A KPI prediction approach with JITL for vehicular Cyber Physical System

Hongpeng Zhou, Hao Ju, Tianyu Tan, Tianyi Gao
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Abstract

Intelligent transportation is a hot research field in Cyber-Physical System (CPS). In order to improve the driving safety, many studies have been conducted to predict collision probability and send out warning signal timely. However, most of these studies are model based with limited prediction accuracy. Moreover, the abundant historical data is leave-off. In this paper, a data-driven method is proposed to achieve the same objective, which could acquire a more satisfactory result and provide an accurate prediction for two key performance indicator(i.e. throttle and brake). A vehicle cyber-physical system (VCPS) benchmark is built on the professional software CarSim. The algorithm just-in time learning (JITL) would process motivation data produced by the benchmark and compute out the prediction result. For testifying the advantages of the proposed method, the other two fitting algorithms (i.e. PLS and KPLS) are compared with it. The simulation results prove that JITL could consume much lesser time and receive a more precise prediction.
基于JITL的车辆网络物理系统KPI预测方法
智能交通是信息物理系统(CPS)中的一个研究热点。为了提高行车安全性,人们进行了大量的碰撞概率预测和及时发出预警信号的研究。然而,这些研究大多是基于模型的,预测精度有限。此外,丰富的历史资料是留下的。本文提出了一种数据驱动的方法来实现同样的目标,该方法可以获得更令人满意的结果,并对两个关键绩效指标(即:油门和刹车)。在专业软件CarSim上建立了车辆网络物理系统(VCPS)基准。jit (just-in - time learning)算法对基准测试产生的动机数据进行处理并计算预测结果。为了验证所提方法的优越性,将另外两种拟合算法(PLS和KPLS)与所提方法进行了比较。仿真结果表明,JITL可以节省大量的时间,获得更精确的预测结果。
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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