Urban Autonomous Driving

Urban autonomous driving research aims to develop vehicles capable of safely and efficiently navigating complex city environments. Current efforts concentrate on improving perception (e.g., using multi-LiDAR systems and transformer-based scene representation learning), planning (leveraging model predictive control, hierarchical reinforcement learning, and imitation learning with personalized adaptations), and control, often integrating these aspects for enhanced performance and robustness. This field is significant for its potential to revolutionize transportation, improving safety, efficiency, and accessibility, while also driving advancements in artificial intelligence, robotics, and sensor technologies.