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

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 ex...

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Bibliographic Details
Published in:Journal of mechanical science and technology 2020, 34(5), , pp.2161-2170
Main Authors: Chen, Xuanwei, Chen, Wei, Hou, Liang, Hu, Huosheng, Bu, Xiangjian, Zhu, Qingyuan
Format: Article
Language:English
Subjects:
Accidents
Control
Dynamic models
Dynamic stability
Dynamical Systems
Engineering
Industrial and Production Engineering
Lateral stability
Mechanical Engineering
Modelling
Original Article
Risk assessment
Rollover
Steering
Vehicles
Vibration
Wavelet transforms
기계공학
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Summary: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.
ISSN:1738-494X
1976-3824
DOI:10.1007/s12206-020-0437-4