Multi-Lane Detection and Tracking Using Vision for Traffic Situation Awareness

Situation awareness in the transport system has been significant for understanding the cause of traffic congestion, potential risks of accidents, and so on, and many efforts have been made to recognize surrounding situations by vehicle onboard cameras for low-cost sensing. These methods use computer vision technologies to recognize objects in the vicinity of the vehicle in the video image. However, to correctly recognize the positional relationship between the surrounding objects and the vehicle, lane detection, particularly multiple lane detection, should robustly be performed. In this paper, we propose a method for detecting multiple lanes from video images taken by onboard cameras. Since frame-by-frame spatial lane detection does not often work in a severe environment on multi-lane roads, the proposed method leverages temporal change detection, which enables complementing such lanes that are not detected in a frame and eliminating wrongly-detected ones for robust detection and tracking. In the experimental using the video images, we have achieved an accuracy of more than 90% in multi-lane environments with different conditions such as lane boundary occlusion by other vehicles, nighttime, rain, side road junctions.Besides, we achieved 96.47% accuracy to detect lanes on which vehicles are driving, while 88.70% in the comparative method.