< Zurück   INHALT   Weiter >

Literatur

1. Rasmussen, J.: Skills, Rules, and Knowledge; Signals, Signs, and Symbols, and Other Distinctions in Human Performance Models. IEEE Transactions On Systems, Man, and Cybernetics SMC-13(3), 257–266 (1983)

2. Donges, E.: Fahrerverhaltensmodelle. In: Winner, Hakuli, Wolf (Hrsg.) Handbuch Fahrerassistenzsysteme, pp. 15–23 (2011)

3. Oswald, W.D.: Automobilismus und die „gefährlichen Alten“. In: G. Schmidt (Hrsg.) Technik und Gesellschaft. Automobil und Automobilismus, vol. 10, pp. 183–195 (1999)

4. Williams, A.F.: Teenage drivers: patterns of risk. Journal of safety research 34(1), 5–15 (2003)

5. Burgard, E.: Fahrkompetenz im Alter: Die Aussagekraft diagnostischer Instrumente bei Senioren und neurologischen Patienten. Dissertation, LMU (2005)

6. Funk, W., Grüninger, M., Dittrich, L., Goßler, J., Hornung, C., Kreßner, I., Libal, I., Limberger, S., Riedel, C., Schaller, S.: Begleitetes Fahren ab 17 – Prozessevaluation des bundesweiten Modellversuchs. Berichte der Bundesanstalt für Straßenwesen. Unterreihe Mensch und Sicherheit.(213) (2010)

7. Mitchell, T.M.: Machine Learning. McGraw-Hill series in computer science. McGraw-Hill, New York (1997)

8. Breiman, L.: Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author). Statist. Sci., 199–231 (2001)

9. Carbonell, J., Michalski, R., Mitchell, T.: An Overview of Machine Learning. In: Michalski, R., Carbonell, J., Mitchell, T. (Hrsg.) Machine Learning. Symbolic Computation, pp. 3–23. Springer Berlin Heidelberg (1983)

10. Ertel, W.: Grundkurs Künstliche Intelligenz. Eine praxisorientierte Einführung, 3rd edn. Lehrbuch. Springer Fachmedien, Wiesbaden (2013)

11. Sewell, M.: Machine Learning. machine-learning.martinsewell.com/ (zuletzt geprüft 15.07.2014) (2009)

12. Sammut, C. (ed.): Encyclopedia of machine learning. 78 tables. springer reference. Springer, New York (2011)

13. Shaoning Pang, Nikola Kasabov: Inductive vs transductive inference, global vs local models: SVM, TSVM, and SVMT for gene expression classification problems – Neural Networks. Proceedings. 2004 IEEE International Joint Conference on (2004)

14. Russell, S., Norvig, P., Intelligence, A.: A modern approach. Artificial Intelligence. Prentice-Hall, Egnlewood Cliffs 25 (1995)

15. March, J.G.: Exploration and exploitation in organizational learning. Organization science 2(1), 71–87 (1991)

16. Nusser, S.: Robust Learning in Safety-Related Domains. Machine Learning Methods for Solving Safety-Related Application Problems, Otto-von-Guericke-Universität Magdeburg (2009)

17. Nusser, S., Otte, C., Hauptmann, W., Leirich, O., Krätschmer, M., Kruse, R.: Maschinelles Lernen von validierbaren Klassifikatoren zur autonomen Steuerung sicherheitsrelevanter Systeme. at-Automatisierungstechnik Methoden und Anwendungen der Steuerungs-, Regelungs- und Informationstechnik 57(3), 138–145 (2009)

18. Pomerleau, D.: Neural Network Vision for Robot Driving. In: Hebert, M., Thorpe, C., Stentz, A. (Hrsg.) Intelligent Unmanned Ground Vehicles, vol. 388. The Springer International Series in Engineering and Computer Science, pp. 53–72. Springer US (1997)

19. Gusikhin, O., Rychtyckyj, N., Filev, D.: Intelligent systems in the automotive industry: applications and trends. Knowl Inf Syst 12(2), 147–168 (2007). doi: 10.1007/s10115-006-0063-1

20. DIN 31000:2011-05: Allgemeine Leitsätze für das sicherheitsgerechte Gestalten von Produkten

21. Deng, L., Li, X.: Machine Learning Paradigms for Speech Recognition: An Overview. IEEE Trans. Audio Speech Lang. Process. 21(5), 1060–1089 (2013). doi: 10.1109/TASL.2013.2244083

22. Caelen, O., Bontempi, G., Barvais, L.: Machine learning techniques for decision support in anesthesia. In: Artificial Intelligence in Medicine, pp. 165–169. Springer (2007)

23. Widodo, A., Yang, B.-S.: Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing 21(6), 2560–2574 (2007). doi: 10.1016/j. ymssp.2006.12.007

24. Bainbridge, L.: Ironies of automation. Automatica 19(6), 775–779 (1983). doi: 10.1016/00051098(83)90046-8

25. Otte, C.: SCI 445 – Safe and Interpretable Machine Learning: A Methodological Review. In: Moewes, C., Nürnberger, A. (Hrsg.) Computational intelligence in intelligent data analysis. Studies in computational intelligence, vol. 445. Springer, Berlin, New York (2013)

26. Burgdorf, F.: Eine kunden- und lebenszyklusorientierte Produktfamilienabsicherung für die Automobilindustrie, KIT Scientific Publishing; Karlsruher Institut für Technologie (2010)

27. Taylor, B.J.: Methods and procedures for the verification and validation of artificial neural networks. Springer (2006)

28. Nelles, O.: Lernfähige Fuzzy-basierte Fahrstrategie für automatische Getriebe. In: Isermann, R. (Hrsg.) Modellgestützte Steuerung, Regelung und Diagnose von Verbrennungsmotoren, pp. 233–250. Springer Berlin Heidelberg (2003)

29. Cao, C.T., Kronenberg, K., Poljansek, M.: Adaptive transmission control. Google Patents. google.com/patents/US5954777 (1999)

30. Dahm, W.: Perspectives on Verification and Validation in Complex Adaptive Systems, Notre Dame University. Workshop on Verification and Validation in Computational Science (2011). Accessed 22 July 2014

31. Tamura, G., Villegas, N., Müller, H., Sousa, J., Becker, B., Karsai, G., Mankovskii, S., Pezzè, M., Schäfer, W., Tahvildari, L., Wong, K.: Towards Practical Runtime Verification and Validation of Self-Adaptive Software Systems. In: Lemos, R. de, Giese, H., Müller, H., Shaw, M. (Hrsg.) Software Engineering for Self-Adaptive Systems II, vol. 7475. Lecture Notes in Computer Science, pp. 108–132. Springer Berlin Heidelberg (2013)

32. Isermann, R.: Fault-diagnosis systems. An introduction from fault detection to fault tolerance. Springer, Berlin, New York (2006)

33. Stokar, R. von: Software-Updates Effiziente Nutzung von Connected Cars. ATZ Elektron 9(1), 46–51 (2014). doi: 10.1365/s35658-014-0387-7

34. Eugen Käfer: Situationsklassifikation und Bewegungsprognose in Verkehrssituationen mit mehreren Fahrzeugen, Universität Bielefeld (2013). Accessed 7 July 2014

35. Eidehall, A., Petersson, L.: Statistical Threat Assessment for General Road Scenes Using Monte Carlo Sampling. Intelligent Transportation Systems, IEEE Transactions on 9(1), 137–147 (2008). doi: 10.1109/TITS.2007.909241

36. Benmimoun, M., Fahrenkrog, F., Zlocki, A., Eckstein, L.: Erkennung und Klassifizierung Kritischer Fahrsituationen Mittels Fahrzeugdaten. ATZ Automobiltech Z 114(10), 820–826 (2012). doi: 10.1007/s35148-012-0485-x

37. Guo, F., Klauer, S., Hankey, J., Dingus, T.: Near Crashes as Crash Surrogate for Naturalistic Driving Studies. Transportation Research Record: Journal of the Transportation Research Board 2147(–1), 66–74 (2010). doi: 10.3141/2147-09

38. Ching-Yao Chan (ed.): Defining Safety Performance Measures of Driver-Assistance Systems for Intersection Left-Turn Conflicts. Intelligent Vehicles Symposium, 2006 IEEE. Intelligent Vehicles Symposium, 2006 IEEE (2006)

39. Winner, H., Geyer, S., Sefati, M.: Maße für den Sicherheitsgewinn von Fahrerassistenzsystemen. In: Winner, H., Bruder, R. (Hrsg.) Maßstäbe des sicheren Fahrens. 6. Darmstädter Kolloquium Mensch + Fahrzeug. Ergonomia Verlag, Stuttgart (2013)

40. Dijkstra, A., Drolenga, H.: Safety effects of route choice in a road network. Simulation of changing route choice. SWOV Institute for Road Safety Research, Leidschendam, Netherlands (2008)

41. Yang, H.: Simulation-based evaluation of traffic safety performance using surrogate safety measures (2012)

42. Zhang, Y., Antonsson, E.K., Grote, K.: A new threat assessment measure for collision avoidance systems. Intelligent Transportation Systems Conference, 2006. ITSC '06. IEEE

43. Jansson, J.: Collision avoidance theory with application to automotive collision mitigation. Linköping studies in science and technology. Dissertations, vol. 950. Dept. of Electrical Enginering, Univ., Linköping (2005)

44. Horst, A. R. A. van der: A time-based analysis of road user behaviour in normal and critical encounters. Institute for Perception TNO, Soesterberg, Netherlands (1990)

45. Tamke, A., Dang, T., Breuel, G.: A flexible method for criticality assessment in driver assistance systems. Intelligent Vehicles Symposium (IV), 2011 IEEE

46. Althoff, M., Stursberg, O., Buss, M.: Model-Based Probabilistic Collision Detection in Autonomous Driving. Intelligent Transportation Systems, IEEE Transactions on 10(2), 299–310 (2009). doi: 10.1109/TITS.2009.2018966

47. Althoff, D., Wollherr, D., Buss, M.: Safety assessment of trajectories for navigation in uncertain and dynamic environments. Robotics and Automation (ICRA), 2011 IEEE International Conference on

48. Althoff, D., Kuffner, J., Wollherr, D., Buss, M.: Safety assessment of robot trajectories for navigation in uncertain and dynamic environments. Auton Robot 32(3), 285–302 (2012). doi: 10.1007/s10514-011-9257-9

49. Meier, A., Gonter, M., Kruse, R.: Symbolic Regression for Precrash Accident Severity Prediction In: Polycarpou, M , Carvalho, A de, Pan, J -S , Wozniak, M , Quintian, H , Corchado, E (Hrsg.) Hybrid Artificial Intelligence Systems, vol. 8480. Lecture Notes in Computer Science, pp. 133–144. Springer International Publishing (2014)

50. Mukherjee, S., Chawla, A., Mohan, D., Singh, M., Dey, R.: Effect of vehicle design on head injury severity and throw distance variations in bicycle crashes. Proceedings From 20th International Technical Conference on the Enhanced Safety of Vehicles. Lyon (2007)

51. Czarnowski, I , Jydrzejowicz, P : Machine Learning and Multiagent Systems as Interrelated Technologies In: Czarnowski, I , Jydrzejowicz, P , Kacprzyk, J (Hrsg ) Agent-Based Optimization, vol. 456. Studies in computational intelligence, pp. 1–28. Springer Berlin Heidelberg (2013)

52. Miller, P.: Die Intelligenz des Schwarms. Was wir von Tieren für unser Leben in einer komplexen Welt lernen können. Campus-Verl., Frankfurt am Main [u.a.] (2010)

53. Gifford, C.M.: Collective Machine Learning: Team Learning and Classification in Multi-agent Systems. Dissertation, University of Kansas (2009)

 
< Zurück   INHALT   Weiter >