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Evidential Combination Operators for Entrapment Prediction in Advanced Driver Assistance Systems

  • Alexander Karlsson
  • Anders Dahlbom
  • Hui Zhong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8502)

Abstract

We propose the use of evidential combination operators for advanced driver assistance systems (ADAS) for vehicles. More specifically, we elaborate on how three different operators, one precise and two imprecise, can be used for the purpose of entrapment prediction, i.e., to estimate when the relative positions and speeds of the surrounding vehicles can potentially become dangerous. We motivate the use of the imprecise operators by their ability to model uncertainty in the underlying sensor information and we provide an example that demonstrates the differences between the operators.

Keywords

Evidential combination operators advanced driver assistance systems Bayesian theory credal sets Dempster-Shafer theory 

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References

  1. 1.
    Brannstrom, M., Sandblom, F., Hammarstrand, L.: A probabilistic framework for decision-making in collision avoidance systems. IEEE Transactions on Intelligent Transportation Systems 14(2), 637–648 (2013)CrossRefGoogle Scholar
  2. 2.
    Dempster, A.P.: A generalization of bayesian inference. Journal of the Royal Statistical Society 30(2), 205–247 (1969)MathSciNetGoogle Scholar
  3. 3.
    Arnborg, S.: Robust Bayesianism: Imprecise and paradoxical reasoning. In: Proceedings of the 7th International Conference on Information fusion (2004)Google Scholar
  4. 4.
    Arnborg, S.: Robust Bayesianism: Relation to evidence theory. Journal of Advances in Information Fusion 1(1), 63–74 (2006)Google Scholar
  5. 5.
    Karlsson, A., Johansson, R., Andler, S.F.: Characterization and empirical evaluation of bayesian and credal combination operators. Journal of Advances in Information Fusion 6(2), 150–166 (2011)Google Scholar
  6. 6.
    Levi, I.: The Enterprise of Knowledge: An Essay on Knowledge, Credal Probability, and Chance. MIT Press (1983)Google Scholar
  7. 7.
    Walley, P.: Towards a unified theory of imprecise probability. International Journal of Approximate Reasoning 24, 125–148 (2000)CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Walley, P.: Statistical Reasoning with Imprecise Probabilities. Chapman and Hall (1991)Google Scholar
  9. 9.
    Berger, J.O.: An overview of robust Bayesian analysis. Test 3, 5–124 (1994)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Couso, I., Moral, S., Walley, P.: A survey of concepts of independence for imprecise probabilities. Risk Decision and Policy 5, 165–181 (2000)CrossRefGoogle Scholar
  11. 11.
    Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press (1976)Google Scholar
  12. 12.
    Smets, P.: Analyzing the combination of conflicting belief functions. Information Fusion 8, 387–412 (2007)CrossRefGoogle Scholar
  13. 13.
    Irpino, A., Tontodonato, V.: Cluster reduced interval data using Hausdorff distance. Computational Statistics 21, 241–288 (2006)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Helldin, T., Falkman, G., Riveiro, M., Davidsson, S.: Presenting system uncertainty in automotive uis for supporting trust calibration in autonomous driving. In: Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. AutomotiveUI 2013, pp. 210–217. ACM, New York (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alexander Karlsson
    • 1
  • Anders Dahlbom
    • 1
  • Hui Zhong
    • 2
  1. 1.Informatics Research CenterUniversity of SkövdeSkövdeSweden
  2. 2.Advanced Technology & Research, Volvo Group Trucks TechnologyGothenburgSweden

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