Automated drowsiness detection for improved driving safety

Several approaches were proposed for the detection and prediction of drowsiness. The approaches can be categorized as estimating the fitness of duty, modeling the sleep-wake rhythms, measuring the vehicle based performance and online operator monitoring. Computer vision based online operator monitoring approach has become prominent due to its predictive ability of detecting drowsiness. Previous studies with this approach detect driver drowsiness primarily by making preassumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to datamine actual human behavior during drowsiness episodes. Automatic classifiers for 30 facial actions from the Facial Action Coding system were developed using machine learning on a separate database of spontaneous expressions. These facial actions include blinking and yawn motions, as well as a number of other facial movements. In addition, head motion was collected through automatic eye tracking and an accelerometer. These measures were passed to learning-based classifiers such as Adaboost and multinomial ridge regression. The system was able to predict sleep and crash episodes during a driving computer game with 96% accuracy within subjects and above 90% accuracy across subjects. This is the highest prediction rate reported to date for detecting real drowsiness. Moreover, the analysis revealed new information about human behavior during drowsy drivin

Driver Alertness Detection Research Using Capacitive Sensor Array

The research project compared and analyzed physiological and performance data for 13 subjects driving a vehicle simulator. Each subject drove the simulator for morning, afternoon, and late night sessions. These sessions were intended to represent alertness conditions during an “awake” baseline period and the secondary and primary circadian sleep cycle periods. The sessions were approximately one hour, two hours, and two or three hours in length, respectively. With one exception, the subjects had experienced normal sleep the night before the test. Five men and eight women participated, ranging in age from 25 to 59. Physiological data included: real-time PERCLOS (percentage of slow-eye closure over one minute) using an infrared-reflective camera; head position coordinates using an overhead capacitive sensor array; and video of the right front of the subject’s face. Performance data included: vehicle speed, lane departures, lane deviation, and steering/turn signal data. The research manager maintained logs of unusual circumstances such as departing the roadway, falling asleep at the wheel, excessive speeding, etc. Head position data was analyzed and compared to the videos. A multi-element algorithm was developed which captured patterns of head motion found to be characteristic of drowsiness. The algorithm output was compared to roadway departures noted in the research manager’s logs of unusual events. The comparison showed a capability of advance detection of about 87% of driver roadway departures with a false positive rate of about 15%

Promoting Public Health and Safety: A Predictive Modeling Software Analysis on Perceived Road Fatality Contributory Factors

Extensive literature search was conducted to computationally analyze the relationship between key perceived road fatality factors and public health impacts, in terms of mortality and morbidity. Heterogeneous sources of data on road fatality 1970-2005 and that based on interview questionnaire on European road drivers’ perception were sourced. Computational analysis was performed on these data using the Multilayer Perceptron model within the dtreg predictive modeling software. Driver factors had the highest relative significance. Drivers played significant role as causative agents of road accidents. A good degree of correlation was also observed when compared with results obtained by previous researchers. Sweden, UK, Finland, Denmark, Germany, France, Netherlands, and Austria, where road safety targets were set and EU targets adopted, experienced a faster and sharper reduction of road fatalities. However, Belgium, Ireland, Italy, Greece and Portugal experienced slow, but little reduction in cases of road fatalities. Spain experienced an increase in road fatalities possibly due to road fatalities enhancing factors. Estonia, Slovenia, Cyprus, Hungry, Czech Republic, Slovakia and Poland experienced a fluctuating but decreasing trend. Enforcement of road safety principles and regulations are needed to decrease the incidences of fatal accidents. Adoption of the EU target of -50% reductions of fatalities in all countries will help promote public health and safety

The reliability of sensing fatigue from neurophysiology

To date no-study has tested the reproducibility of electroencephalography (EEG) changes that occur during driver fatigue. For the EEG changes to be useful in the development of a fatigue sensing and countermeasure device the EEG response during each onset period of fatigue in individuals needs to be reproducible. The aim of the present study was to investigate the reproducibility of the EEG changes during fatigue in professional drivers in order to identify the feasibility of the EEG measure for a fatigue sensor. Twenty professional drivers were assessed during two separate sessions of a driver simulator task

Drowsy driver data acquisition system

This thesis focuses on detecting the drowsiness of a driver based on differentiation of the EEG signal activity between the eyes open and eyes closed states. Here, it is observed that there is a significant increase \u27in a 10 Hz component of the alpha rhythm activity when the subject under test closes his / her eyes. This phenomenon was observed when electrodes were attached to the occipital region. A more desirable approach is to develop a non-intrusive measurement based on a multiturn differential coil combination utilizing a low noise high gain amplifier. The system developed here used an 80,000 turn 2 coil differential combination. A 10 Hz band pass amplifier with a gain of 68 db confirmed the assumed changes when electrodes were used. However, when differential coils were used (80,000 differential coils), the system failed to validate the expected changes. Due to insufficient sensitivity, it was impossible to reach a conclusion and determine whether the increased 10 Hz activity corresponded to brain signals or increased feedback gain resulting in an internal oscillation within the high gain amplification of the developed system. Further studies are suggested to reduce the losses due to magnetic core material and design an amplifier with a lower noise figure. The system developed utilized a DaqCard-1200 data acquisition card and MATLAB for signal processing

A Light on Physiological Sensors for Efficient Driver Drowsiness Detection System

International audienceThe significant advance in bio-sensor technologies hold promise to monitor human physiologicalsignals in real time. In the context of public safety, such technology knows notable research investigations toobjectively detect early stage of driver drowsiness that impairs driver performance under various conditions.Seeking for low-cost, compact yet reliable sensing technology that can provide a solution to drowsy stateproblem is challenging. While some enduring solutions have been available as prototypes for a while, many ofthese technologies are now in the development, validation testing, or even commercialization stages. Thecontribution of this paper is to assess current progress in the development of bio-sensors based driver drowsinessdetection technologies and study their fundamental specifications to achieve accuracy requirements. Existingmarket and research products are then ranked following the discussed specifications. The finding of this work isto provide a methodology to facilitate making the appropriate hardware choice to implement efficient yet lowcostdrowsiness detection system using existing market physiological based sensors