Development of an Advanced Driver Assistance System Using RGB-D Camera

  • Alin Pantea
  • Florin Girbacia
  • Teodora Girbacia
Conference paper


In recent years, the automotive industry has shown increased interest in Advanced Driver Assistance Systems (ADAS), especially those based on bio-signals. Recent advances in RGB-D technologies have provided effective solutions for tracking human activity based on depth data. In this paper is presented an ADAS system based on Kinect RGB-D camera for the identification of the driver’s distraction. Using depth and colour information the proposed ADAS system is be able to identify the driver’s head orientation and eye position. Based on this data, the driver’s inattention is detected and the driver is warned by audio signals. The proposed ADAS system was evaluated using a Virtual Reality driver simulator for manual and visual distraction. The results show accurate recognition of driver’s distraction.


Driver distraction RGB-D sensor ADAS 



This paper is supported by the Romanian Government, specifically MEN – UEFISCDI authority under the program PNII “Partnerships in priority areas”, under the project number 240⁄2014 - NAVIEYES, supporting the collaboration between the company Route 66 and University Transilvania of Brasov.


  1. 1.
    Byington, K.W., Schwebel, D.C.: Effects of mobile Internet use on college student pedestrian injury risk. Accid. Anal. Prev. 51, 78–83 (2013)CrossRefGoogle Scholar
  2. 2.
    Duguleana, M., Dumitru, A., Postelnicu, C., Mogan, G.: Video-based evaluation of driver’s visual attention using smartphones. In: IEEE 6th International Conference on Information, Intelligence, Systems and Applications (IISA), pp. 1–5 (2015)Google Scholar
  3. 3.
    Craye, C., Rashwan, A., Kamel, M.S., Karray, F.: A multi-modal driver fatigue and distraction assessment system. Int. J. Intell. Transp. Syst. Res. 14(3), pp. 1–22 (2015)Google Scholar
  4. 4.
    Gallahan, S.L., Golzar, G.F., Jain, A.P., Samay, A.E., Trerotola, T.J., Weisskopf, J.G., Lau, N.: Detecting and mitigating driver distraction with motion capture technology: distracted driving warning system. In: Systems and Information Engineering Design Symposium (SIEDS), pp. 76–81 (2013)Google Scholar
  5. 5.
    Hu, Z., Uchida, N., Wang, Y., Dong, Y.: Face orientation estimation for driver monitoring with a single depth camera. In: 2015 IEEE Intelligent Vehicles Symposium (IV), pp. 958–963 (2015)Google Scholar
  6. 6.
    Trivedi, M.M., Gandhi, T., McCall, J.: Looking-in and looking-out of a vehicle. Selected investigations in computer vision based enhanced vehicle safety. IEEE Trans. Intell. Transp. Syst. 8(1), 108–120 (2007)CrossRefGoogle Scholar
  7. 7.
    Kondyli, A., Sisiopiku, V.P., Zhao, L., Barmpoutis, A.: Computer assisted analysis of drivers’ body activity using a range camera. IEEE Intell. Transp. Syst. Mag. 7(3), 18–28 (2015)CrossRefGoogle Scholar
  8. 8.
    NHTSA:, official US government website for distracted driving (2016).
  9. 9.
    Postelnicu C.C., Machidon, O., Girbacia, M., Voinea, D., Duguleana, M.: Effects of playing mobile games while driving. In: International Conference on Distributed, Ambient, and Pervasive Interactions, pp. 291–301 (2016)Google Scholar
  10. 10.
  11. 11.
    Logitech G29 driving force. Accesed July 2016
  12. 12.
  13. 13.
  14. 14.
    NaviEyes research project site.

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Alin Pantea
    • 1
  • Florin Girbacia
    • 1
  • Teodora Girbacia
    • 1
  1. 1.Transilvania University of BraşovBraşovRomania

Personalised recommendations