A driver support system should provide assistance and security to the driver. For navigaton tasks it is neccessary to determine position of the ego vehicle relative to the road. One of the principal approaches is to detect road boundaries and lanes using a vision system in the vehicle. Within the European research project "Secure Propulsion using Advanced Redundant Control (SPARC)" different approaches of lane detection are developed to meet the needs of real traffic situations. The vision module presented here is based on several image filters that provide diverse information about the environment. A set of hypotheses about the state of the system is generated by a probabilistic particle filter. Assuming a predefined model of the road the particles are tested according to image filters to infere the best belief vehicle position. Emphasis was placed on extracting relevant information from the scene and efficient testing. In particular, a new testing module based on Canny edge filter and Hough transform increased the accuracy and robustness of estimation. Perfomance of the vision module was tested under various real-road conditions.