期刊 复杂环境下智能汽车自动驾驶系统研究  被引量:4

Study of Intelligent Vehicle Autonomous Driving System Under Complex Environment

作  者:李盛福[1] 

LI Sheng-fu(Department of Automotive Engineering,Guangxi Vocational and Technical College of Industry,Guangxi Guigang 537100,China)

机构地区:[1]广西工业职业技术学院汽车工程系

出  处:《机械设计与制造》2019年第5期199-203,207,共6页Machinery Design & Manufacture

Study of Intelligent Vehicle Autonomous Driving System Under Complex Environment

基  金:广西职业教育改革研究项目(GXGZJG2016A092)

摘  要:为了提高智汽车的驾驶安全性,研究了智能汽车自动驾驶系统,包括路径规划和跟踪两个方面。对于路径规划,在传统RRT算法基础上,将路径转角引入度量函数中,实现了对距离和平滑性的综合度量;将目标偏向策略和相向扩展策略引入到扩展方法中,将随机扩展改进为启发扩展,从而提出了CE-RRT算法,经仿真验证,CE-RRT算法不仅提高了路径规划成功率,而且缩短了规划时间。对于路径跟踪,建立了车辆路径跟踪的线性时变误差模型,设计了线性时变模型预测控制器,通过仿真验证,控制器可以实现对直线路径的完全跟踪,对圆形路径也有很高的跟踪精度。

In order to improve driving safety of intelligent vehicle,autonomous driving system of intelligent vehicle is studied,including path planning and tracking. For path planning,on basis of traditional RRT algorithm,corner angle is introduced to metric function,which can balance distance and smoothness. Target-tendency and meat each other extension strategies are introduced to extension method,which changes random extension to heuristic extension,so that CE-RRT algorithm is proposed. Clarified by simulation,CE-RRT algorithm not only can improve success rate,but can also shorten planning time.For path tracking,linear time varying error model of path tracking is built,and linear time varying model prediction controller is designed. It can be seen by simulation,the controller can track straight path completely,and can track circular path accurately.

关 键 词:智能汽车 自动驾驶系统 CE-RRT算法 目标偏向策略 相向扩展策略 线性时变模型预测控制器 

Intelligent Vehicle Autonomous Driving System CCE-RRT Algorithm Target-Tendency Strategy Meet Each Other Extension Strategy Liner Time Varying Model Prediction Controller 

分 类 号:TH16[机械工程—机械制造及自动化] U463[机械工程—车辆工程]

 

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