An RRT-Dijkstra-Based Path Planning Strategy for Autonomous Vehicles

被引:30
作者
Chen, Ruinan [1 ,2 ,3 ]
Hu, Jie [1 ,2 ,3 ]
Xu, Wencai [1 ,2 ,3 ]
机构
[1] Wuhan Univ Technol, Hubei Res Ctr New Energy & Intelligent Connected V, Wuhan 430070, Peoples R China
[2] Wuhan Univ Technol, Hubei Collaborat Innovat Ctr Automot Components Te, Wuhan 430070, Peoples R China
[3] Wuhan Univ Technol, Hubei Key Lab Adv Technol Automot Components, Wuhan 430070, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 23期
关键词
autonomous vehicle; path planning; RRT-Dijkstra-based strategy; FRAMEWORK;
D O I
10.3390/app122311982
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
It is challenging to plan paths for autonomous vehicles on half-structured roads because of the vast planning area and complex environmental constraints. This work aims to plan optimized paths with high accuracy and efficiency. A two-step path planning strategy is proposed. The classic planning problem is divided into two simpler planning problems: reduction problems for a vast planning area and solving problems for weighted directed graphs. The original planning area is first reduced using an RRT (Rapidly Exploring Random Tree) based guideline planner. Second, the path planning problem in the smaller planning region is expanded into a weighted directed graph and transformed into a discrete multi-source cost optimization problem, in which a potential energy field based discrete cost assessment function was designed considering obstacles, lanes, vehicle kinematics, and collision avoidance performances, etc. The output path is then obtained by applying a Dijkstra optimizer. Comparative simulations are conducted to assess the effectiveness of the proposed strategy. The results shows that the designed strategy balances efficiency and accuracy with enough planning flexibility and a 22% improvement in real-time performance compared to the classic Lattice planner, without significant loss of accuracy.
引用
收藏
页数:16
相关论文
共 26 条
[21]   Driving safety field theory modeling and its application in pre-collision warning system [J].
Wang, Jianqiang ;
Wu, Jian ;
Zheng, Xunjia ;
Ni, Daiheng ;
Li, Keqiang .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2016, 72 :306-324
[22]   Collision-Free Navigation of Autonomous Vehicles Using Convex Quadratic Programming-Based Model Predictive Control [J].
Wang, Zhuping ;
Li, Gangbin ;
Jiang, Houjie ;
Chen, Qijun ;
Zhang, Hao .
IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2018, 23 (03) :1103-1113
[23]   Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame [J].
Werling, Moritz ;
Ziegler, Julius ;
Kammel, Soeren ;
Thrun, Sebastian .
2010 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), 2010, :987-993
[24]   Optimal Vehicle Lane Change Trajectory Planning in Multi-Vehicle Traffic Environments [J].
Zhang, Senlin ;
Deng, Guohong ;
Yang, Echuan ;
Ou, Jian .
APPLIED SCIENCES-BASEL, 2022, 12 (19)
[25]  
Zhonglin Hou, 2019, 2019 IEEE 19th International Conference on Communication Technology (ICCT), P1348, DOI 10.1109/ICCT46805.2019.8947197
[26]   Application of improved Dijkstra algorithm in intelligent ship path planning [J].
Zhu, Zhenyu ;
Li, Lianbo ;
Wu, Wenhao ;
Jiao, Yang .
PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021), 2021, :4926-4931