CLOVER: Closed-Loop Value Estimation and Ranking for End-to-End Autonomous Driving Planning

• URL: http://arxiv.org/abs/2605.15120v2
• Date: Fri, 15 May 2026 11:07:11 GMT
• Title: CLOVER: Closed-Loop Value Estimation and Ranking for End-to-End Autonomous Driving Planning
• Abstract summary: CLOVER is a framework for end-to-end autonomous driving planning.<n>A generator produces diverse candidate trajectories, and a scorer predicts planning-metric sub-scores.<n>The generator is refined toward teacher-selected top-$k$ and vector-Pareto targets.
• Score: 11.2767413780552
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: End-to-end autonomous driving planners are commonly trained by imitating a single logged trajectory, yet evaluated by rule-based planning metrics that measure safety, feasibility, progress, and comfort. This creates a training--evaluation mismatch: trajectories close to the logged path may violate planning rules, while alternatives farther from the demonstration can remain valid and high-scoring. The mismatch is especially limiting for proposal-selection planners, whose performance depends on candidate-set coverage and scorer ranking quality. We propose CLOVER, a Closed-LOop Value Estimation and Ranking framework for end-to-end autonomous driving planning. CLOVER follows a lightweight generator--scorer formulation: a generator produces diverse candidate trajectories, and a scorer predicts planning-metric sub-scores to rank them at inference time. To expand proposal support beyond single-trajectory imitation, CLOVER constructs evaluator-filtered pseudo-expert trajectories and trains the generator with set-level coverage supervision. It then performs conservative closed-loop self-distillation: the scorer is fitted to true evaluator sub-scores on generated proposals, while the generator is refined toward teacher-selected top-$k$ and vector-Pareto targets with stability regularization. We analyze when an imperfect scorer can improve the generator, showing that scorer-mediated refinement is reliable when scorer-selected targets are enriched under the true evaluator and updates remain conservative. On NAVSIM, CLOVER achieves 94.5 PDMS and 90.4 EPDMS, establishing a new state of the art. On the more challenging NavHard split, it obtains 48.3 EPDMS, matching the strongest reported result. On supplementary nuScenes open-loop evaluation, CLOVER achieves the lowest L2 error and collision rate among compared methods. Code data will be released at https://github.com/WilliamXuanYu/CLOVER.

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