Skip to main content

Towards Large Scale Urban Traffic Reference Data: Smart Infrastructure in the Test Area Autonomous Driving Baden-Württemberg

  • Conference paper
  • First Online:
Intelligent Autonomous Systems 15 (IAS 2018)

Abstract

This paper presents the concept, realization and evaluation of a flexible and scalable setup for smart infrastructure at the example of the Test Area Autonomous Driving Baden-Württemberg.

In verification and validation of autonomous driving systems, there exists a gap between virtual validation and real road tests: Simulation provides an easy and efficient way to assess a system’s performance under a variety of environmental constraints, but is restricted to model assumptions and scenarios, which might ignore important aspects. Whereas expensive real road tests promise an unexpected environment for statistical evaluation of traffic scenarios, but lack of observability. Our setup for smart infrastructure is supposed to close the gap by tackling this issue by observing and providing reference data of traffic scenarios for application in different testing and evaluation settings.

We present the approach of implementing a distributed intelligent infrastructure capable of handling traffic light states, road topology and especially information about locally observed traffic participants. The data is provided online via Vehicle-to-X (V2X) communication for live testing and sensor range extension as well as offline via a backend for high-precision analysis and application of machine learning techniques. To obtain information about traffic participants, a camera based object tracking was realised. To cope with the high amount of information to be transmitted via V2X and to use the available bandwidth optimally, the standard for broadcasting vehicle information is modified by applying a form of data compression through prioritization.

The setup is initially evaluated at a large intersection in Karlsruhe, Germany.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    For more information about the Test Area Autonomous Driving Baden-Württemberg see https://taf-bw.de/en.

References

  1. ALP.Lab GmbH (Austrian Light Vehicle Proving Region for Automated Driving). http://www.alp-lab.at/. Accessed 15 May 2018

  2. Federal Ministry of Transport and Digital Infrastructure: Digital Test Beds. http://www.bmvi.de/EN/Topics/Digital-Matters/Digital-Test-Beds/digital-test-beds.html. Accessed 15 May 2018

  3. MCity Headquarters: MCity Test Facility. https://mcity.umich.edu/our-work/mcity-test-facility/. Accessed 20 May 2018

  4. Toyota Research Institute: Opening in October - Toyota Research Institute Automated Vehicle Test Facility. http://www.tri.global/news/opening-in-october-toyota-research-institute-auto-2018-5-3. Accessed 20 May 2018

  5. Aniss, H.: Overview of an ITS Project: SCOOP@F. In: Communication Technologies for Vehicles. Springer International Publishing (2016)

    Google Scholar 

  6. Bar-Shalom, Y., Willett, P., Tian, X.: Tracking and Data Fusion: A Handbook of Algorithms. YBS Publishing, Storrs (2011)

    Google Scholar 

  7. Barrios, C., Motai, Y.: Predicting Vehicle Trajectory. CRC Press, Boca Raton (2017)

    Book  Google Scholar 

  8. Blackman, S., Popoli, R.: Design and Analysis of Modern Tracking Systems. Artech House, Boston (1999)

    MATH  Google Scholar 

  9. Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: Computer Vision and Pattern Recognition (CVPR) (2016)

    Google Scholar 

  10. EN 302 637-2 V1.3.2; Intelligent Transport Systems (ITS); Vehicular Communications; Basic Set of Applications; Part 2: Specification of Cooperative Awareness Basic Service. Standard, ETSI, Sophia Antipolis Cedex - FRANCE, November 2014

    Google Scholar 

  11. European Commission: Cooperative, connected and automated mobility (C-ITS). https://ec.europa.eu/transport/themes/its/c-its_en. Accessed 15 May 2018

  12. Girshick, R.: Fast R-CNN. In: International Conference on Computer Vision (ICCV) (2015)

    Google Scholar 

  13. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Computer Vision and Pattern Recognition (CVPR) (2014)

    Google Scholar 

  14. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: International Conference on Computer Vision (ICCV) (2017)

    Google Scholar 

  15. Heidenreich, T., Spehr, J., Stiller, C.: LaneSLAM - simultaneous pose and lane estimation using maps with lane-level accuracy. In: International Conference on Intelligent Transportation Systems (ITSC) (2015)

    Google Scholar 

  16. Hübner, D., Riegelhuth, G.: A new system architecture for cooperative traffic centres - the SimTD field trial. In: 19th ITS World Congress (2012)

    Google Scholar 

  17. Hubschneider, C., Doll, J., Weber, M., Klemm, S., Kuhnt, F., Zöllner, J.M.: Integrating end-to-end learned steering into probabilistic autonomous driving. In: International Conference on Intelligent Transportation Systems (ITSC) (2017)

    Google Scholar 

  18. Jodoin, J.P., Bilodeau, G.A., Saunier, N.: Urban Tracker webpage. https://www.jpjodoin.com/urbantracker/dataset.html. Accessed 20 May 2018

  19. Jodoin, J.P., Bilodeau, G.A., Saunier, N.: Urban tracker: multiple object tracking in urban mixed traffic. In: Computer Vision (WACV) (2014)

    Google Scholar 

  20. Kuhnt, F., Pfeiffer, M., Zimmer, P., Zimmerer, D., Gomer, J.M., Kaiser, V., Kohlhaas, R., Zöllner, J.M.: Robust environment perception for the audi autonomous driving cup. In: International Conference on Intelligent Transportation Systems (ITSC) (2016)

    Google Scholar 

  21. Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: common objects in context. In: European Conference on Computer Vision (ECCV) (2014) (2014)

    Google Scholar 

  22. Meyer, F., Kropfreiter, T., Williams, J.L., Lau, R.A., Hlawatsch, F., Braca, P., Win, M.Z.: Message passing algorithms for scalable multitarget tracking. In: Proceedings of the IEEE, vol. 106 (2018)

    Article  Google Scholar 

  23. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Computer Vision and Pattern Recognition (2016)

    Google Scholar 

  24. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Neural Information Processing Systems (NIPS) (2017)

    Article  Google Scholar 

  25. Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach (2016)

    Google Scholar 

  26. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: OverFeat: integrated recognition, localization and detection using convolutional networks (2014)

    Google Scholar 

  27. Sjoberg, K., Andres, P., Buburuzan, T., Brakemeier, A.: Cooperative intelligent transport systems in Europe: current deployment status and outlook. IEEE Veh. Technol. Mag. (2017)

    Google Scholar 

  28. Strigel, E., Meissner, D., Seeliger, F., Wilking, B., Dietmayer, K.: The Ko-PER Intersection Laserscanner and Video Dataset. In: International Conference on Intelligent Transportation Systems (ITSC) (2014)

    Google Scholar 

  29. Tomatis, A., Miche, M., Haeusler, F., Lenardi, M., Bohnert, T.M., Radusch, I.: A test architecture for V-2-X cooperative systems field operational tests. In: International Conference on Intelligent Transport Systems Telecommunications (ITST) (2009)

    Google Scholar 

  30. Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. (2000)

    Google Scholar 

  31. Zofka, M.R., Kuhnt, F., Kohlhaas, R., Zollner, J.M.: Simulation framework for the development of autonomous small scale vehicles for autonomous robots (SIMPAR) (2016)

    Google Scholar 

Download references

Acknowledgement

This work was done within the project “Digitales Testfeld BW für automatisiertes und vernetztes Fahren”, referred to as “Testfeld Autonomes Fahren Baden-Württemberg”, funded by the Ministry of Transport Baden-Württemberg.

Under the direction of the FZI Research Center for Information Technology, a consortium of the City of Karlsruhe, the Karlsruhe Institute of Technology, Karlsruhe University of Applied Sciences, Heilbronn University of Applied Sciences, the Fraunhofer Institute for Optronics, System Technology and Image Evaluation IOSB and the City of Bruchsal and other associate partners is implementing the development of the Test Area.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christian Hubschneider .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Fleck, T. et al. (2019). Towards Large Scale Urban Traffic Reference Data: Smart Infrastructure in the Test Area Autonomous Driving Baden-Württemberg. In: Strand, M., Dillmann, R., Menegatti, E., Ghidoni, S. (eds) Intelligent Autonomous Systems 15. IAS 2018. Advances in Intelligent Systems and Computing, vol 867. Springer, Cham. https://doi.org/10.1007/978-3-030-01370-7_75

Download citation

Publish with us

Policies and ethics