  This survey explores the adaptation of visual transformer models in
Autonomous Driving, a transition inspired by their success in Natural Language
Processing. Surpassing traditional Recurrent Neural Networks in tasks like
sequential image processing and outperforming Convolutional Neural Networks in
global context capture, as evidenced in complex scene recognition, Transformers
are gaining traction in computer vision. These capabilities are crucial in
Autonomous Driving for real-time, dynamic visual scene processing. Our survey
provides a comprehensive overview of Vision Transformer applications in
Autonomous Driving, focusing on foundational concepts such as self-attention,
multi-head attention, and encoder-decoder architecture. We cover applications
in object detection, segmentation, pedestrian detection, lane detection, and
more, comparing their architectural merits and limitations. The survey
concludes with future research directions, highlighting the growing role of
Vision Transformers in Autonomous Driving.
