SocialFormer: Social Interaction Modeling with Edge-enhanced Heterogeneous Graph Transformers for Trajectory Prediction
arxiv(2024)
摘要
Accurate trajectory prediction is crucial for ensuring safe and efficient
autonomous driving. However, most existing methods overlook complex
interactions between traffic participants that often govern their future
trajectories. In this paper, we propose SocialFormer, an agent
interaction-aware trajectory prediction method that leverages the semantic
relationship between the target vehicle and surrounding vehicles by making use
of the road topology. We also introduce an edge-enhanced heterogeneous graph
transformer (EHGT) as the aggregator in a graph neural network (GNN) to encode
the semantic and spatial agent interaction information. Additionally, we
introduce a temporal encoder based on gated recurrent units (GRU) to model the
temporal social behavior of agent movements. Finally, we present an information
fusion framework that integrates agent encoding, lane encoding, and agent
interaction encoding for a holistic representation of the traffic scene. We
evaluate SocialFormer for the trajectory prediction task on the popular
nuScenes benchmark and achieve state-of-the-art performance.
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