  Cooperative autonomous driving plays a pivotal role in improving road
capacity and safety within intelligent transportation systems, particularly
through the deployment of autonomous vehicles on urban streets. By enabling
vehicle-to-vehicle communication, these systems expand the vehicles
environmental awareness, allowing them to detect hidden obstacles and thereby
enhancing safety and reducing crash rates compared to human drivers who rely
solely on visual perception. A key application of this technology is vehicle
platooning, where connected vehicles drive in a coordinated formation. This
paper introduces a vehicle platooning approach designed to enhance traffic flow
and safety. Developed using deep reinforcement learning in the Unity 3D game
engine, known for its advanced physics, this approach aims for a high-fidelity
physical simulation that closely mirrors real-world conditions. The proposed
platooning model focuses on scalability, decentralization, and fostering
positive cooperation through the introduced predecessor-follower "sharing and
caring" communication framework. The study demonstrates how these elements
collectively enhance autonomous driving performance and robustness, both for
individual vehicles and for the platoon as a whole, in an urban setting. This
results in improved road safety and reduced traffic congestion.
