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Evolving Traffic Scenarios to Test Driver Assistance Systems in Simulations

  • Torsten Steiner
Conference paper
  • 1.1k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9066)

Abstract

Nowadays, driver assistance systems involve an ever increasing degree of automatization. Costly effort is put into testing the individual components to ensure proper functioning by the vehicle manufacturers.

However, problems can also arise on a macroscopic scale, as vehicles and infrastructure are recently equipped with short range radio communication (“Car2X”). These problems caused by interaction are of even greater concern than “normal” bugs, as the final product might already have been deployed when the issues first become apparent.

Multi-Agent System (MAS) research refers to such issues as “emergent misbehavior”. The said field also brought up an approach to automatically discover the worst consequences of the malfunctions. Hence, a given system under test can already be revised during development, saving a tremendous amount of resources.

The approach from MAS is adapted to the domain of testing driver assistance systems in traffic simulations. A green-light optimal-speed advisory (GLOSA) algorithm is used as an example in which conceptual problems are discovered by the testing system after simpler issues are eliminated.

Keywords

Genetic Algorithm Assistance System Conceptual Problem Driver Assistance System Intelligent Transport System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Alam, S., Shafi, K., Abbass, H., Barlow, M.: Evolving Air Traffic Scenarios for the Evaluation of Conflict Detection Models. In: 6th Eurocontrol Innovation Research Workshop and Conference, Eurocontrol Experiment Research Centre, pp. 1–8 (2007)Google Scholar
  2. 2.
    Bergmann, K.: Vulnerability Testing In Wireless Ad-hoc Networks Using Incremental Adaptive Corrective Learning. Dissertation, University of Calgary (2014), http://theses.ucalgary.ca/handle/11023/1504
  3. 3.
    Chao-Qun, M., Hai-Jun, H., Tie-Qiao, T.: Improving Urban Traffic by Velocity Guidance. In: 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA), vol. 2, pp. 383–387. IEEE (October 2008), http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4659788
  4. 4.
    Eckhoff, D., Halmos, B., German, R.: Potentials and limitations of Green Light Optimal Speed Advisory systems. VNC (Section IV) (2013), http://www7old.informatik.uni-erlangen.de/~eckhoff/publications/pdf/eckhoff2013potentials.pdf
  5. 5.
    ETSI: TS 102 894-1 - V1.1.1 - Intelligent Transport Systems (ITS); Users and applications requirements; Part 1: Facility layer structure, functional requirements and specifications 1, 1–56 (2013)Google Scholar
  6. 6.
    Hudson, J., Denzinger, J., Kasinger, H., Bauer, B.: Efficiency Testing of Self-Adapting Systems by Learning of Event Sequences. In: ADAPTIVE 2010. pp. 200–205 (2010)Google Scholar
  7. 7.
    Katsaros, K., Kernchen, R., Dianati, M., Rieck, D.: Performance study of a Green Light Optimized Speed Advisory (GLOSA) application using an integrated cooperative ITS simulation platform. In: 2011 7th International Wireless Communications and Mobile Computing Conference, pp. 918–923. IEEE (July 2011)Google Scholar
  8. 8.
    Koukoumidis, E., Peh, L., Martonosi, M.: SignalGuru: leveraging mobile phones for collaborative traffic signal schedule advisory. In: Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services, June 28-July 01, pp. 127–140. ACM (2011)Google Scholar
  9. 9.
    Krajzewicz, D., Bieker, L., Erdmann, J.: Preparing Simulative Evaluation of the GLOSA Application. elib.dlr.de, pp.1–11 (October 2012), http://elib.dlr.de/78905/1/ITSW2012_GLOSA.pdf
  10. 10.
    Krauß, S.: Microscopic modeling of traffic flow: Investigation of collision free vehicle dynamics. D L R - Forschungsberichte (1998)Google Scholar
  11. 11.
    Niebel, W.: Cost-Benefit-Based Implementation Strategy for Green Light Optimised Speed Advisory (GLOSA). Activities of Transport Telematics 2013(C), 312–320 (2013), http://link.springer.com/chapter/10.1007/978-3-642-41647-7_38
  12. 12.
    Sanchez, M., Cano, J.C., Kim, D.: Predicting Traffic lights to Improve Urban Traffic Fuel Consumption. In: 2006 6th International Conference on ITS Telecommunications, pp. 331–336. IEEE (June 2006)Google Scholar
  13. 13.
    Seredynski, M., Mazurczyk, W., Khadraoui, D.: Multi-segment Green Light Optimal Speed Advisory. In: 2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum, pp. 459–465 (May 2013)Google Scholar
  14. 14.
    Sommer, C., Eckhoff, D., Dressler, F.: Improving the Accuracy of IVC Simulation Using Crowd-sourced Geodata. PIK - Praxis der Informationsverarbeitung und Kommunikation 33(4), 278–283 (2010), http://www.degruyter.com/view/j/piko.2010.33.issue-4/piko.2010.047/piko.2010.047.xml CrossRefGoogle Scholar
  15. 15.
    Wegener, A., Hellbruck, H., Wewetzer, C., Lubke, A.: VANET Simulation Environment with Feedback Loop and its Application to Traffic Light Assistance. In: 2008 IEEE Globecom Workshops, pp. 1–7. IEEE (November 2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Torsten Steiner
    • 1
  1. 1.Fraunhofer ESKMunichGermany

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