2022
AAAI
AAAI 2022
Adaptive Safe Behavior Generation for Heterogeneous Autonomous Vehicles Using Parametric-Control Barrier Functions (Student Abstract)
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.
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Interdisciplinary Bridge
— Artificial Intelligence and Reinforcement Learning and Robotics
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio