Driving Lane Detection Based on Recognition of Road Boundary Situation

This paper presents the method that recognizes the road boundary situation from a single image and detects a driving lane based on the recognition result. Driving lane detection is important for lateral motion control of the vehicle and it usually realized based on lane mark detection. However, there are some roads where lane marks such as white lines are not drawn. Also, when the road is covered with snow, lane marks cannot be seen. In these cases, it's necessary to detect the boundary line between the roadside object and the road surfaces. Since traffic lanes are divided by various roadside objects, such as curbs, grass, walls and so on, it's difficult to detect all kinds of road boundary including lane marks by a single algorithm. Therefore, we propose the method which changes the driving lane detection method according to the road boundary situation. At first, the situation of the road boundary is identified as some classes, such as white line, curb, grass and so on, by the Convolutional Neural Network (CNN). Then, based on this result, the lane mark or the boundary between the road surface and the roadside object is detected as the lane boundary. When a clear lane mark is drawn on a road, this situation is identified as a class of "White line" and a lane mark is detected as a lane boundary. On the other hand, when a lane mark is not present, this situation is identified as the other class and the boundary of the roadside object corresponding to the identified class is detected as the lane boundary. Experimental results using the KITTI dataset and our own dataset show the effectiveness of the proposed method. In addition, the result of the proposed method is compared with the boundary of the road area extracted by some semantic segmentation method.