Road Detection through Supervised Classification

TL;DR

This paper presents a new annotated dataset and a supervised classification framework for road detection using gray-scale images in autonomous driving.

Contribution

It introduces an annotated urban road dataset and a supervised classification method with hand-crafted features for road detection.

Findings

Successful creation of an urban road dataset

Effective supervised classification for road detection

Potential for improved autonomous navigation

Abstract

Autonomous driving is a rapidly evolving technology. Autonomous vehicles are capable of sensing their environment and navigating without human input through sensory information such as radar, lidar, GNSS, vehicle odometry, and computer vision. This sensory input provides a rich dataset that can be used in combination with machine learning models to tackle multiple problems in supervised settings. In this paper we focus on road detection through gray-scale images as the sole sensory input. Our contributions are twofold: first, we introduce an annotated dataset of urban roads for machine learning tasks; second, we introduce a road detection framework on this dataset through supervised classification and hand-crafted feature vectors.