Scalable Autonomous Driving

Scalable autonomous driving aims to develop robust and generalizable self-driving systems capable of navigating diverse and unpredictable environments. Current research heavily emphasizes the development of world models, often leveraging deep learning architectures like graph neural networks and vision-based approaches (e.g., processing point clouds and bird's-eye-view representations) for perception, prediction, and planning. These advancements focus on improving the efficiency and safety of autonomous driving through pre-training on large datasets and incorporating techniques like self-supervised learning and imitation learning. The ultimate goal is to create reliable and widely deployable autonomous vehicles, significantly impacting transportation and logistics.