automated vehicle technologies for mobility-as-a-service

© fka GmbH19cbu0018.pptx

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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




» 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




» 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




» 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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fka GmbH

Steinbachstr. 7

52074 Aachen

Germany

Christian Burkard

phone +49 241 8861 116

e-mail [email protected]

web www.fka.de

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