Learning Direct Control Policies with Flow Matching for Autonomous Driving

Abstract:We present a flow-matching planner for autonomous driving that directly outputs actionable control trajectories defined by acceleration and curvature profiles. The model is conditioned on a bird's-eye-view (BEV) raster of the surrounding scene and generates control sequences in a small number of Ordinary Differential Equations (ODE) integration steps, enabling low-latency inference suitable for real-time closed-loop re-planning. We train exclusively on urban scenarios (real urban city streets, intersections and roundabouts of the city of Parma, Italy) collected from a 2D traffic simulator with reactive agents, and evaluate in closed-loop on both in-distribution and markedly out-of-distribution environments, including multi-lane highways and unseen urban scenarios. Our results show that the model generalizes reliably to these unseen conditions, maintaining stable closed-loop control and successfully completing scenarios that differ substantially from the training distribution. We attribute this to the BEV representation, which provides a geometry-centric view of the scene that is inherently less sensitive to distributional shifts, and to the flow-matching formulation, which learns a smooth vector field that degrades gracefully under distribution shift. We provide video demonstrations of closed-loop behavior at this https URL.

16 pages, 6 figures, 2 tables. Accepted for oral presentation at the 2026 IEEE International Conference on Intelligent Transportation Systems (ITSC 2026)

Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2605.14832 [cs.RO]

(or arXiv:2605.14832v2 [cs.RO] for this version)