

18th century

Steam-powered vehicle

Early 20th century

Electric automobiles

From 21st century

CAV vehicles

Roads of the future might require higher flexibility…

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Vision of the Future: Forever Open RoadLa

mb,

M. J

., C

ollis

, R.,

Dei

x, S

., K

riege

r, B

., H

autie

re, N

., &

IFS

TT

AR

, F

. (20

11, S

epte

mbe

r). T

he f

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road

. Def

inig

the

next

gen

erat

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road

. In

AIP

CR

Wor

ld C

ongr

ess,

Mex

ico.

“The For-ever Open Road will:

• Be constructed from pre-fabricated elements, built and maintained using sustainable materials.

• Have adaptable capacity provision, and built-in services and communication systems.

• Measure its own condition, harvest energy, and clean and repair itself.

• Communicate with vehicles and will allow for automated driving”.

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Do we need dedicated lanes for automated vehicles?

Automated driving & Infrastructure

Will there be more or less congestion?

What should be the maintenance level of lane marking?

Will we need more or less road infrastructures?

What is the right vehicle grouping strategy?

What do we need to do in pavement design / maintenance to be ready for automated driving?

Do we allow mixed human and automated traffic?

What is the effect on merging / weaving configuration?

How accurate and detailed the dynamic digital maps of the infrastructure and its surrounding need to be?

Do we still need road signs?

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Operational Design Domain (ODD)

ODD Definition:

“Operating conditions under which a given driving automation system or feature thereof is specifically designed to function, including, but not limited to, environmental, geographical, and time-of-day restrictions, and/or the requisite presence or absence of certain traffic or roadway characteristics”. (SAE 2018)

Developments in road infrastructure are necessary in order to realize the vision of automated transportation

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Operational Design Domain (ODD)

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23Source: Tom Alkim's conceptual structuring of ODDs.

Limited ODD

24Source: Tom Alkim's conceptual structuring of ODDs.

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25Source: Tom Alkim's conceptual structuring of ODDs.

26Source: Tom Alkim's conceptual structuring of ODDs.

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Physical infrastructure consists of

the:

• Roads

• Road signs

• Road markings

• Gantries

• etc.

that form part of the physical world where vehicles operate.

Physical and Digital Infrastructure

Digital infrastructure is defined as

the static and dynamic digital representation of the physical world with which the vehicle will have to

interact:

• High definition maps

• Dynamic traffic information

• Advanced advice related to optimum routing.

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Physical Infrastructure: Geometrical road design

Case of full automation and connectivity (100% penetration):• Reduced width standards• New additional lanes• Platooning• Higher speeds

Experimentation and first adoption areas

Sight distance:• Limited line of

sight• Connectivity V2X

Braking distance:• Human tolerance• Energy consumption• Emissions• Other human drivers

(case of mixed traffic)

Lane marking/ signs:• Design• Visibility• Positioning• Harmonization/

consistency

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Physical Infrastructure: Structural pavement design & structures

Precise positioning

Reduce lane width

New wear patterns

Program AV to drive more

evenly across the lane width

Can we still reduce the lane

width?

Increased lane capacity Decreased

wheel wander

Increased traffic speed

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Digital Infrastructure

Dig

ital

Infr

astr

uctu

re

Sensors, Connectivity & Cloud

Digital Maps and Road Database

Exact Positioning of the Vehicle

V2X

Challenges & milestones

• Affordable sensing technology

• High-precision positioning• Communication technology• Digital maps

Digitalization of road infrastructure is a key issue for road operators.

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Digital Infrastructure: Sensors, Connectivity & Cloud

Existing infrastructure sensors (such as traffic cameras)

Info on vehicle surrounding, missed by vehicle on-board

sensors

Requirements for connectivity at Level 4 automation

• Speed limit beacons (controlling speed)

• Magnetic nails/ reflective striping (lanekeeping)

• Infrastructure-assisted merging and lane-changing (aided by RSUs)

• Safety and warning messages (queues/ enhancing traffic signal operation)

ITS message signs• Can be replaced

by V2X• Needed in case of

connectivity failure

Autonomous Vehicular Clouds (AVCs)

vehicular computing, communication, sensing, power and physical resources

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Digital Infrastructure: Digital Maps and Road Database

For AVs, the road database is considered as the most fundamental element.

Digital maps need to be:

• Highly detailed (3D lane geometry)

• Highly accurate

• Richly attributed (lane-level attributes, road DNA)

• Includes static and dynamic data

Velocity Profile Planning ModuleAdapts the speed of the vehicle based

on the road design characteristics.

In-vehicle control processes can be supported by:

• Info on pavement surface quality, curves, hills, lanes, speed limits, signs, traffic signals;

• Info on difficult weather conditions;

• Non-moving road items (reduction in sensor recognition);

• Using landmarks with reliable positions (improves relative positioning).

Crowd Crowd Crowd Crowd sourcingsourcingsourcingsourcing

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Digital Infrastructure: Exact Positioning of the Vehicle

Challenges

Challenges

Challenges

Challenges

Matching events to the

in-car map-database

Real-time (latency)

Precise positioningCooperative Systems

Real time Real time Real time Real time accuracy in accuracy in accuracy in accuracy in positioningpositioningpositioningpositioning

Can be improved by fusion of Can be improved by fusion of Can be improved by fusion of Can be improved by fusion of several inseveral inseveral inseveral in----vehicle sensors & vehicle sensors & vehicle sensors & vehicle sensors & calibration of the Oncalibration of the Oncalibration of the Oncalibration of the On----BoardBoardBoardBoard----Equipment at gantries along Equipment at gantries along Equipment at gantries along Equipment at gantries along the road.the road.the road.the road.

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Physical infrastructure:

Should the infrastructure be separated for different levels of automation? Or, can roads be designed to safely mix vehicles at different automation levels? Do we need dedicated lanes? Do we need transition zones?

What are the implications of reducing lane width on (1) the deterioration of roads , and the required maintenance frequency; (2) traffic safety performance , and the safety perception of drivers/passengers, and driver behavioral adaptation to conventional roads?

Is it possible to eventually have shorter merging/ weaving configurations and on/off ramps , and do we need longer lengths in intermediate stages when we have mixed traffic?

Do we need more separation between VRU and AVs?

Future Research Directions

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Future Research Directions

Digital infrastructure:

How accurate and detailed the digital maps need to b e and what are the necessary details regarding the geometric design of the roads? What responsibility do road authorities have in providing these maps?

Do we still need road signs and traffic management s ystems in the era of digital maps, accurate positioning, and connectivity between vehicles? to what extent? and how would this change with the increase in automation, and its reliability?

Is the installations of road sensors are critical fo r the safe operation of vehicles , or are the sensors installed in vehicles would be enough to gather and share important information?

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Transitions of Control(Authority Transitions)

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37Source: Tom Alkim's conceptual structuring of ODDs.

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The “Out-of-the-Loop” concept

Mer

at, N

., S

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., Lo

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., E

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Lee,

J. D

., Jo

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E.,

& M

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. (20

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21(

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.

In the loopIn physical control of the vehicle and monitoring the driving situation;

On the loopNot in physical control of the vehicle, but monitoring the driving situation;

Out of the loopNot in physical control of the vehicle, and not monitoring the driving situation, OR in physical control of the vehicle but not monitoring the driving situation.

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• Non-critical control transactions;

• Comparison driving simulator vs. on-road;

• Strong positive correlations were found;

• Drivers were generally faster to resume control in the on-road experiment;

Research Findings: Taking Back Control

Control transition times (seconds) in the on road, and simulator condition

Erik

sson

, A.,

Ban

ks, V

. A.,

& S

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. A.

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Control transitions between ACC and manual driving

System switches off

Control transitions between ACC and manual driving

Driver switches off

Control transitions are not modelled

Microscopic traffic flow models

ACC vehicles have an effect on traffic flow efficiency(Klunder, et al. 2009; Van Driel & Van Arem 2010)

PhD

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is: S

ilvia

Var

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(20

18),

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Con

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Tra

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Control transitions between ACC and manual driving

Peak hours, A99 ~ 35.5 km

23 participants

BMW 5 series with full range ACC

Drive as you do in real life and use the system onlywhen you think it is appropriate

Controlled on-road experiment

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Trajectory of a test vehicle (blue line) and time-space speed contour plots of the lane in which the vehicle was in during the experiment.

Dark blue dots represent ACC Inactive, blue ACC Active, and light blue ACC Active and accelerate.

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DIDC to Active and accelerate

Time after activation

Ramps

When the vehicle decelerates

Cut-in anticipation

Ramps & exits

Approaching a slower leader

DIDC to Inactive

Driver heterogeneityDriver heterogeneity

Control transitions between ACC and manual driving

Var

otto

, S. F

., F

arah

, H.,

Tol

edo,

T.,

Van

Are

m, B

., H

ooge

ndoo

rn, S

. P. (

2017

).R

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Res

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.

The main factors influencing drivers’ choice to resume manual control (not involving lane-changes) with full-range ACC:

DIDC - ‘Driver Initiates transition, and Driver in Control after’

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Behavioral Adaptation

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Behavioral Adaptation

Adaptations in driving behaviour are defined as the collection of behavioural aspects that arise following a change in the road traffic:

Direct when these are intentionally realised through system parameters set by the manufacturer of the system;

Indirect when these are unintended;

(Martens & Jenssen, 2012).

Example:

Technology: ACC systems drivers show an impaired ability to respond to emergency situations.

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STADProject Work packages:

SP1 – Travel and location choice behavior

SP2 – Freight and logistics applications

SP3 – Infrastructure service networks

SP4 – Urban design and traffic safetySP5 – Spatial structure and economy

SP6 - The STAD modelling environment

SP7 – Use cases & demonstrators

Pablo Núñez Velasco

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Trust? Expectations?

Behavior?

https://www.mercedes-benz.com/en/mercedes-benz/innovation/research-vehicle-f-015-luxury-in-motion/

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Interaction between Automated Vehicles & VRU

(Vulnerable) road users • Will they adapt? How? • What do they expect of automated

vehicles? • Trust in automated vehicles?

Infrastructure • Does it need to change based on what

we know about road users? How?

Interactions • How will the interactions (road user –

automated vehicle – infrastructure) look like?

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Virtual Reality Experiment• WEpod

• 360º recordingswith a dedicated camera

• VR glasses

Núñez Velasco, et al. (in prep)

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VR Experiment (continued)

Variables:– Vehicle: WEpod x ‘Normal’ vehicle (Volvo)

– Speed: 10 km/h x 20 km/h– Gaps: 2 seconds x 4 seconds

– Crossing facilities : Zebra x None– eHMI: Green x Stop x None

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VR Experiment (continued)

• 55 participants (32 males; 23 females)• Age of participants: 21 – 37• Mostly recruited from TU Delft• Experiment total duration (50 minutes) • Compensation 15€

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Conclusions• Type of vehicle is not significant (however similar to conclusions

of other studies);

• Crossing intention was higher with zebra crossing and with bigger gap size;

• Speed of the vehicle showed a counter intuitive result;

• Trust in automated vehicles affected the crossing intention positively;

• Recognizing the Wepod as an AV affected the crossing intentions negatively;

• eHMIs affected significantly the crossing intentions.

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Future SystemsTo be implemented in real-world need to be:

• Validated (proof their capabilities);

• Sensitive to cultural and individual differences;

• Adaptive to the environment;

• Scalable (to be able to operate with any number of vehicles or VRUs on the street).

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Human Drivers’ Behavioural Adaptation when Driving Next

to a Platoon of Automated Vehicles on a Dedicated Lane

Mas

ter

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All the four scenarios had an identical environment (e.g. buildings, trees andlandscape), except for the design configuration of the DL.

A driving simulator experiment was designed to test four design configurations;

34 participants (21 males), 20-30 years old (M = 23.9, Std. = 2.2)

2-10 years of driving experience (M = 5.1; Std. = 1.5)

Driving Simulator Experiment

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Driving Simulator Experiment

(a) Baseline scenario (BL)

(b) Separated continuous access lane (CAL)

(c) Separated limited access lane with buffer (LAL)

(d) Separated limited access barrier lane (LABL)

The four road design configurations tested in the driving simulator – following the Dutch design standards.

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Driving Simulator Results

Average THW over time per scenario.

Scenario Average THW (s) Std. THW (s)

Baseline (BL) 3.24 1.55

Limited Access Barrier Lane (LABL) 3.17 1.56

Limited Access Lane with (LAL) 2.69 1.33

Continuous Access Lane (CAL) 2.48 1.26

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VISSIM Microscopic Simulation

Parameter Levels Values

Road design Scenario 4 BL, LABL, LAL, CAL

Traffic intensity (veh/h) 2 5500, 5000

Penetration rates of Avs (%) 9 10-50, in steps of 5

Weaving section length (m) 3 600, 800, 1000

Lane change distance (m) 6 200-1200, in steps of 200

Parameters and levels in VISSIM

VISSIM was applied To investigate the impacts of a dedicated lane for AVs on traffic flow.

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VISSIM Microscopic Simulation Results

a. Intensity of 5000 veh/h b. Intensity of 5500 veh/h

Traffic flow over different traffic intensities.

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Conclusions

• Human drivers reduced their THW when they drove in the proximity of a platoon of AVs on the continuous access dedicated lane;

• Similar results were found for a dedicated lane with limited access by using a continuous line as a separation;

• Having a guardrail as a separation leads to similar observed THW as in the baseline scenario;

• A dedicated lane for AVs has a positive influence on the traffic flow when a certain penetration rate of AVs is reached:

o 15-20% for Continuous Access Lane, and

o 30-35% for Limited Access Lanes.

• A dedicated lane is most beneficial with higher traffic intensities.

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Thank you for your attention!