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Inference of Friction Coefficient between Tire and Road

By 学 田口, Manabu Taguchi, 勝也 松永, Katsuya Matsunaga, 裕二 松木, Yuji Matsuki, 和晃 合志, Kazuaki Goshi, 和則 志堂寺 and Kazunori Shidoji


Car accidents occur when headway distance is shorter than the stopping distance. Stopping distance consists of reaction distance and breaking distance. It is necessary for drivers to understand the stopping distance in real time to preventing traffic accidents from occurring. To estimate the breaking distance in real time, it is required to presume friction coefficient, which is one of the factors in determining breaking distance and it was difficult to calculate in real time in driving. So we examined whether it was possible to presume friction coefficient in real time. In this study, we analyzed the noise that was generated by friction between tire and road to presume friction coefficient in non real time, using Fast Fourier Transform (FFT) and a back propagation algorithm for the neural network. We could presume the proper friction coefficient and breaking distance from the analysis. It suggests that we can presume the friction coefficient in real time

Topics: 摩擦係数, Friction coefficient, 実時間, Real time, 停止距離, Stopping distance, 遮断距離, Breaking distance, ニューラルネットワーク, Neural network, 交通事故, Traffic accident
Publisher: Faculty of Information Science and Electrical Engineering, Kyushu University
Year: 2001
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