Lane detection and tracking in challenging environments based on a weighted graph and integrated cues

Real-time lane detection and localization is one of the key issues for many intelligent transportation systems. In this paper, we present a lane detection and tracking approach designed to work in challenging environments where lane boundaries may be low-contrast and changeful with noise due to a number of factors such as wear, type, lighting and weather conditions, etc. In the method, a sophisticated cascade lane feature detector is applied to cope with challenging environments at the very beginning of the detection and a weighted graph is subsequently constructed by integrating intensity as well as geometry cues, reflecting the confidence of each pixel as a lane feature. In order to deal with complex road geometry, we employ Catmull-Rom splines to represent lane boundaries and the left and right lane boundaries are estimated separately in a tracking process using particle filter based on the weighted graph. In the proposed framework, unlike most of previous methods we lay a strong emphasis on accurate and effective lane feature detection since the challenges happen in the very first step of lane detection, and accurately detected lane features can be expected to reduce the complexity and difficulty, as well as improve the accuracy of lane detection in the following steps.