Detecting drivable space in traffic scene understanding

Traffic scene understanding and perception is an important issue for intelligent vehicles and autonomous mobile robots. Especially in dynamic environments, the determination of drivable space and moving obstacles are fundamental requirement for road scene understanding. In this paper, we propose a vision-based approach combining road geometry and color features to percept road and moving obstacles in a dynamic environment from the camera mounted on the host vehicle. In the approach, a free road surface is detected first based on feature similarity search using statistical feature analysis (SFA) combined with a breadth-first search (BFS) algorithm to segment different intensity similarity regions in a road image. Then, the similarity between the road model (its color distribution) and the road region candidates is expressed by a metric derived from the Bhattacharyya distance. With the free road surface, the relative distance of preceding obstacles can easily be estimated using the obstacle scanning mechanism (OSM) and online camera calibration scheme. The experimental results have shown that the proposed approach can detect the drivable region and estimate the relative distance of preceding obstacles in real traffic scenes.