Human-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
1. Introduction
2. Driver Behaviour Recognition Based on Eye-gaze
3. Driver Behaviour Recognition Based on Hand-gesture
4. Driver Behaviour Recognition Based on Head Movement
5. Driver Behaviour Recognition Based on the Fusion of Head Movement and Hand
Movement
6. Real-time Driver Behaviour Recognition
7. The Implication of Non-driving Tasks on the Take-over Process
8. Driver Workload Estimation
9. Neuromuscular Dynamics Characterization for HumanMachine Interface
10. Driver Steering Intention Prediction using Neuromuscular Dynamics
11. Intelligent Haptic Interface Design for HumanMachine Interaction in
Automated Vehicles
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. 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. 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.
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