基于认知风险动态平衡的智能汽车跟车模型

Aiming at the decision-making difficulties of the trade-off between the vehicle following risk and the driving efficiency caused by heterogeneous vehicle types in the complex traffic environment，a human-like vehicle following model for intelligent vehicles based on dynamic balance of perception risk is proposed on the basis of analyzing natural driving data. Firstly，an empirical model of the vehicle following distance is established for vehicle following modes with four different truck-car combination，and the“two invariances”law of time headway（THW）and inverse time to collision（i-TTC）existing in the driver’s steady-state vehicle following behavior is discovered，with the balance lines obtained by drawing method. Then，the mechanism of vehicle following decision-making is revealed from the perspective of the dynamic balance between perception risk and acceleration response during driving，and the commonly-used vehicle following models are unified within the framework of dynamic balance of perception risk. Finally，a simple nonlinear function is proposed as a mathematical expression of dynamic balance of perception risk，and the accuracy of the model is verified by using the tested vehicle following data.