Automated Testing of Vehicle Instrument Cluster Based on Computer Vision

  • Tan Wei Ren
  • Wan Shahmisufi bin Wan Jamaludin
  • Kueh Ying Lin
  • Muhammad Nasiruddin Mahyuddin
  • Bakhtiar Affendi Bin RosdiEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 547)


With advancement of technologies, instrument cluster is become more complex. Conventional manual testing and validation is difficult to cover all the test cases to provide flawless delivery of product within restricted development timescale. As for now, we are proposing to use computer vision system in automotive manufacturing for automated design validation testing process. The purpose of the inclusion of computer vision system is to replace the conventional design validation testing process which is time consuming and extremely labor intensive. The speedometer, tachometer, fuel gauge and temperature gauge are inspected by comparing the accuracy of the pointer detected using the developed algorithms with the pointer position displayed on the meter. Besides that, the signal indicators status can be inspected using pixel intensity test. Under the assumption of controlled light surrounding and fixed position of the camera, pixel intensity can produce accurate results. The deviation error of needle gauge test and signal indicator test are 2.3%, those error occur while there is some noises influence the threshold value. Besides that, processing time of computer vision is within 0.5 s which is quite efficiency in testing process. In conclusion, the machine vision system is able to help for spectating the automated instrument cluster testing process.


Needle gauge test White pixel intensity test Automated instrument cluster test (AIC) 


  1. 1.
    Automotive Electronics—Instrument Clusters. Accessed 12 May 2016. Retrieved from
  2. 2.
    Hage, P., Jones, B.: Machine vision-based quality control systems for the automotive industry. Assembly Autom. 15, 32–34 (1995)CrossRefGoogle Scholar
  3. 3.
    Quan, S., Ning, X., Yifan, C.: Development of an automatic optical measurement system for automotive part surface inspection. In: Proceedings, 2005 IEEE/ASME International Conference on Advanced Intelligent Mechatrocnis, pp. 1557–1562 (2005)Google Scholar
  4. 4.
    Das, S., Das, P.: Competitiveness and its impact on research and development in indian automobile industry. Int. J. Manag. Transform. 5, 79–89 (2011)Google Scholar
  5. 5.
    Sharmila, P.B., Venkadesan, P.S.P.: Automation for instrument cluster panel testing using machine vision and NI-PXI hardware-in-the-loop. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 4 (2015)Google Scholar
  6. 6.
    Tsampardoukas, G., Mouzakitis, A.: Hardware-in-the-loop visual display validation utilising vision system, in Control. UKACC Int. Conf. 2010, 1–5 (2010)Google Scholar
  7. 7.
    Yingping, H., Mouzakitis, A., McMurran, R., Dhadyalla, G., Jones, R.P.: Design validation testing of vehicle instrument cluster using machine vision and hardware-in-the-loop. In: 2008 IEEE International Conference on Vehicular Electronics and Safety, ICVES, pp. 265–270 (2008)Google Scholar
  8. 8.
    Zhang, J., Wang, Y., Lin, F.: Automatic reading recognition system for analog measuring instruments base on digital image processing. J. Appl. Sci. 13, 2562 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Tan Wei Ren
    • 1
  • Wan Shahmisufi bin Wan Jamaludin
    • 1
  • Kueh Ying Lin
    • 1
  • Muhammad Nasiruddin Mahyuddin
    • 1
  • Bakhtiar Affendi Bin Rosdi
    • 1
    Email author
  1. 1.School of Electrical & Electronic Engineering CampusUniversiti Sains MalaysiaNibong TebalMalaysia

Personalised recommendations