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An Earlier Predictive Rollover Index Designed for Bus Rollover Detection and Prevention

机译:专为公交侧翻检测和预防而设计的早期预测侧翻指数

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摘要

As vehicle rollovers annually cause a great deal of traffic-related deaths, an increasing number of vehicles are being equipped with rollover prevention systems with the aim of avoiding such accidents. To improve the functionality of active rollover prevention systems, this study provided a potential enhanced method with the intention to predict the tendency of the lateral load transfer ratio (111), which is the most common rollover index. 'Ibis will help provide a certain amount of lead time for the control system to respond more effectively. Before the prediction process, an estimation equation was proposed to better estimate the LTR; the equation was validated using Simulink and TruckSim. Further, to eliminate the influence of drawbacks and make this method practical, a buffer operator was added. Simulation results showed that grey LTR (GLTR) was able to roundly predict the future trend of the LTR based on current and previous data. Under the tests of "Sine with Dwell" (Sindwell) and double lane change (DLC), the GLTR could provide the control system with sufficient time beforehand. Additionally, to further examine the performance of the G LTR, a differential system model was adopted to verify its effectiveness. Through the Sindwell maneuver, it was demonstrated that the GLTR index could improve the performance of the rollover prevention systems by achieving the expected response.
机译:由于每年的车辆侧翻会导致大量与交通有关的死亡,因此越来越多的车辆配备了防侧翻系统,目的是避免此类事故的发生。为了改善主动防侧翻系统的功能,本研究提供了一种潜在的增强方法,旨在预测侧向载荷传递比(111)的趋势,这是最常见的侧翻指标。宜必思将帮助控制系统提供一定量的交货时间,以便更有效地响应。在预测过程之前,提出了一个估计方程以更好地估计LTR。该方程使用Simulink和TruckSim进行了验证。此外,为了消除缺陷的影响并使该方法实用,添加了一个缓冲运算符。仿真结果表明,灰色LTR(GLTR)能够根据当前和以前的数据全面预测LTR的未来趋势。在“正弦驻留”(Sindwell)和双车道变更(DLC)的测试下,GLTR可以提前为控制系统提供足够的时间。此外,为了进一步检查G LTR的性能,采用了差分系统模型来验证其有效性。通过辛德威尔(Sindwell)演算,表明GLTR指数可以通过实现预期的响应来提高防侧翻系统的性能。

著录项

  • 来源
    《Journal of Advanced Transportation》 |2018年第6期|2713868.1-2713868.10|共10页
  • 作者单位

    Changan Univ Sch Automobile Xian 710064 Shaanxi Peoples R China;

    Univ Iowa Natl Adv Driving Simulator Iowa City IA 52242 USA;

    Changan Univ Sch Construct Machinery Xian 710064 Shaanxi Peoples R China;

    Inha Univ Dept Mech Engn Incheon 2212 South Korea;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-18 05:03:39

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