Driver Drowsiness Detection Research Articles

Driver Drowsiness detection based on monitoring of eye bank rate

Driver Drowsiness detection based on monitoring of eye bank rate

ECG-Based Driving Fatigue Detection Using Heart Rate Variability Analysis with Mutual Information

One of the WHO’s strategies to reduce road traffic injuries and fatalities is to enhance vehicle safety. Driving fatigue detection can be used to increase vehicle safety. Our previous study developed an ECG-based driving fatigue detection framework with AdaBoost, producing a high cross-validated accuracy of 98.82% and a testing accuracy of 81.82%; however, the study did not consider the driver’s cognitive state related to fatigue and redundant features in the classification model. In this paper, we propose developments in the feature extraction and feature selection phases in the driving fatigue detection framework. For feature extraction, we employ heart rate fragmentation to extract non-linear features to analyze the driver’s cognitive status. These features are combined with features obtained from heart rate variability analysis in the time, frequency, and non-linear domains. In feature selection, we employ mutual information to filter redundant features. To find the number of selected features with the best model performance, we carried out 28 combination experiments consisting of 7 possible selected features out of 58 features and 4 ensemble learnings. The results of the experiments show that the random forest algorithm with 44 selected features produced the best model performance testing accuracy of 95.45%, with cross-validated accuracy of 98.65%.

Two-Stream Spatial–Temporal Transformer Networks For Driver Drowsiness Detection

<p>For driver drowsiness detection in the real world, the existing methods have good performance in general. However, when the face is blocked, the light is dim, and the driver’s head posture changes, the performance will deteriorate significantly. In this paper, a two-stream Spatial-Temporal transformer network intended to perform diver drowsiness detection task is proposed to solve the above problems. The spatial-temporal graph is extracted from the video and then the results are obtained from 2s-STTN. The model is a two-stream transformer network model. In our model, the Spatial Self-Attention module is used to learn the embedding of different facial landmarks, and the Temporal Self-Attention module is used to learn the correlation between the frames of facial feature points. Different activated facial landmarks are separated and recognized by class activation mapping technology. Each flow recognizes different activated facial features, extracts spatial or temporal features, and integrates the information about facial features, so as to improve the performance of the system. 2s-STTN can not only mine the long-term dependence of driver behavior from video, but also mine the driver drowsiness information provided by the unobstructed facial signs when the face is blocked. By conducting experiments and comparing our model with other models, it is demonstrated that the proposed model has good performance in driver drowsiness detection.</p> <p> </p>

Detection of driver drowsiness using transfer learning techniques

Detection of driver drowsiness using transfer learning techniques

Drowsiness detection system using deep learning based data fusion approach

Drowsiness detection system using deep learning based data fusion approach

Computer vision-based driver fatigue detection framework with personalization threshold and multi-feature fusion

Computer vision-based driver fatigue detection framework with personalization threshold and multi-feature fusion

Face Positioned Driver Drowsiness Detection Using Multistage Adaptive 3D Convolutional Neural Network

Accidents due to driver drowsiness are observed to be increasing at an alarming rate across all countries and it becomes necessary to identify driver drowsiness to reduce accident rates. Researchers handled many machine learning and deep learning techniques especially many CNN variants created for drowsiness detection, but it is dangerous to use in real time, as the design fails due to high computational complexity, low evaluation accuracies and low reliability. In this article, we introduce a multistage adaptive 3D-CNN model with multi-expressive features for Driver Drowsiness Detection (DDD) with special attention to system complexity and performance. The proposed architecture is divided into five cascaded stages: (1) A three level Convolutional Neural Network (CNN) for driver face positioning (2) 3D-CNN based Spatio-Temporal (ST) Learning to extract 3D features from face positioned stacked samples. (3) State Understanding (SU) to train 3D-CNN based drowsiness models (4) Feature fusion using ST and SU stages (5) Drowsiness Detection stage. The Proposed system extract ST values from the face positioned images and then merges it with SU results from each state understanding sub models to create conditional driver facial features for final Drowsiness Detection (DD) model. Final DD Model is trained offline and implemented in online, results show the developed model performs well when compared to others and additionally capable of handling Indian conditions. This method is applied (Trained and Evaluated) using two different datasets, Kongu Engineering College Driver Drowsiness Detection (KEC-DDD) own dataset and National Tsing Hua University Driver Drowsiness Detection (NTHU-DDD) Benchmark Dataset. The proposed system trained with KEC-DDD dataset produces accuracy of 77.45% and 75.91% using evaluation set of KEC-DDD and NTHU-DDD dataset and capable to detect driver drowsiness from 256×256 resolution images at 39.6 fps at an average of 400 execution seconds.

Behavior Based Human Driver Fatigue Detection System Using Deep Learning

The "Driver Fatigue Detection System Using Deep Learning" project is an innovative initiative that aims to improve road safety by utilizing advanced technology. It addresses the problem of accidents caused by driver fatigue, which leads to numerous injuries and fatalities each year. The project focuses on developing a smart real- time monitoring system that employs deep learning algorithms to identify and counteract driver fatigue. This system uses sophisticated deep learning techniques, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks, to process a variety of data sources such as in-cabin camera feeds, vehicle performance metrics, and biometric data like eye movements and facial expressions. By analyzing this data, the system can accurately determine the driver's level of alertness and detect signs of fatigue. The key components of the system involve data preprocessing, extracting relevant features, training the model, and providing real-time alerts. When significant indicators of fatigue are detected, the system immediately issues alerts, such as audio warnings or adjustments to adaptive cruise control, to help the driver regain focus and prevent accidents caused by fatigue. This project not only showcases the adaptability of deep learning but also highlights its potential to revolutionize driver monitoring, thereby significantly improving road safety. Keywords: Fatigue Detection, Audio Warning, Safety.

A Deep-Learning Approach to Driver Drowsiness Detection

Drowsy driving is a widespread cause of traffic accidents, especially on highways. It has become an essential task to seek an understanding of the situation in order to be able to take immediate remedial actions to detect driver drowsiness and enhance road safety. To address the issue of road safety, the proposed model offers a method for evaluating the level of driver fatigue based on changes in a driver’s eyeball movement using a convolutional neural network (CNN). Further, with the help of CNN and VGG16 models, facial sleepiness expressions were detected and classified into four categories (open, closed, yawning, and no yawning). Subsequently, a dataset of 2900 images of eye conditions associated with driver sleepiness was used to test the models, which include a different range of features such as gender, age, head position, and illumination. The results of the devolved models show a high degree of accountability, whereas the CNN model achieved an accuracy rate of 97%, a precision of 99%, and recall and F-score values of 99%. The VGG16 model reached an accuracy rate of 74%. This is a considerable contrast between the state-of-the-art methods in the literature for similar problems.

Machine Learning Approaches for Detecting Driver Drowsiness: A Critical Review

Driver drowsiness is a serious issue that poses a significant threat to road safety, as it can lead to accidents and injuries. In response to this problem, a thorough review of machine learning techniques for detecting driver drowsiness was conducted. The review examined a range of techniques, including more recent approaches that use machine learning and deep learning algorithms as well as different types of data sources driver behaviours, physiological signals, and vehicle behaviours. The primary objective of this paper was to critically analyse and provide a comprehensive overview of the current state-of-the-art in detecting driver drowsiness, evaluate the effectiveness of each technique in terms of accuracy and reliability, and identify potential areas for future research and improvement. In order to achieve this, a systematic review of relevant research studies was undertaken. The review determined that machine learning-based techniques can improve the accuracy and reliability of driver drowsiness detection systems. However, certain limitations, such as the need for large amounts of data, feature extraction, and model structure, must be addressed. By overcoming these limitations, machine learning-based systems have the potential to enhance road safety and prevent accidents. In conclusion, this paper provides a thorough review of machine learning techniques for driver drowsiness detection, evaluates their effectiveness, identifies potential research directions, and highlights their significance and contribution to road safety. The insights gained from this study can be used to guide the development of more effective driver drowsiness detection systems and improve road safety for the community.

Data-driven approach for effective fatigue detection in drivers during driving events

The effectiveness of transportation and the safety of the road depend heavily on drivers during driving events. The way people drive, and the likelihood of accidents or other occurrences may both be strongly impacted by their actions and behaviors. Promoting safe and sensible driving behaviors requires an understanding of the traits and variables that affect drivers during driving incidents. The rising number of traffic accidents highlights the critical necessity to rein in and lessen the prevalence of careless driving. One of the most common causes of these serious mistakes is drowsiness while driving. To combat this problem, algorithms have been created to identify signs of driver weariness and sound an alarm. The developed algorithms have a serious flaw in their accuracy, and it also takes too long to identify driver drowsiness before alerting them. Timeliness and precision are two of the most important factors in preventing traffic mishaps. Multiple datasets have been utilized to improve methods for identifying signs of exhaustion or drowsiness. These data were acquired either through video streaming records of the driver's behavior or from the driver's brain electroencephalogram (EEG) readings. In order to create a high-performance fatigue detection system, this research designs a novel firefly-integrated optimum cascaded convolutional neural network (FI-OCCNN). The suggested approach offers the greatest detection accuracy among the existing classifiers, up to 98.75%. The studies further show that the recommended methods provide the greatest level of detecting precision with the quickest testing time (TT) compared to all other existing and successful tiredness detection techniques.

Multiple robust approaches for EEG-based driving fatigue detection and classification

Multiple robust approaches for EEG-based driving fatigue detection and classification

A Deep Learning model for driver drowsiness detection in extremely low-light conditions

Accidents occurring at night account for a significant percentage when compared to those that happen during the daytime. One of the leading causes of such accidents is driver drowsiness, which could result from inadequate sleep or exhaustion. Continuous monitoring of the facial expressions of the driver can detect signs of drowsiness, and an alarm system can alert the driver and prevent potential accidents. Various research studies have been conducted in this area. However, the low lighting conditions at night make it challenging to obtain detailed images, making it difficult to apply the proposed models effectively. This paper presents an effective model for detecting driver drowsiness using deep learning techniques. The study utilizes AlexNet as a classifier and attains an impressive accuracy of 98.3% after hyperparameter tuning.

Fatigue Driving Detection Method Based on IPPG Technology

Physiological signal index can accurately reflect the degree of fatigue, but the contact detection method will greatly affect the driver's driving. This paper presents a non-contact method for detecting tired driving. It uses cameras and other devices to collect information about the driver's face. By recording facial changes over a period and processing the captured video, pulse waves are extracted. Then the frequency domain index and nonlinear index of heart rate variability were extracted by pulse wave characteristics. Finally, the experiment proves that the method can clearly judge whether the driver is tired. In this study, the Imaging Photoplethysmography (IPPG) technology was used to realise non-contact driver fatigue detection. Compared with the non-contact detection method through identifying drivers' blinking and yawning, the physiological signal adopted in this paper is more convincing. Compared with other methods that detect physiological signals to judge driver fatigue, the method in this paper has the advantages of being non-contact, fast, convenient and available for the cockpit environment.

Efficient and Robust Driver Fatigue Detection Framework Based on the Visual Analysis of Eye States

Fatigue detection based on vision is widely employed in vehicles due to its real-time and reliable detection results. With the coronavirus disease (COVID-19) outbreak, many proposed detection systems based on facial characteristics would be unreliable due to the face covering with the mask. In this paper, we propose a robust visual-based fatigue detection system for monitoring drivers, which is robust regarding the coverings of masks, changing illumination and head movement of drivers. Our system has three main modules: face key point alignment, fatigue feature extraction and fatigue measurement based on fused features. The innovative core techniques are described as follows: (1) a robust key point alignment algorithm by fusing global face information and regional eye information, (2) dynamic threshold methods to extract fatigue characteristics and (3) a stable fatigue measurement based on fusing percentage of eyelid closure (PERCLOS) and proportion of long closure duration blink (PLCDB). The excellent performance of our proposed algorithm and methods are verified in experiments. The experimental results show that our key point alignment algorithm is robust to different scenes, and the performance of our proposed fatigue measurement is more reliable due to the fusion of PERCLOS and PLCDB.

Fatigue Detection Through Car Driver’s Face Using Boosting Local Binary Patterns

The general population is concerned with traffic accidents. Driver fatigue is one of the leading causes of car accidents. Several factors, including nighttime driving, sleep deprivation, alcohol consumption, driving on monotonous roads, and drowsy and fatigue-inducing drugs, can contribute to fatigue. This study proposes a facial appearance-based driver fatigue detection system. This is based on the assumption that facial features can be used to identify driver fatigue. We categorize driver conditions into three groups: normal, talking, and yawning. In this study, we used Adaboost to propose Boosting Local Binary Patterns (LBP) to improve the image features of fatigue drivers in the Support Vector Machine (SVM) model. The experimental results indicate that the system's optimal performance achieves an accuracy value of 93.68%, a recall value of 94%, and a precision value of 94%.
 

Exploring contributing factors to improper driving actions in single-vehicle work zone crashes: A mixed logit analysis considering heterogeneity in means and variances, and temporal instability

A work-zone introduces unexpected conditions for road users and may cause severe safety issues. This study aims to understand drivers’ pre-crash improper actions, which are the major contributing factor to the crash occurrence and outcomes severity in single-vehicle (SV) work-zone crashes. A mixed logit model with heterogeneity in mean and variance was developed using Florida’s seven-year work-zone crash data. The estimated models investigated five pre-crash improper actions (no contributing action, careless driving, speeding, swerving lanes, and other pre-crash actions) in SV work-zone crashes. Year-paired likelihood ratio tests identified temporal instability existing in the dataset over the study period and suggested yearly model estimation to capture the temporal instability. The model results identified a wide range of variables that impact drivers’ pre-crash improper actions, such as driver and weather conditions, work-zone type, and temporal and lighting characteristics. The findings of this study imply that driving psychology plays a critical role in safety-related driving behaviors in work-zone areas. Engineering or education countermeasures that could increase driver alertness of potential hazards are suggested to reduce improper actions by drivers and improve work-zone safety.

Facial feature fusion convolutional neural network for driver fatigue detection

Driver fatigue detection is critical for preventing traffic accidents. However, in real driving scenarios, low-quality input situations often occur due to occlusion, large-turn head deformation, and inconsistent eye states, which introduce noise and degrade the performance of single-stream networks. To address this, we propose a multi-stream facial feature fusion convolution neural network (FFF–CNN) that enhances performance on low-quality inputs. The FFF-CNN includes a global face stream and two local eye streams, with feature fusion used for eye state classification. To facilitate feature fusion, we implemented two preprocessing modules, the feature attention module (FAM) and the stream interaction module (SIM). FAM emphasizes important features in the global face stream, while SIM corrects information from the two local eye streams. The proposed FFF-CNN achieved good performance on both our dataset and the public Closed Eyes in the Wild (CEW) dataset. Compared to single-stream networks, the FFF-CNN improved performance by 4.95% and 4.27% on our dataset and the CEW dataset, respectively. Furthermore, it achieved state-of-the-art performance on the CEW dataset with an accuracy of 98.35%. The effectiveness of the proposed FAM and SIM has been verified by experiments. Notably, our experimental results show that the proposed FFF-CNN effectively improved fatigue detection performance on low-quality input, approaching the performance on normal situations.

Detecting slow eye movements using multi-scale one-dimensional convolutional neural network for driver sleepiness detection

Background:Slow eye movements (SEMs), which occurs during eye-closed periods with high time coverage rate during simulated driving process, indicate drivers’ sleep onset. New Method:For the multi-scale characteristics of slow eye movement waveforms, we propose a multi-scale one-dimensional convolutional neural network (MS-1D-CNN) for classification. The MS-1D-CNN performs multiple down-sampling processing branches on the original signal and uses the local convolutional layer to extract the features for each branch. Results:We evaluate the classification performance of this model on ten subjects’ standard train–test datasets and continuous test datasets by means of subject-subject evaluation and leave-one-subject-out cross validation, respectively. For the standard train–test datasets, the overall average classification accuracies are about 99.1% and 98.6%, in subject-subject evaluation and leave-one-subject-out cross validation, respectively. For the continuous test datasets, the overall average values of accuracy, precision, recall and F1-score are 99.3%, 98.9%, 99.5% and 99.1% in subject-subject evaluation, are 99.2%, 98.8%, 99.3% and 99.0% in leave-one-subject-out cross validation. Comparison with existing method:Results of the standard train–test datasets show that the overall average classification accuracy of the MS-1D-CNN is quite higher than the baseline method based on hand-designed features by 3.5% and 3.5%, in subject-subject evaluation and leave-one-subject-out cross validation, respectively. Conclusions:These results suggest that multi-scale transformation in the MS-1D-CNN model can enhance the representation ability of features, thereby improving classification accuracy. Experimental results verify the good performance of the MS-1D-CNN model, even in leave-one-subject-out cross validation, thus promoting the application of SEMs detection technology for driver sleepiness detection.

Fatigue in transportation operations: A contextual factors survey among Iranian suburban drivers.

Fatigue, as a persistent and serious occupational hazard, plays an important role in traffic accidents by reducing the driver's ability to maneuver with the vehicle and increasing the likelihood of falling asleep at the wheel. The purpose of this study was to identify the individual contextual factors, sleep condition, lifestyle, job characteristics, environmental, and economic conditions that affect the fatigue and alertness of Iranian suburban bus drivers. A questionnaire-based cross-sectional survey was used for this study. Non-probability sampling was used to study 401 suburban bus drivers from Tehran province, Iran, ranging in age from 24 to 67 years. The SPSS22 statistical software V27 was used for the analysis. Approximately half of the participants (50.5%) had experienced fatigue while driving in the previous six months. According to a logistic regression analysis, the contextual factors were all independently related to falling asleep and fatigue while driving. This study provides a thorough understanding of the contextual factors related to drowsy driving and emphasizes the importance of taking these things into consideration when developing interventions aimed at improving the driver's wellbeing and health and lowering the risk of errors and accidents.