AI RESEARCH
RetroMotion: Retrocausal Motion Forecasting Models are Instructable
arXiv CS.AI
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ArXi:2505.20414v2 Announce Type: replace-cross Motion forecasts of road users (i.e., agents) vary in complexity depending on the number of agents, scene constraints, and interactions. In particular, the output space of joint trajectory distributions grows exponentially with the number of agents. Therefore, we decompose multi-agent motion forecasts into (1) marginal distributions for all modeled agents and (2) joint distributions for interacting agents. Using a transformer model, we generate joint distributions by re-encoding marginal distributions followed by pairwise modeling.