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Fahrerabsichtserkennung und Risikobewertung

  • Martin Liebner
  • Felix Klanner
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Zusammenfassung

Obwohl die Zahl der Verkehrstoten in den letzten Jahrzehnten ständig zurückgegangen ist und 2013 mit 3340 Toten einen neuen historischen Tiefstand erreichte [1], besteht nach wie vor die Notwendigkeit, diese auch in Zukunft weiter zu reduzieren. Entsprechende Zielsetzungen kommen hierbei sowohl von europäischer Seite [2] wie auch von Seiten der Bundesregierung [3]. Neben straßenbaulichen Maßnahmen und der Verbesserung des Insassenschutzes sind insbesondere auch Fahrerassistenzsysteme in der Lage, hierzu einen wesentlichen Beitrag zu leisten. Während frühe Systeme wie ABS und ESC auf die Unterstützung der Fahrzeugsteuerung beschränkt waren, existieren mittlerweile eine Vielzahl von Fahrerassistenzsystemen, die den Fahrer aktiv auf bestehende Gefahren hinweisen und es ihm dadurch ermöglichen, einen Großteil der Unfälle zu verhindern [4].

Besonders deutlich wird das Potenzial von Fahrerassistenzsystemen vor dem Hintergrund, dass 69 % der Verkehrsunfälle mit Personenschaden innerorts stattfinden und dass es sich bei 61 % der hierbei Getöteten um Fußgänger und Radfahrer handelt [5]. Im Gegensatz zu den Fahrzeuginsassen verfügen diese im Falle einer Kollision nur über minimale Schutzmöglichkeiten – die vollständige Vermeidung von Unfällen oder zumindest die Reduktion der Kollisionsgeschwindigkeit stehen somit an oberster Stelle.

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Literatur

  1. 1.
    Statistisches Bundesamt: 7,2 % weniger Todesopfer auf deutschen Straßen im Jahr 2013 (2014)Google Scholar
  2. 2.
    European Transport Safety Council: Road Safety Manifesto for the European Parliament Elections May 2014 (2013)Google Scholar
  3. 3.
    Bundesministerium für Verkehr, Bau und Stadtentwicklung: Verkehrssicherheitsprogramm 2011 vom 28.10.2011 (2011)Google Scholar
  4. 4.
    Deutscher Verkehrssicherheitsrat e. V.: Was leisten Fahrerassistenzsysteme? (2009)Google Scholar
  5. 5.
    Statistisches Bundesamt: Verkehrsunfälle Fachserie 8 Reihe 7 Jahr 2013 (2014)Google Scholar
  6. 6.
    Endsley, M.: Toward a theory of situation awareness in dynamic systems. Human Factors: The Journal of the Human Factors and Ergonomics Society 37, 32–64 (1995)CrossRefGoogle Scholar
  7. 7.
    Naujoks, F., Grattenthaler, H., Neukum, A.: Zeitliche Gestaltung effektiver Fahrerinformationen zur Kollisionsvermeidung auf der Basis kooperativer Perzeption 8. Workshop Fahrerassistenzsysteme., S. 107–117 (2012)Google Scholar
  8. 8.
    Liebner, M., Klanner, F., Stiller, C.: Der Fahrer im Mittelpunkt – Eye Tracking als Schlüssel zum mitdenkenden Fahrzeug? 8. Workshop Fahrerassistenzsysteme, Walting: UniDAS e. V.., S. 87–96 (2012)Google Scholar
  9. 9.
    Brännström, M., Coelingh, E., Sjoberg, J.: Model‐Based Threat Assessment for Avoiding Arbitrary Vehicle Collisions. IEEE Transactions on Intelligent Transportation Systems 11(3), 658–669 (2010)CrossRefGoogle Scholar
  10. 10.
    Brännström, 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
  11. 11.
    Ammoun, S., Nashashibi, F.: Real time trajectory prediction for collision risk estimation between vehicles 2009 5th IEEE Conference on Intelligent Computer Communication and Processing (ICCP), S. 417–422 (2009)Google Scholar
  12. 12.
    Hillenbrand, J., Spieker, A., Kroschel, K.: A Multilevel Collision Mitigation Approach – Its Situation Assessment, Decision Making, and Performance Tradeoffs. IEEE Transactions on Intelligent Transportation Systems 7(4), 528–540 (2006)CrossRefGoogle Scholar
  13. 13.
    Lytrivis, P., Thomaidis, G., Tsogas, M., Amditis, A.: An Advanced Cooperative Path Prediction Algorithm for Safety Applications in Vehicular Networks. IEEE Transactions on Intelligent Transportation Systems 12(3), 669–679 (2011)CrossRefGoogle Scholar
  14. 14.
    Tamke, A., Dang, T., Breuel, G.: A Flexible Method for Criticality Assessment in Driver Assistance Systems. In: 2009 5th IEEE Conference on Intelligent Computer Communication and Processing (ICCP). S. 697–702. (2011)Google Scholar
  15. 15.
    Eidehall, A., Petersson, L.: Statistical Threat Assessment for General Road Scenes Using Monte Carlo Sampling. IEEE Transactions on Intelligent Transportation Systems 9(1), 137–147 (2008)CrossRefGoogle Scholar
  16. 16.
    Althoff, M., Stursberg, O., Buss, M.: Safety Assessment of Autonomous Cars using Verification Techniques. In: 2007 American Control Conference, S. 4154–4159. (2007)CrossRefGoogle Scholar
  17. 17.
    Petrich, D., Dang, T., Kasper, D., Breuel, G., Stiller, C.: Map‐based long term motion prediction for vehicles in traffic environments. In: 2013 16th International IEEE Conference on Intelligent Transportation Systems (ITSC), Oct. 2013, S. 2166–2172. (2013)Google Scholar
  18. 18.
    Althoff, M., Althoff, D.: Safety verification of autonomous vehicles for coordinated evasive maneuvers. In: 2009 IEEE Intelligent Vehicles Symposium (IV) (2010)Google Scholar
  19. 19.
    Berthelot, A., Tamke, A., Dang, T., Breuel, G.: Handling Uncertainties in Criticality Assessment. In: 2011 IEEE Intelligent Vehicles Symposium (IV), S. 571–576. (2011)CrossRefGoogle Scholar
  20. 20.
    Weidl, G., Singhal, V., Petrich, D., Kasper, D., Wedel, A., Breuel, G.: Collision Detection and Warning at Road Intersections using an Object Oriented Bayesian Network. In: 2013 IEEE 9th International Conference on Intelligent Computer Communication and Processing (ICCP) (2013)Google Scholar
  21. 21.
    Althoff, M., Stursberg, O., Buss, M.: Model‐Based Probabilistic Collision Detection in Autonomous Driving. IEEE Transactions on Intelligent Transportation Systems 10(2), 299–310 (2009)CrossRefGoogle Scholar
  22. 22.
    Althoff, M., Mergel, A.: Comparison of Markov Chain Abstraction and Monte Carlo Simulation for the Safety Assessment of Autonomous Cars. IEEE Transactions on Intelligent Transportation Systems 12(4), 1237–1247 (2011)CrossRefGoogle Scholar
  23. 23.
    Broadhurst, A., Baker, S., Kanade, T.: Monte Carlo road safety reasoning. In: 2005 IEEE Intelligent Vehicles Symposium, 2005, S. 319–324. (2005)Google Scholar
  24. 24.
    Greene, D., Liu, J., Reich, J., Hirokawa, Y., Shinagawa, A., Ito, H., Mikami, T.: An Efficient Computational Architecture for a Collision Early‐Warning System for Vehicles, Pedestrians, and Bicyclists. IEEE Transactions on Intelligent Transportation Systems 12(4), 942–953 (2011)CrossRefGoogle Scholar
  25. 25.
    Aoude, G., Luders, B., Lee, K., Levine, D., How, J.: Threat Assessment Design for Driver Assistance System at Intersections. In: 2010 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), S. 1855–1862. (2010)Google Scholar
  26. 26.
    Schwering, C., Lakemeyer, G.: Spatio‐Temporal Reasoning about Traffic Scenarios. In: 2013 11th International Symposium on Logical Formalizations of Commonsense Reasoning (2013)Google Scholar
  27. 27.
    Vacek, S.: „Videogestützte Umfelderfassung zur Interpretation von Verkehrssituationen für kognitive Automobile”. Dissertation, Universität Karlsruhe (TH), 2008Google Scholar
  28. 28.
    Boury-Brisset, A.-C., Tourigny, N.: Knowledge capitalisation through case bases and knowledge engineering for road safety analysis. Knowledge‐Based Systems 13(5), 297–305 (2000)Google Scholar
  29. 29.
    Vacek, S., Gindele, T., Zollner, J., Dillmann, R.: Situation classification for cognitive automobiles using case‐based reasoning. In: 2007 IEEE Intelligent Vehicles Symposium (IV), June 2007, S. 704–709. (2007)CrossRefGoogle Scholar
  30. 30.
    Graf, R., Deusch, H., Fritzsche, M., Dietmayer, K.: A learning concept for behavior prediction in traffic situations. In: 2013 IEEE Intelligent Vehicles Symposium (IV), June 2013, S. 672–677. (2013)CrossRefGoogle Scholar
  31. 31.
    Chinea, A., Parent, M.: Risk Assessment Algorithms Based on Recursive Neural Networks. In: 2007 International Joint Conference on Neural Networks, Aug. 2007, S. 1434–1440. (2007)CrossRefGoogle Scholar
  32. 32.
    Salim, F., Loke, S., Rakotonirainy, A., Srinivasan, B., Krishnaswamy, S.: Collision Pattern Modeling and Real‐Time Collision Detection at Road Intersections. In: 2007 IEEE Intelligent Transportation Systems Conference, Sept. 2007, S. 161–166. (2007)CrossRefGoogle Scholar
  33. 33.
    Morris, B., Doshi, A., Trivedi, M.: Lane Change Intent Prediction for Driver Assistance : On‐Road Design and Evaluation. In: 2011 IEEE Intelligent Vehicles Symposium (IV), S. 895–901. (2011)CrossRefGoogle Scholar
  34. 34.
    Gindele, T., Brechtel, S., Dillmann, R.: A probabilistic model for estimating driver behaviors and vehicle trajectories in traffic environments. In: 2010 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), S. 1625–1631. (2010)Google Scholar
  35. 35.
    Kasper, D., Weidl, G., Dang, T., Breuel, G., Tamke, A., Rosenstiel, A.: Object‐Oriented Bayesian Networks for Detection of Lane Change Maneuvers. In: 2011 IEEE Intelligent Vehicles Symposium (IV), S. 673–678. (2011)CrossRefGoogle Scholar
  36. 36.
    Ortiz, M., Kummert, F., Schmudderich, J.: Prediction of driver behavior on a limited sensory setting. In: 2012 15th International Conference on Intelligent Transportation Systems (ITSC), S. 638–643. (2012)CrossRefGoogle Scholar
  37. 37.
    Kretschmer, M., König, L., Neubeck, J., Wiedemann, J.: Erkennung und Prädiktion des Fahrerverhaltens während eines Überholvorgangs. In: 2. Tagung Aktive Sicherheit durch Fahrerassistenz, Garching (2006)Google Scholar
  38. 38.
    Firl, J.: Probabilistic Maneuver Prediction in Traffic Scenarios. In: 2011 European Conference on Mobile Robots (ECMR) (2011)Google Scholar
  39. 39.
    Tran, Q., Firl, J.: A probabilistic discriminative approach for situation recognition in traffic scenarios. In: 2012 IEEE Intelligent Vehicles Symposium (IV), June 2012, S. 147–152. (2012)CrossRefGoogle Scholar
  40. 40.
    Berndt, H., Dietmayer, K.: Driver intention inference with vehicle onboard sensors. In: 2009 IEEE International Conference on Vehicular Electronics and Safety (ICVES, S. 102. (2009)CrossRefGoogle Scholar
  41. 41.
    Klanner, F.: „Entwicklung eines kommunikationsbasierten Querverkehrsassistenten im Fahrzeug”. Dissertation, Technische Universität Darmstadt, 2008Google Scholar
  42. 42.
    Lidström, K., Larsson, T.: Model‐based Estimation of Driver Intentions Using Particle Filtering. In: 2008 11th International IEEE Conference on Intelligent Transportation Systems (ITSC), Oct. 2008, S. 1177–1182. (2008)Google Scholar
  43. 43.
    Cheng, S., Trivedi, M.: Turn‐intent analysis using body pose for intelligent driver assistance. Pervasive Computing, IEEE 5(4), 28–37 (2006)CrossRefGoogle Scholar
  44. 44.
    Aoude, G., Desaraju, V.: Driver behavior classification at intersections and validation on large naturalistic data set. IEEE Transactions on Intelligent Transportation Systems 13(2), 724–736 (2012)CrossRefGoogle Scholar
  45. 45.
    Armand, A., Filliat, D., Ibanez-Guzmán, J.: Modelling Stop Intersection Approaches using Gaussian Processes. In: 2013 16th International Conference on Intelligent Transportation Systems (ITSC), S. 1650–1655. (2013)Google Scholar
  46. 46.
    McCall, J., Trivedi, M.: Driver behavior and situation aware brake assistance for intelligent vehicles. Proceedings of the IEEE 95(2), 374–387 (2007)CrossRefGoogle Scholar
  47. 47.
    Schneider, J., Wilde, A., Naab, K.: Probabilistic approach for modeling and identifying driving situations. In: 2008 IEEE Intelligent Vehicles Symposium (IV), June 2008, S. 343–348. (2008)CrossRefGoogle Scholar
  48. 48.
    Lefèvre, S., Laugier, C.: Exploiting Map Information for Driver Intention Estimation at Road Intersections. In: 2011 IEEE Intelligent Vehicles Symposium (IV), S. 583–588. (2011)CrossRefGoogle Scholar
  49. 49.
    Schendzielorz, T., Mathias, P., Busch, F.: Infrastructure‐based Vehicle Maneuver Estimation at Urban Intersections. In: 6th International IEEE Annual Conference on Intelligent Transportation Systems (ITSC), S. 1442–1447. (2013)Google Scholar
  50. 50.
    Gindele, T., Brechtel, S., Dillmann, R.: Learning context sensitive behavior models from observations for predicting traffic situations. In: 2013 16th International IEEE Conference on Intelligent Transportation Systems (ITSC), Oct. 2013, S. 1764–1771. (2013)Google Scholar
  51. 51.
    Zhang, J., Roessler, B.: „Situation analysis and adaptive risk assessment for intersection safety systems in advanced assisted driving. In: Dillmann, R., Beyerer, J., Stiller, C., Zöllner, J.M., Gindele, T. (Hrsg.) Autonome Mobile Systeme, S. 249–258. Springer, Berlin, Heidelberg, New York (2009)Google Scholar
  52. 52.
    Herrmann, S., Schroven, F.: Situation analysis for driver assistance systems at urban intersections. In: 2012 IEEE International Conference on Vehicular Electronics and Safety (ICVES), July 2012, S. 151–156. (2012)Google Scholar
  53. 53.
    Liebner, M., Klanner, F., Baumann, M., Ruhhammer, C., Stiller, C.: Velocity‐Based Driver Intent Inference at Urban Intersections in the Presence of Preceding Vehicles. Intelligent Transportation Systems 5(2), 10–21 (2013)Google Scholar
  54. 54.
    Liebner, M., Ruhhammer, C., Klanner, F., Stiller, C.: Generic driver intent inference based on parametric models. In: 2013 16th International IEEE Conference on Intelligent Transportation Systems (ITSC), Oct. 2013, S. 268–275. (2013)Google Scholar
  55. 55.
    Doshi, A., Trivedi, M.: A comparative exploration of eye gaze and head motion cues for lane change intent prediction. In: 2008 IEEE Intelligent Vehicles Symposium (IV), June 2008, S. 49–54. (2008)CrossRefGoogle Scholar
  56. 56.
    McCall, J., Wipf, D., Trivedi, M., Rao, B.: Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning. IEEE Transactions on Intelligent Transportation Systems 8(3), 431–440 (2007)CrossRefGoogle Scholar
  57. 57.
    Doshi, A., Trivedi, M.: Tactical driver behavior prediction and intent inference: A review. In: 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), S. 1892–1897. (2011)CrossRefGoogle Scholar
  58. 58.
    Ortiz, G., Fritsch, J., Kummert, F., Gepperth, A.: Behavior prediction at multiple time‐scales in inner‐city scenarios. In: 2011 IEEE Intelligent Vehicles Symposium (IV), S. 1066–1071. (2011)Google Scholar
  59. 59.
    Kumar, P., Perrollaz, M., Laugier, C.: Learning‐Based Approach for Online Lane Change Intention Prediction. In: 2013 IEEE Intelligent Vehicles Symposium (IV) (2013)Google Scholar
  60. 60.
    Mabuchi, R., Yamada, K.: Study on Driver‐Intent Estimation at Yellow Traffic Signal by Using Driving Simulator. In: 2011 IEEE Intelligent Vehicles Symposium (IV) (2011)Google Scholar
  61. 61.
    Tipping, M.: Sparse Bayesian learning and the relevance vector machine. The Journal of Machine Learning Research 1, 211–244 (2001)zbMATHMathSciNetGoogle Scholar
  62. 62.
    Psorakis, I., Damoulas, T., Girolami, M.: Multiclass relevance vector machines: sparsity and accuracy. IEEE transactions on neural networks/a publication of the IEEE Neural Networks Council 21(10), 1588–1598 (2010)CrossRefGoogle Scholar
  63. 63.
    Reichel, M., Botsch, M., Rauschecker, R., Siedersberger, K., Maurer, M.: Situation aspect modelling and classification using the Scenario Based Random Forest algorithm for convoy merging situations. In: 2010 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), S. 360–366. (2010)Google Scholar
  64. 64.
    Hermes, C., Wöhler, C., Schenk, K., Kummert, F.: Long‐term vehicle motion prediction. In: 2009 IEEE Intelligent Vehicles Symposium (IV), S. 652–657. (2009)CrossRefGoogle Scholar
  65. 65.
    Käfer, E., Hermes, C., Wöhler, C., Ritter, H., Kummert, F.: Recognition of situation classes at road intersections. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), S. 3960–3965. (2010)CrossRefGoogle Scholar
  66. 66.
    von Eichhorn, A., Werling, M., Zahn, P., Schramm, D.: Maneuver prediction at intersections using cost‐to‐go gradients. In: 2013 16th International IEEE Conference on Intelligent Transportation Systems (ITSC), Oct. 2013, S. 112–117. (2013)Google Scholar
  67. 67.
    Lefèvre, S., Laugier, C., Ibañez-Guzmán, J.: Context‐based Estimation of Driver Intent at Road Intersections. In: 2011 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems, S. 583–588. (2011)CrossRefGoogle Scholar
  68. 68.
    Perl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Franciso (1988)Google Scholar
  69. 69.
    Gipps, P.: A behavioural car‐following model for computer simulation. Transportation Research Part B: Methodological I(2), 105–111 (1981)CrossRefGoogle Scholar
  70. 70.
    Treiber, M., Helbing, D.: Realistische Mikrosimulation von Straßenverkehr mit einem einfachen Modell. In: 16th Symposium Simulationstechnik ASIM, 2002, S. 80. (2002)Google Scholar
  71. 71.
    Land, M., Lee, D.: Where do we look when we steer. Nature 369, 742–744 (1994)CrossRefGoogle Scholar
  72. 72.
    Rahman, M., Chowdhury, M., Xie, Y., He, Y.: Review of Microscopic Lane‐Changing Models and Future Research Opportunities. IEEE Transactions on Intelligent Transportation Systems 1(4), 1942–1956 (2013)CrossRefGoogle Scholar
  73. 73.
    Hochstädter, A., Zahn, P., Breuer, K.: Ein universelles Fahrermodell mit den Einsatzbeispielen Verkehrssimulation und Fahrsimulator A universal driver model with the applications. In: Fahrzeug‐ und Motorentechnik, 9. Aachener Kolloquium (2000)Google Scholar
  74. 74.
    Salvucci, D.: Modeling driver behavior in a cognitive architecture. Human factors 48(2), 362–380 (2006)CrossRefGoogle Scholar
  75. 75.
    Meyer-Delius, D.: Probabilistic situation recognition for vehicular traffic scenarios. In: Robotics and Automation (ICRA), 2009 IEEE International Conference on, S. 459–464. (2009)CrossRefGoogle Scholar
  76. 76.
    Rabiner, L.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  77. 77.
    Tran, Q., Firl, J.: Modelling of traffic situations at urban intersections with probabilistic non‐parametric regression. In: 2013 IEEE Intelligent Vehicles Symposium (IV), June 2013, S. 334–339. (2013)CrossRefGoogle Scholar
  78. 78.
    Laugier, C., Paromtchik, I., Perrollaz, M., Yong, M., Yoder, J.-D., Tay, C., Mekhnacha, K., Nègre, A.: Probabilistic Analysis of Dynamic Scenes and Collision Risks Assessment to Improve Driving Safety. In: IEEE Intelligent Transportation Systems Magazine, October 2011, S. 4–19. (2011)Google Scholar
  79. 79.
    Dagli, I., Brost, M., Breuel, G., et al.: Action Recognition and Prediction for Driver Assistance Systems Using Dynamic Belief Networks. In: Kowalczyk, (Hrsg.) Agent Technologies, Infrastructures, Tools, and Applications for E‐Services, S. 179–194. Springer‐Verlag, Berlin, Heidelberg (2003)CrossRefGoogle Scholar
  80. 80.
    Oliver, N., Pentland, A.: Graphical models for driver behavior recognition in a SmartCar. In: 2000 IEEE Intelligent Vehicles Symposium (IV), S. 7–12. (2000)CrossRefGoogle Scholar
  81. 81.
    Lefèvre, S., Laugier, C., Ibañez-Guzmán, J.: Risk assessment at road intersections: Comparing intention and expectation. In: 2012 IEEE Intelligent Vehicles Symposium (IV), S. 165–171. (2012)CrossRefGoogle Scholar
  82. 82.
    Brechtel, S., Gindele, T., Dillmann, R.: Probabilistic MDP behavior planning for cars. In: 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), S. 1537–1542. (2011)Google Scholar
  83. 83.
    Dagli, I., Reichardt, D.: Motivation‐based approach to behavior prediction. In: Intelligent Vehicles Symposium (IV), 2002 IEEE, S. 227–233. (2002)Google Scholar
  84. 84.
    Koller, D.: Object‐Oriented Bayesian Networks. In: 1997 13th Annual Conference on Uncertainty in Artificial Intelligence (UAI), S. 302–313. (1997)Google Scholar
  85. 85.
    Liebner, M.: „Fahrerabsichtungserkennung und Risikobewertung für warnende Fahrerassistenzsysteme”‐ Dissertation, Karlsruher Institut für Technologie, 2014Google Scholar
  86. 86.
    Schroven, F., Giebel, T.: Fahrerintentionserkennung für Fahrerassistenzsysteme. In: VDI‐FVT‐Jahrbuch, S. 54–58. (2009)Google Scholar
  87. 87.
    Liebner, M., Baumann, M., Klanner, F., Stiller, C.: Driver intent inference at urban intersections using the intelligent driver model. In: 2012 IEEE Intelligent Vehicles Symposium (IV), S. 1162–1167. (2012)CrossRefGoogle Scholar

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© Springer Fachmedien Wiesbaden 2015

Authors and Affiliations

  • Martin Liebner
    • 2
  • Felix Klanner
    • 1
  1. 1.BMW GroupMünchenDeutschland
  2. 2.BMW GroupMünchenDeutschland

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