一体式车辆避撞轨迹规划与跟踪控制

doi:10.16097/j.cnki.1009-6744.2022.02.012

交通运输系统工程与信息 ›› 2022, Vol. 22 ›› Issue (2): 127-136.DOI: 10.16097/j.cnki.1009-6744.2022.02.012

• 智能交通系统与信息技术 • 上一篇下一篇

一体式车辆避撞轨迹规划与跟踪控制

王国栋^{1, 2}，刘立¹，孟宇^{* 1}，马智萍³，郑淏清¹，顾青^{1, 4}，白国星¹

1. 北京科技大学，机械工程学院，北京 100083；2. 汽车噪声振动和安全技术国家重点实验室，重庆 401122； 3. 北京青年政治学院，管理系，北京 100102；4. 北京科技大学，顺德研究生院，广东佛山 528300

收稿日期:2021-08-04 修回日期:2021-12-19 接受日期:2021-12-24 出版日期:2022-04-25 发布日期:2022-04-23
作者简介:王国栋(1995- )，男，河南夏邑人，博士生
基金资助:
国家重点研发计划；汽车噪声振动和安全技术国家重点实验室开放基金；广东省基础与应用基础研究基金

Integrated Control of Trajectory Planning and Tracking for Vehicle Collision Avoidance

WANG Guo-dong^{1, 2} , LIU Li¹ , MENG Yu^*1 , MA Zhi-ping³, ZHENG Hao-qing¹ , GU Qing^{1, 4}, BAI Guo-xing¹

1. School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China; 2. State Key Laboratory of Vehicle NVH and Safety Technology, Chongqing 401122, China; 3. Department of Management, Beijing Youth Politics College, Beijing 100102, China; 4. Shunde Graduate School, University of Science and Technology Beijing, Foshan 528300, Guangdong, China

Received:2021-08-04 Revised:2021-12-19 Accepted:2021-12-24 Online:2022-04-25 Published:2022-04-23
Supported by:
National Key Research and Development Program of China(2018YFE0192900)；Open Funds of State Key Laboratory of Vehicle NVH and Safety Technology(NVHSKL- 202107)；Guangdong Basic and Applied Basic Research Foundation(2019A1515111015)。

摘要/Abstract

摘要： 为提升复杂交通环境中智能车辆的避撞能力，将路径规划、速度规划及跟踪控制整合为一个优化问题，提出一种基于模型预测控制(MPC)的一体式车辆避撞轨迹规划和跟踪控制方法。首先，分析实际交通环境中的避撞场景，将智能车辆的避撞控制问题转化为多约束优化问题；其次，搭建7DOF(七自由度)车辆动力学模型和复合滑移工况的UniTire轮胎模型设计MPC控制器；再次，针对变速控制问题中传统基于时域预测模型的MPC控制方法无法在预测时域中实现车辆空间和位姿约束的问题，设计了基于空间域预测模型的MPC控制器；最后，基于Matlab和CarSim联合仿真平台设计了不同避撞场景验证所提方法，并与现有基于恒速假设的一体式避撞控制方法进行对比。仿真结果表明：所提方法能够充分发挥车辆的机动性能，解决现有一体式控制方法在复杂环境中避撞失败的问题，并保证避撞过程稳定和轨迹平滑。

关键词: 智能交通, 避撞控制, 模型预测控制, 智能车辆, 轨迹规划, 轨迹跟踪

Abstract: To improve the collision avoidance ability of intelligent vehicles in complex traffic environments, the path planning, speed planning, and tracking control are integrated into one optimization problem, and an integrated control method of trajectory planning and tracking for vehicle collision avoidance based on model predictive control (MPC) is proposed. Firstly, the collision avoidance scene in the real traffic environment is analyzed, and the collision avoidance control problem of intelligent vehicles is transformed into a multi-constraint optimization problem. Secondly, a 7DOF vehicle dynamic model and a UniTire model with combined slip conditions are established for MPC controller design. Thirdly, to solve the problem that, in the variable speed control problem, the traditional MPC based on the time-domain prediction model cannot accurately express the spatial and posture constraints of the vehicle over the prediction horizon, an MPC controller based on a spatial-domain prediction model is designed. Finally, based on the co-simulation platform of Matlab and CarSim, different collision avoidance scenarios are designed to verify the proposed method, and the existing integrated collision avoidance control method based on constant speed assumption is compared with the proposed method. The simulation results show that the proposed method can make full use of the vehiclemaneuverability, solve the problem of collision avoidance failure of existing integrated control methods in complex environments, and ensure the stability of the collision avoidance process and smoothness of trajectory.

Key words: intelligent transportation, collision avoidance control, model predictive control, intelligent vehicle; trajectory planning, trajectory tracking

中图分类号:

U495

王国栋, 刘立, 孟宇, 马智萍, 郑淏清, 顾青, 白国星. 一体式车辆避撞轨迹规划与跟踪控制[J]. 交通运输系统工程与信息, 2022, 22(2): 127-136.

WANG Guo-dong , LIU Li , MENG Yu , MA Zhi-ping, ZHENG Hao-qing , GU Qing, BAI Guo-xing. Integrated Control of Trajectory Planning and Tracking for Vehicle Collision Avoidance[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 127-136.

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一体式车辆避撞轨迹规划与跟踪控制

Integrated Control of Trajectory Planning and Tracking for Vehicle Collision Avoidance

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