An Empirical Evaluation of Deep Learning on Highway Driving

TL;DR

This paper empirically evaluates deep learning techniques for highway perception, demonstrating their effectiveness in real-time car and lane detection using large datasets, supporting their potential in autonomous driving.

Contribution

It provides a comprehensive empirical assessment of deep learning methods on highway data, highlighting their feasibility for real-time autonomous driving applications.

Findings

CNNs can perform lane and vehicle detection at real-time frame rates.

Deep learning methods show promise for robust highway perception.

Large datasets are essential for practical deep learning autonomous driving systems.

Abstract

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.