Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Radar for Autonomous Driving – Paradigm Shift from Mere Detection to Semantic Environment Understanding Read chapter Read first chapter

2019 | OriginalPaper | Chapter

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

Authors : Raphael Pfeffer, Patrick Ukas, Eric Sax

Published in: Fahrerassistenzsysteme 2018

Publisher: Springer Fachmedien Wiesbaden

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
previous chapter An Overview of Deep Learning and Its Applications
next chapter Incorporating Human Driving Data into Simulations and Trajectory Predictions
Footnotes
1
CarMaker by IPG Automotive GmbH (www.​ipg-automotive.​com).
 
Literature
1.
2.
Maurer, M., Gerdes, J.C., Lenz, B., Winner, H.: Autonomes Fahren. Springer Vieweg, Berlin (2015)CrossRef
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)
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)
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
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)
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)
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)
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)
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)
11.
Peng, X., Sun, B., Ali, K., Saenko, K.: Learning deep object detectors from 3D models. In: Computer Vision and Pattern Recognition (2015)
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
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
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)
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
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)
Metadata
Title
Potential of Virtual Test Environments for the Development of Highly Automated Driving Functions Using Neural Networks
Authors
Raphael Pfeffer
Patrick Ukas
Eric Sax
Copyright Year
2019
Book
Fahrerassistenzsysteme 2018

Print ISBN: 978-3-658-23750-9

Electronic ISBN: 978-3-658-23751-6

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-658-23751-6_18

Premium Partner

    Image Credits