Safety-Critical Lane-Change Control for CAV Platoons in Mixed Autonomy Traffic Using Control Barrier Functions
Fengqing Hu, Huan Yu
TL;DR
The paper addresses safety-critical lane-change maneuvers for CAV platoons in mixed autonomy traffic. It introduces a two-layer framework where a higher-level FSM handles lane-change decisions and a per-vehicle CLF-CBF-QP safety controller enforces both longitudinal and lateral safety through constraints such as $L_fV_{l,i}+L_gV_{l,i}\mathbf{u}_i\le -\alpha_lV_{l,i}+\delta_{l,i}$ and $\frac{\partial h_i}{\partial t}+L_fh_i+L_gh_i\mathbf{u}_i\ge -\gamma h_i$. CLF constraints govern spacing with $s_{d,i}=s_0+v_i\tau_i$ and CBFs manage interactions across front/target/back vehicles and lateral lane-change safety. Four safety-critical scenarios are analyzed to validate the approach, demonstrating that CLF-CBF-QP maintains safety while enabling platoon lane changes, outperforming a CLF-QP baseline which may fail under aggressive cut-ins or abrupt decelerations. The work advances practical lane-change safety in mixed autonomy traffic and informs robust cooperative platoon operations.
Abstract
Platooning can serve as an effective management measure for connected and autonomous vehicles (CAVs) to ensure overall traffic efficiency. Current study focus on the longitudinal control of CAV platoons, however it still remains a challenging problem to stay safe under lane-change scenarios where both longitudinal and lateral control is required. In this paper, a safety-critical control method is proposed conduct lane-changing maneuvers for platooning CAVs using Control Barrier Functions (CBFs). The proposed method is composed of two layers: a higher-level controller for general lane change decision control and a lower-level controller for safe kinematics control. Different from traditional kinematics controllers, this lower-level controller conducts not only longitudinal safety-critical control but also critically ensures safety for lateral control during the platooning lane change. To effectively design this lower-level controller, an optimization problem is solved with constraints defined by both CBFs and Control Lyapunov Functions (CLFs). A traffic simulator is used to conduct numerical traffic simulations in four safety-critical scenarios and showed the effectiveness of the proposed controller.