ProDrive: 自己と環境の共進化による自動運転のためのプロアクティブ計画
ProDrive: Proactive Planning for Autonomous Driving via Ego-Environment Co-Evolution
現在の観測のみに依存する反応的な自動運転計画の問題を解決するため、自己と環境の共進化を可能にするワールドモデルベースのプロアクティブ計画フレームワークを提案した。
詳しい要約
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著者: Chuyao Fu, Shengzhe Gan, Zhuoli Ouyang, Yuhan Rui, Xiaowei Chi, Sirui Han, Jiankun Wang, Hong Zhang
分類: cs.RO
原文アブストラクト
End-to-end autonomous driving planners typically generate trajectories from current observations alone. However, real-world driving is highly dynamic, and such reactive planning cannot anticipate future scene evolution, often leading to myopic decisions and safety-critical failures. We propose ProDrive, a world-model-based proactive planning framework that enables ego-environment co-evolution for autonomous driving. ProDrive jointly trains a query-centric trajectory planner and a bird's-eye-view (BEV) world model end-to-end: the planner generates diverse candidate trajectories and planning-aware ego tokens, while the world model predicts future scene evolution conditioned on them. By injecting planner features into the world model and evaluating all candidates in parallel, ProDrive preserves end-to-end gradient flow and allows future outcome assessment to directly shape planning. This bidirectional coupling enables proactive planning beyond current-observation-driven decision-making. Experiments on NAVSIM v1 show that ProDrive outperforms strong baselines in both safety and planning efficiency, while ablations validate the effectiveness of the proposed ego-environment coupling design.
