The Community for Technology Leaders
Green Image
Issue No. 04 - July-Aug. (2012 vol. 27)
ISSN: 1541-1672
pp: 57-62
Chunzhao Guo , Toyota Central R&D Laboratories
Seiichi Mita , Toyota Technological Institute
This article presents a semantic graph representation for vision-based intelligent vehicle systems. It can represent the traffic scene with both perceptional meaning of object classes and the spatial relations between them. Using such graphs offers superior performance in terms of both accuracy and robustness. Furthermore, a stereovision-based road boundary estimation system, designed for navigating an intelligent vehicle through challenging traffic scenarios, is introduced, which exemplifies the advantages of the semantic graph.
Road vehicles, Semantics, Image edge detection, Intelligent vehicles, Image segmentation, Stereo vision, Hidden Markov models, roadmap, intelligent vehicles, semantic graph, stereo vision

S. Mita and C. Guo, "A Semantic Graph of Traffic Scenes for Intelligent Vehicle Systems," in IEEE Intelligent Systems, vol. 27, no. , pp. 57-62, 2012.
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