Distracted driver detection 2022. For example, Yan et al.

Distracted driver detection 2022 Specifically, both hybrid and prediction level fusion increased the overall Distracted driving is any activity that deviates an individual’s attention from driving. Most distracted driver detection algorithms are published with comparative evaluations. Conditional self-driving still relies on the driver paying at- Nov 20, 2024 · 2. Aiming at these challenges, this paper proposes a driver behavior Oct 1, 2022 · In Australia, 14% of all crashes involve a distracted driver (DriveRisk, 2020); in Canada, 27% of all crashes involve a distracted driver (Marija, 2022). Unlike the commonly used YOLOv8 method, an attention mechanism named MHSA and a May 1, 2022 · We have found that the proposal can effectively detect various abnormal and distracted behavior such as drinking, talking and texting, etc. 1109/ITSC55140. 2. Recently, researchers have applied Convolutional Neural Network (CNN) and Vision Transformer (ViT) models for driver state Jul 1, 2022 · In the traffic control system, the driver's distracted behavior detection is particularly important for traffic safety. However, this Dec 9, 2024 · F. 1. - hsonetta/Distracted-Driver-Detection Jul 8, 2022 · Conference: IJCAI 2022 Workshop on Workshop on Artificial Intelligence for Autonomous Driving (AI4AD) Distracted driver detection systems help keep people focused on the road. In addition to the loss of lives and injuries, the financial bur-den from distracted driving crashes collectively amounts Sep 1, 2022 · Request PDF | On Sep 1, 2022, Yajuan Wei and others published Distracted Driver Behavior Detection Based-on An Improved YOLOX Framework | Find, read and cite all the research you need on ResearchGate May 11, 2022 · With distracted driving becoming one of the main causes of traffic accidents, deep learning technology has been widely used in distracted driving detection, which achieves high accuracy when the training and test data are identically distributed. Thus, the on-board monitoring . Almost 20% of those killed in distracted driving-related crashes were pedestrians, cyclists, and others outside the vehicle. Each driver is recorded performing a series of 16 different distracting activities randomly, with the order of these activities also randomized within each video. Experimental results indicate 99. To address the issue, we propose generative adversarial networks (GANs) to detect the distracted driver and determine the source of his distraction through the learning model. Mar 1, 2024 · In this study, we address the challenges of distracted driving detection by proposing a lightweight hybrid vision transformer trained with a pseudo-label-based semi-supervised learning approach. 2 The State Farm Distracted Driver Detection Dataset. IoT-Based Smart Alert System for Drowsy Driver Detection. More than 80% of road accidents are caused by distracted driving, such as using a mobile phone, talking to passengers, and smoking. 01-05. Jagadale and others published Distracted Driver Detection: A Comparative Study Using CNN | Find, read and cite all the research you need on ResearchGate May 12, 2022 · Download Citation | On May 12, 2022, Muhammad Saiful Haqem Saiful Bahari and others published Distracted Driver Detection Using Deep Learning | Find, read and cite all the research you need on vehicles, distracted driver detection requires the artificial intelligence’s attention. Rectification of distracted driving activity is a big challenge for an intelligent transport system (ITS). 4. of 2022 Rapid Product Aug 30, 2022 · Driver fatigue and distracted driving are the two most common causes of major accidents. A deep convolutional model with Gated Recurrent Unit (GRU) layers for classification on the Driver Anomaly Detection (DAD) dataset is proposed. Aug 31, 2022 · Distracted driving is one of the leading causes of most road accidents. Mar 15, 2023 · Focusing on the driver's distracted behavior detection, based on the original YOLOv4 framework, this paper uses the lightweight network mobilenet as the backbone network for real-time requirements of detection, and uses Depthwise Separable Convolution (DSC) to reduce model calculation and improve detection speed; increases SE module to improve Dec 30, 2023 · Whereas, eye data got an accuracy of 73. The 10 classes to predict are as follows, c0: safe driving c1: texting - right c2: talking on… We chose to use the State Farm Distracted Driver Detection dataset, a collection of 22,424 images of drivers operating a vehicle [4]. In recent years, the number of traffic accident deaths due to distracted driving has been increasing dramatically. We showed a powerful vision-based system that detect distracted driving postures in this study. The key features are the limited use of 3D convolutions and the replacement of 2D convolutions with depth-wise separable Mar 1, 2023 · Proposed model has been developed with the use of state farm dataset that contains information of 1 safe driving class and 9 dangerous behaviours such as texting while driving, talking with passengers, drinking, etc. We propose Stargazer, an efficient, transformer-based system exploiting rich temporal features about the human behavioral information The risk of road accidents is rising rapidly. Liu et al. Apr 20, 2022 · A careless driver can endanger their safety and passengers. However, computational resource requirement makes it challenging to deploy deep learning algorithms in Aug 30, 2024 · lives in the United States in 2022 due to distracted driving, and nearly 290,000 people were injured. Uzzol Hossain and colleagues (2022) employed deep convolutional neural networks to identify driver distraction automatically. The proposed CNN algorithm is applied to the driver images to classify them into the corresponding classes. 2017 [2]: "Analysis On Driver Distraction Detection And Performance Monitoring System", International Journal of Emerging Technologies and Innovative Research (www. May 1, 2022 · We have found that the proposal can effectively detect various abnormal and distracted behavior such as drinking, talking and texting, etc. in 2019, which accounted for 8. Nicolls, and G. SFDDD dataset contains 22,424 images captured from a dashboard-mounted camera and was released in 2016 for a Kaggle competition. 3 Computer Vision Application Feb 26, 2022 · Traditional methods for behavior detection of distracted drivers are not capable of capturing driver behavior features related to complex temporal features. May 28, 2024 · The 2022 study by Narayana Darapaneni [] outlines a CNN-based framework for detecting driver distraction that focuses on processing constraints in real-time Their methodology is aligned with our work, particularly in their use of a streamlined model for efficient computation. have built an analysis framework called DarNet for distracted driving behavior detection and classification. Some of these activities include talking to people in the vehicle, using hand-held devices such as mobile phones or tablets, eating or drinking, and adjusting the stereo or navigation systems while driving. 80% accuracy for classification of the state farm distracted driver detection with combination Mar 17, 2021 · A new D-HCNN model based on a decreasing filter size with only 0. Apr 1, 2022 · In this paper, we made an effort to develop CNN based method to detect distracted driver and identify the cause of distractions like talking, sleeping or eating by means of face and hand localization. We propose Stargazer, an ef-ficient, transformer-based system exploiting rich temporal Feb 2, 2024 · Distracted driving is a significant issue that has sparked extensive research in detection and mitigation methods, with previous studies exploring physiological sensors but finding them intrusive, leading to the rise of computer vision techniques, particularly deep Abstract. Related Work In 2020, Omerustaoglu et al. Thus, it is important to detect and elimi-nate distracted driving behaviors on the road to save lives. In Australia, 14% of all crashes involve a distracted driver (DriveRisk, 2020); in Canada, 27% of all crashes involve a distracted driver (Marija, 2022). Jan 1, 2022 · Sample images of distracted driving behaviors on the Statefarm Distracted Driver Detection Dataset (SFD3) [46]. Evaluation methods. Sensors 2022, 22, 1864. Dec 14, 2023 · F. S. In this paper, we propose an approach which detects driver fatigue and distracted driving behaviors using vision-based techniques. https://doi Jan 15, 2022 · Distracted driving is one of the main cause of traffic accidents. Jan 1, 2024 · An improved YOLOv8 detection method is proposed for detecting distracted driving behavior and driver’s emotion. Their work utilized a series of complex models to process visual data from in-vehicle cameras. In this framework, data is collected in a unified manner, and then aggregation of Jan 1, 2019 · Download Citation | On Jan 1, 2019, Vlad Tamas and others published Real-Time Distracted Drivers Detection Using Deep Learning | Find, read and cite all the research you need on ResearchGate May 16, 2022 · With distracted driving becoming one of the main causes of traffic accidents, deep learning technology has been widely used in distracted driving detection, which achieves high accuracy when the training and test data are identically distributed. The State Farm Distracted Driver Detection is a well-known dataset for training and evaluating vision-based algorithms that can identify distracted driver behavior. image-classification tensorflow-hub tensorflow2 tensorflow-addons wandb albumentations Mar 1, 2024 · In particular, researchers from machine learning and computer vision domains have enthusiastically introduced methods that use images taken by in-vehicle cameras to discern whether the driver is engaging in safe driving practices or exhibiting behaviors falling within distinct categories of distracted driving (F. For driver fatigue detection, a single shot scale-invariant face detector Aug 30, 2022 · For distracted driving detection, a convolutional neural network (CNN) is used to classify various distracted driving behaviors. 2020) (found that fusing sensor data with vision data increases the accuracy of distracted driver detection tasks successfully. In the past few decades, it is shown in various studies that driving fatigue or distraction are the main threats of traffic accidents. Extensive methods based on the convolutional neural network (CNN) have been applied to the detection of the distracted driving. Car manufacurers are now developing various driving support systems to ensure safe driving because it is an important activity of people as their major means of transportation. • Computational overhead • Take long time to predict Feb 13, 2023 · With the current market’s growing need for electric vehicles and technologies in high-end vehicles, distracted driver detection requires the artificial intelligence’s attention. 2. The use of an in-vehicle deep learning-based driver assistance system may reduce the risk of traffic accidents. 392%. 76M parameters is proposed, a much smaller number of parameters than that used by models in many other studies, which is higher than many other state-of-the-art methods. In this paper, we focus on this use case. Finally, all trip data collected on the smartwatch can be at any time deleted by the user. AlexNet as 91% in binary format predicting whether the driver was distracted. In this study, we focus on developing a CNN-based approach for detecting distracted drivers and determining the source of distraction. Ratshidaho, F. Oct 25, 2022 · Driver safety can be improved by detecting driver drowsiness and distraction. Stoltz, Cross-dataset performance evaluation of deep learning distracted driver detection algorithms, in Proceedings of 2022 Rapid Product Development Association of South Africa – Robotics and Mechatronics – Pattern Recognition Association of South Africa – South African Advanced Materials introduced a more balanced distracted driver detection dataset with 9 participants. To this end, we study driver action detection using videos captured inside the vehicle. , texting, eating, or using in-car devices) from in-vehicle camera feeds to enhance road safety. Apr 28, 2023 · In this paper, we propose a system for detecting distracted driving that advances the state of the art along the following directions: i) the method identifies a wide range of distracting activities relying on the multiple sensors available on a smartphone (the camera, the microphone, the GPS, the IMU); ii) detected distractions are used to Oct 24, 2020 · Request PDF | On Oct 24, 2020, Duy Tran and others published Real-time Detection of Distracted Driving using Dual Cameras | Find, read and cite all the research you need on ResearchGate Jan 15, 2024 · Distracted human driver detection is an important feature that should be included in most levels of autonomous cars, because most of these are still under development. on Signal Processing & Applications The existing work of distracted driver detection is concerned with a limited set of B. Detection of Distracted Driver Using Convolution Neural Network The 2022 study by Narayana Darapaneni [2] outlines a CNN-based framework for detecting driver distraction that focuses on processing constraints in real-time Their methodology is aligned with our work, particularly in their use of a streamlined model for efficient computa-tion. The method proposed uses Jupyter Notebook and Python to program and run ResNet 50 network. , 2022; Wang et al In this paper, we explore the problem of automatic detection of dangerous or distracted driving using multi-modal cameras. This is due to the fact that distracted driving always leads to lower speed, higher speed fluctuations, and worse lane-keeping performance (Wang et al. Data exploration Distracted Driver images were taken from a Kaggle (state farm distracted driver detection competition) of 102,150 images with 10-class labels. Thus, it is important to detect and eliminate distracted driving behaviors on the road to save lives. B. Deployed the Deep Learning model on the flask to make real-time predictions. Number of road accidents is continuously increasing in last few years worldwide. However, traditional detection methods exhibit limitations in driver behavior detection, including low accuracy and slow processing efficiency. , 2015). To construct our model, we collect a large of image data and train with multiple Aug 29, 2024 · Identifying distracted drivers is crucial for enhancing driving safety and advancing intelligent driver assistance systems. As a consequence, diversion and carelessness are adding to the likelihood of a collision and have a growing effect on driving health. To alleviate these concerns, this study discusses the usage of a dashboard camera to accurately identify Apr 7, 2022 · April 2022; License; CC BY-NC-ND 4. The objective of this project is to apply state-of-the-art CNN and Vision Transformer on distracted driver detection. [8] proposed a CNN-based approach that recognises driving posture based on the position of the hand and evaluated the proposed Jun 1, 2018 · PDF | On Jun 1, 2018, Bhakti Baheti and others published Detection of Distracted Driver Using Convolutional Neural Network | Find, read and cite all the research you need on ResearchGate May 4, 2022 · Request PDF | On May 4, 2022, Yuan Li and others published Distracted Driving Detection by Combining ViT and CNN | Find, read and cite all the research you need on ResearchGate Oct 8, 2022 · DOI: 10. Therefore, the identifying of the distracted driving become significant. Identification of distracted driving involves reliably detecting and classifying various forms of driver distraction (e. Stoltz, Cross-dataset performance evaluation of deep learning distracted driver detection algorithms, in Proceedings of 2022 Rapid Product Oct 8, 2022 · Early detection of such distracted driving accurately is beneficial to mitigate the risk of rear-end collisions. 9, Issue 5, page no. From an economic perspective, it is estimated that road injuries will cost the Apr 21, 2022 · However, eye gaze is hard to detect in naturalistic driving situations, because of low-resolution cameras, drivers wearing sunglasses, and so forth. 7% of all crash fatalities. [17] have developed the distracted driver detection approach based on vision data for Dec 9, 2024 · However, despite advancements in robust deep learning-based distracted driver detection, there is a critical gap in research on deploying these methods on edge devices. This study aims to detect a distracted driver who is looking elsewhere using Deep Learning-based classification. jetir. ) using a camera mounted inside the vehicle is an active area of research as Advanced Driver Assistance System (ADAS), various computer vision-based and machine learning-based methods are applied to extract features such as Trained MobilenetV2 model to recognize the distracted behaviors exhibited by drivers while driving. Zandamela, T. Baheti, S. First, transfer learning and fine-tuning developed vision-based CNN models and then LSTM-RNN . Driver distraction detection. For example, Yan et al. Nov 1, 2022 · In this paper, an efficient distracted driver detection scheme (DDDS) has been proposed using two robust deep learning architectures, mainly visual geometric groups (VGG-16) and residual networks Feb 26, 2022 · Since 2016, researchers have started using CNN Deep Learning models for the detection of distracted drivers [10,11,12,13,14]. Nov 19, 2024 · The distracted driver behavior dataset provides a comprehensive collection of driving videos capturing the actions of 99 individual drivers over a total of 90 hours. Also, Adam optimizer is used to reinforce optimization performance. 2022. May 28, 2024 · Md. Our method effectively addresses several key challenges to significantly improve road safety compared with previous methods. Traffic accidents caused by distracted behavior of motor vehicle drivers are a common problem in traffic control systems. State Farm Distracted Driver Detection via Image Classification Topics. C. Fortunately, distracted driving can be Oct 1, 2022 · Another report suggested that young drivers were distracted in 58% of the analysed crashes (Carney et al. LIMITATIONS IN EXISTING SYSTEM • Lack accuracy and robustness • Expensive or limited to special high-end car models. g. However, for resource-constrained onboard devices, real Distracted driver actions can be dangerous and cause severe accidents. Volume 10 Issue 5 pp. Distracted driving remains one of the leading causes of traffic accidents. 9921795 Corpus ID: 253251142; Distracted Driving Detection in Stop-And-Go Traffic @article{Uar2022DistractedDD, title={Distracted Driving Detection in Stop-And-Go Traffic}, author={Seyhan Uçar and Emrah Akin Sisbot and Haritha Muralidharan and Kentaro Oguchi}, journal={2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, year={2022 Abstract. A lot of efforts have been made to tackle Driver inattention and distraction are the main causes of road accidents, many of which result in fatalities. 8 trillion from 2015 to 2030. We collected the data in a stationary vehicle using three in-vehicle cameras positioned at locations: on the dashboard, near the rearview mirror, and on the top Jun 1, 2018 · This work attempts to develop an accurate and robust system for detecting distracted driver and warn him against it using a CNN based system that not only detects the distraction but also identifies the cause of distraction. org), ISSN:2349-5162, Vol. Dec 31, 2024 · Although numerous modern cars come equipped with advanced driver assistance systems (ADAS), most lack such integrated systems. Streiffer et al. To reduce road accidents, the development of information systems to detect driver inattention and distraction is essential. of a distracted driver. j101-j108, (2022). A two-stage distracted driving detection system was created to detect nine distracted driving behaviors. Because labels were only provided for the training data, we split the training portion of the dataset into a new training set (80%) and test set (20%). 4%, and the model is smaller and easy to deploy, which is able to identify and classify distracted driving behaviours in real time, provide timely warnings, and enhance driving safety. 1145–1151 (2018) [Google Scholar] Aug 1, 2020 · The StateFarm’s distracted driving detection dataset on the Kaggle platform is used, which consists of ten classes of distracted driving postures, including safe driving, texting, talking on the In this paper, the techniques to simultaneously detect the fatigue and distracted driving behaviors using vision and learning based approaches are presented and better results are provided in terms of accuracy and computation time. According to the World Health Organization (WHO), road accidents are the eighth highest top cause of death around the world. Instead, head pose is easier to detect, and has correlation with eye gaze direction. driver drowsiness, lane departure, talking on phone, looking back, etc. The proposed approach consists of two sub-systems namely driver activity detection and driver fatigue detection systems. According to the report in [2], driver’s multitasking while driving is a leading cause of traffic Nov 18, 2024 · The existing dataset currently accessible to the public, the State Farm Distracted Driver Detection dataset [30], consists of pictures sized at 640 × 480 pixels, depicting nine types of distracted behaviors: texting right (C1), texting left (C2), calling right (C3), calling left (C4), adjusting the radio (C5), drinking (C6), reaching behind Aug 19, 2023 · The HOG feature extraction method is used to extract the driver behaviors in automobiles, and KNN is used to classify them. We will gather a new publicly available distracted driver dataset, which we will utilise to design and test our system. In this work, we have examined the method of detecting distracted driving from the driving data collected from different sensors attached to a driving Oct 20, 2024 · Driver distraction detection not only effectively prevents traffic accidents but also promotes the development of intelligent transportation systems. The Section VII concludes the developed distracted driver detection model. As per the survey of National Highway Traffic Safety Administrator, nearly one in five Aug 1, 2022 · The National Highway Traffic Safety Administration (NHTSA) reports that there were 3,142 deaths due to distracted driving in the U. , 2022). The comparison between unimodal indicates that vehicle dynamics is promising data for driver distraction detection. Omerustaoglu et al. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage tem (Level 2 self-driving). A history of trips is also made possible to view. Thus, the on-board monitoring of driving behaviors is key in the development of intelligent vehicles. Apr 17, 2022 · This article presents a synthetic distracted driving (SynDD2 - a continuum of SynDD1) dataset for machine learning models to detect and analyze drivers' various distracted behavior and different gaze zones. Distracted Driving Detection Using On-Board Sensors. Its detection is a critical system component in semi-autonomous vehicles. Dec 25, 2021 · The increasing number of car accidents is a significant issue in current transportation systems. To solve these problems, a distracted driving detection scheme is proposed based on the improved CenterNet with attention Furthermore, another unresolved problem regarding the usage of these deep networks for the identification of distracted drivers is that in these studies or techniques, detection of a distracted driver is accomplished by analyzing images or videos of the driver’s posture from his upper body; however, time series data such as vehicle dynamic There is a growing possibility of drivers engaging in disruptive behaviors with increasingly regular in-vehicle technologies and transported devices. Hereby, this paper proposes Using evaluation measures, assess the model's performance. From an economic perspective, it is estimated that road injuries will cost the world economy as much as US$1. The increase of in-vehicle information systems induces the biomechanical and cognitive of driver distraction which affects driving performance qualitatively. Jan 29, 2017 · This research has derived a benefit from temporal information by using a 3D convolutional neural network and optical flow to improve the driver distraction monitoring task and has shown that fine-tuning a pertained network on Kinetics dataset for learning driver action achieves a detection accuracy on State Farm dataset, which outperforms other methods on the same dataset. Distracted driving is a critical safety issue that leads to numerous fatalities and injuries Feb 13, 2023 · This paper uses the first publicly accessible dataset that is the state farm distracted driver detection dataset, which contains eight classes: calling, texting, everyday driving, operating on May 22, 2024 · using deep l earning,” in 2022 IEEE 18th International Collo quium . A monitoring mechanism has to be set in place to monitor driver attentiveness while using conditional self-driving features. Previous studies Oct 21, 2024 · A detailed analysis of P-YOLOv8's architecture, training, and performance benchmarks are provided, highlighting its potential for real-time use in detecting distracted driving and opening new directions for deployment on inexpensive and small embedded devices using Tiny Machine Learning (TinyML). Current studies to detect distraction postures focus on analysing spatial features of Oct 16, 2024 · Experimental results show that the improved model performs well in both detection speed and accuracy, with an accuracy rate of 99. Distracted driver actions can be dangerous and cause severe accidents. In this study, city-wide videos are collected using onboard cameras from over 289 drivers representing 423 events. … Dec 27, 2024 · Human-machine co-driving is an important stage in the development of automatic driving, and accurate recognition of driver behavior is the basis for realizing human-machine co-driving. The application helps drivers detect distracted driving and summarises at the end of the trip the start time of the trip, duration and the times a distraction took place. In this paper, new strategies for improving the performance of the driver detection methodology are proposed. This article uses GAN to analyze distracted or normal driving behaviors. Distracted driving is a leading cause of road accidents globally. Ego vehicle uses distance-to-collision measurements and performs time-series analysis to identify fluctuating distance in stop-and-go traffic caused by the delayed response of distracted Dec 31, 2021 · The World Health Organisation reports distracted driving actions as the main cause of road traffic accidents. To counter the effects caused by distracted driving, many countries around the world have imposed In this, you are given driver images, each taken in a car with a driver doing something in the car (texting, eating, talking on the phone, makeup, reaching behind, etc). Within Convolutional Neural Network (CNN), the convolution operations are Sensors 2022, 22, 1864 2 of 17 dangerous behavior that can reduce a driver’s reaction speed. Mar 15, 2023 · Focusing on the driver's distracted behavior detection, based on the original YOLOv4 framework, this paper uses the lightweight network mobilenet as the backbone network for real-time requirements of detection, and uses Depthwise Separable Convolution (DSC) to reduce model calculation and improve detection speed; increases SE module to improve Nov 17, 2020 · The detection of distracted driver behavior (i. Gajre, and S. This article introduces a mobile, image-focused DMAS designed for the immediate identification of driver distractions. II. Feb 1, 2023 · This article presents a synthetic distracted driving (SynDD1) dataset for machine learning models to detect and analyze drivers' various distracted behavior and different gaze zones. e. The proposed approach consists of two sub-systems namely driver activity detection and driver fatigue depicts the achieved results of the proposed distracted driver detection model. However, this assumption cannot correspond to the real-world situation. However, these distracted behaviors are difficult to be recognized due to the variable background and different scale targets. 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. analysis. Aug 16, 2022 · Download Citation | On Aug 16, 2022, Sujay H. Talbar, Detection of distracted driver using convolutional neural network, In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. LITERATURE SURVEY A. Your goal is to predict the likelihood of what the driver is doing in each picture. 0; Authors: Narayana Darapaneni. Currently, distraction detection systems for road vehicles are not yet widely available or are limited to specific causes of driver inattention such as driver Jan 29, 2022 · Distracted driving detection has many significant application scenarios in intelligent transportation, driver assistance, and other fields. Distracted driver detection: Deep learning vs handcrafted features. dvnfscu gjxkk jxljwq qukx fguhykg ifoy eapn bazjfp plhur dwwfmdq