@phdthesis{Goldhammer2016, author = {Goldhammer, Michael}, title = {Selbstlernende Algorithmen zur videobasierten Absichtserkennung von Fußg{\"a}ngern}, doi = {10.19211/KUP9783737602532}, school = {Technische Hochschule Aschaffenburg}, year = {2016}, subject = {Fahrerassistenzsystem}, language = {de} } @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} } @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{GoldhammerDollBrunsmannetal.2014, author = {Goldhammer, Michael and Doll, Konrad and Brunsmann, Ulrich and Gensler, Andre and Sick, Bernhard}, title = {Pedestrian's Trajectory Forecast in Public Traffic with Artificial Neural Networks}, series = {22nd International Conference on Pattern Recognition (ICPR 2014), Stockholm, Sweden}, booktitle = {22nd International Conference on Pattern Recognition (ICPR 2014), Stockholm, Sweden}, publisher = {IEEE}, isbn = {978-1-4799-5208-3}, issn = {1051-4651}, doi = {10.1109/ICPR.2014.704}, pages = {4110 -- 4115}, year = {2014}, subject = {Fahrerassistenzsystem}, 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{GoldhammerGerhardZernetschetal.2013, author = {Goldhammer, Michael and Gerhard, Matthias and Zernetsch, Stefan and Doll, Konrad and Brunsmann, Ulrich}, title = {Early Prediction of a Pedestrian's Trajectory at Intersections}, series = {16th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2013), The Hague, The Netherlands}, booktitle = {16th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2013), The Hague, The Netherlands}, publisher = {IEEE}, isbn = {978-1-4799-2914-6}, doi = {10.1109/ITSC.2013.6728239}, pages = {237 -- 242}, year = {2013}, subject = {Fahrerassistenzsystem}, language = {en} } @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} }