@inproceedings{BauerKoehlerDolletal.2010, author = {Bauer, Sebastian and K{\"o}hler, Sebastian and Doll, Konrad and Brunsmann, Ulrich}, title = {FPGA-GPU Architecture for Kernel SVM Pedestrian Detection}, series = {2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR, San Francisco}, booktitle = {2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR, San Francisco}, isbn = {978-1-4244-7029-7}, doi = {10.1109/CVPRW.2010.5543772}, pages = {61 -- 68}, year = {2010}, subject = {Field programmable gate array}, language = {en} } @article{KoehlerGoldhammerBaueretal.2013, author = {K{\"o}hler, Sebastian and Goldhammer, Michael and Bauer, Sebastian and Zecha, Stephan and Doll, Konrad and Brunsmann, Ulrich and Dietmayer, Klaus}, title = {Stationary Detection of the Pedestrian's Intention at Intersections}, series = {IEEE Intelligent Transportation Systems Magazine}, volume = {2013}, journal = {IEEE Intelligent Transportation Systems Magazine}, number = {5}, issn = {1939-1390}, pages = {87 -- 99}, year = {2013}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{KoehlerGoldhammerBaueretal.2012, author = {K{\"o}hler, Sebastian and Goldhammer, Michael and Bauer, Sebastian and Doll, Konrad and Brunsmann, Ulrich and Dietmayer, Klaus}, title = {Early Detection of the Pedestrian's Intention to Cross the Street}, series = {15th International IEEE Conference on Intelligent Transportation Systems (ITSC 2012), Anchorage, Alaska, USA.}, booktitle = {15th International IEEE Conference on Intelligent Transportation Systems (ITSC 2012), Anchorage, Alaska, USA.}, doi = {10.1109/ITSC.2012.6338797}, pages = {1759 -- 1764}, year = {2012}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{GoldhammerKoehlerDolletal.2016, author = {Goldhammer, Michael and K{\"o}hler, Sebastian and Doll, Konrad and Sick, Bernhard}, title = {Track-Based Forecasting of Pedestrian Behavior by Polynomial Approximation and Multilayer Perceptrons}, series = {Intelligent Systems and Applications - Extended and Selected Results from the SAI Intelligent Systems Conference (IntelliSys) 2015}, booktitle = {Intelligent Systems and Applications - Extended and Selected Results from the SAI Intelligent Systems Conference (IntelliSys) 2015}, publisher = {Springer International Publishing}, isbn = {978-3-319-33386-1}, pages = {259 -- 279}, year = {2016}, abstract = {We present an approach for predicting continuous pedestrian trajectories over a time horizon of 2.5 s by means of polynomial least squares approximation and multilayer perceptron (MLP) artificial neural networks. The training data are gathered from 1075 real urban traffic scenes with uninstructed pedestrians including starting, stopping, walking and bending in. The polynomial approximation provides an extraction of the principal information of the underlying time series in the form of the polynomial coefficients. It is independent of sensor parameters such as cycle time and robust regarding noise. Approximation and prediction can be performed very efficiently. It only takes 35 ms on an Intel Core i7 CPU. Test results show 28\% lower prediction errors for starting scenes and 32\% for stopping scenes in comparison to applying a constant velocity movement model. Approaches based on MLP without polynomial input or Support Vector Regression (SVR) models as motion predictor are outperformed as well.}, subject = {Fußg{\"a}nger}, language = {en} } @misc{BrunsmannDollHellertetal.2010, author = {Brunsmann, Ulrich and Doll, Konrad and Hellert, Christian and Kempf, Johannes and K{\"o}hler, Sebastian and Saxen, Frerk and Weimer, Daniel}, title = {Intelligente Verkehrssicherheits- und Informationssysteme}, series = {Safety Expo, Aschaffenburg}, journal = {Safety Expo, Aschaffenburg}, year = {2010}, abstract = {Poster}, subject = {Verkehrssicherheit}, language = {de} } @inproceedings{WeimerKoehlerHellertetal.2011, author = {Weimer, Daniel and K{\"o}hler, Sebastian and Hellert, Christian and Doll, Konrad and Brunsmann, Ulrich and Krzikalla, Roland}, title = {GPU Architecture for Stationary Multisensor Pedestrian Detection at Smart Intersections}, series = {IEEE Intelligent Vehicles Symposium, Baden Baden, Germany}, booktitle = {IEEE Intelligent Vehicles Symposium, Baden Baden, Germany}, publisher = {IEEE}, doi = {10.1109/IVS.2011.5940411}, pages = {89 -- 94}, year = {2011}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{GoldhammerHubertKoehleretal.2014, author = {Goldhammer, Michael and Hubert, Andreas and K{\"o}hler, Sebastian and Zindler, Klaus and Brunsmann, Ulrich and Doll, Konrad and Sick, Bernhard}, title = {Analysis on Termination of Pedestrians' Gait at Urban Intersections}, series = {Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on}, booktitle = {Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on}, publisher = {IEEE}, address = {Qingdao, China}, doi = {10.1109/ITSC.2014.6957947}, pages = {1758 -- 1763}, year = {2014}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{GoldhammerKoehlerDolletal.2015, author = {Goldhammer, Michael and K{\"o}hler, Sebastian and Doll, Konrad and Sick, Bernhard}, title = {Camera Based Pedestrian Path Prediction by Means of Polynomial Least-squares Approximation and Multilayer Perceptron Neural Networks}, series = {SAI Intelligent Systems Conference (IntelliSys), 2015}, booktitle = {SAI Intelligent Systems Conference (IntelliSys), 2015}, publisher = {IEEE}, doi = {10.1109/IntelliSys.2015.7361171}, pages = {390 -- 399}, year = {2015}, abstract = {This paper provides a method to forecast pedestrian trajectories by means of polynomial least-squares approximation and multilayer perceptron artificial neural networks for traffic safety applications. The approach uses camera based head tracking as input data to predict a continuous trajectory for a 2.5 s future time horizon. Training and test is performed using 1075 recorded tracks of uninstructed pedestrians in common public traffic situations, including many challenging scenarios like starting, stopping and bending in. The neural network approach has the ability to handle these scenes by learning a single implicit movement model independent of a specific motion type. The polynomial approximation provides an extraction of the principal information of the underlying time series in the form of the polynomial coefficients, high independence of input data, e.g., sample rate, and additional noise resistance. Our test results show 24\% lower prediction errors for starting scenes and 29\% for stopping scenes in comparison to a constant velocity Kalman filter. Approaches using MLP without polynomial input and the usage of Support Vector}, subject = {Neuronales Netz}, language = {en} } @inproceedings{KoehlerGoldhammerZindleretal.2015, author = {K{\"o}hler, Sebastian and Goldhammer, Michael and Zindler, Klaus and Doll, Konrad and Dietmayer, Klaus}, title = {Stereo-Vision-Based Pedestrian's Intention Detection in a Moving Vehicle}, series = {Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on}, booktitle = {Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on}, doi = {10.1109/ITSC.2015.374}, pages = {2317 -- 2322}, year = {2015}, abstract = {We present a method to detect starting, stopping and bending in intentions of pedestrians from a moving vehicle based on stereo-vision. The method focuses on urban scenarios where these pedestrian movements are common and may result in critical situations. Pedestrian intentions are determined by means of an image-based motion contour histogram of oriented gradient descriptor. It is based on silhouettes gathered from stereo data and does not require any compensation of appearance changes resulting from the ego-motion of a vehicle. Nevertheless, it covers small movements indicating a pedestrian's intention. A linear support vector machine with probabilistic estimates is used for classification. We evaluated our method on the publicly available Daimler Pedestrian Path Prediction Benchmark Dataset containing detections of a stateof-the-art pedestrian detector. We detect a pedestrian's stopping intention from 125 ms to 500 ms before standing still within an accuracy range of 80\% to 100\%. Bending in is detected from 320 ms to 570 ms after a first visible lateral body movement in the same accuracy range. The intention to cross the road from standing still (starting) is detected 250 ms after the first visible motion and, therefore, within the first step with an accuracy of 100\%.}, subject = {Bildfolgenanalyse}, language = {en} } @inproceedings{KoehlerDollKebingeretal.2018, author = {K{\"o}hler, Sebastian and Doll, Konrad and Kebinger, Sophie and Schmitt, Daniel and Kr{\"o}hn, Michael and Fried, Maik and B{\"o}rsig, Rainer}, title = {Prozessinnovation: Hochautomatisiert lernendes Assistenzsystem f{\"u}r die manuelle Montage}, address = {K{\"o}ln}, isbn = {978-3-8007-4522-7}, pages = {153 -- 164}, year = {2018}, abstract = {Trotz hoher Automatisierungsgrade in der produzierenden Industrie, sind manuelle Montageprozesse durch den Menschen, sei es aufgrund der geforderten Flexibilit{\"a}t, insbesondere bei kleinen Losgr{\"o}ßen oder der erforderlichen Pr{\"a}zision, unverzichtbar. Um eine hohe Produktivit{\"a}t und niedrige Ausschussraten zu gew{\"a}hrleisten, ist es sinnvoll, den Menschen in seiner Montaget{\"a}tigkeit zu unterst{\"u}tzen oder zu entlasten. Intelligente Assistenzsysteme k{\"o}nnen den Menschen dahingehend unterst{\"u}tzen, dass sie bspw. schwere T{\"a}tigkeiten kollaborativ {\"u}bernehmen, Prozesse gezielt steuern oder Informationen bei Bedarf kontextsensitiv bereitstellen. In diesem Beitrag wird das Konzept eines intelligent hochautomatisiert lernenden Assistenzsystems vorgestellt, dessen Ziel es ist, anhand von bereits gelernten Abl{\"a}ufen an einem manuellen Montagearbeitsplatz fehlerhafte Abl{\"a}ufe zu erkennen oder neue valide Abl{\"a}ufe hochautomatisiert und un{\"u}berwacht zu lernen. Manuelle Montageprozesse bestehen aus Abfolgen von Handgriffen. Diese Handgriffe werden vom Assistenzsystem als Gesten erkannt und verarbeitet, sodass der Gesamtablauf in Teilschritte zerlegt werden kann. Weiterhin soll dieses Assistenzsystem Informationen zur Behebung des Fehlers direkt am Arbeitsplatz verf{\"u}gbar machen. Das Assistenzsystem, dessen Architektur in Abb. 1 dargestellt ist, besteht aus einem zustandsgesteuerten, lernenden Steuersystem mit einem Microsoft Kinect-v2-Sensor, der den Arbeitsplatz aus der {\"U}berkopfperspektive erfasst und die Montageschritte erkennt. Da das Grundprinzip des Assistenzsystems in der Langzeitbeobachtung und -unterst{\"u}tzung der Montaget{\"a}tigkeit liegt, werden die kontinuierlich aggregierten Daten zur Erweiterung und Verbesserung des Assistenzsystems genutzt. Es lernt somit hochautomatisiert neue oder abgewandelte Montageabl{\"a}ufe. Hieraus ergibt sich eine Herausforderung an die Bewertung der G{\"u}te eines solchen Systems. W{\"a}hrend eine hohe Erkennungsrate der einzelnen Aktivit{\"a}ten innerhalb eines Montageablaufs unabdingbar ist, wird f{\"u}r das vorgeschlagene System dar{\"u}ber hinaus gefordert, dass komplette Abl{\"a}ufe korrekt, d.h. in der richtigen Reihenfolge ohne einzelne Falschklassifikationen, erkannt werden. Nur hierdurch lassen sich real ge{\"a}nderte Abl{\"a}ufe hochautomatisiert und un{\"u}berwacht im Zustandsautomat des Steuersystems online einlernen.}, subject = {Automatisierungstechnik}, language = {de} } @inproceedings{KoehlerDollKebingeretal.2018, author = {K{\"o}hler, Sebastian and Doll, Konrad and Kebinger, Sophie and Schmitt, Daniel and Kr{\"o}hn, Michael and Fried, Maik and B{\"o}rsig, Rainer}, title = {Gestenerkennung in einem hochautomatisiert lernenden Assistenzsystem f{\"u}r manuelle Montageprozesse}, series = {AUTOMATION 2018, VDI-Berichte 2330}, booktitle = {AUTOMATION 2018, VDI-Berichte 2330}, address = {Baden-Baden}, isbn = {978-3-18-092330-7}, pages = {145 -- 156}, year = {2018}, subject = {Assistenzsystem}, language = {de} } @article{GoldhammerKoehlerZernetschetal.2019, author = {Goldhammer, Michael and K{\"o}hler, Sebastian and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard and Dietmayer, Klaus}, title = {Intentions of Vulnerable Road Users - Detection and Forecasting by Means of Machine Learning}, series = {IEEE Transactions on Intelligent Transportation Systems}, volume = {2020}, journal = {IEEE Transactions on Intelligent Transportation Systems}, number = {21 / 7}, doi = {10.1109/TITS.2019.2923319}, pages = {3035 -- 3045}, year = {2019}, subject = {Fahrerassistenzsystem}, language = {en} } @misc{DollKoehlerGoldhammeretal.2015, author = {Doll, Konrad and K{\"o}hler, Sebastian and Goldhammer, Michael and Brunsmann, Ulrich}, title = {Pedestrian Movement Modelling and Trajectory Prediction at Urban Intersections}, series = {International IEEE Conference on Intelligent Transportation Systems (ITSC 2015)}, journal = {International IEEE Conference on Intelligent Transportation Systems (ITSC 2015)}, address = {Las Palmas de Gran Canaria, Spain}, year = {2015}, abstract = {Vortrag}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{KoehlerSchreinerRonalteretal.2013, author = {K{\"o}hler, Sebastian and Schreiner, Brian and Ronalter, Steffen and Doll, Konrad and Brunsmann, Ulrich and Zindler, Klaus}, title = {Autonomous Evasive Maneuvers Triggered by Infrastructure-Based Detection of Pedestrian Intentions}, series = {IEEE Intelligent Vehicles Symposium (IV' 13), Gold Coast, Australien, 23.-26. Juni}, booktitle = {IEEE Intelligent Vehicles Symposium (IV' 13), Gold Coast, Australien, 23.-26. Juni}, issn = {1931-0587}, doi = {10.1109/IVS.2013.6629520}, pages = {519 -- 526}, year = {2013}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{KoehlerDollBrunsmann2012, author = {K{\"o}hler, Sebastian and Doll, Konrad and Brunsmann, Ulrich}, title = {Videobasierte Erkennung von Fußg{\"a}ngerintentionen zur Steigerung der Verkehrssicherheit}, series = {Messe-Exponat und Vortrag, Vision 2012, Stuttgart, 06.-08. November}, booktitle = {Messe-Exponat und Vortrag, Vision 2012, Stuttgart, 06.-08. November}, year = {2012}, subject = {Fahrerassistenzsystem}, language = {de} }