TY - JOUR
T1 - Detection of driver's anomaly behavior using wireless 3D-accelerometers
AU - Tada, Masahiro
AU - Naya, Futoshi
AU - Okada, Masaya
AU - Noma, Haruo
AU - Toriyama, Tomoji
AU - Kogure, Kiyoshi
N1 - Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2008
Y1 - 2008
N2 - In this paper, we propose a method for directly measuring and analyzing driving behavior using wireless SD-accelerometers. Whereas existing systems installed many sensors into a specially equipped test vehicle, to indirectly measure driving behaviors, our method uses wireless 3D-accelerometers attached to a driver for directly measuring his/her behaviors in a vehicle. After applying independent component analysis (ICA) to reduce car-caused noise, our method detects anomalies in driving behaviors using one-class SVM. By directly measuring driving behavior, our method allows to point out anomalies in driving behaviors characteristics to novice drivers with precision of 71.1% and recall of 73.9%.
AB - In this paper, we propose a method for directly measuring and analyzing driving behavior using wireless SD-accelerometers. Whereas existing systems installed many sensors into a specially equipped test vehicle, to indirectly measure driving behaviors, our method uses wireless 3D-accelerometers attached to a driver for directly measuring his/her behaviors in a vehicle. After applying independent component analysis (ICA) to reduce car-caused noise, our method detects anomalies in driving behaviors using one-class SVM. By directly measuring driving behavior, our method allows to point out anomalies in driving behaviors characteristics to novice drivers with precision of 71.1% and recall of 73.9%.
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U2 - 10.1527/tjsai.23.105
DO - 10.1527/tjsai.23.105
M3 - Article
AN - SCOPUS:42949165012
SN - 1346-0714
VL - 23
SP - 105
EP - 116
JO - Transactions of the Japanese Society for Artificial Intelligence
JF - Transactions of the Japanese Society for Artificial Intelligence
IS - 3
ER -