Journal
2023 IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR AUTOMOTIVE, METROAUTOMOTIVE
Volume -, Issue -, Pages 199-204Publisher
IEEE
DOI: 10.1109/MetroAutomotive57488.2023.10219125
Keywords
Internet of Things; driver behavior; unsupervised learning; k-fix AutoCloud algorithm; TinyML; online learning; road safety; data processing
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The integration of IoT technologies in the automotive sector has impacted driver behavior research, and a new unsupervised online algorithm is proposed for detecting driver behavior patterns, suitable for low-power IoT devices.
The Internet of Things (IoT) has significantly impacted various industries, particularly the automotive sector, due to the growing integration of IoT technologies in vehicles. As a result, the volume of data generated by vehicular sensors has increased, leading to a surge in studies focusing on driver behavior to enhance road safety and optimize transportation networks. However, traditional approaches to analyzing driver behavior have relied on supervised offline learning models, which are unsuitable for handling data streams in online learning environments. This study introduces an unsupervised online k-fix AutoCloud algorithm for detecting driver behavior patterns, leveraging the concepts of typicity and eccentricity while considering the historical-temporal relationships between samples. Furthermore, the algorithm autonomously and adaptively evolves without requiring a supervised training phase, making it compatible with the TinyML concept, encompassing Artificial Intelligence algorithms designed for low-power IoT devices. To validate the proposed method, a real case study was conducted over four days using a vehicle to compare the quantity and quality of clusters generated by the algorithm. The findings demonstrate the potential of the proposed approach for optimizing data processing with minimal computational power.
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