Formal Safety Guarantees for Autonomous Vehicles using Barrier Certificates

2026-01-08

AI Summary

This work addresses the safety risks faced by autonomous vehicles in mixed traffic due to the lack of interpretability and formal safety guarantees in data-driven modules. The authors propose a verifiable safety framework that, for the first time, integrates barrier certificates with an interpretable traffic conflict metric—Time-to-Collision (TTC)—to formally verify safety conditions using SMT solvers and enforce safety constraints in real time through an adaptive control mechanism. Experimental evaluation on real-world highway datasets demonstrates that the approach reduces unsafe interactions with TTC below 3 seconds by up to 40%, achieving complete conflict elimination in certain lanes. The framework thus offers both interpretability and rigorous formal safety assurance.

Technology Category

• Natural Language Processing: Safety and Robustness
• Computer Vision: Interpretability, Explainability, and Transparency
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Modern AI technologies enable autonomous vehicles to perceive complex scenes, predict human behavior, and make real-time driving decisions. However, these data-driven components often operate as black boxes, lacking interpretability and rigorous safety guarantees. Autonomous vehicles operate in dynamic, mixed-traffic environments where interactions with human-driven vehicles introduce uncertainty and safety challenges. This work develops a formally verified safety framework for Connected and Autonomous Vehicles (CAVs) that integrates Barrier Certificates (BCs) with interpretable traffic conflict metrics, specifically Time-to-Collision (TTC) as a spatio-temporal safety metric. Safety conditions are verified using Satisfiability Modulo Theories (SMT) solvers, and an adaptive control mechanism ensures vehicles comply with these constraints in real time. Evaluation on real-world highway datasets shows a significant reduction in unsafe interactions, with up to 40\% fewer events where TTC falls below a 3 seconds threshold, and complete elimination of conflicts in some lanes. This approach provides both interpretable and provable safety guarantees, demonstrating a practical and scalable strategy for safe autonomous driving.

Problem

• Autonomous Vehicles
• Safety Guarantees
• Barrier Certificates
• Mixed-Traffic Environments
• Time-to-Collision

Innovation

• Barrier Certificates
• Time-to-Collision
• Formal Verification
• SMT Solvers
• Autonomous Vehicle Safety