Vision-Based Real-Time Lane Marking Detection and Tracking

Abstract

Detection and tracking of lane marking is essential for driving safety and intelligent vehicle. In this paper, an algorithm is presented which allows detection and tracking of multiple lane markings. Edge points cue is used to detect the lane marking and a road orientation estimation method is used to delete the edge lines which are impossible attribute to lane markings. In order to select the candidate lane marking, a confidence measures method is proposed. Then a finite-state machine decides whether or not a lane marking is really detected by fusion multi-frame detection results. Specifically, a particle filter is used to predict the future values of the lane marking model parameters, based on past observations. With particle filtering and confidence measures method, lane markings on various road scenes are detected and tracked. Experimental in different conditions, including illumination, weather and road, demonstrates its effectiveness and robustness. The algorithm runs in real-time at rates of about 30 Hz.