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 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 gen...
| Authors | Sining Ang, Yuguang Yang, Canyu Chen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 14 May 2026 |
| Last updated at source | 15 May 2026 |
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