Advertisement

Real-Time Data Processing in Autonomous Vehicles Based on Distributed Architecture: A Case Study

  • Yassine El HafidEmail author
  • Abdessamad El Rharras
  • Abdellah Chehri
  • Rachid Saadane
  • Mohammed Wahbi
Conference paper
  • 45 Downloads
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 185)

Abstract

This work aims to evaluate the real-time processing system in the context of an autonomous vehicle with limited hardware and software capabilities. We elaborate algorithm implemented in 1/10 scale electric car using a line scan camera, speed sensors, and embedded electronic control system. The vehicle navigates in an arbitrary one-lane circuit using an edge detection algorithm. The challenge was to make a complete one loop of the arbitrary circuit in the shortest time with various lighting conditions. The experiments show that several issues were revealed in each step of data sensors processing and need a robust algorithm to handle exceptions caused by multiple disturbances. Furthermore, we paid particular attention to time constraints in embedded processor calculation and actuators response time to achieve reliable critical software control algorithms.

Keywords

Autonomous vehicle Real-time data processing Embedded system 

References

  1. 1.
    Katare, D., El-Sharkawy, M.: Embedded system enabled vehicle collision detection: an ANN classifier. In: 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0284–0289. IEEE (2019)Google Scholar
  2. 2.
    Gao, T., Lai, Z., Mei, Z., Wu, Q.: Hybrid SVM-CNN classification technique for moving targets in automotive FMCW radar system. In: 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP), pp. 1–6. IEEE (2019)Google Scholar
  3. 3.
    Ren, J., Ren, R., Green, M., Huang, X.: A deep learning method for fault detection of autonomous vehicles. In: 2019 14th International Conference on Computer Science & Education (ICCSE), pp. 749–754. IEEE (2019)Google Scholar
  4. 4.
    Essaid, M., Idoumghar, L., Lepagnot, J., Brévilliers, M.: GPU parallelization strategies for metaheuristics: a survey. Int. J. Parall. Emerg. Distrib. Syst. 34(5), 497–522 (2019)CrossRefGoogle Scholar
  5. 5.
    Hamm, M., Huhn, W.: Glare investigations and safety research on digital light technologies. In: SAE Technical Paper (2019)Google Scholar
  6. 6.
    Verma, M., Collette, C.: Active vibration isolation system for drone cameras. In: Proceedings of International Conference on Vibration Problems: ICOVP (2019)Google Scholar
  7. 7.
    Alejandre, I., Artés, M.: Method for the evaluation of optical encoders performance under vibration. Precis. Eng. 31(2), 114–121 (2007)CrossRefGoogle Scholar
  8. 8.
    Shariff, H.M., Rahiman, M.H.F., Adnan, R., Marzaki, M.H., Tajjudin, M., Jalil, M.H.A.: The PID integrated anti-windup scheme by Ziegler-Nichols tuning for small-scale steam distillation process. In: 2019 IEEE 9th International Conference on System Engineering and Technology (ICSET), pp. 391–395 (2019)Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Yassine El Hafid
    • 1
    Email author
  • Abdessamad El Rharras
    • 1
  • Abdellah Chehri
    • 2
  • Rachid Saadane
    • 1
  • Mohammed Wahbi
    • 1
  1. 1.Laboratory Engineering SystemSIRC/LAGeS-EHTP Hassania School of Public WorksCasblancaMorocco
  2. 2.Department of Applied SciencesUniversity of Quebec in ChicoutimiChicoutimiCanada

Personalised recommendations