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

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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.

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Acknowledgments

This work was financially supported by the National Natural Science Foundation of China (Grant No. 51575463 and Grand No. 51975495), as well as was supported by Fujian Collaboration Innovation Centre for R&D of Coach and Special Vehicle (Grant No. 2016BJC016), Major science and technology projects of Xiamen of China (3502Z20191019).

Author information

Authors and Affiliations

  1. Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen, 361005, China

    Xuanwei Chen, Wei Chen, Liang Hou, Xiangjian Bu & Qingyuan Zhu

  2. School of Computer Science & Electronic Engineering, University of Essex, Colchester, CO4 3SQ, UK

    Huosheng Hu

  3. Fujian Collaboration Innovation Centre for R&D of Coach and Special Vehicle, Xiamen University of Technology, Xiamen, 361005, China

    Qingyuan Zhu

Authors
  1. Xuanwei Chen
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  2. Wei Chen
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  3. Liang Hou
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  4. Huosheng Hu
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  5. Xiangjian Bu
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  6. Qingyuan Zhu
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Corresponding author

Correspondence to Qingyuan Zhu.

Additional information

Recommended by Editor Ja Choon Koo

Xuanwei Chen received his B.Eng. in Mechanical and Electrical Engineering, Tan Kah Kee College, Xiamen University, China where he is now a graduate student. His research interest is in data mining.

Wei Chen received his B.Eng. from Huaqiao University in July, 2014. He is currently working toward the Ph.D. in Mechanical and Electrical Engineering, Xiamen University, China. His research interests include artificial intelligence and computer vision.

Liang Hou received the B.S. from Tian-jin University of Technology in 1996, the M.S, in 1999, and Ph.D., 2002. He is currently a Professor of Mechanical and Electrical Engineering, Xiamen University. His research interests are in industrial big data and smart manufacturing.

Huosheng Hu is a Professor of Computer Science and Electronic Engineering, University of Essex, U.K., where he leads the Robotics Research Group. His research interests include robotics, human- robot interaction, mechatronics, embedded systems, and cloud computing.

Xiangjian Bu received the B.S. in Engineering from Henan University of Technology in 2011 and the M.S. in 2014. His research interests are in mechanical optimization design and vibration noise control.

Qingyuan Zhu received the B.S. in Vehicle Engineering in 2002, and Ph.D. from China Agricultural University, Beijing, China, in 2009. He is currently a Professor of Mechanical and Electrical Engineering, Xiamen University, Xiamen, China. His current research interests include advanced driver assistance system, sensing and modelling, heavy-duty construction equipment and off-road vehicles.

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Chen, X., Chen, W., Hou, L. et al. A novel data-driven rollover risk assessment for articulated steering vehicles using RNN. J Mech Sci Technol 34, 2161–2170 (2020). https://doi.org/10.1007/s12206-020-0437-4

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  • DOI: https://doi.org/10.1007/s12206-020-0437-4

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