BeyondSight: エンドツーエンド自動運転のための物体永続性
BeyondSight: Object Permanence for End-to-End Autonomous Driving
自動運転において、長時間遮蔽されたアクターの存在を維持する永続性を考慮したエンドツーエンド駆動フレームワークを提案し、遮蔽下での認識・予測・計画性能を向上させた。
詳しい要約
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著者: Sandro Papais, Letian Wang, Mudit Jain, Behnaz Rezaei, Steven L. Waslander
分類: cs.RO
原文アブストラクト
Autonomous driving operates in partially observable environments where actors may become fully occluded by other vehicles or infrastructure. Most end-to-end driving systems implicitly couple actor existence to instantaneous observations, causing actor hypotheses to degrade or disappear during prolonged occlusion and removing potentially critical agents from downstream prediction and planning. We introduce BeyondSight, a permanence-aware end-to-end driving framework that decouples actor existence from observability by maintaining persistent actor hypotheses over time. BeyondSight propagates actor queries temporally and updates them with observation-conditioned evidence, enabling joint perception, prediction, and planning to reason about actors even when they are temporarily unobservable. To enable principled training and evaluation of persistence-aware models, we further introduce nuScenes-Permanence, an extension of nuScenes that provides supervision and observability-conditioned evaluation for unobservable actors. Experiments show that BeyondSight substantially improves reasoning under occlusion, increasing detection performance for unobservable actors from 0 to 0.249 mAP while reducing planning error from 0.61 to 0.54 L2avg. These results highlight object permanence as an important modeling principle for robust end-to-end autonomous driving.
