The Ego-Lane Detection under rainy condition

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Generation of Lane-marker candidate-imageThe Lane-Marker of the road in both fine and rainy weather proved to have the feature that the intensity of the image became peak around the Lane-Marker area by examining the profile of road surface and the area. By using the feature, we tried to extract the Lane-Marker's candidate-regions which have high probability to be white lines, where input sequential images taken from the camera installed on the ruuning car.Then, by speeding up our system with the image processor, we could also adopt the information of previous frames. As a result, our system could generate more hopefull Lane-Marker candidate-image. Robust Recognition of Ego-LaneWe considered a road boundary as an arc model, and we extracted the road boundary by our template matching process between the Lane-Marker candidate-image. The measurement of road's radius of curvature and the position and moving direction of the car could be obtained, that is, the Ego-Lane could be recognized, where the direction of the car was parallel with the road's line.Then, we improved the above method by adding a line model to road model, etc. At first, the straight line model were used as a template since we assumed the Lane-Maker was approaching to a straight line near the car, and the position and moving direction of the car could be obtained. Next, by using these information and applying the arc model, the road's radius of curvature can be obtained.As the necessary model parameters were increased, the reseaching space of the template matching process became large. But we could reduce the space by using our method in order which was expalined in the previous statements, and ccould get a better capability to recognize the Ego-Lane.Meanwhile, the motion of wiper in rainy weather is also considered. Since the motion disturbs a good scene for detection of Lane-Marker, we proposed a method to eliminate the scene with wiper so as not to reduce the detection capability.