Road Driving

Autonomous off-road driving research focuses on developing robust control systems capable of navigating challenging, unstructured terrains at high speeds while ensuring safety. Current efforts leverage various machine learning techniques, including reinforcement learning (with model predictive control and risk-sensitive adaptations), and neuro-symbolic approaches that integrate physics-based models with neural networks, to improve trajectory planning and vehicle control. These advancements are driven by the need for more efficient and reliable autonomous systems in diverse applications, from agriculture and mining to search and rescue operations, and are supported by the development of large-scale, multi-modal datasets for model training and evaluation.