Human-Machine Interaction for Automated Vehicles

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

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.