[1]ZHAO Jingwen,LI Xuanpeng,ZHANG Weigong.Vehicle trajectory prediction method based on modeling of multi-agent interaction behavior[J].CAAI Transactions on Intelligent Systems,2023,18(3):480-488.[doi:10.11992/tis.202201022]
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Vehicle trajectory prediction method based on modeling of multi-agent interaction behavior

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 3 Page number: 480-488 Column: 学术论文—机器感知与模式识别 Public date: 2023-07-05
Title:
Vehicle trajectory prediction method based on modeling of multi-agent interaction behavior
Author(s):
ZHAO Jingwen;  LI Xuanpeng;  ZHANG Weigong
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Keywords:
trajectory prediction; attention mechanism; multi-agent interaction; multimodal trajectory; conditional variational auto-encoder; endpoint generating; inverse reinforcement learning; decision refinement
CLC:
TP391
DOI:
10.11992/tis.202201022
Abstract:
Predicting trajectories of surrounding agents is critical to realize the decision-making planning of autonomous driving behaviors. Facing the difficulties brought by complex vehicle interaction and multimodal driving intention, this paper proposes a trajectory prediction method based on vehicle multi-agent interaction behavior modeling. The method uses conditional variational autoencoder to generate multi-modal results of the trajectory endpoints. By combination with the self-attention mechanism and multi-head attention mechanism, the influence of group interaction between vehicles is captured. Finally, the inverse reinforcement learning is used to output the optimal decision of multi-modal trajectory, realizing synchronous prediction of multi-agent trajectory. An experiment has been carried out on the NGSIM, which is a real-world trajectory prediction dataset on the highway traffic scene. The results prove effectiveness of the model, and the prediction effect is better than existing methods as a whole.
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