On Driver's Workload in Dangerous Scenes Based on EEG Data

Scientific measurement of the risk degree of traffic scenes and accurate assessment of driver's workload are conducive to reducing driving risk and road traffic accidents. In this paper, EEG signal evaluation method based on "driver's" perspective is used to describe the risk of traffic scene objectively and quantitatively. The traffic scenes with dynamic traffic environment factors are taken as the research objects, including the pedestrian scene and the variable-speed vehicle scene. The drivers’ EEG signals are used as the indicators to evaluate the risk degree of the traffic scene. Based on research objects and indicators, the internal relationship between drivers' EEG signals and traffic dangerous environment factors are explored, and the evaluation models of traffic scene risk degree based on drivers' EEG signals are established.