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El. knyga: Driving Automation: A Human Factors Perspective [Taylor & Francis e-book]

(Loughborough University, UK), (University of Southampton, UK)
  • Formatas: 274 pages, 14 Tables, black and white; 26 Line drawings, black and white; 13 Halftones, black and white; 39 Illustrations, black and white
  • Serija: Human Factors, Simulation and Performance Assessment
  • Išleidimo metai: 10-Mar-2023
  • Leidėjas: CRC Press
  • ISBN-13: 9781003374084
Kitos knygos pagal šią temą:
  • Taylor & Francis e-book
  • Kaina: 180,03 €*
  • * this price gives unlimited concurrent access for unlimited time
  • Standartinė kaina: 257,19 €
  • Sutaupote 30%
Driving Automation: A Human Factors PerspectiveDidesnis paveikslėlis
  • Formatas: 274 pages, 14 Tables, black and white; 26 Line drawings, black and white; 13 Halftones, black and white; 39 Illustrations, black and white
  • Serija: Human Factors, Simulation and Performance Assessment
  • Išleidimo metai: 10-Mar-2023
  • Leidėjas: CRC Press
  • ISBN-13: 9781003374084
Kitos knygos pagal šią temą:
Taylor & Francis e-booksMore info about Taylor & Francis e-books
Partnerių interneto svetainė: https://www.taylorfrancis.com/books/9781003374084
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 factors 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 covers important topics including mental workload and malleable attentional resources theory, effects of automation on driver performance, in-vehicle interface 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 engineering, mechanical engineering, and health and safety.
Preface xiii
Acknowledgements xvii
Author Biographies xix
Glossary xxi
STAGE I Setting out
1(78)
1 Context
3(28)
Overview
3(1)
Prelude
3(2)
Timeline
5(1)
Past
5(2)
Present
7(2)
Future
9(2)
Definitions
11(3)
Taxonomies
14(1)
Classical taxonomies
14(3)
Contemporary taxonomies
17(4)
Driving automation taxonomies
21(6)
The human factor
27(1)
Key points
28(3)
2 Promises, promises...
31(30)
Overview
31(1)
Introduction
31(2)
Lessons learned from aviation
33(6)
Automotive accidents of automation
39(1)
Collision between a Tesla Model S and a lorry, Williston, Florida, 7 May 2016 (NTSB, 2017)
40(1)
Collision between Uber's developmental automated vehicle and a pedestrian, Tempe, Arizona, 18 March 2018 (NTSB, 2019b)
41(1)
Collision between a Tesla Model X and a crash attenuator, Mountain View, California, 23 March 2018 (NTSB, 2020)
42(1)
Lessons learned
43(1)
Problems and ironies
43(4)
Vigilance
47(1)
Trust
48(1)
Complacency
49(1)
Behavioural adaptation
50(3)
Situation awareness
53(2)
Mental workload
55(2)
Conclusions
57(1)
Key points
58(3)
3 Pay attention
61(18)
Overview
61(1)
Introduction
61(1)
Mental workload revisited
62(1)
Attention
63(2)
Automaticity
65(4)
The `problem' of underload
69(4)
Malleable attentional resources theory (MART)
73(4)
Key points
77(2)
STAGE 2 Taking the load off
79(78)
4 How low is too low?
81(22)
Overview
81(1)
Introduction
81(1)
General methodology
82(2)
Driving performance data
84(1)
Mental workload data
85(4)
Attention data
89(2)
Method - the present study
91(1)
Design
91(1)
Procedure
92(1)
Results
93(1)
Driving performance data
93(1)
Mental workload data
93(1)
Attention ratio data
94(3)
Discussion
97(1)
Implications: mental workload and performance
97(1)
Implications: malleable attentional resources theory
98(3)
Conclusions
101(1)
Key points
102(1)
5 When is ACC not ACC?
103(14)
Overview
103(1)
Introduction
103(3)
Experiment 1 Straight roads
106(1)
Method
106(1)
Results
107(1)
Discussion
108(2)
Experiment 2 variable-speed lead vehicle
110(1)
Method
110(1)
Results
110(1)
Discussion
111(2)
General discussion
113(1)
Summary of results
113(1)
Implications: mental workload and adaptive cruise control
114(1)
Conclusions
114(1)
Key points
115(2)
6 What's skill got to do with it?
117(14)
Overview
117(1)
Introduction
117(4)
Method
121(1)
Design
121(1)
Procedure
121(1)
Results
122(1)
Driving performance data
122(2)
Mental workload data
124(1)
Attention ratio data
124(2)
Discussion
126(1)
Implications: mental workload and performance
126(1)
Implications: malleable attentional resources theory and skill
126(2)
Conclusions
128(1)
Key points
129(2)
7 I thought you were driving!
131(26)
Overview
131(1)
Introduction
131(1)
Responses to automation failure
132(2)
Reaction times to automation failure
134(2)
The experiment
136(2)
Method
138(1)
Design
138(3)
Procedure
141(1)
Results
142(1)
Reaction frequencies
142(1)
Reaction time data
143(1)
Physiological arousal
144(1)
Subjective mental workload
145(2)
Discussion
147(1)
Implications: malleable attentional resources theory and automaticity
147(4)
Implications: automation and driver skill
151(1)
Brake reaction times
152(1)
Conclusions
153(2)
Key points
155(2)
STAGE 3 Human-centred automation
157(46)
8 What can automation do for us?
159(22)
Overview
159(1)
Introduction
159(1)
Transient impairment: distraction
160(1)
In-car distractors
161(4)
External distractors
165(1)
Automation-related distractors
166(1)
Perceptual impairment: eyesight
167(3)
Cognitive impairment: ageing
170(4)
Automation lends a hand
174(5)
Key points
179(2)
9 How do we get along?
181(22)
Overview
181(1)
Introduction
181(1)
In-vehicle interface design
182(3)
Ecological interface design
185(1)
EID for eco-driving
186(1)
Development of the Foot-LITE EID
187(3)
Evaluation
190(2)
Adaptive interfaces
192(4)
Adaptive automation
196(2)
Driver monitoring
198(2)
Conclusions
200(1)
Key points
201(2)
STAGE 4 Letting George do it
203(28)
10 An autopian future?
205(26)
Overview
205(1)
Introduction
205(1)
What was the problem with automation again?
206(3)
How to design an automated driving system (from a human factors perspective)
209(2)
Design philosophies for human-centred automation
211(1)
Hard or soft, vehicle or driving?
211(2)
Problem-driven automation
213(1)
`Cliff-edge' automation
214(2)
Human-automation teaming
216(7)
Driver training
223(2)
Which way now?
225(3)
Key points
228(3)
References 231(40)
Index 271
Mark S. Young is a visiting professor, Loughborough Design School, Loughborough University, UK. He has a BSc in Psychology and a Ph.D. in Human Factors and is a Chartered Fellow of the UK Chartered Institute of Ergonomics and Human Factors (CIEHF). His research interests focus on the human factors of transport systems and much of his research has been based on simulators, investigating issues such as driver workload, distraction, and the effects of automation and novel technologies.

Neville A. Stanton is a chartered psychologist, chartered ergonomist, and chartered engineer. He is a professor emeritus of human factors engineering in the School of Engineering at the University of Southampton, UK. He has degrees in Occupational Psychology (BSc), Applied Psychology (MPhil), and Human Factors Engineering (PhD, DSc) and has worked at the Universities of Aston, Brunel, Cornell, and MIT. His research interests include modelling, predicting, analysing, and evaluating human performance in systems as well as designing the interfaces and interaction between humans and technology.
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