マニューバ分解による障害物知覚安全ゾーンの精緻化
Refining Obstacle Perception Safety Zones via Maneuver-Based Decomposition
自動運転車のマニューバ情報を活用し、ハミルトン・ヤコビ到達可能性に基づく知覚安全ゾーンを時間的畳み込みで分解することで、完全性を保ちつつ最大76%縮小する手法を提案した。
著者: Sever Topan, Yuxiao Chen, Edward Schmerling, Karen Leung, Jonas Nilsson, Michael Cox, Marco Pavone
分類: cs.RO
原文アブストラクト
A critical task for developing safe autonomous driving stacks is to determine whether an obstacle is safety-critical, i.e., poses an imminent threat to the autonomous vehicle. Our previous work showed that Hamilton Jacobi reachability theory can be applied to compute interaction-dynamics-aware perception safety zones that better inform an ego vehicle's perception module which obstacles are considered safety-critical. For completeness, these zones are typically larger than absolutely necessary, forcing the perception module to pay attention to a larger collection of objects for the sake of conservatism. As an improvement, we propose a maneuver-based decomposition of our safety zones that leverages information about the ego maneuver to reduce the zone volume. In particular, we propose a "temporal convolution" operation that produces safety zones for specific ego maneuvers, thus limiting the ego's behavior to reduce the size of the safety zones. We show with numerical experiments that maneuver-based zones are significantly smaller (up to 76% size reduction) than the baseline while maintaining completeness.
