Autonomous Off Road Driving

Autonomous off-road driving research aims to enable vehicles to navigate unstructured, unpredictable terrains safely and efficiently at high speeds. Current efforts focus on improving perception through sensor fusion (e.g., camera and LiDAR) and robust mapping techniques, often employing deep learning architectures like convolutional neural networks (CNNs) and transformers, alongside model predictive control (MPC) and reinforcement learning (RL) for planning and control. These advancements are crucial for expanding the capabilities of autonomous vehicles in diverse applications, such as search and rescue, agriculture, and planetary exploration, by addressing challenges like terrain variability, limited sensing, and the need for robust, real-time decision-making.