Latency Assessment in Vehicle Embedded Monitoring Systems with Machine Learning

• Hilton Machado UFRN
• Matheus Andrade UFRN https://orcid.org/0009-0002-0268-2247
• Thaís Medeiros UFRN https://orcid.org/0000-0002-6447-3806
• Marianne Silva UFRN / UFAL https://orcid.org/0000-0002-8277-7571
• Ivanovitch Silva UFRN https://orcid.org/0000-0002-0116-6489

DOI: https://doi.org/10.5753/wtf.2025.8831

Abstract

The advancement of automotive technologies and the widespread adoption of OBD-II (On-Board Diagnostics) devices have enabled real-time vehicle data collection, supporting diagnostics and performance analysis applications. Although the OBD-II protocol is standardized, its implementation across different hardware and vehicles can significantly impact communication latency with the Electronic Control Unit (ECU). This study investigates how such variability affects the efficiency of data acquisition and the performance of embedded machine learning algorithms in a mobile application. Through a case study involving multiple vehicle models and data collection devices, ECU response times and algorithm execution times were analyzed under different operating conditions. The results show that system performance is sensitive to vehicle and interface hardware characteristics, highlighting the need to adjust acquisition parameters to ensure reliable and accurate real-time data processing. The findings contribute to improving embedded solutions for smart mobility and vehicle monitoring.

Keywords: OBD-II, Latency, ECU, Embedded Machine Learning, Real-Time

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