@article{ref1,
title="Online driving anomaly detection based on parameter estimation of intelligent driver model",
journal="Transactions of Society of Automotive Engineers of Japan",
year="2023",
author="Suzuki, Hironori and Ohkubo, Shouma",
volume="54",
number="2",
pages="300-305",
abstract="This paper aims to develop an online driving abnormality detection system based on a data-driven approach. It assumes that driving abnormalities are reflected in vehicle acceleration and attempts to estimate the parameters of a non-linear intelligent driver model (IDM) by using a dual particle filter (DPF). For normal driving, DPF can accurately reproduce acceleration by estimating only two of the five model parameters and assuming the remaining parameters are constant. However, if there were situations that could not be reproduced accurately with only two parameter estimates, it can be defined that the driving behavior was so unusual and far from normal that the remaining three parameters cannot be treated as constants. Numerical analysis of the data collected in the driving simulator experiment showed that abnormal driving was significantly detected regardless of the hazardous situation and that the detection sensitivity was high and accurate.<p /> <p>Language: ja</p>",
language="ja",
issn="0287-8321",
doi="10.11351/jsaeronbun.54.300",
url="http://dx.doi.org/10.11351/jsaeronbun.54.300"
}