Color-based road detection and its evaluation on the KITTI road benchmark

Road detection is one of the key issues of scene understanding for Advanced Driving Assistance Systems (ADAS). Recent approaches has addressed this issue through the use of different kinds of sensors, features and algorithms. KITTI-ROAD benchmark has provided an open-access dataset and standard evaluation mean for road area detection. In this paper, we propose an improved road detection algorithm that provides a pixel-level confidence map. The proposed approach is inspired from our former work based on road feature extraction using illuminant intrinsic image and plane extraction from v-disparity map segmentation. In the former research, detection results of road area are represented by binary map. The novelty of this improved algorithm is to introduce likelihood theory to build a confidence map of road detection. Such a strategy copes better with ambiguous environments, compared to a simple binary map. Evaluations and comparisons of both, binary map and confidence map, have been done using the KITTI-ROAD benchmark.

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