Human-Machine Interaction for Automated Vehicles
Driver Status Monitoring and the Takeover Process
- 1st Edition - May 24, 2023
- Imprint: Academic Press
- Authors: Yifan Zhao, Chen Lv, Lichao Yang
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 8 9 9 7 - 5
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 8 9 9 8 - 2
Human-Machine Interaction for Automated Vehicles: Driver Status Monitoring and the Takeover Process explains how to design an intelligent human-machine interface by character… Read more
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Request a sales quoteHuman-Machine Interaction for Automated Vehicles: Driver Status Monitoring and the Takeover Process explains how to design an intelligent human-machine interface by characterizing driver behavior before and during the takeover process. Multiple solutions are presented to accommodate different sensing technologies, driving environments and driving styles. Depending on the availability and location of the camera, the recognition of driving and non-driving tasks can be based on eye gaze, head movement, hand gesture or a combination. Technical solutions to recognize drivers various behaviors in adaptive automated driving are described with associated implications to the driving quality.
Finally, cutting-edge insights to improve the human-machine-interface design for safety and driving efficiency are also provided, based on the use of this sensing capability to measure drivers’ cognition capability.
- Covers everything needed to design an effective driver monitoring system, including sensors, areas to monitor, computing devices, and data analysis algorithms
- Explores aspects of driver behavior that should be considered when designing an intelligent HMI
- Examines the L3 take-over process in detail
Researchers and engineers interested in human-machine interaction in automated vehicles.
- Cover image
- Title page
- Table of Contents
- Copyright
- Chapter one. Introduction
- Abstract
- Table of Contents
- 1.1 Automation level
- 1.2 Summary of the book
- References
- Chapter two. Driver behaviour recognition based on eye gaze
- Abstract
- Table of Contents
- 2.1 Introduction
- 2.2 Methodology
- 2.3 Results
- 2.4 Discussion
- 2.5 Conclusions
- References
- Chapter three. Driver behaviour recognition based on hand-gesture
- Abstract
- Table of Contents
- 3.1 Introduction
- 3.2 Methodology
- 3.3 Results
- 3.4 Discussion
- 3.5 Conclusion
- References
- Chapter four. Driver behaviour recognition based on head movement
- Abstract
- Table of Contents
- 4.1 Introduction
- 4.2 Methodology
- 4.3 Results
- 4.4 Conclusion
- References
- Chapter five. Driver behaviour recognition based on the fusion of head movement and hand movement
- Abstract
- Table of Contents
- 5.1 Introduction
- 5.2 Related works
- 5.3 Methodology
- 5.4 Dataset and training
- 5.5 Results
- 5.6 Visualisation and discussion
- 5.7 Conclusion
- References
- Chapter six. Real-time driver behaviour recognition
- Abstract
- Table of Contents
- 6.1 Introduction
- 6.2 Methodology
- 6.3 Results
- 6.4 Conclusion
- References
- Chapter seven. The implication of non-driving tasks on the take-over process
- Abstract
- Table of Contents
- 7.1 Introduction
- 7.2 Methodology
- 7.3 Results
- 7.4 Conclusion
- References
- Chapter eight. Driver workload estimation
- Abstract
- Table of Contents
- 8.1 Introduction
- 8.2 The hybrid methods
- 8.3 Dataset and pre-processing
- 8.4 Results and discussions
- 8.5 Conclusion
- Appendix
- References
- Chapter nine. Neuromuscular dynamics characterisation for human–machine interface
- Abstract
- Table of Contents
- 9.1 Introduction
- 9.2 Dynamic model of the human–machine interacting system
- 9.3 System identification methodology
- 9.4 Experiment design and data collection
- 9.5 Experiment results
- 9.6 Result analysis and discussion
- 9.7 Conclusions and future work
- References
- Chapter ten. Driver steering intention prediction using neuromuscular dynamics
- Abstract
- Table of Contents
- 10.1 Introduction
- 10.2 High-level architecture of the system
- 10.3 Experiment design and data analysis
- 10.4 Hybrid-learning-based time-series model
- 10.5 Experiment results
- 10.6 Conclusion
- References
- Chapter eleven. Intelligent haptic interface design for human–machine interaction in automated vehicles
- Abstract
- Table of Contents
- 11.1 Introduction
- 11.2 Experimental results
- 11.3 Discussion
- 11.4 Experimental methods
- References
- Index
- Edition: 1
- Published: May 24, 2023
- Imprint: Academic Press
- No. of pages: 260
- Language: English
- Paperback ISBN: 9780443189975
- eBook ISBN: 9780443189982
YZ
Yifan Zhao
Dr Yifan Zhao is a Reader in Data Science in the School of Aerospace, Transport and Manufacturing at Cranfield University and the academic lead of the Through-life Engineering Services Lab. He has over 20 years of experience in solving Inverse Problems based on computer vision, Artificial Intelligence (AI), signal processing, and nonlinear system identification. The covered themes include asset management of construction, non-destructive testing & evaluation (NDT&E) in Digital Manufacturing, driver monitoring and human-machine interface for Intelligent Transport, and brain functional imaging & analysis for Digital Healthcare. He has produced over 150 publications, 3 books and 3 patents.
Affiliations and expertise
Reader in Data Science, School of Aerospace, Transport and Manufacturing, Cranfield University, UKCL
Chen Lv
Dr Chen Lv is an Assistant Professor at the School of Mechanical and Aerospace Engineering and the Cluster Director in Future Mobility Solutions at Nanyang Technological University. His research focuses on intelligent vehicles, automated driving, and human-machine systems, where he has contributed 2 books, more than 100 papers, and obtained 12 Chinese patents. He serves as Associate Editor for IEEE T-ITS, IEEE TVT, and IEEE T-IV. He received IEEE IV Best Workshop/Special Session Paper Award in 2018, Automotive Innovation Best Paper Award in 2020, the winner of Waymo Open Dataset Challenges at CVPR 2021, and Machines Young Investigator Award in 2022.
Affiliations and expertise
Assistant Professor, School of Mechanical and Aerospace Engineering, Nanyang Technological University, SingaporeLY
Lichao Yang
Dr Lichao Yang is a research fellow in Computer Vision and Artificial Intelligence in the School of Aerospace, Transport and Manufacturing at Cranfield University. He received the B.Eng. degree in automotive engineering from Coventry University in 2017 and M.Sc. and Ph.D. degrees in automotive mechatronics and manufacturing from Cranfield University in 2018 and 2021. His research interests include computer vision, image processing, machine learning, and human behaviour analysis. He was awarded The Worshipful Company of Founders Award for the best PhD/EngD thesis for the academic year 2020/2021 and the Chinese Government Award for Outstanding Self-financed Student Abroad in 2021.
Affiliations and expertise
Research Fellow in Computer Vision and Artificial Intelligence, School of Aerospace, Transport and Manufacturing, Cranfield University, UKRead Human-Machine Interaction for Automated Vehicles on ScienceDirect