Drowsiness Detection Research Articles

Advanced Driver-Assistance Systems: Features Journey for Tomorrow

Intelligent associated vehicles (ICVs) are accepted to completely change people sooner rather than later by making the transportation more secure, cleaner and more agreeable. Albeit numerous models of ICVs have been created to demonstrate the idea of independent driving and the plausibility of further developing traffic effectiveness, there actually exists a critical hole prior to accomplishing large scale manufacturing of undeniable level ICVs. The goal of this study is to introduce an outline of both the cutting edge and future viewpoints of key necessary advances for future ICVs. It is a moving undertaking to survey every connected work and foresee their future viewpoints, particularly for such a perplexing and interdisciplinary area of examination. Advanced driver-assistance systems (ADASs) have become a salient feature for safety in modern vehicles. They are also a key underlying technology in emerging autonomous vehicles. State-of-the-art ADASs are primarily vision based, various type of features for ex. Lane Departure Warning (LDW), Blind Spot Monitoring (BSM), Forward Collision Warning (FCW), Automatic Emergency Braking (AEB), Traffic Sign Recognition, High Beam Assist, Rear Cross Traffic Alert, Driver Drowsiness Detection, Obstacle Aware Acceleration, Auto-steer etc. The paper aims at giving a complete picture focusing on the ADAS features for the user-friendly design of human-machine interfaces between driver and assistance system.

Drowsiness Detection using EAR (Eye Aspect Ratio) by Machine Learning

Drowsiness detection is critical in many sectors, including transportation, healthcare, and workplace safety, since it may have a substantial influence on human performance and safety. Traditional sleepiness detection approaches are frequently subjective, time intensive, and unsuitable for real-time applications. In recent years, computer vision-based techniques that use eye-related characteristics to identify tiredness have shown promise. The eye aspect ratio, a geometric measure determined from ocular landmarks that indicates the openness or closure of the eyes, is one such trait. We present a sleepiness detection method in this research that blends eye aspect ratio computation with machine learning techniques to provide real-time and accurate drowsiness evaluation. We offer a thorough technique that includes calculating the eye-aspect ratio, extracting features, and classifying them with machine learning methods. The ability of our proposed technique is evaluated using a dataset of eye pictures acquired from individuals under various sleepiness situations. Our experimental findings show that our technique is successful in detecting sleepiness with good accuracy, sensitivity, and specificity. Our suggested method includes applications such as sleepy driving detection, tiredness monitoring in hospital settings, and workplace safety. This research advances the area of sleepiness detection by combining the ocular aspect ratio and machine learning to measure tiredness in real time. KEYWORDS: Drowsiness detection, Eye aspect ratio, Machine learning, Eye tracking, Blink rate, Pupil dilation, Real-time monitoring, Non-invasive, Safety, Transportation

Intelligent and secure real-time auto-stop car system using deep-learning models

In this study, we introduce an innovative auto-stop car system empowered by deep learning technology, specifically employing two Convolutional Neural Networks (CNNs) for face recognition and travel drowsiness detection. Implemented on a Raspberry Pi 4, our system is designed to cater exclusively to certified drivers, ensuring enhanced safety through intelligent features. The face recognition CNN model accurately identifies authorized drivers, employing deep learning techniques to verify their identity before granting access to vehicle functions. This first model demonstrates a remarkable accuracy rate of 99.1%, surpassing existing solutions in secure driver authentication. Simultaneously, our second CNN focuses on real-time detecting+ of driver drowsiness, monitoring eye movements, and utilizing a touch sensor on the steering wheel. Upon detecting signs of drowsiness, the system issues an immediate alert through a speaker, initiating an emergency park and sending a distress message via Global Positioning System (GPS). The successful implementation of our proposed system on the Raspberry Pi 4, integrated with a real-time monitoring camera, attains an impressive accuracy of 99.1% for both deep learning models. This performance surpasses current industry benchmarks, showcasing the efficacy and reliability of our solution. Our auto-stop car system advances user convenience and establishes unparalleled safety standards, marking a significant stride in autonomous vehicle technology.

Studying Drowsiness Detection Performance While Driving Through Scalable Machine Learning Models Using Electroencephalography

Driver drowsiness is a significant concern and one of the leading causes of traffic accidents. Advances in cognitive neuroscience and computer science have enabled the detection of drivers’ drowsiness using Brain-Computer Interfaces (BCIs) and Machine Learning (ML). However, the literature lacks a comprehensive evaluation of drowsiness detection performance using a heterogeneous set of ML algorithms, being also necessary to study the performance of scalable ML models suitable for groups of subjects. To address these limitations, this work presents an intelligent framework employing BCIs and features based on electroencephalography for detecting drowsiness in driving scenarios. The SEED-VIG dataset is used to evaluate the best-performing models for individual subjects and groups. Results show that Random Forest (RF) outperformed other models used in the literature, such as Support Vector Machine (SVM), with a 78% f1-score for individual models. Regarding scalable models, RF reached a 79% f1-score, demonstrating the effectiveness of these approaches. This publication highlights the relevance of exploring a diverse set of ML algorithms and scalable approaches suitable for groups of subjects to improve drowsiness detection systems and ultimately reduce the number of accidents caused by driver fatigue. The lessons learned from this study show that not only SVM but also other models not sufficiently explored in the literature are relevant for drowsiness detection. Additionally, scalable approaches are effective in detecting drowsiness, even when new subjects are evaluated. Thus, the proposed framework presents a novel approach for detecting drowsiness in driving scenarios using BCIs and ML.

Comparing Haar Cascade and YOLOFACE for Region of Interest Classification in Drowsiness Detection

Comparing Haar Cascade and YOLOFACE for Region of Interest Classification in Drowsiness Detection

SleepyWheels: An ensemble model for drowsiness detection leading to accident prevention

<p>Approximately 30% of traffic accidents in the world are attributed to driver drowsiness. Various approaches have been suggested by different research teams to detect drowsiness, but their methods have drawbacks. Some involve invasive techniques that cause driver discomfort, some involve too many false positives that cause distractions while driving while others rely on complex models that are too resource-intensive. In this paper, we present SleepyWheels, a novel drowsiness detection system. Our key insight is the combined use of two lightweight neural networks: a binary classifier and a facial landmark detector. This innovative approach minimizes false positives and proves resilient across diverse testing scenarios, such as different camera positions, variations in skin tone and when there is obscurement of facial features by objects like eyewear. Research outcomes include a working prototype of the system, a custom dataset for training the classifier and a trained model, which attains an impressive 97% accuracy rate. Deployment and testing were performed on Windows 10 but the system can be deployed on edge devices like Raspberry Pi. The lightweight nature of the models unlocks possibilities of deployment on mobile and embedded devices for use in vehicles.</p>

IoT-Assisted Automatic Driver Drowsiness Detection through Facial Movement Analysis Using Deep Learning and a U-Net-Based Architecture

The main purpose of a detection system is to ascertain the state of an individual’s eyes, whether they are open and alert or closed, and then alert them to their level of fatigue. As a result of this, they will refrain from approaching an accident site. In addition, it would be advantageous for people to be promptly alerted in real time before the occurrence of any calamitous events affecting multiple people. The implementation of Internet-of-Things (IoT) technology in driver action recognition has become imperative due to the ongoing advancements in Artificial Intelligence (AI) and deep learning (DL) within Advanced Driver Assistance Systems (ADAS), which are significantly transforming the driving encounter. This work presents a deep learning model that utilizes a CNN–Long Short-Term Memory network to detect driver sleepiness. We employ different algorithms on datasets such as EM-CNN, VGG-16, GoogLeNet, AlexNet, ResNet50, and CNN-LSTM. The aforementioned algorithms are used for classification, and it is evident that the CNN-LSTM algorithm exhibits superior accuracy compared to alternative deep learning algorithms. The model is provided with video clips of a certain period, and it distinguishes the clip by analyzing the sequence of motions exhibited by the driver in the video. The key objective of this work is to promote road safety by notifying drivers when they exhibit signs of drowsiness, minimizing the probability of accidents caused by fatigue-related disorders. It would help in developing an ADAS that is capable of detecting and addressing driver tiredness proactively. This work intends to limit the potential dangers associated with drowsy driving, hence promoting enhanced road safety and a decrease in accidents caused by fatigue-related variables. This work aims to achieve high efficacy while maintaining a non-intrusive nature. This work endeavors to offer a non-intrusive solution that may be seamlessly integrated into current automobiles, hence enhancing accessibility to a broader spectrum of drivers through the utilization of facial movement analysis employing CNN-LSTM and a U-Net-based architecture.

A Survey on Drowsiness Detection – Modern Applications and Methods

A Survey on Drowsiness Detection – Modern Applications and Methods

A Real-time Light-weight Computer Vision Application for Driver’s Drowsiness Detection

A Real-time Light-weight Computer Vision Application for Driver’s Drowsiness Detection

A Novel Brain-Machine Safety System Based on Drowsiness Detection using the PNN and MLP Algorithms

This work aims to develop a safety system capable of detecting drowsiness, in order to reduce the accident rate, the main idea is based on the classification of the cerebral signal captured by the electroencephalograph, after processing to eliminate all noise, the resulting data will then be segmented into two classes, the first category is devoted to the state of wakefulness and the second will be devoted to drowsiness. This categorization is based on the combination of features such as mean and standard deviation, and performed by two neural network algorithms, the probabilistic neural network and the multilayer perceptron, whose performance is evaluated according to machine learning metrics such as accuracy, error, specificity, sensitivity, F1 indicator and ROC curve representation. The binary classification results are highly significant in terms of class discrimination, with both models achieving excellent performance, with the MLP classifier outperforming the PNN model with an accuracy of 96.10% and an accuracy rate of 98.43%. This suggests that the MLP model can be effectively applied to critical tasks where good discrimination is required, such as detecting drowsiness in drivers to avoid accidents.

Drowsiness Detection in Transportation: a Deep Cascaded Convolutional Neural Network (DCCNN) Aproach for Improving Road Safety

Drowsiness Detection in Transportation: a Deep Cascaded Convolutional Neural Network (DCCNN) Aproach for Improving Road Safety

Driver Drowsiness Detection Based on Joint Human Face and Facial Landmark Localization With Cheap Operations

Driver Drowsiness Detection Based on Joint Human Face and Facial Landmark Localization With Cheap Operations

A sophisticated Drowsiness Detection System via Deep Transfer Learning for real time scenarios

<abstract> <p>Driver drowsiness is one of the leading causes of road accidents resulting in serious physical injuries, fatalities, and substantial economic losses. A sophisticated Driver Drowsiness Detection (DDD) system can alert the driver in case of abnormal behavior and avoid catastrophes. Several studies have already addressed driver drowsiness through behavioral measures and facial features. In this paper, we propose a hybrid real-time DDD system based on the Eyes Closure Ratio and Mouth Opening Ratio using simple camera and deep learning techniques. This system seeks to model the driver's behavior in order to alert him/her in case of drowsiness states to avoid potential accidents. The main contribution of the proposed approach is to build a reliable system able to avoid false detected drowsiness situations and to alert only the real ones. To this end, our research procedure is divided into two processes. The offline process performs a classification module using pretrained Convolutional Neural Networks (CNNs) to detect the drowsiness of the driver. In the online process, we calculate the percentage of the eyes' closure and yawning frequency of the driver online from real-time video using the Chebyshev distance instead of the classic Euclidean distance. The accurate drowsiness state of the driver is evaluated with the aid of the pretrained CNNs based on an ensemble learning paradigm. In order to improve models' performances, we applied data augmentation techniques for the generated dataset. The accuracies achieved are 97 % for the VGG16 model, 96% for VGG19 model and 98% for ResNet50 model. This system can assess the driver's dynamics with a precision rate of 98%.</p> </abstract>

Driver Distraction and Drowsiness Detection Based on Object Detection Using Deep Learning Algorithm

Driver Distraction and Drowsiness Detection Based on Object Detection Using Deep Learning Algorithm

A Systematic Review on Driver Drowsiness Detection Using Eye Activity Measures

A Systematic Review on Driver Drowsiness Detection Using Eye Activity Measures

Novel Transfer Learning Approach for Driver Drowsiness Detection Using Eye Movement Behavior

Novel Transfer Learning Approach for Driver Drowsiness Detection Using Eye Movement Behavior

Driving Towards Safety: Examining the Role of Drowsiness Detection Systems in Accident Prevention

Driving Towards Safety: Examining the Role of Drowsiness Detection Systems in Accident Prevention

Biosignals Monitoring for Driver Drowsiness Detection Using Deep Neural Networks

Biosignals Monitoring for Driver Drowsiness Detection Using Deep Neural Networks

Comprehensive Survey of Driver Drowsiness Systems

Driver monitoring systems have been improved over time as artificial intelligence and computer technology have advanced. Several experimental studies have collected real-world driver drowsiness data and used various artificial intelligence algorithms and feature combinations to dramatically improve the real-time effectiveness of these systems. This study presents an updated assessment of the driver sleepiness detection systems implemented over the last decade. In modern automobiles, assessing the driver's cognitive condition is an important aspect of passenger safety. The term cognitive state refers to a driver's mental and emotional state, which has a substantial impact on their ability to drive safely. Drivers' cognitive states may be altered by factors such as fatigue, distraction, stress, or disability. Intelligent automotive technology may be able to adapt and aid the driver by identifying varied conditions in real-time, reducing the frequency of accidents. The face, being an integral component of the body, communicates a significant quantity of information. The facial expressions, such as blinking and yawning patterns, exhibit changes in a driver when they are experiencing fatigue.

Analysis of heart rate indices at different levels of sleepiness

The objective detection of sleepiness through physiological indices is crucial for ensuring transportation and industrial safety. As gadgets become more commonplace for measuring heart rate, it is important to identify valid indices that correlate with the level of sleepiness. Research indicates that time-domain and frequency-domain heart rate indices align with levels of alertness and sleepiness [e.g., 1, 2]. The aim of this paper is to identify heart rate indices that correlate with varying degrees of evening sleepiness in vivo, whether low or high. The data analyzed in this paper was obtained from 32 recordings from the SSDD (Subjective Sleepiness Dynamics Dataset) collected at the UNN Cyberpsychology Laboratory since 2022. The experiment was designed in the following manner: participants put on a heart rhythm sensor (Polar H10) at 7:40 PM in their homes and connected it to a smartphone app. Participants then completed a self-reported questionnaire in the electronic system. Starting at 8:00 PM, and every 30 minutes thereafter, the participant recorded data on the subjective level of sleepiness via the Karolinska (KSS) and Stanford (SSS) sleepiness scales up until the time of the participant’s bedtime. The selection of 32 recordings met specific criteria: all participants went to bed between 10:30 and 11:00 PM and completed both the KSS and SSS on time at each time point (08:00, 08:30, 09:00, 09:30, and 10:00 PM). For each time point, an integral sleepiness score was calculated according to the formula: sl=(KSS/10+SSS/7)/2. The 4-minute rhythmogram recordings corresponding to the time points of KSS and SSS filling were chosen for analysis. Data were processed in Jupyter Notebook. Heart rhythm indices, including time-domain, frequency-domain, and nonlinear indices, were computed for each time point from the rhythmogram — NN-interval sequence — using the “hrv-analysis” package. The study employed Student’s t-test to compare indices for varying levels of sleepiness based on integral scores, where “low level” was defined as sl 0.45 and “high level” was defined as sl 0.55. Additionally, the Pearson criterion was used to evaluate the correlation between sl and heart rate indices at each time interval. The indices responsible for the variability of the NN-interval sequence, namely NNi_50 (p=0.027), NNi_20 (p=0.007), and pNNi_20 (p=0.024), exhibited lower values during periods of “low” sleepiness (N=54) as compared to “high” sleepiness (N=58). Moreover, we observed that the autonomic balance index, i.e., the ratio of the power of the heart rate variability spectrum in the low-frequency band to that in the high-frequency band, was higher during ‘high’ sleepiness (p=0.015). Analysis revealed that correlations between integral sleepiness score and heart rate indices were present only for the 08:30 PM time point. Correlations of sl with the NN-interval range (R=–0.388; p=0.028), the severity of sympathetic regulatory circuit activity (R=–0.383; p=0.031), and the nonlinear cardiovagal index (R=–0.359; p=0.043) were found. Thus, it can be inferred that increased sleepiness is associated with decreased heart rate variability indices, along with decreased functioning of the autonomic nervous system overall.