Hybrid car following control for CAVs: Integrating linear feedback and deep reinforcement learning to stabilize mixed traffic.

This paper introduces a novel hybrid car-following strategy for connected automated vehicles (CAVs) to mitigate traffic oscillations while simultaneously improving CAV car-following (CF) distance-maintaining efficiencies. To achieve this, our proposed control framework integrates two controllers: a linear feedback controller and a deep reinforcement learning controller. Firstly, a cutting-edge linear feedback controller is developed by non-linear programming to maximally dampen traffic oscillations in the frequency domain while ensuring both local and string stability. Based on that, deep reinforcement learning (DRL) is employed to complement the linear feedback controller further to handle the unknown traffic disturbance quasi-optimally in the time domain. This unique approach enhances the control stability of the traditional DRL approach and provides an innovative perspective on CF control. Simulation experiments were conducted to validate the efficacy of our control strategy. The results demonstrate superior performance in terms of training convergence, driving comfort, and dampening oscillations compared to existing DRL-based controllers. • A novel hybrid car-following strategy for CAVs to mitigate traffic oscillations. • A novel control framework by integrating linear feedback control and DRL. • Excellent control performance on distance maintaining and smooth driving. • Better capability to handle traffic corner cases. [ABSTRACT FROM AUTHOR]