• Journal of the Korean Society for Precision Engineering
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• v.24 no.2 s.191
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• pp.47-57
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• 2007

The vibration environment essentially companied by vehicle operation on the road is determined by the shape of road surface, which is called profile. In general, the profile and severity of unpaved road is an important issue in the reliability of durability test for vehicles. In order to maintain severity of unpaved road, it is necessary to develop profilometer system. We developed profilometer system which is composed of data processing computer, power unit, air compressor and sensors. This paper focuses on the severity management of unpaved test courses using neural networks. This paper presents the maintenance range for cross-country course in CPG(Chang-won Proving Ground) and the evaluation of similarity degree between unpaved roads.

• Journal of the Korean Society of Systems Engineering
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• v.1 no.2
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• pp.26-33
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• 2005

In this paper, an algorithm is presented to recognize road based on unpaved test courses image. The road images obtained by a video camera undergoes a pre-processing that includes filtering, gray level slicing, masking and identification of unpaved test courses. After this pre-processing, a part of image is grouped into 27 sub-windows and fed into a three-layer feed-forward neural network. The neural network is trained to indicate the road direction. The proposed algorithm has been tested with the images different from the training images, and demonstrated its efficacy for recognizing unpaved road. Based on the test results, it can be said that the algorithm successfully combines the traditional image processing and the neural network principles towards a simpler and more efficient driver warning or assistance system.