Machine learning methods for driver behaviour classification

Ghandour, Raymond; Potams, Albert Jose; Boulkaibet, Ilyes; Neji, Bilel; Barakeh, Zaher Al; Karar, Abdullah S.

doi:10.1109/biosmart54244.2021.9677801

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…Furthermore, the issue of vehicle speed must be taken into account when detecting distractions. Gradient Boosting Classifier is a competition-winning idea dealing with hard-to-predict observations through iterative boosting of weak learners and loss function optimization [2]. It does better than other classifiers which are such as ANN, RF, and LR by having uniformity across all the classes and using micro scores for imbalanced datasets.…”

Section: Related Workmentioning

confidence: 99%

Driver Distraction Detection Using Machine Learning

2024

IJSREM

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The problem of driver distraction has become serious, and this is due to physical distractions that can be dangerous on the roads. Convolutional Neural Networks (CNNs) are used in this research to introduce a new approach to identifying physical distractions. To train the designed CNN model, a large dataset of labeled images that depict different physical distractions such as eating, texting, and manipulating controls is utilized. We make use of the spatial hierarchies learned by CNNs that enable us to extract important information for distraction identification with high efficiency. The experimental results validate the proposed method’s ability to recognize physical distractions in real driving scenarios effectively. Incorporating such models into in-car systems could therefore result in reduced distractions and consequently fewer accidents and additionally offer immediate warnings or interventions thus improving driver safety considerably. Key Words: Distraction, Machine Learning, Convolution Neural Network, Road accidents.

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Section: Related Workmentioning

confidence: 99%

Driver Distraction Detection Using Machine Learning

2024

IJSREM

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“…On the other hand, SVM produced a good tradeoff in terms of a low computational cost and an encouraging accuracy of 94%. In [29], a comparison of three classification methods, including random forest, gradient boosting, and logistic regression are presented in terms of the classification of driver distraction behavior. The results showed that the gradient-boosting algorithm achieved the best performance.…”

Section: B Traditional Machine Learning-based Techniquesmentioning

confidence: 99%

IoT-Based Non-Intrusive Automated Driver Drowsiness Monitoring Framework for Logistics and Public Transport Applications to Enhance Road Safety

et al. 2023

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The exponential growth in road accidents has led to a need for continuous driver monitoring to enhance road safety. Existing techniques rely on vehicle sensor-based and behavior analysis-based approaches, where the behavior analysis-based approaches are generally considered more desirable as they enable reliable detection of a more elaborate set of driver behaviors. They are categorized as intrusive and non-intrusive approaches. Unlike intrusive approaches that generally rely on constant direct human contact with sensors (physiological signals) and are sensitive to artifacts, non-intrusive approaches offer a more effective behavior monitoring using computer vision-based techniques. This paper proposes an end-to-end non-intrusive IoT-based automated framework to monitor driver behaviors, designed specifically for logistic and public transport applications. It consists of an embedded system, edge computing and cloud computing modules, and a mobile phone application, in an attempt to provide a holistic unified solution for drowsiness detection, monitoring, as well as evaluation of drivers. Drowsiness detection is based on detecting sleeping, yawning, and distraction behaviors using an image processing-based technique. To minimize the effects of latency, throughput, and packet losses, edge computing is performed using commercial off-the-shelf embedded boards. Moreover, a cloud-hosted real-time database for remote monitoring on interactive Android mobile application has been set up, where admin can add multiple drivers to get drowsiness notifications along with other useful related information for driver evaluation. An extensive experimental testing has been performed, obtaining encouraging results. An overall accuracy of 96% is achieved along with an enhanced robustness, portability, and usability of the proposed framework.INDEX TERMS ADAS, driver drowsiness, road safety, driver monitoring, IoT, edge computing, mobile application, road accidents, cloud computing.

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“…"Machine learning methods for driver behaviour classification" [8] To To increase safety and comfort during driving Traffic simulation…”

Section: Title Objective Type Of Analyzed Data Conclusionmentioning

confidence: 99%

“…Articles [8,11,17] describe the application of machine learning techniques for driver behavior data. All applied methods-Kalman filter, linear regression, logistic regression, gradient boosting, and random forest-are suitable for analyzing driver behavior.…”

Section: Title Objective Type Of Analyzed Data Conclusionmentioning

confidence: 99%

“…Another important component of the road traffic simulation model is the driver behavior model. This takes into account characteristics such as mood, fatigue, and responses to distracting conditions [7,8]. The relationships between external factors and the way drivers perform tasks, such as choices of headway during car-following, gap acceptance, overtaking, speed choice, and lateral and longitudinal control, can also be represented in the model [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Modeling Driver Behavior in Road Traffic Simulation

Mecheva¹,

Furnadzhiev²,

Kakanakov³

2022

Sensors

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Driver behavior models are an important part of road traffic simulation modeling. They encompass characteristics such as mood, fatigue, and response to distracting conditions. The relationships between external factors and the way drivers perform tasks can also be represented in models. This article proposes a methodology for establishing parameters of driver behavior models. The methodology is based on road traffic data and determines the car-following model and routing algorithm and their parameters that best describe driving habits. Sequential and parallel implementation of the methodology through the urban mobility simulator SUMO and Python are proposed. Four car-following models and three routing algorithms and their parameters are investigated. The results of the performed simulations prove the applicability of the methodology. Based on more than 7000 simulations performed, it is concluded that in future experiments of the traffic in Plovdiv it is appropriate to use a Contraction Hierarchies routing algorithm with the default routing step and the Krauss car-following model with the default configuration parameters.

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Machine learning methods for driver behaviour classification

Cited by 6 publications

References 9 publications

Driver Distraction Detection Using Machine Learning

Driver Distraction Detection Using Machine Learning

IoT-Based Non-Intrusive Automated Driver Drowsiness Monitoring Framework for Logistics and Public Transport Applications to Enhance Road Safety

Modeling Driver Behavior in Road Traffic Simulation

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