[2603.22035] Future-Interactions-Aware Trajectory Prediction via Braid Theory

Computer Science > Artificial Intelligence arXiv:2603.22035 (cs) [Submitted on 23 Mar 2026] Title:Future-Interactions-Aware Trajectory Prediction via Braid Theory Authors:Caio Azevedo, Stefano Sabatini, Sascha Hornauer, Fabien Moutarde View a PDF of the paper titled Future-Interactions-Aware Trajectory Prediction via Braid Theory, by Caio Azevedo and 3 other authors View PDF HTML (experimental) Abstract:To safely operate, an autonomous vehicle must know the future behavior of a potentially high number of interacting agents around it, a task often posed as multi-agent trajectory prediction. Many previous attempts to model social interactions and solve the joint prediction task either add extensive computational requirements or rely on heuristics to label multi-agent behavior types. Braid theory, in contrast, provides a powerful exact descriptor of multi-agent behavior by projecting future trajectories into braids that express how trajectories cross with each other over time; a braid then corresponds to a specific mode of coordination between the multiple agents in the future. In past work, braids have been used lightly to reason about interacting agents and restrict the attention window of predicted agents. We show that leveraging more fully the expressivity of the braid representation and using it to condition the trajectories themselves leads to even further gains in joint prediction performance, with negligible added complexity either in training or at inference time. We...