視覚ベースのEnd-to-End自動運転のための微分可能制御バリア関数
Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Driving
微分可能な制御バリア関数を組み込んだ学習システムを提案し、視覚入力からのEnd-to-End自動運転において安全性を保証しつつ、実車で車線維持と障害物回避を実現した。
著者: Wei Xiao, Tsun-Hsuan Wang, Makram Chahine, Alexander Amini, Ramin Hasani, Daniela Rus
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
Guaranteeing safety of perception-based learning systems is challenging due to the absence of ground-truth state information unlike in state-aware control scenarios. In this paper, we introduce a safety guaranteed learning framework for vision-based end-to-end autonomous driving. To this end, we design a learning system equipped with differentiable control barrier functions (dCBFs) that is trained end-to-end by gradient descent. Our models are composed of conventional neural network architectures and dCBFs. They are interpretable at scale, achieve great test performance under limited training data, and are safety guaranteed in a series of autonomous driving scenarios such as lane keeping and obstacle avoidance. We evaluated our framework in a sim-to-real environment, and tested on a real autonomous car, achieving safe lane following and obstacle avoidance via Augmented Reality (AR) and real parked vehicles.
