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.

[1]  Eiji Mizutani,et al.  Totally model-free reinforcement learning by actor-critic Elman networks in non-Markovian domains , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[2]  Leon O. Chua,et al.  Spatial logic algorithms using basic morphological analogic CNN operations , 1996, Int. J. Circuit Theory Appl..

[3]  Leon O. Chua,et al.  Linear and nonlinear circuits , 1987 .

[4]  Leon O. Chua,et al.  Detecting simple motion using cellular neural networks , 1990, IEEE International Workshop on Cellular Neural Networks and their Applications.

[5]  L. Chua,et al.  An analytic method for designing simple cellular neural networks , 1991 .

[6]  Dean Pomerleau,et al.  Rapidly Adapting Artificial Neural Networks for Autonomous Navigation , 1990, NIPS.

[7]  Leon O. Chua,et al.  The CNN paradigm , 1993 .

[8]  Lin-Bao Yang,et al.  Cellular neural networks: theory , 1988 .