A sliding window technique for vision-based autonomous navigation applications is a common approach to path mapping from extracted features. Mapping a path inside a captured image requires finding a series of waypoints representing the path. Previous approaches find these points by sliding a window along the path in fixed increments across one image dimension. After each slide, the center of the window in the other dimension is adjusted so that the window maximally covers the path in that area. These approaches, however, fails to map paths that experience sharp curvature since the windows slide along only one dimension. The method proposed herein uses a pseudo-derivative approach to sliding windows that improves upon the traditional technique by dynamically adjusting the windows along both image dimensions during each slide. In this method, the directional components of a vector representing the previous slide are used as a naive estimation to perform the current slide. If this fails to map the path, the vector direction is used to enlarge the window dimensions. The method was tested in the domain of autonomous vehicles for lane- following based on lane-markings. The algorithm proved to be successful with lanes possessing sharp curvature and discontinuities as compared to previous sliding window approaches.