Statistical characterization of the visual characteristics of painted lane markings

Most vision-based systems for lane detection and tracking use painted lane markings as the visual cues which determine the location of the camera relative to the lane. Almost all of the work that has been done in the area of evaluating the performance of these systems has focused on the accuracy of the recovered lane geometry. Reliability of feature detection as a function of intrinsic marking properties, ambient lighting and weather conditions, and viewing geometry is an equally important aspect of algorithm performance which must be explored if progress is to continue in this area of research. This paper reports a small scale effort to attack one aspect of this problem, the automated characterization of the intrinsic visual properties of white painted lane markings. Images of the right lane marking are taken by a camera mounted in an trailer enclosure towed behind a vehicle, allowing control of the lighting conditions. The intensity histogram of each image is examined to select a threshold which is used to classify each pixel as pavement or stripe. The edges of the white stripe are located using robust estimation and a shared vanishing point constraint. Once the stripe edges are located in an image, stripe properties such as width, brightness, and contrast with the pavement are calculated