GATraj: A Graph- and Attention-based Multi-Agent Trajectory Prediction Model

Hao Cheng, Mengmeng Liu, Lin Chen, Hellward Broszio, Monika Sester, Michael Ying Yang

Trajectory prediction has been a long-standing problem in intelligent systems such as autonomous driving and robot navigation. Recent state-of-the-art models trained on large-scale benchmarks have been pushing the limit of performance rapidly, mainly focusing on improving prediction accuracy. However, those models put less emphasis on efficiency, which is critical for real-time applications. This paper proposes an attention-based graph model named GATraj with a much higher prediction speed. Spatial-temporal dynamics of agents, e.g., pedestrians or vehicles, are modeled by attention mechanisms. Interactions among agents are modeled by a graph convolutional network. We also implement a Laplacian mixture decoder to mitigate mode collapse and generate diverse multimodal predictions for each agent. Our model achieves performance on par with the state-of-the-art models at a much higher prediction speed tested on multiple open datasets.

Knowledge Graph



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