Learning the human longitudinal control behavior with a modular hierarchical Bayesian mixture-of-behaviors model

IEEE Intelligent Vehicles Symposium, Proceedings

Volumn , Issue , 2011, Pages 540-545

  Eilers, Mark ^a   Möbus, Claus ^b  

^a OFFIS INSTITUTE FOR INFORMATION TECHNOLOGY   (Germany)

^b UNIVERSITY OF OLDENBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ASSISTANCE SYSTEM; BAYESIAN INFORMATION CRITERION; DRIVER MODELS; HIERARCHICAL BAYESIAN; HUMAN DRIVERS; INTELLIGENT ASSISTANCES; MACHINE-LEARNING; SOFTWARE ENGINEERING PROCESS; STRUCTURE-LEARNING; TOPDOWN;

HIERARCHICAL SYSTEMS; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; LONGITUDINAL CONTROL; MODELS; RAPID PROTOTYPING; SOFTWARE ENGINEERING; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

BEHAVIORAL RESEARCH;

EID: 79960803093     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IVS.2011.5940530     Document Type: Conference Paper

References (33)

1. Cacciabue, P.C.: Modelling Driver Behaviour, in: Automotive Environments, London: Springer, ISBN-10: 1-84628-617-4, 2007
2. Jürgensohn, Th.: Control Theory Models of the Driver, in: Cacciabue, 2007, p.277-292, 2007
3. Salvucci, D.D., and Gray, R.: A Two-Point Visual Control Model of Steering, Perception, 33, pp. 1233-1248, 2004
4. Weir, D.H. and Chao, K.C.: Review of Control Theory Models for Directional and Speed Control, in: Cacciabue, P.C. (ed.), p. 293-311, 2007
5. Salvucci, D.D.: Integrated Models of Driver Behavior, in: Gray, W. D. (ed.): Integrated models of cognitive systems, New York: Oxford University Press, pp. 356-367, 2007
6. Chattington, M., Wilson, M., Ashford, D. and Marple-Horvat, D.E.: Eye-Steering Coordination, in: Natural Driving, Experimental Brain Research, 180, pp. 1-14, 2007
7. Land, M.F.: The Visual Control of Steering, in: L.R. Harris & M. Jenkin (eds.), Vision and Action, Cambridge: Cambridge University Press, pp. 163-180, 1998
8. Land, M. and Horwood, J.: Which Parts of the Road Guide Steering?, Nature, 377, pp.339-340, 1995
9. Land, M. and Lee, D.N.: Where we look when we steer, in: Nature, 369, pp. 742-744, 1994
10. Lee, D.N.: How Movement is Guided, http://www.perception-inaction. ed.ac.uk/PDF-s/Howmovementisguided.pdf, 2005
11. Wilkie, R.M., Poulter, D.R., and Wann, J.P.: Where You Look When You Learn to Steer, in: Journal of Vision, 4(8), 1a, http://journalofvision.org/4/8/ 1/, doi:10.1167/4.8.1., 2004
12. Wilkie, R.M. and Wann, J.P.: Eye-Movements Aid the Control of Locomotion, in: Journal of Vision, 3, pp. 677-68, Appendix: Springer-Author Discount, 2003
13. Möbus, C., Eilers, M. and Garbe, H.: Predicting the Focus of Attention and Deficits in Situation Awareness with a Modular Hierarchical Bayesian Driver Model, in: Vincent G. Duffy (ed.), Digital Human Modeling, Heidelberg: Springer, LNCS 6777, (in press), 2011
14. Bessière, P., Laugier, Ch., and Siegwart, R. (eds.): Probabilistic Reasoning and Decision Making in Sensory-Motor Systems, Berlin: Springer, ISBN 978-3-540-79006-8, 2008.
15. Möbus, C., and Eilers, M.: Prototyping Smart Assistance with Bayesian Autonomous Driver Models, in: Mastrogiovanni F. and Chong, N-Y.(eds): Handbook of Research on Ambient Intelligence and Smart Environments: Trends and Perspectives, IGI Global publications, (in press), ISBN13: 978-1-61692-857-5, 2011
16. Xu, Y. and Lee, K.K.C.: Human Behavior Learning and Transfer, CRC Press, Boca Raton, 2006
17. Möbus, C. and Eilers, M.: Further Steps Towards Driver Modeling according to the Bayesian Programming Approach, in: Conference Proceedings, HCII 2009, Digital Human Modeling, pp. 413-422, LNCS (LNAI), Springer, San Diego, ISBN 978-3-642-02808-3, 2009
18. Möbus, C., Eilers, M., Garbe, H. and Zilinski, M.: Probabilistic, and Empirical Grounded Modeling of Agents in Partial Cooperative (Traffic) Scenarios, in: Vincent G. Duffy (Ed.), Digital Human Modeling, Conference Proceedings, HCI 2009, San Diego, CA. USA, Springer: Lecture Notes in Computer Science (LNCS 5620) and Lecture Notes in Artificial Intelligence (LNAI), ISBN 978-3-642-02808-3, pp. 423-432, 2009
19. Forbes, J., Huang, T., Kanazawa, K. and Russell, S.: Towards a Bayesian Autonomous Taxi, 1995
20. Bessière, P. and the BIBA INRIA Research Group: Survey: Probabilistic Methodology and Techniques for Artefact Conception and Development, Technical Report RR-4730, INRIA, 2003.
21. Eilers, M. and Möbus, C.,: Learning the Relevant Percepts of Modular Hierarchical Bayesian Driver Models using a Bayesian Information Criterion, in: Vincent G. Duffy (ed.), Digital Human Modeling, Heidelberg: Springer, LNCS 6777, (in press), 2011
22. Möbus, C. and Eilers, M.: Mixture of Behaviors and Levels-of-Expertise in a Bayesian Autonomous Driver Model, in: Vincent G. Duffy (ed): Advances in Applied Digital Human Modeling, pp. 425-435, CRC Press, Taylor & Francis Group, Boca Raton, 2010/2011, ISBN 978-1-4398-3511-1, 2010
23. Eilers, M. and Möbus, C.: Learning of a Bayesian Autonomous Driver Mixture-of-Behaviors (BAD-MoB) Model, in: Advances in Applied Digital Human Modeling, pp. 436-445, Vincent G. Duffy (ed), CRC Press, Taylor & Francis Group, Boca Raton, 2010/2011, ISBN 978-1-4398-3511-1, 2010
24. Eilers, M. and Möbus, C.: Lernen eines modularen Bayesian Autonomous Driver Mixture-of-Behaviors (BAD MoB) Modells, in: Kolrep, H. and Jürgensohn, Th. (eds.): Fahrermodellierung - Zwischen kinematischen Menschmodellen und dynamisch-kognitiven Verhaltensmodellen, 3. Berliner Fachtagung für Fahrermodellierung, Fortschrittsbericht des VDI in der Reihe 22 (Mensch-Maschine-Systeme), Nr.32, pp. 61-74, Düsseldorf: VDI-Verlag, ISBN 978-3-18-303222-8, 2010
25. Horrey, W.J., Wickens, Ch.D., and Consalus, K.P.: Modeling Driver's Visual Attention Allocation While Interacting With In-Vehicle Technologies, J.Exp.Psych., 12, pp.67-78, 2006
26. Jordan, M. I. and Jacobs, R.A.: Hierarchical mixture of experts and the EM algorithm, in: Neural Computation, 1994.
27. Kassner, A. and Baumann, M.: Situation Awareness bei unterschiedlichen Automationsgraden, in: Beiträge zur 53. Tagung experimentell arbeitender Psychologen, Lengerich, Pabst Science Publishers, ISBN 978-3-89967-698-3, 2011
28. Cheng, J. and Greiner, R.: Learning Bayesian Belief Network Classifiers: Algorithms and Systems, in: Advances in Artificial Intelligence, Lecture Notes in Computer Science, Volume 2056/2001, pp. 141-151, DOI: 10.1007/3-540-45153-6- 14, 2001
29. Schwarz, G.: Estimating the Dimension of a Model, in: The Annals of Statistics, Vol. 6, Nr. 2, pp. 461-464, 1978
30. Cowell, R.G., Dawid, A.Ph., Lauritzen, St.L., and Spiegelhalter, D.J.: Probabilistic Networks and Expert Systems, Berlin: Springer, ISBN 0-387-98767-3, 1999
31. Jensen, F.V. and Nielsen, Th.D.: Bayesian Networks and Decision Graphs (2nd edition), Springer, ISBN 0-387-68281-3, 2007
32. Domingos, P and Pazzani, M: On the Optimality of the Simple Bayesian Classifier under Zero-One Loss, in: Machine Learning, 29, pp. 103-130, 1997
33. Pietquin, O. Tango, F. and Aras, R.: Batch Reinforcement Learning for Optimizing Longitudinal Driving Assistance Strategies, in: Proceedings of the IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS 2011), 2011.