@article{ref1,
title="Path-planning algorithms for self-driving vehicles based on improved RRT-Connect",
journal="Transportation safety and environment",
year="2023",
author="Li, Jin and Huang, Chaowei and Pan, Minqiang",
volume="5",
number="3",
pages="tdac061-tdac061",
abstract="This study aims to solve path planning of intelligent vehicles in self-driving. In this study, an improved path-planning method combining 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 curvature 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 m-1 and 0.1512 m-1 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.<p /> <p>Language: en</p>",
language="en",
issn="2631-4428",
doi="10.1093/tse/tdac061",
url="http://dx.doi.org/10.1093/tse/tdac061"
}