In-Vehicle Prediction of Truck Driver Sleepiness : Steering Related Variables

Abstract: In this master thesis project quantitative testing in a truck simulator with 22 participants were conducted during which ten in-vehicle variables were measured. Examples of measured variables are steering wheel torque, lateral position and yaw angle. These measured variables were then used to calculate 17 independent variables that all to some extent explain the sleepiness level of the driver. The drivers’ sleepiness level was measured using the Karolinska Sleepiness Scale (KSS) in order to judge the performance of the independent variables. The combination of the 17 independent variables that best explain the sleepiness level of the driver is then extracted using multiple regression analysis with forward selection. Sometimes some of the independent variables are not defined; therefore different models were created to handle all possible combinations of valid and invalid independent variables. The final system uses six different models to predict the sleepiness level of the driver. The performance of the final system showed promising results. The system can correctly classify the drivers in approximately 87% of the cases. The number of occasions when the system classify the driver as sleepy when he/she is still alert is very low, approximately 0.7%.