Probabilistic Safe Adaptive Merging Control for Autonomous Vehicles Under Motion Uncertainty
Abstract
In this work we address the safe adaptive control problem for autonomous vehicles in the highway on-ramp merging scenario. We argue that by developing a Control Barrier Function (CBF)-based method, autonomous vehicles are able to perform adaptive interactions with human drivers with safety guarantees under uncertainty. We propose a novel extension of traditional CBF to a probabilistic setting for stochastic system dynamics with provable chance-constrained safety and provide a theoretical analysis to discuss the solution feasibility guarantee and design factors reflecting different vehicle behaviors. This allows for adaptation to different driving strategies with a formally provable feasibility guarantee for the ego vehicle’s safe controller. The results demonstrate the enhanced safety and adaptability of our proposed approach.
BibTeX
@workshop{Lyu-2021-134885,author = {Yiwei Lyu and Wenhao Luo and John M. Dolan},
title = {Probabilistic Safe Adaptive Merging Control for Autonomous Vehicles Under Motion Uncertainty},
booktitle = {Proceedings of Safe Robot Control with Learned Motion and Environment Models},
year = {2021},
month = {June},
keywords = {autonomous driving, merging, adaptive control, safety control, Control Barrier Function,},
}