Test-Time Trajectory Optimization for Autonomous Driving

Abstract:End-to-end planners for autonomous driving typically generate a set of candidate trajectories, score each one, and return the highest-scoring candidate. However, the scorer is applied only after the proposals are generated and cannot influence the set of trajectories: a weak set of candidates limits planning performance regardless of the scorer's quality. We instead treat the scorer as a learned trajectory-level reward function and search for trajectories that maximize it. Our method, TOAD, runs the Cross-Entropy Method at test time, warm-started from the planner's proposals. It requires no retraining and is plug-and-play for existing planners. Across six base planners, TOAD improves results on NAVSIM-v1 (94.7 PDMS), NAVSIM-v2 (56.3 EPDMS), and the closed-loop HUGSIM benchmark. The code will be made publicly available via the project page: this https URL.

Robotics (cs.RO)

arXiv:2606.07170 [cs.RO]

(or arXiv:2606.07170v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2606.07170 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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