Path-planning algorithms for self-driving vehicles basedon improved RRT-Connect  

作　　者：Jin Li Chaowei Huang Minqiang Pan 

机构地区：[1]School of Intelligent Engineering,Shaoguan University,Shaoguan 512005,Guangdong,China [2]The School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510640,Guangdong,China

出　　处：《Transportation Safety and Environment》2023年第3期92-101,共10页交通安全与环境（英文）

基　　金：Supported by Guangdong Natural Science Foundation(Grant No.2015A030310411);Guangdong University Char acteristic Innovation(Grant No.2018KQNCX207);Shaoguan science and technology plan(Grant No.2018sn043);Shaoguan university scientific research(Grant No.sz2018KJ06).

摘　　要：This study aims to solve path planning of ntelligent vehicles in self driving In this study,an improved path planning method com-bining constraints of the environment and vehicle is proposed.The algorithm designs a reasonable path cost function,then uses a heuristic guided search strategy to improve the speed and quality of path planning,and finally generates smooth and continuous cur-vature paths based on the path post-processing method focusing on the requirements of path smoothness.A simulation test shows that compared with the basic rapidly-exploring random tree(RRT),RRT-Connect and RRT*algorithms,the path length of the proposed algorithm can be reduced by 19.7%,29.3%and 1%respectively,and the maximum planned path curvature of the proposed algorithm is 0.0796 mr1 and 0.1512 mi respectively.under the condition of a small amount of planning time.The algorithm can plan the more suitable driving path for intelligent vehicles in a complex environment.

关 键 词：path planning rapidly-exploring random tree(RRT) double tree expansion autonomous driving curvature constraint 

分 类 号：U491[交通运输工程—交通运输规划与管理]