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针对城市复杂路况下智能网联汽车频繁换道过程中的平顺性差以及跟踪精度低等问题,提出了基于模型预测控制(MPC)理论的轨迹跟踪控制方法,对智能网联汽车在执行自主换道时的轨迹规划和轨迹跟踪进行了研究。根据车载传感器对周围环境检测识别结果,采用5次多项式进行车辆换道轨迹规划,利用MPC理论的轨迹跟踪控制方法设计轨迹,使跟踪控制器对轨迹跟踪不断滚动优化,并搭建了CarSim与Simulink联合虚拟仿真平台进行仿真实验。结果表明:在城市复杂路况中,智能网联汽车在一定车速范围内使用该方法进行频繁换道时,车辆的平顺性和跟踪精确度均较好。
Abstract：In order to solve the shortage of process smoothness and track tracking accuracy of the intelligent network car's frequent lane change in the complex city road conditions,a trajectory tracking control method based on MPC is proposed,and the track planning and track tracking of intelligent network connected cars when performing autonomous lane changes are studied by the proposed MPC theory.According to the results of vehicle sensor detection and recognition of the surrounding environment,five polynomial vehicle are used to plan the track change.The continuous rolling of track tracking is optimized by the application of the MPC-based theoretical design tracking controller,and furthermore,the simulation experiment of CarSim and Simulink joint virtual simulation platform are established.It has certain reference value to the frequent track change of intelligent network car in the complex city road conditions.The results show that both the smoothness and tracking accuracy of the vehicle are better when applying the method for frequent lane changes in a certain speed range of the intelligent connected vehicle in complex urban road conditions.
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基本信息:
DOI：10.19573/j.issn2095-0926.202104004
中图分类号:U463.6
引用信息:
[1]王丹萍,关志伟,刘云鹏,等.智能网联汽车自主换道控制策略与仿真[J].天津职业技术师范大学学报,2021,31(04):19-24.DOI:10.19573/j.issn2095-0926.202104004.
基金信息:
天津市人工智能科技重大专项(17ZXRGGX00070); 天津市教委科研计划重点项目(2019ZD20)
2021-12-21
2021-12-21