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Proceedings ArticleDOI

Drunk driving and drowsiness detection

23 Jun 2017-pp 1-6
TL;DR: This paper aims towards the detection of driver's drowsiness using the visual features approach along with drunk detection using alcohol sensor, thus covering the major reasons behind road accidents.
Abstract: Development of safety features to prevent drunk and drowsy driving is one of the major technical challenges in the automobile industry. Driving while being drunk or drowsy is a major reason behind road accidents especially in the modern age. Driving when drowsy can lead to higher crash risk than being in alert state. Therefore, by using assistive systems to monitor driver's level of alertness can be of significant help in prevention of accidents. This paper aims towards the detection of driver's drowsiness using the visual features approach along with drunk detection using alcohol sensor. Driver drowsiness is based on real-time detection of the driver's head, face and mouth, where-in HAAR-Cascade classifier for face and eye detection and template matching in the mouth region for yawning detection. The system will also have an alcohol detection sensor which will determine whether the driver is drunk or not, thus covering the major reasons behind road accidents.
Citations
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Journal ArticleDOI
TL;DR: A system whose main objective is identifying a person having alcohol in the blood through supervised classification of sensor-generated and computer-vision-based data and reaches a classification performance of 98% while ensures adequate operation conditions for the embedded system is introduced.
Abstract: Car drivers under the influence of alcohol is one of the most common causes of road traffic accidents. To tackle this issue, an emerging, suitable alternative is the use of intelligent systems -traditionally based on either sensor networks or artificial vision- that are aimed to prevent starting the car when drunk status on the car driver is detected. In such vein, this paper introduces a system whose main objective is identifying a person having alcohol in the blood through supervised classification of sensor-generated and computer-vision-based data. To do so, some drunk-status criteria are considered, namely: the concentration of alcohol in the car environment, the facial temperature of the driver and the pupil width. Specifically, for data acquisition purposes, the proposed system incorporates a gas sensor, temperature sensor and a digital camera. Acquired data are analyzed into a two-stages machine learning system consisting of feature selection and supervised classification algorithms. Both acquisition and analysis stages are to be performed into a embedded system, and therefore all procedures and algorithms are designed to work at low-computational resources. As a remarkable outcome, due mainly to the incorporation of feature selection and relevance analysis stages, proposed approach reaches a classification performance of 98% while ensures adequate operation conditions for the embedded system.

15 citations

Journal ArticleDOI
TL;DR: In this article, the authors present a polychotomous view of DM through a pair of interlocked tables that relate these states to their indicators and the sensors that can access each of these indicators (e.g., a camera).
Abstract: Road-vehicle accidents are mostly due to human errors, and many such accidents could be avoided by continuously monitoring the driver. Driver monitoring (DM) is a topic of growing interest in the automotive industry, and it will remain relevant for all vehicles that are not fully autonomous, and thus for decades for the average vehicle owner. The present paper focuses on the first step of DM, which consists in characterizing the state of the driver. Since DM will be increasingly linked to driving automation (DA), this paper presents a clear view of the role of DM at each of the six SAE levels of DA. This paper surveys the state of the art of DM, and then synthesizes it, providing a unique, structured, polychotomous view of the many characterization techniques of DM. Informed by the survey, the paper characterizes the driver state along the five main dimensions--called here "(sub)states"--of drowsiness, mental workload, distraction, emotions, and under the influence. The polychotomous view of DM is presented through a pair of interlocked tables that relate these states to their indicators (e.g., the eye-blink rate) and the sensors that can access each of these indicators (e.g., a camera). The tables factor in not only the effects linked directly to the driver, but also those linked to the (driven) vehicle and the (driving) environment. They show, at a glance, to concerned researchers, equipment providers, and vehicle manufacturers (1) most of the options they have to implement various forms of advanced DM systems, and (2) fruitful areas for further research and innovation.

12 citations

Proceedings ArticleDOI
13 Mar 2020
TL;DR: This product will come to prepare a combination of face detection and face contours along with an additional feature of alcohol consumption of driver and as an accordance, the vehicle acceleration is kept.
Abstract: This system aims to make the driving vehicle safer and protect from drowsiness, alcohol detection avoids accidents and collision between the vehicles while driving and minimizes the road accidents. This product will come to prepare a combination of face detection and face contours along with an additional feature of alcohol consumption of driver and as an accordance, the vehicle acceleration is kept. This product consists of deep learning algorithms applied with microcontroller and interface with the microprocessor. The face will detect using computer vision and forms contours around the face. The person is checked with drowsiness detection then alcohol detection through a set of the device. The set of device checks for alcohol parameters taken by the person. The device used in this paper uses a display interface to show and notify alertness. It messages the concerned person to pick up the person who is being alcoholic. The ignition lock will be removed on resetting and checking of a person's quality of alcohol consumed. It will check from which the alcohol consumption device will be moving out. The device will give alcohol consumption reading and stop the vehicle as per it. The OpenCV library is being used to facilitate face drowsy detection. For collision detection, The product comes with a distance tracker at every side of the vehicle which detects the distance between the vehicle with the model and the vehicles around it. On colliding, it will give an alert to the driver and show the message with detailed collision and stored detail about collision in a black box. It will also help to guide the driver to avoid a collision.

8 citations


Cites methods from "Drunk driving and drowsiness detect..."

  • ..., et al [12] observed the eye movement and face of the driver, the image collected from the camera is analyzed by using Support Vector Machine (SVM) and Logistic Regression Model (LRM)....

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Book ChapterDOI
01 Jan 2021
TL;DR: In this paper, a non-intrusive drowsiness detection system is implemented, which alerts the driver on the onset of Drowsiness using two machine learning techniques, namely LDA and SVM.
Abstract: Forewarning drowsy drivers can reduce the number of road accidents. A non-intrusive drowsiness detection system is implemented, which alerts the driver on the onset of drowsiness. A Pi camera module attached to Raspberry Pi is used to acquire and process the live video of the driver. Haar face detector in OpenCV is used for face detection followed by 68 points of facial landmark identification. Eye and Mouth Aspect Ratios, blink rate and yawning rate are the features extracted. Drowsiness detection is done using two methodologies viz. a threshold-based one and the other, employing artificial intelligence. The machine learning techniques used are LDA and SVM. Feedback is provided as an alarm if a driver is found to be drowsy. The analysis shows that machine learning-based techniques viz. LDA and SVM outperform threshold technique for the dataset considered.

6 citations

DOI
TL;DR: In this paper , the authors used output-only measurements available from motion sensors in the longitudinal drive of mixed autonomous and human-driven platoons to detect drunk drivers' behavior in human driven vehicles within the same platoon.
Abstract: Drunk drivers, who critically and continuously threaten road safety, are usually detected using biological sensors within the same vehicle or after drunk driving behavior is observed by police patrols. Observing an obvious drunk driving behavior indicates a relatively high blood alcohol concentration, and thus, detecting drunk drivers with a relatively low blood alcohol concentration is difficult. This study uses output-only measurements available from motion sensors in the longitudinal drive of mixed autonomous and human-driven platoons to detect drunk drivers’ behavior in human-driven vehicles within the same platoon. The human-driven vehicle is assumed to be between two autonomous vehicles that are able to share information with each other. The proposed approach relates a set of motion sensor measurements with another within the platoon and does not require the knowledge of the excitation signal or the dynamics of the platoon. Numerical simulations are first implemented to test the proposed approach, and then, VISSIM software is implemented to simulate realistic road topography. Further validations on laboratory mobile robots are presented in this article, where a class of abnormal driving conditions that includes human-driven vehicles is simulated. Experiments are carried out by measuring abnormal human-driven vehicles within connected autonomous robots. This method is shown to deal with various system uncertainties, and the approach can deal with the drunk driver case. The drunken driving conditions are modeled mathematically with different blood alcohol concentration levels, which correspond to different effects and different drunk driving behaviors. The proposed transmissibility-based drunk driver detection algorithm is shown to detect drivers with risky relatively low alcohol concentrations.

3 citations

References
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Journal ArticleDOI
TL;DR: A proposal is made for the active of such systems into car-to-car communication to support vehicular ad hoc network's (VANET) primary aim of safe driving and the dissemination of driver behavior via C2C communication.
Abstract: Driver drowsiness and distraction are two main reasons for traffic accidents and the related financial losses. Therefore, researchers have been working for more than a decade on designing driver inattention monitoring systems. As a result, several detection techniques for the detection of both drowsiness and distraction have been proposed in the literature. Some of these techniques were successfully adopted and implemented by the leading car companies. This paper discusses and provides a comprehensive insight into the well-established techniques for driver inattention monitoring and introduces the use of most recent and futuristic solutions exploiting mobile technologies such as smartphones and wearable devices. Then, a proposal is made for the active of such systems into car-to-car communication to support vehicular ad hoc network's (VANET's) primary aim of safe driving. We call this approach the dissemination of driver behavior via C2C communication. Throughout this paper, the most remarkable studies of the last five years were examined thoroughly in order to reveal the recent driver monitoring techniques and demonstrate the basic pros and cons. In addition, the studies were categorized into two groups: driver drowsiness and distraction. Then, research on the driver drowsiness was further divided into two main subgroups based on the exploitation of either visual features or nonvisual features. A comprehensive compilation, including used features, classification methods, accuracy rates, system parameters, and environmental details, was represented as tables to highlight the (dis)advantages and/or limitations of the aforementioned categories. A similar approach was also taken for the methods used for the detection of driver distraction.

279 citations


"Drunk driving and drowsiness detect..." refers background or methods in this paper

  • ...A similar approach was also taken for the methods used for the detection of driver distraction [1]....

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  • ...studies indicate that 25%–30% of driving accidents are caused due to drowsiness [1]....

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  • ...Recent Technologies that have been examined are explained from [1-2, 4, 6-13]....

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Journal ArticleDOI
TL;DR: Experimental results show that the proposed scheme offers high classification accuracy with acceptably low errors and false alarms for people of various ethnicity and gender in real road driving conditions.
Abstract: This paper presents visual analysis of eye state and head pose (HP) for continuous monitoring of alertness of a vehicle driver. Most existing approaches to visual detection of nonalert driving patterns rely either on eye closure or head nodding angles to determine the driver drowsiness or distraction level. The proposed scheme uses visual features such as eye index (EI), pupil activity (PA), and HP to extract critical information on nonalertness of a vehicle driver. EI determines if the eye is open, half closed, or closed from the ratio of pupil height and eye height. PA measures the rate of deviation of the pupil center from the eye center over a time period. HP finds the amount of the driver's head movements by counting the number of video segments that involve a large deviation of three Euler angles of HP, i.e., nodding, shaking, and tilting, from its normal driving position. HP provides useful information on the lack of attention, particularly when the driver's eyes are not visible due to occlusion caused by large head movements. A support vector machine (SVM) classifies a sequence of video segments into alert or nonalert driving events. Experimental results show that the proposed scheme offers high classification accuracy with acceptably low errors and false alarms for people of various ethnicity and gender in real road driving conditions.

177 citations


"Drunk driving and drowsiness detect..." refers background or methods in this paper

  • ...The proposed scheme used visual features such as Eye Index (EI), Pupil Activity (PA), and HP to extract critical information on non-alertness of the driver [2]....

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  • ...The non-alert state represented that the driver is either drowsy or distracted [2]....

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  • ...Thus, making it convenient for signal acquisition but highly dependent on the type of vehicle, driver experience, and the road conditions [2]....

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  • ...The optical flow finds an estimate of the feature points between two video frames extracted using the good features to track method [2]....

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  • ...Recent Technologies that have been examined are explained from [1-2, 4, 6-13]....

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Journal ArticleDOI
TL;DR: The manifold testing of the system demonstrates the practical use of multiple features, particularly with discrete methods, and their fusion enables a more authentic and ample fatigue detection.
Abstract: Driver drowsiness is among the leading causal factors in traffic accidents occurring worldwide. This paper describes a method to monitor driver safety by analyzing information related to fatigue using two distinct methods: eye movement monitoring and bio-signal processing. A monitoring system is designed in Android-based smartphone where it receives sensory data via wireless sensor network and further processes the data to indicate the current driving aptitude of the driver. It is critical that several sensors are integrated and synchronized for a more realistic evaluation of the driver behavior. The sensors applied include a video sensor to capture the driver image and a bio-signal sensor to gather the driver photoplethysmograph signal. A dynamic Bayesian network framework is used for the driver fatigue evaluation. A warning alarm is sounded if driver fatigue is believed to reach a defined threshold. The manifold testing of the system demonstrates the practical use of multiple features, particularly with discrete methods, and their fusion enables a more authentic and ample fatigue detection.

164 citations


"Drunk driving and drowsiness detect..." refers background in this paper

  • ...A warning alarm was also sounded if driver fatigue was believed to reach a defined threshold [7]....

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  • ...Recent Technologies that have been examined are explained from [1-2, 4, 6-13]....

    [...]

Journal ArticleDOI
TL;DR: This paper designs and implements an automatic system, using computer vision, which runs on a computationally limited embedded smart camera platform to detect yawning, and uses a significantly modified implementation of the Viola-Jones algorithm for face and mouth detections.
Abstract: Yawning detection has a variety of important applications in a driver fatigue detection, well-being assessment of humans, driving behavior monitoring, operator attentiveness detection, and understanding the intentions of a person with a tongue disability. In all of the above applications, an automatic detection of yawning is one important system component. In this paper, we design and implement such automatic system, using computer vision, which runs on a computationally limited embedded smart camera platform to detect yawning. We use a significantly modified implementation of the Viola-Jones algorithm for face and mouth detections and, then, use a backprojection theory for measuring both the rate and the amount of the changes in the mouth, in order to detect yawning. As proof-of-concept, we have also implemented and tested our system on top of an actual smart camera embedded platform, called APEX from CogniVue Corporation. In our design and implementations, we took into consideration the practical aspects that many existing works ignore, such as real-time requirements of the system, as well as the limited processing power, memory, and computing capabilities of the embedded platform. Comparisons with existing methods show significant improvements in the correct yawning detection rate obtained by our proposed method.

93 citations


"Drunk driving and drowsiness detect..." refers background or methods in this paper

  • ...The basic idea in the back-projection theory is to create a similar image giving the similarity of each pixel of the candidate object to be matched (the candidate) with the object of interest (the reference) [4]....

    [...]

  • ...Recent Technologies that have been examined are explained from [1-2, 4, 6-13]....

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  • ...Implementation of the Viola–Jones algorithm for face and mouth detections and, use of a back-projection theory for measuring both the rate and the amount of the changes in the mouth, to detect yawning along with the histogram of the grayscale image [4]....

    [...]

Journal ArticleDOI
TL;DR: Three paramount efforts in the development of DFD systems are covered: academic, governmental, and corporate; the reader will see the complete picture of this area in just one article.
Abstract: Driving and transporting goods are necessary for human activity. As a consequence of drivers spending a considerable amount of time at the workplace, and usually under pressure, vehicular accidents have become a great contributor to mortality in several countries. Traffic accidents in countries such as the United States are a central concern. For instance, the U.S. National Highway Traffic Safety Administration (NHTSA) Fatality Analysis Reporting System Encyclopedia [1] shows that there were approximately 55,926 vehicles involved in collisions in 2007, 9,797 of which were due to driver fatigue and inattention. The reported driver-related factors include the driver was drowsy, sleepy, asleep, and/or fatigued, the driver was under the influence of alcohol, drugs, and/or medication, the driver was inattentive (talking, eating, etc.), a cellular telephone was present in the vehicle, a cellular telephone was in use in vehicle.

46 citations


"Drunk driving and drowsiness detect..." refers background in this paper

  • ...Table 1: Total vehicle collusion due to fatigue and inattention (2003-2007) [3]...

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  • ...1: Aspects that can be used to determine the level of drowsiness of a driver [3]...

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