Skip to main content
main-content

Tipp

Weitere Kapitel dieses Buchs durch Wischen aufrufen

2019 | OriginalPaper | Buchkapitel

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

verfasst von: Raphael Pfeffer, Patrick Ukas, Eric Sax

Erschienen in: Fahrerassistenzsysteme 2018

Verlag: Springer Fachmedien Wiesbaden

share
TEILEN

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.

Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

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

  • über 69.000 Bücher
  • über 500 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

Testen Sie jetzt 15 Tage kostenlos.

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 50.000 Bücher
  • über 380 Zeitschriften

aus folgenden Fachgebieten:

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




Testen Sie jetzt 15 Tage kostenlos.

Fußnoten
1
CarMaker by IPG Automotive GmbH (www.​ipg-automotive.​com).
 
Literatur
2.
Zurück zum Zitat Maurer, M., Gerdes, J.C., Lenz, B., Winner, H.: Autonomes Fahren. Springer Vieweg, Berlin (2015) CrossRef Maurer, M., Gerdes, J.C., Lenz, B., Winner, H.: Autonomes Fahren. Springer Vieweg, Berlin (2015) CrossRef
3.
Zurück zum Zitat 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) 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.
Zurück zum Zitat 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) 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.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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) 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.
Zurück zum Zitat 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) 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.
Zurück zum Zitat 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) 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.
Zurück zum Zitat 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) 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.
Zurück zum Zitat Rajpura, P.S., Bojinov, H., Hegde, R.S.: Object detection using deep CNNs trained on synthetic images. In: Computer Vision and Pattern Recognition (2017) 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.
Zurück zum Zitat Peng, X., Sun, B., Ali, K., Saenko, K.: Learning deep object detectors from 3D models. In: Computer Vision and Pattern Recognition (2015) Peng, X., Sun, B., Ali, K., Saenko, K.: Learning deep object detectors from 3D models. In: Computer Vision and Pattern Recognition (2015)
12.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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) 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.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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) 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)
Metadaten
Titel
Potential of Virtual Test Environments for the Development of Highly Automated Driving Functions Using Neural Networks
verfasst von
Raphael Pfeffer
Patrick Ukas
Eric Sax
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-658-23751-6_18

Premium Partner