Physiological measurement used in real time experiment to detect driver cognitive distraction

Azman, Azreen; Ibrahim, Siti Zainab; Meng, Qinggang; Edirisinghe, Eran A.

doi:10.1109/elinfocom.2014.6914389

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…Several methods and techniques have been used in the literature to detect abnormal driver behavior, such as artificial neural networks, gradient boosting machine [36], dynamic Bayesian network, logic regressions, support vector machine and various machine learning methods: decision tree, random forest, k-NN, SVM and Naive Bayes [37][38][39][40]. They have been used to detect the physiological and visual attributes to identify and to propose distraction reduction techniques.…”

Section: Driving Behavior Classification Methodsmentioning

confidence: 99%

Driver Behavior Classification System Analysis Using Machine Learning Methods

et al. 2021

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Distraction while driving occurs when a driver is engaged in non-driving activities. These activities reduce the driver’s attention and focus on the road, therefore increasing the risk of accidents. As a consequence, the number of accidents increases and infrastructure is damaged. Cars are now equipped with different safety precautions that ensure driver awareness and attention at all times. The first step for such systems is to define whether the driver is distracted or not. Different methods are proposed to detect such distractions, but they lack efficiency when tested in real-life situations. In this paper, four machine learning classification methods are implemented and compared to identify drivers’ behavior and distraction situations based on real data corresponding to different behaviors such as aggressive, drowsy and normal. The data were randomized for a better application of the methods. We demonstrate that the gradient boosting method outperforms the other used classifiers.

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Section: Driving Behavior Classification Methodsmentioning

confidence: 99%

Driver Behavior Classification System Analysis Using Machine Learning Methods

et al. 2021

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“…The suggested methodology has a certain advantage over other DD detection methods described in Section II. Particularly, as compared to the methods, where the behavioral and psychological attributes are applied [6][7][8][9][10][11][12][13][14][15][16][17][18], the proposed approach does not require additional devices, such as cameras and neuroscan systems. Those devices increase the system cost [8], what in its turn is a potential resistance for system application in a commercial passenger vehicle.…”

Section: Discussionmentioning

confidence: 99%

“…The glance region prediction algorithm was designed using random forest classifier in [10] and convolutional NN -in [11]. In [12], dynamic Bayesian network (BN) outperformed logic regression (LR), static BN, and support vector machine (SVM) approaches in cognitive DD detection. SVM together with semi-supervised extreme learning machine were combined for the DD detection in [5].…”

Section: Related Work and Problem Statementmentioning

confidence: 99%

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

Aksjonov

Nedoma

Vodovozov

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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In addition to vehicle control, drivers often perform secondary tasks that impede driving. Reduction of driver distraction is an important challenge for the safety of intelligent transportation systems. In this paper, a methodology for detection and evaluation of driver distraction while performing secondary tasks is described and an appropriate hardware and software environment is offered and studied. The system includes a model of normal driving, a subsystem for measuring the errors from the secondary tasks, and a module for total distraction evaluation. A new machine learning algorithm defines driver performance in lane keeping and speed maintenance on a specific road segment. To recognize the errors, a method is proposed, which compares normal driving parameters with ones obtained while conducting a secondary task. To evaluate distraction, an effective fuzzy logic algorithm is used. To verify the proposed approach, a case study with driver-in-the-loop experiments was carried out, in which participants performed the secondary task, namely chatting on a cell phone. The results presented in this research confirm its capability to detect and to precisely measure a level of abnormal driver performance.

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A Study on the Development of Driver Behavior Simulation Dummy for the Performance Evaluation of Driver Monitoring System

Yae,

Oh,

Kim

et al. 2024

Int. J. Precis. Eng. Manuf.

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Driver monitoring system (DMS) was mainly developed to prevent accident risks by analyzing facial movements related to drowsiness and carelessness in real time such as driver’s gaze, blink, and head angle through cameras and warning the driver. Recently, the scope has been expanded to monitor passengers, and it has been linked to safety functions such as neglecting children, empty seats, or controlling airbags on seats with people under safety weight. However, evaluation research for algorithm advancement and performance optimization is relatively insufficient. In addition, the verification system is facing limitations such as personal information protection problems caused by the subject’s face data, errors in reproducing the subject’s drowsy and careless behavior, and differences in behavior according to individual differences. Therefore, as the importance of traffic safety is emphasized, an evaluation tool that can more efficiently and systematically evaluate the performance of DMS is needed. In this study, a driver behavior simulation dummy was developed that can quantitatively control the movement of the driver’s face and upper body. The driver behavior simulation dummy was developed in three stages in the order of function and specification definition, design and manufacture according to specifications, and verification through error tests for each function.

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Physiological measurement used in real time experiment to detect driver cognitive distraction

Cited by 4 publications

References 3 publications

Driver Behavior Classification System Analysis Using Machine Learning Methods

Driver Behavior Classification System Analysis Using Machine Learning Methods

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

A Study on the Development of Driver Behavior Simulation Dummy for the Performance Evaluation of Driver Monitoring System

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