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Obstacle Detection and Distance Estimation for Autonomous Electric Vehicle Using Stereo Vision and DNN

  • Sarma Emani
  • K. P. Soman
  • V. V. Sajith VariyarEmail author
  • S. Adarsh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 898)

Abstract

Automation—replacement of humans with technology—is everywhere. It is going to become far more widespread, as industries are continuing to adapt to new technologies and are trying to find novel ways to save time, money, and effort. Automation in automobiles aims at replacing human intervention during the run time of vehicle by perceiving the environment around automobile in real time. This can be achieved in multitude of ways such as using passive sensors like camera and applying vision algorithms on their data or using active sensors like RADAR, LIDAR, time of flight (TOF). Active sensors are costly and not suitable for use in academic and research purposes. Since we have advanced computational platforms and optimized vision algorithms, we can make use of low-cost vision sensors to capture images in real time and map the surroundings of an automobile. In this paper, we tried to implement stereo vision on autonomous electric vehicle for obstacle detection and distance estimation.

Keywords

Radar Lidar TOF Stereo vision Object detection Distance estimation 

References

  1. 1.
    Bagolee, S.A., Tvana, M., Asadi, M., Oliver, T.: Autonomous vehicles: challenges, opportunities, and future implications for transportation policies. J.M.T. 24, 284–303 (2016)Google Scholar
  2. 2.
    Gruel, W., Stanford, J.M.: Assessing the long-term effects of autonomous vehicles: a speculative approach. Transp. Res. Procedia 13, 18–29 (2016)CrossRefGoogle Scholar
  3. 3.
    Aboali, M., Manap, N.A., Darsono, A.M., Yusof, Z.M.: Review on three-dimensional (3-D) acquisition and range imaging techniques. Int. J. Appl. Eng. Res. 12, 2409–2421 (2017)Google Scholar
  4. 4.
    Aish, D.: Stereo vision facing the challenges and seeing the opportunities for ADAS applications (2016). http://www.ti.com/lit/wp/spry300/spry300.pdf
  5. 5.
    Magad, A., Nurulfajar, M., Majad, D., Zulkalnain, M.Y.: Review on 3-D imaging and range imaging techniques. Int. J. Appl. Eng. Res. 12, 2409–2421 (2017)Google Scholar
  6. 6.
    Zhencheng, H., Uchimura, K.: U-V-disparity: an efficient algorithm for stereovision based scene analysis. In: IEEE Proceedings, Intelligent Vehicles Symposium, pp. 48–54 (2005)Google Scholar
  7. 7.
    Patel, D.K., Pankaj A.B., Nirav, R.S.: Distance measurement system using binocular stereo vision approach. IJERT 2, 2409–2421 (2017)Google Scholar
  8. 8.
    Appiah, N., Bandaru, N.: Obstacle detection using stereo vision for self-driving cars (2015)Google Scholar
  9. 9.
    Kwon, S., Lee, H.: Dense stereo-based real-time ROI generation for on-road obstacle detection. In: International SoC Design Conference, pp. 179–180, Jeju (2016)Google Scholar
  10. 10.
    Tianyu, G.: Real time obstacle depth perception using stereo vision. Masters thesis, University of Florida, Florida, USA (2014)Google Scholar
  11. 11.
    Jernej, M., Damir, V.: Distance measuring based on stereoscopic pictures. In: 9th International PhD Workshop on Systems and Control: young Generation Viewpoint, Slovenia (2008)Google Scholar
  12. 12.
    Deepika, N., Sajith, V.V.: Obstacle classification and detection for vision based navigation for autonomous driving. In: Proceedings, ICACCI. pp. 2092–2097. IEEE Press, Udupi (2017)Google Scholar
  13. 13.
    Shimil, J., Sajith, V.V., Soman, K.P.: Effective utilization and analysis of ROS on embedded platform for implementing autonomous car vision and navigation modules. In: Proceedings, ICACCI. pp. 877–882. IEEE Press, Udupi (2017)Google Scholar
  14. 14.
    Howard, G.A., Menglong, Z., Bo, C., Dmitry, K., Weijun, W., Tobias, W., Marko, A., Hartwig, A.: MobileNets: efficient convolutional neural networks for mobile vision applications (2017). arXiv:1704.04861
  15. 15.
    Wei, L., Dragomir, A., Dumitru, E., Christian, S., Scott R., Fu, C.-Y., Berg, A.C.: SSD: Single shot multibox detector. arXiv:1512.02325 (2016)

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Sarma Emani
    • 1
  • K. P. Soman
    • 2
  • V. V. Sajith Variyar
    • 2
    Email author
  • S. Adarsh
    • 1
  1. 1.Department of Electronics and Communication EngineeringAmrita School of Engineering, Coimbatore, Amrita Vishwa VidyapeethamCoimbatoreIndia
  2. 2.Center for Computational Engineering and Networking (CEN)Amrita School of Engineering, Coimbatore, Amrita Vishwa VidyapeethamCoimbatoreIndia

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