Vision transformers started making waves in deep learning by replacing the typical convolutional neural networks (CNN) in tasks like image classification, Object detection, segmentation etc. the implementation of vision transformers can be further extended to autonomous cars. As it was proven that pure transformer architecture can outperform CNNs when trained over a large dataset by using comparatively less computational resources [1]. These vision transformers can be implemented in selfdriving cars to calculate the optimal steering angle by capturing images of the surroundings. The vision transformers can take advantage of the attention mechanism to focus on the most important things in the image like road lanes, other vehicles, traffic signs, etc. and produce better results compared to CNN models. The paper discusses about implementation behavior cloning using vision transformers in a self-driving car which is simulated in a Udacity self-drive car simulator.

Keywords : Transformers, Self-Driving Vehicles, Vision Transformers, Convolutional Neural Networks CNN, Behavior Cloning.