Real World Autonomous

Real-world autonomous systems aim to develop vehicles capable of navigating and operating in complex, unpredictable environments without human intervention. Current research heavily focuses on improving perception and planning capabilities, employing techniques like end-to-end learning, behavior cloning, hybrid imitation learning, and reinforcement learning with world models, often utilizing convolutional neural networks and transformers. These advancements are crucial for enhancing safety, efficiency, and reliability in autonomous driving, impacting both the scientific understanding of complex AI systems and the development of practical self-driving technologies.