@inproceedings{BieshaarZernetschDeppingetal.2017, author = {Bieshaar, Maarten and Zernetsch, Stefan and Depping, Malte and Sick, Bernhard and Doll, Konrad}, title = {Cooperative Starting Intention Detection of Cyclists based on Smart Devices and Infrastructure}, series = {2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)}, booktitle = {2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Yokohama, Japan}, doi = {10.1109/ITSC.2017.8317691}, pages = {1 -- 8}, year = {2017}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{BieshaarReitbergerKressetal.2017, author = {Bieshaar, Maarten and Reitberger, G{\"u}nther and Kreß, Viktor and Zernetsch, Stefan and Doll, Konrad and Fuchs, Erich and Sick, Bernhard}, title = {Highly Automated Learning for Improved Active Safety of Vulnerable Road Users}, series = {ACM Chapters Computer Science in Cars Symposium (CSCS-17)}, volume = {2017}, booktitle = {ACM Chapters Computer Science in Cars Symposium (CSCS-17)}, address = {M{\"u}nchen}, year = {2017}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{KressSchreckZernetschetal.2020, author = {Kress, Viktor and Schreck, Steven and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, title = {Pose Based Action Recognition of Vulnerable Road Users Using Recurrent Neural Networks}, series = {2020 IEEE Symposium Series on Computational Intelligence (SSCI)}, booktitle = {2020 IEEE Symposium Series on Computational Intelligence (SSCI)}, publisher = {IEEE}, address = {Canberra, Australia}, isbn = {978-1-7281-2548-0}, doi = {10.1109/SSCI47803.2020.9308462}, pages = {2723 -- 2730}, year = {2020}, abstract = {This work investigates the use of knowledge about three dimensional (3D) poses and Recurrent Neural Networks (RNNs) for detection of basic movements, such as wait, start, move, stop, turn left, turn right, and no turn, of pedestrians and cyclists in road traffic. The 3D poses model the posture of individual body parts of these vulnerable road users (VRUs). Fields of application for this technology are, for example, driver assistance systems or autonomous driving functions of vehicles. In road traffic, VRUs are often occluded and only become visible in the immediate vicinity of the vehicle. Hence, our proposed approach is able to classify basic movements after different and especially short observation periods. The classification will then be successively improved in case of a longer observation. This allows countermeasures, such as emergency braking, to be initiated early if necessary. The benefits of using 3D poses are evaluated by a comparison with a method based solely on the head trajectory. We also investigate the effects of different observation periods. Overall, knowledge about 3D poses improves the basic movement detection, in particular for short observation periods. The greatest improvements are achieved for the basic movements start, stop, turn left, and turn right.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{ZernetschSchreckKressetal.2020, author = {Zernetsch, Stefan and Schreck, Steven and Kress, Viktor and Doll, Konrad and Sick, Bernhard}, title = {Image Sequence Based Cyclist Action Recognition Using Multi-Stream 3D Convolution}, series = {2020 25th International Conference on Pattern Recognition (ICPR)}, booktitle = {2020 25th International Conference on Pattern Recognition (ICPR)}, year = {2020}, abstract = {In this article, we present an approach to detect basic movements of cyclists in real world traffic situations based on image sequences, optical flow (OF) sequences, and past positions using a multi-stream 3D convolutional neural network (3D-ConvNet) architecture. To resolve occlusions of cyclists by other traffic participants or road structures, we use a wide angle stereo camera system mounted at a heavily frequented public intersection. We created a large dataset consisting of 1,639 video sequences containing cyclists, recorded in real world traffic, resulting in over 1.1 million samples. Through modeling the cyclists' behavior by a state machine of basic cyclist movements, our approach takes every situation into account and is not limited to certain scenarios. We compare our method to an approach solely based on position sequences. Both methods are evaluated taking into account frame wise and scene wise classification results of basic movements, and detection times of basic movement transitions, where our approach outperforms the position based approach by producing more reliable detections with shorter detection times. Our code and parts of our dataset are made publicly available.}, subject = {Fahrerassistenzsystem}, language = {en} } @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} }