Telematics and machine learning system for estimating the load condition of a heavy-duty vehicle

This study employs machine learning techniques to determine whether trucks are loaded or empty based solely on data from the vehicle's communication network, especially from the engine module. We achieved an accuracy of over 85% for short hauls (0.5 to 5 km) and nearly 95% for long hauls (5 to...

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Bibliographic Details
Published inProcedia computer science Vol. 232; pp. 2616 - 2625
Main Authors von Glehn, Fabio Ribeiro, Gonçalves, Bruno Henrique Pereira, Neto, Marlipe Garcia Fagundes, Fonseca, João Paulo da Silva
Format Journal Article
LanguageEnglish
Published Elsevier B.V 2024
Subjects
Big data analytics
Fuel saving
Heavy-duty vehicles
Machine learning
Telematics
Telematics
Big data analytics
Heavy-duty vehicles
Machine learning
Fuel saving
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Summary:This study employs machine learning techniques to determine whether trucks are loaded or empty based solely on data from the vehicle's communication network, especially from the engine module. We achieved an accuracy of over 85% for short hauls (0.5 to 5 km) and nearly 95% for long hauls (5 to 500 km). This approach not only streamlines fleet management, reducing the need for communication between managers and drivers but also plays a crucial role in research related to fuel consumption reduction and intelligent fault diagnostics. Minimizing dependence on imprecise and time-delayed human responses opens new avenues for effective studies and innovative solutions in the transportation industry.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2024.02.080