Cheng, Yifan (Tongji University), Zhang, Yuxiang (Jilin University), Jiang, Yuyao (Jilin University, China), Chu, Hongqing (Tongji University), Gao, Bingzhao (Tongji University), Chen, Hong (Tongji University)

Disturbance Observer-Enhanced Adaptive CBFs for Safety-Critical Motion Control of Autonomous Vehicles

Scheduled for presentation during the Invited Session "Enhancing Trustworthiness and Resilience of Connected and Autonomous Vehicles in Adversarial Environments" (FrAT7), Friday, September 27, 2024, 10:50−11:10, Salon 15

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

The standalone adaptability in the penalty parameters of adaptive control barrier functions (ACBFs), may be insufficient to meet the high requirements for safety and robustness of autonomous vehicles (AVs) motion control, especially under system disturbances. This paper integrates the disturbance observer theory into the ACBFs, in order to improve the robustness and adaptability of the corresponding optimization framework. The enhanced performance is mainly realized in the following aspects: firstly, an auxiliary system for safety-critical control design is defined, and the constraints, related to the penalty parameters, are introduced. Secondly, the disturbance observers are introduced to estimate the unknown parts of high-order safety and stability constraints. Thirdly, the estimated error bounds are introduced into the original framework of ACBF-CLF-QPs to guarantee robustness and adaptive safety. The superior performance compared to the original ACBFs is verified in the AVs motion control under the classical scenario, and the simulation results indicate that, the proposed method has stronger robustness and adaptability to system disturbances while meeting the safety constraints.