Goal-Guided and Interaction-Aware State Refinement Graph Attention Network for Multi-Agent Trajectory Prediction

Multi-agent trajectory prediction plays a pivotal role for intelligent transportation and autonomous driving. Modeling the social interaction among agents and revealing the inherent relationship between interaction and future trajectory are crucial for accurate trajectory prediction. To address these challenges, this letter proposes a goal-guided and interaction-aware state refinement graph attention network (SRGAT) for multi-agent trajectory prediction. Specifically, the trajectory sequence of individual agent is firstly converted into a compact representation by exploiting Transformer encoder and gated recurrent unit (GRU). Then, a social state refinement (SSR) module for modeling social influence between agents is proposed to include more interaction-related features. Subsequently, multiple goals are predicted for each agent, followed by another goal-guided SSR module to incorporate goal information into social interaction. Finally, the multimodal trajectory is forecasted by fusing the features from forward and backward GRU. Experiments on public benchmark datasets are carried out to evaluate the effectiveness of our model. The results demonstrate the superior performance of our model compared with the state-of-the-art methods.