• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.158-164
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• 2015

Lane detection is one of the key parts among autonomous vehicle technologies because lane keeping and path planning are based on lane detection. Camera is used for lane detection but there are severe limitations such as narrow field of view and effect of illumination. On the other hands, Lidar sensor has the merits of having large field of view and being little influenced by illumination because it uses intensity information. Existing researches that use methods such as Hough transform, histogram hardly handle multiple lanes in the co-occuring situation of lanes and road marking. In this paper, we propose a method based on RANSAC and regularization which provides a stable and precise detection result in the co-occuring situation of lanes and road marking in highway scenarios. This is performed by precise lane point extraction using circular model RANSAC and regularization aided least square fitting. Through quantitative evaluation, we verify that the proposed algorithm is capable of multi lane detection with high accuracy in real-time on our own acquired road data.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.1
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• pp.108-114
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• 2012

We propose a dispersive finite-difference time domain(FDTD) algorithm suitable for the electromagnetic analysis of the human body. In this work, the dispersion relation of the human body is modeled by a quadratic complex rational function(QCRF), which leads to an accurate and efficient FDTD algorithm. Coefficients(involved in QCRF) for various human tissues are extracted by applying a weighted least square method(WLSM), referred to as the complex-curve fitting technique. We also presents the FDTD formulation for the QCRF-based dispersive model in detail. The QCRFbased dispersive model is significantly accurate and its FDTD implementation is more efficient than the counterpart of the Cole-Cole model. Numerical examples are used to show the validity of the proposed FDTD algorithm.