Driver Distraction Detection Research Articles

Driver Distraction Detection Based on Fusion Enhancement and Global Saliency Optimization

Driver distraction detection not only effectively prevents traffic accidents but also promotes the development of intelligent transportation systems. In recent years, thanks to the powerful feature learning capabilities of deep learning algorithms, driver distraction detection methods based on deep learning have increased significantly. However, for resource-constrained onboard devices, real-time lightweight models are crucial. Most existing methods tend to focus solely on lightweight model design, neglecting the loss in detection performance for small targets. To achieve a balance between detection accuracy and network lightweighting, this paper proposes a driver distraction detection method that combines enhancement and global saliency optimization. The method mainly consists of three modules: context fusion enhancement module (CFEM), channel optimization feedback module (COFM), and channel saliency distillation module (CSDM). In the CFEM module, one-dimensional convolution is used to capture information between distant pixels, and an injection mechanism is adopted to further integrate high-level semantic information with low-level detail information, enhancing feature fusion capabilities. The COFM module incorporates a feedback mechanism to consider the impact of inter-layer and intra-layer channel relationships on model compression performance, achieving joint pruning of global channels. The CSDM module guides the student network to learn the salient feature information from the teacher network, effectively balancing the model’s real-time performance and accuracy. Experimental results show that this method outperforms the state-of-the-art methods in driver distraction detection tasks, demonstrating good performance and potential application prospects.

Real time distracted driver detection using Xception architecture and Raspberry Pi

Researchers are concentrating on developing technologies to identify and caution drivers against driving while distracted because it is a major cause of traffic accidents. According to the National Highway Traffic Safety Administrator's report, distracted driving is to blame for roughly one in every five car accidents.Our goal is to create an accurate and dependable method for identifying distracted drivers and alerting them to their lack of focus. We take inspiration from the success of convolutional neural networks in computer vision to do this. Our strategy entails putting in place a CNN-based system that can recognize when a driver is distracted as well as pinpoint the precise cause of their preoccupation. Real-time detection, however, necessitates three apparently mutually exclusive requirements for an optimal network: a small number of parameters, high accuracy, and fast speed.

A Multimodal Recurrent Model for Driver Distraction Detection

A Multimodal Recurrent Model for Driver Distraction Detection

A New Approach to Detect Driver Distraction to Ensure Traffic Safety and Prevent Traffic Accidents: Image Processing and MCDM

One of the factors that threaten traffic safety and cause various traffic problems is distracted drivers. Various studies have been carried out to ensure traffic safety and, accordingly, to reduce traffic accidents. This study aims to determine driver-distraction classes and detect driver violations with deep learning algorithms and decision-making methods. Different driver characteristics are included in the study by using a dataset created from five different countries. Weight classification in the range of 0–1 is used to determine the most important classes using the AHP method, and the most important 9 out of 23 classes are determined. The YOLOv8 algorithm is used to detect driver behaviors and distraction action classes. The YOLOv8 algorithm is examined according to performance-measurement criteria. According to mAP 0.5:0.95, an accuracy rate of 91.17% is obtained. In large datasets, it is seen that a successful result is obtained by using the AHP method, which is used to reduce transaction complexity, and the YOLOv8 algorithm, which is used to detect driver distraction. By detecting driver distraction, it is possible to partially avoid traffic accidents and the negative situations they create. While detecting and preventing driver distraction makes a significant contribution to traffic safety, it also provides a significant improvement in traffic accidents and traffic congestion, increasing transportation efficiency and the sustainability of cities. It also serves sustainable development goals such as energy efficiency and reducing carbon emissions.

Domain Adaptive Driver Distraction Detection Based on Partial Feature Alignment and Confusion-Minimized Classification

Domain Adaptive Driver Distraction Detection Based on Partial Feature Alignment and Confusion-Minimized Classification

LGD-FCOS: driver distraction detection using improved FCOS based on local and global knowledge distillation

LGD-FCOS: driver distraction detection using improved FCOS based on local and global knowledge distillation

Real-time driver drowsiness and distraction detection using convolutional neural network with multiple behavioral features

Real-time driver drowsiness and distraction detection using convolutional neural network with multiple behavioral features

Driver Distraction Detection and Alert System Using Convolutional Neural Networks

Abstract: The Driver Distraction and Alert System Using Convolutional Neural Networks provide a real-time machine learning and webcam-based monitoring system for driver distraction detection. The system uses a sophisticated Convolutional Neural Network (CNN) architecture trained on the State Form of Distracted Driver Detection dataset, which is made possible by utilizing the OpenCV, TensorFlow, and Pandas packages. With improvements like dropout layers and Xavier weight initialization, the model efficiently classifies ten driver attention states through preprocessing stages like image scaling and normalization. The results of the experiment show how well the suggested method works to identify distracted driving behaviors; a revised model on the Kaggle competition platform achieved a competitive public score of 2.67118

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

Distracted driving is a major global contributing factor to traffic accidents. Distracted drivers are three times more likely to be involved in an accident than non-distracted drivers. This is why detecting driver distraction is essential to improving road safety. Several prior studies have proposed a range of methods for identifying driver distraction, including as image, sensor, and machine learning-based approaches. However, these methods have limitations in terms of accuracy, complexity, and real-time performance. By combining a convolutional neural network (CNN) with the You Only Look Once (YOLO) object identification method, this study suggests a unique way to driver distraction detection The two primary phases of the suggested paradigm are object identification utilizing Yolo and classification of the identified things. The YOLO algorithm is used to identify and pinpoint the driver's hands, face, and any other objects that might draw their attention away from the road. The objects that have been observed are then categorized using a CNN to determine whether or not the driver is distracted. When evaluated on a publicly available dataset, the proposed model shows good performance in detecting driver preoccupation. Utilize the CNN algorithm in addition to ocular features to determine the driver's level of fatigue. The proposed method might be incorporated into advanced driver assistance systems with real-time environment to improve road safety.

Towards Safer Roads: A Deep Learning Approach to Driver Distraction Detection in Four-wheeler Cars

The fact that there are more than 50 million cars sold annually and more than 1.3 million fatal motor vehicle accidents each year indicates the urgent need for stronger road safety regulations. Driver behaviour needs to be addressed, especially in emerging nations like India, which is responsible for 11% of all road fatalities worldwide. Diversion has been identified as the leading cause of 78% of accidents involving drivers. Distractions can take many different forms, from using a phone to interacting with others, and they greatly hinder road safety. This work aims to address this important problem by creating a highly effective machine learning (ML) model that employs computer vision techniques to classify various driver distractions in real-time. Utilising cutting-edge models, such as an ensemble of CNNs and convolutional neural networks (CNNs) like ResNet50. Our goal is to effectively identify and categorise distractions through the use of deep learning and picture recognition, allowing for proactive intervention to avert mishaps. Beyond classification accuracy, this study evaluates the model’s overall speed and scalability, which are critical for deployment on edge devices. We assess the practical practicality and wide-spread adoption of our approach by analysing performance parameters like inference time and resource utilisation

Detection of distracted driving through the analysis of real-time driver, vehicle, and roadway volatilities

Distracted driving adversely impacts drivers’ decision-making and leads to safety-critical events (SCEs). Early detection of driver distraction is critical to prevent traffic crashes by providing warning messages to drivers and the surrounding vehicles. This study harnesses real-time multidimensional data collected through sensors that examine the variations in driver biometrics, vehicle kinematics, and roadway surroundings in different driving scenarios conducted on a Multimodal Virtual Reality Simulator. The driving behaviors of the study participants were examined under various visual detection response tasks of increasing complexity. The study classifies driving behaviors on a 5-level ordinal scale by estimating a Panel Ordered Logit Model, Random Forest, and Artificial Neural Network, using real-time volatilities in driver biometric signals, vehicle speed and acceleration, and roadway surroundings. The study results reveal that the driver gaze and the coefficients of variation in vehicle speed, driver eye movements, vehicular distances from the lane centerline, and the following vehicle significantly impact distracted driving. The study’s findings align with the principles of the safe systems approach by emphasizing the development of proactive safety measures in the form of feedback and warning the driver and surrounding vehicles of a potential distracted driving event, helping to foster safer user behavior and vehicles.

Driver Drowsiness and Distraction Detection: An Image Processing-Based Comparative Analysis for Improved Accuracy

This research presents a comprehensive examination and implementation of driver drowsiness, distraction, and detection systems utilizing advanced image processing techniques. The literature review encompasses an in-depth analysis of drowsiness, distraction, and detection parameters, presented in tabulated form. The proposed architecture is detailed through flow charts outlining both software and hardware components. A comparative analysis of key parameters, along with their corresponding accuracy percentages, is provided in a structured table. The findings demonstrate that the proposed system exhibits superior accuracy compared to existing results. Through practical implementation, the system proves effective in accurately detecting driver sleepiness, classifying states as Sleepy, Drowsy, or Active. Notably, the proposed work achieves high accuracy, with eye detection accuracy at 98% and drowsiness accuracy at 96%, showcasing an improvement of approximately 10% when compared to existing solutions.

Three stage classification framework with ranking scheme for distracted driver detection using heuristic-assisted strategy

In recent days, the number of road accidents are greatly increased in world wide. The people around the world died due to the rapid road crush. The driver behavior must be detected to neglect the occurrence of road accidents. In this work, the three stage deep learning-based techniques are developed to detect the distracted driver action. The developed model is implemented in three phase to detect the distracted behavior of the driver during the driving process. In the first stage, the images are gathered from standard public resources regarding various drivers while driving. Next, the deep features retrieved using VGG16 and Inception and Xception networks. Then, the weighted fusedfusion is done by gathered features, where the weight parameter is optimized through the Adaptive Gradient-based Optimizer (AGBO) algorithm. Further, the driver behavior classification is done through a Deep Belief Network (DBN) with Radial Basis Functions Neural Networks (RBF), where the hyperparameters of DBN and RBF are optimized through the same AGBO algorithm. Finally, the first set of classified outcomes is attained from the images. In the second stage, the gathered images are fed to the feature extraction stage, where the texture features and motion features are gathered from the images. These collected features are fed to the same optimized DBN with RBF and the second set of classified outcomes with human behaviors has been attained.In the third stage, the gathered image fralgoriom the dataset is fed to the PCA for extracting the features. The gathered features are given to the same optimized DBN with the RBF classifier to get the classified outcome. Later, the classified outcomes from the three stages are fed to the ranking procedure for detecting the distracted driver action. When considering the evaluation indices, the suggested distracted driver prediction model attains 97.67% of accuracy, 97.68% of sensitivity, 97.67% of specificity, and 97.55% of precision value. Thus, the performance of the developed model in the distracted driver prediction is enhanced than the existing approaches.

Eye Blink Detection- A New System for Driver Drowsiness and Distraction Detection

The advancement of technology has led to innovative solutions for ensuring road safety, particularly in the detection of driver drowsiness and distraction. This abstract presents a novel approach using eye blink detection as a fundamental component in a system designed to mitigate the risks associated with drowsy and distracted driving. The proposed system integrates cutting-edge computer vision techniques to accurately detect and analyze eye blinks in real-time. By utilizing high-resolution cameras installed within the vehicle, the system captures and processes images of the driver's face, focusing on the eyes to track blinking patterns and durations. Through sophisticated algorithms and machine learning models, it distinguishes between normal blinks and extended periods of eye closure, indicative of drowsiness or distraction. Furthermore, the system incorporates contextual awareness by considering various factors such as environmental conditions, driving behavior, and facial expressions to enhance the accuracy of detection. Machine learning algorithms trained on diverse datasets enable the system to adapt and improve its performance over time, making it more adept at recognizing subtle variations in blink patterns unique to each driver. The applications of this system are multifaceted. Firstly, it serves as an early warning system, alerting drivers when signs of drowsiness or distraction are detected. This proactive approach aims to prevent accidents by prompting drivers to take necessary breaks or refocus their attention on the road. Moreover, the system can integrate with existing driver assistance technologies, allowing for automated interventions, such as adjusting seat vibrations or emitting audible alerts to re-engage the driver. In conclusion, the proposed eye blink detection system represents a significant advancement in the realm of driver safety technologies. By leveraging sophisticated algorithms and real-time analysis of blink patterns, it offers a promising solution to mitigate the risks associated with driver drowsiness and distraction, ultimately contributing to safer roads and reducing the likelihood of accidents. KEYWORDS: Eye blinking, Detection, Driver, Drowsiness, Distraction, Detection, computer vision, machine learning, Deep learning, image processing.

Towards driver distraction detection: a privacy-preserving federated learning approach

Towards driver distraction detection: a privacy-preserving federated learning approach

A lightweight model combining convolutional neural network and Transformer for driver distraction recognition

Driver distraction recognition has been studied by many researchers. However, most studies have failed to balance the efficiency and accuracy of models. In this study, a lightweight network called CaTNet is proposed. The CaTNet is a simplified framework based on the existing model ConvNeXt, which prunes redundant feature layers. And a whole new module CaT containing self-attention is introduced in tandem, both of which are combined to enhance the feature characterization. It captures long-range dependencies and retains the local inductive bias provided by the convolutional neural network (CNN). The proposed method is verified on the dataset of American University in Cairo (AUC) and the State Farm Distracted Driver Detection (SFD3) Dataset. The CaTNet achieves 94.82% and 99.91% Top-1 accuracy while the number of model parameters is only 2.832M, with 12.44 frames per second (FPS) running on Jetson Nano. These results are superior to other existing models.

Highly Discriminative Driver Distraction Detection Method Based on Swin Transformer

Driver distraction detection not only helps to improve road safety and prevent traffic accidents, but also promotes the development of intelligent transportation systems, which is of great significance for creating a safer and more efficient transportation environment. Since deep learning algorithms have very strong feature learning abilities, more and more deep learning-based driver distraction detection methods have emerged in recent years. However, the majority of existing deep learning-based methods are optimized only through the constraint of classification loss, making it difficult to obtain features with high discrimination, so the performance of these methods is very limited. In this paper, to improve the discrimination between features of different classes of samples, we propose a high-discrimination feature learning strategy and design a driver distraction detection model based on Swin Transformer and the highly discriminative feature learning strategy (ST-HDFL). Firstly, the features of input samples are extracted through the powerful feature learning ability of Swin Transformer. Then, the intra-class distance of samples of the same class in the feature space is reduced through the constraint of sample center distance loss (SC loss), and the inter-class distance of samples of different classes is increased through the center vector shift strategy, which can greatly improve the discrimination of different class samples in the feature space. Finally, we have conducted extensive experiments on two publicly available datasets, AUC-DD and State-Farm, to demonstrate the effectiveness of the proposed method. The experimental results show that our method can achieve better performance than many state-of-the-art methods, such as Drive-Net, MobileVGG, Vanilla CNN, and so on.

A Lightweight and Efficient Distracted Driver Detection Model Fusing Convolutional Neural Network and Vision Transformer

A Lightweight and Efficient Distracted Driver Detection Model Fusing Convolutional Neural Network and Vision Transformer

A Novel Approach Distracted Driver Detection System Using ML Techniques

A Novel Approach Distracted Driver Detection System Using ML Techniques

L-TLA: A Lightweight Driver Distraction Detection Method Based on Three-Level Attention Mechanisms

L-TLA: A Lightweight Driver Distraction Detection Method Based on Three-Level Attention Mechanisms