Skip to main content

Potential of Virtual Test Environments for the Development of Highly Automated Driving Functions Using Neural Networks

Part of the Proceedings book series (PROCEE)

Abstract

This paper outlines the implications and challenges that modern algorithms such as neural networks may have on the process of function development for highly automated driving. In this context, an approach is presented how synthetically generated data from a simulation environment can contribute to accelerate and automate the complex process of data acquisition and labeling for these neural networks. A concept of an exemplary implementation is shown and first results of the training of a convolutional neural network using these synthetic data are presented.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-658-23751-6_18
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-658-23751-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   139.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.

Notes

  1. 1.

    CarMaker by IPG Automotive GmbH (www.ipg-automotive.com).

References

  1. SAE International: Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles (2017). www.sae.org/autodrive. 22 Feb 2018

  2. Maurer, M., Gerdes, J.C., Lenz, B., Winner, H.: Autonomes Fahren. Springer Vieweg, Berlin (2015)

    CrossRef  Google Scholar 

  3. Pfeffer, R., Leichsenring, T.: Continuous development of highly automated driving functions with vehicle-in-the-loop using the example of euro NCAP scenarios. In: 7th Conference Simulation and Testing for Vehicle Technology, Berlin (2016)

    Google Scholar 

  4. Otten, S., Bach, J., Wohlfahrt, C.,King, C., Lier, J., Schmid, H., Schmerler, S., Sax, E.: Automated assessment and evaluation of digital test drives. In: Zachäus, C., Müller, B., Meyer, G. (eds.) Advanced Microsystems for Automotive Applications 2017. Lecture Notes in Mobility. Springer, Cham (2017)

    Google Scholar 

  5. Lutz, A., Schick, B., Holzmann, H.: Simulation methods supporting homologation of Electronic stability control in vehicle variants. Veh. Syst. Dyn. 55(10), 1432–1497 (2017)

    CrossRef  Google Scholar 

  6. Johnson-Roberson, M., Barto, C., Mehta, R., Sridhar, S., Rosaen, K., Vasudevan, R.: Driving in the matrix: can virtual worlds replace human-generated annotations for real world tasks? In: Proceedings of International Conference on Robotics and Automation (ICRA) (2017)

    Google Scholar 

  7. Marin, J., Vazquez, D., Geronimo, D., Lopez, A.M.: Learning appearance in virtual scenarios for pedestrian detection. In: IEEE Computer Vision and Pattern Recognition (CVPR) (2010)

    Google Scholar 

  8. Nilsson, J., Fredriksson, J., Gu, I.Y.-H., Andersson, P.: Pedestrian detection using augmented training data. In: 22nd International Conference on Pattern Recognition (ICPR) (2014)

    Google Scholar 

  9. Barbosa, I., Cristani, M., Caputo, B., Rognhaugen, A., Theoharis, T.: looking beyond appearances: synthetic training data for deep CNNS in re-identification. In: Computer Vision and Pattern Recognition (2017)

    Google Scholar 

  10. Rajpura, P.S., Bojinov, H., Hegde, R.S.: Object detection using deep CNNs trained on synthetic images. In: Computer Vision and Pattern Recognition (2017)

    Google Scholar 

  11. Peng, X., Sun, B., Ali, K., Saenko, K.: Learning deep object detectors from 3D models. In: Computer Vision and Pattern Recognition (2015)

    Google Scholar 

  12. Falcini, F., Lami, G., Constanza, A.: Deep learning in automotive software. In: IEEE Software, May/June 2017, pp. 56–63. IEEE Computer Society (2017)

    CrossRef  Google Scholar 

  13. Vondrick, C., Patterson, D., Ramanan, D.: Efficiently scaling up crowdsourced video annotation – a set of best practices for high quality, economical video labeling. Int. J. Comput. Vis. 101(1), 184–204 (2013)

    CrossRef  Google Scholar 

  14. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.: SSD: Single Shot MultiBox Detector. In: Computer Vision and Pattern Recognition (2015)

    Google Scholar 

  15. Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)

    CrossRef  Google Scholar 

  16. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI Vision Benchmark Suite. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Raphael Pfeffer .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Pfeffer, R., Ukas, P., Sax, E. (2019). Potential of Virtual Test Environments for the Development of Highly Automated Driving Functions Using Neural Networks. In: Bertram, T. (eds) Fahrerassistenzsysteme 2018. Proceedings. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-23751-6_18

Download citation