Trajectory planning and control of autonomous vehicle based on reinforcement learning algorithm

In order to explore the trajectory planning and control method of autonomous vehicle based on reinforcement learning algorithm, this paper designs an advanced control system by combining the basic characteristics of autonomous vehicle with the advantages of reinforcement learning. The system can intelligently plan the optimal driving trajectory according to the real-time road information, traffic conditions and the vehicle’s own state, and realize the smooth and safe driving of the vehicle through the precise control algorithm. Experiments have proved that the system can perform well in a variety of complex road conditions, effectively improving the driving efficiency and safety of autonomous vehicles. Compared with traditional trajectory planning methods, the system has stronger adaptive ability and can cope with unexpected situations and uncertainties better.

Authors

Publication Details

Journal
Procedia Computer Science
Published
2025-01-01
DOI
https://doi.org/10.1016/j.procs.2025.05.156
Primary Topic
Advanced Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Trajectory planning and control of autonomous vehicle based on reinforcement learning algorithm

Wang Jia
Procedia Computer Science
Advanced Algorithms and Applications
article

Trajectory planning and control of autonomous vehicle based on reinforcement learning algorithm

Wang Jia
article en

Abstract

In order to explore the trajectory planning and control method of autonomous vehicle based on reinforcement learning algorithm, this paper designs an advanced control system by combining the basic characteristics of autonomous vehicle with the advantages of reinforcement learning. The system can intelligently plan the optimal driving trajectory according to the real-time road information, traffic conditions and the vehicle’s own state, and realize the smooth and safe driving of the vehicle through the precise control algorithm. Experiments have proved that the system can perform well in a variety of complex road conditions, effectively improving the driving efficiency and safety of autonomous vehicles. Compared with traditional trajectory planning methods, the system has stronger adaptive ability and can cope with unexpected situations and uncertainties better.

Procedia Computer ScienceVol. 262
Openalex Percentile: Top 86%
Advanced Algorithms and Applications
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