Road Detection through Supervised Classification
This work addresses road detection for autonomous vehicles, but it is incremental as it applies existing methods to a new dataset.
The paper tackles road detection for autonomous driving by using supervised classification on a new annotated dataset of urban roads, achieving results through hand-crafted feature vectors.
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.