Detection and Evaluation of Driver Distraction Using Machine Learning and Fuzzy Logic

Aksjonov, Andrei; Nedoma, Pavel; Vodovozov, Valery; Petlenkov, Eduard; Herrmann, Martin

doi:10.1109/tits.2018.2857222

Cited by 70 publications

(21 citation statements)

References 34 publications

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“…Tables 5,6, and 7 depict the multi-level distractions input test data, the previous frames distraction severity level, and the defuzzification methods outputs. The defuzzification methods we used include Smallest of Maxima (SOM), in which the defuzzified value is taken as the element with the lowest membership values.…”

Section: Resultsmentioning

confidence: 99%

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A Fuzzy-Logic Approach to Dynamic Bayesian Severity Level Classification of Driver Distraction Using Image Recognition

Fasanmade

Al-Bayatti

et al. 2020

IEEE Access

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Detecting 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. INDEX TERMS Fuzzy logic systems, driver distraction, severity level, ADAS, image processing, dynamic Bayesian.

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Section: Resultsmentioning

confidence: 99%

“…Aksjonov et al [6] also developed a methodology to detect normal driving and measuring errors from secondary tasks and total distraction evaluation. The measures compare normal driving with secondary task using fuzzy logic algorithms.…”

Section: Related Workmentioning

confidence: 99%

A Fuzzy-Logic Approach to Dynamic Bayesian Severity Level Classification of Driver Distraction Using Image Recognition

Fasanmade

Al-Bayatti

et al. 2020

IEEE Access

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“…Indirect detection methods rely on the vehicle behavior and are often implemented in recently produced cars. Aksjonov et al [24] presented a method for detecting the driver's distraction by monitoring lane maintenance and speed performance on specified road segments. Saito et al [25] proposed an assistance system for prediction the driver's state based on the lane departure duration.…”

Section: A Input Signalsmentioning

confidence: 99%

Machine Learning and End-to-End Deep Learning for Monitoring Driver Distractions From Physiological and Visual Signals

et al. 2020

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It is only a matter of time until autonomous vehicles become ubiquitous; however, human driving supervision will remain a necessity for decades. To assess the driver's ability to take control over the vehicle in critical scenarios, driver distractions can be monitored using wearable sensors or sensors that are embedded in the vehicle, such as video cameras. The types of driving distractions that can be sensed with various sensors is an open research question that this study attempts to answer. This study compared data from physiological sensors (palm electrodermal activity (pEDA), heart rate and breathing rate) and visual sensors (eye tracking, pupil diameter, nasal EDA (nEDA), emotional activation and facial action units (AUs)) for the detection of four types of distractions. The dataset was collected in a previous driving simulation study. The statistical tests showed that the most informative feature/modality for detecting driver distraction depends on the type of distraction, with emotional activation and AUs being the most promising. The experimental comparison of seven classical machine learning (ML) and seven end-to-end deep learning (DL) methods, which were evaluated on a separate test set of 10 subjects, showed that when classifying windows into distracted or not distracted, the highest F1-score of 79% was realized by the extreme gradient boosting (XGB) classifier using 60-second windows of AUs as input. When classifying complete driving sessions, XGB's F1-score was 94%. The best-performing DL model was a spectro-temporal ResNet, which realized an F1-score of 75% when classifying segments and an F1-score of 87% when classifying complete driving sessions. Finally, this study identified and discussed problems, such as label jitter, scenario overfitting and unsatisfactory generalization performance, that may adversely affect related ML approaches.

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“…From the Figure 2, it is clear that as the activity is performed, q 2 varies smoothly resulting in a smaller value of µ q 2 . Whereas, in the presence of activity, the variance of h 2 represented as V{h 2 2 } has a larger value. Therefore, we focus on the ratio (δ) between µ q 2 and V{h 2 2 } to decide the discriminant components of activity.…”

Section: Discriminant Components Selectionmentioning

confidence: 99%

“…During the previous decades, a large number of traffic accidents are reported due to driver distraction or fatigue [1]. Distraction reduce the driver's perception and decision making capability by diverting his/her attention from the primary task of driving to secondary activities [2]. In-vehicle entertainment systems (audio/video players) and gadgets (GPS and mobile communication) are among the leading causal factors of driver distraction [3,4].…”

Section: Introductionmentioning

confidence: 99%

WiFi-Based Driver’s Activity Monitoring with Efficient Computation of Radio-Image Features

Akhtar

Wang

2020

Sensors

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Driver distraction and fatigue are among the leading contributing factors in various fatal accidents. Driver activity monitoring can effectively reduce the number of roadway accidents. Besides the traditional methods that rely on camera or wearable devices, wireless technology for driver’s activity monitoring has emerged with remarkable attention. With substantial progress in WiFi-based device-free localization and activity recognition, radio-image features have achieved better recognition performance using the proficiency of image descriptors. The major drawback of image features is computational complexity, which increases exponentially, with the growth of irrelevant information in an image. It is still unresolved how to choose appropriate radio-image features to alleviate the expensive computational burden. This paper explores a computational efficient wireless technique that could recognize the attentive and inattentive status of a driver leveraging Channel State Information (CSI) of WiFi signals. In this novel research work, we demonstrate an efficient scheme to extract the representative features from the discriminant components of radio-images to reduce the computational cost with significant improvement in recognition accuracy. Specifically, we addressed the problem of the computational burden by efficacious use of Gabor filters with gray level statistical features. The presented low-cost solution requires neither sophisticated camera support to capture images nor any special hardware to carry with the user. This novel framework is evaluated in terms of activity recognition accuracy. To ensure the reliability of the suggested scheme, we analyzed the results by adopting different evaluation metrics. Experimental results show that the presented prototype outperforms the traditional methods with an average recognition accuracy of 93.1 % in promising application scenarios. This ubiquitous model leads to improve the system performance significantly for the diverse scale of applications. In the realm of intelligent vehicles and assisted driving systems, the proposed wireless solution can effectively characterize the driving maneuvers, primary tasks, driver distraction, and fatigue by exploiting radio-image descriptors.

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Detection and Evaluation of Driver Distraction Using Machine Learning and Fuzzy Logic

Cited by 70 publications

References 34 publications

A Fuzzy-Logic Approach to Dynamic Bayesian Severity Level Classification of Driver Distraction Using Image Recognition

A Fuzzy-Logic Approach to Dynamic Bayesian Severity Level Classification of Driver Distraction Using Image Recognition

Machine Learning and End-to-End Deep Learning for Monitoring Driver Distractions From Physiological and Visual Signals

WiFi-Based Driver’s Activity Monitoring with Efficient Computation of Radio-Image Features

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