Journal article
An Earlier Predictive Rollover Index Designed for Bus Rollover Detection and Prevention
Journal of advanced transportation, Vol.2018, pp.1-10
01/01/2018
DOI: 10.1155/2018/2713868
Abstract
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.
Details
- Title: Subtitle
- An Earlier Predictive Rollover Index Designed for Bus Rollover Detection and Prevention
- Creators
- Shun Tian - Changan Univ, Sch Automobile, Xian 710064, Shaanxi, Peoples R ChinaLang Wei - Changan Univ, Sch Automobile, Xian 710064, Shaanxi, Peoples R ChinaChris Schwarz - University of IowaWenCai Zhou - Chang'an UniversityYuan Jiao - Changan Univ, Sch Construct Machinery, Xian 710064, Shaanxi, Peoples R ChinaYanQin Chen - Inha University
- Resource Type
- Journal article
- Publication Details
- Journal of advanced transportation, Vol.2018, pp.1-10
- Publisher
- Wiley-Hindawi
- DOI
- 10.1155/2018/2713868
- ISSN
- 0197-6729
- eISSN
- 2042-3195
- Number of pages
- 10
- Grant note
- 51278062 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC)
- Language
- English
- Date published
- 01/01/2018
- Academic Unit
- Iowa Technology Institute; Driving Safety Research Institute
- Record Identifier
- 9984627188502771
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