Michailidis Emmanouel T, Panagiotopoulou Antigoni, Papadakis Andreas
Department of Digital Systems, School of Information and Communication Technologies, University of Piraeus, GR18534 Piraeus, Greece.
Department of Electrical and Electronics Engineering Educators, School of Pedagogical and Technological Education, GR15122 Athens, Greece.
Sensors (Basel). 2025 Jun 29;25(13):4057. doi: 10.3390/s25134057.
The On-Board Diagnostics II (OBD-II) system, driven by a wide range of embedded sensors, has revolutionized the automotive industry by enabling real-time monitoring of key vehicle parameters such as engine load, vehicle speed, throttle position, and diagnostic trouble codes. Concurrently, recent advancements in machine learning (ML) have further expanded the capabilities of OBD-II applications, unlocking advanced, intelligent, and data-centric functionalities that significantly surpass those of conventional methodologies. This paper presents a comprehensive investigation into ML-based applications that leverage OBD-II sensor data, aiming to enhance sustainability, operational efficiency, safety, and security in modern vehicular systems. To this end, a diverse set of ML approaches is examined, encompassing supervised, unsupervised, reinforcement learning (RL), deep learning (DL), and hybrid models intended to support advanced driving analytics tasks such as fuel optimization, emission control, driver behavior analysis, anomaly detection, cybersecurity, road perception, and driving support. Furthermore, this paper synthesizes recent research contributions and practical implementations, identifies prevailing challenges, and outlines prospective research directions.
车载诊断系统II（OBD-II）由大量嵌入式传感器驱动，通过实时监测发动机负载、车速、节气门位置和诊断故障码等关键车辆参数，彻底改变了汽车行业。与此同时，机器学习（ML）的最新进展进一步扩展了OBD-II应用的功能，解锁了先进、智能且以数据为中心的功能，这些功能大大超越了传统方法。本文对利用OBD-II传感器数据的基于ML的应用进行了全面研究，旨在提高现代车辆系统的可持续性、运营效率、安全性和安保性。为此，研究了一系列不同的ML方法，包括监督学习、无监督学习、强化学习（RL）、深度学习（DL）以及旨在支持高级驾驶分析任务（如燃油优化、排放控制、驾驶员行为分析、异常检测、网络安全、道路感知和驾驶支持）的混合模型。此外，本文综合了近期的研究贡献和实际应用，识别了当前面临的挑战，并概述了未来的研究方向。
