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In-vehicle prediction of truck driver sleepiness: lane position variables
2007 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Drivers falling asleep behind the steering wheel are the cause of many traffic accidents, and the statistics show that the number of sleepiness related accidents are escalating. Commercial drivers represent a large part of the sleepiness accident statistics, probably depending on much time spent on the road, long driving hours and the monotonous character of the roads traveled. Systems for sleepiness detection exist but the evidence to judge their applications and performance is inadequate. Sleepiness detection from cameras monitoring the driver and other driver related measures can be hard and expensive to implement. A system only using variables that could be measured from the vehicle itself, preferably using already existing sensors, would be desirable. The assignment of this master thesis project, commissioned by Scania CV AB in Södertälje, was to investigate the possibility to develop an algorithm that detects a sleepy driving behavior, using in-vehicle variables only. This project is a continuation of a previous master thesis project that investigated a patent claiming to be able to detect inattentive driving. The authors came to the conclusion that two of the variables in the patent showed promising results that should be further investigated. These were to be tried out in this project, along with other variables proved to predict driver sleepiness, by performing extensive tests. Quantitative testing, where 22 subjects drove a simulator while sleep deprived, enabled the collection of ten raw variables, measured from either the steering wheel or lane position. Examples of raw variables are steering wheel torque, yaw angle rate and lateral acceleration. These were combined in different ways to form 17 transformed variables that according to literature had shown to be correlated with a sleepy driving behavior, like the number of lane exceedances or the variance of lateral position. To be able to judge the performance of the different transformed variables, a reliable measure of the driver’s actual sleepiness was needed. A subjective measure called Karolinska Sleepiness Scale (KSS) was chosen, where the drivers estimate their own sleepiness on a 1-9 scale. The best version of each transformed variable was optimized compared to the KSS and forward selection with regression analysis was used to extinguish which variables should be combined to make the best formula to detect sleepiness. Since some transformed variables were not defined for all time intervals, different formulas had to be created depending on which variables that was available. This created a selection model where six different formulas were used. The algorithm performance was judged and it proved to give good results. The formulas combined in the algorithm make correct classifications, sleepy or alert driver, in more than 87 % of the cases when sleepiness threshold was set to eight, with a low false alarm rate of less than one percent. This is a promising result considering that only in-vehicle variables were used. A better performance would probably come from combining the detection from in- vehicle variables with another sleepiness measure. The project is done in collaboration Jens Berglund from Linköping University. The work was divided during the literature study and the identification of the transformed variables, where this report focused on the lane position measurements and frequency analysis of the raw variables and Berglund (2007) addressed the steering wheel related measurers. The result and conclusion came from the combination of the steering wheel variables, lane position variables and frequency analysis variables.

Place, publisher, year, edition, pages
Keyword [en]
Technology, Sleepiness detection, lane position, raw variables, transformed variables, truck drivers, algorithms
Keyword [sv]
URN: urn:nbn:se:ltu:diva-50180ISRN: LTU-EX--07/107--SELocal ID: 77635883-a0a6-4ca7-bd3d-51abf618ce72OAI: diva2:1023537
Subject / course
Student thesis, at least 30 credits
Educational program
Media Engineering, master's level
Validerat; 20101217 (root)Available from: 2016-10-04 Created: 2016-10-04Bibliographically approved

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