@inproceedings{KressZernetschDolletal.2020, author = {Kreß, Viktor and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, title = {Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks}, series = {ICPR 2021: Pattern Recognition. ICPR International Workshops and Challenges}, booktitle = {ICPR 2021: Pattern Recognition. ICPR International Workshops and Challenges}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-030-68763-2}, doi = {https://doi.org/10.1007/978-3-030-68763-2_5}, pages = {57 -- 71}, year = {2020}, abstract = {In this work, we use Recurrent Neural Networks (RNNs) in form of Gated Recurrent Unit (GRU) networks to forecast trajectories of vulnerable road users (VRUs), such as pedestrians and cyclists, in road traffic utilizing the past trajectory and 3D poses as input. The 3D poses represent the postures and movements of limbs and torso and contain early indicators for the transition between motion types, e.g. wait, start, move, and stop. VRUs often only become visible from the perspective of an approaching vehicle shortly before dangerous situations occur. Therefore, a network architecture is required which is able to forecast trajectories after short time periods and is able to improve the forecasts in case of longer observations. This motivates us to use GRU networks, which are able to use time series of varying duration as inputs, and to investigate the effects of different observation periods on the forecasting results. Our approach is able to make reasonable forecasts even for short observation periods. The use of poses improves the forecasting accuracy, especially for short observation periods compared to a solely head trajectory based approach. Different motion types benefit to different extent from the use of poses and longer observation periods.}, 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{HubertZernetschDolletal.2017, author = {Hubert, Andreas and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, title = {Cyclists starting behavior at intersections}, series = {2017 IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {2017 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Los Angeles, CA, USA}, doi = {10.1109/IVS.2017.7995856}, pages = {1071 -- 1077}, year = {2017}, subject = {Fahrerassistenzsystem}, language = {en} } @article{KroehnReuterHahnleetal.2016, author = {Kr{\"o}hn, Michael and Reuter, Norbert and Hahnle, Michael and Prochus, Tobias and Doll, Konrad}, title = {Gestengesteuerte Prozessanalyse}, series = {atp edition - Automatisierungstechnische Praxis}, volume = {58}, journal = {atp edition - Automatisierungstechnische Praxis}, number = {12}, pages = {26 -- 33}, year = {2016}, subject = {Prozessanalyse}, language = {de} } @inproceedings{ZernetschReichertKressetal.2022, author = {Zernetsch, Stefan and Reichert, Hannes and Kress, Viktor and Doll, Konrad and Sick, Bernhard}, title = {A Holistic View on Probabilistic Trajectory Forecasting - Case Study. Cyclist Intention Detection}, series = {2022 IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {2022 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, doi = {https://doi.org/10.1109/IV51971.2022.9827220}, pages = {265 -- 272}, year = {2022}, subject = {Autonomes Fahrzeug}, language = {en} } @inproceedings{BieshaarZernetschRiepeetal.2021, author = {Bieshaar, Maarten and Zernetsch, Stefan and Riepe, Katharina and Doll, Konrad and Sick, Bernhard}, title = {Cyclist Motion State Forecasting - Going beyond Detection}, series = {2021 IEEE Symposium Series on Computational Intelligence (SSCI)}, booktitle = {2021 IEEE Symposium Series on Computational Intelligence (SSCI)}, publisher = {IEEE}, doi = {10.1109/SSCI50451.2021.9660151}, pages = {1 -- 8}, year = {2021}, subject = {Autonomes Fahrzeug}, language = {en} } @inproceedings{SchneegansEilbrechtZernetschetal.2021, author = {Schneegans, Jan and Eilbrecht, Jan and Zernetsch, Stefan and Bieshaar, Maarten and Doll, Konrad and Stursberg, Olaf and Sick, Bernhard}, title = {Probabilistic VRU Trajectory Forecasting for Model-Predictive Planning A Case Study: Overtaking Cyclists}, series = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, booktitle = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, publisher = {IEEE}, doi = {10.1109/IVWorkshops54471.2021.9669208}, pages = {272 -- 279}, year = {2021}, subject = {Fahrerassistenzsystem}, language = {en} } @article{KressJeskeZernetschetal.2023, author = {Kress, Viktor and Jeske, Fabian and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, title = {Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users' Trajectories}, series = {IEEE Transactions on Intelligent Vehicles}, volume = {8}, journal = {IEEE Transactions on Intelligent Vehicles}, number = {3}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, issn = {2379-8904}, doi = {10.1109/TIV.2022.3149624}, pages = {2592 -- 2603}, year = {2023}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{ZernetschTruppKressetal.2021, author = {Zernetsch, Stefan and Trupp, Oliver and Kress, Viktor and Doll, Konrad and Sick, Bernhard}, title = {Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information}, series = {2021 IEEE International Smart Cities Conference (ISC2)}, booktitle = {2021 IEEE International Smart Cities Conference (ISC2)}, publisher = {IEEE}, doi = {10.1109/ISC253183.2021.9562857}, pages = {1 -- 7}, year = {2021}, subject = {Autonomes Fahrzeug}, language = {en} } @inproceedings{JungHubertDolletal.2022, author = {Jung, Janis and Hubert, Andreas and Doll, Konrad and Kr{\"o}hn, Michael and Stadler, Jochen}, title = {Prozessinnovation}, series = {Wissenstransfer im Spannungsfeld von Autonomisierung und Fachkr{\"a}ftemangel, Tagungsband, 18. AALE-Konferenz, Pforzheim, 09.03.-11.03.2022}, booktitle = {Wissenstransfer im Spannungsfeld von Autonomisierung und Fachkr{\"a}ftemangel, Tagungsband, 18. AALE-Konferenz, Pforzheim, 09.03.-11.03.2022}, publisher = {Hochschule f{\"u}r Technik, Wirtschaft und Kultur Leipzig}, doi = {10.33968/2022.20}, year = {2022}, abstract = {Manuelle Montageprozesse sind nach wie vor unverzichtbar in vielen Bereichen der produzierenden Industrie. Vor allem die Qualit{\"a}tskontrolle, sowie das Einlernen neuer Mitarbeitenden stellen Betriebe durch die voranschreitende Digitalisierung vor neue Herausforderungen. Assistenzsysteme k{\"o}nnen hier helfen, die L{\"u}cke zwischen Anforderungen und Qualifikation zu {\"u}berbr{\"u}cken. Wir stellen einen Ansatz zur intelligenten Assistenz vor, welcher auf einer kamerabasierten Erkennung von Arbeitsabl{\"a}ufen mit Hilfe von Methoden des maschinellen Lernens beruht. Das Assistenzsystem erzeugt automatisiert Hilfsmaterial zur Unterst{\"u}tzung der Werkenden. Zus{\"a}tzlich zur Darstellung der technischen Aspekte, werden psychologische Aspekte, wie Akzeptanz und Motivation untersucht.}, subject = {Assistenzsystem}, language = {de} } @inproceedings{SerflingReichertBayerleinetal.2025, author = {Serfling, Benjamin and Reichert, Hannes and Bayerlein, Lorenzo and Doll, Konrad and Radkhah-Lens, Kati}, title = {LiDAR Based Semantic Perception for Forklifts in Outdoor Environments}, series = {2025 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)}, booktitle = {2025 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)}, publisher = {IEEE}, doi = {https://doi.org/10.1109/SOLI66289.2025.11396313}, pages = {40 -- 45}, year = {2025}, subject = {Gabelstapler}, language = {en} } @inproceedings{HubertGuoDolletal.2025, author = {Hubert, Andreas and Guo, Chenxi and Doll, Konrad and Sick, Bernhard}, title = {Improving Fast 6D Pose Estimation with Adaptive Model Fusion}, series = {2025 5th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)}, booktitle = {2025 5th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)}, publisher = {IEEE}, doi = {https://doi.org/10.1109/MLISE66443.2025.11100239}, pages = {58 -- 63}, year = {2025}, subject = {Maschinelles Lernen}, language = {en} } @incollection{HubertGuoDolletal.2025, author = {Hubert, Andreas and Guo, C. and Doll, Konrad and Kuhn, F. and Kr{\"o}hn, M.}, title = {Multimodales hochautomatisiert lernendes Assistenzsystem f{\"u}r manuelle Montageprozesse}, series = {Automation 2025}, booktitle = {Automation 2025}, publisher = {VDI Verlag}, isbn = {9783181024577}, doi = {https://doi.org/10.51202/9783181024577-575}, pages = {575 -- 590}, year = {2025}, subject = {Maschinelles Lernen}, language = {de} }