Advertisement

Statistical Model Checking for Scenario-Based Verification of ADAS

  • Sebastian GerwinnEmail author
  • Eike Möhlmann
  • Anja Sieper
Chapter
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 476)

Abstract

The increasing complexity of advanced driver assistant systems requires a thorough investigation of a high-dimensional input space. To alleviate this problem, simulation-based approaches have recently been proposed. An overall safety assessment, however, requires the simulation results to relate to the safety of such systems in real-world scenes. To this end, we propose a rigorous method of specifying requirements for these systems depending on the environmental situation and generating statistical evidence for the safety of the system in the specified environmental situations. We demonstrate this process in an exemplary highway scenario involving decision and perception uncertainty.

Keywords

Formal Methods ADAS Traffic Sequence Charts Statistical Model Checking 

References

  1. 1.
    Bartels, A., Meinecke, M.M., Steinmeyer, S.: Fahrstreifenwechselassistenz. In: Handbuch Fahrerassistenzsysteme, pp. 959–974. Springer, Berlin (2015)Google Scholar
  2. 2.
    Bubeck, S., Munos, R., Stoltz, G., Szepesvari, C.: X-armed bandits. J. Mach. Learn. Res. 12(May), 1655–1695 (2011)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Damm, W., Harel, D.: LSCs: breathing life into message sequence charts. Formal Methods Syst. Des. 19(1), 45–80 (2001)CrossRefGoogle Scholar
  4. 4.
    Damm, W., Kemper, S., Möhlmann, E., Peikenkamp, T., Rakow, A.: Traffic Sequence Charts - From Visualization to Semantics. Reports of SFB/TR 14 AVACS 117, SFB/TR 14 AVACS (2017). http://www.avacs.org/fileadmin/Publikationen/Open/avacs_technical_report_117.pdf, http://www.avacs.org
  5. 5.
    Damm, W., Kemper, S., Mhlmann, E., Peikenkamp, T., Rakow, A.: Traffic Sequence Charts - A Visual Language for Capturing Traffic Scenarios. In: Embedded Real Time Software and Systems - ERTS2018 (2018). To appearGoogle Scholar
  6. 6.
    Damm, W., Mhlmann, E., Peikenkamp, T., Rakow, A.: A formal semantics for traffic sequence charts. In: Festschrift in honor of Edmund A. Lee (2017). To appearGoogle Scholar
  7. 7.
    David, A., Jensen, P.G., Larsen, K.G., Mikučionis, M., Taankvist, J.H.: Uppaal stratego. In: International Conference on Tools and Algorithms for the Construction and Analysis of Systems, pp. 206–211. Springer, Berlin (2015)Google Scholar
  8. 8.
    Ellen, C., Gerwinn, S., Fränzle, M.: Statistical model checking for stochastic hybrid systems involving nondeterminism over continuous domains. Int. J. Softw. Tools Technol. Transfer 17(4), 485–504 (2015)CrossRefGoogle Scholar
  9. 9.
    ENABLE-S\(^3\): (European initiative to enable validation for highly automated safe and secure systems). Grant nr. 692455-2 Call H2020-ECSEL-2015-2-IA-two-stage call (2016)Google Scholar
  10. 10.
    Fränzle, M., Hermanns, H., Teige, T.: Stochastic satisfiability modulo theory: a novel technique for the analysis of probabilistic hybrid systems. In: International Workshop on Hybrid Systems: Computation and Control, pp. 172–186. Springer, Berlin (2008)Google Scholar
  11. 11.
    Henzinger, T.A.: The theory of hybrid automata. In: Proceedings, 11th Annual IEEE Symposium on Logic in Computer Science, New Brunswick, NJ, USA, July 27–30, 1996, pp. 278–292. IEEE Computer Society (1996).  https://doi.org/10.1109/LICS.1996.561342
  12. 12.
    Kemper, S., Etzien, C.: A visual logic for the description of highway traffic scenarios. In: Complex Systems Design & Management, pp. 233–245. Springer, Berlin (2014)CrossRefGoogle Scholar
  13. 13.
    Linker, S., Hilscher, M.: Proof theory of a multi-lane spatial logic. In: International Colloquium on Theoretical Aspects of Computing, pp. 231–248. Springer, Berlin (2013)CrossRefGoogle Scholar
  14. 14.
    Loos, S.M., Platzer, A., Nistor, L.: Adaptive cruise control: hybrid, distributed, and now formally verified. In: International Symposium on Formal Methods, pp. 42–56. Springer, Berlin (2011)Google Scholar
  15. 15.
    Nidhi Kalra, S.M.P.: Driving to Safety: How Many Miles of Driving Would It Take to Demonstrate Autonomous Vehicle Reliability? RAND Corporation (2016). http://www.jstor.org/stable/10.7249/j.ctt1btc0xwGoogle Scholar
  16. 16.
    PEGASUS: (projekt zur etablierung von generell akzeptierten gütekriterien, werkzeugen und methoden sowie szenarien und situationen zur freigabe hochautomatisierter fahrfunktionen). Funding: BMWI, Germany (2016)Google Scholar
  17. 17.
    Shmarov, F., Zuliani, P.: Probreach: verified probabilistic delta-reachability for stochastic hybrid systems. In: Proceedings of the 18th International Conference on Hybrid Systems: Computation and Control, pp. 134–139. ACM, New York (2015)Google Scholar
  18. 18.
    Stellet, J.E., Zofka, M.R., Schumacher, J., Schamm, T., Niewels, F., Zöllner, J.M.: Testing of advanced driver assistance towards automated driving: a survey and taxonomy on existing approaches and open questions. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems (ITSC), pp. 1455–1462. IEEE, New York (2015)Google Scholar
  19. 19.
    Vidyasagar, M.: Randomized algorithms for robust controller synthesis using statistical learning theory. Automatica 37(10), 1515–1528 (2001)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Winner, H., Weitzel, A.: Quo vadis, fas? In: Handbuch Fahrerassistenzsysteme, pp. 658–667. Springer, Berlin (2012)CrossRefGoogle Scholar
  21. 21.
    Younes, H.L., Kwiatkowska, M., Norman, G., Parker, D.: Numerical versus statistical probabilistic model checking. Int. J. Softw. Tools Technol. Transfer 8(3), 216–228 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Sebastian Gerwinn
    • 1
    Email author
  • Eike Möhlmann
    • 1
  • Anja Sieper
    • 1
  1. 1.OFFIS - Institute for Information TechnologyOldenburgGermany

Personalised recommendations