Road lane marker extraction by motion-detector CNNs

This paper describes an application of motion-detector CNN (cellular nonlinear/neural networks) to road lane-marker extraction for automobile position assessment. We assume that an automated vehicle drives down a freeway by tracking lane markers. A vehicle vision system, while taking road snapshots, needs to extract lane marker information from sequential road images. This feature-extraction task requires fast image processing to timely adjust vehicle heading under changing road conditions. Since the CNN has been employed for a variety of image processing, we have tested a motion-detector CNN for the lane-marker extraction. We shall demonstrate the power of the motion-detector CNN and present its current limitations, as well as its promising possibilities. We believe this application example may help pave the way for future autonomous vehicle control.