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Drowsy driving is a main cause of severe accidents. Drowsiness is responsible for 30% to 37% of fatal road accident in Iran. In this paper, driver drowsiness is detected based on features related to the steering wheel angle and the lateral position of the vehicle. Data from the vehicle and the virtual road are used to extract drowsiness features. Experimental results using a driving simulator are presented. Participants were 21 to 28 year-old males with a high tendency to sleep (Epworth Sleepiness Scale≥10). The subjects had to drive a lane keeping scenario on a long and monotonous virtual road in both drowsy and alert states. The drowsiness criteria are validated with Karolinska Sleepiness Scale (KSS) and video rating based on KSS measurements. The results illustrate that the phase diagram of the steering wheel angle (Ellipse criterion), the standard deviation of the steering wheel angle, and the mean and the standard deviation of the lateral position of the vehicle are highly correlated with drowsiness. The accuracy of the diagnosis was 77% for the Ellipse criterion, 76% for the standard deviation of the steering wheel angle, 67% for the standard deviation of the lateral position, and 65% for the mean value of the lateral position.

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In this paper, an active caster mechanism has been introduced for the purpose of vehicle handling enhancement. To this end, a 9-DOF nonlinear vehicle model, consisting of steering system dynamic equations, derived by using Kane dynamics method, and Magic Formula tyre model, is used for the simulation purposes. The effect of caster angle variations on the steady state response of the vehicle, was investigated in the next step. Based on the knowledge, extracted by the mentioned approach, a fuzzy logic controller (FLC) is designed for controlling caster angle. According to the yaw rate error, between actual and desired values, and the vehicle lateral acceleration, the controller produces the required caster angle in order to reach to stable state of the vehicle. The desired vehicle dynamics motion is assumed in the form of the steady motion of the bicycle model. Also the variation of caster angle was limited in a conventional range. During some critical maneuvers, the performance of the caster angle controller was tested and compared with the uncontrolled vehicle. Simulation results show that the caster variation control has a high capability to enhance vehicle handling dynamics.