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Digital Maps for Driving Assistance Systems and Autonomous Driving

  • Alexandre Armand
  • Javier Ibanez-Guzman
  • Clément Zinoune
Chapter

Abstract

Modern passenger vehicles increasingly incorporate perception systems that allow the deployment of advanced driving assistance systems which in turn shall led to highly automated systems and ultimately to autonomous vehicles. Despite the numerous advances in sensors and communications technologies applied to passenger vehicles, perception remains a major challenge due to the complexity of the task, the vehicle geometric constraints and cost. Digital navigation maps have proven themselves to be essential for driver guidance and have replaced paper maps. They store the geometric description of roads and associated features. Due to the limits of perception systems, errors occur on the understanding and relevance of the detected objects, when using multiple sensors discordances occur that lead to unknowns. By projecting sensor information from the perceived environment into digital navigation maps, information can be contextualized. The resulting representation of the world is much easier to interpret by a machine, but also to ensure the coherence of the perceived information.

This article deals with two aspects of perception: situation understanding and the detection of errors in maps. One goal is to demonstrate how contextualized information can be used to facilitate situation understanding, that is to provide meaning to the spatio-temporal relationship between perceived objects, road features, and the subject vehicle. Situation understanding is a fundamental feature for a machine to take decisions in an appropriate manner, particularly, if the navigation of the vehicle is to be governed by it. The approach is based on the application of ontologies to facilitate contextual descriptions by enabling the formalization of information in a semantic manner. The digital map on board the vehicle stores a priori knowledge about the environment, that is the underlying structure and static context. Digital maps contain different errors, particularly regarding road geometry. In case of errors, the association between the perceived objects and maps for situation understanding will be wrong. This will propagate through the system and lead to hazardous situations. The chapter is thus completed by introducing a mathematical formalism that allows for the detection of geometric map errors in real time. The uniqueness of this formalism is that it uses production type components, namely GNSS receivers and vehicle state data. The approach includes a mechanism that allows for the identification of the error, which could be in the digital map itself or due to errors in the position estimates of the vehicle.

The chapter combines theoretical developments with experimental data, as the proposed solutions were tested in public road networks using purposely equipped vehicles to demonstrate the viability and advantages of the theoretical developments.

Keywords

Fault detection isolation and adaptation Map integrity Ontology Situation understanding Statistical test 

References

  1. 1.
    S. Abburu, A survey on ontology reasoners and comparison. Int. J. Comput. Appl. 57 (17), 33–39 (2012)Google Scholar
  2. 2.
    A. Armand, D. Filliat, J. Ibañez-Guzmán, Modelling stop intersection approaches using gaussian processes, in Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems-ITSC (2013)Google Scholar
  3. 3.
    F. Baader, The Description Logic Handbook: Theory, Implementation, and Applications (Cambridge University Press, Cambridge, 2003)zbMATHGoogle Scholar
  4. 4.
    M. Basseville, I.V. Nikiforov, Detection of Abrupt Changes: Theory and Application (Prentice-Hall, Englewood Cliffs, NJ, 1993)Google Scholar
  5. 5.
    D.J. Buckley, The GIS primer an introduction to geographic information systems. Technical Report, Innovative, 1997Google Scholar
  6. 6.
    K. Dentler, R. Cornet, A.T. Teije, N. De Keizer, Comparison of reasoners for large ontologies in the OWL 2 EL profile. Semantic Web 2 (2), 71–87 (2011)Google Scholar
  7. 7.
    S. Geyer, M. Baltzer, B. Franz, S. Hakuli, M. Kauer, M. Kienle, S. Meier, T. Weißgerber, K. Bengler, R. Bruder et al., Concept and development of a unified ontology for generating test and use-case catalogues for assisted and automated vehicle guidance. IET Intell. Transp. Syst. 8 (3), 183–189 (2013)CrossRefGoogle Scholar
  8. 8.
    P.-Y. Gilliéron, H. Gontran, B. Merminod, Cartographie routière précise pour les systèmes d’́assistance à la conduite, in Proceedings of the GIS-SIT Conference. TOPO-CONF-2006-015 (2006)Google Scholar
  9. 9.
    T.R. Gruber, Toward principles for the design of ontologies used for knowledge sharing? Int. J. Hum. Comput. Stud. 43 (5), 907–928 (1995)CrossRefGoogle Scholar
  10. 10.
    T. Gruber, Ontology, in The Encyclopedia of Database Systems, ed. by L. Liu, M.T. Özsu (Springer, Berlin, 2009)Google Scholar
  11. 11.
    P.J. Hayes, The second naive physics manifesto, Formal Theories of the Commonsense World (1985), pp. 1–36Google Scholar
  12. 12.
    M. Horridge, S. Bechhofer, The OWL API: a java API for OWL ontologies. Semantic Web 2 (1), 11–21 (2011)Google Scholar
  13. 13.
    M. Horridge, S. Jupp, G. Moulton, A. Rector, R. Stevens, C. Wroe, A practical guide to building OWL ontologies using protégé 4 and CO-ODE tools edition 1. 2. The University of Manchester (2009)Google Scholar
  14. 14.
    I. Horrocks, P.F. Patel-Schneider, H. Boley, S. Tabet, B Grosof, M. Dean et al., Swrl: a semantic web rule language combining OWL and RuleML. W3C Member Submission 21, 79 (2004)Google Scholar
  15. 15.
    M. Hulsen, J.M. Zollner, N. Haeberlen, C. Weiss, Asynchronous real-time framework for knowledge-based intersection assistance, in 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC) (IEEE, New York, 2011), pp. 1680–1685Google Scholar
  16. 16.
    M. Hulsen, J.M. Zollner, C. Weiss, Traffic intersection situation description ontology for advanced driver assistance, in 2011 IEEE Intelligent Vehicles Symposium (IV) (IEEE, New York, 2011), pp. 993–999Google Scholar
  17. 17.
    Hummel, W. Thiemann, I. Lulcheva, Scene understanding of urban road intersections with description logic, in Dagstuhl Seminar Proceedings (Schloss Dagstuhl-Leibniz-Zentrum fr Informatik, Dagstuhl, 2008)Google Scholar
  18. 18.
    C.G. Keller, C. Sprunk, C. Bahlmann, J. Giebel, G. Baratoff, Real-time recognition of U.S. speed signs, in 2008 IEEE Intelligent Vehicles Symposium (2008), pp. 518–523Google Scholar
  19. 19.
    R. Kohlhaas, T. Bittner, T. Schamm, J.M. Zollner, Semantic state space for high-level maneuver planning in structured traffic scenes, in 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC) (IEEE, New York, 2014), pp. 1060–1065Google Scholar
  20. 20.
    L. Lamard, R. Chapuis, J.-P. Boyer, Multi target tracking with CPHD filter based on asynchronous sensors, in 2013 16th International Conference on Information Fusion (FUSION) (IEEE, New York, 2013), pp. 892–898Google Scholar
  21. 21.
    D. Maquin, J. Ragot, Diagnostic des systèmes linéaires (Lavoisier, Paris, 2000)Google Scholar
  22. 22.
    D.L McGuinness, F. Van Harmelen et al., Owl web ontology language overview. W3C Recommendation 10 (10), 2004 (2004)Google Scholar
  23. 23.
    B. Motik, R. Shearer, I. Horrocks, Optimized reasoning in description logics using hypertableaux, in Automated Deduction–CADE-21 (Springer, Berlin, 2007), pp. 67–83CrossRefGoogle Scholar
  24. 24.
    F. Moutarde, A. Bargeton, A. Herbin, L. Chanussot, Robust on-vehicle real-time visual detection of American and European speed limit signs, with a modular traffic signs recognition system, in 2007 IEEE Intelligent Vehicles Symposium (2007), pp. 1122–1126Google Scholar
  25. 25.
    M. Platho, J. Eggert, Deciding what to inspect first: incremental situation assessment based on information gain, in 2012 15th International IEEE Conference on Intelligent Transportation Systems (ITSC) (IEEE, New York, 2012), pp. 888–893Google Scholar
  26. 26.
    M. Platho, H.-M. Gros, J. Eggert, Predicting velocity profiles of road users at intersections using configurations, in 2013 IEEE Intelligent Vehicles Symposium (IV) (IEEE, New York, 2013), pp. 945–951CrossRefGoogle Scholar
  27. 27.
    E. Pollard, P. Morignot, F. Nashashibi, An ontology-based model to determine the automation level of an automated vehicle for co-driving, in 2013 16th International Conference on Information Fusion (FUSION) (IEEE, New York, 2013), pp. 596–603Google Scholar
  28. 28.
    V. Popovic, B. Vasic, Review of hazard analysis methods and their basic characteristics. FME Trans. 36 (4), 181–187 (2008)Google Scholar
  29. 29.
    Protégé website, http://protege.stanford.edu/. Accessed 29 April 2015
  30. 30.
    Protégé Wiki website, http://protegewiki.stanford.edu/wiki/webprotege. Accessed 29 April 2015
  31. 31.
    Protobuf Website, https://developers.google.com/protocol-buffers/. Accessed 29 April 2015
  32. 32.
    R. Regele, Using ontology-based traffic models for more efficient decision making of autonomous vehicles, in Fourth International Conference on Autonomic and Autonomous Systems, 2008. ICAS 2008 (IEEE, New York, 2008), pp. 94–99Google Scholar
  33. 33.
    R.A. Retting, B.N. Persaud, P.E. Garder, D. Lord, Crash and injury reduction following installation of roundabouts in the united states. Am. J. Public Health 91 (4), 628–631 (2001)CrossRefGoogle Scholar
  34. 34.
    T. Schamm, J.M. Zollner, A model-based approach to probabilistic situation assessment for driver assistance systems, in 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC) (IEEE, New York, 2011), pp. 1404–1409Google Scholar
  35. 35.
    L. Serafini, A. Tamilin, Local tableaux for reasoning in distributed description logics, in Proceedings of the International Workshop on Description Logics, DL, vol. 4 (2004), pp. 100–109Google Scholar
  36. 36.
    R.M. Smullyan, First-Order Logic (Courier Corporation, North Chelmsford, MA, 1995)zbMATHGoogle Scholar
  37. 37.
    S. Vacek, T. Gindele, J.M. Zollner, R. Dillmann, Situation classification for cognitive automobiles using case-based reasoning, in 2007 IEEE Intelligent Vehicles Symposium (IEEE, New York, 2007), pp. 704–709CrossRefGoogle Scholar
  38. 38.
    R. Wang, H.J. Ruskin, R. Wang, H.J. Ruskin, Modeling traffic flow at a single lane urban roundabout. Comput. Phys. Commun. 147, 570–576 (2002). Proceedings of the Europhysics Conference on Computational Physics Computational Modeling and Simulation of Complex SystemsGoogle Scholar
  39. 39.
    L. Zhao, R. Ichise, S. Mita, Y. Sasaki, An ontology-based intelligent speed adaptation system for autonomous cars, in Semantic Technology (Springer, Berlin, 2014), pp. 397–413Google Scholar
  40. 40.
    C. Zinoune, P. Bonnifait, J. Ibanez-Guzman, Sequential FDIA for autonomous integrity monitoring of navigation maps on board vehicles. IEEE Trans. Intell. Transp. Syst. 17 (1), 143–155 (2016)CrossRefGoogle Scholar

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© Springer International Publishing Switzerland 2017

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Authors and Affiliations

  • Alexandre Armand
    • 1
  • Javier Ibanez-Guzman
    • 1
  • Clément Zinoune
    • 1
  1. 1.Renault S.A.S., Technocentre de Guyancourt, Research DepartmentGuyancourtFrance

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