考虑前车加速度信息的改进IDM模型研究

An improved intelligent driver model considering the acceleration information of the frontvehicle

通过引入驾驶员反应时间、反应车型特征的期望跟驰间距系数及前车加速度信息,提出了一种考虑前车加速度信息的改进智能驾驶员模型(AIDM)。稳定性分析结果表明:考虑前车加速度信息能进一步提高交通流的稳定性,有效抑制交通拥堵。利用城市道路的实车数据对模型中的前车加速度信息敏感系数进行标定,理论分析和仿真结果表明:与IDM相比,提出的AIDM的拟合精度提高了1.41%。改进后的模型能有效描述实际交通现象,可为智能网联驾驶的相关研究提供参考。

This paper improves the IDM model by introducing the driver’s reaction time,the expected car-following distance coefficient that reflects the characteristics of the vehicle type,and the acceleration information of the front vehicle,and proposes an AIDM model.The stability analysis results show that the acceleration information of the front vehicle can further improve the stability of the traffic flow and effectively suppress traffic congestion.The actual vehicle data of urban roads is used to calibrate the acceleration information sensitivity coefficient of the front vehicle in the model.Theoretical analysis and simulation results show that compared with IDM,the accuracy of the proposed AIDM is improved by 1.41%.The model in this paper can effectively describe the actual traffic phenomenon,which provides a basis for related research on intelligent networked driving.