对基于OBD-II的机器学习应用程序进行审查,以实现可持续,高效,安全和安全的车辆驾驶
Emmanouel T Michailidis1,2, Antigoni Panagiotopoulou2, Andreas Papadakis2
1Department of Digital Systems, School of Information and Communication Technologies, University of Piraeus, GR18534 Piraeus, Greece.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
机器学习增强了车载诊断II (OBD-II) 系统,用于更智能的车辆. 这项研究探讨了使用OBD-II数据来提高汽车系统的燃油效率,安全性和可持续性的ML应用.
科学领域:
- 汽车工程 汽车工程
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 车载诊断II (OBD-II) 系统通过嵌入式传感器收集实时车辆数据.
- 与新兴的机器学习 (ML) 能力相比,传统的OBD-II应用是有限的.
研究的目的:
- 为了研究基于ML的应用程序,利用OBD-II传感器数据.
- 提高现代车辆的可持续性,运营效率,安全性和安全性.
主要方法:
- 检查监督,无监督,强化学习 (RL),深度学习 (DL) 和混合ML模型.
- 对燃料优化,排放控制,驾驶员行为,异常检测,网络安全,道路感知和驾驶支持等ML应用的分析.
主要成果:
- 在利用OBD-II数据来实现先进的汽车功能方面,ML显著超过传统方法.
- 确定了一套多样化的ML方法,适用于各种驾驶分析任务.
结论:
- 基于ML驱动的OBD-II应用程序在车辆性能和安全方面提供了显著的改进.
- 未来的研究应该解决挑战,并探索汽车系统的ML的新方向.
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