STAR-Drive: Selective Trajectory Adoption and Replanning for Autonomous Driving

Vision–language–action (VLA) models bring visual understanding and reasoning to autonomous driving, but their high inference cost makes frequent replanning expensive. In per-step replanning (Full Replan), each valid prediction immediately replaces the current driving plan, coupling the decision to request a new prediction with the decision to use it. However, the controller can continue following the current plan between model calls, and a newly generated plan need not be a better replacement. We introduce STAR-Drive, a training-free runtime that decides when to request a new plan and whether to adopt it, while leaving the VLA and feedback controller unchanged. Trajectory-guided scheduling uses the active trajectory’s age and upcoming motion to schedule model calls, limiting how long the plan can be reused and querying earlier before planned turns or speed changes. After each query, world-aware trajectory selection compares the candidate with the remaining active trajectory over the same future interval, checking for substantially reduced travel distance and vehicle footprints crossing mapped road boundaries. Rejecting a new plan does not extend the current plan’s reuse deadline; recovery rules govern execution when normal reuse is unavailable. Across 913 closed-loop AlpaSim scenes with Alpamayo 1.5, STAR-Drive increases the driving score, Score_AF, from 59.63 to 65.70 on the $\times 100$ scale and reduces the off-road rate from 7.89% to 4.16% compared with Full Replan. It achieves these gains with 75.53% fewer VLA calls and a $2.81\times$ speedup in mean end-to-end latency, measured after initial rendering and excluding setup.