DSBTR: A Diffusion Schrödinger Bridge Trajectory Refiner for Multi-Agent Trajectory Prediction

Multi-agent trajectory prediction is crucial for ensuring autonomous driving safety. Although diffusion models show great potential in trajectory refinement, existing diffusion-based refiners suffer from severe exposure bias. This bias stems from the distribution mismatch between the noisy training endpoint and the noise-free inference starting point, which frequently causes negative optimization during the refinement process. To resolve this issue, this paper proposes the Diffusion Schrödinger Bridge Trajectory Refiner (DSBTR). By introducing a tractable Schrödinger bridge with dual-terminal constraints, DSBTR ensures that the forward evolution endpoint strictly degenerates into a deterministic and noise-free coarse prediction prior. This theoretical alignment eliminates the initial boundary error and constrains the reverse solving process to a strict residual approximation. Based on the semi-linear structure of DSBTR, we derive a tailored high-order DPM-Solver to accelerate the sampling process and meet autonomous driving application requirements. Extensive experiments on the ETH/UCY and SDD benchmarks demonstrate that DSBTR serves as an effective plug-and-play module. It consistently improves the prediction accuracy of various baseline architectures and successfully avoids negative optimization.