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CLOVER framework enhances autonomous driving planning with closed-loop value estimation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CLOVER, a novel framework designed to improve end-to-end autonomous driving planning systems. This approach addresses the common training-evaluation mismatch by generating diverse candidate trajectories and using a scorer to predict planning-metric sub-scores for ranking. CLOVER employs a closed-loop self-distillation method to refine both the generator and scorer, leading to state-of-the-art performance on benchmarks like NAVSIM and NavHard. AI

IMPACT Introduces a new method to improve the safety and performance of autonomous driving systems by refining planning algorithms.

RANK_REASON The cluster contains an academic paper detailing a new framework for autonomous driving planning. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

CLOVER framework enhances autonomous driving planning with closed-loop value estimation

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The cluster contains an academic paper detailing a new framework for autonomous driving planning. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Yan Wang ·

    CLOVER: Closed-Loop Value Estimation \& 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 logge…

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