Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical evaluations of recent deep learning advances. Computer vision, combined with deep learning, has the potential to bring about a relatively inexpensive, robust solution to autonomous driving. To prepare deep learning for industry uptake and practical applications, neural networks will require large data sets that represent all possible driving environments and scenarios. We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection. We show how existing convolutional neural networks (CNNs) can be used to perform lane and vehicle detection while running at frame rates required for a real-time system. Our results lend credence to the hypothesis that deep learning holds promise for autonomous driving.
@article{arxiv.1504.01716,
title = {An Empirical Evaluation of Deep Learning on Highway Driving},
author = {Brody Huval and Tao Wang and Sameep Tandon and Jeff Kiske and Will Song and Joel Pazhayampallil and Mykhaylo Andriluka and Pranav Rajpurkar and Toki Migimatsu and Royce Cheng-Yue and Fernando Mujica and Adam Coates and Andrew Y. Ng},
journal= {arXiv preprint arXiv:1504.01716},
year = {2015}
}