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Abstract or Description
Highway driving can be more safe and reliable when maps contain lane-level detailed cartographic information. Such maps are a resource for driving-assistance systems, enabling them to provide human drivers with precise lane-by-lane advice.
This paper proposes new aerial image analysis algorithms that, fromhighway orthoimages, produce lane-level detailed maps. We analyze screenshots of road vectors to obtain the relevant spatial and photometric patterns of road image-regions. We then refine the obtained patterns to generate hypotheses about the true road-lanes. A roadlane hypothesis, since it explains only a part of the true road-lane, is then linked to other hypotheses to completely delineate boundaries of the true road-lanes. Finally, some of the refined image cues about the underlying road network are used to guide a linking process of road-lane hypotheses.
We tested the accuracy and robustness of our algorithms with high-resolution, intercity highway ortho-images. Experimental results show promise in producing lane-level detailed highway maps from ortho-image analysis – 89% of the true road-lane boundary pixels were successfully detected and 337 out of 417 true road-lanes were correctly recovered.