Lane detection for driver assistance and intelligent vehicle applications

Since the 1990s, there have been various lane detection systems designed to suit various road conditions such as highways, urban and rural roads. Current reserach has shown to predominantly detect only 1 lane marking set in real time and is unable to provide additional lanes for support in situations such as lane closures, road work conditions and car accidents that may obstruct the driving lane. These driver assistance systems are limited in their ability to assist the driver in these conditions. In this paper we propose a method to determine the markings of 2 lanes which can be used in conjunction with a detection system to detect obstacles present in front of the driver on the road. We first determine a suitable threshold for the perspective image to extract these road markings and signs, then apply morphological transformations to counter possible ‘deviations’ that may arise in this feature extraction technique. This method provides a robust approach to lane detection and works considerably well in various weather conditions. The resulting images show that the method developed can be used for lane-departure warning, as well as for obsatcle detection, in driver assistance.