Human factors in automotive engineering and technology

Tai ngay!!! Ban co the xoa dong chu nay!!! Human Factors in Automotive Engineering and Technology Human Factors in Road and Rail Transport Series Editors Dr Lisa Dorn Director of the Driving Research Group, Department of Human Factors, Cranfield University Dr Gerald Matthews Associate Research Professor, Institute for Simulation and Training, University of Central Florida Dr Ian Glendon Associate Professor, School of Psychology, Griffith University Today’s society confronts major land transport problems Human and financial costs of road vehicle crashes and rail incidents are increasing, with road vehicle crashes predicted to become the third largest cause of death and injury globally by 2020 Several social trends pose threats to safety, including increasing vehicle ownership and traffic congestion, advancing technological complexity at the human-vehicle interface, population ageing in the developed world, and ever greater numbers of younger vehicle drivers in the developing world Ashgate’s Human Factors in Road and Rail Transport series makes a timely contribution to these issues by focusing on human and organisational aspects of road and rail safety The series responds to increasing demands for safe, efficient, economical and environmentally-friendly land-based transport It does this by reporting on state-of-the-art science that may be applied to reduce vehicle collisions and improve vehicle usability as well as enhancing driver wellbeing and satisfaction It achieves this by disseminating new theoretical and empirical research generated by specialists in the behavioural and allied disciplines, including traffic and transportation psychology, human factors and ergonomics The series addresses such topics as driver behaviour and training, in-vehicle technology, driver health and driver assessment Specially commissioned works from internationally recognised experts provide authoritative accounts of leading approaches to real-world problems in this important field Human Factors in Automotive Engineering and Technology Guy H Walker Heriot-Watt University, UK Neville A Stanton University of Southampton, UK and Paul M Salmon University of the Sunshine Coast, Australia © Guy H Walker, Neville A Stanton and Paul M Salmon 2015 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 without the prior permission of the publisher Guy H Walker, Neville A Stanton and Paul M Salmon have asserted their right under the Copyright, Designs and Patents Act, 1988, to be identified as the authors of this work Published by Ashgate Publishing Limited Ashgate Publishing Company Wey Court East 110 Cherry Street Union Road Suite 3-1 Farnham Burlington, VT 05401-3818 Surrey, GU9 7PT USA England www.ashgate.com British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library The Library of Congress has cataloged the printed edition as follows: Walker, Guy, author Human factors in automotive engineering and technology / by Guy H Walker, Neville A Stanton and Paul M Salmon pages cm (Human factors in road and rail transport) Includes bibliographical references and index ISBN 978-1-4094-4757-3 (hbk) ISBN 978-1-4094-4758-0 (ebook) -ISBN 978-1-4724-0628-6 (epub : alk paper) Automobiles Design and construction Human engineering I Stanton, Neville A (Neville Anthony), 1960- author II Salmon, Paul M., author III Title IV Series: Human factors in road and rail transport TL250.W35 2015 629.2’31 dc23 2014046296 ISBN: 9781409447573 (hbk) ISBN: 9781409447580 (ebk – PDF) ISBN: 9781472406286 (ebk – ePUB) Printed in the United Kingdom by Henry Ling Limited, at the Dorset Press, Dorchester, DT1 1HD Contents List of Figures List of Tables About the Authors   Acknowledgements   Glossary   vii ix xi xiii xv The Car of the Future, Here Today   A Technology Timeline   13 Lessons from Aviation   27 Defining Driving   39 Describing Driver Error   49 Examining Driver Error and its Causes   75 A Psychological Model of Driving   95 Vehicle Feedback and Driver Situational Awareness   111 Vehicle Automation and Driver Workload   131 10 Automation Displays   141 11 Trust in Vehicle Technology   159 12 A Systems View of Vehicle Automation   179 13 Conclusions   193 Appendix    Further Reading   References   Bibliography   Index   203 273 275 295 303 This page has been left blank intentionally List of Figures 1.1 1.2 1.3 1.4 1.5 2.1 2.2 2.3 2.4 3.1 3.2 4.1 5.1 5.2 5.3 5.4 5.5 6.1 6.2 6.3 6.4 6.5 6.6 The driving simulator laboratory has been through several iterations in its 20-year history: This is the first, dating from 1995 and based around the front portion of a Ford Orion   The Brunel University Driving Simulator (BUDS) in 2000   The current iteration (2013): The Southampton University Driving Simulator (SUDS)   Sadly, the all-too-common experience is that human factors insights are discovered to be needed too late: Too late to be cheap and too late to be as effective as they could be    11 By far the best place to employ human factors insights is early in the design process    11 One of the first implementations of solid-state electronics in vehicles was electronic ignition, which replaced the mechanical distributor and its troublesome contact breaker points   14 Collision warning with brake support system   21 Handling management system   22 Adaptive Cruise Control    23 Allocation of function (adapted from Singleton, 1989)   35 Allocation of function matrix   36 Top level of the HTAoD    45 The perceptual cycle in driving   52 Illustration of the multi-modality of a typical infotainment system   56 Levels of cognitive control (adapted from Rasmussen, 1986)   57 Percentage of errors implicated in crashes (from Treat et al., 1979)  60 Contributing factors taxonomy (from Wierwille et al., 2002)   67 On-road study methodology   76 The instrumented vehicle (ORTeV)   77 Frequency of different error types made during on-road study   83 Participant’s head rotation, gaze and the lateral position of the vehicle during speeding violation event   85 Participant midway through right-hand turn on the red arrow: Overlaid circles show the straight on green traffic signal (left-hand side of the driver view window) and the red right-hand turn traffic signal (right-hand side of the driver window view)   88 Head rotation, gaze angle and lateral position during ‘perceptual failure’ error event   90 viii 7.1 7.2 Human Factors in Automotive Engineering and Technology Information flow between driver, automatics and vehicle sub-systems (from Stanton and Marsden, 1996)   A group of 29 drivers were asked ‘what you think the oil warning light means?’ (The correct answer is low oil pressure)   7.3 Hypothesised relationship between psychological factors    8.1 Quantity of knowledge extracted by the drivers of high and low feedback vehicles across the four encoding categories (n=12)    8.2 Median values of da characterising probe recall performance in each of the vehicle feedback conditions   9.1 The driver’s view of the road, instruments and secondary task (see bottom-left of picture)   9.2 Correct responses to the secondary task in the manual and ACC conditions    9.3 Driver reactions to ACC failure   10.1 Functional diagram of S&G-ACC   10.2 Display types   10.3 Change detection rates with the three interfaces    10.4 Self-reported workload with the three displays   11.1 The TPB can be used as a simplified behavioural model within which to situate trust and its effects on behaviour   11.2 Trust curves and the relationship between objective system reliability and driver trust: The dotted line is a theoretical trust continuum, whereas the solid curved line is an approximate one based on empirical studies (e.g., Kantowitz, Hanowski and Kantowitz, 1997; and Kazi et al., 2007)   11.3 Indicative trust calibration curve overlain across sampling behaviour curve to reveal an important intermediate region where sampling and trust changes rapidly   11.4 Different methods of assessing driver trust can be applied at different points in the system design lifecycle   12.1 Results for vehicle speed    12.2 Results for lateral position    12.3 Results for workload/frustration   12.4 Results for overall situational awareness    13.1 Hollnagel and Woods’ (2005) self-reinforcing complexity cycle   96 106 108 120 127 134 136 137 142 143 150 151 160 166 170 175 184 185 186 187 195 List of Tables 3.1 3.2 4.1 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12 5.13 5.14 6.1 6.2 6.3 6.4 6.5 8.1 8.2 8.3 Types of driver error and their (potential) technological solution   29 Degrees of automation for driver tasks    37 Comparison of two-litre saloon cars since 1966   40 The SHERPA method provides a simple way to systematically and exhaustively identify credible error types based on a task analysis and external error modes    51 Three types of schema-based errors    53 Reason’s error taxonomy    54 Example error types for Reason’s errors and violations taxonomy (adapted from Reason, 1990)   59 Classification of driver errors (from Reason et al., 1990)   59 Driver error and incident causation factors (adapted from Wierwille et al., 2002)   61 Principal causal-factor taxonomy for accident analysis (adapted from Najm et al., 1995)    62 Contribution of vehicle manoeuvres to road accidents in the UK (adapted from Brown, 1990)    63 Drivers’ errors as contributing to accidents (adapted from Sabey and Staughton, 1975)    64 Human error and causal factors taxonomy (from Sabey and Taylor, 1980)   65 Errors associated with accident scenarios (adapted from Verway et al., 1993)   65 Generic driver error taxonomy with underlying psychological mechanisms: Action errors    68 Driver error causal factors    70 Potential technological solutions for driver errors    71 CDM probes used during on-road study   78 Different error types (frequency and proportion of all errors) made by drivers during the on-road study   81 CDM extract for unintentional speeding violation   84 CDM extract for intentional speeding violation   86 Extracts from CDM transcript for ‘perceptual failure’ error   88 Sample vehicles    115 Descriptive analysis of the control measures    118 Inferential and effect size analysis of the control measures    119 296 Human Factors in Automotive Engineering and Technology Carsten, O (2005) Mind over matter: Who’s controlling the vehicle and how we know In G Underwood (ed.), Traffic and Transport Psychology Oxford: Elsevier, pp 231–42 Chase, W G and Simon, H A (1973) Perception in chess Cognitive Psychology, 55–81 Divey, S T (1991) The Accident Liabilities of Advanced Drivers Crowthorne: TRRL Dorn, L and Barker, D (2005) The effects of driver training on simulated driving performance Accident Analysis and Prevention, 37, 63–9 Endsley, M R and Kaber, D B (1999) Level of automation effects on performance, situation awareness and workload 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design of 191–2 failure scenarios 137 false sense of security 191 feedback to drivers 180, 182, 182 hypervigilance 138 impact of on driver behaviour 179–92, 182, 184, 185, 186, 187, 188–9 as improvement on CC 132 interfaces, driver 191–2 introduction of 131 locus of control 180, 185, 188 mental models 180–1, 187, 188 mode errors 154 over-reliance on 133 and perceived behavioural control 167–9 problems with automation 132–3 reclaiming control from 131, 137–8 simulator study 134, 134–7, 136, 137 situational awareness (SA) 180, 186–7, 187, 188, 190, 191 speed 184, 188–9 stop-and-go 141–4, 142, 143, 146, 146–56, 148, 150, 150, 151, 152 s tress in driving 181, 189, 190–1 trust 180, 185, 188 unintended acceleration 138 workload 181, 182, 182, 184–6, 185, 186, 188–9, 190 Adler, S 97–8 age and human error 60 aggression in driving 27–8 airbags, smart 21 aligning torque 6, 20 allocation of system function generic process for 35 hierarchical task analysis of driving (HTAoD) 36–7, 37 hypothetical-deductive model (HDM) 35–6, 36 tables of relative merit (TRM) 35 anger in driving 27–8 Annett, J 42–3, 97 anti-lock braking systems (ABS) 16, 165–6 appraisal in stress management 102–3 Arthur, J.W 100 Ashleigh, M.J 174 Astley, J.A 43 auditory feedback to drivers 5–6 automation allocation of system function 34–7, 35, 36, 37 arguments for 4, 27–30, 29, 132, 194, 196 in aviation 30–4 aviation as basic model of 27 cognitive stress, automation-induced 30–1 economic case for 29–30 error, human, reduction of 28 error-inducing equipment design 33–4 304 Human Factors in Automotive Engineering and Technology f eedback to drivers 5–7, 95 frustration-aggression hypothesis 27–8 full/cooperative 37, 37 human factors, importance of in 7–8 intermittant failures in 31 mode errors problems and ironies 4–7, 8–9, 132–3, 197–8 reliability of equipment 31–2 shortfalls in expected benefits 30–1 soft 177–8 task allocation 101–2 training and skills maintenance 32–3 trust in 98–9 see also adaptive cruise control (ACC); trust aviation automation in 30–4 as basic model of automation 8, 27 error-inducing equipment design 33–4 human error, role of in accidents 49 mode errors 5, 55 reliability of equipment 31–2 shortfalls in expected benefits from automation 30–1 technological risks from automation in 30–4 training and skills maintenance 32–3 Bainbridge, L 106 Barber, B 98–9 Barley, S 33 Becker, A.B 107 Bies, R.J 171–2 Billings, C.E 32 Boehm-Davies, D.A 34 Bolte, B 156 brake-by-wire systems 19–20 braking performance 40 Brewer, W.F 105 Brookhuis, K.A 144, 155 Brown, I 62–3, 63, 98 Bygrave, H.M 181 calibration, trust 165–6, 166 capabilities of cars, use made of 39–41, 40 Carter, C 98 Chapanis, A 50, 55 Cherns, A 200 classification of human error 50–8, 51, 53, 54, 56, 57 Clegg, C.W 200 co-evolutionary spiral 193 cognition 104 cognitive control, errors in levels of 56–8, 57 cognitive stress, automation-induced 30–1 collisions sensing 20–1, 22 warning and avoidance 21, 21 Computer Car 2030 202 computing see automation; technology Comrey, A.L 100, 168 conceptual model building 175, 175–6 confidence, driver 167–9 cooperative/full automation 37, 37 critical decision method (CDM) interviews 78, 78–9, 83, 84–5, 86–7, 88–9 cruise control (CC) systems 4, 131, 132 see also adaptive cruise control (ACC) cue utilisation theory 102 dashboard displays 24 design of 191–2 mental workload 155–6 mode errors 154 optimisation of workload as aim 145 representations of information 156–7 S&G-ACC study on workload 146, 146–56, 148, 150, 150, 151, 152 situational awareness (SA) 142–4, 143, 154–5 stop-and-go adaptive cruise control (ACC) 142 Davis, L.E 200 de-skilling 32–3 decision errors 60, 60 dependability 161–2 design adaptive cruise control (ACC) 191–2 dominant paradigm for 198–9 error-inducing 33–4 feedback to drivers 6–7 human error as product of 50 human factors, integration of into 10, 11 mode errors 55 Index o ptimisation of workload as aim 145 situational awareness (SA) 156 socio-technical principles for future 200–1, 200–1 trends in and feedback to drivers 112 trust, recommendations regarding 176–8 Desmond, P.A 96, 101, 103, 107, 181 displays, dashboard see dashboard displays distributed situation awareness (DSA) 104 distrust 169, 169 Doverspike, D 100 drive-by-wire systems 7, 18, 20 driver-automation-vehicle system 95, 95 Driver Behaviour Questionnaire (DBQ) 58 drivers co-evolutionary spiral with vehicles 193 impact of ACC on behaviour 179–92, 182, 184, 185, 186, 187, 188–9 monitoring of 24 see also psychological model of driving; trust driving dynamic capabilities of cars, use made of 39–41, 40 large human role in 39 as stressful 27–8 see also task analysis of driving dry brake-by-wire systems 19–20 Duncan, J 98 dynamic capabilities of cars, use made of 39–41 Easterbrook, J.A 102 economic case for automation 29–30 electronic architecture (OSEK) 26 electronic ignition 14, 14 enabling technology 25–6 Endsley, M.R 66, 156, 191 engine management systems 15–16 enjoyment in driving 29–30 error, human see human error externality/internality 99–100, 168–9, 180 Fairclough, S.H 98, 107–8 faith 163–5, 166 feedback to drivers 305 a daptive cruise control (ACC) 180, 182 aligning torque 6, 20 auditory 5–6 from automated systems 95 and automation 5–7 dashboard displays 24 design of vehicles, trends in 112 designing out of 6–7 drive-by-wire systems 20 implicit vehicle feedback 111 naturalistic driving study 114–21, 115, 116, 118, 119, 120, 121, 122, 128–9 as psychological factor 97–8 sensitivity to 6–7, 111–12 simulated road environment study 122–8, 123, 126, 127 in skills acquisition 97–8 sophistication needed in automation 8–9 tactile transparency of technology 176–7 frustration-aggression hypothesis 27–8 fuel injection 14–15 full/cooperative automation 37 Fuller, R 39 Geels-Blair, K 162 Gillan, D.J 174 Godthelp, H 39 Goom, M 101 Gopher, D 101 Griffin, M.J Ground Proximity Warning Systems (GPWS) 31 Gruppe, T.R 101 Gulian, E 103 Hanowski, R.J 167, 174 Hartley, D.C 6–7, 112 hierarchical task analysis (HTA) 42–4 hierarchical task analysis of driving (HTAoD) 36–7, 37, 45, 197, 203–72 Hoffman, E.R 6, 112 Hollnagel, E 195, 195–6 human error and age 60 causal factors, taxonomy of 70, 70–1 306 Human Factors in Automotive Engineering and Technology classification schemes 50–8, 51, 53, 54, 56, 57 cognitive control, errors in levels of 56–8, 57 contributing conditions 58–67, 59, 60, 61, 62, 63, 64, 65–6, 67, 70, 70–1 decision errors 60, 60 as design/system product 50 errors/violations 58–9, 59 looked-but-did-not-see (LBDNS) errors 62–3 mode errors 53, 55 objectivity as missing from analysis 75–6 performance errors 60, 60 recognition errors 60, 60 reduction of 28, 29 risk to others, degree of 59, 59 role of in accidents 49–50 schema theory 51–3, 52, 53 situational awareness (SA) 66 Skill, Rule, Knowledge (SRK) framework 56–8, 57 slips, lapses, mistakes and violations 54, 54–5 Systematic Human Error Reduction Approach (SHERPA) 51, 51 systems view 58 taxonomy of, proposed 67–71, 68–9 technological solutions to 71, 71–3, 196 see also multi-method framework for studying error human factors implicit theory based around 199–200 importance of in automation 7–8 integration into design process 10, 11 see also feedback to drivers; human error; locus of control; psychological model of driving; situational awareness (SA); stress in driving; trust; workload Human Robot Interaction Trust Scale 174 hypervigilance 138 hypothetical-deductive model (HDM) 35–6, 36 Ilgen, D.R 164 image, system 164 inertial navbigation systems (INS) 34 information management 24, 25 infrastructure-induced errors 87–90, 88, 88–9, 90 instrument clusters 24 intentionality of vehicle systems 164, 177–8 interfaces, driver design of 191–2 mental workload 155–6 mode errors 154 optimisation of workload as aim 145 representations of information 156–7 S&G-ACC study on workload 146, 146–56, 148, 150, 150, 151, 152 situational awareness (SA) 142–4, 143, 154–5 stop-and-go adaptive cruise control (ACC) 142 intermittant failures in automation 31 internality/externality 99–100, 168–9, 180 Johnson-Laird, P.N 105 Jones, D.E 156 Jordan, P.W 30 Joubert, P.N 6, 112 Joy, T.J.P 6–7, 112 Kantowitz, B.H 167, 174 Kantowitz, S.C 167, 174 Kay, H 97 Keller, D 162 Kelly, G 174 Kimchi, R 101 Kluger, A.N 97–8 knowledge and skills maintenance 32–3 knowledge of results 97–8 lapses 54, 54–5 Lechner, D 41 Lee, J.D 144, 159–60, 167 Leplat, J 101 Lewandowsky, S 164 locus of control 99–100, 168–9, 180, 185, 188 looked-but-did-not-see (LBDNS) errors 62–3 Index maintenance of knowledge and skills 32–3 Mansfield, N.J Marsden, P 95, 132–3 Matthews, G 96, 101, 103, 107, 181 May, A.J 98 McKnight, A.J 41–2, 45 McRuer, D.T 42 Meister, D 105 mental models 52 adaptive cruise control (ACC) 180–1, 187, 188 as psychological factor 104–7, 106 mental workload 25, 96, 100–2, 155–6 Merritt, S.M 164 mistakes 54, 54–5 mistrust 169, 169 mode awareness 25 mode errors 5, 53, 55, 154 monitoring of drivers 24 Montag, I 100, 168 Moray, N 95, 167, 173 Muir, B.M 95, 98–9, 167, 169, 173, 180 multi-method framework for studying error application of error taxonomy 91 critical decision method (CDM) interviews 78, 78–9, 83, 84–5, 86–7, 88–9 error classification 79 in-depth analysis of errors 83–90, 84–5, 85, 86–7, 88, 88–9, 90, 91–2 infrastructure-induced errors 87–90, 88, 88–9, 90 initial error classification 81, 81–2, 83 materials used 80 methodology 76 on-road test vehicle (ORTeV) 77, 77 participants in study 79–80 procedure 80–1 route taken 80 signalling design at intersection 87–90, 88, 88–9, 90, 92 speeding errors 84–5, 84–7, 85, 86–7 taxonomy-based error classification 82–3, 83 verbal protocol analysis (VPA) 78 Najm, W.G 62, 62 navigation systems and trust 166–7 307 Neisser, U 52, 52 network analysis 175, 175 Nilsson, L 133, 138 Norman, Donald 1, 8–9, 17–18, 51–3, 52, 53, 111, 137, 154, 156 Okada, Y 101 on-road study of performance and errors application of error taxonomy 91 critical decision method (CDM) interviews 78, 78–9, 83, 84–5, 86–7, 88–9 error classification 79 in-depth analysis of errors 83–90, 84–5, 85, 86–7, 88, 88–9, 90, 91–2 infrastructure-induced errors 87–90, 88, 88–9, 90 initial error classification 81, 81–2, 83 materials used 80 methodology 76 on-road test vehicle (ORTeV) 77, 77 participants in study 79–80 procedure 80–1 route taken 80 signalling design at intersection 87–90, 88, 88–9, 90, 92 speeding errors 84–5, 84–7, 85, 86–7 taxonomy-based error classification 82–3, 83 verbal protocol analysis (VPA) 78 opaque technology 23, 23–5 Open Systems and the Corresponding Interfaces for Automotive Electronics (OSEK) 26 over-reliance on technology 31–2 perceptual cycle 52, 52 performance enevelope of cars 39–41, 40 performance errors 60, 60 Perrin, C 41 planned behaviour, theory of (TPB) 160, 160–1 pleasure in driving 30 predictability 161 primary task measures 173 psychological model of driving driver-automation-vehicle system 95, 95 308 Human Factors in Automotive Engineering and Technology f eedback to drivers as factor 97–8 hypothesised model 107–8, 108 internality/externality 99–100 interrelations between variables 107–8 locus of control 99–100 mental representations 104–7, 106 mental workload 100–2 situational awareness (SA) 103–4 stress 102–3 trust 98–9 Rajan, J.A 107 Rasmussen, Jens 56–8, 57 Reason, J 28, 58–9, 59, 66–7 Reason, James 54, 54–5, 56 recognition errors 60, 60 Reinartz,, S.J 101 reliability 166–7 reliability of equipment 31–2 repertory grids 174 revenge 170–2, 172 Rice, S 162 Rimmo, P 60 Rips, L.J 105 risk homeostasis 16, 20, 22 risk to others, degree of 59, 59 Rogers, S.B 138 Rotter, J.B 99–100 route guidance systems 166–7 Sabey, B.E 64, 64, 65 schema theory 51–3, 52, 53 Schlegel, R.E 100 Schwark, J 162 See, K.A 159–60, 167 self-reinforcing complexity cycle 195, 195–6 sensitivity to feedback 6–7, 111–12 Seppelt, B.D 144 signalling design at intersection 87–90, 88, 88–9, 90, 92 simulator, driving, evolution of 1, 2, 3, situational awareness (SA) 5, 66, 96 adaptive cruise control (ACC) 180, 186–7, 187, 188, 190, 191 definitions 112–13 design principles supporting 156 information elements 113–14 i nterfaces, driver 142–4, 143, 154–5 measurement methods 113–14 naturalistic driving study 114–21, 115, 116, 118, 119, 120, 121, 122, 128–9 as psychological factor 103–4 self-awareness of 128–9, 130 simulated road environment study 122–8, 126, 127 and technology 129–30 Skill, Rule, Knowledge (SRK) framework 56–8, 57 skills feedback in acquisition of 97–8 maintenance 32–3 slips 54, 54–5 smart airbags 21 soft automation 177–8 Sparkes, T.J 181 speeding errors 84–5, 84–7, 85, 86–7 Stammers, R.B 43 Stanton, N.A 4, 5, 95, 132–3, 174, 191 Staughton, G.C 64, 64 steer-by-wire systems 19 stop-and-go adaptive cruise control (ACC) 141–4, 142, 143, 146, 146–56, 148, 150, 150, 151, 152 stress in driving 27–8, 102–3, 181, 189, 190–1 system image 164 system-wide trust theory 162–3 Systematic Human Error Reduction Approach (SHERPA) 51, 51 systems approach ACC, study of impact of 182, 182–92, 184, 185, 186, 187, 188–9 analysis of errors 50 behaviour of drivers 179–81 benefits of 92 human error as product of 50 problems through elements combining 197 tables of relative merit (TRM) 35 tactile feedback to drivers task allocation 101–2 task analysis of driving assumptions regarding 42 hierarchical task analysis (HTA) 42–4 Index hierarchical task analysis of driving (HTAoD) 37, 44–6, 45, 197, 202–72 lack of 41–2 task defined 41 validation of 46–7 task underload 25 taxonomies of human error 50–8, 51, 53, 54, 56, 57 causal factors 70, 70–1 proposed 67–71, 68–9 see also on-road study of performance and errors Taylor, H 65 technology 42-volt vehicle electrics 26 anti-lock braking systems (ABS) 16 brake-by-wire systems 19–20 collision sensing 20–1, 22 collision warning and avoidance 21, 21 dashboard displays 24 drive-by-wire systems 18, 20 electronic ignition 14, 14 enabling 25–6 engine management systems 15–16 evolution of 14–17 fuel injection 14–15 information management 24, 25 instrument clusters 24 mental underload 25 mode awareness 25 monitoring of drivers 24 opaque 23, 23–5 OSEK 26 over-reliance on 31–2 risk homeostasis 16, 22 risks from in aviation 30–4 self-reinforcing complexity cycle 195, 195–6 and situational awareness (SA) 129–30 smart airbags 21 solutions to human error 71, 71–3, 196 steer-by-wire systems 19 survey of trends 17 timescale for the future 17–18 traction control 16 transparency of 13, 176–7 309 t ransparent 18–22, 21, 22 trust in 98–9 ubiquity of 13 voice activation 25 yaw stability control 21–2, 22 see also adaptive cruise control (ACC); trust test drive in 2030 9–10, 202 theory of planned behaviour (TPB) 160, 160–1 traction control 16 train driving training and feedback 97–8 knowledge and skills maintenance 32–3 transparency of technology 13, 18–22, 21, 22, 176–7 Treat, J.R 60–2 Tripp, T.M 171–2 trust ACC and perceived behavioural control 167–9 adaptive cruise control (ACC) 180, 185, 188 anti-lock braking case 165–6 aspects of 159–60 calibration 165–6, 166 conceptual model building 175, 175–6 confidence, driver 167–9 defined 159 and dependability 161–2 design recommendations 176–8 distrust 169, 169 establishing 161 exploration of system by driver 177 and faith 163–5 Human Robot Interaction Trust Scale 174 as important intervening variable 161 intentionality of vehicle systems 164, 177–8 interest in 159 measuring 173–6 mistrust 169, 169 navigation systems 166–7 need for 161 network analysis 175, 175 and predictability 161 310 Human Factors in Automotive Engineering and Technology p rimary task measures 173 propensity to 164 regaining 178 and reliability 166–7 repertory grids 174 revenge 170–2, 172 soft automation 177–8 subjective scales 173–4 system image 164 system-wide trust theory 162–3 theory of planned behaviour (TPB) 160, 160–1 training 177 transparency of technology 176–7 Tucker, P 98 2030 test drive 9–10, 202 ubiquity of technology 13 underload, mental 25, 102, 145 unintended acceleration 138 verbal protocol analysis (VPA) 78 Verwey, W.B 65–6, 65–6 violations 54 voice activation 25 Wagenaar, W.A 66–7 Walker, G.H Wang, Z 100–1 Welford, A.T 97 wet brake-by-wire systems 19 Wickens, C.D 55, 102 Wierwille, W.W 61, 67, 138 Williams, P 98 Wilson, J.R 107 Woods, D.D 195, 195–6 workload adaptive cruise control (ACC) 181, 182, 182, 184–6, 185, 186, 188–9, 190 adaptive interfaces 102 high/low 25 mental 96, 100–2 optimisation of 145 S&G-ACC study 146, 146–56, 148, 150, 150, 151, 152 and task performance 28 underload/overload 25, 102, 145 see also adaptive cruise control (ACC) Yagoda, R.E 174 yaw stability control 21–2, 22 Young, M.S 4, 5, 191