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

End-to-end autonomous driving planners are commonly trained by imitating a single logged trajectory, yet they are 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 still violate planning rules, while alternative trajectories farther from the demonstration can remain valid and high-scoring. The mismatch is especially limiting for proposal-selection planners, whose performance depends on both candidate-set coverage and scorer ranking quality. We propose CLOVER, a Closed-LOop Value Estimation and Ranking framework for end-to-end driving planning. CLOVER first expands single-trajectory imitation into set-level proposal coverage by constructing evaluator-filtered pseudo-expert trajectories. It then performs conservative closed-loop self-distillation: a trajectory-level scorer is fitted to true evaluator sub-scores on generated proposals, while the generator is refined toward teacher-selected top-k and vector-Pareto proposal targets with stability regularization. We also analyze when an imperfect scorer can improve the generator, showing that scorer-mediated refinement is reliable under local scorer accuracy, conservative updates, and selected-set enrichment. On NAVSIM, CLOVER achieves 94.5 PDMS and 90.4 EPDMS, establishing a new state-of-the-art performance. On the more challenging NavHard split, it obtains 48.3 EPDMS, matching the strongest reported result. Supplementary evaluations on nuScenes and Bench2Drive cover open-loop planning and reactive closed-loop driving, respectively: CLOVER achieves the lowest nuScenes L2 error and collision rate among compared methods and reaches a 70.5% success rate and an 89.2 Driving Score on Bench2Drive. Code will be released.