Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Driving
This work addresses safety guarantees for perception-based autonomous driving, which is critical for real-world deployment but incremental as it builds on existing control barrier function methods.
The paper tackles the challenge of ensuring safety in vision-based end-to-end autonomous driving systems by introducing a framework with differentiable control barrier functions (dCBFs) trained end-to-end via gradient descent, achieving safe lane following and obstacle avoidance in sim-to-real and real-world tests on an autonomous car.
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.