Driving Model

Driving models aim to replicate human driving behavior for applications in autonomous vehicles and traffic simulation. Current research emphasizes end-to-end learning approaches, often integrating multimodal sensor data (vision, LiDAR) with techniques like differentiable optimal control, reinforcement learning (e.g., using Deep Q-Networks or Trust Region Policy Optimization), and large language models to improve decision-making and handle complex scenarios. These advancements focus on enhancing model accuracy, efficiency, and robustness, particularly in challenging situations like merging and navigating urban environments, ultimately contributing to safer and more efficient transportation systems.