Reinforcement Learning Autonomous Driving Trajectory Prediction Based on Directed Graph

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Reinforcement Learning Autonomous Driving Trajectory Prediction Based on Directed Graph

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[1]CUI Jianming,LIN Fanrong,ZHANG Di,et al.Reinforcement Learning Autonomous Driving Trajectory Prediction Based on Directed Graph[J].Journal of Zhengzhou University (Engineering Science),2023,44(05):53-61.[doi:10.13705/j.issn.1671-6833.2023.05.002]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44卷 Number of periods: 2023 05 Page number: 53-61 Column: Public date: 2023-08-20

Title:: Reinforcement Learning Autonomous Driving Trajectory Prediction Based on Directed Graph

Author(s):: CUI Jianming¹; LIN Fanrong¹; ZHANG Di¹ ; ZHANG Luning¹; LIU Ming²; 1. School of Information Engineering,Chang′an University, Xi′an 710018, China; 2. National Computer Network Emergency Response Technical Team / Coordination Center of China, Beijing 100029, China

Keywords:: autonomous driving; trajectory prediction; directed graph; reinforcement learning; GAIL; attention mechanism; multimodal predictio

CLC:: O211. 62；TP183

DOI:: 10.13705/j.issn.1671-6833.2023.05.002

Abstract:: As an important part of autonomous driving, trajectory prediction aimed to forcast the vehicle′s driving path, so that the vehicle could make path planning according to the driving estimation, so as to make safe and accurate decisions. Firstly, in order to improve the accuracy of vehicle trajectory prediction, the directed graph method was used to construct a high-definition driving scene map, and the directed graph method vectorized the map information to effectively extract the map topology. Secondly, GAIL was used to learn the driving strategy of the dataset through the confrontation game between the generator and the discriminator, so as to adopt the corresponding driving behavior according to the current state. Finally, the multimodal prediction trajectory scheme was obtained by sampling traversal. Simulation was carried out on the nuScenes motion prediction dataset. The quantitative results showed that compared with other methods, when K = 5, the minimum final displacement error MinFDE5 was increased by 10. 8%; when K = 10, the minimum fianl displacement error MinFDE10 increased by 17. 53%, the minimum average displacement error MinADE10 increased by 9. 52%, and the error rate MissRate10 decreased by 28. 26%. The evaluation showed that the generated trajectories were multimodal, could conform to the basic structure of the scene, with improved accuracy.

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Last Update: 2023-09-04