Estimating the Maximum Road Friction Coefficient with Uncertainty Using Deep Learning

Song, Seungmok; Min, Kyushik; Park, Jongwon; Kim, Hayoung; Huh,Kunsoo

doi:10.1109/ITSC.2018.8569965

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Estimating the Maximum Road Friction Coefficient with Uncertainty Using Deep Learning

Song, Seungmok;
Min, Kyushik;
Park, Jongwon;
Kim, Hayoung;
Huh,Kunsoo

Citations

SCOPUS

27

초록

Estimating the maximum road friction coefficient with high reliability in various driving situation is one of the most significant issue in the field of automotive research. Numerous study has been done in this field, however, because of the several limitations and problems, researches in this field are still active. This paper uses a deep learning method to estimate the maximum road friction coefficient. The network of this study is mainly composed of convolutional neural network, recurrent neural network with deep ensemble architecture. In addition, through Prioritized Batch Selection (PBS), which is proposed in this paper, the training result is dramatically enhanced. The performance of the proposed estimator is verified in simulation of test driving scenarios.

키워드

Friction; Intelligent systems; Intelligent vehicle highway systems; Recurrent neural networks; Roads and streets; Uncertainty analysis; Convolutional neural network; Driving situations; High reliability; Learning methods; Road friction coefficients; Test drivings; Deep learning

제목: Estimating the Maximum Road Friction Coefficient with Uncertainty Using Deep Learning

저자: Song, Seungmok; Min, Kyushik; Park, Jongwon; Kim, Hayoung; Huh,Kunsoo

DOI: 10.1109/ITSC.2018.8569965

발행일: 2018-12

유형: Conference Paper

저널명: IEEE International Conference on Intelligent Transportation Systems

권: 2018-November

페이지: 3156 ~ 3161