This manuscript develops a visual lane detection algorithm using the Hough transform to detect strong lines in a road image as candidates for lane boundaries. The search space in the Hough transform is reduced by searching for lane boundaries where they were detected in the previous video frame. The perspective transform is applied to determine the position and orientation of candidate lines, which are trusted as true boundaries if the detected lane width falls within a specified tolerance of the actual width. Results from a nearly 8-minute long video of highway driving in rain indicate that lane boundaries are correctly identified in 95% of the images. Detection errors occur primarily during lane changes and poor lighting when entering underpasses. Including data from

inertial measurements, location on digital maps, and steering direction would help to reduce or eliminate the instances of incorrectly detected lane location.