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Commercial motor vehicle driver impairment due to drowsiness is known to be a major contributing factor in many crashes. This report details the steps taken to develop a prototype driver drowsiness monitoring system (DDMS). The first area of consideration was the basic design requirements that would pertain to all driver drowsiness monitors, such a...
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Every year, thousands of vehicles are involved in crashes which are attributed to the onset of driver drowsiness. To address this issue, a prototype integrated system was developed that combined machine-vision based drowsy driver monitoring technology and the analysis of operator/vehicle performance parameters to reliably assess driver drowsiness....
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... The subject's cognitive state and task engagement were monitored via a surveillance video and the vehicle trajectory to decipher the attentiveness of the subject, which was used to reject the trials when the steering wheel was not synchronistic with the visual effect. The driving experiment was conducted in the early afternoon (13:00-15:00) after lunch because the circadian rhythm of sleepiness was at its peak at noon [47,48]. Additionally, the highway scene was monotonous and the task demand was low and hence were likely to induce drowsiness [43,49]. ...
Predicting a driver's cognitive state, or more specifically, modeling a driver's reaction time (RT) in response to the appearance of a potential hazard warrants urgent research. In the last two decades, the electric field that is generated by the activities in the brain, monitored by an electroencephalogram (EEG), has been proven to be a robust physiological indicator of human behavior. However, mapping the human brain can be extremely challenging, especially owing to the variability in human beings over time, both within and among individuals. Factors such as fatigue, inattention and stress can induce homeostatic changes in the brain, which affect the observed relationship between brain dynamics and behavioral performance, and thus make the existing systems for predicting RT difficult to generalize. To solve this problem, an ensemble-based weighted prediction system is presented herein. This system comprises a set of prediction submodels that are individually trained using groups of data with similar EEG-RT relationships. To obtain a final prediction, the prediction outcomes of the sub-models are then multiplied by weights that are derived from the EEG alpha coherences of 10 channels plus theta band powers of 30 channels, whose changes were found to be indicators of variations in the EEG-RT relationship. The results thus obtained reveal that the proposed system with a time-varying adaptive weighting mechanism significantly outperforms the conventional system in modeling a driver's RT. The adaptive design of the proposed system demonstrates its feasibility in coping with the variability in the brain-behavior relationship. In this contribution surprisingly simple EEG-based adaptive methods are used in combination with an ensemble scheme to significantly increase system performance.
... The objective of this study was to evaluate the sensitivity to fatigue of lane position and steering measurements. Fatigued driving is a major cause of traffic accidents, especially those involving serious injuries or fatalities (1). Many studies have proposed methods for monitoring or quantifying drivers' drowsiness or fatigue status. ...
... Other studies have proposed measuring eye and head movements (5)(6)(7). Such measurements of driver operation or vehicular movement derive the driver's physiological status without direct interference (1,8,9). ...
The parameter value chosen to measure driving performance affects the accuracy of the estimated fatigue level. Methods to analyze the sensitivity of these parameter values were proposed. Standard deviation of lane position (SDLP) and steering reversal rate (SRR) were considered to assess fatigue, and the sensitivity of these parameters was analyzed from the time domain and value domain. Thirty-six male drivers participated in a field test. Lane position, steering wheel angle data, and self-reported fatigue level (scored on the Karolinska sleepiness scale) were recorded. SDLP results indicate that the maximum average coefficient with fatigue level reached .11, with a unified statistical interval of 202 s when the consecutive analysis method was used; the maximum average coefficient was .12 with a unified interval of 120 s when the maximum analysis method was used. SRR results indicate that a steering angle difference of 6° was the most sensitive threshold for driver fatigue level and has an average correlation coefficient of .42, which demonstrated that SRR was more reliable than SDLP for monitoring fatigue level. With the use of the optimal parameter value, the variation results of SDLP and SRR at each fatigue level were examined, and results indicate that driving ability was impaired as fatigue level increased. The methods and results can be applied to analyses of fatigued or drowsy driving.
... Such systems can also be widely used in various types of safety systems. For example assessment of driver drowsiness systems which is already implemented by most innovative automotive manufactures [9]. The great interest of this kind of research comes also from the Police and Security Forces, who see an opportunity for extraction a much larger amount of information recorded during interrogations and video surveillance. ...
This paper describes background and state of art in automatic emotion recognition systems which are the essential part of affective comput-ing. Affective computing is a relatively new field of study concerning recog-nition and processing of emotions in computer systems. Providing capabili-ties of emotion recognition into computer systems could significantly redefine Human-Computer Interactions. Computers and software could adapt their behavior to user needs. The emotion recognition systems are on the eye of large corporations, who want to know at all costs whether their products, services and marketing strategy addresses the needs and tastes of customers. Finally, paper presents the concept and design of an ongoing project of de-velopment the Multimodal Emotion Recognition System.
... These recommendations were based on the study data that showed the DDWS failed to work optimally in a number of real-world operational conditions. Therefore, based on this evaluation, a study was undertaken to develop a more robust drowsy driver field system [27]. The concept of developing a more robust system was to combine multiple drowsiness measures that would not only strengthen an overall drowsiness measure, but provide backup measures when individual sensor conditions were insufficient for effective operation. ...
... However, a typical feature of these systems is that they are based on a single metric to measure drowsiness. The purpose of the Bowman et al. [27] effort was to demonstrate that a single-metric system may not be the optimal approach, particularly as designers move from labs and simulators into the real-world and rugged environment characteristic of commercial vehicle operation. ...
Driver drowsiness is one of the most widely discussed safety issues in long-haul trucking. The nature of the trucking profession, and the vigilance required to operate a large vehicle hundreds of miles each day, suggests that concern regarding driver drowsiness may be warranted. A fundamental problem with understanding the scope of the drowsiness problem, or other driver errors, is that the chief method used to assess driver behavior-related crash causal factors has been epidemiological studies. Though epidemiology, or the analysis of crash databases, can provide a wealth of information about the characteristics of the crash, assessing driver behavior or driver state immediately preceding a crash is a limitation of this approach. Though the results from an epidemiological or "traditional" approach to studying driver behavior are typically referenced in both the literature and popular press, the data are only as good as the reporting-officers' investigative skills. And, when it comes to subtle issues, such as where the driver was looking immediately preceding the crash, even the most skilled reporting officer cannot say with much certainty. However, the miniaturization of technology, coupled with the ability to continuously record and store thousands of hours of driving data (including video of the driver's face and eyes), has made naturalistic data collection a viable approach to reliably evaluate on-road driver behavior, performance, and error in real-time. This technique serves as an "instant replay" where data and video immediately preceding a crash can be reviewed, and re-viewed, to determine driver behavior-related contributing factors (including drowsiness and distraction). This chapter highlights the naturalistic data collection approach applied to a large-scale study of commercial vehicle operations. The benefits and limitations of naturalistic data collection, along with epidemiological and empirical data collection, are highlighted. Keeping with the theme of the book, and with a focus on understanding drowsiness-related issues in trucking, a study is described that evaluated a drowsy driver warning system. Though the study was successful in terms of investigating the safety benefits and limitations of a particular device, the naturalistic dataset collected during the study was, in and of itself, a key outcome of the study. As will be described, this dataset has been used for further development of drowsiness-related technology, driver training and education, and assessment of the U.S. Department of Transportation's (US DOT) revised Hours-of-Service (HOS) regulations for commercial motor vehicle (CMV) drivers. Additionally, information on the sleep of commercial vehicle drivers has also resulted from this naturalistic study providing important information on the impact of the revised HOS policy and the sleep habits of U.S. truck drivers.
The purpose of this paper is to design the metal object detection system for drive inside protection. To do this, we propose the algorithm for designing the color filter that can detect the metal object using fuzzy theory and the algorithm for detecting area of the driver`s face using fuzzy skin color filter. Also, by using the proposed algorithm, we propose the algorithm for detecting the metallic object candidate regions. And, the metallic object color filter is then applied to find the candidate regions. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.
Every year, thousands of vehicles are involved in crashes which are attributed to the onset of driver drowsiness. To address this issue, a prototype integrated system was developed that combined machine-vision based drowsy driver monitoring technology and the analysis of operator/vehicle performance parameters to reliably assess driver drowsiness. PERCLOS (a measure of eye closure) is considered to be the “gold standard” of drowsiness detection metrics. Systems have been developed to measure PERCLOS. However, issues including eyewear, ambient illumination, and head movement present hurdles which can be difficult to overcome. Research has investigated driver control metrics associated with drowsiness, and lane position appears to be a key indicator. This paper reports on a project aimed at integrating PERCLOS with other drowsiness metrics to form a new measure, PERCLOS+ (PERCLOS plus other measures), that may prove to be a more robust measure in a real-world, field application as compared to a single metric system.
