基于改进驾驶员模型的车道保持控制模型

Research on lane keeping system based on improved algorithm for optimal preview acceleration driver model

为解决传统车道保持系统控制精度较差、驾驶员模型对大曲率道路适应性不足的问题,对传统预瞄加速度驾驶员模型非零频率点进行泰勒展开以改进模型;利用驾驶模拟器试验数据和快速傅里叶方法（FFT）研究双移线道路下的主要转向频率,并搭建Simulink/Car Sim联合仿真模型以验证改进前后驾驶员模型的双移线路径跟踪精度。仿真试验表明：改进后模型的横向位移误差比改进前明显减小,车速为60、70和80 km/h时的最大横向位移误差比改进前分别降低了0.2、0.15和0.11 m;与常规道路人工势场车道保持系统的对比,改进驾驶员模型能够更好地控制车辆跟随期望路径行驶,体现出良好的车道保持能力。

To solve the problem that the traditional lane keeping system has inadequate control precision and the driver model has insufficient adaptability to the road with large curvature,an improved algorithm for the optimal preview acceleration driver model is investigated by Taylor expansion at non-zero frequency point. The driving simulator data and the fast Fourier transformation（FFT） method were used to study the main steering frequency under double lane change. And a Simulink/Car Sim co-simulation model was built as well. Double lane change simulation experiments were carried out. The results show that the lateral displacement error of the improved model is significantly smaller than that of original one,which the maximum displacement errors are reduced by 0. 2,0. 15 and 0. 11 m for vehicle speeds of 60,70 and80 km/h respectively,and that the improved driver model can control the vehicle to follow the expected path better than the road artificial potential field method.