Driving Automation: A Human Factors Perspective

296 Pages·2023·12.103 MB·English

by  Mark S Young

Driving Automation
The technology behind self-driving cars is being heavily promulgated as the
solution to a variety of transport problems including safety, congestion, and
impact on the environment. This text examines the key role that human fac-
tors plays in driving forward future vehicle automation in a way that realizes
the benefits while avoiding the pitfalls.
Driving Automation: A Human Factors Perspective addresses a range of issues
related to vehicle automation beyond the ‘can we’ to ‘how should we’. It cov-
ers important topics including mental workload and malleable attentional
resources theory, effects of automation on driver performance, in-vehicle inter-
face design, driver monitoring, eco-driving, responses to automation failure,
and human-centred automation.
The text will be useful for graduate students and professionals in diverse areas
such as ergonomics/human factors, automobile engineering, industrial engi-
neering, mechanical engineering, and health and safety.
The Human Factors of Simulation and Assessment Series
Series Editors:
Michael Lenné, Monash University Accident Research Centre, Australia
Mark S. Young, Loughborough Design School, Loughborough University, UK
Peter Hancock, Department of Psychology, University of Central Florida
Ongoing advances in lower-cost technologies are supporting a substantive
growth worldwide in the use of simulation and naturalistic performance
assessment methods for research, training and operational purposes in
domains such as road, rail, aviation, mining and healthcare. However, this
has not been accompanied by a similar growth in the expertise required to
develop and use such systems for evaluating human performance. Whether
for research or practitioner purposes, many of the challenges in assessing
operator performance, both using simulation and in natural environments,
are common. What performance measures should be used, what technology
can support the collection of these measures across the different designs, how
can other methods and performance measures be integrated to complement
objective data, how should behaviours be coded and the performance stan-
dards measured and defined? How can these approaches be used to support
product development and training, and how can performance within these
complex systems be validated? This series addresses a shortfall in knowledge
and expertise by providing a unique and dedicated forum for researchers and
experienced users of simulation and field-based assessment methods to share
practical experiences and knowledge in sufficient depth to facilitate delivery
of practical guidance.
Simulators for Transportation Human Factors: Research and Practice
Edited by Mark S. Young, Michael G. Lenné
Integrating Human Factors Methods and Systems Thinking for Transport
Analysis and Design
Gemma J. M. Read, Vanessa Beanland, Michael G. Lenné, Neville A. Stanton,
Paul M. Salmon
Increasing Motorcycle Conspicuity: Design and Assessment of Interventions
to Enhance Rider Safety
Lars Rößger, Michael G. Lenné
Distributed Situation Awareness in Road Transport: Theory, Measurement,
and Application to Intersection Design
Paul M. Salmon, Gemma Jennie Megan Read, Guy H. Walker, Michael G.
Lenné, Neville A. Stanton
Driving Automation: A Human Factors Perspective
Mark S. Young and Neville A. Stanton
Driving Automation
A Human Factors Perspective
Mark S. Young and Neville A. Stanton
Designed cover image: © shutterstock
First edition published 2023
by CRC Press
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and by CRC Press
4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN
CRC Press is an imprint of Taylor & Francis Group, LLC
© 2023 Mark S. Young and Neville A. Stanton
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but the author and publisher cannot assume responsibility for the validity
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Library of Congress Cataloging-in-Publication Data
Names: Young, Mark S., author. | Stanton, Neville A. (Neville Anthony),
1960- author.
Title: Driving automation : a human factors perspective / Mark S. Young and
Neville A. Stanton.
Description: First edition. | Boca Raton : CRC Press, 2023. | Includes
bibliographical references and index.
Identifiers: LCCN 2022045891 (print) | LCCN 2022045892 (ebook) |
ISBN 9780367754457 (hardback) | ISBN 9781032448244 (paperback) |
ISBN 9781003374084 (ebook)
Subjects: LCSH: Automobiles--Automatic control. | Automobile
driving--Human factors. | Automated vehicles.
Classification: LCC TL152.8 .Y675 2023 (print) | LCC TL152.8 (ebook) |
DDC 629.04/6--dc23/eng/20221101
LC record available at https://lccn.loc.gov/2022045891
LC ebook record available at https://lccn.loc.gov/2022045892
ISBN: 978-0-367-75445-7 (hbk)
ISBN: 978-1-032-44824-4 (pbk)
ISBN: 978-1-003-37408-4 (ebk)
DOI: 10.1201/9781003374084
Typeset in Sabon
by KnowledgeWorks Global Ltd.
For my Dad, who always drove.
-MSY

Contents
Preface xiii
Acknowledgements xvii
Author Biographies xix
Glossary xxi
STAGE 1
Setting out 1
1 Context 3
Overview 3
Prelude 3
Timeline 5
Past 5
Present 7
Future 9
Definitions 11
Taxonomies 14
Classical taxonomies 14
Contemporary taxonomies 17
Driving automation taxonomies 21
The human factor 27
Key points 28
2 Promises, promises… 31
Overview 31
Introduction 31
Lessons learned from aviation 33
Automotive accidents of automation 39
Collision between a Tesla Model S and a lorry, Williston,
Florida, 7 May 2016 (NTSB, 2017) 40
vii
viii Contents
Collision between Uber’s developmental automated vehicle
and a pedestrian, Tempe, Arizona, 18 March 2018
(NTSB, 2019b) 41
Collision between a Tesla Model X and a crash
attenuator, Mountain View, California, 23 March
2018 (NTSB, 2020) 42
Lessons learned 43
Problems and ironies 43
Vigilance 47
Trust 48
Complacency 49
Behavioural adaptation 50
Situation awareness 53
Mental workload 55
Conclusions 57
Key points 58
3 Pay attention 61
Overview 61
Introduction 61
Mental workload revisited 62
Attention 63
Automaticity 65
The ‘problem’ of underload 69
Malleable attentional resources theory (MART) 73
Key points 77
STAGE 2
Taking the load off 79
4 How low is too low? 81
Overview 81
Introduction 81
General methodology 82
Driving performance data 84
Mental workload data 85
Attention data 89
Method – the present study 91
Design 91
Procedure 92
Contents ix
Results 93
Driving performance data 93
Mental workload data 93
Attention ratio data 94
Discussion 97
Implications: mental workload and performance 97
Implications: malleable attentional resources theory 98
Conclusions 101
Key points 102
5 When is ACC not ACC? 103
Overview 103
Introduction 103
Experiment 1: straight roads 106
Method 106
Results 107
Discussion 108
Experiment 2: variable-speed lead vehicle 110
Method 110
Results 110
Discussion 111
General discussion 113
Summary of results 113
Implications: mental workload and adaptive cruise control 114
Conclusions 114
Key points 115
6 What’s skill got to do with it? 117
Overview 117
Introduction 117
Method 121
Design 121
Procedure 121
Results 122
Driving performance data 122
Mental workload data 124
Attention ratio data 124
Discussion 126
Implications: mental workload and performance 126
Implications: malleable attentional resources theory
and skill 126