Beyond Joint Refinement: Bounded Residual Flows for Multi-Modal Trajectory Forecasting

Multi-future trajectory prediction is a safety-critical bottleneck between perception and planning: an over-confident wrong-mode prediction can steer a planner into an unsafe manoeuvre. The dominant paradigm appends a refinement module to a strong backbone and trains the two jointly from random initialisation — leaving the refined output without an a-priori bound and letting the refiner lean on a learned gate that masks a poorly-behaved refiner on clean data and fails wherever its protection lapses: out-of-distribution input, closed-loop recursion, or low-bit quantisation. We therefore argue that a refiner should be judged by whether its raw correction is valid without the gate, and we construct one that is. BReF (Bounded Residual Flow) is a plug-and-play module that attaches to any frozen multi-modal predictor and refines its trajectories by integrating a learned drift field along a latent “refinement” time, kept separate from the physical trajectory time. A smoothness penalty in this latent time turns the refiner into a contraction, so its raw correction stays small and physically plausible on its own and the gate becomes near-redundant. Because the refiner is identity-initialised on the frozen backbone, the backbone acts as a verifiable performance floor that jointly-trained refiners cannot give. On nuScenes, BReF improves a strong backbone among twenty recent baselines on every multi-mode metric and on single-mode minADE while preserving the full mode set; the decisive gate-forced diagnostic — removing the gate at inference — separates refiners that are indistinguishable on displacement error by nearly three orders of magnitude, tracing the advantage to the latent-time contraction.