  Traffic intersections present significant challenges for the safe and
efficient maneuvering of connected and automated vehicles (CAVs). This research
proposes an innovative roadside unit (RSU)-assisted cooperative maneuvering
system aimed at enhancing road safety and traveling efficiency at intersections
for CAVs. We utilize RSUs for real-time traffic data acquisition and train an
offline reinforcement learning (RL) algorithm based on human driving data.
Evaluation results obtained from hardware-in-loop autonomous driving
simulations show that our approach employing the twin delayed deep
deterministic policy gradient and behavior cloning (TD3+BC), achieves
performance comparable to state-of-the-art autonomous driving systems in terms
of safety measures while significantly enhancing travel efficiency by up to
17.38% in intersection areas. This paper makes a pivotal contribution to the
field of intelligent transportation systems, presenting a breakthrough solution
for improving urban traffic flow and safety at intersections.
