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Object-Oriented Bayesian Networks for Detection of Lane Change Maneuvers

  • This article introduces a novel approach towards the recognition of typical driving maneuvers in structured highway scenarios and shows some key benefits of traffic scene modeling with object-oriented Bayesian networks (OOBNs). The approach exploits the advantages of an introduced lane-related coordinate system together with individual occupancy schedule grids for all modeled vehicles. This combination allows an efficient classification of the existing vehicle-lane and vehicle-vehicle relations in traffic scenes and thus substantially improves the understanding of complex traffic scenes. Probabilities and variances within the network are propagated systematically which results in probabilistic sets of the modeled driving maneuvers. Using this generic approach, the network is able to classify a total of 27 driving maneuvers including merging and object following.

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Author:Dietmar Kaspar, Galia WeidlORCiD, Thao Dang, Gabi Breuel, Andreas Tamke, Andreas Wedel, Wolfgang Rosenstiel
URL:https://www.researchgate.net/publication/249012939_Object-Oriented_Bayesian_Networks_for_Detection_of_Lane_Change_Maneuvers#fullTextFileContent
DOI:https://doi.org/10.1109/MITS.2012.2203229
Parent Title (English):IEEE Intelligent Transportation Systems Magazine
Document Type:Article
Language:English
Year of Completion:2012
Release Date:2023/12/07
Tag:Artificial Intelligence; ITS (Intelligent Transportation Systems)
GND Keyword:Fahrerassistenzsystem; Künstliche Intelligenz
Volume:2012
Issue:4/3
First Page:19
Last Page:31
Urheberrecht:1
Institutes:Einrichtungen / Kompetenzzentrum Künstliche Intelligenz
research focus :Intelligent Systems / Artifical Intelligence and Data Science
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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