Lane Change Decision

Autonomous lane-changing decision-making is a critical area of research in autonomous driving, aiming to develop safe and efficient algorithms for vehicles to change lanes in dynamic traffic environments. Current research heavily utilizes deep reinforcement learning (DRL), employing architectures like Q-learning and actor-critic methods, often incorporating multi-agent approaches to handle interactions between vehicles and account for both individual and collective benefits. This research is crucial for improving traffic flow, enhancing fuel efficiency, and ultimately increasing the safety and reliability of autonomous vehicles on roadways.