An Improved Road Network Modeling and Map Matching for Precise Vehicle Localization

Wang, Chenhao; Hu, Zhencheng; Hamada, Naoko; Uchimura, Keiichi

doi:10.1007/978-3-642-10817-4_17

Cited by 3 publications

(1 citation statement)

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“…GPS Information: In recent years, the wide range of highquality location information combined with affordable global positioning system (GPS) receivers has enabled mass-market mapping systems, such as vehicle navigation (e.g., localizing the vehicle within a map) [14] or visual guidance using augmented reality [15]. However, these approaches use information from the current image to perform their task, and thus, they fail with extreme image acquisition conditions.…”

Section: Related Workmentioning

confidence: 99%

Combining Priors, Appearance, and Context for Road Detection

Álvarez

López

Gevers

et al. 2014

IEEE Trans. Intell. Transport. Syst.

101

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Abstract-Detecting the free road surface ahead of a moving vehicle is an important research topic in different areas of computer 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 conditions, 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 detection method that is, to a large degree, robust to varying imaging conditions, road types, and scenarios.

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Section: Related Workmentioning

confidence: 99%