High-speed pilotage assisted driving system is one of the current research hotspots in the field of intelligent vehicles, and autonomous lane change is the core function of navigate on autopilot (NOA) system. At present, NOA system has gradually matured in lane centering control and adaptive cruise, but the autonomous lane changing function needs further improvement in adapting to different traffic scenarios and planning paths, especially speed planning, to ensure the safety, optimal speed and optimal path of lane changing. Therefore, this paper focuses on speed planning in intelligent vehicle lane changing. To address the problems of large randomness, path curvature, and non-compliance with vehicle constraints in the RRT algorithm for speed planning, this study adopts a strategy combining progressive region sampling and target bias sampling to ensure that the path has directionality. Secondly, the lateral deviation of the path, the distance cost and the geometric size of the vehicle as well as the front wheel turning angle constraint are considered to ensure the reasonableness of the path and the speed; then, the S–T graph method is used to transform the state space of the reference path, and the optimal trajectory is selected according to the value function after the speed search, and the optimal trajectory is smoothed by the polynomial regression method. Finally, the speed planning algorithm designed in this paper is simulated and verified based on MATLAB. The results show that the trajectory and speed planned by the algorithm have continuity and robustness, which meet the requirements of intelligent vehicle driving. The research results can provide a reference strategy and theoretical basis for the mass production of automatic lane change function.
Research on Intelligent Vehicle Speed Planning Algorithm Based on the Improved Rapidly Exploring Random Tree
Lect. Notes Electrical Eng.
International Conference on Green Intelligent Transportation System and Safety ; 2022 ; Qinghuangdao, China September 16, 2022 - September 18, 2022
2024-12-31
16 pages
Article/Chapter (Book)
Electronic Resource
English
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