冰雪条件下中国驾驶员跟驰行为及模型研究

分析驾驶员在冰雪条件下的驾驶行为特性,建立考虑驾驶员行为特性的跟驰模型,有助于丰富现有交通流理论.通过招募驾驶员开展实车跟驰试验,对比分析正常条件与冰雪条件下的驾驶行为差异.进而基于任务难度均衡理论构建包含人类因素参数的任务难度模块,引入改进后的智能驾驶员模型,并采用车辆轨迹数据对模型进行标定和有效性验证.研究表明：驾驶员在跟驰行驶过程中受外界刺激及自身驾驶能力影响时会对车辆行驶状态进行动态调整,试图保持期望间距,且速度与前车一致的状态;冰雪条件下驾驶员采取风险补偿行为,其车头时距波动幅度较正常条件收窄,模型引入人类因素参数可以较好地描述其差异性. 模型有效性验证表明,新模型在6个仿真场景中的表现都优于传统智能驾驶员模型,且表现出更好的鲁棒性.研究结果可为冰雪条件下的交通管理措施制定提供理论支持.

Car-following Behavior and Model of Chinese Drivers under Snow and Ice Conditions

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 carfollowing 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.