汽车工程 ›› 2020, Vol. 42 ›› Issue (3): 299-306.doi: 10.19562/j.chinasae.qcgc.2020.03.004

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智能网联环境下的多车协同换道策略研究*

刘志强, 韩静文, 倪捷   

  1. 江苏大学汽车与交通工程学院,镇江 212016
  • 收稿日期:2019-04-01 出版日期:2020-03-25 发布日期:2020-04-16
  • 通讯作者: 刘志强,教授,博士生导师,E-mail:Zhqliu@ujs.edu.cn
  • 基金资助:
    *国家自然科学基金(61403172)、江苏省高等学校自然科学研究面上项目(17KJB580004)和江苏省道路载运工具新技术应用重点实验室项目(BM20082061709)资助。

Study on Multi-vehicle Coordinated Lane Change Strategy Under Network Conditions

Liu Zhiqiang, Han Jingwen, Ni Jie   

  1. School of Automobile and Traffic Engineering, Jiangsu University, Zhenjiang 212013
  • Received:2019-04-01 Online:2020-03-25 Published:2020-04-16

摘要: 为提高换道安全性、稳定性和换道效率,本文中提出一种智能网联条件下多车协同安全换道策略。通过建立基于激励模型的换道收益函数进行协同换道可行性判断。基于模型预测控制建立协同换道多目标优化控制函数,实现换道过程的分布式控制。提出一个两阶段协同换道框架,将换道过程分为稀疏纵向距离阶段和换道阶段,以解决由于避撞约束的高维度和车辆运动学的非线性造成的最优控制函数难以求解的问题。采用滚动时域优化算法对优化控制问题逐步动态求解。最后基于美国NGSIM开源交通流数据进行Matlab/Simulink联合仿真,验证了该策略的可行性与准确性。

关键词: 智能驾驶, 协同换道, 模型预测控制, 滚动优化, 车车通信

Abstract: To enhance the safety, stability and efficiency in lane change, a multi-vehicle coordinated lane change strategy under the condition of intelligent network connection is proposed in this paper. The feasibility of coordinated lane change is judged by establishing a gain function based on incentive model. Based on model predictive control, a multi-objective optimization control function for coordinated lane change is built to realize distributed control in lane change process. For overcoming the difficulty in solving optimal control function, caused by the high-dimension of collision avoidance constraint and the nonlinearity of vehicle kinematics, a two-stage coordinated lane change framework is proposed, which divides the lane change process into sparse longitudinal distance phase and lane change phase. The rolling horizon optimization algorithm is adopted to solve the optimization control problem step by step. Finally, a Matlab/Simulink co-simulation is conducted based on US NGSIM open source traffic flow data, verifying the feasibility and correctness of the strategy proposed

Key words: intelligent driving, coordinated lane change, model predictive control, rolling optimization, V2V communication