The integration of Connected Cruise Control (CCC) and wireless Vehicle-to-Vehicle (V2V) communication technology aims to improve driving safety and stability. To enhance CCC’s adaptability in complex traffic conditions, in-depth research into intelligent asymmetrical control design is crucial. In this paper, the intelligent CCC controller issue is investigated by jointly considering the dynamic network-induced delays and target vehicle speeds. In particular, a deep reinforcement learning (DRL)-based controller design method is introduced utilizing the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. In order to generate intelligent asymmetrical control strategies, the quadratic reward function, determined by control inputs and vehicle state errors acquired through interaction with the traffic environment, is maximized by the training that involves both actor and critic networks. In order to counteract performance degradation due to dynamic platoon factors, the impact of dynamic target vehicle speeds and previous control strategies is incorporated into the definitions of Markov Decision Process (MDP), CCC problem formulation, and vehicle dynamics analysis. Simulation results show that our proposed intelligent asymmetrical control algorithm is well-suited for dynamic traffic scenarios with network-induced delays and outperforms existing methods.
Full Text Finder