338 research outputs found
Driver Distraction Identification with an Ensemble of Convolutional Neural Networks
The World Health Organization (WHO) reported 1.25 million deaths yearly due
to road traffic accidents worldwide and the number has been continuously
increasing over the last few years. Nearly fifth of these accidents are caused
by distracted drivers. Existing work of distracted driver detection is
concerned with a small set of distractions (mostly, cell phone usage).
Unreliable ad-hoc methods are often used.In this paper, we present the first
publicly available dataset for driver distraction identification with more
distraction postures than existing alternatives. In addition, we propose a
reliable deep learning-based solution that achieves a 90% accuracy. The system
consists of a genetically-weighted ensemble of convolutional neural networks,
we show that a weighted ensemble of classifiers using a genetic algorithm
yields in a better classification confidence. We also study the effect of
different visual elements in distraction detection by means of face and hand
localizations, and skin segmentation. Finally, we present a thinned version of
our ensemble that could achieve 84.64% classification accuracy and operate in a
real-time environment.Comment: arXiv admin note: substantial text overlap with arXiv:1706.0949
Automatic driver distraction detection using deep convolutional neural networks
Recently, the number of road accidents has been increased worldwide due to the distraction of the drivers. This rapid road crush often leads to injuries, loss of properties, even deaths of the people. Therefore, it is essential to monitor and analyze the driver's behavior during the driving time to detect the distraction and mitigate the number of road accident. To detect various kinds of behavior like- using cell phone, talking to others, eating, sleeping or lack of concentration during driving; machine learning/deep learning can play significant role. However, this process may need high computational capacity to train the model by huge number of training dataset. 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. Four architectures namely CNN, VGG-16, ResNet50 and MobileNetV2 have been adopted for transfer learning. To verify the effectiveness, the proposed model is trained with thousands of images from a publicly available dataset containing ten different postures or conditions of a distracted driver and analyzed the results using various performance metrics. The performance results showed that the pre-trained MobileNetV2 model has the best classification efficiency. © 2022 The Author(s
A Fuzzy-Logic Approach to Dynamic Bayesian Severity Level Classification of Driver Distraction Using Image Recognition
open access articleDetecting and classifying driver distractions is crucial in the prevention of road accidents. These distractions impact both driver behavior and vehicle dynamics. Knowing the degree of driver distraction can aid in accident prevention techniques, including transitioning of control to a level 4 semi- autonomous vehicle, when a high distraction severity level is reached. Thus, enhancement of Advanced Driving Assistance Systems (ADAS) is a critical component in the safety of vehicle drivers and other road users. In this paper, a new methodology is introduced, using an expert knowledge rule system to predict the severity of distraction in a contiguous set of video frames using the Naturalistic Driving American University of Cairo (AUC) Distraction Dataset. A multi-class distraction system comprises the face orientation, drivers’ activities, hands and previous driver distraction, a severity classification model is developed as a discrete dynamic Bayesian (DDB). Furthermore, a Mamdani-based fuzzy system was implemented to detect multi- class of distractions into a severity level of safe, careless or dangerous driving. Thus, if a high level of severity is reached the semi-autonomous vehicle will take control. The result further shows that some instances of driver’s distraction may quickly transition from a careless to dangerous driving in a multi-class distraction context
Ensemble Learning for Fusion of Multiview Vision with Occlusion and Missing Information: Framework and Evaluations with Real-World Data and Applications in Driver Hand Activity Recognition
Multi-sensor frameworks provide opportunities for ensemble learning and
sensor fusion to make use of redundancy and supplemental information, helpful
in real-world safety applications such as continuous driver state monitoring
which necessitate predictions even in cases where information may be
intermittently missing. We define this problem of intermittent instances of
missing information (by occlusion, noise, or sensor failure) and design a
learning framework around these data gaps, proposing and analyzing an
imputation scheme to handle missing information. We apply these ideas to tasks
in camera-based hand activity classification for robust safety during
autonomous driving. We show that a late-fusion approach between parallel
convolutional neural networks can outperform even the best-placed single camera
model in estimating the hands' held objects and positions when validated on
within-group subjects, and that our multi-camera framework performs best on
average in cross-group validation, and that the fusion approach outperforms
ensemble weighted majority and model combination schemes
SleepyWheels: An Ensemble Model for Drowsiness Detection leading to Accident Prevention
Around 40 percent of accidents related to driving on highways in India occur
due to the driver falling asleep behind the steering wheel. Several types of
research are ongoing to detect driver drowsiness but they suffer from the
complexity and cost of the models. In this paper, SleepyWheels a revolutionary
method that uses a lightweight neural network in conjunction with facial
landmark identification is proposed to identify driver fatigue in real time.
SleepyWheels is successful in a wide range of test scenarios, including the
lack of facial characteristics while covering the eye or mouth, the drivers
varying skin tones, camera placements, and observational angles. It can work
well when emulated to real time systems. SleepyWheels utilized EfficientNetV2
and a facial landmark detector for identifying drowsiness detection. The model
is trained on a specially created dataset on driver sleepiness and it achieves
an accuracy of 97 percent. The model is lightweight hence it can be further
deployed as a mobile application for various platforms.Comment: 20 page
Driver Behavior Recognition via Interwoven Deep Convolutional Neural Nets With Multi-Stream Inputs
Recognizing driver behaviors is becoming vital for in-vehicle systems that
seek to reduce the incidence of car accidents rooted in cognitive distraction.
In this paper, we harness the exceptional feature extraction abilities of deep
learning and propose a dedicated Interwoven Deep Convolutional Neural Network
(InterCNN) architecture to tackle the accurate classification of driver
behaviors in real-time. The proposed solution exploits information from
multi-stream inputs, i.e., in-vehicle cameras with different fields of view and
optical flows computed based on recorded images, and merges through multiple
fusion layers abstract features that it extracts. This builds a tight
ensembling system, which significantly improves the robustness of the model. We
further introduce a temporal voting scheme based on historical inference
instances, in order to enhance accuracy. Experiments conducted with a real
world dataset that we collect in a mock-up car environment demonstrate that the
proposed InterCNN with MobileNet convolutional blocks can classify 9 different
behaviors with 73.97% accuracy, and 5 aggregated behaviors with 81.66%
accuracy. Our architecture is highly computationally efficient, as it performs
inferences within 15ms, which satisfies the real-time constraints of
intelligent cars. In addition, our InterCNN is robust to lossy input, as the
classification remains accurate when two input streams are occluded
Automatic Driver Drowsiness Detection System
The proposed system aims to lessen the number of accidents that occur due to drivers’ drowsiness and fatigue, which will in turn increase transportation safety. This has become a common reason for accidents in recent times. Several facial and body gestures are considered signs of drowsiness and fatigue in drivers, including tiredness in the eyes and yawning. These features are an indication that the driver’s condition is improper. EAR (Eye Aspect Ratio) computes the ratio of distances between the horizontal and vertical eye landmarks, which is required for the detection of drowsiness. For the purpose of yawn detection, a YAWN value is calculated using the distance between the lower lip and the upper lip, and the distance will be compared against a threshold value. We have deployed an eSpeak module (text-to-speech synthesiser), which is used for giving appropriate voice alerts when the driver is feeling drowsy or is yawning. The proposed system is designed to decrease the rate of accidents and contribute to technology with the goal of preventing fatalities caused by road accidents. Over the past ten years, advances in artificial intelligence and computing technologies have improved driver monitoring systems. Several experimental studies have gathered data on actual driver fatigue using different artificial intelligence systems. In order to dramatically improve these systems' real-time performance, feature combinations are used. An updated evaluation of the driver sleepiness detection technologies put in place during the previous ten years is presented in this research. The paper discusses and displays current systems that track and identify drowsiness using various metrics. Based on the information used, each system can be categorised into one of four groups. Each system in this paper comes with a thorough discussion of the features, classification rules, and datasets it employs. 
A Novel Driver Distraction Behavior Detection Based on Self-Supervised Learning Framework with Masked Image Modeling
Driver distraction causes a significant number of traffic accidents every
year, resulting in economic losses and casualties. Currently, the level of
automation in commercial vehicles is far from completely unmanned, and drivers
still play an important role in operating and controlling the vehicle.
Therefore, driver distraction behavior detection is crucial for road safety. At
present, driver distraction detection primarily relies on traditional
Convolutional Neural Networks (CNN) and supervised learning methods. However,
there are still challenges such as the high cost of labeled datasets, limited
ability to capture high-level semantic information, and weak generalization
performance. In order to solve these problems, this paper proposes a new
self-supervised learning method based on masked image modeling for driver
distraction behavior detection. Firstly, a self-supervised learning framework
for masked image modeling (MIM) is introduced to solve the serious human and
material consumption issues caused by dataset labeling. Secondly, the Swin
Transformer is employed as an encoder. Performance is enhanced by reconfiguring
the Swin Transformer block and adjusting the distribution of the number of
window multi-head self-attention (W-MSA) and shifted window multi-head
self-attention (SW-MSA) detection heads across all stages, which leads to model
more lightening. Finally, various data augmentation strategies are used along
with the best random masking strategy to strengthen the model's recognition and
generalization ability. Test results on a large-scale driver distraction
behavior dataset show that the self-supervised learning method proposed in this
paper achieves an accuracy of 99.60%, approximating the excellent performance
of advanced supervised learning methods
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