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[abstFig src='/00280006/11.jpg' width='300' text='Road detection method with HOG and SVM' ] This paper describes a road area detection method using a support vector machine (SVM) and histogram of oriented gradient (HOG) features. The boundary lines have many features, such as changes in height, color, and brightness, but these are sensitive to noise. In terms of robustness, it is difficult to match road boundary lines with the boundary lines on 2D maps. Localization methods using texture matching are accurate, but they have disadvantages related to adapting to changes in the environment. We therefore decided to make a classifier to differentiate road areas from other areas by detecting the road plane. First, we calculate the HOG features from range data acquired by 3D LiDAR. We then create the road area classifier by applying SVM. Finally, we evaluate the basic performance of the proposed method in simulation and in the real world.

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This paper proposes a driving safety evaluation method based on the parameter analysis of a driver model. Since the driving abilities, such as reaction, recognition and judgment time decrease with aging, it is difficult for elderly drivers to avoid the traffic accidents instantly. Therefore, the proactive safety driving is important for the elderly drivers to prevent the accidents. Then, the safety evaluation criteria of a driving behavior are useful to encourage the driver to keep proactive safety driving. And these criteria are useful to develop an advanced driver assistance system for elderly drivers. To evaluate the driving behavior, we use the parameters of a risk potential model which is the driver model to represent the speed and course control around the risk factors. Since the risk potential model represents the driving behavior by using a small number of the parameters, we make the assumption that the parameters are highly abstract the driving behavior and it is useful for analyzing the safety level of the driver. To evaluate the assumption experimentally, we collect the actual driving data of elderly, general and normative drivers for analyzing the parameters of the risk potential model and the safety level of the driver. Finally, the results show that the parameters of the risk potential model can be used to indicate the features of the driving behavior of the elderly drivers.

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