114,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Wiley - IEEE
- Sprache: Englisch
AUTOMATED VEHICLES AND MaaS A topical overview of the issues facing automated driving systems and Mobility as a Service, identifies the obstacles to implementation and offers potential solutions Advances in cooperative and automated vehicle (CAV) technologies, cultural and socio-economic shifts, measures to combat climate change, social pressures to reduce road deaths and injuries, and changing attitudes toward self-driving cars, are creating new and exciting mobility scenarios worldwide. However, many obstacles remain and are compounded by the consequences of COVID-19. Mobility as a Service (MaaS) integrates various forms of public and private transport services into a single on-demand mobility service. Combining trains, cars, buses, bicycles, and other forms of transport, MaaS promises a convenient, cost-effective, and eco-friendly alternative to private automobiles. Automated Vehicles and MaaS: Removing the Barriers is an up-to-date overview of the contemporary challenges facing CAVs and MaaS. Written in a clear and accessible style, this timely volume summarizes recent research studies, describes the evolution of automated driving systems and MaaS, identifies the barriers to their widespread adoption, and proposes potential solutions to overcome and remove these barriers. The text focuses on the claims, realities, politics, new organizational roles, and implementation problems associated with CAVs and MaaS--providing industry professionals, policymakers, planners, administrators, and investors with a clear understanding of the issues facing the introduction of automated driving systems and MaaS. This important guide and reference: * Provides an overview of recent progress, the current state of the art, and discussion of future objectives * Presents both technical background and general overview of automated driving systems and MaaS * Covers political, commercial, and practical issues, as well as technical and research content, yet suitable for non-specialists * Helps readers make informed decisions and realistic estimates for implementing mobility solutions and new business models for transport services * Includes an extensive bibliography with direct links to in-depth technical engineering and research information Automated Vehicles and MaaS: Removing the Barriers is an essential resource for transport providers, vehicle manufacturers, urban and transport planners, students of transportation, vehicle technology, and urban planning, and transport policy and strategy managers, advisors, and reviewers.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 450
Veröffentlichungsjahr: 2021
Ähnliche
Table of Contents
Cover
Title Page
Copyright
Dedication
Preface
Acknowledgements
Table of Abbreviations
1 The Promise and Hype Regarding Automated Driving and MaaS
1.1 The Promise
1.2 What Do We Mean by the Term ‘Automated Driving’?
1.3 The Hype
2 Automated Driving Levels
2.1 SAE J3016
2.2 The Significance of Operational Design Domain (ODD)
2.3 Deprecated Terms
2.4 No Relative Merit
2.5 Mutually Exclusive Levels
2.6 J3016 Limitations
2.7 Actors in the Automated Vehicle Paradigm
2.8 Other Functions
3 The Current Reality
3.1 UNECE WP 29
3.2 Social Acceptance
3.3 SMMT
3.4 Other Observations
3.5 The European Commission
3.6 Legislation
3.7 Subsidiarity
3.8 Viewpoints
4 Automated Driving Paradigms
4.1 OECD
4.2 Communications Evolution
4.3 Cooperative ITS
4.4 The C‐ITS Platform
4.5 Holistic Approach
4.6 It Won't Happen Quickly
4.7 Implications of Fully Automated Vehicles
5 The MaaS Paradigm
5.1 Purist Definition for MaaS
5.2 Vehicle Manufacturer Perspective for MaaS
5.3 Traditional Transport Service Provider Perspective for MaaS
5.4 MaaS from the Perspective of the MaaS Broker
5.5 MaaS as a Tool for Social Engineering
5.6 MaaS Experience to Date
5.7 MaaS and Covid‐19
6 Challenges Facing Automated Driving
7 Potential Problems Hindering the Instantiation of MaaS
7.1 Root Causes of Obstacles
7.2 Level of Community Readiness
7.3 Level of Social Engineering Readiness
7.4 Perception of Risks
7.5 Level of Market Readiness
7.6 Level of Software Solution Readiness
7.7 Training
7.8 Timing
7.9 Institutional and Governance
8 Potential Solutions to Overcoming Barriers to Automated Driving
8.1 Vehicle Manufacturers Flawed Paradigm of the Automated Vehicle
8.2 Vehicle Manufacturers Using Different Paradigms for Competitive Advantage
8.3 Road Operator's Responsibilities
8.4 New Modes of Transport and New Mobility Services Must Be Safe andSecure by Design
8.5 How Other Road Users Interact with AVs
8.6 Automated Vehicles Will Have to Be Able to Identify and Consistently Respond to Different Forms of Communication
8.7 AVs by Themselves Will Not Necessarily Be Smarter than Conventional Vehicles
8.8 Congestion Levels Will Not Drop Significantly
8.9 Automated Vehicles Will Release Unsatiated Demand
8.10 Safety and Some Operational Data Must Be Freely Shared
8.11 Mixed AV and Conventional Traffic
8.12 AV Acceptability
8.13 Low Latency Communication
8.14 Roads Could Be Allocated Exclusively to AVs
8.15 Automated and Connected Vehicles Bring New Requirements
8.16 Cybersecurity
8.17 Changing Speed Limits and Even Getting Signs Put Up Can Take Years
8.18 Political Decisions Needed
8.19 Role of Government
8.20 Fallback to Driver
8.21 Range of Services Supported
8.22 Young Drivers and Experience
8.23 Liability
8.24 Level 5 May Take a Long Time to Instantiate
9 Potential Solutions to Overcoming Barriers to MaaS
9.1 Addressing General Issues
9.2 Essentials to Enable MaaS
9.3 Removing Obstacles to MaaS
9.4 Innovative Enablers for MaaS
10 The C‐ART Innovation
10.1 Overview
10.2 Policy Context
10.3 Key Conclusions
10.4 C‐ART Scenarios
11 Potential Solutions to Instantiate AVs and MaaS: Managed Optimisation Architecture for Transportation (MOAT)
11.1 Managed Not Controlled
11.2 High Level Actors in the MOAT Architecture
11.3 MOAT from the Subscriber/User Perspective
11.4 MOAT from the Travel Service Provider Perspective
11.5 MOAT from the Road Operator Perspective
11.6 MOAT from the AV Operator (AVO) Perspective
11.7 MOAT from the Travel Optimisation Service (TOS) Perspective
11.8 MOAT from the Traffic Management Centre (TMC) Perspective
11.9 MOAT from the Travel Information Provider (TIP) Perspective
11.10 MOAT and Privacy
11.11 The MOAT Overview Architecture
11.12 The MOAT Systems Architecture
Note
12 The Business Case for MaaS
12.1 The Challenge
12.2 The Solution
12.3 The Outlook
13 The Business Case for Automated Vehicles
13.1 The Challenge
13.2 The Solution
13.3 The Outlook
14 Timescales to Successful Implementation
14.1 Caveat
14.2 Phased MOAT
14.3 Timescales MaaS
14.4 Timescales for Automated Vehicles
14.5 The First Half of the Twentieth Century
14.6 The Second Half of the Twentieth Century
14.7 2000–2009
14.8 2010–2019
14.9 2020–2029
14.10 2030–2039
14.11 2040–2050
14.12 2050–2060
14.13 In Summary
Bibliography
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Summary of levels of driving automation.
Table 2.2 User roles while a driving automation system is engaged.
Chapter 8
Table 8.1 Human factors.
Chapter 10
Table 10.1 C‐art qualitatively assessed.
List of Illustrations
Chapter 1
Figure 1.1 1950s image of highway of the future:
Popular Mechanics
magazine....
Figure 1.2 Mercedes F 015 concept AV at Detroit Motor Show (2015).
Figure 1.3 Screenclip from Renault website.
Figure 1.4 SMMT Potential overall impact of CAV
'
s on the UK economy (scr...
Figure 1.5 Screenclip from press report.
Figure 1.6 Publicly available EC Document from EC project Ensemble.
Chapter 2
Figure 2.1 SAE J3016 freely available document.
Figure 2.2 J3016 schematic (not a control diagram) view of driving task show...
Figure 2.3 Automated car architecture Schoettle and Sivak (2015).
Figure 2.4 Ford virtual driving system (eenews Automotive, 2019).
Figure 2.5 Actors in a CAV paradigm.
Figure 2.6 Actors in a CAV paradigm..
Figure 2.7 Automated vehicle system high‐level communications taxonomy.....
Chapter 4
Figure 4.1 CALM 2001 architecture separating communications from application...
Figure 4.2 ITS station architecture (ISO 21217).
Figure 4.3 C‐ART Projection.
Figure 4.4 Czech National Traffic Information Centre.
Chapter 5
Figure 5.1 MaaS concept vehicles.
Figure 5.2 MaaS current situation.
Figure 5.3 MaaS paradigm: simplification of the interfaces for the user of a...
Figure 5.4 The main services enabling the integrated mobility service.
Figure 5.5 Example MaaS architecture (EC Project IMOVE).
Figure 5.6 Main functional responsibilities in MaaS.
Figure 5.7 Integrated mobility value networks.
Chapter 8
Figure 8.1 Roadsigns that confuse CAVs.
Figure 8.2 Roadsigns that confuse CAVs.
Figure 8.3 Roadsigns that confuse CAVs.
Figure 8.4 Roadsigns that confuse CAVs.
Figure 8.5 Current road regulation written a long time ago.
Figure 8.6 METR architecture.
Figure 8.7 METR perspective.
Figure 8.8 AV crash.
Figure 8.9 Advanced traveller information systems.
Figure 8.10 Reduced speed zone warning/lane closure.
Figure 8.11 Curve speed warning.
Figure 8.12 Queue warning.
Figure 8.13 Vehicle data for traffic operations.
Figure 8.14 Warnings about upcoming work zone.
Figure 8.15 Reduced speed zone warning/lane closure.
Figure 8.16 Spot weather impact warning.
Figure 8.17 Emergency brake light.
Figure 8.18 Emergency vehicle preemption.
Figure 8.19 Object registration and discovery.
Figure 8.20 Pedestrian in signalised crosswalk warning.
Figure 8.21 In‐vehicle signage.
Figure 8.22 Stop sign violation warning.
Figure 8.23 Freight signal priority.
Figure 8.24 Transit signal priority.
Figure 8.25 Eco‐approach and departure at signalised intersections.
Figure 8.26 TM02 vehicle‐based traffic surveillance.
Figure 8.27 Electric charging stations management.
Figure 8.28 VS12 pedestrian and cyclist safety.
Figure 8.29 PT11 transit pedestrian indication.
Figure 8.30 PM01 parking space management.
Figure 8.31 Traveller information – smart parking.
Figure 8.32 Smart park and ride system.
Figure 8.33 TI03 dynamic route guidance.
Figure 8.34 Intelligent traffic signal system.
Figure 8.35 I04 infrastructure‐provided trip planning and route guidance....
Figure 8.36 Special vehicle alert.
Figure 8.37 Advanced traveller information systems.
Figure 8.38 Connected vehicle system monitoring and management.
Figure 8.39 Intersection safety warning and collision avoidance.
Figure 8.40 Electronic regulations.
Figure 8.41 Speed harmonisation.
Chapter 9
Figure 9.1 Conceptual governance reference architecture.
Figure 9.2 Example of how cooperation between the United States and the EU c...
Figure 9.3 Example of governance reference architecture.
Figure 9.4 Organisational aspects (high level).
Figure 9.5 Organisational roles in governance.
Figure 9.6 Simplified SCMS architecture.
Chapter 10
Figure 10.1 ENISA Gateway ecu.
Chapter 11
Figure 11.1 MOAT paradigm from the perspective of the subscriber and the use...
Figure 11.2 MOAT.
Figure 11.3 MOAT.
Figure 11.4 MOAT and
management of electronic traffic regulations
‘
METR
’.
Figure 11.5 IMOVE.
Chapter 14
Figure 14.1
C‐ITS connected and automated vehicles
(
CCAM
) deployment....
Figure 14.2
Connected and autonomous vehicle
(
CAV
) take‐up.
Guide
Cover
Table of Contents
Begin Reading
Pages
iii
iv
v
xiii
xv
xvii
xviii
xix
xx
xxi
xxii
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
251
252
253
254
255
256
267
Automated Vehicles and MaaS
Removing the Barriers
Bob Williams
Senior Consultant
CSi (UK)
Oxfordshire
UK
This edition first published 2021
© 2021 John Wiley & Sons Ltd
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of Bob Williams to be identified as the author of this work has been asserted in accordance with law.
Registered Offices
John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA
John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK
Editorial Office
The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK
For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
Wiley also publishes its books in a variety of electronic formats and by print‐on‐demand. Some content that appears in standard print versions of this book may not be available in other formats.
Limit of Liability/Disclaimer of Warranty
While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
Library of Congress Cataloging‐in‐Publication Data
Names: Williams, Bob, author. | John Wiley & Sons, Ltd., publisher.
Title: Automated vehicles and MaaS : removing the barriers / Bob Williams. Description: Hoboken, NJ : Wiley, 2021. | Includes bibliographical references and index.
Identifiers: LCCN 2020033769 (print) | LCCN 2020033770 (ebook) | ISBN 9781119765349 (hardback) | ISBN 9781119765332 (adobe pdf) | ISBN 9781119765387 (epub)
Subjects: LCSH: Intelligent transportation systems. | Automated vehicles. | Transportation and state.
Classification: LCC TE228.3 .W54 2021 (print) | LCC TE228.3 (ebook) | DDC 629.28/30285–dc23
LC record available at https://lccn.loc.gov/2020033769
LC ebook record available at https://lccn.loc.gov/2020033770
Cover Design: Wiley
Cover Image: © Vladimir Kramin/Shutterstock
I dedicate this book to my tolerant partner Isabelle, and my patient daughter Juliette, who have both enabled me the time in a limited amount of free time (for this is a very busy and exciting period for those of us who work on aspects of intelligent transport systems) to develop and write this book.
I dedicate this book also to those who provide a lot of time, skill and expertise, often largely unpaid, to develop standards that enable us to realise and benefit from the potential offered by intelligent transport systems, and who help to realise and achieve its safety of life potential.
Preface
There has been much hype, publicity and many optimistic claims regarding connected and automated vehicles and the concept of ‘Mobility as a Service’. This book confronts, with a feet‐on‐the‐floor approach, why these forecasts are so difficult to achieve, what is reasonably probable and achievable, and provides guidance, from an expert involved in the development of intelligent transport systems since 1988, on how to remove the barriers to the successful introduction of automated vehicles and Mobility as a Service.
Acknowledgements
I take this opportunity to acknowledge and thank my colleagues in CEN TC278 and its sister organisation ISO TC204 for their work in achieving practical, fair and open standards for intelligent transport systems. I thank them for their patience with me, and continual sharing of expertise that have helped me through a long career in this area, which has enabled me to pull together the information and experience necessary to write this book.
Table of Abbreviations
E & OE, abbreviations/acronyms in this book that are not detailed below may generally be understood to be the names of companies or trading entities. Abbreviations/acronyms used in this book are to be interpreted as follows:
2D
2 Dimension/2 Dimensional
3D
3 Dimension/3 Dimensional
3G
3rd Generation
3GPP
3rd Generation Partnership Project
4G
4th Generation
5G
5th Generation
AAM
Alliance of Automobile Manufacturers
ACC
adaptive cruise control
ACM
Association for Computing Machinery (USA)
ADAS
advanced driver assistance system
ADS
automated driving system
AEB(s)
automated emergency braking (system)
AEF
Association d'Economie Financière
AEV
automated and electric vehicles
AI
artificial intelligence
AMC
American Motor Company (American Motors) (USA).
APEC
Asia‐Pacific Economic Cooperation (Organisation)
API
application program interface
ARK‐Invest
(Company name
ARC‐IT
Architecture Reference for Cooperative and Intelligent Transport
ASEAN
association of south east asian nations
ASN.1
abstract syntax notation 1
Auto ISAC
automotive information sharing and analysis centre
AV
automated vehicle
AVO
AV operator
BAST
Bundesanstalt für Straßenwesen (German: Federal Highway Research Institute)
BMW
Bayerische Motoren Werke GmbH
BSM
basic safety message
CA
certificate authority
CALM
communications architecture for land mobiles
CAM
cooperative awareness message
C‐ART
Name of JRC project (Coordinated Automated Road Transport)
CAMP
Crash Avoidance Metrics Partnership
CAV
connected and automated vehicle(s)
CCAM
cooperative connected automated mobility
CCMS
C‐ITS credential management system (EU)
CES
Consumer Electronics Show (Consumer Technology Association trade show)
CEO
chief executive officer
C‐ITS
cooperative‐ITS
CMS
credential management system
CNECT
Name of European Commission Directorate General (DG) for Communications Networks, Content and Technology.
CONVERGE
Communication Network Vehicle Road Global Extension (EC Project)
C‐ROADS
connected‐roads (EC DG MOVE Initiative)
CRL
certificate revocation list
CVT
continuously variable transmission
C=V2X
communications – vehicle to anything
DARPA
Defense Advanced Research Projects Agency (USA)
DAS
driving automation system (sae) or driving assistance system (others)
DATEX
data exchange standard for exchanging traffic information between traffic management centres, traffic service providers, traffic operators and media partners
DDT
dynamic driving task
DENM
decentralized environmental notification message
DfT
Department for Transport (UK)
DoT
Department of Transport (USA and others)
DG
Directorate General (European Commission)
DG‐CNECT
DG‐ Communications Networks, Content and Technology (EC)
DG‐GROW
DG‐ for Internal Market, Industry, Entrepreneurship and SMEs (EC)
DG‐INFSO
DG‐ Information society and Media (EC: Now DG‐CNECT)
DG‐MOVE
DG‐ Mobility and Transport (EC)
DSRC
dedicated short range communication
DSS
driver support system
DSSAD
data storage system for automated driving vehicles
EC
European Commission
ECTL
European certificate trust list
ECU
electronic communications unit
EDR
event data recorder
EEA
European Economic Area
EFTA
European Free Trade Agreement
ENISA
European Union Agency for Networks and Communication
ESC
electronic stability control
ETSC
European Transport Safety Council
ETSI
European Telecommunications Standards Institute
ETRAC
European Road Transport Research Advisory Council
EU
European Union
ExVe
extended vehicle
EZ‐GO
(Trade name [Renault])
FHWA
Federal Highway Administration (USA)
FRAV
functional requirements for automated vehicles
FSD
Full Self‐Driving (Tesla product name)
GAD
(UK) Government Actuaries Dept
GDP
gross domestic product
GDPR
General Data Protection Regulation (EU)
GEAR 2030
name of high‐level group on the competitiveness and sustainable growth of the automotive industry in the European Union (EC)
GLOSA
green light optimal speed advisory
GM
General Motors
GMC
governance management committee
GNSS
global navigation satellite system
GPS
global positioning system (US Department of Defence GNSS)
GRB
UNECE WP 29 former name of working party subcommittee on noise and tyres
GRBP
UNECE WP29 Working Party on Noise and Tyres (Groupe Rapporteur Bruit et Pneumatiques)
GRE
UNECE WP 29 Working Party on Lighting and Light‐Signalling (Groupe Rapporteur Electrique)
GRPE
UNECE WP 29 Working Party on Pollution and Energy (Groupe Rapporteur pollution et énergie)
GRRF,
UNECE WP 29 Working Party on Brakes and Running Gear (Groupe Rapporteur Freins & roulants)
GRSG
UNECE WP 29 Working Party on General Safety Provisions (Groupe Rapporteur Securite Generale)
GRSP
UNECE WP 29 Working Party on Passive Safety (Groupe Rapporteur sécurité passive)
GRVA,
UNECE WP 29 Working Party of automated vehicles (Groupe de Rapporteurs pour les Véhicules Autonomes)
HARTS
Harmonised Architecture for Transport Systems (HTG initiative)
HMI
human >< machine interface
HOV
high occupancy vehicle
HP
Hewlett Packard
HTG
Harmonization Task Group (a collaboration of US DOT, the EU Joint Research Council (JRC), EC DG CNECT and the Transport Certification Authority (TCA) of Australia),
ICT
Information and Communications Technologies
IEEE
Institute of Electrical and Electronics Engineers
IGEAD
Informal Group of Experts on Automated Driving (UNECE WP29)
IMOVE
(name of EC project)
IMS
IP multimedia subsystem
INSPIRE
(Name of EU Directive: Directive 2007/2/EC of the European Parliament and of the Council of 14 March 2007 establishing an Infrastructure for Spatial Information in the European Community
IP
internet protocol
IP
intellectual property
IPR
intellectual property rights
ISA
intelligent speed assistance
ISO
International Standards Organisation
ITF
International Trade Forum
ITS
intelligent transport systems
ITS‐G5
intelligent transport systems – 5 ghz wifi for transport communications
ITS‐S
intelligent transport systems – station
ITS‐SU
intelligent transport systems – station unit
JRC
Joint Research Centre (of European Commission)
LA
local authority
LAN
local area network
LKA
lane keeping assistance
LOM
la loi d'orientation des mobilités or, mobility orientation law) (France)
LTE
Long Term Evolution (3GPP)
MaaS
Mobility as a Service
METR
Management of Electronic Traffic Regulations
MOAT
Managed Optimisation Architecture for Transportation
NASA
National Aeronautics and Space Administration (USA)
NHTSA
National Highway Traffic Safety Administration (USA)
NIS(D)
Network and Information Security Directive (EC)
NTSB
National Transportation Safety Board (USA)
OD
operational design or operational domain
ODD
operational design domain
OECD
Organisation for Economic Co‐operation and Development
OEDR
object and event detection and response
OEM,
original equipment manufacturer
OICA,
Organisation Internationale des Constructeurs d'Automobiles
OTA
over the air
PHV
private hire vehicle(s)
PKC
public key certificate
PKI
public key infrastructure
PMC
policy management committee
PS‐LTE,
packet switched‐long‐term evolution (3GPP)
PT
public transport/public transit
PTA
public transport authority(ies)/ public transit authority(ies)
PTO
public transport operator
PTW
powered two‐wheeler
RA
registration authority
RAN,
radio access network
RB
resource block
RCA
root certificate authority
RER
Réseau Express Régional (Paris)
RGMC
regional governance management committee
RO
road operator
RTMS
road traffic management system
R&D
research & development
SAE
Society of Automotive Engineers (USA)
SAEV
shared autonomous electric vehicles
SCMS
security credential management system
SDO
standards development organization
SMMT
Society of Motor Manufacturers and Traders (UK)
SSP
system security plan
SUV
sports utility vehicle
TBD
to be decided
TC
technical committee
TCA
Transport Certification Australia
TfL
Transport for London
TIP
travel information provider
TLM
trust list manager
TMC
traffic management centre
TNC
transportation network company
TN‐ITS
Traffic Navigation‐ITS (Organisation, EU)
TOS
travel optimisation service
TRAMAN21
Traffic Management for the 21st Century (EC Project name)
TSP
travel service provider
UI
user interface
UK
United Kingdom of Great Britain and Northern Ireland
UMTS
Universal Mobile Telecommunications Service
UNECE
United Nations, Economic Commission for Europe
USA
United States of America
UU
user equipment to the UMTS terrestrial radio (interface)
UVAR
urban vehicle access regulations/restrictions
V2I
vehicle to/from infrastructure
V2V
vehicle to/from vehicle
V2X
vehicle to/from anything
VACS
vehicle automation and communication systems (TRAMAN21 term)
VANET
vehicular ad hoc networks
VDA
Verband Der Automobilindustrie E.V. (German Automobile Industry Association)
VKT
vehicle kilometres travelled
VMS
variable message sign
VRU
vulnerable road user
VW
Volkswagen
WAVE
wireless access in vehicular environments
WG
working group
WHO
World Health Organisation
WLAN
wireless local area network
WP
working party
WWII
World War II
1The Promise and Hype Regarding Automated Driving and MaaS
Figure 1.1 1950s image of highway of the future: Popular Mechanics magazine.
Source: Popular Mechanics magazine.
1.1 The Promise
It is possible that the fully automated car was first seen in a road safety awareness film ‘The Safest Place’ (1935). ‘The vehicle always stays in its lane, never forgets to signal when turning, obeys all stop signs and never overtakes on dangerous corners’ Kröger (2016).
By 1939, at the World's Fair, General Motors ‘Futurama’ featured a model of future transport systems with automated highways in an imagined world of 1960 Weber (2014). Please note with a smile, futurologists usually overestimate the speed of development and uptake of their subject (Figure 1.1).
Advances in computer technology have seen the rapid development of automation over the past 50 years. Combined with innovative engineering, this has led to developments from unmanned aerial vehicles (UAVs/drones) to armed robotic rovers. The US Armed Forces and DARPA built on the philosophy of ‘development through competition’ based on the early twentieth‐century Orteig Prize (US$25 000) offered in 1919 by French hotelier Raymond Orteig for the first nonstop flight between New York City and Paris that helped prod the development of air flight, and that spurred Charles Lindbergh to make his solo flight across the Atlantic Ocean in 1927. DARPA have sponsored a number of competitions to accelerate the development of everything from automatic weaponry to private sector space flight.
In 2004, DARPA established the ‘Grand Challenge’, a competition designed to encourage the development of technologies needed to create the first fully autonomous ground vehicles.
The first Grand Challenge took place on 13 March 2004 and involved 15 self‐driving ground vehicles navigating a 228 km (142 mi) course across the desert in Primm, Nevada (https://www.wired.com/story/autonomous-car-chaos-2004-darpa-grand-challenge/). The prize was $1 million but the desert course proved to be too hard. No team finished the course, and the prize went unclaimed.
The second event was held on 8 October 2005 in southern Nevada with 5 of the original 195 teams completing the 212 km (132 mi) and the $2 million prize was won by Stanford University.
For the third event, held in November 2007, DARPA extended the challenge to include a mock urban environment. Driving in traffic and typical vehicle manoeuvres and highway crossings were involved. Tartan Racing, a team from Carnegie Mellon University in Pittsburgh, Pennsylvania, claimed the $2 million prize with their vehicle ‘Boss’, a converted Chevrolet Tahoe.
Thus the race to the development of automated vehicles kicked off and was incentivised, and its progress has only accelerated thereafter.
* * * * *
We already live in a world where vehicles are to some extent ‘connected’. New model vehicles in Europe have a system called ‘eCall’, which automatically contacts and puts the occupants of the vehicle in touch with the emergency services in the event of an accident. Volvo Assistance, BMW Connected Drive, GM Onstar, Mercedes ‘Me’ and ‘Rescue’ as well as Citroen Assistance are examples of breakdown, emergency and driver support systems that are connected to resources outside of the vehicle, connected by 2G/3G/4G, and soon to be 5G, mobile telephony.
The modern vehicle also ‘connects’ to its environment in many ways, largely through sensors, to assist with the driving experience. Electronic stability control (ESC) is now mandatory on all new cars sold in Europe. Lane‐keeping systems (LKS), adaptive cruise control (ACC), automated emergency braking (AEB), and intelligent speed assistance (ISA) systems are increasingly commonplace, as are automatic headlight dipping, traction control, tyre‐pressure monitoring, etc. It is thought‐provoking to consider that most of what these systems do is to use technology to compensate, to some extent, for human error, often taking some control away from the driver under certain circumstances.
Modern sat‐nav systems download and take into account dynamic congestion and traffic incident information in their route planning, and guidance by sat‐nav providers communicate this data wirelessly to the on‐board sat‐nav system. Researchers and developers are close to the fruition of car‐to‐car and car‐to‐infrastructure communication developments, that will enable a truly ‘connected’ vehicle (‘cooperative ITS’ or ‘C‐ITS’ as it is known in the trade).
Moving beyond such connectivity‐enabled functions, attention has now moved to the often misnomered ‘autonomous’ vehicle that will understand its environment and the requirements of its passengers, and the requirements of the road infrastructure, and operate the vehicle without the assistance of a driver (more correctly called the ‘automated’ vehicle). It will also ‘learn’ to react and adapt to different situations during the entire driving process.
Over the next 10–50 years, the transport sector may expect to undergo a significant change, and potentially, transformation, as connected and automated vehicle technology is introduced.
With the impending take‐up and spread of cooperative ITS (C‐ITS) systems in vehicles, informative features will be complemented by, or evolve, cooperative features that will enable vehicles to interact with each other and with the surrounding infrastructure (i.e. vehicle‐to‐vehicleV2V and vehicle‐to‐infrastructureV2I communication). Full‐scale deployment of C‐ITS enabled vehicles that communicate with other vehicles concerning potentially dangerous situations and communicate with local road infrastructure is expected in the near term, and indeed may be required by regulation (for new vehicles), at least in Europe, by the early 2020s.
Many future projections estimate that by 2025, high automation driving will be available on highways and by 2030 in cities. The EC's Joint Research Centre further forecasts the year 2050 as a realistic timescale for the transition to a future mobility paradigm.
In order to summarise the potential of automated driving, ETSC, the European Transport Safety Council refers to the European Road Transport Research Advisory Council, who have summarised “safety and the potential to reduce accidents caused by human error” is one of the main drivers for higher levels of automated driving. “Automated driving can therefore be considered as a key aspect to support several EU transport policy objectives including road safety”.
Automated and connected vehicles have the easy to understand potential to substantially reduce road accidents, traffic congestion, traffic pollution and energy use, and are therefore seem attractive to and are often encouraged/incentivised by governments. Automated vehicles also promise to increase productivity and comfort and to facilitate a greater inclusion in the mobility of specific groups of individuals such as disabled or elderly. But other projections for instantiation in other paradigms predict the opposite in respect of automated vehicles, i.e. an increase in traffic congestion, an increase in traffic pollution and an increase in energy use, and other studies indicate that, particularly in the early years, may also actually increase accidents (even though the accidents may not be the fault of the automated vehicle, but how others react to it).
What is clear is that we are not dealing solely with the efficiency of vehicle control functions to automatically drive vehicles, but with the road transport system as a whole, which is a complex one where road users, vehicles and infrastructure interact with each other and millions of decentralised decisions are taken every second, by human drivers, and other road users, and within which automated vehicles will have to operate as a managed part of the system.
Automated and connected vehicles potential contribution to reduce road accidents is achieved primarily by eliminating human errors, which are a contributing factor in a vast majority of road accidents. And it is also generally recognised that most accidents occur due to risks that human drivers continuously take (consciously and unconsciously) as a result of collective experience gained in more than 100 years of driving activities, and of past driving experiences of the driver over his/her lifetime.
But, as the C‐ART report (EC JRC 2017) points out, ‘if on the one hand these risks generate road accidents with all their negative consequences, on the other hand these risks usually have a positive effect on the capacity of the road transport system. The introduction of automated vehicles, which by definition will be designed to minimize the risk of accidents, could therefore have a negative effect on road capacity especially in a transition period where a mix of conventional and automated vehicles will be sharing the same infrastructure’.
Features most prominently making progress at the moment are so called ADAS (advanced driver assistance system) functions. These systems generally refer to systems such as automatic braking, collision protection and emergency assistance. As the technologies evolve and mature, ADAS will soon evolve into part of the automated driving package.
Telematics and infotainment services that are already in place in modern vehicles use connectivity features. These services will expand, probably rapidly, sometimes as part of the selling options, sometimes as subscription services, and sometimes simply through smartphone apps integration.
1.2 What Do We Mean by the Term ‘Automated Driving’?
Automated driving combines a wide range of technologies and infrastructures, and importantly, connectivity. Automated driving should also be seen within the broader context, not just of taking the driving function away from the user of the vehicle, but also enabling new disruptive paradigms for mobility that may change the way we travel, change the vehicle ownership paradigm, change where we choose to live, etc., especially in urban environment.
Automated vehicles are those which blend autonomous control with communication with other vehicles and with the infrastructure in order to control and manage vehicle movements from start point to destination without direct driver input. Automated vehicles use a mix of on‐board sensors, cameras, Global Navigation Satellite Systems (GNSS), and telecommunications to obtain information in order to make their own judgements regarding safety‐critical situations and the general management of the journey.
Vehicle manufacturers frequently use the term ‘autonomous vehicle’ (the definition of autonomous is ‘having the freedom to govern itself or control its own affairs’ or similar), although what they go on to describe is clearly a vehicle communicating with its environment, and not one solely relying on the vehicle's own systems and without communicating with other vehicles or the infrastructure. The author observes that this highlights the shortcomings of the vehicle manufacturer's vehicle centric visions of the paradigm for automated driving, which is focussed on the vehicle controlling its movements through the environment, rather than the reality that the vehicle is only allowed to operate within the limits set by traffic management control and regulations.
While the auto manufacturers largely see the vehicle controlling its movements as the controller of the paradigm, in instantiation, automation has several key stakeholders, starting with the road authority or local/city administration (because they provide the road infrastructure, regulations, street equipment and signage and in the future may operate transport optimisation services). Transport optimisation services are key stakeholders (because they dynamically control all traffic movements through the network). Of course, the automotive manufacturers, and the users of the vehicle, while not being the sole controllers of the automated vehicle, are key stakeholders. Similarly, as these vehicles are communicating and receiving communications, communications providers are also key stakeholders, likewise, transport managers (road, rail, metro, parking facility operators, bus, cycle and scooter share, etc.).
With many ‘Mobility as a Service’ (MaaS) paradigms involving automated driving, vehicle‐sharing service providers are another potentially key stakeholder. There are also other stakeholders such as technology providers, insurance companies, and aftermarket service providers. And there will be other actors involved such as driver clubs/associations, universities and research centres.
In Europe, EU Regulation adopts UNECE (United Nations, Economic Commission for Europe) type approval regulations to provide (and require) access to basic raw (on board diagnostic) data from the vehicle by regulation, and there is serious consideration as to what additional data should – will have to – be made available for cooperative safety services, and to enable a fair and open after‐market (although the vehicle manufacturers continue to try to control access to data, and where possible use it to generate an income stream). In North America the situation is currently less regulated, therefore more unclear, but one way or another these ‘connected’ services will continue to develop, and push towards the automation of more and more services.
1.3 The Hype
Most of the leading vehicle manufacturers are bullish about the prospects for self‐driving vehicles. So rather than my version of what they are claiming, I simply turn to what is on their websites:
The Ford Motor company website (2019) stated:
Looking Further
Ford will have a fully autonomous vehicle in operation by 2021
No driver required. Thanks to Ford, that statement will be possible in 2021, the year that we will have a fully autonomous vehicle in commercial operation. To make this possible, we have partnered or invested with four different technology companies, along with doubling our Silicon Valley presence.
The effort to build fully autonomous vehicles by 2021 is a main pillar of Ford Smart Mobility: our plan to be a leader in autonomy, connectivity, mobility, customer experience, and analytics. The vehicle will operate without a steering wheel, gas pedal or brake pedal within geo‐fenced areas as part of a ride sharing or ride hailing experience. By doing this, the vehicle will be classified as a SAE Level 4 capable‐vehicle, or one of High Automation that can complete all aspects of driving without a human driver to intervene.
The SAE International six levels of automation rating system is used by the U.S. Department of Transportation to classify a vehicle's automation capabilities. The system starts at Level 0 – No Automation – which is defined as a vehicle that requires a human driver for all aspects of the driving task, and goes up to Level 5 – Full Automation – in which a vehicle can perform all driving tasks, no matter the environmental or roadway conditions. By mass producing a Level 4 capable vehicle, Ford will have achieved the highest level of automation by any automotive maker to date.
In order to reach this ambitious goal, Ford has committed to expanding its research in advanced algorithms, 3‐D mapping, radar technology and camera sensors. To help accelerate the development of these new technologies, we have announced four key investments and collaborations with Velodyne, SAIPS, Nirenberg Neuroscience LLC and Civil Maps. These companies bring their own unique skill sets and experiences to the table, and have proven to be dedicated to making the world a better place through their technological endeavors.
Since becoming the first automaker to begin testing fully autonomous vehicles inside Mcity, the University of Michigan's simulate urban environment, Ford has made enormous strides in researching how these vehicles operate in hazardous conditions, such as snow and complete darkness. Over the next two years, we will have tripled our autonomous vehicle test fleet to 30 Fusion Hybrid sedans in 2017 and will have 90 by 2018. These sedans will be taking the roads in California, Arizona, and Michigan for extensive development and testing.
In addition to the extensive testing of these vehicles and intensive collaboration with outside partners, Ford is focusing on expanding its Silicon Valley presence by creating a dedicated campus in Palo Alto to ensure that these innovations will be made. The Ford Research and Innovation Center that was initially created in 2015 will have two new buildings and 150,000 square feet of work and lab space added, and the current Palo Alto staff of 130 people will be doubled by the end of 2017. (Source: Looking Further, Ford will have a fully autonomous vehicle in operation by 2021, Autonomous 2021. © 2019, Ford Motor Company.)
The Daimler/Mercedes (Figure 1.2) websites (2019) states :
Prototypes such as the Mercedes‐Benz S‐Class S 500 INTELLIGENT DRIVE, the F 015 Luxury in Motion or the Future Truck 2025 show that the technical conditions for autonomous driving are already well established. And demonstrations of our intelligent Highway Pilot in the Freightliner Inspiration Truck in Nevada (USA) and a series production Mercedes‐Benz Actors in Germany have proven that it is ready for autonomous driving on public roads.
The required sensors and cameras have long been used in series production vehicles and undertake increasing numbers of tasks on the driver's behalf. Today's discussion no longer revolves around whether the technology will deliver on its promise but whether people want what the technology can deliver. And whether society and legislators are ready for this “revolution in automobility.”
Figure 1.2 Mercedes F 015 concept AV at Detroit Motor Show (2015).
Source: Mercedes‐Benz.
When it comes to passenger transport, autonomous driving ensures more safety, more comfort and more mobility. A purpose‐designed research vehicle, the F 015 Luxury in Motion, shows what an autonomous Mercedes‐Benz may look like in the future. We are convinced that the car can be more than just a means of transport: we see it as a private retreat that offers more freedom. Because autonomous driving allows us to use our time on the move as we wish. Naturally the design of this private retreat reflects the Mercedes‐Benz brand.
When it comes to freight transport autonomous driving brings additional advantages. More efficiency: a more steady flow of traffic reduces fuel consumption and emissions. Better coordination of all processes: thanks to the connection to telematics solutions for fleets, routes and trips, diagnosis and servicing can be better planned. And what difference will it make for the drivers? On the one hand, time pressure will be reduced. After all, if all partners are informed about the progress of the journey in real time, there is no reason to explain a delay. On the other hand, drivers can “hand over control” on monotonous stretches of road. Anyone picturing a driver dozing in a moving truck at this point is very much on the wrong track, however. The driver is a key part of the system. In certain traffic situations, for example on motorways and rural roads, in city traffic and when connecting semitrailers and making deliveries, the driver must retain control of the truck.
Autonomous mobility will bring about major changes and involve many different parties. Psychological barriers must be overcome in the same way that social acceptance must be gained. This requires a sound legal basis across country borders that regulates autonomous traffic and covers questions of liability in the event of a collision.
French manufacturer Renault, says on its website (2019):
“Our goal is to provide our customers with models that feature a delegated autonomous driving mode from 2020 onward. This technology will make driving safer and more pleasant while also freeing up time for drivers.” Laurent Taupin, Chief Engineer – Autonomous Vehicle
From ADAS to Autonomous Driving
Groupe Renault currently offers advanced driver assistance systems on its vehicles. These ADAS improve safety and act for the most part without human input, as is the case for automatic emergency braking (AEBS). They serve as a gateway to autonomous vehicles, even though they are initially only there to provide assistance to the driver, who remains in charge of the vehicle.
Eyes off/hands off technology
“Eyes Off/Hands Off” technology is a form of autonomous driving with no driver supervision. When drivers delegate driving activities, they no longer need to watch the road or have their hands on the steering Wheel ‐ driving is now fully delegated to the vehicle. This feature is intended for the most boring kinds of driving ‐ driving in stop‐and‐go traffic, for instance ‐ and only on approved highways.
Eyes off/hands off mode
When autonomous driving mode is activated, a set of sensors monitor the road and provide 360° surveillance of the vehicle: lidars (long‐range laser scanners), long‐range frontal radar, medium‐range corner radar, frontal digital cameras, four 180° digital cameras, an ultrasound belt and more. The data collected by these sensors is analyzed by the many embedded software “brains” that tell the vehicle what to do.
Figure 1.3 Screenclip from Renault website.
Source: Renault.
Eyes off/ hands off benefits
With the autonomous “Eyes Off/Hands Off” mode, Renault's goal is to change the experience of riding in cars, making it more pleasant, more interesting and safer. This will significantly reduce the risk of accidents! Trips will be less stressful and more productive. Drivers can make better use of their time by using in‐vehicle connectivity to answer emails or watch videos. They can do so safely, outside conditions permitting, as long as applicable laws and regulations are followed, once these are updated to authorize these new features. (Source: Renault, Autonomous Vehicle, © 2019, Renault.)
EZ‐GO Concept
The robot‐vehicle that reinvents the relationship between space and time. In an urban environment, EZ‐GO Concept is the first incarnation of autonomous, connected and shared mobility using an electric engine, without a steering wheel or a driver (Figure 1.3).
Mobility on demand for everyone
EZ‐GO Concept is a “robot‐vehicle”: both a vehicle and a transport service but also plays an integral part of the urban ecosystem. It has a positive impact on city life by providing mobility that is more respectful of the environment. The use of this autonomous vehicle for public transport takes it to the forefront of a new urban way of life. A facilitator for everyday life, EZ‐GO Concept offers a genuinely connected and customised experience to its passengers. With frontal doors, limited speed and autonomous driving, EZ‐GO Concept puts the safety of passengers at the forefront. The AD lighting signature, messages from the illuminated scrolling displays and the vehicle's exterior sounds ensure the safety of pedestrians. (Source: Renault, EZ‐GO Concept, © 2019, Renault.)
The Verge, an online multimedia publication designed to examine how technology will change life in the future, recently posted (2019)
General Motors plans to mass‐produce self‐driving cars that lack traditional controls like steering wheels and pedals by 2019, the company announced today. It's a bold declaration for the future of driving from one of the country's Big Three automakers, and one that is sure to shake things up for the industry as the annual Detroit Auto Show kicks off next week.
The car will be the fourth generation of its driverless, all‐electric Chevy Bolts, which are currently being tested on public roads in San Francisco and Phoenix. And when they roll off the assembly line of GM's manufacturing plant in Orion, Michigan, they'll be deployed as ride‐hailing vehicles in a number of cities.
“It's a pretty exciting moment in the history of the path to wide scale [autonomous vehicle] deployment and having the first production car with no driver controls,” GM President Dan Ammann told The Verge. “And it's an interesting thing to share with everybody.”
“THE FIRST PRODUCTION CAR WITH NO DRIVER CONTROLS”
The announcement coincides with the tail end of CES, where a number of big companies announced their own plans to deploy autonomous vehicles, and right before the Detroit Auto Show, where the industry will have on display all the trucks and SUVs that make its profits.
By committing to rolling out fully driverless cars in a shortened timeframe, GM is seeking to outmaneuver rivals both old and new in the increasingly hyper competitive race to build and deploy robot cars. Ford has said it will build a steering‐wheel‐and‐pedal‐less autonomous car by 2021, while Waymo, the self‐driving unit of Google parent Alphabet, is preparing to launch its first commercial ride‐hailing service in Phoenix featuring fully driverless minivans (though still with traditional controls).
Unlike those other companies, GM provided a sneak peek at how its new, futuristic cars will look on the inside. In some ways, [it's] the vehicular version of a [Rorschach] inkblot test. The bilateral symmetry of the interior looks both unnerving and yet completely normal at the same time. Instead of a steering wheel, in its place is blank real estate. Under the dash, more empty space.
The automaker submitted a petition to the National Highway Traffic Safety Administration for permission to deploy a car that doesn't comply with all federal safety standards. Ammann said the company wasn't seeking an exemption from the Federal Motor Vehicle Safety Standards — something the government caps at 2,500 — just a new way around a few of the requirements.
GM is proposing to “meet that standard in a different kind of way,” Ammann said. “A car without a steering wheel can't have a steering wheel airbag,” he said. “What we can do is put the equivalent of the passenger side airbag on that side as well. So its to meet the standards but meet them in a way that's different than what's exactly prescribed, and that's what the petition seeks to get approval for.” (Source: Andrew J. Hawkins, GM will make an autonomous car without steering wheel or pedals by 2019, Jan 12, © 2018, Vox Media.)
The Volvo website (2019) offers:
What is autonomous driving?
We believe that mobility should be safer, sustainable and more convenient. For Volvo Cars, technology should make people's lives easier. That's why our approach to autonomous driving is all about the people that will use them. Our future cars will be able to navigate without human input, equipped with sensors that read the surroundings, adapting to changing traffic conditions.
Unsupervised driving
In unsupervised autonomous mode, a vehicle performs all the driving because it is safe to rely on the technology to steer, brake and accelerate. People on board the vehicle are not expected to have control of the car.
Why autonomous cars?
Unsupervised autonomous cars will revolutionise society, boost global economies and transform the way we manage our time. As the biggest change to personal mobility since the invention of the car 130 years ago, we think there's a lot to look forward to. At Volvo Cars we believe that our first unsupervised autonomous vehicles will be in the market by 2021. What makes our approach to autonomous driving so unique is that we focus on people – not just on technology (Source: Volvo, Autonomous Driving. © 2019, Volvo Car Corporation.)
Even the austerity‐following UK Chancellor of the Exchequer, Phillip Hammond, not one noted for his optimism, quoted early in 2019, that…. “the autonomous car, probably powered by an electric motor, will be on British roads, unsupervised, by 2021.” (The Guardian2019)
Anthony Cuthbertson writing for the middle of the road UK newspaper, The Independent, shortly after, (2019) reported:
Driverless cars to be rolled out on UK roads by end of 2019, government announces
‘Key priority must be ensuring cyber security defences are deployed so this fantastic, ground‐breaking technology does not fall victim to hackers,’ …….
Self‐driving cars without a human supervisor will be tested on public roads in the UK by the end of the year, under government plans.
Fully driverless trials have previously only taken place on a limited scale in the US and Europe.
The Department of Transport said the move towards advanced trials would push the UK to the forefront of the industry.
‘Thanks to the UK's world class research base, this country is in the vanguard of the development of new transport technologies, including automation,’ said Jesse Norman, the transport minister.
‘The government is supporting the safe, transparent trialling of this pioneering technology, which could transform the way we travel.’
‘The UK has a rich heritage in automotive development and manufacturing, with automated and electric vehicles set to transform the way we all live our lives,’ said Richard Harrington, the automotive minister.
Uber plans self‐driving bicycles and scooters.
‘We need to ensure we take the public with us as we move towards having self‐driving cars on our roads by 2021. The update to the code of practice will provide clearer guidance to those looking to carry out trials on public roads.’
Advanced driverless trials on UK roads by the end of 2019 will also help the government keep to its commitment of having self‐driving vehicles on UK roads by 2021, ministers said. (Source: Anthony Cuthbertson, Driverless cars to be rolled out on UK roads by end of 2019, government announces, 6 February. © 2019, The Independent.)
And even the normally staid departments of government are getting excited. The UK Government Actuaries Dept (GAD), in September 2017 published:
Self‐Driving Cars
Once just viewed as part of science fiction, self‐driving cars, perhaps more correctly referred to asconnected and autonomous vehicles (CAVs), are already here in various forms. Connected vehicles are those which are able to communicate with their surroundings providing information on road, traffic and weather conditions. The next level is automation where the vehicle uses its connection to assist the driver, examples include autonomous emergency braking, adaptive cruise control and park assist. Testing is now also well under way for vehicles that take full control from start to finish – fully autonomous vehicles.
The vast majority of road accidents relate to human error and reducing such accidents is projected to contribute £2 billion of savings to the economy by 2030. The total projected economic benefits from all sources are in excess of £51 billion. These figures highlight the importance of developing this technology.
The UK Society of Motor Manufacturers and Traders (SMMT) issued a report (2019).
This report ‘offers a detailed assessment of connected and autonomous vehicle (CAV) development, and crucially deployment, in the UK’. The report envisages the economic benefit to the UK from the deployment of CAVs (connected and automated vehicles), to be in the region of £62 billion per annum by 2030. The SMMT report forecasts the benefits, just for UK, to be (Figure 1.4):
‘The emergence of CAVs will bring unprecedented change to the automotive industry worldwide. More than 18 million new automated vehicles are expected to be added to the global motor parc by 2030, significantly changing the way people commute. Over the next decade, for instance, new mobility modes such as automated shuttles could address gaps in first and last mile mobility’.
