Adaptive Safe Behavior Generation for Heterogeneous Autonomous Vehicles Using Parametric-Control Barrier Functions (Student Abstract)

Authors

  • Yiwei Lyu Carnegie Mellon University
  • Wenhao Luo University of North Carolina at Charlotte
  • John M. Dolan Carnegie Mellon University

DOI:

https://doi.org/10.1609/aaai.v36i11.21641

Keywords:

Intelligent Transportation System, Autonomous Driving, Safe Control, Control Barrier Function

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.

Downloads

Published

2022-06-28

How to Cite

Lyu, Y., Luo, W., & Dolan, J. M. (2022). Adaptive Safe Behavior Generation for Heterogeneous Autonomous Vehicles Using Parametric-Control Barrier Functions (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13009-13010. https://doi.org/10.1609/aaai.v36i11.21641