Attention-aware Social Graph Transformer Networks for Stochastic Trajectory Prediction

• URL: http://arxiv.org/abs/2312.15881v2
• Date: Sat, 11 May 2024 14:38:52 GMT
• Title: Attention-aware Social Graph Transformer Networks for Stochastic Trajectory Prediction
• Authors: Yao Liu, Binghao Li, Xianzhi Wang, Claude Sammut, Lina Yao,
• Abstract summary: Trajectory prediction is fundamental to various intelligent technologies, such as autonomous driving and robotics. Current trajectory prediction research faces problems of complex social interactions, high dynamics and multi-modality. We propose Attention-aware Social Graph Transformer Networks for multi-modal trajectory prediction.
• Score: 16.55909815712467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trajectory prediction is fundamental to various intelligent technologies, such as autonomous driving and robotics. The motion prediction of pedestrians and vehicles helps emergency braking, reduces collisions, and improves traffic safety. Current trajectory prediction research faces problems of complex social interactions, high dynamics and multi-modality. Especially, it still has limitations in long-time prediction. We propose Attention-aware Social Graph Transformer Networks for multi-modal trajectory prediction. We combine Graph Convolutional Networks and Transformer Networks by generating stable resolution pseudo-images from Spatio-temporal graphs through a designed stacking and interception method. Furthermore, we design the attention-aware module to handle social interaction information in scenarios involving mixed pedestrian-vehicle traffic. Thus, we maintain the advantages of the Graph and Transformer, i.e., the ability to aggregate information over an arbitrary number of neighbors and the ability to perform complex time-dependent data processing. We conduct experiments on datasets involving pedestrian, vehicle, and mixed trajectories, respectively. Our results demonstrate that our model minimizes displacement errors across various metrics and significantly reduces the likelihood of collisions. It is worth noting that our model effectively reduces the final displacement error, illustrating the ability of our model to predict for a long time.

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