Latest revision as of 21:15, 22 March 2021

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 (LTR), which is the most common rollover index. This 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 GLTR, 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.

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The different versions of the original document can be found in:

• http://downloads.hindawi.com/journals/jat/2018/2713868.pdf under the license https://creativecommons.org/licenses/by

• http://dx.doi.org/10.1155/2018/2713868 under the license cc-by

• http://downloads.hindawi.com/journals/jat/2018/2713868.pdf,

http://downloads.hindawi.com/journals/jat/2018/2713868.xml,

http://dx.doi.org/10.1155/2018/2713868 under the license http://creativecommons.org/licenses/by/4.0

• http://dx.doi.org/10.1155/2018/2713868,

https://doaj.org/toc/0197-6729,

https://doaj.org/toc/2042-3195 under the license http://creativecommons.org/licenses/by/4.0/

• https://www.hindawi.com/journals/jat/2018/2713868,

http://downloads.hindawi.com/journals/jat/2018/2713868.pdf,

https://academic.microsoft.com/#/detail/2898696697