Learning context sensitive behavior models from observations for predicting traffic situations

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Volumn , Issue , 2013, Pages 1764-1771

  Gindele, Tobias ^a   Brechtel, Sebastian ^a   Dillmann, Rudiger ^a  

^a KARLSRUHE INSTITUTE OF TECHNOLOGY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84894346767     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ITSC.2013.6728484     Document Type: Conference Paper

References (24)

1. S. Brechtel, T. Gindele, and R. Dillmann, "Probabilistic MDPBehavior Planning for Cars," in IEEE International Conference on Intelligent Transportation Systems, Washington DC, USA, 2011, pp. 1537-1542.
2. I. Dagli, M. Brost, and G. Breuel, "Action recognition and prediction for driver assistance systems using dynamic belief networks," Lecture Notes in Computer Science, pp. 179-194, 2003.
3. I. Dagli and D. Reichardt, "Motivation-based approach to behavior prediction," in Proceedings of the IEEE Intelligent Vehicles Symposium, 2002.
4. T. Gindele, S. Brechtel, and R. Dillmann, "A Probabilistic Model for Estimating Driver Behaviors and Vehicle Trajectories in Traffic Environments," in IEEE International Conference on Intelligent Transportation Systems, Madeira, Portugal, 2010, pp. 1625-1631.
5. G. Agamennoni, J. I. Nieto, and E. M. Nebot, "Estimation of Multivehicle Dynamics by Considering Contextual Information," IEEE Transactions on Robotics, vol. 28, no. 4, pp. 855-870, 2012.
6. A. Alin, M. V. Butz, and J. Fritsch, "Incorporating environmental knowledge into bayesian filtering using attractor functions," in IEEE Intelligent Vehicles Symposium. IEEE, 2012, pp. 476-481.
7. D. Meyer-Delius, C. Plagemann, G. Von Wichert, W. Feiten, G. Lawitzky, and W. Burgard, "A Probabilistic Relational Model for Characterizing Situations in Dynamic Multi-Agent Systems," in Proceedings of the 31st Annual Conference for Data Analysis, Machine Learning and Applications, 2007.
8. S. Bonnin, F. Kummert, and J. Schmudderich, "A generic concept of a system for predicting driving behaviors," in IEEE International Conference on Intelligent Transportation Systems. IEEE, 2012, pp. 1803-1808.
9. M. G. Ortiz, J. Schmudderich, F. Kummert, and A. Gepperth, "Situation-specific learning for ego-vehicle behavior prediction systems," in IEEE International Conference on Intelligent Transportation Systems. IEEE, 2011, pp. 1237-1242.
10. J. Zhang and B. Rössler, "Situation Analysis and Adaptive Risk Assessment for Intersection Safety Systems in Advanced Assisted Driving," in Proceedings of the 21th Fachgespräch Autonome Mobile Systeme, 2009, pp. 249-258.
11. S. Lefèvre, J. Ibanez-Guzman, and C. Laugier, "Context-based estimation of driver intent at road intersections," in IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems, 2011.
12. K. Murphy, Machine Learning: a Probabilistic Perspective. The MIT Press, 2012.
13. D. Koller and N. Friedman, Probabilistic graphical models: principles and techniques. The MIT Press, 2009.
14. L. Breiman, "Random forests," Machine learning, vol. 45, no. 1, pp. 5-32, 2001.
15. D. Koller and A. Pfeffer, "Object-Oriented Bayesian Networks," in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, 1997, pp. 302-313.
16. D. Patterson, L. Liao, D. Fox, and H. Kautz, "Inferring high-level behavior from low-level sensors," in UbiComp 2003: Ubiquitous Computing. Springer, 2003, pp. 73-89.
17. A. Demcenko, M. Tamosiunaite, A. Vidugiriene, and L. Jakevicius, "Lane marker parameters for vehicle's steering signal prediction," WSEAS Transactions on Systems, vol. 8, no. 2, pp. 251-260, 2009.
18. J. Pearl, Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, 1988.
19. S. Blackman and R. Popoli, Design and analysis of modern tracking systems. Norwood, MA: Artech House, 1999.
20. V. Vapnik, The nature of statistical learning theory. Springer-Verlag New York Inc, 2000.
21. A. Dempster, N. Laird, and D. Rubin, "Maximum Likelihood from Incomplete Data via the EM Algorithm," Journal of the Royal Statistical Society. Series B (Methodological), vol. 39, no. 1, pp. 1-38, 1977.
22. R. A. Levine and G. Casella, "Implementations of the monte carlo em algorithm," Journal of Computational and Graphical Statistics, vol. 10, no. 3, pp. 422-439, 2001.
23. R. Neal and G. Hinton, "A view of the em algorithm that justifies incremental, sparse, and other variants," Learning in graphical models, vol. 89, pp. 355-368, 1998.
24. E. Ikonomovska, "Algorithms for Learning Regression Trees and Ensembles on Evolving Data Streams," Ph.D. dissertation, Jozef Stefan International Postgraduate School, Ljubljana, Slovenia, October 2012.