@inproceedings{ReichertLangRoschetal.2021, author = {Reichert, Hannes and Lang, Lukas and Rosch, Kevin and Bogdoll, Daniel and Doll, Konrad and Sick, Bernhard and Rellss, Hans-Christian and Stiller, Christoph and Zollner, J. Marius}, title = {Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems}, series = {2021 IEEE International Smart Cities Conference (ISC2)}, booktitle = {2021 IEEE International Smart Cities Conference (ISC2)}, publisher = {IEEE}, doi = {https://doi.org/10.1109/ISC253183.2021.9562912}, pages = {1 -- 4}, year = {2021}, subject = {Autonomes Fahrzeug}, language = {en} } @incollection{LampertJungHubertetal.2022, author = {Lampert, Pascal and Jung, Janis and Hubert, Andreas and Doll, Konrad}, title = {Looping Through Color Space: A Simple Augmentation Method to Improve Biased Object Detection}, series = {Lecture Notes in Networks and Systems}, booktitle = {Lecture Notes in Networks and Systems}, publisher = {Springer Nature Singapore}, address = {Singapore}, isbn = {9789811916069}, issn = {2367-3370}, doi = {10.1007/978-981-19-1607-6_61}, pages = {687 -- 698}, year = {2022}, subject = {Objekterkennung}, language = {en} } @inproceedings{SchreckReichertHetzeletal.2023, author = {Schreck, Steven and Reichert, Hannes and Hetzel, Manuel and Doll, Konrad and Sick, Bernhard}, title = {Height Change Feature Based Free Space Detection}, series = {2023 11th International Conference on Control, Mechatronics and Automation (ICCMA)}, booktitle = {2023 11th International Conference on Control, Mechatronics and Automation (ICCMA)}, publisher = {IEEE}, doi = {https://doi.org/10.1109/ICCMA59762.2023.10374705}, pages = {171 -- 176}, year = {2023}, subject = {Gabelstapler}, language = {en} } @inproceedings{ReichertHetzelHubertetal.2024, author = {Reichert, Hannes and Hetzel, Manuel and Hubert, Andreas and Doll, Konrad and Sick, Bernhard}, title = {Sensor Equivariance: A Framework for Semantic Segmentation with Diverse Camera Models}, series = {2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}, booktitle = {2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}, publisher = {IEEE}, doi = {https://doi.org/10.1109/CVPRW63382.2024.00132}, pages = {1254 -- 1261}, year = {2024}, subject = {Bildverarbeitung}, language = {en} } @article{ZindlerDollHuber2017, author = {Zindler, Klaus and Doll, Konrad and Huber, Bertold}, title = {Sicher unterwegs - Fortschritte beim aktiven Fußg{\"a}ngerschutz}, series = {messtec drives Automation}, volume = {25}, journal = {messtec drives Automation}, number = {03}, editor = {WILEY-VCH, Verlag}, pages = {82 -- 82}, year = {2017}, subject = {Fahrerassistenzsystem}, 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} } @inproceedings{BieshaarReitbergerZernetschetal.2017, author = {Bieshaar, Maarten and Reitberger, G{\"u}nther and Zernetsch, Stefan and Sick, Bernhard and Fuchs, Erich and Doll, Konrad}, title = {Detecting intentions of vulnerable road users based on collective intelligence}, series = {AAET - Automatisiertes und vernetztes Fahren}, booktitle = {AAET - Automatisiertes und vernetztes Fahren}, pages = {67 -- 87}, year = {2017}, abstract = {Vulnerable road users (VRUs, i.e. cyclists and pedestrians) will play an important role in future traffic. To avoid accidents and achieve a highly efficient traffic flow, it is important to detect VRUs and to predict their intentions. In this article a holistic approach for detecting intentions of VRUs by cooperative methods is presented. The intention detection consists of basic movement primitive prediction, e.g. standing, moving, turning, and a forecast of the future trajectory. Vehicles equipped with sensors, data processing systems and communication abilities, referred to as intelligent vehicles, acquire and maintain a local model of their surrounding traffic environment, e.g. crossing cyclists. Heterogeneous, open sets of agents (cooperating and interacting vehicles, infrastructure, e.g. cameras and laser scanners, and VRUs equipped with smart devices and body-worn sensors) exchange information forming a multi-modal sensor system with the goal to reliably and robustly detect VRUs and their intentions under consideration of real time requirements and uncertainties. The resulting model allows to extend the perceptual horizon of the individual agent beyond their own sensory capabilities, enabling a longer forecast horizon. Concealments, implausibilities and inconsistencies are resolved by the collective intelligence of cooperating agents. Novel techniques of signal processing and modelling in combination with analytical and learning based approaches of pattern and activity recognition are used for detection, as well as intention prediction of VRUs. Cooperation, by means of probabilistic sensor and knowledge fusion, takes place on the level of perception and intention recognition. Based on the requirements of the cooperative approach for the communication a new strategy for an ad hoc network is proposed. The evaluation is done using real data gathered with a research vehicle, a research intersection with public traffic and mobile devices.}, subject = {Verkehrsverhalten}, language = {en} } @inproceedings{KressJungZernetschetal.2019, author = {Kreß, Viktor and Jung, Janis and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, title = {Pose Based Start Intention Detection of Cyclists}, publisher = {IEEE}, address = {Auckland}, doi = {10.1109/ITSC.2019.8917215}, pages = {2381 -- 2386}, year = {2019}, abstract = {In this work, we present a new approach for start intention detection of cyclists based on 3D human pose estimation to increase their safety in road traffic. Start intention detection is realized using sequences of frame-wise estimated 3D poses. The poses were obtained by image sequences recorded by a stereo camera mounted behind the windshield of a moving vehicle. For training and evaluation, a dataset with 206 starting cyclists was created in real traffic. We demonstrate the advantages of this approach by comparing it to an existing, solely head trajectory based method. In particular, we investigate the performance for different observed time horizons ranging from 0.12 s up to 1.0 s as inputs for the two methods. This is of special importance for the protection of cyclists in road traffic, as they often only become visible to approaching vehicles shortly before dangerous situations occur. With an input length of 1.0 s the solely head trajectory based approach detects starting motions on average 0.834 s after the first motion of the bicycle with an F1-score of 97.5 \%. The pose based approach outperforms these results by achieving the same F1-score 0.135 s earlier. The advantages of the pose based method become even more obvious with shorter input lengths. With an input length of 0.12 s, the head based approach achieves an F1-score of 93.5 \% after 2.37 s, while the same score is reached after 0.668 s using poses.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{KressJungZernetschetal.2019, author = {Kreß, Viktor and Jung, Janis and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, title = {Start Intention Detection of Cyclists using an LSTM Network}, series = {INFORMATIK 2019: 50 Jahre Gesellschaft f{\"u}r Informatik - Informatik f{\"u}r Gesellschaft (Workshop-Beitr{\"a}ge)}, booktitle = {INFORMATIK 2019: 50 Jahre Gesellschaft f{\"u}r Informatik - Informatik f{\"u}r Gesellschaft (Workshop-Beitr{\"a}ge)}, editor = {Draude, Claude and Lange, Martin and Sick, Bernhard}, publisher = {Gesellschaft f{\"u}r Informatik e.V.}, address = {Bonn}, isbn = {978-3-88579-689-3}, issn = {1617-5468}, doi = {10.18420/inf2019_ws25}, pages = {219 -- 228}, year = {2019}, abstract = {In this article, we present an approach for start intention detection of cyclists based on their head trajectories. Therefore, we are using a network architecture based on Long Short-Term Memory (LSTM) cells, which is able to handle input sequences of different lengths. This is important because, for example, due to occlusions, cyclists often only become visible to approaching vehicles shortly before dangerous situations occur. Hence, the dependency of the results on the input sequence length is investigated. We use a dataset with 206 situations where cyclists were transitioning from waiting to moving that was recorded from a moving vehicle in inner-city traffic.With an input sequence length of 1.0 s we achieve an F1-score of 96.2\% on average 0.680 s after the first movement of the bicycle. We obtain similar results for sequence lengths down to 0.2 s. For shorter sequences, the results regarding the F1-score and the mean detection time deteriorate considerably.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{ReitbergerBieshaarZernetschetal.2018, author = {Reitberger, G{\"u}nther and Bieshaar, Maarten and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard and Fuchs, Erich}, title = {Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure}, series = {21st International Conference on Intelligent Transportation Systems (ITSC) 2018}, booktitle = {21st International Conference on Intelligent Transportation Systems (ITSC) 2018}, publisher = {IEEE}, address = {Maui, HI, USA}, doi = {10.1109/ITSC.2018.8569267}, year = {2018}, abstract = {In future traffic scenarios, vehicles and other traffic participants will be interconnected and equipped with various types of sensors, allowing for cooperation based on data or information exchange. This article presents an approach to cooperative tracking of cyclists using smart devices and infrastructure-based sensors. A smart device is carried by the cyclists and an intersection is equipped with a wide angle stereo camera system. Two tracking models are presented and compared. The first model is based on the stereo camera system detections only, whereas the second model cooperatively combines the camera based detections with velocity and yaw rate data provided by the smart device. Our aim is to overcome limitations of tracking approaches based on single data sources. We show in numerical evaluations on scenes where cyclists are starting or turning right that the cooperation leads to an improvement in both the ability to keep track of a cyclist and the accuracy of the track particularly when it comes to occlusions in the visual system. We, therefore, contribute to the safety of vulnerable road users in future traffic.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{ZernetschKohnenGoldhammeretal.2016, author = {Zernetsch, Stefan and Kohnen, Sascha and Goldhammer, Michael and Doll, Konrad and Sick, Bernhard}, title = {Trajectory prediction of cyclists using a physical model and an artificial neural network}, series = {Intelligent Vehicles Symposium (IV), 2016}, booktitle = {Intelligent Vehicles Symposium (IV), 2016}, doi = {10.1109/IVS.2016.7535484}, pages = {833 -- 838}, year = {2016}, abstract = {This article presents two methods for predicting the trajectories of cyclists at an intersection and compares them to a Kalman Filter (KF) approach. The first method uses a physical model of cyclists to predict their future position. The second method is based on a polynomial least-squares approximation in combination with a multilayer perceptron artificial neural network and is able to predict the future position of cyclists independent of their motion type such as "Starting", "Stopping", "Waiting" or "Passing". To evaluate the performance of the methods, 566 tracks (394 for training, 172 for testing) of uninstructed cyclists were recorded at a public intersection using a wide angle stereo camera system and laser scanners. Using the tracks as input data, the future trajectory was predicted for a time horizon of 2.5 s. For starting motions the prediction using the physical model leads to 27\% more accurate positions than the KF approach for a forecast horizon of 2.5 s. The neural network shows a 34\% more accurate result for starting and stopping motions and a similar result for waiting and passing motions.}, subject = {Kalman-Filter}, language = {en} }