Valet Parking

Automated valet parking (AVP) systems aim to autonomously park vehicles, addressing the "last-mile" challenge in autonomous driving. Current research heavily focuses on robust perception in challenging parking environments using multi-sensor fusion (cameras, LiDAR, IMU) and bird's-eye-view (BEV) representations, often incorporating deep learning models like convolutional neural networks and transformers for tasks such as object detection, semantic segmentation, and localization. These advancements are crucial for improving the safety and reliability of AVP systems, paving the way for wider adoption in real-world applications and contributing significantly to the development of safer and more efficient autonomous driving technologies.