Cooperative Lane

Cooperative lane changing (CLC) research focuses on developing algorithms that enable autonomous vehicles (AVs) to safely and efficiently change lanes by coordinating their maneuvers with other vehicles, minimizing disruption to traffic flow. Current research heavily utilizes multi-agent reinforcement learning (MARL) techniques, such as actor-critic networks and proximal policy optimization (PPO), to train AVs to make optimal lane-change decisions in complex, mixed-traffic environments. These advancements aim to improve traffic efficiency, safety, and driver comfort, contributing significantly to the development of safer and more effective autonomous driving systems.