Scalable Machine Learning and Analytics of the Vehicle Data to derive Vehicle Health and Driving Characteristics

Shreyash Sabde, P. Pagala, Kartik Mudaliar, K. Ashwini
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引用次数: 1

Abstract

Many vehicles have the facility to check the health of the engine, driving characteristics, ADAS, and many more advanced features. But they all are observed in high-end cars which are costly. This paper focuses on creating a low-cost framework for existing vehicles, where the framework will contain the driving environment characteristics, driving analysis, and vehicle health characteristics. We have utilized the On-Board Diagnostic II (OBD II) module and also smartphone to build the ML and analytics model and this model is used in our proposed framework. So, with the help of OBD II and smartphone, we can collect the vision data, sensor data which we can further use in our model to derive the result which will help the driver in regards to car maintenance, driving skills in a way to improve driving skills, and also to government by reporting road anomalies.
车辆数据的可扩展机器学习和分析,以获得车辆健康和驾驶特征
许多车辆都有检查发动机健康状况、驾驶特性、ADAS和许多更先进功能的设施。但它们都是在昂贵的高端汽车上观察到的。本文的重点是为现有车辆创建一个低成本的框架,该框架将包含驾驶环境特征、驾驶分析和车辆健康特征。我们利用车载诊断II (OBD II)模块和智能手机来构建机器学习和分析模型,该模型用于我们提出的框架中。因此,在OBD II和智能手机的帮助下,我们可以收集视觉数据,传感器数据,我们可以在我们的模型中进一步使用这些数据来得出结果,这将有助于驾驶员进行汽车维护,提高驾驶技能,并通过报告道路异常情况向政府报告。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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