Visual perception is the most critical input for driving decisions. In this study, our aim is to understand relationship between saliency and driving decisions. We present a novel attention-based saliency map prediction model for making braking decisions This approach constructs a holistic model to the driving task and can be extended for other driving decisions like steering and acceleration. The proposed model is a deep neural network model that feeds extracted features from input image to a recurrent neural network with an attention mechanism. Then predicted saliency map is used to make braking decision. We trained and evaluated using driving attention dataset BDD-A, and saliency dataset CAT2000.
@article{arxiv.2002.11020,
title = {See, Attend and Brake: An Attention-based Saliency Map Prediction Model for End-to-End Driving},
author = {Ekrem Aksoy and Ahmet Yazıcı and Mahmut Kasap},
journal= {arXiv preprint arXiv:2002.11020},
year = {2020}
}