【摘要】 针对复杂交通环境下异质车型带来的跟车风险与行车效率权衡的决策难题，在分析自然驾驶数据的基础上，提出基于认知风险动态平衡的智能汽车拟人化跟车模型。首先，针对4种不同的货车-轿车组合跟车模式，建立跟车距离的经验模型，提炼出驾驶人稳态跟车行为中存在的车头时距和逆碰撞时间的“两不变”规律，通过作图法获得平衡线；其次，从驾驶过程中认知风险与加速度响应之间动态平衡的角度揭示了跟车决策的机理，将常用的跟车模型统一在认知风险动态平衡的框架内；最后，提出一种简洁的非线性函数实现认知风险动态平衡的数学表述，利用实测跟车数据验证了模型的准确性。更多还原

【Abstract】 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.更多还原