Human Driven Vehicle

Human-driven vehicle (HDV) behavior modeling and prediction are crucial for developing safe and efficient autonomous driving systems. Current research focuses on improving trajectory prediction using deep learning architectures like convolutional and recurrent neural networks, often combined with reinforcement learning algorithms (e.g., DDPG, TD3, PPO) to optimize autonomous vehicle (AV) decision-making in mixed traffic scenarios. This work aims to enhance AV safety and efficiency by accurately anticipating HDV actions, considering factors like driver cooperation, risk assessment, and communication between vehicles. The resulting advancements have implications for improving traffic flow, reducing accidents, and ultimately shaping the future of transportation systems.