A Control Framework for Stabilizing a Mixed Platoon With Connected Automated Vehicles

Human-Driven Vehicles (HDVs) and Connected Automated Vehicles (CAVs) will coexist for a long period. However, existing studies on CAV control find it challenging to stabilize a mixed platoon with HDVs and CAVs considering the uncertainty of HDVs. Besides, most methods are limited to specific platoon compositions and have advantages only in certain penetration rates which hinders application of CAVs. In this study, a novel control framework named Cooperative Adaptive Cruise Control in a mixed platoon (CACCm) is proposed to stabilize mixed platoons as well as smooth the traffic flow. The framework consists of two modules: a control strategy for CAVs in a platoon with random compositions and a control parameter optimization process improving string stability and mitigating oscillations. A reactive controller is designed for a generic platoon by constructing a car-following model with uncertainty and a feedforward filter, which have more adaptability in mixed scenarios. To strengthen stability and mitigate oscillations caused by HDVs, properties of HDVs’ motion in frequency domain are analyzed and its predominant frequency range is extracted using real HDV trajectories from the reconstructed Next Generation Simulation (NGSIM) dataset and Fast Fourier Transformation. Two types of indicators for a mixed platoon including string stability ratio and disturbance damping ratio are defined. Based on this, an optimization process is conducted for control parameters to increase the disturbance suppression and string stability. Verified by real trajectory data simulation experiments, CACCm can significantly mitigate the speed oscillations, and improve comfort, safety and fuel consumption compared with existing approaches.