基于轨迹预测与模型预测的换道路径跟踪控制

针对自主车辆换道轨迹跟踪精度较低等问题进行了研究。提出了基于轨迹预测的多点预瞄权重增益分配的方法。首先,根据车辆与路径的实时横向偏差以及航向角偏差,建立驾驶员转向模型,获得最优方向盘转角;其次,为了提高车辆换道路径跟踪时的稳定性,采用线性模型预测控制(linear model predictive control,L-MPC)策略设计轨迹跟踪控制器。最后,搭建Carsim&Simulink联合仿真模型,针对不同车速设置对比实验进行分析,结果表明基于轨迹预测的驾驶员模型能较好地跟踪换道轨迹,且稳态行驶下的路径跟踪最大横向误差为8.1%,但在高速极限工况时路径跟踪适应性较差,而L-MPC策略在高速时具有更好的路径跟踪精度及稳定性,其跟踪误差小于4%。

Modelling of vehicle interaction behavior during discretionary lane-changing preparation process on freeway

In order to increase the accuracy of microscopic traffic flow simulation,two acceleration models are presented to simulate car-following behaviors of the lane-changing vehicle and following putative vehicle during the discretionary lanechanging preparation( DLCP) process, respectively. The proposed acceleration models can reflect vehicle interaction characteristics. Samples used for describing the starting point and the ending point of DLCP are extracted from a real NGSIM vehicle trajectory data set. The acceleration model for a lanechanging vehicle is supposed to be a linear acceleration model.The acceleration model for the following putative vehicle is constructed by referring to the optimal velocity model,in which optimal velocity is defined as a linear function of the velocity of putative leading vehicle. Similar calibration,a hypothesis test and parameter sensitivity analysis were conducted on the acceleration model of the lane-changing vehicle and following putative vehicle,respectively. The validation results of the two proposed models suggest that the training and testing errors are acceptable compared with similar works on calibrations for car following models. The parameter sensitivity analysis shows that the subtle observed error does not lead to severe variations of car-following behaviors of the lane-changing vehicle and following putative vehicle.