Dissertations / Theses on the topic 'Driving drowsiness'

Few attempts have been made to use physical and physiological driver characteristics to predict driver drowsiness. As a result, a reliable drowsy driver detection system has yet to be devised. Thus, the primary objectives of this research were to determine whether driving characteristics and response variables could be used to detect eyelid closure associated with edrowsiness, and. to provide ‘potential measures of driver· drowsiness. In. the study, eyelid closure was defined as the measurement standard of drowsiness. Eyelid closure, in studies conducted at Duke University, was a reliable measure of drowsiness. A computer simulated nighttime driving task introduced 90 minutes of typical highway driving to twenty driver/subjects seated ixx a moving-base driving simulator. Each driver/subject drove under two conditions--rested and after 19 hours of being awake. During the 90 minutes of driving, two types of low—level stimuli, steering wheel torque and front wheel displacement, were applied to the simulation. Responses to these stimuli as well as driving I measures from the intervals between stimuli were analyzed for variations associated with eyelid closure. Seventeen dependent variables were investigated.
Ph. D.
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The development of technologies for preventing drowsiness at the wheel is a major challenge in the field of accident avoidance systems. Preventing drowsiness during driving requires a method for accurately detecting a decline in driver alertness and a method for alerting and refreshing the driver. As a detection method, the authors have developed a system that uses image processing technology to analyse images of the road lane with a video camera integrated with steering wheel angle data collection from a car simulation system. The main contribution of this study is a novel algorithm for drowsiness detection and tracking, which is based on the incorporation of information from a road vision system and vehicle performance parameters. Refinement of the algorithm is more precisely detected the level of drowsiness by the implementation of a support vector machine classification for robust and accurate drowsiness warning system. The Support Vector Machine (SVM) classification technique diminished drowsiness level by using non intrusive systems, using standard equipment sensors, aim to reduce these road accidents caused by drowsiness drivers. This detection system provides a non-contact technique for judging various levels of driver alertness and facilitates early detection of a decline in alertness during driving. The presented results are based on a selection of drowsiness database, which covers almost 60 hours of driving data collection measurements. All the parameters extracted from vehicle parameter data are collected in a driving simulator. With all the features from a real vehicle, a SVM drowsiness detection model is constructed. After several improvements, the classification results showed a very good indication of drowsiness by using those systems.

Two previous studies performed at Virginia Tech have shown that it is feasible to detect drowsy drivers using driving performance and physiological measures. Therefore, assuming that drowsiness can be detected, it becomes important to develop methods (countermeasures) by which drivers can regain and maintain alertness. The current study was thus undertaken in an attempt to evaluate three subsidiary tasks which differed only in regard to input modality (auditory, tactual, or visual) in terms of: 1) the degree to which they aided the driver by maintaining or restoring alertness; and 2) the degree to which the responses to these tasks could be used to detect drowsiness. Subjective measures of drowsiness were also obtained to provide an additional source of verification of level of drowsiness. To accomplish these objectives, a total of 12 male and female driver-subjects drove a moving-base simulator continuously from 12:30 a.m. to 3:00 a.m. During this time, the subjects performed each of the subsidiary tasks for a 30-minute period; they also drove for a 30-minute period during which no subsidiary task was performed. During the simulated, nighttime, highway driving scenario, 20 driving performance, behavioral, and physiological measures were collected for each 3-minute driving interval, along with 5 subsidiary task measures and subjective alertness ratings. The experimental results indicated that none of the three subsidiary tasks provided an effective means of maintaining driver alertness. However, the results of a second series of discriminant analyses did indicate that driver impairment due to drowsiness could be reliably detected with linear combinations of subsidiary task and driving measures. In fact, promising discriminant models for the auditory and visual tasks were identified which employed a subsidiary task response measure of the number of correct responses to the subsidiary task during each 6-minute driving interval as well as a physiological measure of the subject's heart rate variance; these models showed overall classification error percentages as low as 3% and 8%. Finally, the analyses of the subjective alertness ratings indicated that subjects' ratings were not significantly affected by either the type of subsidiary task performed or time-on-task.
Ph. D.

Driver distraction has become a major concern for the U.S. Department of Transportation (US DOT).  Performance decrements are typically the result of driver distraction because attentional resources are limited, which are limited; fatigue and drowsiness limit attentional resources further.  The purpose of the current research is to gain an understanding of the relationship between mobile device use (MDU), fatigue, through driving time and time on duty, and drowsiness, through time of day and amount of sleep, for commercial motor vehicle drivers.  A re-analysis of naturalistic driving data was used to obtain information about the factors, MDU, safety-critical events (SCE), and normal driving epochs.  Odds ratios were used to calculate SCE risk for 6 mobile device use subtasks and each of the factors, which were divided into smaller bins of hours for more specific information.  A generalized linear mixed model and chi-square test were used to assess MDU for each factor and the associated bins.  Results indicated visually demanding subtasks were associated with an increase in SCE risk, but conversation on a hands-free cell phone decreased SCE risk.  There was an increase in SCE risk for visual manual subtasks for all bins in which analyses were possible.  Drivers had a higher proportion of MDU in the early morning (circadian low period) than all other times of day that were analyzed.  These results will be used to create recommended training and evaluate policy and technology and will help explain the relationship between MDU, fatigue, and drowsiness.
Master of Science

Driver distraction has become a major concern for the U.S. Department of Transportation (US DOT).  Performance decrements are typically the result of driver distraction because attentional resources are limited, which are limited; fatigue and drowsiness limit attentional resources further.  The purpose of the current research is to gain an understanding of the relationship between mobile device use (MDU), fatigue, through driving time and time on duty, and drowsiness, through time of day and amount of sleep, for commercial motor vehicle drivers.  A re-analysis of naturalistic driving data was used to obtain information about the factors, MDU, safety-critical events (SCE), and normal driving epochs.  Odds ratios were used to calculate SCE risk for 6 mobile device use subtasks and each of the factors, which were divided into smaller bins of hours for more specific information.  A generalized linear mixed model and chi-square test were used to assess MDU for each factor and the associated bins.  Results indicated visually demanding subtasks were associated with an increase in SCE risk, but conversation on a hands-free cell phone decreased SCE risk.  There was an increase in SCE risk for visual manual subtasks for all bins in which analyses were possible.  Drivers had a higher proportion of MDU in the early morning (circadian low period) than all other times of day that were analyzed.  These results will be used to create recommended training and evaluate policy and technology and will help explain the relationship between MDU, fatigue, and drowsiness.
Master of Science

In the human control of motor vehicles, there are situations regularly encountered wherein the vehicle operator becomes drowsy and fatigued due to the influence of long work days, long driving hours, or low amounts of sleep. Although various methods are currently proposed to detect drowsiness in the operator, they are either obtrusive, expensive, or otherwise impractical. The method of drowsy driving detection through the collection of Steering Wheel Movement (SWM) signals has become an important measure as it lends itself to accurate, effective, and cost-effective drowsiness detection. In this dissertation, novel technologies for drowsiness detection using Inertial Measurement Units (IMUs) are investigated and described. IMUs are an umbrella group of kinetic sensors (including accelerometers and gyroscopes) which transduce physical motions into data. Driving performances were recorded using IMUs as the primary sensors, and the resulting data were used by artificial intelligence algorithms, specifically Support Vector Machines (SVMs) to determine whether or not the individual was still fit to operate a motor vehicle. Results demonstrated high accuracy of the method in classifying drowsiness. It was also shown that the use of a smartphone-based approach to IMU monitoring of drowsiness will result in the initiation of feedback mechanisms upon a positive detection of drowsiness. These feedback mechanisms are intended to notify the driver of their drowsy state, and to dissuade further driving which could lead to crashes and/or fatalities. The novel methods not only demonstrated the ability to qualitatively determine a drivers drowsy state, but they were also low-cost, easy to implement, and unobtrusive to drivers. The efficacy, ease of use, and ease of access to these methods could potentially eliminate many barriers to the implementation of the technologies. Ultimately, it is hoped that these findings will help enhance traveler safety and prevent deaths and injuries to users.

Road traffic accidents are a serious burden to the health systems of many countries especially in South Africa. Research aimed at reducing traffic related accidents is of importance as traffic crashes are rated as the second leading cause of fatalities in South Africa and ninth in the world. Despite the extensive efforts into research and development of new technology, driver fatigue still remains a cause of vehicle accidents worldwide. Fatigue plays a role in up to 20% of vehicle accidents with many being serious or fatal. Numerous coping behaviours are employed by drivers to counteract the negative effects of fatigue. The most common coping behaviours include taking short naps, talking to passengers, listening to the radio, opening windows and drinking stimulants. Driving breaks have long been identified as an effective countermeasure against fatigue. Most research done in driving breaks has investigated the duration of the breaks, activity undertaken during the break and the frequency of the breaks taken outside the vehicle. However limited literature is available on the effectiveness of breaks in counteracting the effects of fatigue. The objective of the current study was aimed at assessing whether short duration breaks are an effective countermeasure against fatigue. Physiological, neurophysiological, subjective and performance measures were used as indicators for fatigue. Additional focus of the research was determining whether breaks were more or less effective at counteracting the effects of fatigue at different times of day. Twelve participants were recruited for the study, six males and six females. The participants were required to perform a driving task on a simulator for 90 minutes. The study consisted of four independent conditions, namely driving during the day with breaks, driving during the day without breaks, driving during the night with breaks and driving during the night without breaks. The without breaks conditions were similar except that they occurred at different times of the day, one session at night and the other session during day time, as was the case for the conditions with breaks. The driving task used in the current study was a low fidelity simulator tracking task. The participants were required to follow a centre line displayed on a tracking path as accurately as possible. The measurements that were recorded in this study included physiological, performance, subjective and neurophysiological. Physiological measures included heart rate and heart rate variability (frequency domain) and core body temperature. The ascending threshold of the critical flicker fusion frequency was the only neurophysiological measurement included in the current investigation. Performance was quantified by mean deviation from a centre line participants were meant to track. Two rating scales were used: Karolinska sleepiness scale and the Wits sleepiness scale were used for the measurement of subjective sleepiness. Heart rate, heart rate variability and mean deviation were measured continuously throughout the 90 minute driving task. Critical flicker fusion frequency, temperature and the subjective scales were measured before and after the 90 minute driving task. The results indicated that the short duration breaks during day time had a positive effect on driving performance; however the breaks at night had a negative effect on driving performance. Heart rate was higher during the day compared to night time and the heart rate variability high frequency spectrum values were lower during the day condition, to show the activation of the sympathetic nervous system which is characteristic of day time. The night conditions had lower heart rate values and higher heart rate variability high frequency values, which show the activation of the parasympathetic nervous system which is dominant during periods of fatigue and night time. Subjective sleepiness levels were also higher at night compared to day time.

L'hypovigilance du conducteur (que ce soit provoquée par la distraction ou la somnolence) est une des menaces principales pour la sécurité routière. Cette thèse s'encadre dans le projet Toucango, porté par la start-up Innov+, qui vise à construire un détecteur d'hypovigilance en temps réel basé sur la fusion d'un flux vidéo en proche infra-rouge et d'informations environnementales. L'objectif de cette thèse consiste donc à proposer des techniques d'extraction des indices pertinents ainsi que des algorithmes de fusion multimodale qui puissent être embarqués sur le système pour un fonctionnement en temps réel. Afin de travailler dans des conditions proches du terrain, une base de données en conduite réelle a été créée avec la collaboration de plusieurs sociétés de transports. Dans un premier temps, nous présentons un état de l'art scientifique et une étude des solutions disponibles sur le marché pour la détection de l'hypovigilance. Ensuite, nous proposons diverses méthodes basées sur le traitement d'images (pour la détection des indices pertinents sur la tête, yeux, bouche et visage) et de données (pour les indices environnementaux basés sur la géolocalisation). Nous réalisons une étude sur les facteurs environnementaux liés à l'hypovigilance et développons un système d'estimation du risque contextuel. Enfin, nous proposons des techniques de fusion multimodale de ces indices avec l'objectif de détecter plusieurs comportements d'hypovigilance : distraction visuelle ou cognitive, engagement dans une tâche secondaire, privation de sommeil, micro-sommeil et somnolence
Driver hypovigilance (whether caused by distraction or drowsiness) is one of the major threats to road safety. This thesis is part of the Toucango project, hold by the start-up Innov+, which aims to build a real-time hypovigilance detector based on the fusion of near infra-red video evidence and environmental information. The objective of this thesis is therefore to propose techniques for extracting relevant indices as well as multimodal fusion algorithms that can be embedded in the system for real-time operation. In order to work near ground truth conditions, a naturalistic driving database has been created with the collaboration of several transport companies. We first present a scientific state of the art and a study of the solutions available on the market for hypovigilance detection. Then, we propose several methods based on image (for the detection of relevant indices on the head, eyes, mouth and face) and data processing (for environmental indices based on geolocation). We carry out a study on the environmental factors related to hypovigilance and develop a contextual risk estimation system. Finally, we propose multimodal fusion techniques of these indices with the objective of detecting several hypovigilance behaviors: visual or cognitive distraction, engagement in a secondary task, sleep deprivation, microsleep and drowsiness

Ce travail de thèse a pour contexte la surveillance des systèmes homme-machine, où l'opérateur est le conducteur d'un système de transport ferroviaire. Notre objectif est d'améliorer la sécurité du système en prévenant et en évitant les facteurs pouvant augmenter le risque d'une erreur humaine. Deux verrous majeurs sont identifiés : l'aspect caractérisation, ou comment déterminer les phases indicatives et discernables de l'activité de conduite et l'aspect représentation, ou comment décrire et codifier les actions de conduite de l'opérateur et leurs répercussions sur le système ferroviaire dans un formalisme mathématique permettant une analyse sans équivoque. Pour solutionner ces verrous, nous proposons en premier lieu un modèle comportemental de l'opérateur humain permettant de représenter son comportement de contrôle en temps continu. Afin de tenir compte des différences inter- et intra-individuelles des opérateurs humains, ainsi des changements de situations, nous proposons une transformation du modèle comportemental initialement présenté, dans un nouveau espace de représentation. Cette transformation est basée sur la théorie des chaines cachées de Markov, et sur l'adaptation d'une technique particulière de reconnaissance de formes. Par la suite, nous définissons une modélisation comportementale en temps discret de l'opérateur humain, permettant en même temps de représenter ses actions et de tenir compte des erreurs et des évènements inattendus dans l'environnement de travail. Cette modélisation est inspirée des modèles cognitifs d’opérateur. Les deux aspects permettent d'interpréter les observables par rapport à des situations de référence. Afin de caractériser l'état global de l'opérateur humain, différentes informations sont prises en considération ; ces informations sont hétérogènes et entachées d’incertitudes de mesure, nécessitant une procédure de fusion de données robuste qui est effectuée à l'aide d'un réseau Bayésien. Au final, les méthodologies de modélisation et de fusion proposées sont exploitées pour la conception d'un système de vigilance fiable et non-intrusif. Ce système permet d'interpréter les comportements de conduite et de détecter les états à risque du conducteur (ex. l'hypovigilance). L'étude théorique a été testée en simulation pour vérifier sa validité. Puis, une étude de faisabilité a été menée sur des données expérimentales obtenues lors des expériences sur la plate-forme de conduite ferroviaire COR&GEST du laboratoire LAMIH. Ces résultats ont permis de planifier et de mettre en place les expérimentations à mener sur le futur simulateur de conduite multimodal "PSCHITT-PMR"
The scope of the thesis is the monitoring of human-machine systems, where the operator is the driver of rail-based transportation system. Our objective is to improve the security of the system preventing and avoiding factors that increase the risk of a human error. Two major problems are identified: characterization, or how to determine indicative and discernible phases of driver's activity and representation, or how to describe and codify driver's actions and its repercussions on the rail system in a mathematical formalism that will allow unequivocal analysis. In order to bring a solution to those problems, we propose, first-of-all, a behavioral model of the human operator representing his control behavior in continuous-time. To consider inter- and intra-individual differences of human operators and situation changes, we propose a transformation of the latter behavioral model in a new space of representation. This transformation is based on the theory of Hidden Markov Models, and on an adaptation of a special pattern recognition technique. Then, we propose a discrete-time behavioral modeling of the human operator, which represents his actions and takes account of errors and unexpected events in work environment. This model is inspired by cognitive models of human operators. These two aspects allow us to interpret observables with respect to reference situations in order to characterize the overall human operator state. Different information sources are considered; as a result the data are heterogeneous and subject to measuring uncertainties, needing a robust data fusion approach that is performed using a Bayesian Network. Finally, the proposed modeling and fusion methodologies are used to design a reliable and unintrusive vigilance system. This system can interpret driving behaviors and to detect driver’s risky states in order to prevent drowsiness. The theoretical study was tested in simulation to check the validity. Then, a feasibility study was conducted using data obtained during experiments on the LAMIH laboratory railroad platform “COR&GEST”. These results allowed us to plan and implement experiments to be conducted on the future multimodal driving simulator “PSCHITT-PMR”

碩士
國立聯合大學
資訊工程學系碩士班
106
Driving in drowsiness is a very dangerous driving behavior. Especially, many fatal accidents occur due to driver drowsiness from many news. The study on drowsy driving detection attracts the attention from many academic researches and information technology (IT) companies. Drivers have to put on the sensors on head in sensor-based detection. However, it is uncomfortable and drivers always forget to put on. In this thesis, an image-based drowsy detection has been developed on driving recorders. Currently, drowsiness detection algorithms using images are implemented in general personal computers with high computational power and storage. However, it is expensive and hard to implement on low-end driving recorders because of the cost. We modified the Viola’s face detection and implemented on the embedded systems. After face detection, facial landmarks are identified using face alignment algorithm. This algorithm is a forest tree-based search method with local binary features(LBF). the locations of eye’s landmarks are used to determine the eye and mouth status. In addition, the panning angle of head is calculated according the detected landmarks. The eye status and panning angle of head determine if the drivers are in the dangerous driving status or not. All programs are implemented in C programing language. To evaluate the effectiveness of the proposed algorithm, the program is also implemented on PC for simulation. More than 10 video clips with 3,000 face images are tested in which facial landmarks were manually labelled. The implemented algorithm is compared with that of Open-CV tool kit. The detected errors of facial landmarks are acceptable. 4-6 frames per second (fps) are achieved on the embedded systems.

碩士
國立交通大學
生物科技系所
96
Many traffic accidents have resulted from loss of alertness, lack of attention, or poor decision-making of truck and auto drivers. Catastrophic errors can be caused by momentary lapses in alertness and attention during periods of relative inactivity. Therefore, accurate and non-intrusive real-time monitoring of operator alertness would thus be highly desirable in a variety of operational environments. The aim of this study is to investigate the continuous electroencephalogram (EEG) fluctuations from alertness to drowsiness in a realistic virtual-reality-based (VR) driving environment that comprises a 360° virtual reality scene and a driving simulator. Sixteen healthy subjects (aged between 18 and 28) performed 1-hour lane-keeping driving task while their 32-channel EEG signals and driving behavior data were simultaneously recorded at 256 Hz. EEG data, after artifact removal, were processed by independent component analysis (ICA), component cluster analysis and time-frequency analysis to assess EEG correlates of cognitive-state changes. The bi-lateral occipital (BLO), occipital midline (OM), frontal central midline (FCM), central midline (CM), central parietal midline (CPM), left-central parietal (LCP) and right-central parietal (RCP) component clusters exhibited monotonic alpha-band (8-12 Hz) power increase during the transition from alertness to very-slight and slight drowsiness, but remain constant or slight decrease during the extreme drowsiness period. On the other hand, the theta-band (4-7 Hz) power for BLO, OM, FCM, CM, CPM, LCP and RCP component clusters increased monotonically during the transition from slight to extreme drowsiness. Additionally, we compared the EEG between different component clusters diversity of EEG power changes with respect to the transition from alertness to drowsiness and found that alpha power of BLO and OM component were most stable and desirable EEG feature for very-slight and slight drowsiness detection. The theta power of BLO and OM component were the most stable and desirable EEG feature for slight and extreme drowsiness detection.

碩士
國立交通大學
多媒體工程研究所
96
Previous studies showed that the alpha power increases in the occipital lobe highly related to human drowsiness. However, the acquisition of occipital EEG signals with the traditional electrode cap is inconvenient. Thus, the main purpose of this study was to confirm whether the forehead EEG signals could reflect the driver’s drowsiness and be able to use to estimate driver’s driving trajectory for constructing a feasible detecting system that can be applied in real life. Brain signals acquired from the occipital and the frontal lobe were analyzed and compared in this study. The frequency power changes in these components were used as features and fed into linear regression model to predict driver’s driving performance. Results showed the highest estimation accuracy was yielded with the features extracted from the occipital ICs cluster. We also found that there is another drowsiness-related brain source located in the frontal lobe. Furthermore, the increases of the theta power in the frontal lobe also highly correlated to the driver’s drowsiness. Comparing the conventional methods using the occipital activities, the estimation accuracy using the forehead signals is slightly lower but the estimation accuracy was still higher than 0.8. Results demonstrated that forehead signals could be used to estimate the drivers’ drowsiness. The new detecting system, using forehead signals, not only can correctly estimate the user’s drowsiness but also can drastically reduce the preparation time. In the future, such detection system will be easily and widely applied in the real operational environments.

碩士
國立交通大學
電機與控制工程系所
93
Preventing accidents caused by drowsiness has become a major focus of active safety driving in recent years. It requires an optimal estimation system to online continuously detect drivers’ cognitive state related to abilities in perception, recognition and vehicle control. The propose of this thesis is to develop an adaptive drowsiness estimation system based on electroencephalogram (EEG) by combining with independent component analysis (ICA), time-frequency spectral analysis, correlation analysis and fuzzy neural network model to estimate a driver’s cognitive state in Virtual-Reality (VR) dynamic driving simulator. Moreover, the VR-based motion platform with EEG measured system is the innovation of brain and cognitive engineering researches. Firstly, there is good evidence to show that the necessary of VR-based motion platform for brain research in driving simulation. This is an important fact to stress that the kinesthetic stimuli obviously influence the cognitive states and the phenomenon can be indicated by the EEG signals. Secondly, a single-trial event-related potential (ERP) is applied to recognize different brain potentials by the five degrees of drowsiness in driving. And we demonstrate a close relationship between the fluctuations in driving performance and the EEG signal log bandpower spectrum. Our Experimental results show that it is feasible to accurately estimate the driving performance. Then we observe that the brain source related to drowsiness is on cerebral cortex. Finally, the spiked dry electrodes and the corresponding movement artifact removal technology were designed to replace the regular wet electrode for the purpose of applications in the realistic driving or working environments.

碩士
南臺科技大學
電子工程系
107
In recent years, most vehicle drivers suffered poor mental status due to long hours of works and lack of sleep, resulting in a rate increase in vehicle accidents. To avoid or reduce the disaster caused by fatigue driving, the drowsiness-fatigue-detection (DFD) systems have widely been researched and developed. However, it still lacks related functions such as a useful/accepted DFD system and management of recording fatigue driving. To solve the problem mentioned above, this thesis proposes a DFD system, which is based on wearable smart glasses. The proposed system consists of a pair of smart glasses, in-vehicle infotainment system (IVI system), Sigfox cloud platform, and online information platform. A band-pass infrared sensing module is mounted on the proposed smart glasses and uses infrared reflection principle to detect the state of the driver’s eyes. We implement an algorithm in microcontroller to identify whether the driver is entering fatigue state. When the driver is in a state of fatigue, thus the proposed smart glasses will transmit a fatigue event to the IVI system via Bluetooth low energy (BLE). At this time, the IVI system will immediately trigger a sound to wake up the fatiguing driver.Furthermore, the IVI system also transmits the fatigue event to the Sigfox could platform via the Sigfox low power wide area network (LPWAN). Finally, the SigFox cloud platform adopts callbacks method to send fatigue events to the online information platform. The online information can display and check the driver’s fatigue event information such as license plate number, date, time, location, etc. As a result, the proposed DFD system can be achieved the purpose of road safety.