automated vehicle technologies for mobility-as-a-service
TRANSCRIPT
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Automated Vehicle Technologies for
Mobility-as-a-Service (MaaS) Solutions
International Automotive Congress 2019, Shanghai
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Agenda
» Introduction and motivation
» Industry activities in the field of Mobility-as-a-Service
» Key technologies for Automated Mobility-as-a-Service
» Summary
Slide No. 2
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Setting the scene – Drivers for Mobility-as-a-Service
Slide No. 3
Urbanisation
70 % of world population
living in cities by 2050
154 h lost due to traffic
congestion in Berlin
Inner city restrictions
Environmental
Protection
Safety
Customer Demands 23 % of emissions caused
by road transport
Local emissions in cities,
ban of ICE vehicles
Liveable cities, reduction of
parking areas
1.2 million people die in
road accidents every year
94 % of crashes involve
human error
~ 600 bn $ harm from loss
of life and injury per year in
USA
Drivers license penetration
decreasing among young
adults (- 7 % in Germany from
2010 to 2018)
Increasing costs and
decreasing relevance of car
ownership
Ageing society
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Definition of Mobility-as-a-Service
Slide No. 4
MaaS definition – Key characteristics
Definition of MaaS Alliance:
The aim of MaaS is to provide an alternative to the use of the private car that may be as convenient, more sustainable, help to reduce congestion and constraints in transport capacity, and can be even cheaper.
Integration of various forms of transport services into a single mobility service
Single application to provide access to mobility, with a single payment channel
New opportunities to serve unmet
demand, helping to meet mobility needs and solve the inconvenient parts of individual journeys
Source: Frost & Sullivan
Types of Mobility-as-a-Service offers
Distance travelled
Co
st &
Co
nve
nie
nce
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Mobility-as-a-Service as a driver for Automated Vehicles
Slide No. 5
Economic efficiency of Automated MaaS Prices per kilometer driven with an autonomous taxi/shuttle*
Source: Deloitte
*Calculation for Germany
Mobility-as-a-Service with Automated Vehicles economically highly attractive:
Autonomous taxis or shuttles are probably cheaper than private car usage or public transportation
Especially in cities with inner city restrictions and high parking fees
Based on calculations from Deloitte:
Kilometer price for an autonomous taxi is expected to be 0.34 € (compared to 2.60 €/km for conventional taxis and 0.44 €/km for private mid-range car)
Kilometer price for an autonomous shuttle with an average of three people is estimated to be 0.15 €
Automated MaaS can be highly competitive for private cars and public transport for everyday trips in cities
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Customer opinions towards Automated MaaS
Slide No. 6
36,0%
56,0%
67,6%
73,5%
Miscellaneous
More Comfort
Increased Safety
Less TrafficCongestions
Opinions towards autonomous vehicles and taxis
85,0%
15,0%
Open for autonomous vehicles?
Yes No Yes No
81,1%
18,9%
Sharing an autonomous taxi?
Results of our survey with 616 part icipants in Germany.
Source: ika, RWTH Aachen University
Which advantages do you expect from autonomous vehicles?
Source: ika, RWTH Aachen University
Time efficiency
Environmentally friendly
Stress reduction
No need to search for parking spaces
Cost reduction
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Agenda
» Introduction and motivation
» Industry activities in the field of Mobility-as-a-Service
» Key technologies for Automated Mobility-as-a-Service
» Summary
Slide No. 7
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Automotive production
OEM (97) & Supplier (46)
143
Automotive ecosystem in Germany
national turnover in billion € (2017)
22
Fueling / charging
Private parking (1)
26
Vehicle insurance
32 Repair & maintenance
64
New car sales
66
Used car sales
33Leasing
Vehicle ecosystem
Mobility ecosystem
Sources: VDA, BDL, Statista
Taxi
Ride hailing
Car sharing
Car rental
Intermodal
mobility
Driverless transport
Logistics
services
Ride sharing
Approaches of incumbents and new entrants towards
Mobility-as-a-Service
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22
Fueling / charging
143
Automotive ecosystem in Germany
national turnover in billion € (2017)
Private parking (1)
26
Vehicle insurance
32 Repair & maintenance
64
New car sales
66
Used car sales
33Leasing
Sources: VDA, BDL, Statista
Taxi
Car rental
Intermodal
mobility
Driverless transport
Logistics
services
Ride sharing
Vehicle ecosystem
Mobility ecosystem
Automotive production
OEM (97) & Supplier (46)
Approaches of incumbents and new entrants towards
Mobility-as-a-Service
Ride hailing
Car sharing
Key takeaways
1
3
2
In future, the mobility ecosystem will be a major profit pool, where today’s players already try to gain their market position
Established players try to extend their business into the new mobility ecosystem – from vehicle production to MaaS provider
New players directly step into the mobility ecosystem trying to make access to the vehicle ecosystem (design, interface etc.)
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Emergence of Mobility-as-a-Service (MaaS) will fundamentally
change the Mobility Ecosystem
Slide No. 10
MaaS ecosystem – Key stakeholders
Mobility users
MaaS operators
SW-Integrators
Vehicle manufacturer
Intermediaries
Cities
Description of the emerging ecosystem
Automotive incumbents have to redefine their position within
the emerging MaaS ecosystem:
Focus on vehicle manufacturing only
Developing and marketing of an own MaaS offer
Definition of economically viable operator models:
Passenger transport vs. transport of goods
Definition of economic requirements, e.g.: operating costs
Key activities of SW-Integrators:
Operating systems for MaaS fleets
SW for control centers
Emergence of intermediaries:
Data platforms (city or traffic infrastructure related data),
Booking and payment platforms, end-user applications
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Overview of worldwide demonstration projects for
Automated Mobility-as-a-Service
Slide No. 11
Navya
EasyMile
Lyft
Waymo
Uber
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Categorization of Automated Vehicle Concepts
Slide No. 12
Luxury vehicles
Volume vehicles Robot taxis
Autonomous shuttles
Focus for Automated MaaS
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Agenda
» Introduction and motivation
» Industry activities in the field of Mobility-as-a-Service
» Key technologies for Automated Mobility-as-a-Service
» Summary
Slide No. 13
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Automation Level Established 2018 2020 2022 2024 2026 2028 2030 ...
Level 5Full Automation
Level 4High Automation
Level 3Conditional Automation
Level 2Partial Automation
Level 1Driver Assistance
Level 0No Automation
(support beyond human
capability to act)
Traffic Jam Assist
Parking Assist
Adaptive Cruise Control
Stop & Go
Lane Keeping Assist
Lane Change Assist
Parking Assist
Lane Departure Warning
Blind-spot Warning
Forward Collision Warning
ABS, ESC
Emergency Brake
Automated Urban Bus Chauffeur
Automated PRT/Shuttles on dedicated roads
Automated Buses on dedicated roads
Fully Automated
Urban Mobility Vehicles
Urban Bus Assist
Automated PRT/Shuttles in mixed traffic
Automated Buses in mixed traffic
Timeframe to reach levels of TRL 7-9, to be understood as ready
from a technology perspective:
TRL 7: System prototype demonstration in operational environment
TRL 8: System complete and qualified
TRL 9: Actual system proven in operational environment
Roadmap of Automated Urban Mobility Vehicles
Focus
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Operation conditions for Automated Urban Mobility Vehicles
Operation in designated lanes / dedicated infrastructure Operation in mixed traffic
In specific areas in Europe today high automation in
transit areas exist with specific solutions requiring low
vehicle speed and/or dedicated infrastructure
Models of operation of collective & individual (”taxi”)
character
The services based on these kind of vehicles will be
most probably integrated with traditional public
transport services
Automated PRT/Shuttles will be used both individually
& collectively, no passenger intervention in driving task
Drive in mixed traffic in same speed as other traffic
Will be most probably integrated into a smart,
seamlessly connected ecosystem by mobility services
The emergence of the shuttle segment is a result of rising
demand for ridesharing services & could be
available 24/7
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Vehicle Body AUTOtaxi
Taxi service
Order, open, interact via
CE device
Cooperative & agile …
Vehicle Body AUTOshuttle
Supplement to public
transport
6 - 8 people
Behaves like a rail vehicle
Vehicle Body AUTOelfe
Private „butler / nanny“
Completes autonomously
private journeys to school,
sport, …
Privacy, individual …
Vehicle Body AUTOliefer
Autonomous delivery
service
Automated loading concept
„Mobile parcel station“
Motion Platform
Modular design consisting of 4 dynamic modules,
energy module, brain stem + self-awareness
Scalable, different vehicle sizes representable
Electrically (48 volt) & functionally safe
Modularization of vehicle body structures
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Modularization of vehicle body structures –
Example research project unicaragil
autoELF
PrivateButler / Nanny
autoCARGO
Pick-up / Delivery Service
autoTAXI
Taxi Service
autoSHUTTLE
Public Transport System
[Image sources: UNICARagil project]
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Passive Safety for Automated Shuttles
Local Motors Olli – Crash test with 3 mph Local Motors Olli – Crash test with 25 mph
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Passive Safety for Automated Shuttles –
Existing approaches
Detailed description
Already realized and presented autonomous shuttle
concepts (SAE L5) only offer basic or no passive
safety features
Level of passive safety comparable to public city bus,
however significantly lower vehicle mass compared to
city bus
Seat belts are sometimes integrated, often only lap belt
Some autonomous shuttle concepts also include
places for standing passengers
Presented autonomous shuttle concepts and studies
also indicate new usage concepts, e.g. office on
wheels or café on wheels, making passive safety
concepts impossible
Discussion about required and acceptable level of
crash safety ongoing
Limitations of vehicle speed (typically < 25 mph) and
areas of application (gated areas, separated lanes,
designated lanes) could decrease relevance for passive
safety systems
Illustrations
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Sensor setup for Automated Vehicles –
Example Navya Arma shuttle
6x Mono-layer lidar sensor
180° coverage
Long detection range
2x Mono cameras
Front & rear camera
2x Multi-layer lidar
360° coverage (Velodyne)
Peripheral vision
GNSS antennae
Precise positioning
GNSS RTK system
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Sensor setup for Automated Vehicles –
Example Waymo self-driving taxi
Supplemental sensors
Audio detection system
GPS
Radar sensors (min. 4)
Continuous 360° view
Located at the corners
Lidar system (5 sensors)
Short-range lidar
High-resolution mid-range
lidar
Long-range lidar
Camera system (5 cameras)
Front- and side-facing
360 ° field of view
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Lidar Multi line laser scanner 360° rotating laser scanner Solid State Lidar
Infrared laser light is illuminated
and distance is measured by
time-of-flight principle
For each line, one laser is emitting
light in changing directions,
changed by a moving mirror
Many lasers emitting light in
changing direction by rotating
housing and mirrors
Has a semiconductor-based gain
medium that is solid and contains
no moving or vibrating parts
Technical Specs
Range
Horizontal FOV
Vertical FOV
Rotating Speed
20 m
27°
12°
150 m
145° (Resolution of 0.25°)
3,2° (4 Layers of 0.8° each)
100 m
360° (Resolution of 0.1°)
30° (16 Layers of 2° each)
5-20 Hz
150 m
120°
10°
¥ (< 250 $)¥ ¥ ¥ (~ 4,000 $)¥ ¥ (~ 300 $)¥
Description
Manufacturer/
Supplier
Cost
Low costs due to no rotating
parts; state of research
360° horizontal view; contains
rotating parts; expensive
technology
Long range of detection; detects
environment in 3D
Assessment/
Evaluation
Already in series production;
cheap; short detection range
Overview of laser-based sensors –
A key sensor type for Automated Driving
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Functional Architecture for Automated Vehicles
Control Room
Sensors
Communication
Traffic Situation
Steering Powertrain Braking
Predicition of Traffic Situation
Safety Strategy
Driving Condition Coordination
Vehicle
Actuators
Decision about Driving BehaviorNavigation
Safe Behaviorin Degradation
Mode
Planning ofTrajectories
Sensor Drone MAP
Environment Information
Collective Environment Model
Collective Memory
Collective Behavior
Cloud
Navig
ation
Guida
nce
Stabil
izatio
n
Sensors
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Agenda
» Introduction and motivation
» Industry activities in the field of Mobility-as-a-Service
» Key technologies for Automated Mobility-as-a-Service
» Summary
Slide No. 24
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Summary –
Key technologies for Automated Mobility-as-a-Service
Environment perception:
Increase of sensors
Vehicle Intelligence: Sensor fusion,
environment modelling, planning &
decision algorithms
Electrics/Electronics:
New E/E architectures
Increase of functional safety
New redundancy concepts
Fail-safe functions
HMI design & UX:
Display of system state (for drivers
and other road users)
Increase of number & size of
displays
Augmented Reality concepts
Operating & functional software:
Interpretation of large data sets
(environment data)
Functional safety
Artificial intelligence for vehicle
functions
New vehicle & interior concepts:
New urban vehicle concepts
New interior designs & seating
positions
New mobility concepts: Mobility-as-
a-service, ride-sharing, car-sharing
Backend software:
Planning & dispatching
Vehicle routing
Remote control
Vehicle booking & payment
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Christian Burkard
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