Authors : Brajesh Kumar Sen; Dr. R. S. Verma

Volume/Issue : Volume 11 - 2026, Issue 9 - September

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Google Scholar : https://tinyurl.com/2ypxfe6p

DOI : https://doi.org/10.38124/ijisrt/26sep770

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : Heavy commercial vehicles (HCVs) are responsible for a disproportionately high number of fatal road traffic crashes worldwide, due to long driving hours, mechanical fatigue and dangerous driving behaviour. In this paper, a smart embedded system for real-time monitoring, tracking and safety management of HCVs is designed, developed and experimentally validated using IoT, AI and ML. The proposed framework is composed of an embedded acquisition layer based on GPS-GSM-sensors and a data preprocessing and analytics pipeline based on Python that can ingest real-time vehicle, driver, environmental and road-condition data.

Keywords : Internet of Things; Heavy Commercial Vehicles; Machine Learning; Driver Behaviour Classification; Predictive Maintenance; Real-Time Safety Monitoring; GPS-GSM Telematics; Decision Support System.

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