Combining Priors, Appearance, and Context for Road Detection

Abstract—Detecting the free road surface ahead of a moving vehicle is an important research topic in different areas of com-puter vision, such as autonomous driving or car collision warning. Current vision-based road detection methods are usually based solely on low-level features. Furthermore, they generally assume structured roads, road homogeneity, and uniform lighting condi-tions, constraining their applicability in real-world scenarios. In this paper, road priors and contextual information are introduced for road detection. First, we propose an algorithm to estimate road priors online using geographical information, providing relevant initial information about the road location. Then, contextual cues, including horizon lines, vanishing points, lane markings, 3-D scene layout, and road geometry, are used in addition to low-level cues derived from the appearance of roads. Finally, a generative model is used to combine these cues and priors, leading to a road detec-tion method that is, to a large degree, robust to varying imaging conditions, road types, and scenarios. Index Terms—Illuminant invariance, lane markings, road detection, road prior, road scene understanding, vanishing point, 3-D scene layout. I