@inproceedings{WeidlSinghalPetrichetal.2013, author = {Weidl, Galia and Singhal, Virat and Petrich, Dominik and Kaspar, Dietmar and Wedel, Andreas and Breuel, Gabi}, title = {Collision Risk Prediction and Warning at Road Intersections Using an Object Oriented Bayesian Network}, series = {5th International Conference Automotive User Interfaces and Interactive Vehicular Applications (Automotive UI,13), Oct.28-30, 2013}, booktitle = {5th International Conference Automotive User Interfaces and Interactive Vehicular Applications (Automotive UI,13), Oct.28-30, 2013}, doi = {10.1145/2516540.2516577}, year = {2013}, abstract = {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.}, subject = {Fahrerassistenzsystem}, language = {en} } @article{KasparWeidlDangetal.2011, author = {Kaspar, Dietmar and Weidl, Galia and Dang, Thao and Breuel, Gabi and Tamke, Andreas and Rosenstiel, Wolfgang}, title = {Object-Oriented Bayesian Networks for Detection of Lane Change Maneuvers}, series = {IEEE Intelligent Transportation Systems Magazine}, journal = {IEEE Intelligent Transportation Systems Magazine}, number = {4(3)}, doi = {10.1109/IVS.2011.5940468}, pages = {19 -- 31}, year = {2011}, abstract = {In this paper we introduce a novel approach towards the recognition of typical driving maneuvers in structured highway scenarios and identify some of the 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 grids for all vehicles. This combination allows for an efficient classification of the existing vehiclelane and vehicle-vehicle relations in a traffic scene and thus substantially improves the understanding of complex traffic scenes. We systematically propagate probabilities and variances within our network which results in probabilistic sets of the modeled driving maneuvers. Using this generic approach, we are able to classify a total of 27 driving maneuvers including merging and object following.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{KasparWeidlDangetal.2011, author = {Kaspar, Dietmar and Weidl, Galia and Dang, Thao and Breuel, Gabi and Tamke, Andreas and Rosenstiel, Wolfgang}, title = {Erkennung von Fahrman{\"o}vern mit objektorientierten Bayes-Netzen}, series = {7. Workshop. Fahrerassistenzsysteme. FAS2011}, booktitle = {7. Workshop. Fahrerassistenzsysteme. FAS2011}, year = {2011}, abstract = {In diesem Artikel wird ein Ansatz zur Erkennung von Spurwechselman{\"o}vern mit Hilfe von objekt-orientierten Bayes Netzen beschrieben. Dieser Ansatz ist eine Erweiterung von Grundlagenarbeiten zur Einscherererkennung. Zun{\"a}chst werden die zur Erkennung von Spurwechselvorg{\"a}ngen erforderlichen Fahrsituationsmerkmale vorgestellt. Darauf aufbauend wird das entwickelte objekt-orientierte Bayes Netz zur Modellierung der Spurwechsel erl{\"a}utert. Dabei wird ein Spurwechsel als eine Beziehung zwischen zwei Fahrzeugen betrachtet. Dabei kann ein Fahrzeug in der Spur bleiben, die Spur nach links oder nach rechts wechseln. Durch die Kombination der M{\"o}glichkeiten der beiden Fahrzeuge entstehen 9 Klassen. Aus den relativen Positionen der Fahrzeuge ergeben sich aus den 9 Situationsklassen 27 m{\"o}gliche Spurwechselman{\"o}ver. Dabei ist das Einschervorgang ein Sonderfall der modellierten Fahrman{\"o}ver.}, subject = {Fahrerassistenzsystem}, language = {de} } @article{KasparWeidlDangetal.2012, author = {Kaspar, Dietmar and Weidl, Galia and Dang, Thao and Breuel, Gabi and Tamke, Andreas and Wedel, Andreas and Rosenstiel, Wolfgang}, title = {Object-Oriented Bayesian Networks for Detection of Lane Change Maneuvers}, series = {IEEE Intelligent Transportation Systems Magazine}, volume = {2012}, journal = {IEEE Intelligent Transportation Systems Magazine}, number = {4/3}, doi = {10.1109/MITS.2012.2203229}, pages = {19 -- 31}, year = {2012}, abstract = {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.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{WeidlMadsenKasparetal.2014, author = {Weidl, Galia and Madsen, Anders L. and Kaspar, Dietmar and Breuel, Gabi}, title = {Optimizing Bayesian networks for recognition of driving maneuvers to meet the automotive requirements}, series = {The 2014 IEEE Multi-Conference on Systems and Control (MSC 2014)}, booktitle = {The 2014 IEEE Multi-Conference on Systems and Control (MSC 2014)}, doi = {10.1109/ISIC.2014.6967630}, year = {2014}, abstract = {An Object Oriented Bayesian Network for recognition of maneuver in highway traffic has demonstrated an acceptably high recognition performance on a prototype car with a Linux PC having an i7 processor. This paper is focusing on keeping the high recognition performance of the original OOBN, while evaluating alternative modelling techniques and their impact on the memory and time requirements of an ECU-processor for automotive applications. New challenges are faced, when the prediction horizon is to be further extended.}, subject = {Fahrerassistenzsystem}, language = {en} } @article{WeidlMadsenWangetal.2018, author = {Weidl, Galia and Madsen, Anders L. and Wang, Stevens Ruixi and Kaspar, Dietmar and Karlsen, Martin}, title = {Early and Accurate Recognition of Highway Traffic Maneuvers Considering Real-World Application : A Novel Framework Using Bayesian Networks}, series = {IEEE Intelligent Transportation Systems Magazine}, volume = {2018}, journal = {IEEE Intelligent Transportation Systems Magazine}, number = {10/3}, doi = {10.1109/MITS.2018.2842049}, pages = {146 -- 158}, year = {2018}, abstract = {This paper presents a novel application of artificial cognitive systems to traffic scene understanding and early recognition of highway maneuvers. This is achieved by use of Bayesian networks for knowledge representation, to mimic the human reasoning on situation analysis and to manage inherited uncertainties in the automotive domain, that requires efficient and effective analysis of high volume and frequency data streams. The maneuver recognition uses features, analyzing the observed vehicles behavior and available free space on the target lane. Dynamic Bayesian networks (DBN) capture the motion of the own and surrounding vehicles as a dynamic process, following the trend development of lateral motion features. The static and dynamic models for maneuver recognition are statistically evaluated with real highway driving data sequences. The DBN demonstrates earlier recognition (∼ 1.1 seconds) and higher accuracy (1.12\% error rate) with more stable performance than the static models. The system is deployed on an experimental vehicle, where a Divide-and-conquer approach to inference in object-oriented Bayesian networks is introduced and its implementation is shown to require computation time of 0.15 milliseconds and ROM memory ∼ 10 2 kilobytes. The promising performance evaluation results are confirmed by test drives in real highway traffic.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{WeidlMadsenTereshchenkoetal.2015, author = {Weidl, Galia and Madsen, Anders L. and Tereshchenko, Viacheslav and Kaspar, Dietmar and Breuel, Gabi}, title = {Early Recognition of Maneuvers in Highway Traffic}, series = {European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty, July 2015}, booktitle = {European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty, July 2015}, doi = {10.1007/978-3-319-20807-7_48}, year = {2015}, 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.}, subject = {Fahrerassistenzsystem}, language = {en} }