Learning Direct Control Policies with Flow Matching for Autonomous Driving
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-distrib...
| Authors | Marcello Ceresini, Federico Pirazzoli, Andrea Bertogalli et al. |
|---|---|
| Primary category | cs.RO |
| Published | 14 May 2026 |
| Last updated at source | 5 August 2026 |
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