TrajFlow: Multi-modal Motion Prediction via Flow Matching
To address the inefficiency and inadequate modeling of diversity and uncertainty in multimodal motion prediction for autonomous driving in dynamic scenarios, this paper proposes a single-forward-pass inference framework based on flow matching. Our key contributions are: (1) a novel single-pass multimodal trajectory generation mechanism that eliminates iterative sampling; (2) incorporation of a Plackett–Luce ranking loss to explicitly model confidence-based trajectory ordering, thereby improving uncertainty estimation quality; and (3) a self-conditioned training strategy that reuses intermediate predictions to construct noisy inputs, enhancing temporal consistency. Experiments on the Waymo Open Motion Dataset demonstrate state-of-the-art performance: a 4.2% reduction in final displacement error (FDE), a 23% improvement in trajectory diversity, and a 68% reduction in computational overhead—achieving a superior balance among accuracy, diversity, and inference efficiency.