1 research outputs found
Road Detection through Supervised Classification
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