Enforcing Safety for Mixed Traffic Control via a Control Barrier Function Quadratic Program

Zhao, Chenguang; Yu, Huan · 2023 · IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2023.10.1151

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This paper addresses the challenge of ensuring safety in mixed-autonomy traffic systems, where Connected Automated Vehicles (CAVs) interact with human-driven vehicles (HDVs). While CAVs are recognized as transformative for improving traffic performance, such as dissipating stop-and-go waves, their control design often fails to fully account for safety impacts. Specifically, existing controllers may cause rear-end collisions in safety-critical scenarios, hindering the broader application of CAV technology. The authors aim to design a Safety-critical Traffic Controller (STC) for a leading CAV that guarantees both safety and stability within the closed-loop mixed traffic system. The proposed method utilizes Control Barrier Functions (CBFs) to enforce safety constraints. The authors first define safe driving constraints for the CAV and HDVs, using CBFs to penalize any potential safety violations. They then synthesize a safety-critical controller by integrating these CBF constraints with a nominal control input. This integration is achieved by solving a Quadratic Programming (QP) problem. The resulting controller ensures that the CAV maintains string stability and adheres to safety boundaries, even when interacting with unpredictable human-driven vehicles. The approach focuses on a leading CAV scenario, providing a rigorous mathematical framework for real-time control decisions that prioritize collision avoidance without sacrificing traffic flow efficiency. The study demonstrates that the proposed STC effectively prevents rear-end collisions in safety-critical situations where traditional controllers might fail. By embedding safety constraints directly into the control optimization via CBFs, the controller guarantees that the system remains within a safe set of states. The numerical results indicate that the method successfully stabilizes the mixed vehicle platoon while maintaining the necessary safety margins between the CAV and surrounding HDVs. This ensures that the CAV can operate reliably in mixed traffic environments, addressing the specific gap in current literature regarding the safety implications of CAV control designs. The significance of this work lies in its contribution to the safe deployment of automated vehicles in real-world, mixed-autonomy settings. By providing a provably safe control strategy, the paper offers a solution to the critical barrier of trust and safety in CAV adoption. The use of CBFs combined with QP provides a computationally efficient and theoretically sound method for real-time implementation. This approach not only enhances the safety of individual CAVs but also contributes to the overall stability and safety of the traffic system, facilitating the transition toward more automated transportation networks.

Key finding

The proposed Safety-critical Traffic Controller using Control Barrier Functions and Quadratic Programming successfully enforces safety and string stability for mixed traffic platoons in numerical simulations.

Methodology

simulation_modeling

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via author_sweep_intake on 2026-05-28.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• teleoperation remote driving