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The detection of free space and obstacles in a scene is essential for safe driving. Among sensors for environment perception, a stereo-vision is promising as it provides 3D perception information. Moreover current decreasing price of a camera module makes a vision sensor attractive, taking into account the consumer product. In this paper we propose an algorithm for the detection of free space and obstacles in a scene, using a stereo-vision. Contrary to previous generic obstacle detection methods that have a strong assumption of camera placement with respect to the ground or estimate ground parameters for free space/obstacles separation, our method analyzes 3D reconstructued structures of an environment. The environment is represented on the proposed probabilistic volume polar grid map. The probabilistic volume polar grid map is a simplified representation of the environment, using the volumes generated from the reconstructed point hypotheses. The volume polar grid map combines with an image plane, so that the reconstruction uncertainty representation and the free space computation are straightforward. The map is analyzed by the two ways: (1) the analysis of the structural characteristics of volumes and (2) the traversability analysis. The traversability analysis gives the free space and the nearest obstacle in each search direction, while the structural characteristics analysis provides potential obstacles in a more wide range. The results from both analysis modules are combined to provide information of the free space, obstacles and potential obstacles in the given scene, which is useful for safe driving. Our system is expected to be used as a driving assistance system.