Causality-Based Parametric Control Barrier Function for Safe Multi-Vehicle Interaction
The paper extends Parametric Control Barrier Function (Parametric-CBF) by embedding causality inference to explicitly reason over inter-vehicle influence, enabling an adaptive safety-critical controller that avoids overly conservative behavior and improves task efficiency in multi-vehicle interactions.
[2606.25134] Causality-Based Parametric Control Barrier Function for Safe Multi-Vehicle Interaction
[Submitted on 23 Jun 2026]
Title:Causality-Based Parametric Control Barrier Function for Safe Multi-Vehicle Interaction
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Abstract:Safe control has been widely studied in various safety-critical applications, for instance, autonomous driving. In order to ensure the autonomous vehicle does not collide with other vehicles, it is essential to obtain an accurate expectation of surrounding vehicles' behavior and react adaptively. Instead of assuming fully cooperative and homogeneous vehicles using the same safety-critical controllers, recent works have been exploring different data-driven approaches to model the neighboring vehicles' underlying controllers with observed data. However, existing works either suffer from 1) the inter-vehicle influence during the multi-vehicle interaction, which makes it hard to determine the causality of surrounding vehicles' behavior in controller modeling, or 2) being dominated by the worst-case analysis, which may lead to overly conservative behavior. In this paper, we extend the prior work on Parametric-Control Barrier Function (Parametric-CBF) to multi-robot interactions with embedded causality inference to explicitly reason over the inter-vehicle influence. Given the learned Causality-based Parametric-CBF, we present an adaptive safety-critical controller that allows the ego vehicle to safely react to surrounding vehicles with the learned expectation. We demonstrate that by leveraging the motion flexibility among multi-vehicle systems, task efficiency can be greatly improved in various interaction-intensive scenarios.
Comments: accepted ICRA 2026
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2606.25134 [cs.RO]
(or arXiv:2606.25134v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.25134
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Caleb Chang [view email] [v1] Tue, 23 Jun 2026 20:06:30 UTC (792 KB)
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