Closed-Loop Aware Planning via Imitation-Free Speed Scoring for End-to-End Autonomous Driving

What happened

arXiv:2610.36079v1 Announce Type: new Abstract: End-to-end autonomous driving has predominantly relied on open-loop imitation learning for training, which presents two limitations. On Bench2Drive-Speed, CLAP-AD sets a new state of the art Speed-Adherence Score of 82.53, topping TFv6, the strongest baseline on this metric, by 12.9 points while retaining strong closed-loop driving performance.

arXiv Robotics (cs.RO) reports that it introduces a planning paradigm that learns the path through imitation while scoring a set of candidate speeds across multiple criteria with a learned function supervised by physically grounded safety constraints. First, expert demonstrations show only one behavior per scenario, so the model cannot learn alternative valid behaviors and, in turn, cannot adapt to user-specified preferences at test time.

Second, noise or ambiguity in these demonstrations can produce inconsistent, unreliable supervision for the end-to-end models. Driving is inherently multi-modal: multiple different behaviors can be valid responses to the same scene, and this diversity arises largely from speed rather than path.

On Bench2Drive, it improves driving score by +28.5 and +32.7 over UniAD and VAD, and on NAVSIM v2, built on real-world driving data, EPDMS by +6.1 over its backbone. Project page: this https URL Comments: Accepted to WACV 2027. Personal use of this material is permitted.