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Analyzing the Next Generation Airborne Collision Avoidance System

  • Christian von Essen
  • Dimitra Giannakopoulou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8413)

Abstract

The next generation airborne collision avoidance system, ACAS X, departs from the traditional deterministic model on which the current system, TCAS, is based. To increase robustness, ACAS X relies on probabilistic models to represent the various sources of uncertainty. The work reported in this paper identifies verification challenges for ACAS X, and studies the applicability of probabilistic verification and synthesis techniques in addressing these challenges. Due to shortcomings of off-the-shelf probabilistic analysis tools, we developed a framework that is designed to handle systems with similar characteristics as ACAS X. We describe the application of our framework to AC

Keywords

Markov decision processes probabilistic verification probabilistic synthesis aircraft collision avoidance 

References

  1. 1.
    Chatterjee, K.: Markov decision processes with multiple long-run average objectives. In: Arvind, V., Prasad, S. (eds.) FSTTCS 2007. LNCS, vol. 4855, pp. 473–484. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Forejt, V., Kwiatkowska, M., Parker, D.: Pareto curves for probabilistic model checking. In: Chakraborty, S., Mukund, M. (eds.) ATVA 2012. LNCS, vol. 7561, pp. 317–332. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Hansson, H., Jonsson, B.: A logic for reasoning about time and reliability. Formal Aspects of Computing 6, 102–111 (1994)CrossRefGoogle Scholar
  4. 4.
    Johnson, C.: Final report: review of the BFU Überlingen accident report. Contract C/1.369/HQ/SS/04 to Eurocontrol (2004), http://www.dcs.gla.ac.uk/~johnson/Eurocontrol/Ueberlingen/Ueberlingen_Final_Report.PDF
  5. 5.
    Katoen, J.-P., Zapreev, I.S., Hahn, E.M., Hermanns, H., Jansen, D.N.: The ins and outs of the probabilistic model checker MRMC. Perform. Eval. 68(2) (2011)Google Scholar
  6. 6.
    Kochenderfer, M.J., Chryssanthacopoulos, J.P.: Robust airborne collision avoidance through dynamic programming. Project Report ATC-371, Massachusetts Institute of Technology, Lincoln Laboratory (2011)Google Scholar
  7. 7.
    Kuchar, J., Drumm, A.C.: The traffic alert and collision avoidance system. Lincoln Laboratory Journal 16(2), 277 (2007)Google Scholar
  8. 8.
    Kwiatkowska, M., Norman, G., Parker, D.: PRISM 4.0: Verification of probabilistic real-time systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 585–591. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Rennen, G., van Dam, E.R., den Hertog, D.: Enhancement of sandwich algorithms for approximating higher-dimensional convex Pareto sets. INFORMS Journal on Computing 23(4), 493–517 (2011)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Zuliani, P., Platzer, A., Clarke, E.M.: Bayesian statistical model checking with application to Stateflow/Simulink verification. Formal Methods in System Design 43(2), 338–367 (2013)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Christian von Essen
    • 1
  • Dimitra Giannakopoulou
    • 2
  1. 1.VerimagGrenobleFrance
  2. 2.NASA Ames Research CenterMoffett FieldUSA

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