Modeling Driver Risk Perception on City Roads Using Deep Learning.

Research on how risk is perceived by drivers is vital to driving behavior research and driving safety. As risk can be divided into subjective and objective risk, in this paper, we focus on modeling subjective risk perception by drivers using a deep learning method. Different drivers often perceive different levels of subjective risk under the same driving conditions. In addition, different driving conditions or driving events will have different effects on drivers. Based on these two risk perception features, in this paper, we first design an experiment on a city road with two lanes to assess the level of subjective risk perceived by drivers belonging to different groups. We then use a deep learning network-based method to abstract features of the driving environment. These environmental features are integrated with driver risk perception data and this information is used as training and testing data for the learning network. Finally, a long-short-term memory-based method is adopted to model the subjective risk perception of individual drivers based on traffic conditions and vehicle operation data from the driver's vehicle. Our results show that the proposed method can effectively model the subjective risk perception behavior of drivers, allowing for end-to-end risk perception prediction in future driving assistance systems.