Energy-guided diffusion for socially plausible multiagent trajectory prediction
TL;DR
An energy-guided diffusion framework that injects interpretable constraints directly into the denoising process at inference time is proposed that shows consistent improvements in best-of-K accuracy (minADE/minFDE) and a substantial reduction in collision rate.
Abstract
Accurate multi-agent trajectory prediction requires modeling inherently multi-modal futures while maintaining physical and social plausibility. Diffusion-based predictors provide strong generative coverage but can still produce unsafe samples such as inter-agent collisions, especially under short observation horizons. We propose an energy-guided diffusion framework that injects interpretable constraints directly into the denoising process at inference time. Our model first encodes agent interactions into condition tokens and then samples K future trajectories with a conditional diffusion generator. During sampling, we apply per-step gradient guidance derived from a differentiable energy decomposition that penalizes social collisions and encourages intent/goal consistency, enabling plug-and-play controllability without retraining. Experiments on ETH/UCY and SDD under the standard 8 → 12 protocol demonstrate consistent improvements in best-of-K accuracy (minADE/minFDE) and a substantial reduction in collision rate. Ablation studies further show that the social energy term drives most safety gains, the intent term improves long-horizon accuracy, and continuous per-step guidance is essential compared to late-step or post-hoc alternatives.