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.

Hybrid Embedded-Systems-Based Approach to in-Driver Drunk Status Detection Using Image Processing and Sensor Networks

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.

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Survey and synthesis of state of the art in driver monitoring.

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.

Drowsiness Alert, Alcohol Detect and Collision Control for Vehicle Acceleration

This work presents a system for detecting excess alcohol in drivers to reduce road traffic accidents. To do so, criteria such as alcohol concentration the environment, a facial temperature of the driver and width of the pupil are considered. To measure the corresponding variables, the data acquisition procedure uses sensors and artificial vision. Subsequently, data analysis is performed into stages for prototype selection and supervised classification algorithms. Accordingly, the acquired data can be stored and processed in a system with low-computational resources. As a remarkable result, the amount of training samples is significantly reduced, while an admissible classification performance is achieved - reaching then suitable settings regarding the given device’s conditions.

Multivariate Approach to Alcohol Detection in Drivers by Sensors and Artificial Vision

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.

Driver Drowsiness Detection System Using Conventional Machine Learning

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.

Drunk driving and drowsiness detection

Advancement of safety features to avert drunk and drowsy driving has been one of the leading technical challenges in the automobile business. Especially in this modern age where people are under serious work pressure has led to higher crash rates. To prevent such accidents this paper discusses the use of nonintrusive techniques by using visual features to determine whether driver is driving in alert state. Drowsiness detection has been implemented using HAAR Cascade for face and eye closure detection and yawn detection implemented using Template matching in visual studio 2013. For drunk state detection, an alcohol sensor (MQ-3) has been implemented to avoid drunk driving. If the driver is found to be in drunk or drowsy condition, then an alarm would be generated and the driver being alerted using a buzzer and a vibrator that can be placed in the seatbelt or under driver seat thus preventing from mishaps taking place.

Vivek R. Nair

Papers

Drunk driving and drowsiness detection

Drunk Driving and Drowsiness Detection Alert System