Causality-Based Parametric Control Barrier Function for Safe Multi-Vehicle Interaction

2026-06-23

AI Summary

This work addresses the challenges of ambiguous causal relationships in multi-vehicle interaction modeling and the excessive conservatism arising from worst-case safety analysis. To overcome these issues, the authors propose a novel approach that integrates causal inference with parametric control barrier functions (Parametric-CBFs). By explicitly embedding causal reasoning into the CBF framework for the first time, the method constructs a causality-aware Parametric-CBF that accurately captures the underlying causal mechanisms governing neighboring vehicles’ behaviors. An adaptive safety controller is then designed based on this formulation. Evaluated in high-density interactive traffic scenarios, the proposed method significantly improves task execution efficiency while rigorously guaranteeing system safety, thereby effectively mitigating overly conservative control actions.

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.

Problem

• multi-vehicle interaction
• causality inference
• safe control
• behavior modeling
• conservative safety

Innovation

• Causality inference
• Parametric Control Barrier Function
• Multi-vehicle interaction
• Adaptive safety control
• Safe autonomy