[Submitted on 26 Oct 2026]
Title:Closed-Loop Aware Planning via Imitation-Free Speed Scoring for End-to-End Autonomous Driving
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Abstract:End-to-end autonomous driving has predominantly relied on open-loop imitation learning for training, which presents two limitations. 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. We introduce 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. We realize this paradigm as Closed-Loop Aware Planning for Autonomous Driving (CLAP-AD), a plug-in planning head that integrates with existing end-to-end driving models and, to our knowledge, is the first to enable explicit test-time control over their speed selection without speed-conditioned training. 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. 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. 17 pages (8-page main paper, references, and supplementary material), 5 figures, 7 tables. Project page: this https URL. \c{opyright} 2027 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2610.36079 [cs.RO]
(or arXiv:2610.36079v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.36079
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Emad Aghajanzadeh [view email] [v1] Mon, 26 Oct 2026 18:05:26 UTC (750 KB)
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