The detection of driving space is the most fundamental step in intelligent vehicle control. This research paper proposes a generic vision based algorithm for identifying driving surfaces in various indoor and outdoor environments. In this paper, instead of relying on a static model for demarcating the boundaries of the driving surfaces, we propose a novel algorithm that provides an adaptive method to detect a drivable surface in any environment. The uniqueness of the proposed algorithm lies in the robustness of the adaptive model that caters for changes in the environment. These changes may be in the form of light composition, off road disturbances, on road static and dynamic objects, shadows and variations in texture for indoor environment. It basically provides a highly dynamic online mechanism for changing the parameters of the Canny Edge Enhancement algorithm. This enables us to accurately determine the starting point and orientation of the driving surface boundary. Subsequently weighted average is used on the candidate edges to optimize the edge detection results. Experiments were carried out on our university's Intelligent DRIving System (IDRIS) for outdoor environments and on P3AT for indoor purposes. The experimentation results show that the proposed method can detect the driving surface boundaries in real-time for various different environments.