AcademicArticleSCO_0041530757
Overview
abstract
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© 2000 IEEE. In this work, we present a statistical analysis applied to different sensors on the driver's impairment detection problem. Our goal is to get a minimal number of discriminating sensors to match the requirements of an industrial prototype. The signals coming from these group of sensors are used to create artificial variables based on several mathematical transformations (wavelets, standard deviations) in order to fusion this information at a first level. Our statistical study is based on several steps: 1) the observation of the variation of the mean and variance of the different variables, 2) the test of Hypotheses F concerning a population's variance and then, an hypotheses test based on Student's t distribution for the means, 3) Principal Components Analysis (PCA); and 4) General Performance of the Diagnosis system using or not the different variables. This study has been performed using experimental data coming from 10 drivers in real experiments involving fatigue drivers at a closed circuit and over a motorway. These experiments had been realised using the CopiTech demonstrator which is equipped with a group of sensors measuring physiologic, mechanics and environmental status in real time. The analyse is validated by the physical state of the driver based on EEG. Results stress that the better discriminate sensors are: a) Lateral Position, b) steering wheel angle, and c) vehicle speed.