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- Title
A novel data-driven rollover risk assessment for articulated steering vehicles using RNN.
- Authors
Chen, Xuanwei; Chen, Wei; Hou, Liang; Hu, Huosheng; Bu, Xiangjian; Zhu, Qingyuan
- Abstract
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.
- Publication
Journal of Mechanical Science & Technology, 2020, Vol 34, Issue 5, p2161
- ISSN
1738-494X
- Publication type
Academic Journal
- DOI
10.1007/s12206-020-0437-4