核心概念
Vision Transformers are revolutionizing Autonomous Driving by outperforming traditional neural networks, offering advanced capabilities for real-time scene processing.
摘要
Vision Transformers are reshaping the landscape of Autonomous Driving by leveraging their success in Natural Language Processing. They excel in tasks like object detection, lane detection, and segmentation, providing a comprehensive understanding of dynamic driving environments. The survey explores the structural components of Transformers, such as self-attention and multi-head attention mechanisms. It delves into the applications of Vision Transformers in 3D and 2D perception tasks, highlighting their impact on autonomous vehicle technology. Additionally, it discusses challenges, trends, and future directions for Vision Transformers in Autonomous Driving.
統計資料
"BERT, GPT, and T5 setting new standards in language understanding."
"Models like BERT have revolutionized Natural Language Processing."
"ViTs have significantly evolved showcasing their versatility."
"DETR extended principles to 3D object detection."
"PETR uses position embedding transformations for enhanced image features."
引述
"Transformers are gaining traction in computer vision."
"ViTs have significantly evolved showcasing their versatility."
"Vision Transformers offer promise for Autonomous Driving but face hurdles like data collection."