自動運転の安全性に向けた汎用制御修正手法
A Generalized Control Revision Method for Autonomous Driving Safety
ベクトル化知覚と占有グリッドマップを入力とし、新たなバリア関数で多様な交通シーン制約を統合的に扱う制御修正手法を提案し、複雑なシーンでの安全性を検証した。
著者: Zehang Zhu, Yuning Wang, Tianqi Ke, Zeyu Han, Shaobing Xu, Qing Xu, John M. Dolan, Jianqiang Wang
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Safety is one of the most crucial challenges of autonomous driving vehicles, and one solution to guarantee safety is to employ an additional control revision module after the planning backbone. Control Barrier Function (CBF) has been widely used because of its strong mathematical foundation on safety. However, the incompatibility with heterogeneous perception data and incomplete consideration of traffic scene elements make existing systems hard to be applied in dynamic and complex real-world scenarios. In this study, we introduce a generalized control revision method for autonomous driving safety, which adopts both vectorized perception and occupancy grid map as inputs and comprehensively models multiple types of traffic scene constraints based on a new proposed barrier function. Traffic elements are integrated into one unified framework, decoupled from specific scenario settings or rules. Experiments on CARLA, SUMO, and OnSite simulator prove that the proposed algorithm could realize safe control revision under complicated scenes, adapting to various planning backbones, road topologies, and risk types. Physical platform validation also verifies the real-world application feasibility.
