计算机与现代化 ›› 2022, Vol. 0 ›› Issue (05): 119-126.

• 人工智能 • 上一篇    

智能车逆向超车控制算法

  

  1. (1.长安大学信息工程学院,陕西西安710064;2.河北省高速公路延崇筹建处,河北张家075400)
  • 出版日期:2022-06-08 发布日期:2022-06-08
  • 作者简介:阮仕峰(1997—),男,陕西安康人,硕士研究生,研究方向:自动驾驶轨迹规划,E-mail: simonryan@chd.edu.cn; 惠飞(1982—),男,教授,研究方向:车联网技术及应用; 于建游(1973—),男,教授级高级工程师,研究方向:交通规划与管理; 张志刚(1975—),男,高级工程师,研究方向:交通规划与管理; 杜绎如(1996—),女,硕士研究生,研究方向:大数据分析; 郭星(1998—),女,硕士研究生,研究方向:车联网通信加密。
  • 基金资助:
    国家重点研发计划项目子课题(2018YFB1600604); 陕西省重点研发计划项目(2020ZDLGY16-06); 河北省省级科技计划项目 (20470801D); 中央高校基本科研业务费资助项目(3001102249503) 

Reverse Overtaking Control Algorithm for Autonomous Vehicles

  1. (1.School of Information Engineering, Chang’an University, Xi’an 710064, China;
    2.Yanchong Preparatory Office of Hebei Expressway, Zhangjiakou 075400, China)
  • Online:2022-06-08 Published:2022-06-08

摘要: 针对双向车道因受限于道路条件及交通特性仅能借用对向车道完成超车(逆向超车)的问题,通过采用车联网以及车载传感器获取环境车辆的速度、加速度等全局信息,将多车场景中各个实体所造成的影响纳入超车决策中,从而提出一种基于图搜索和模型预测控制(Model Predictive Control, MPC)的逆向超车控制方法。首先,根据车车通信获取的全局信息,结合非合作博弈,对各车在整个时段内的行为进行预测,并根据预测情况对道路的各个区域进行安全评估,评估依据为该区域在下一时刻出现车辆的概率。对道路完成评估后,得到碰撞概率热区图,之后采用A*算法搜索安全路径,根据安全路径完成目标车辆的轨迹规划,并设计模型预测控制器来对主车进行实时控制,使车辆按照既定轨迹行驶。最后,借助Carsim与MATLAB/Simulink搭建联合仿真平台,对提出的算法进行验证。仿真实验结果表明,该模型的控制误差最大不超过0.15 m,平均误差率约为1.7%,能实现对车辆的精准控制,保证被控车辆安全完成逆向超车。

关键词: 逆向超车, 有向图, A*算法, 车车通信, 博弈论, 模型预测控制

Abstract: In order to solve the problem that two-way lanes are limited by road conditions and traffic characteristics, a reverse overtaking control strategy based on graph search and model predictive control (MPC) is proposed. The strategy obtains global information such as speed and acceleration of the environment vehicles with the help of telematics and in-vehicle sensors, and incorporate the impact of each entity in the multi-vehicle scenario into the overtaking decision. Firstly, based on the global information obtained from vehicle-vehicle communication, combined with a non-cooperative game, each vehicle is predicted within the action of the entire time period, and each area of the road is evaluated for safety based on the prediction, and the evaluation is based on the probability of a vehicle appearing in that area at the next moment. After completing the assessment of the road, the collision probability hot zone map is obtained, and then the safe path is searched by the A* algorithm, and the trajectory planning of the main vehicle is completed according to the safe path. After that, the model prediction controller is designed to control the main vehicle in real time so that the vehicle follows the established trajectory. Finally, the proposed algorithm is verified by building a joint simulation platform with the help of Carsim and MATLAB/Simulink. The simulation test results show that the maximum control error of the model does not exceed 0.15 m, and the average error rate is about 1.7%, which can realize the accurate control of the vehicle and ensure the controlled vehicle to complete the reverse overtaking safely.

Key words: reverse overtaking, directed graph, A* algorithm, Vehicle-vehicle communication, game theory, model predictive control