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首页> 外文期刊>IEEE Transactions on Intelligent Transportation Systems >Cloud Computing-Based Analyses to Predict Vehicle Driving Shockwave for Active Safe Driving in Intelligent Transportation System
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Cloud Computing-Based Analyses to Predict Vehicle Driving Shockwave for Active Safe Driving in Intelligent Transportation System

机译：基于云计算的分析预测智能交通系统中主动安全驾驶的车辆驾驶冲击波

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摘要

In the cloud computing era, the analyses of various types of big data gathered from the Internet of Vehicles, the Internet of Things, and smart sensors/devices achieve convenient services. Some famous applications include vehicle traffic loading on roads (from Google MAP), video sharing for fans group (from Facebook or Youtube), and so on. However, to achieve real-time active safe driving (RT-ASD) under unstable driving in high-threat areas on roads becomes an open issue in cloud computing-based intelligent transportation system. This paper proposes a predictive backward shockwave analysis approach (PSA) to achieve the RT-ASD based on the analyses of macroscopic traffic shockwave (PSA_MA) and microscopic car-following (PSA_mi). The PSA contributes in several aspects to active safe driving: 1) predicting and analyzing high threat of backward shockwaves from the gathered big data of the driving state information vehicles; 2) informing the analyzed threat messages to the vehicles in high-threat areas via the 3-Tier hierarchical cloud computing mechanism; 3) reducing the driving threat certainly; and 4) PSA_MA and PSA_mi can be applied for achieving active safe driving in autonomous self-driving vehicles and human-driving vehicles. The numerical results show that the PSA outperforms in approaches to relative error rate prediction, the accuracy of the backward shockwave determination, average vehicle velocity, average travel time, number of goodput vehicles, time-to-collision, and distance-to-collision.
机译：在云计算时代，对从车联网，物联网和智能传感器/设备收集的各种类型的大数据的分析实现了便捷的服务。一些著名的应用程序包括道路上的车辆交通负荷（来自Google MAP），爱好者小组的视频共享（来自Facebook或Youtube）等等。然而，在道路上高威胁区域的不稳定驾驶情况下实现实时主动安全驾驶（RT-ASD）成为基于云计算的智能交通系统中的一个悬而未决的问题。本文基于宏观交通冲击波（PSA_MA）和微观跟踪车（PSA_mi）的分析，提出了一种预测后向冲击波分析方法（PSA）来实现RT-ASD。 PSA在主动安全驾驶方面有几个方面的贡献：1）从收集的驾驶状态信息车辆的大数据中预测和分析后向冲击波的高威胁; 2）通过三层分层云计算机制将分析后的威胁信息告知高威胁区域的车辆; 3）一定要减少驾驶威胁; 4）PSA_MA和PSA_mi可用于实现自动驾驶和自动驾驶车辆的主动安全驾驶。数值结果表明，PSA在相对误差率预测，后向冲击波确定的准确性，平均车速，平均行驶时间，吞吐车辆数量，碰撞时间和碰撞距离等方面表现均优于同类。

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