Research on trajectory prediction of vehicle lane change for autonomous driving based on inverse reinforcement learning

Ming Zhan; Jingjing Fan; Linhao Jin

doi:10.1117/12.3015773

20 February 2024 Research on trajectory prediction of vehicle lane change for autonomous driving based on inverse reinforcement learning

Ming Zhan, Jingjing Fan, Linhao Jin

Author Affiliations +

Proceedings Volume 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023); 130643A (2024) https://doi.org/10.1117/12.3015773
Event: 7th International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 2023, Dalian, China

Abstract

Autonomous vehicles improve the safety and efficiency of vehicles in complex traffic scenarios through autonomous decision-making intelligence technology. To address the requirements of the self-driving vehicle lane change scenario for the accuracy of vehicle lane change trajectory prediction, in this paper, we propose a lane change trajectory prediction method for self-driving vehicles based on inverse reinforcement learning. We model the inverse reinforcement learning process through a maximum entropy mechanism to learn the optimal reward function that infers the potential end targets during the vehicle lane change. This reward model is used to construct the optimal policy that can be sampled for planning in the grid world. Conditioned on the sequence of state actions sampled by this maximum entropy policy, we generate vehicle lane change prediction trajectories. We conduct training experiments on lane change scenario data from the publicly available nuScenes dataset for autonomous driving, which shows that our method can meet the vehicle lane change requirements in real scenarios and validate the accuracy and reasonableness of the lane change trajectories.

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

Citation Download Citation

Ming Zhan, Jingjing Fan, and Linhao Jin "Research on trajectory prediction of vehicle lane change for autonomous driving based on inverse reinforcement learning", Proc. SPIE 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 130643A (20 February 2024); https://doi.org/10.1117/12.3015773

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KEYWORDS

Autonomous vehicles

Autonomous driving

Motion models

Detection and tracking algorithms

Decision making

Feature extraction

Transportation

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