Maximum entropy inverse reinforcement learning-based trajectory planning for autonomous driving

Puhao Zhang; Shumin Xie; Xiaoya Lu; Zuodong Zhong; Qing Li

doi:10.1117/12.3026671

9 February 2024 Maximum entropy inverse reinforcement learning-based trajectory planning for autonomous driving

Puhao Zhang, Shumin Xie, Xiaoya Lu, Zuodong Zhong, Qing Li

Author Affiliations +

Proceedings Volume 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023); 130730K (2024) https://doi.org/10.1117/12.3026671
Event: Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 2023, Changsha, China

Abstract

With the rapid advancement of autonomous driving technology, effective trajectory planning has become crucial for ensuring road safety and driving efficiency. Traditional trajectory planning methods often rely on preset rules and models, making them ill-suited for the complex and dynamic traffic environment. To address this, a Maximum Entropy Inverse Reinforcement Learning (MaxEnt IRL) based trajectory planning method is proposed in this paper, aiming at learning from expert driving behaviors to infer an efficient reward function, which in turn guides decision-making and path planning for autonomous vehicles. This study begins by analyzing expert driving data to extract key state and action features. Then, the MaxEnt IRL algorithm is applied to learn the reward function underlying these features, reflecting the decision-making logic of expert drivers. The learned reward function is subsequently used to guide the trajectory planning of the autonomous driving system, generating safe and efficient driving paths. A series of experiments conducted in a simulated environment demonstrate that the MaxEnt IRL-based method proposed in this paper exhibits higher adaptability and efficiency in handling complex traffic scenarios compared to traditional trajectory planning methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Puhao Zhang, Shumin Xie, Xiaoya Lu, Zuodong Zhong, and Qing Li "Maximum entropy inverse reinforcement learning-based trajectory planning for autonomous driving", Proc. SPIE 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 130730K (9 February 2024); https://doi.org/10.1117/12.3026671

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