Real-Time road detection is a demanding task in active safety and autodriving of vehicle. Vision is the most popular sensing method for road detection, but it is easier to be influenced by illumination, shadows, shield, etc. To overcome those difficulties, the light detection and ranging (Lidar) sensor is a good choice for road detection. For either the urban environment or the rural areas, the important feature of the road is that the road surface could be approximately represented by some planes. Hence, the Lidar's scanning plane and the road surface intersect at a set of line segments, and a line segment means a road plane. To extract the line segments from a scan, a least mean square problem is proposed, which is solved by a distance segment approach and an iterative line fitting approach. To eliminate the perception dead zone, some suitable historical data are adopted combining with the fresh data. In order to initial the road search range, a hypothesis is given that the area under the vehicle is road. The road detection is achieved for the scans from the close to the distance and in the meanwhile the search range of the next scan is updated. A lot of experiment results demonstrate the robustness and efficiency of the proposed approach for real-time auto-driving of the intelligent vehicles.
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