Yifan Zhao, Chen Lv, Lichao Yang

Human-Machine Interaction for Automated Vehicles

Driver Status Monitoring and the Takeover Process

• Broschiertes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 260
• Erscheinungstermin: 25. Mai 2023
• Englisch
• Abmessung: 153mm x 229mm x 15mm
• Gewicht: 370g
• ISBN-13: 9780443189975
• ISBN-10: 0443189978
• Artikelnr.: 67199256

Autorenporträt

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.

Inhaltsangabe

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 Human-Machine Interface 10. Driver Steering Intention
Prediction using Neuromuscular Dynamics 11. Intelligent Haptic Interface
Design for Human-Machine Interaction in Automated Vehicles