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首页> 外文期刊>Journal of communications and networks >Drivingstyles: a mobile platform for driving styles and fuel consumption characterization
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Drivingstyles: a mobile platform for driving styles and fuel consumption characterization

机译：Drivingstyles：用于驾驶风格和油耗表征的移动平台

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Intelligent transportation systems (ITS) rely on connected vehicle applications to address real-world problems. Research is currently being conducted to support safety, mobility and environmental applications. This paper presents the DrivingStyles architecture, which adopts data mining techniques and neural networks to analyze and generate a classification of driving styles and fuel consumption based on driver characterization. In particular, we have implemented an algorithm that is able to characterize the degree of aggressiveness of each driver. We have also developed a methodology to calculate, in real-time, the consumption and environmental impact of spark ignition and diesel vehicles from a set of variables obtained from the vehicle’s electronic control unit (ECU). In this paper, we demonstrate the impact of the driving style on fuel consumption, as well as its correlation with the greenhouse gas emissions generated by each vehicle. Overall, our platform is able to assist drivers in correcting their bad driving habits, while offering helpful tips to improve fuel economy and driving safety.
机译：智能交通系统（ITS）依靠联网的车辆应用程序来解决实际问题。目前正在进行研究以支持安全性，机动性和环境应用。本文提出了DrivingStyles架构，该架构采用数据挖掘技术和神经网络来分析和生成基于驾驶员特征的驾驶风格和油耗分类。特别是，我们已经实现了一种能够表征每个驾驶员的攻击程度的算法。我们还开发了一种方法，可以根据从车辆的电子控制单元（ECU）获得的一组变量实时计算火花点火和柴油车辆的消耗和环境影响。在本文中，我们演示了驾驶方式对燃油消耗的影响，以及其与每辆车产生的温室气体排放的相关性。总体而言，我们的平台能够协助驾驶员纠正不良的驾驶习惯，同时提供有益的提示，以改善燃油经济性和驾驶安全性。

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