Connected Behavior

Connected behavior research focuses on understanding and leveraging the interactions between connected and automated vehicles (CAVs) to improve traffic efficiency and safety. Current research emphasizes developing advanced algorithms, such as multi-agent reinforcement learning and deep learning models with attention mechanisms, for tasks like trajectory prediction, collaborative perception, and coordinated decision-making in complex traffic scenarios. This work is significant because it addresses critical challenges in autonomous driving, paving the way for safer and more efficient transportation systems by enabling CAVs to effectively share information and coordinate their actions. The development of robust and reliable models for connected behavior is crucial for the successful deployment of CAV technology.