Analysis and detection of driver fatigue caused by sleep deprivation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2007.Includes bibliographical references (leaves 167-181).Human errors in attention and vigilance are among the most common causes of transportation accidents. Thus, effective countermeasures are crucial for enhancing road safety. By pursuing a practical and reliable design of an Active Safety system which aims to predict and avoid road accidents, we identify the characteristics of drowsy driving and devise a systematic way to infer the state of driver alertness based on driver-vehicle data. Although sleep and fatigue are major causes of impaired driving, neither effective regulations nor acceptable countermeasures are available yet. The first part of this thesis analyzes driver-vehicle systems with discrete sleep-deprivation levels, and reveals differences in the performance characteristics of drivers. Inspired by the human sleep-wake cycle mechanism and attributes of driver-vehicle systems, we design and perform human-in-the-loop experiments in a test bed built with STISIM Drive, an interactive fixed-based driving simulator. In the simulated driving, participants were given various driving tasks and secondary tasks for both non and partially sleep-deprived conditions. This experiment demonstrates that sleep deprivation has a greater effect on rule-based tasks than on skill-based tasks; when drivers are sleep-deprived, their performance of responding to unexpected disturbances degrades while they are robust enough to continue such routine driving tasks as straight lane tracking, following a lead vehicle, lane changes, etc. In the second part of the thesis we present both qualitative and quantitative guidelines for designing drowsy driver detection systems in a probabilistic framework based on the Bayesian network paradigm and experimental data.(cont.) We consider two major causes of sleep, i.e., sleep debt and circadian rhythm, in the framework with various driver-vehicle parameters, and also address temporal aspects of drowsiness and individual differences of subjects. The thesis concludes that detection of drowsy driving based on driver-vehicle data is a feasible but difficult problem which has diverse issues to be addressed; the ultimate challenge lies in the human operator.by Ji Hyun Yang.Ph.D

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

Detecting driver fatigue using heart rate variability: A systematic review

Driver fatigue detection systems have potential to improve road safety by preventing crashes and saving lives. Conventional driver monitoring systems based on driving performance and facial features may be challenged by the application of automated driving systems. This limitation could potentially be overcome by monitoring systems based on physiological measurements. Heart rate variability (HRV) is a physiological marker of interest for detecting driver fatigue that can be measured during real life driving. This systematic review investigates the relationship between HRV measures and driver fatigue, as well as the performance of HRV based fatigue detection systems. With the applied eligibility criteria, 18 articles were identified in this review. Inconsistent results can be found within the studies that investigated differences of HRV measures between alert and fatigued drivers. For studies that developed HRV based fatigue detection systems, the detection performance showed a large variation, where the detection accuracy ranged from 44% to 100%. The inconsistency and variation of the results can be caused by differences in several key aspects in the study designs. Progress in this field is needed to determine the relationship between HRV and different fatigue causal factors and its connection to driver performance. To be deployed, HRV-based fatigue detection systems need to be thoroughly tested in real life conditions with good coverage of relevant driving scenarios and a sufficient number of participants

SUBJECTIVE METHODS FOR ASSESSMENT OF DRIVER DROWSINESS

The paper deals with the issue of fatigue and sleepiness behind the wheel, which for a long time has been of vital importance for the research in the area of driver-car interaction safety. Numerous experiments on car simulators with diverse measurements to observe human behavior have been performed at the laboratories of the faculty of the authors. The paper provides analysis and an overview and assessment of the subjective (self-rating and observer rating) methods for observation of driver behavior and the detection of critical behavior in sleep deprived drivers using the developed subjective rating scales

The effect of electronic word of mouth communication on purchase intention moderate by trust: a case online consumer of Bahawalpur Pakistan

The aim of this study is concerned with improving the previous research finding complete filling the research gaps and introducing the e-WOM on purchase intention and brand trust as a moderator between the e-WOM, and purchase intention an online user in Bahawalpur city Pakistan, therefore this study was a focus at linking the research gap of previous literature of past study based on individual awareness from the real-life experience. we collected data from the online user of the Bahawalpur Pakistan. In this study convenience sampling has been used to collect data and instruments of this study adopted from the previous study. The quantitative research methodology used to collect data, survey method was used to assemble data for this study, 300 questionnaire were distributed in Bahawalpur City due to the ease, reliability, and simplicity, effective recovery rate of 67% as a result 202 valid response was obtained for the effect of e-WOM on purchase intention and moderator analysis has been performed. Hypotheses of this research are analyzed by using Structural Equation Modeling (SEM) based on Partial Least Square (PLS). The result of this research is e-WOM significantly positive effect on purchase intention and moderator role of trust significantly affects the relationship between e-WOM, and purchase intention. The addition of brand trust in the model has contributed to the explanatory power, some studied was conduct on brand trust as a moderator and this study has contributed to the literature in this favor. significantly this study focused on current marketing research. Unlike past studies focused on western context, this study has extended the regional literature on e-WOM, and purchase intention to be intergrading in Bahawalpur Pakistan context. Lastly, future studies are recommended to examine the effect of trust in other countries allow for the comparison of the findings

Väsymyksen vaikutus ajosuoriutumiseen liukkaalla tiellä

Tavoitteet Kuljettajan väsymys vaikuttaa keskeisesti niin ajokykyyn kuin onnettomuusriskiinkin. Ajo-olosuhteet taas määrittävät sen miten kuljettajat kokevat väsymyksen ajon aikana. Haastavissa olosuhteissa ajaminen saattaa lisätä väsyneiden kuljettajien tarkkaavaisuutta ajotapahtumaa kohtaan, mutta tämä voi lisätä myös kognitiivista taakkaa ja aiheuttaa näin lisää väsymystä. Tämän tutkimuksen tavoitteena on selvittää miten liukkaalla tiellä ajaminen vaikuttaa yhdessä univajeesta johtuvan väsymyksen kanssa kuljettajan ajosuoriutumiseen. Menetelmät Kaksitoista miespuolista koehenkilöä (i’iltään 19–21) ajoivat ajosimulaattorissa 52.5 km matkan neljässä eri asetelmassa (päivä- ja yöaikaan sekä kuivalla ja liukkaalla tiellä). Subjektiivista uneliaisuutta mitattiin Karolinska Sleepiness Scalen avulla ja fysiologista väsymystä silmänräpäysten pituuden perusteella elektro-okulografiaa käyttäen. Kuljettajien ajosuoriutumista arvioitiin kolmella muuttujalla: auton sivuttaissijainnin keskipoikkeama ajoradalla, ohjausliikkeiden amplitudien keskiarvo ja ohjausliikkeiden huippunopeuden keskiarvo. Ajosession jälkeen koehenkilöt ajoivat liikennekartiopujottelutehtävän, jossa onnistumista arvioitiin erikseen. Tulokset Liukkaalla tiellä ajaminen paransi univajeesta kärsivien kuljettajien ajosuoriutumista kaikilla kolmella muuttujalla mitattuna. Kolmisuuntainen yhdysvaikutus kuljettajan tilan, tieolosuhteiden ja ajan välillä oli merkitsevä vain subjektiivisen uneliaisuuden kohdalla. Univajeisten kuljettajien uneliaisuus lisääntyi nopeammin liukkaalla tiellä, mutta tämä ei vaikuttanut heidän ajosuoriutumiseensa. Johtopäätökset Haastavissa olosuhteissa ajaminen voi lisätä jo valmiiksi väsyneiden kuljettajien väsymystä, mutta muutokset suorituskyvyssä voivat näkyä vasta viipeellä. Suuret yksilölliset erot niin väsymys- kuin ajo-olosuhdevasteissa vaativat lisätutkimusta.Objectives Fatigue is a major factor affecting driving performance and traffic accident risk. Driving conditions influence how people experience fatigue while driving. Driving in demanding conditions may increase vigilance in tired drivers; however, it may also increase cognitive load and become an additional source of fatigue. The current study investigated how driving on a slippery road interacts with fatigue caused by sleep deprivation and how it influences driving performance. Methods Twelve male participants (aged 19–21) drove 52.5 km in a driving simulator in four different conditions (day vs night and dry vs slippery road). Subjective sleep-related fatigue was measured with the Karolinska Sleepiness Scale and physiological fatigue in blink durations with electro-oculography. Three measures were used for driver performance: standard deviation of lateral position, mean steering wheel movement amplitude and mean steering wheel movement peak velocity. After each driving session, participants negotiated a cone track. The success rate for this task was analysed separately. Results Driving on slippery roads improved performance in all three performance metrics in sleep-deprived drivers. The three-way interaction between driver condition, road condition and time-on-task was significant for subjective sleep-related fatigue but not for performance. Sleep-deprived drivers became increasingly sleepy over time when driving in slippery conditions; however, this did not negatively affect their performance. Conclusions Driving in demanding weather conditions can increase the fatigue experienced by drivers; however, this change may not be initially detectable in performance. Large individual variability in response to both fatigue and driving conditions requires further research

Sensors and Systems for Monitoring Mental Fatigue: A systematic review

Mental fatigue is a leading cause of motor vehicle accidents, medical errors, loss of workplace productivity, and student disengagements in e-learning environment. Development of sensors and systems that can reliably track mental fatigue can prevent accidents, reduce errors, and help increase workplace productivity. This review provides a critical summary of theoretical models of mental fatigue, a description of key enabling sensor technologies, and a systematic review of recent studies using biosensor-based systems for tracking mental fatigue in humans. We conducted a systematic search and review of recent literature which focused on detection and tracking of mental fatigue in humans. The search yielded 57 studies (N=1082), majority of which used electroencephalography (EEG) based sensors for tracking mental fatigue. We found that EEG-based sensors can provide a moderate to good sensitivity for fatigue detection. Notably, we found no incremental benefit of using high-density EEG sensors for application in mental fatigue detection. Given the findings, we provide a critical discussion on the integration of wearable EEG and ambient sensors in the context of achieving real-world monitoring. Future work required to advance and adapt the technologies toward widespread deployment of wearable sensors and systems for fatigue monitoring in semi-autonomous and autonomous industries is examined.Comment: 19 Pages, 3 Figure