Augmented Mixed Vehicular Platoon Control With Dense Communication Reinforcement Learning for Traffic Oscillation Alleviation

Traffic oscillations present significant challenges to road transportation systems, resulting in reduced fuel efficiency, heightened crash risks, and severe congestion. Recently emerging Augmented Intelligence of Things (AIoT) technology holds promise for enhancing traffic flow through vehicle-road cooperation. A representative application involves using Deep Reinforcement Learning (DRL) techniques to control Connected Autonomous Vehicle (CAV) platoons to alleviate traffic oscillations. However, uncertainties in Human-Driven Vehicles (HDVs) driving behavior and the random distribution of CAVs make it challenging to achieve effective traffic oscillation alleviation in the IoT environment. Existing DRL-based mixed vehicular platoon control strategies underutilize downstream traffic data, impairing CAVs’ ability to predict and mitigate traffic oscillations, leading to inefficient speed adjustments and discomfort. This paper proposes a dense communication cooperative RL policy for mixed vehicular platoons to address these challenges. It employs a parameter-sharing structure and a dense information flow topology, enabling CAVs to proactively respond to traffic oscillations while accommodating arbitrary vehicle distributions and communication failures. Experimental results demonstrate superior performance of the proposed strategy in driving efficiency, comfort, and safety, particularly in scenarios involving multi-vehicle cut-ins or cut-outs and communication failures.