Detecting Driver Sleepiness Using Consumer Wearable Devices in Manual and Partial Automated Real-Road Driving

Driver sleepiness constitutes a well-known traffic safety risk. With the introduction of automated driving systems, the chance of getting sleepy and even falling asleep at wheel could increase further. Conventional sleepiness detection methods based on driving performance and behavior may not be available under automated driving. Methods based on physiological measurements such as heart rate variability (HRV) becomes a potential solution under automated driving. However, with reduced task load, HRV could potentially be affected by automated driving. Therefore, it is essential to investigate the influence of automated driving on the relation between HRV and sleepiness. Data from real-road driving experiments with 43 participants were used in this study. Each driver finished four trials with manual and partial automated driving under normal and sleep-deprived condition. Heart rate was monitored by consumer wearable chest bands. Subjective sleepiness based on Karolinska sleepiness scale was reported at five min interval as ground truth. Reduced heart rate and increased overall variability were found in association with severe sleepy episodes. A binary classifier based on the AdaBoost method was developed to classify alert and sleepy episodes. The results indicate that partial automated driving has small impact on the relationship between HRV and sleepiness. The classifier using HRV features reached area under curve (AUC) = 0.76 and it was improved to AUC = 0.88 when adding driving time and day/night information. The results show that commercial wearable heart rate monitor has the potential to become a useful tool to assess driver sleepiness under manual and partial automated driving

Motivation to continue driving while sleepy: the effects on sleepiness and performance levels

Driver sleepiness contributes to a substantial proportion of fatal and severe road crashes and potentially contributes to a greater proportion of less serious crashes. A number of survey studies have reported that some drivers choose to continue to drive while sleepy despite being aware of an increasing level of sleepiness. Additionally, drivers’ motivations to continue driving while sleepy is a stronger predictor of sleepy driving behaviours, overshadowing crash risk perception of sleepy driving. While several survey studies have quantified self-reported aspects of continuing to drive while sleepy, there appears to be lack of studies that examine the actual psychophysiological and performance sequela of continuing to drive when sleepy. The current study sought to examine the effect motivating oneself to apply extra effort to the task of driving when sleepy on physiological and subjective sleepiness and driving performance. In total, 18 participants undertook a 60 minute Hazard Perception test on four occasions – on the four occasions, the participants motivation level (motivated and non-motivated) and sleepiness level (sleepy-alert) were experimentally manipulated. Physiological, subjective, and performance indices of sleepiness were obtained with respect to the effects of the manipulation of motivation and sleepiness levels. The results suggest that no effect of motivation was observed in the Hazard Perception test data. Physiological and subjective sleepiness were both greater in the sleepy conditions than the alert conditions and over the duration of both tests, sleepiness levels increased regardless of the motivation or sleepiness conditions. Considered together, these findings suggest that sleepiness is very resilient to motivations to stay alert and improve performance levels. The present results suggest that continuing to drive while sleepy whereby the drivers motivate themself to apply extra effort to the task of driving is a dangerous driving behaviour

Sleepy drivers on a slippery road : A pilot study using a driving simulator

Sleepy drivers have problems with keeping the vehicle within the lines, and might often need to apply a sudden or hard corrective steering wheel movement. Such movements, if they occur while driving on a slippery road, might increase the risk of ending off road due to the unforgiving nature of slippery roads. We tested this hypothesis. Twelve young men participated in a driving simulator experiment with two counterbalanced conditions; dry versus slippery road x day (alert) versus night (sleepy) driving. The participants drove 52.5 km on a monotonous two-lane highway and rated their sleepiness seven times using the Karolinska Sleepiness Scale. Blink durations were extracted from an electrooculogram. The standard deviation of lateral position and the smoothness of steering events were measures of driving performance. Each outcome variable was analysed with mixed-effect models with road condition, time-of-day and time-on-task as predictors. The Karolinska Sleepiness Scale increased with time-on-task (p < 0.001) and was higher during night drives (p < 0.001), with a three-way interaction suggesting a small increased sleepiness with driving time at night with slippery road conditions (p = 0.012). Blink durations increased with time-on-task (p < 0.01) with an interaction between time-of-day and road condition (p = 0.040) such that physiological sleepiness was lower for sleep-deprived participants in demanding road conditions. The standard deviation of lateral position increased with time-on-task (p = 0.026); however, during night driving it was lower on a slippery road (p = 0.025). The results indicate that driving in demanding road condition (i.e. slippery road) might further exhaust already sleepy drivers, although this is not clearly reflected in driving performance.Peer reviewe

Deteksi Mata Mengantuk pada Pengemudi Mobil Menggunakan Metode Viola Jones

Computer Vision is one of the branches of Image processing science that allows a combination of human beings, such as identifying an object like an eye and taking a decision. Many of the face detection systems use the Viola Jones method as an object detection method. The method of Viola Jones is known by having high speed and accuracy because it is useful to combine several concepts such as (Haar Features, Integral Image, AdaBoost, and Cascade Classifier) into a major method for detecting objects. The programming language used in this study uses the MATLAB programming language to facilitate the process of creating the system. The research aims to implement Viola Jones into a simple eye-sensing drowsiness system by utilizing the existing libraries in the MATLAB programming language. Once the system is completed, a system test is performed against the detected drowsiness detection characteristics. This eye drowsiness detection system aims to determine if the car rider is sleepy or not when driving with an input in the form of eye detection taken using a digital camera and then inserted into a language Programming GUI Matlab where the value is taken binary eyes, sleepy eyes and not sleepy that will be a reference that will be processed later so that it can produce the output of a warning sound to the rider of the sleepy car vehicle or not The sleepy automatically. The testing of the program gained an amount detected 7 eyes of 10 eyes by using BW 0255 level which is useful to accelerate a program to detect sleepy eyes

Managing driver fatigue: education or motivation?

Fatigue has been recognised as the primary contributing factor in approximately 15% of all fatal road crashes in Australia. To develop effective countermeasures for managing fatigue, this study investigates why drivers continue to drive when sleepy, and driver perceptions and behaviours in regards to countermeasures. Based on responses from 305 Australian drivers, it was identified that the major reasons why these participants continued to drive when sleepy were: wanting to get to their destination; being close to home; and time factors. Participants’ perceptions and use of 18 fatigue countermeasures were investigated. It was found that participants perceived the safest strategies, including stopping and sleeping, swapping drivers and stopping for a quick nap, to be the most effective countermeasures. However, it appeared that their knowledge of safe countermeasures did not translate into their use of these strategies. For example, although the drivers perceived stopping for a quick nap to be an effective countermeasure, they reported more frequent use of less safe methods such as stopping to eat or drink and winding down the window. This finding suggests that, while practitioners should continue educating drivers, they may need a greater focus on motivating drivers to implement safe fatigue countermeasures

Deteksi Rasa Kantuk Pengendara Kendaraan Bermotor Menggunakan Image Prosessing

Abstract - The number of traffic accidents in Indonesia is increasing. One of the main causes of this condition is drowsy drivers. Things like this need to be considered so that the number of accidents due to these factors can be avoided. Therefore, a research was conducted using a digital image processing system to detect driver sleepiness.Digital image processing is intended to determine whether the driver is not sleepy or while driving with input in the form of eye images taken using a digital camera and then entered into the Matlab programming language where the image is taken the value of bw of the sleepy eye area and not a reference image which will be processed with image processing such as cropping, grayscale, iris extraction, thresholding, and analyzed by the bwarea method compared with the image to be identified. The output is information on whether the driver is sleepy or not. Keywords: Bw area, Iris, Digital Image Processing, Thresholdin

A Sleep Apnea Program for Commercial Drivers

Sleepy driving is a major contributing factor to the motor vehicle crash risk associated with commercial drivers (Pack et al., 2006). In commercial vehicles, approximately 31% to 41% of major crashes can be linked to sleepy driving (Gurubhagavatula et al., 2004). An average non-fatality crash involving a commercial motor vehicle costs about $75,637 while a fatality crash averages about$3.54 million (Gurubhagavatula et al., 2004). A contributing factor to sleepy driving is obstructive sleep apnea (Pack et al., 2006). The purposes of this study were to: (1) determine how many commercial motor vehicle drivers referred to a sleep center as a result of a clinical positive screen by a commercial motor vehicle driver medical examiner had a true positive quantitative test for obstructive sleep apnea, and (2) to determine, on average, how many drivers treated for obstructive sleep apnea met minimum treatment requirements using positive airway pressure therapy at one week, one month, three months, six months and one year. The electronic medical records of 128 commercial motor vehicle drivers were reviewed for diagnosis of obstructive sleep apnea with the following findings: 19 (14.9%) had no clinically significant obstructive sleep apnea, 51 (39.8%) had mild to moderate obstructive sleep apnea, and 58 (45.3%) had moderate to severe obstructive sleep apnea. Of the original 83 drivers prescribed positive airway pressure therapy, 25 (30.1%) were meeting the minimal adherence goal at 1 year of treatment. Of drivers with moderate to severe obstructive sleep apnea, 21 (36.2%) of the original 58 prescribed treatment were meeting the adherence goal at 1 year while 4 (16%) of the original 25 drivers with mild to moderate obstructive sleep apnea were meeting this goal

Motorized Driving Safety System Using Eye Detection Analysis Method

Traffic accident, particularly two-wheeled vehicles, is a problem for the Government, especially the Resort Police Traffic Unit (Satlantas Polres). The factor that causes the traffic accident incident is divided into three types, namely human factor, vehicle factor, and road or environment factor. The human factor is the most common factor for an accident. Fatigue factor that causes someone to feel sleepy while driving often results in a traffic accident. Based on the problem, the researcher wanted to create a technology innovation of a motorized driving safety system in the form of a helmet. The researcher made an innovation of a helmet that can detect drowsiness through the driver's eye blink duration. The drowsiness will be detected by using a camera sensor. The camera sensor used was Open MV camera. The method used in detecting sleepy drivers was the eye detection analysis method. The method enable detection based on the data of the duration of eye condition when it is closed and open. The closed eye has a low RBG mean value of 110-113 and an RBG median value of 99-109. Whereas opened eye has a higher RGB mean value of 179-206 and RGB median value of 178-206. The result of the research showed that someone's sleepy condition occurred when closing their eyes for more than 0.4 seconds to 4 seconds. The helmet is also equipped with GPS to monitor the position in the event of an accident as an emergency response effort

Crash risk perception of sleepy driving and its comparisons with drink driving and speeding: Which behavior is perceived as the riskiest?

Objective: Driver sleepiness is a major crash risk factor but may be underrecognized as a risky driving behavior. Sleepy driving is usually rated as less of a road safety issue than more well-known risky driving behaviors, such as drink driving and speeding. The objective of this study was to compare perception of crash risk of sleepy driving, drink driving, and speeding. Methods: Three hundred Australian drivers completed a questionnaire that assessed crash risk perceptions for sleepy driving, drink driving, and speeding. Additionally, the participants' perceptions of crash risk were assessed for 5 different contextual scenarios that included different levels of sleepiness (low, high), driving duration (short, long), and time of day/circadian influences (afternoon, nighttime) of driving. Results: The analysis confirmed that sleepy driving was considered a risky driving behavior but not as risky as high levels of speeding (P <.05). Yet, the risk of crashing at 4 a.m. was considered as equally risky as low levels of speeding (10 km over the limit). The comparisons of the contextual scenarios revealed driving scenarios that would arguably be perceived as quite risky because time of day/circadian influences were not reported as high risk. Conclusions: The results suggest a lack of awareness or appreciation of circadian rhythm functioning, particularly the descending phase of circadian rhythm that promotes increased sleepiness in the afternoon and during the early hours of the morning. Yet, the results suggested an appreciation of the danger associated with long-distance driving and driver sleepiness. Further efforts are required to improve the community's awareness of the impairing effects from sleepiness and, in particular, knowledge regarding the human circadian rhythm and the increased sleep propensity during the circadian nadir

RFID lecturer availability system

In process of learning in polytechnic, having a meeting with a lecturer was an importance thing to make sure that all the task given are clearly understand and submitted on time. As such, RFID Lecturer Availability System (L.A.S) will enable to identify the availability of the lecturer for the students. This will enable the students to get inputs if the lecturer is available or having different work to be done. The advantage of LAS is that it is easy to be used and low costs. This enable an efficient product to ensure students are aware of lecturer availability before commencing any meetings or appointment. In future, the study will focus on the evaluation of the prototype in measuring the usefulness of the application developed