A Survey on State-of-the-Art Drowsiness Detection Techniques
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paper_title: Driver’s Face Detection Using Space-time Restrained Adaboost Method

paper_content:

Face detection is the first step of vision-based driver fatigue detection method. Traditional face detection methods have problems of high false-detection rates and long detection times. A space-time restrained Adaboost method is presented in this paper that resolves these problems. Firstly, the possible position of a driver’s face in a video frame is measured relative to the previous frame. Secondly, a space-time restriction strategy is designed to restrain the detection window and scale of the Adaboost method to reduce time consumption and false-detection of face detection. Finally, a face knowledge restriction strategy is designed to confirm that the faces detected by this Adaboost method. Experiments compare the methods and confirm that a driver’s face can be detected rapidly and precisely.
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paper_title: Real time drowsiness detection using eye blink monitoring

paper_content:

According to analysis reports on road accidents of recent years, it's renowned that the main cause of road accidents resulting in deaths, severe injuries and monetary losses, is due to a drowsy or a sleepy driver. Drowsy state may be caused by lack of sleep, medication, drugs or driving continuously for long time period. An increase rate of roadside accidents caused due to drowsiness during driving indicates a need of a system that detects such state of a driver and alerts him prior to the occurrence of any accident. During the recent years, many researchers have shown interest in drowsiness detection. Their approaches basically monitor either physiological or behavioral characteristics related to the driver or the measures related to the vehicle being used. A literature survey summarizing some of the recent techniques proposed in this area is provided. To deal with this problem we propose an eye blink monitoring algorithm that uses eye feature points to determine the open or closed state of the eye and activate an alarm if the driver is drowsy. Detailed experimental findings are also presented to highlight the strengths and weaknesses of our technique. An accuracy of 94% has been recorded for the proposed methodology.
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paper_title: Driver Drowsiness Detection Using Eye-Closeness Detection

paper_content:

The purpose of this paper was to devise a way to alert drowsy drivers in the act of driving. One of the causes of car accidents comes from drowsiness of the driver. Therefore, this study attempted to address the issue by creating an experiment in order to calculate the level of drowsiness. A requirement for this paper was the utilisation of a Raspberry Pi Camera and Raspberry Pi 3 module, which were able to calculate the level of drowsiness in drivers. The frequency of head tilting and blinking of the eyes was used to determine whether or not a driver felt drowsy. With an evaluation on ten volunteers, the accuracy of face and eye detection was up to 99.59 percent.
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paper_title: Performing systematic literature reviews in software engineering

paper_content:

Context: Making best use of the growing number of empirical studies in Software Engineering, for making decisions and formulating research questions, requires the ability to construct an objective summary of available research evidence. Adopting a systematic approach to assessing and aggregating the outcomes from a set of empirical studies is also particularly important in Software Engineering, given that such studies may employ very different experimental forms and be undertaken in very different experimental contexts.Objectives: To provide an introduction to the role, form and processes involved in performing Systematic Literature Reviews. After the tutorial, participants should be able to read and use such reviews, and have gained the knowledge needed to conduct systematic reviews of their own.Method: We will use a blend of information presentation (including some experiences of the problems that can arise in the Software Engineering domain), and also of interactive working, using review material prepared in advance.
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paper_title: Subjective sleepiness, simulated driving performance and blink duration: examining individual differences.

paper_content:

The present study aimed to provide subject-specific estimates of the relation between subjective sleepiness measured with the Karolinska Sleepiness Scale (KSS) and blink duration (BLINKD) and lane drifting calculated as the standard deviation of the lateral position (SDLAT) in a high-fidelity moving base driving simulator. Five male and five female shift workers were recruited to participate in a 2-h drive (08:00-10:00 hours) after a normal night sleep and after working a night shift. Subjective sleepiness was rated on the KSS in 5-min intervals during the drive, electro-occulogram (EOG) was measured continuously to calculate BLINKD, and SDLAT was collected from the simulator. A mixed model anova showed a significant (P Language: en
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paper_title: Performing systematic literature reviews in software engineering

paper_content:

Context: Making best use of the growing number of empirical studies in Software Engineering, for making decisions and formulating research questions, requires the ability to construct an objective summary of available research evidence. Adopting a systematic approach to assessing and aggregating the outcomes from a set of empirical studies is also particularly important in Software Engineering, given that such studies may employ very different experimental forms and be undertaken in very different experimental contexts.Objectives: To provide an introduction to the role, form and processes involved in performing Systematic Literature Reviews. After the tutorial, participants should be able to read and use such reviews, and have gained the knowledge needed to conduct systematic reviews of their own.Method: We will use a blend of information presentation (including some experiences of the problems that can arise in the Software Engineering domain), and also of interactive working, using review material prepared in advance.
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paper_title: An Evaluation of Emerging Driver Fatigue Detection Measures and Technologies

paper_content:

Operator fatigue and sleep deprivation have been widely recognized as critical safety issues that cut across all modes in the transportation industry. FMCSA, the trucking industry, highway safety advocates, and transportation researchers have all identified driver fatigue as a high priority commercial vehicle safety issue. Fatigue affects mental alertness, decreasing an individual’s ability to operate a vehicle safely and increasing the risk of human error that could lead to fatalities and injuries. Sleepiness slows reaction time, decreases awareness, and impairs judgment. Fatigue and sleep deprivation impact all transportation operators (airline pilots, truck drivers, and railroad engineers, for example). Adding to the difficulty of understanding the fatigue problem and developing effective countermeasures to address operator fatigue is the fact that the incidence of fatigue is underestimated because it is so hard to quantify and measure. Obtaining reliable data on fatigue-related crashes is challenging because it is difficult to determine the degree to which fatigue plays a role in crashes. Fatigue, however, can be managed, and effectively managing fatigue will result in a significant reduction in related risk and improved safety. This study focuses on recent developments in mathematical models and vehicle-based operator alertness monitoring technologies. The major objective of this paper is to review and discuss many of the activities currently underway to develop unobtrusive, in-vehicle, real-time drowsy driver detection and fatigue-monitoring/alerting systems.
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paper_title: Driver Fatigue Detection Using Mouth and Yawning Analysis

paper_content:

Summary Driver fatigue is an important factor in large number of accidents. There has been much work done in driver fatigue detection. This paper presents driver fatigue detection based on tracking the mouth and to study on monitoring and recognizing yawning. The authors proposed a method to locate and track driver’s mouth using cascade of classifiers proposed by Viola-Jones for faces. SVM is used to train the mouth and yawning images. During the fatigue detection mouth is detected from face images using cascade of classifiers. Then, SVM is used to classify the mouth and to detect yawning then alert Fatigue.
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paper_title: Driver drowsiness detection using face expression recognition

paper_content:

An important application of machine vision and image processing could be driver drowsiness detection system due to its high importance. In recent years there have been many research projects reported in the literature in this field. In this paper, unlike conventional drowsiness detection methods, which are based on the eye states alone, we used facial expressions to detect drowsiness. There are many challenges involving drowsiness detection systems. Among the important aspects are: change of intensity due to lighting conditions, the presence of glasses and beard on the face of the person. In this project, we propose and implement a hardware system which is based on infrared light and can be used in resolving these problems. In the proposed method, following the face detection step, the facial components that are more important and considered as the most effective for drowsiness, are extracted and tracked in video sequence frames. The system has been tested and implemented in a real environment.
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paper_title: Video-Based Classification of Driving Behavior Using a Hierarchical Classification System with Multiple Features

paper_content:

Driver fatigue and inattention have long been recognized as one of the main contributing factors in traffic accidents. Therefore, the development of intelligent driver assistance systems, which provides automatic monitoring of driver's vigilance, is an urgent and challenging task. This paper presents a novel system for video-based driving behavior recognition. The fundamental idea is to monitor driver's hand movements and to use these as predictors for safe/unsafe driving behavior. In comparison to previous work, the proposed method utilizes hierarchical classification and treats driving behavior in terms of a spatio-temporal reference framework as opposed to a static image. The approach was verified using the Southeast University Driving-Posture Dataset, a dataset comprised of video clips covering aspects of driving such as: normal driving, responding to a cell phone call, eating and smoking. After pre-processing for illumination variations and motion sequence segmentation, eight classes of behavior were...
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paper_title: A novel approach for drowsy driver detection using head posture estimation and eyes recognition system based on wavelet network

paper_content:

Fatigue and drowsiness are a part of everyday life for millions of people. They reduce reaction time, vigilance, alertness and concentration, therefore, the ability of performing attention-based activities (such as driving) is impaired. In this paper, a drowsy driver detection system has been developed, using video processing analyzing eyes blinking concepts for measuring eyes closure duration and head posture estimation to verify the driver vigilance state. There are two contributions in this paper. the first one resides in combining two parameters of drowsiness analysis. The second is the creation of a new method of head posture estimation.
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paper_title: Driver’s Face Detection Using Space-time Restrained Adaboost Method

paper_content:

Face detection is the first step of vision-based driver fatigue detection method. Traditional face detection methods have problems of high false-detection rates and long detection times. A space-time restrained Adaboost method is presented in this paper that resolves these problems. Firstly, the possible position of a driver’s face in a video frame is measured relative to the previous frame. Secondly, a space-time restriction strategy is designed to restrain the detection window and scale of the Adaboost method to reduce time consumption and false-detection of face detection. Finally, a face knowledge restriction strategy is designed to confirm that the faces detected by this Adaboost method. Experiments compare the methods and confirm that a driver’s face can be detected rapidly and precisely.
---
paper_title: An Evaluation of Emerging Driver Fatigue Detection Measures and Technologies

paper_content:

Operator fatigue and sleep deprivation have been widely recognized as critical safety issues that cut across all modes in the transportation industry. FMCSA, the trucking industry, highway safety advocates, and transportation researchers have all identified driver fatigue as a high priority commercial vehicle safety issue. Fatigue affects mental alertness, decreasing an individual’s ability to operate a vehicle safely and increasing the risk of human error that could lead to fatalities and injuries. Sleepiness slows reaction time, decreases awareness, and impairs judgment. Fatigue and sleep deprivation impact all transportation operators (airline pilots, truck drivers, and railroad engineers, for example). Adding to the difficulty of understanding the fatigue problem and developing effective countermeasures to address operator fatigue is the fact that the incidence of fatigue is underestimated because it is so hard to quantify and measure. Obtaining reliable data on fatigue-related crashes is challenging because it is difficult to determine the degree to which fatigue plays a role in crashes. Fatigue, however, can be managed, and effectively managing fatigue will result in a significant reduction in related risk and improved safety. This study focuses on recent developments in mathematical models and vehicle-based operator alertness monitoring technologies. The major objective of this paper is to review and discuss many of the activities currently underway to develop unobtrusive, in-vehicle, real-time drowsy driver detection and fatigue-monitoring/alerting systems.
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paper_title: Eye tracking system to detect driver drowsiness

paper_content:

This paper describes an eye tracking system for drowsiness detection of a driver. It is based on application of Viola Jones algorithm and Percentage of Eyelid Closure (PERCLOS). The system alerts the driver if the drowsiness index exceeds a pre-specified level.
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paper_title: Real time drowsiness detection using eye blink monitoring

paper_content:

According to analysis reports on road accidents of recent years, it's renowned that the main cause of road accidents resulting in deaths, severe injuries and monetary losses, is due to a drowsy or a sleepy driver. Drowsy state may be caused by lack of sleep, medication, drugs or driving continuously for long time period. An increase rate of roadside accidents caused due to drowsiness during driving indicates a need of a system that detects such state of a driver and alerts him prior to the occurrence of any accident. During the recent years, many researchers have shown interest in drowsiness detection. Their approaches basically monitor either physiological or behavioral characteristics related to the driver or the measures related to the vehicle being used. A literature survey summarizing some of the recent techniques proposed in this area is provided. To deal with this problem we propose an eye blink monitoring algorithm that uses eye feature points to determine the open or closed state of the eye and activate an alarm if the driver is drowsy. Detailed experimental findings are also presented to highlight the strengths and weaknesses of our technique. An accuracy of 94% has been recorded for the proposed methodology.
---
paper_title: Driver Drowsiness Detection Using Eye-Closeness Detection

paper_content:

The purpose of this paper was to devise a way to alert drowsy drivers in the act of driving. One of the causes of car accidents comes from drowsiness of the driver. Therefore, this study attempted to address the issue by creating an experiment in order to calculate the level of drowsiness. A requirement for this paper was the utilisation of a Raspberry Pi Camera and Raspberry Pi 3 module, which were able to calculate the level of drowsiness in drivers. The frequency of head tilting and blinking of the eyes was used to determine whether or not a driver felt drowsy. With an evaluation on ten volunteers, the accuracy of face and eye detection was up to 99.59 percent.
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paper_title: Information data flow in AWAKE multi-sensor driver monitoring system

paper_content:

Hypovigilance detection and warning systems are currently based on stand alone sensor approaches. This paper presents a multisensor system that allows the information fusion of different sources (vehicle, driver and environmental sensing parameters) and contributes to the decrease of false alarms and misses of the hypovigilance detection system. A hybrid scheme- centralized communication and data flow management of integrated stand alone systems- is adopted, which in turn, allows the real time application to monitor the driver and provide imminent and information messages according to his/her state and adapted to the external traffic and environmental scenario. The data flow between all systems, sensors and modules is described to synthesize the functional architecture. The system development is funded by the European so-called AWAKE project.
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paper_title: Subjective sleepiness, simulated driving performance and blink duration: examining individual differences.

paper_content:

The present study aimed to provide subject-specific estimates of the relation between subjective sleepiness measured with the Karolinska Sleepiness Scale (KSS) and blink duration (BLINKD) and lane drifting calculated as the standard deviation of the lateral position (SDLAT) in a high-fidelity moving base driving simulator. Five male and five female shift workers were recruited to participate in a 2-h drive (08:00-10:00 hours) after a normal night sleep and after working a night shift. Subjective sleepiness was rated on the KSS in 5-min intervals during the drive, electro-occulogram (EOG) was measured continuously to calculate BLINKD, and SDLAT was collected from the simulator. A mixed model anova showed a significant (P Language: en
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paper_title: Driver Drowsiness Detection Based on Time Series Analysis of Steering Wheel Angular Velocity

paper_content:

A novel driver drowsiness detection method based on time series analysis of the steering wheel angular velocity is proposed in this paper. Firstly, the steering behavior under the fatigue state is analyzed, followed by the determination of the temporal detection window, and then, the data series of the steering wheel angular velocity in the temporal detection window is selected as the detection feature. IF the detection feature satisfies the extent constraint and the variability constraint in the temporal window, a drowsiness state is detected accordingly. At last, experiment tests validate our method has good performance and could be well used in the real world.
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paper_title: Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions

paper_content:

This paper presents a drowsiness on-line detection system for monitoring driver fatigue level under real driving conditions, based on the data of steering wheel angles (SWA) collected from sensors mounted on the steering lever. The proposed system ﬁrstly extracts approximate entropy (ApEn)featuresfromﬁxedslidingwindowsonreal-timesteeringwheelanglestimeseries. Afterthat, this system linearizes the ApEn features series through an adaptive piecewise linear ﬁtting using a given deviation. Then, the detection system calculates the warping distance between the linear features series of the sample data. Finally, this system uses the warping distance to determine the drowsiness state of the driver according to a designed binary decision classiﬁer. The experimental data were collected from 14.68 h driving under real road conditions, including two fatigue levels: “wake” and “drowsy”. The results show that the proposed system is capable of working online with an average 78.01% accuracy, 29.35% false detections of the “awake” state, and 15.15% false detections of the “drowsy” state. The results also conﬁrm that the proposed method based on SWA signal is valuable for applications in preventing trafﬁc accidents caused by driver fatigue.
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paper_title: Automatic Detection of Driver Fatigue Using Driving Operation Information for Transportation Safety

paper_content:

Fatigued driving is a major cause of road accidents. For this reason, the method in this paper is based on the steering wheel angles (SWA) and yaw angles (YA) information under real driving conditions to detect drivers' fatigue levels. It analyzes the operation features of SWA and YA under different fatigue statuses, then calculates the approximate entropy (ApEn) features of a short sliding window on time series. Using the nonlinear feature construction theory of dynamic time series, with the fatigue features as input, designs a "2-6-6-3" multi-level back propagation (BP) Neural Networks classifier to realize the fatigue detection. An approximately 15-h experiment is carried out on a real road, and the data retrieved are segmented and labeled with three fatigue levels after expert evaluation, namely "awake", "drowsy" and "very drowsy". The average accuracy of 88.02% in fatigue identification was achieved in the experiment, endorsing the value of the proposed method for engineering applications.
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paper_title: EEG-based Driver Fatigue Detection

paper_content:

Fatigue is a gradual process leads to a slower reaction time. Fatigue is the major cause of road accidents around the globe. This paper proposes, implements and tests a system to detect fatigue based on Electroencephalogram (EEG) signal. The system produces fatigue index which is relevant to the level of subject's drowsiness. The input to the system is EEG signal which is measured by inexpensive single electrode neuro-signal acquisition device. The system was tested on locally collected dataset for a car simulation driver in different drowsiness levels. The system was able to detect the fatigue level for all subjects in different levels of tiredness.
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paper_title: Detection of Driver Drowsiness Using Wavelet Analysis of Heart Rate Variability and a Support Vector Machine Classifier

paper_content:

Driving while fatigued is just as dangerous as drunk driving and may result in car accidents. Heart rate variability (HRV) analysis has been studied recently for the detection of driver drowsiness. However, the detection reliability has been lower than anticipated, because the HRV signals of drivers were always regarded as stationary signals. The wavelet transform method is a method for analyzing non-stationary signals. The aim of this study is to classify alert and drowsy driving events using the wavelet transform of HRV signals over short time periods and to compare the classification performance of this method with the conventional method that uses fast Fourier transform (FFT)-based features. Based on the standard shortest duration for FFT-based short-term HRV evaluation, the wavelet decomposition is performed on 2-min HRV samples, as well as 1-min and 3-min samples for reference purposes. A receiver operation curve (ROC) analysis and a support vector machine (SVM) classifier are used for feature selection and classification, respectively. The ROC analysis results show that the wavelet-based method performs better than the FFT-based method regardless of the duration of the HRV sample that is used. Finally, based on the real-time requirements for driver drowsiness detection, the SVM classifier is trained using eighty FFT and wavelet-based features that are extracted from 1-min HRV signals from four subjects. The averaged leave-one-out (LOO) classification performance using wavelet-based feature is 95% accuracy, 95% sensitivity, and 95% specificity. This is better than the FFT-based results that have 68.8% accuracy, 62.5% sensitivity, and 75% specificity. In addition, the proposed hardware platform is inexpensive and easy-to-use.
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paper_title: Detecting Drowsy Driver Using Pulse Sensor

paper_content:

The driver’s condition, which involves staying focus on the road, is the most important aspect to consider whenever one is driving. To ignore the importance of this could result in severe physical injuries, deaths and economic losses. Again, previous researches were focused mainly on the physical conditions of the driver; eg movement of head and drowsiness. However, this research is focused on the driver’s heart rate by using an infrared heart-rate sensor or pulse sensor. These sensors are non-intrusively measured heart pulse wave from the driver’s heart. By doing experiment, the results show clear pulse wave signal can be obtained by looking at the low to high frequency (LF/HF ratio) which calculate HRV frequency domain of the driver’s heart rate time series. The LF/HF ratio shows decreasing trends as the drivers go from the state of being awake and alert to the state of drowsiness. Therefore, accidents can be avoided if there is an alert system to keep the drivers alert and focused on the road.
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paper_title: Wearable driver drowsiness detection system based on biomedical and motion sensors

paper_content:

Driver drowsiness detection system had been developed as mobile device application such as Percentage of Eye Closure (PERCLOS) measured by using mobile device camera. Nevertheless, the mobile device has the potential risk of distracting the driver's attention, causing accidents. Thus, a wearable-type drowsiness detection system is proposed to overcome such issue. The proposed system used self-designed wristband consisted of photoplethysmogram sensor and galvanic skin response sensor. The sensors data are sent to the mobile device which served as a main analyzing processing unit. Those data are analyzed along with the motion sensors, which are the mobile device built-in accelerometer and gyroscope sensors. Five features are extracted accordingly based on the received raw sensors data, including heart rate, pulse rate variability, respiratory rate, stress level, and adjustment counter. Those features are further served as computation parameters to a support vector machine to derive the driver drowsiness state. The testing results indicated that the accuracy of the system with SVM model reached up to 98.3%. In addition, driver will be alerted using graphical and vibration alarm generated by the mobile device. In fact, the integration of driver physical behavior and physiological signals is proven to be an outstanding solution to detect driver drowsiness in a safer, more flexible and portable used.
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paper_title: Detecting Driver Drowsiness Using Wireless Wearables

paper_content:

The National Highway Traffic Safety Administration data show that drowsy driving causes more than 100,000 crashes a year. In order to prevent these devastating accidents, it is necessary to build a reliable driver drowsiness detection system which could alert the driver before a mishap happens. In the literature, the drowsiness of a driver can be measured by vehicle-based, behavior-based, and physiology-based approaches. Comparing with the vehicle-based and behavior-based measurements, the physiological measurement of drowsiness is more accurate. With the latest release of wireless wearable devices such as biosensors that can measure people's physiological data, we aim to explore the possibility of designing a user-friendly and accurate driver drowsiness detection system using wireless wearables. In this paper, we use a wearable biosensor called Bio Harness 3 produced by Zephyr Technology to measure a driver's physiological data. We present our overall design idea of the driver drowsiness detection system and the preliminary experimental results using the biosensor. The detection system will be designed in two phases: The main task of the first phase is to collect a driver's physiological data by the biosensor and analyze the measured data to find the key parameters related to the drowsiness. In the second phase, we will design a drowsiness detection algorithm and develop a mobile app to alert drowsy drivers. The results from this project can lead to the development of real products which can save many lives and avoid many accidents on the road. Furthermore, our results can be widely applied to any situation where people should not fall asleep: from the applications in mission-critical fields to the applications in everyday life.
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paper_title: Driver fatigue detection system

paper_content:

The research aims to detect the onset of drowsiness in drivers, while the vehicle is in motion. Detection is done by continuously looking out for symptoms of drowsiness, while considering both physiological and physical signs. Physiological factors include core body temperature and pulse rate. Both of these parameters will decrease during the onset of drowsiness. These are monitored by using somatic sensors. Physical cues including yawning, drooping eyelids, closed eyes and increased blink durations. Once the system detects that the driver is drowsy by using a combination of these factors, it alerts the driver across multiple stages depending on the severity of the symptoms. The system also becomes attuned to the driver's unique characteristics over time, thus reducing the margin of false positives.
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paper_title: A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability

paper_content:

Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system’s performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear.
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paper_title: A new system for driver drowsiness and distraction detection

paper_content:

Drowsiness especially in long distance journeys is a key factor in traffic accidents. In this paper a new module for automatic driver drowsiness detection based on visual information and Artificial Intelligence is presented. The aim of this system is to locate, track and analyze both the driver's face and eyes to compute a drowsiness index to prevent accidents. Both face and eye detection is performed by Haar-like features and AdaBoost classifiers. In order to achieve better accuracy in face tracking, we propose a new method which is combination of detection and object tracking. Proposed face tracking method, also has capability to self correction. After eye region is found, Local Binary Pattern (LBP) is employed to extract eye characteristics. Using these features, an SVM classifier was trained to perform eye state analysis. To evaluate the effectiveness of proposed method, a drowsy person was pictured, while his EEG signals were taken. In this video we were able to track face by an accuracy of 100% and detecting eye blink by accuracy of 98.4%. Also we can calculate face orientation and tilt using eye position which is valuable knowledge about driver concentration. Finally, we can make a decision about drowsiness and distraction of the driver. Experimental results show high accuracy in each section which makes this system reliable for driver drowsiness detection.
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Title: A Survey on State-of-the-Art Drowsiness Detection Techniques

Section 1: INTRODUCTION
Description 1: Introduce the importance of drowsiness detection, its impact on road safety, and the motivation behind the review.

Section 2: RESEARCH METHODOLOGY
Description 2: Describe the systematic approach used to gather and evaluate research papers on drowsiness detection techniques.

Section 3: DROWSINESS DETECTION TECHNIQUES
Description 3: Provide a detailed review of various drowsiness detection techniques, categorized by their approach, and discuss their pros and cons.

Section 4: A COMPARATIVE STUDY OF DDT
Description 4: Conduct a comparative analysis of different drowsiness detection techniques and discuss their advantages and disadvantages.

Section 5: HYBRID APPROACHES OF DDT
Description 5: Discuss the combination of different drowsiness detection techniques to enhance detection accuracy and reliability.

Section 6: CLASSIFICATION METHODS USED FOR DDT
Description 6: Detail the various classification methods used in drowsiness detection systems, including their pros and cons, and comparative performance.

Section 7: COMPARATIVE STUDY OF CLASSIFICATION METHODS
Description 7: Present a comparative study of different classification methods, discussing their error rates and suitability under various conditions.

Section 8: CONCLUSION
Description 8: Summarize the main findings of the survey, highlighting the most effective drowsiness detection techniques and future research directions.