@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} } @inproceedings{SeeligerWeidlPetrichetal.2014, author = {Seeliger, Florian and Weidl, Galia and Petrich, Dominik and Naujoks, Frederik and Breuel, Gabi and Neukum, Alexandra and Dietmayer, Klaus}, title = {Advisory warnings based on cooperative perception}, series = {IEEE Intelligent Vehicles Symposium Proceedings, June 2014}, booktitle = {IEEE Intelligent Vehicles Symposium Proceedings, June 2014}, doi = {10.1109/IVS.2014.6856479}, year = {2014}, abstract = {The Ko-PER (cooperative perception) research project aims at improvements of active traffic safety through cooperative perception systems. Within the project a prototype of a cooperative warning system was realized. This system provides early advisory warnings which are especially useful in critical situations with occluded conflict partners. The development process was accompanied by a series of driving simulator studies to determine both the potential to reduce traffic conflicts and important design characteristics of early advisory warning signals. The most important details of the prototype system's components inter-vehicle information-fusion and situation analysis are described and the achieved warning timings are compared to the results of the driving simulator studies.}, subject = {Fahrerassistenzsystem}, language = {en} } @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.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} } @incollection{WeidlBreuel2012, author = {Weidl, Galia and Breuel, Gabi}, title = {Overall Probabilistic Framework for Modeling and Analysis of Intersection Situations}, series = {Advanced Microsystems for Automotive Applications 2012}, booktitle = {Advanced Microsystems for Automotive Applications 2012}, publisher = {Springer}, isbn = {978-3-642-29673-4}, doi = {10.1007/978-3-642-29673-4_24}, pages = {257 -- 268}, year = {2012}, abstract = {We propose a system design for preventive traffic safety in general intersection situations involving all present traffic participants (vehicles and vulnerable road users) in the context of their environment and traffic rules. It exploits the developed overall probabilistic framework for modeling and analysis of intersection situations under uncertainties in the scene, in measured data or in communicated information. It proposes OOBN modeling for the cognitive assessment of potential and real danger in intersection situations and presents schematically an algorithm for multistage cognitive situation assessment. A concept for the interaction between situation assessment and the proposed Proactive coaching Safety Assistance System (PaSAS) is outlined. The assessment of danger in a situation development serves as a filter for the output and intensity of HMI-signals for directing driver's attention to essentials.}, 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} } @incollection{WeidlMadsenDahlquist2008, author = {Weidl, Galia and Madsen, Anders L. and Dahlquist, Erik}, title = {Decision Support on Complex Industrial Process Operation}, series = {Bayesian Networks: A Practical Guide to Applications}, booktitle = {Bayesian Networks: A Practical Guide to Applications}, publisher = {Wiley}, doi = {10.1002/9780470994559.ch18}, pages = {313 -- 328}, year = {2008}, abstract = {Introduction: A methodology for Root Cause AnalysisPulp and paper applicationThe ABB Industrial IT platformConclusion}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @article{RoehrlIglesiasRozasWeidl2008, author = {R{\"o}hrl, Norbert and Iglesias-Rozas, Jos{\´e} R. and Weidl, Galia}, title = {A modern reproducible method for the histologic grading of astrocytomas with statistical classification tools}, series = {Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology}, volume = {2008}, journal = {Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology}, number = {30/1}, pages = {33 -- 8}, year = {2008}, abstract = {To investigate whether statistical classification tools can infer the correct World Health Organization (WHO) grade from standardized histologic features in astrocytomas and how these tools compare with GRADO-IGL, an earlier computer-assisted method. A total of 794 human brain astrocytomas were studied between January 1976 and June 2005. The presence of 50 histologic features was rated in 4 categories from 0 (not present) to 3 (abundant) by visual inspection of the sections under a microscope. All tumors were also classified with the corresponding WHO grade between I and IV. We tested the prediction performance of several statistical classification tools (learning vector quantization [LVQ], supervised relevance neural gas [SRNG], support vector machines [SVM], and generalized regression neural network [GRNN]) for this data set. The WHO grade was predicted correctly from histologic features in close to 80\% of the cases by 2 modern classifiers (SRNG and SVM), and GRADO-IGL was predicted correctly in > 84\% of the cases by a GRNN. A standardized report, based the 50 histologic features, can be used in conjunction with modern classification tools as an objective and reproducible method for histologic grading of astrocytomas.}, subject = {Cytologie}, language = {en} } @article{WeidlRoehrlRozas2007, author = {Weidl, Galia and R{\"o}hrl, Norbert and Rozas, Javier}, title = {Causal probabilistic modeling for malignancy grading in pathology with explanations of dependency to the related histological features}, series = {Histology and Histopathology}, volume = {2007}, journal = {Histology and Histopathology}, number = {22/9}, doi = {10.14670/HH-22.947}, pages = {947 -- 962}, year = {2007}, abstract = {This work demonstrates that histological grading of brain tumors and astrocytomas can be accurately predicted and causally explained with the help of causal probabilistic models, also known as Bayesian networks (BN). Although created statistically, this allows individual identification of the grade of malignancy as an internal cause that has enabled the development of the histological features to their observed state. The BN models are built from data representing 794 cases of astrocytomas with their malignant grading and corresponding histological features. The computerized learning process is improved when pre-specified knowledge (from the pathologist) about simple dependency relations to the histological features is taken into account. We use the BN models for both grading and causal analysis. In addition, the BN models provide a causal explanation of dependency between the histological features and the grading. This can offer the biggest potential for choice of an efficient treatment, since it concentrates on the malignancy grade as the cause of pathological observations. The causal analysis shows that all ten histological features are important for the grading. The histological features are causally ordered, implying that features of first order are of higher priority, e.g. for the choice of treatment in order not to allow the malignancy to progress to a higher degree. Due to the explanations of feature relations, the causal analysis can be considered as a powerful complement to any malignancy classification tool and allows reproducible comparison of malignancy grading.}, subject = {Histologie}, language = {en} } @incollection{RoehrlIglesiasRozasWeidl2007, author = {R{\"o}hrl, Norbert and Iglesias-Rozas, Jos{\´e} R. and Weidl, Galia}, title = {Computer Assisted Classification of Brain Tumors}, series = {Data Analysis, Machine Learning and Applications}, booktitle = {Data Analysis, Machine Learning and Applications}, publisher = {Springer}, doi = {10.1007/978-3-540-78246-9_7}, pages = {55 -- 60}, year = {2007}, abstract = {The histological grade of a brain tumor is an important indicator for choosing the treatment after resection. To facilitate objectivity and reproducibility, Iglesias et al. (1986) proposed to use a standardized protocol of 50 histological features in the grading process. We tested the ability of Support Vector Machines (SVM), Learning Vector Quantization (LVQ) and Supervised Relevance Neural Gas (SRNG) to predict the correct grades of the 794 astrocytomas in our database. Furthermore, we discuss the stability of the procedure with respect to errors and propose a different parametrization of the metric in the SRNG algorithm to avoid the introduction of unnecessary boundaries in the parameter space.}, subject = {Hirntumor}, language = {en} }