Vehicle Operating Safe State Monitoring System Modeling Method Based on Automata

  • Jianlong Xu
  • Guixiong Liu
  • Yi Gao
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 146)


This paper aims to clarify the information flow for the vehicles operating safety states monitoring (VOSM) system and optimize VOSM system performance. Based on analyzing VOSM system structure and information sequence, the information model for VOSM system is built by automata theory. The relationship among three key parameters including vehicle’s motion attitude parameter (MAP), dynamic load parameter (DLP) and braking performance parameter (BPP) is analyzed and described in this paper. The results indicate that VOSM system model based on automata can effectively describe VOSM system information sequence, and reflects the process of the system state change. Through the model, it will be able to achieve intelligent scheduling in VOSM system, and provide theory basis and reference model for future development and optimization work.


Automata theory VOSM Information flow System modeling 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pan, M.Y., Zhou, Y.B., Liu, G.X.: Analysis of vehicle driving safety measurement modes and development. Modern Manufacturing Engineering 5, 12–161 (2009)Google Scholar
  2. 2.
    Liu, G.X., Pan, M.Y., Zhou, Y.B.: New progress of vehicle driving safety states monitoring technology. Modern Manufacturing Engineering 9, 8–13 (2009)Google Scholar
  3. 3.
    Liu, G.X., Pan, M.Y., Lin, C.N., Feng, Y.: A wheel movement attitude monitoring method based on wheel embedded intelligent sensors. ZL 200910078476.6 (2009)Google Scholar
  4. 4.
    Liu, G.X., Zhou, Y.B., Huang, G.J., Pan, M.Y.: A wheel dynamic load monitoring method based on wheel embedded intelligent sensors. ZL 200910077794.0 (2009)Google Scholar
  5. 5.
    Liu, G.X., Pan, M.Y., Huang, G.J., Lin, C.N.: A wheel brake performance monitoring method based on wheel embedded intelligent sensors. ZL 2009 1 0077744.2 (2009)Google Scholar
  6. 6.
    Di Lecce, V., Amato, A.: A Distributed Measurement System For Smart Monitoring Of Vehicle Activities. In: IEEE International Instrumentation & Measurement Technology Conference, pp. 1–5 (2010)Google Scholar
  7. 7.
    Mueller, F., Wenzel, A.: Intelligent monitoring and adaptive competence assignment for driver and vehicle. In: 2007 IEEE Intelligent Vehicles Symposium, vol. 1-3, pp. 1254–1259 (2007)Google Scholar
  8. 8.
    Jouvray, C., Gerard, S., Terrier, F.: Smart sensor modeling with the UML for real-time embedded applications. In: 2004 Intelligent Vehicles Symposium, pp. 919–924 (2004)Google Scholar
  9. 9.
    Farooq, A., Hejiao, H., et al.: Analysis of the Petri net model of parallel manufaturing processes with shared resources. Information Sciences 181(23), 5249–5266 (2011)CrossRefGoogle Scholar
  10. 10.
    Dong, J.S., Hao, P., Qin, S.: Timed Automata Patterns. IEEE Transactions on Software Engineering 34(6), 844–859 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Jianlong Xu
    • 1
  • Guixiong Liu
    • 1
  • Yi Gao
    • 1
  1. 1.The Department of Mechanical & Automobile EngineeringSouth China University of TechnologyGuangzhouChina

Personalised recommendations