Multi-modal trajectory forecasting with Multi-scale Interactions and Multi-pseudo-target Supervision

Abstract

Trajectory forecasting is crucial for the advancement of autonomous vehicles. While much progress has been made, extant approaches often fall short in accounting for intricate social interactions and the unpredictability of human behavior. This paper introduces a novel multi-modal trajectory forecasting model named Multi-scale Interaction and Multi-pseudo-target Supervision (MIMS). Central to our approach is a multiscale hypergraph that discerns latent interactions across all scales, evaluating both their strength and type. Further enhancing our model’s capabilities is a causal-effect-estimation-based pseudo-target generation method. This facilitates multi-modality modeling in motion forecasting by offering explicit supervision with multiple latent targets. We validate MIMS using the Argoverse Motion Forecasting dataset. The results reveal that MIMS outperforms current state-of-the-art models across various traffic conditions. Specifically, our multiscale hypergraph exhibits a superior capacity for capturing complex spatiotemporal dependencies compared to pairwise methods. Moreover, our multi-pseudo-target supervision eliminates the pattern convergence of multi-modal forecasting.