Road Environment

Road environment research focuses on enabling robust autonomous navigation in unstructured off-road terrains, addressing challenges like varied terrain types, limited visibility, and unpredictable vehicle-terrain interactions. Current research heavily utilizes deep learning models, including convolutional neural networks (CNNs) and transformers, for tasks such as traversability estimation, semantic mapping, and path planning, often incorporating techniques like reinforcement learning and self-supervised learning to improve efficiency and generalization. These advancements are crucial for improving the safety and reliability of autonomous vehicles in diverse environments, with applications ranging from agricultural robotics to planetary exploration.