智能汽车仿人转向控制驾驶员模型分析

Analysis of human-like steering control driver model for intelligent vehicle

为使智能汽车的弯道行驶具备人类驾驶员操控特征,基于最优曲率预瞄驾驶员模型与T-S模糊推理算法,提出了一种预瞄点横向和纵向自适应调节的仿人转向控制驾驶员模型.在封闭城市双车道弯道工况下,采集了熟练驾驶员在不同车速行驶下的弯道轨迹数据,分析得出了目标行驶轨迹.基于蚁群算法对预瞄点横向和纵向调节模糊规则进行了优化,在PreScan中构建了连续多曲率弯道仿真场景,并基于驾驶模拟仪所采集到的熟练驾驶员驾驶轨迹,对所提出的驾驶员模型的仿人转向特性进行验证.结果表明:优化后的仿人转向驾驶员模型与熟练驾驶员的行驶轨迹相似程度较好,优于最优曲率预瞄驾驶员模型或预瞄距离自适应的驾驶员模型.

To make the intelligent vehicle obtain human-like behavior when passing a curve road,a human-like steering control driver model was proposed based on optimal curvature preview driver model and T-S fuzzy inference system to adjust the preview point on both lateral and longitude directions.The driving data from experienced driver model were collected in an urban curve road,and the optimal target was obtained after data analysis.Ant colony optimization(ACO)was used in tuning the fuzzy parameters in the preview decision module.A multi-curvature driving scenario was built in Prescan,and the human-like characteristic of the proposed driver model was verified by the trajectories of experienced drivers collected by a driving simulator.The results show that the trajectory similarity between the experienced drivers and the human-like steering driver model is higher than those of the optimal curvature preview model and the driver model with adaptive preview distance.