TY - JOUR A1 - Kaspar, Dietmar A1 - Weidl, Galia A1 - Dang, Thao A1 - Breuel, Gabi A1 - Tamke, Andreas A1 - Wedel, Andreas A1 - Rosenstiel, Wolfgang T1 - Object-Oriented Bayesian Networks for Detection of Lane Change Maneuvers JF - IEEE Intelligent Transportation Systems Magazine N2 - 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. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem KW - Künstliche Intelligenz Y1 - 2012 UR - https://www.researchgate.net/publication/249012939_Object-Oriented_Bayesian_Networks_for_Detection_of_Lane_Change_Maneuvers#fullTextFileContent U6 - https://doi.org/10.1109/MITS.2012.2203229 VL - 2012 IS - 4/3 SP - 19 EP - 31 ER - TY - CHAP A1 - Weidl, Galia A1 - Singhal, Virat A1 - Petrich, Dominik A1 - Kaspar, Dietmar A1 - Wedel, Andreas A1 - Breuel, Gabi T1 - Collision Risk Prediction and Warning at Road Intersections Using an Object Oriented Bayesian Network T2 - 5th International Conference Automotive User Interfaces and Interactive Vehicular Applications (Automotive UI,13), Oct.28-30, 2013 N2 - This paper describes a novel approach to situation analysis at intersections using object-oriented Bayesian networks. The Bayesian network infers the collision probability for all vehicles approaching the intersection, while taking into account traffic rules, the digital street map, and the sensors' uncertainties. The environment perception is fused from communicated data, vehicles local perception and self-localization. Thus, a cooperatively validated set of data is obtained to characterize all objects involved in a situation (resolving occlusions). The system is tested with data, acquired by vehicles with heterogenic equipment (without/with perception). In a first step the probabilistic mapping of a vehicle onto a fixed set of traffic lanes and forward motion predictions is introduced. Second, criticality measures are evaluated for these motion predictions to infer the collision probability. In our test vehicle this probability is then used to warn the driver of a possible hazardous situation. It serves as a likelihood alarm parameter for deciding the intensity of HMI acoustic signals to direct the driver's attention. First results in various simulated and live real-time scenarios show, that a collision can be predicted up to two seconds before a possible impact by applying the developed Bayesian network. The extension of this network to further situation features is the content of ongoing research. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem KW - Künstliche Intelligenz Y1 - 2013 UR - https://www.researchgate.net/publication/267764514_Collision_Risk_Prediction_and_Warning_at_Road_Intersections_Using_an_Object_Oriented_Bayesian_Network#fullTextFileContent U6 - https://doi.org/10.1145/2516540.2516577 ER -