Stochastic human trajectory prediction via interaction-aware diffusion model

Human trajectory prediction has significant practical applications in various scenarios, such as autonomous driving, social robots and so on. Recently, it has been widely studied by diffusion models in order to model the inherent multi-modality of human motions. However, existing diffusion-based approaches only focus on modeling the social interactions via a single encoder and neglect the scene interactions, which results in producing unreasonable trajectories across obstacles or road boundaries. To address this issue, we propose the Interaction-Aware Diffusion Model (IADM), a novel diffusion-based framework considering both human motions and surrounding scene layout by treating the social and scene interactions as conditions in the parameterized reverse Markov chain. To implement IADM, we design two encoders, i.e ., social encoder and scene encoder, where the social encoder models the social interactions via attention mechanism, and the scene encoder preserves spatial information of the scene when learning the scene interactions. Furthermore, we devise the dual-guidance decoder consisting of the motion-guided temporal module and the scene-guided spatial module to intensify the collaboratively guidance of the social and scene interactions. Extensive experiments on the ETH/UCY dataset, Stanford Drone Dataset and Intersection Drone Dataset validate the superiority of our method, achieving state-of-the-art results.