Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges

Alkinani, Monagi H.; Khan, Wazir Zada; Arshad, Quratulain

doi:10.1109/access.2020.2999829

Cited by 68 publications

(31 citation statements)

References 91 publications

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“…In the literature, many researchers focus on analyzing how fatigue that increases during driving affects driving performance and road safety [ 104 ]. These studies point out that fatigue is a very important factor that causes a lack of hazard perception [ 105 ]. This may lead to driving accidents [ 106 ].…”

Section: Discussion and Limitations Of Our Experimentsmentioning

confidence: 99%

The Effects of the Driver’s Mental State and Passenger Compartment Conditions on Driving Performance and Driving Stress

Magaña

Scherz²,

Seepold

et al. 2020

Sensors

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Globalization has increased the number of road trips and vehicles. The result has been an intensification of traffic accidents, which are becoming one of the most important causes of death worldwide. Traffic accidents are often due to human error, the probability of which increases when the cognitive ability of the driver decreases. Cognitive capacity is closely related to the driver’s mental state, as well as other external factors such as the CO2 concentration inside the vehicle. The objective of this work is to analyze how these elements affect driving. We have conducted an experiment with 50 drivers who have driven for 25 min using a driving simulator. These drivers completed a survey at the start and end of the experiment to obtain information about their mental state. In addition, during the test, their stress level was monitored using biometric sensors and the state of the environment (temperature, humidity and CO2 level) was recorded. The results of the experiment show that the initial level of stress and tiredness of the driver can have a strong impact on stress, driving behavior and fatigue produced by the driving test. Other elements such as sadness and the conditions of the interior of the vehicle also cause impaired driving and affect compliance with traffic regulations.

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Section: Discussion and Limitations Of Our Experimentsmentioning

confidence: 99%

The Effects of the Driver’s Mental State and Passenger Compartment Conditions on Driving Performance and Driving Stress

Magaña

Scherz²,

Seepold

et al. 2020

Sensors

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“…Recently, deep learning has made a major advances in recognising driver activities from images/videos [18], [14], [6], [17]. Driver's activity recognition can be seen as a subset of the traditional human activity recognition problem.…”

Section: Related Workmentioning

confidence: 99%

“…It is a key component of knowing how vehicles will learn to adapt to various driving conditions and environments. To address this, recent research on recognising basic driver's actions such as eating, drinking, interacting with the vehicle controls, and so on [3], [4], [5], [6], [7], [8], is only the first step. This study advances this by proposing a novel approach to enhance the performance of the automatic recognition of driver's activities from still images captured by vehicle cameras.…”

Section: Introductionmentioning

confidence: 99%

“…Driver action recognition is often focused on vital cues such as head/body pose of the driver, and their interaction with objects in a given scene. Most of the recent approaches [6], [10], [3], [4], [11], [5] focus selectively on these vital cues to improve the recognition accuracy. In machine learning, this kind of processes is often referred to as the attention mechanism.…”

Section: Introductionmentioning

confidence: 99%

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Deep CNN, Body Pose, and Body-Object Interaction Features for Drivers’ Activity Monitoring

Behera

Wharton

Keidel

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Automatic recognition and prediction of in-vehicle human activities has a significant impact on the next generation of driver assistance and intelligent autonomous vehicles. In this paper, we present a novel single image driver action recognition algorithm inspired by human perception that often focuses selectively on parts of the images to acquire information at specific places which are distinct to a given task. Unlike existing approaches, we argue that human activity is a combination of pose and semantic contextual cues. In detail, we model this by considering the configuration of body joints, their interaction with objects being represented as a pairwise relation to capture the structural information. Our body-pose and bodyobject interaction representation is built to be semantically rich and meaningful, and is highly discriminative even though it is coupled with a basic linear SVM classifier. We also propose a Multi-stream Deep Fusion Network (MDFN) for combining highlevel semantics with CNN features. Our experimental results demonstrate that the proposed approach significantly improves the drivers' action recognition accuracy on two exacting datasets. Index Terms-Transfer learning, intelligent vehicles, in-vehicle activity monitoring, deep learning, body pose and contextual descriptor, neural network-based fusion.

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“…1. Previously, researchers used artificial neural network (ANN) for drowsiness detection where information such as features are stored in the entire network and can learn from observing datasets and disappearances of few piece of information does not prevent network from functioning [8,9,14]. However, ANN requires higher computation due to parallel processing is needed which means that overall methodology will not always be robust and little suitable to use in a vehicle constantly [10,11].…”

Section: Background Studymentioning

confidence: 99%

Robust Drowsiness Detection for Vehicle Driver using Deep Convolutional Neural Network

Saif¹,

Mahayuddin²

2020

IJACSA

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Drowsiness detection during driving is still an unsolved research problem which needs to be addressed to reduce road accidents. Researchers have been trying to solve this problem using various methods where most of these solution lacks behind in accuracy, real-time performance, costly, complex to build, and has a higher computational cost with low frame rate. This research proposes robust method for drowsiness detection of vehicle drivers based on head pose estimation and pupil detection by extracting facial region initially. Proposed method used frame aggregation strategy in case of face region cannot be extracted in any frame due to shortcomings, i.e. light reflection, shadow. In order to improve identification under highly varying lighting conditions, proposed research used cascade of regressors cutting edge method where each regression refers estimation of facial landmarks. Proposed method used deep convolutional neural network (DCNN) for accurate pupil detection to learn non linear data pattern. In this context, challenges of varying illumination, blurring and reflections for robust pupil detection are overcome by using batch normalization for stabilizing distributions of internal activations during training phase which makes overall methodology less influenced by parameter initialization. Proposed research performed extensive experimentation where accuracy rate of 98.97% was achieved using frame rate of 35 fps which is higher comparing with previous research results. Experimental results reveal the effectiveness of the proposed methodology.

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Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges

Cited by 68 publications

References 91 publications

The Effects of the Driver’s Mental State and Passenger Compartment Conditions on Driving Performance and Driving Stress

The Effects of the Driver’s Mental State and Passenger Compartment Conditions on Driving Performance and Driving Stress

Deep CNN, Body Pose, and Body-Object Interaction Features for Drivers’ Activity Monitoring

Robust Drowsiness Detection for Vehicle Driver using Deep Convolutional Neural Network

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