冰雪条件下中国驾驶员跟驰行为及模型研究

This paper analyzes the driver's driving behavior characteristics under snow and ice conditions and establishes a car-following model to consider the driver's behavior characteristics. By conducting a real-car following experiment, the driving behaviors of the drivers are compared under normal conditions and snow and ice conditions. Based on the theory of task difficulty balance, a task difficulty module containing human factors parameters is constructed, and it is introduced into the improved Intelligent Driver Model. The vehicle trajectory data is used to calibrate and verify the validity of the model. Research shows that, when affected by external stimuli and his own driving ability, the driver will dynamically adjust the driving state in real time during the car-following process, to keep the expected distance and the speed consistent with the vehicle ahead. Under snow and ice conditions, drivers' choices of time headway and variation of time headway fluctuation amplitude are different, and human factor parameters introduced by the model can better capture such difference. The validation of the model indicated that the performance of the new model was better than the traditional IDM model in 6 simulation scenes, and it has better robustness. The research results can provide theoretical support for the formulation of traffic management measures under snow and ice conditions.