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A novel data-driven rollover risk assessment for articulated steering vehicles using RNN

Chen, Xuanwei and Chen, Wei and Hou, Liang and Hu, Huosheng and Bu, Xiangjian and Zhu, Qingyuan (2020) 'A novel data-driven rollover risk assessment for articulated steering vehicles using RNN.' Journal of Mechanical Science and Technology, 34 (5). pp. 2161-2170. ISSN 1738-494X

JMST-V35-N4-2020-2161-2170.pdf - Accepted Version

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Articulated steering vehicles have outstanding capability operating but suffer from frequent rollover accidents due to their complicated structure. It is necessary to accurately detect their rollover risk for drivers to take action in time. Their variable structure and the variable center of mass exhibit nonlinear time-variant behavior and increase the difficulty of dynamic modelling and lateral stability description. This paper proposes a novel data-driven modelling methodology for lateral stability description of articulated steering vehicles. The running data is first collected based on the typical operations that prone to rollover and then classified into two types: Safety and danger. The data quality is further improved by wavelet transformation. Finally, an RNN model is built on the data. The experimental results show that the output of the RNN model can accurately quantify lateral stability of the vehicle, i.e., the risk of rollover, when it is turning and crossing uneven surfaces or obstacles.

Item Type: Article
Uncontrolled Keywords: Articulated steering vehicles; Lateral stability; Data-driven; Recurrent neural network; Rollover risk
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 18 Jun 2020 12:00
Last Modified: 18 Aug 2022 13:00

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