Efficient Autonomous Driving

Efficient autonomous driving research centers on developing systems that are both safe and operationally efficient. Current efforts focus on improving perception through multimodal data fusion (e.g., radar, lidar, cameras) and advanced algorithms like graph neural networks and deep reinforcement learning for robust trajectory prediction and planning, often incorporating vehicle-to-everything (V2X) communication. These advancements aim to enhance the reliability and safety of autonomous vehicles, ultimately leading to improved traffic flow and reduced accident rates. The development of robust and efficient methods for handling uncertainty, such as adversarial attacks and partial observations, is also a key area of investigation.