Abstract
This paper presents an application of Bayesian networks where early recognition of traffic maneuver intention is achieved using features of lane change, representing the relative dynamics between vehicles on the same lane and the free space to neighbor vehicles back and front on the target lane. The classifiers have been deployed on the automotive target platform, which has severe constraints on time and space performance of the system. The test driving has been performed with encouraging results. Even earlier recognition is possible by considering the trend development of features, characterizing the dynamic driving process. The preliminary test results confirm feasibility.
AMIDST (Analysis of Massive Data Streams) is a project, which has received funding from the European Union’s 7th Framework Programme for research, technological development and demonstration under grant agreement no 619209.
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References
Morris, B., Anup, D., Mohan, T.: Lane change intent prediction for driver assistance: on-road design and evaluation. In: IEEE Intelligent Vehicles Symposium, vol. IV, pp. 895-901 (2011)
Gerdes, A.: Automatic maneuver recognition in the automobile: the fusion of uncertain sensor values using bayesian models. In: 3rd International Workshop on Intelligent Transportation, Braunschweig (2006)
Dogan, Ü., Edelbrunner, J., Iossifidis, I.: Autonomous driving: a comparison of machine learning techniques by means of the prediction of lane change behavior. In: International Conference on Robotics and Biomimetics, Phuket, Thailand (2011)
Kumar, P., Perrollaz, M., Lef`evre, S., Laugier, C.: Learning-based approach for online lane change intention prediction. In: IEEE Intelligent Vehicles Symposium, Gold Coast, Australia (2013)
Hülnhagen, T., Dengler, I., Tamke, A., Dang, T., Breuel, G.: Maneuver recognition using probabilistic finite-state machines and fuzzy logic. In: 2010 IEEE Intelligent Vehicles Symposium, San Diego (2010)
Schlechtriemen, J., Wedel, A., Hillenbrand, J., Breuel, G., Kuhnert, K.-D.: A lane change detection approach using feature ranking with maximized predictive power. In: Proceedings on Intelligent Vehicles Symposium, 8-11 June 2014, pp. 108-114. IEEE (2014)
Kasper, D., Weidl, G., Dang, T., Breuel, G., Tamke, A., Rosenstiel, W.: Object-oriented Bayesian networks for detection of lane change maneuvers. In: Intelligent Vehicles Symposium. IEEE (2011)
Kasper, D., Weidl, G., Dang, T., Breuel, G., Tamke, A., Wedel, A., Rosenstiel, W.: Object-oriented Bayesian networks for detection of lane change maneuvers. IEEE Intell. Transp. Syst. Mag. 4, 19–31 (2012)
Kasper, D.: Erkennung von Fahrmanöovern mit object-orientierten Bayes-Netzen in Autobahnszenarien, Ph.D. thesis. Tübingen University, Germany (2013). http://tobias-lib.uni-tuebingen.de/volltexte/2013/6800/pdf/thesis_kasper_20130426.pdf. Accessed 18 Feb 2015
Weidl, G., Madsen, A.L., Kasper, D., Breuel, G.:Optimizing Bayesian networks for recognition of driving maneuvers to meet the automotive requirements. In: Proceedings of the 2014 IEEE Multi-Conference on Systems and Control, October 8th to 10th, 2014, Nice/Antibes, France (2014)
Tereshchenko, V.: Relative object-object dynamics for earlier recognition of maneuvers in highway traffic. Master’s thesis, University of Stuttgart, IAS, 30 October 2014
http://amidst.eu/use-cases/identification-and-interpretation-of-maneuvers-in-traffic. Accessed 18 Feb 2015, European Union FP7 project AMIDST grant no. 619209
http://amidst.eu/papersandpresentations/deliverables. Deliverable D1.2. Accessed 18 Feb 2015 European Union FP7 project AMIDST grant no. 619209
Friedman, N., Koller, D.: Probabilistic Graphical Models: Principles and Techniques. The MIT Press, Cambridge (2009)
Kjræulff, U.B., Madsen, A.L.: Bayesian Networks and Influence Diagrams - A Guide to Construction and Analysis. Springer, New York (2013)
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Weidl, G., Madsen, A.L., Tereshchenko, V., Kasper, D., Breuel, G. (2015). Early Recognition of Maneuvers in Highway Traffic. In: Destercke, S., Denoeux, T. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2015. Lecture Notes in Computer Science(), vol 9161. Springer, Cham. https://doi.org/10.1007/978-3-319-20807-7_48
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DOI: https://doi.org/10.1007/978-3-319-20807-7_48
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