Safety Critical Control of Mixed-autonomy Traffic via a Single Autonomous Vehicle

Zhou, Jingyuan; Yu, Huan · 2022 · 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

DOI: 10.1109/itsc55140.2022.9921901

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Summary

This paper addresses the safety challenges in mixed-autonomy traffic, where connected and automated vehicles (CAVs) coexist with human-driven vehicles (HDVs). While existing research often focuses on achieving string stability or throughput in such systems, the potential impact of control strategies on collision safety remains underexplored. The authors propose a safety-critical control strategy for a single CAV operating within a Leading Cruise Control (LCC) framework. In this setup, the CAV interacts with both a preceding HDV and a platoon of following HDVs. The primary objective is to guarantee collision-free spacing for all vehicles while maintaining traffic flow stability, specifically addressing risks posed by sudden acceleration or deceleration from surrounding HDVs. The methodology integrates Control Barrier Functions (CBFs) with a nominal feedback controller through a Quadratic Program (QP). The longitudinal dynamics of the HDVs are modeled using a linearized Optimal Velocity Model, while the CAV’s acceleration serves as the control input. The nominal controller is designed to ensure string stability, attenuating velocity fluctuations from the head vehicle. To enforce safety, high-order CBFs are employed to handle the high relative degree of spacing constraints. The QP formulation minimizes the deviation of the actual control input from the nominal input, subject to CBF constraints that guarantee non-negative spacing. Priority is assigned to the safety constraint between the CAV and the preceding HDV, ensuring feasibility. This approach allows the system to operate under the nominal controller for stability unless safety constraints are violated, at which point the control input deviates to prevent collisions. Simulation results validate the proposed strategy in two safety-critical scenarios involving sudden maneuvers by HDVs. The findings demonstrate that the CBF-QP controller successfully maintains safe spacing between the CAV and all surrounding HDVs, preventing collisions even when the nominal controller alone would fail to do so. The system effectively balances the competing objectives of string stability and safety, ensuring that the CAV can stabilize traffic flow in a "collision-free" manner. The study confirms that integrating CBFs into the LCC framework provides a robust solution for mixed-autonomy environments, where the CAV must adapt to unpredictable human driving behaviors. The significance of this work lies in its contribution to the safe deployment of CAVs in transitional traffic environments. By formally guaranteeing safety through CBFs while preserving the benefits of nominal stability controllers, the paper provides a scalable control architecture. The authors note that this safety-critical framework is not limited to string stability but can be extended to other control objectives, such as fuel efficiency and emission reduction. This approach offers a practical pathway for enhancing the reliability of single-CAV interventions in mixed-autonomy traffic, addressing a critical gap in current intelligent transportation systems research.

Key finding

The proposed control barrier function-based quadratic programming strategy successfully guarantees collision-free safety and string stability for a single autonomous vehicle operating in mixed-autonomy traffic.

Methodology

simulation_modeling

Provenance

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

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