Adaptive Safe Behavior Generation for Heterogeneous Autonomous Vehicles Using Parametric-Control Barrier Functions (Student Abstract)
Abstract
Control Barrier Functions have been extensively studied to ensure guaranteed safety during inter-robot interactions. In this paper, we introduce the Parametric-Control Barrier Function (Parametric-CBF), a novel variant of the traditional Control Barrier Function to extend its expressivity in describing different safe behaviors among heterogeneous robots. A parametric-CBF based framework is presented to enable the ego robot to model the neighboring robots behavior and further improve the coordination efficiency during interaction while enjoying formally provable safety guarantees. We demonstrate the usage of Parametric-CBF in behavior prediction and adaptive safe control in the ramp merging scenario.
Cite
Text
Lyu et al. "Adaptive Safe Behavior Generation for Heterogeneous Autonomous Vehicles Using Parametric-Control Barrier Functions (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21641Markdown
[Lyu et al. "Adaptive Safe Behavior Generation for Heterogeneous Autonomous Vehicles Using Parametric-Control Barrier Functions (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/lyu2022aaai-adaptive/) doi:10.1609/AAAI.V36I11.21641BibTeX
@inproceedings{lyu2022aaai-adaptive,
title = {{Adaptive Safe Behavior Generation for Heterogeneous Autonomous Vehicles Using Parametric-Control Barrier Functions (Student Abstract)}},
author = {Lyu, Yiwei and Luo, Wenhao and Dolan, John M.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2022},
pages = {13009-13010},
doi = {10.1609/AAAI.V36I11.21641},
url = {https://mlanthology.org/aaai/2022/lyu2022aaai-adaptive/}
}