MMCloud: um algoritmo de clustering não supervisionado online para análise do comportamento do motorista

Abstract

Evolving and adaptive intelligent systems are essential for handling dynamic data streams from real-world environments, especially in resource-constrained settings such as vehicles enabled with the Internet of Things (IoT). This study proposes an online evolutionary clustering approach for real-time driver behavior diagnosis, leveraging Tiny Machine Learning (TinyML) and soft sensors. It introduces the MMCloud algorithm, an incremental clustering model designed to manage continuous data streams. The algorithm eliminates the need for retraining and is capable of adapting to concept drift and varying driving conditions. The methodology integrates onboard diagnostics (OBD-II) with an edge computing framework to classify driver behavior into three categories: cautious, normal, and aggressive. To validate the proposed approach, two case studies were conducted in urban environments under various traffic conditions. One used a Freematics One+ device, while the other deployed the embedded algorithm in a mobile application. The results demonstrate the system’s ability to accurately identify evolving driving patterns, contributing to safer driving practices, fuel consumption optimization, and intelligent transportation systems (ITS). This research advances the field of evolutionary AI by integrating adaptive machine learning models with embedded IoT solutions, enabling autonomous and self-organizing monitoring of driver behavior.