Autonomous Driving Application

Autonomous driving application research centers on developing safe and reliable self-driving systems, primarily focusing on robust perception, prediction, and planning capabilities. Current efforts involve refining deep learning models, including transformers and convolutional neural networks, for tasks like object detection, lane recognition, and motion prediction, often incorporating multi-modal data from cameras, LiDAR, and radar. These advancements aim to improve the accuracy, efficiency, and safety of autonomous vehicles, impacting both the scientific understanding of complex systems and the practical deployment of self-driving technology in real-world scenarios.