@inproceedings{ReichertHetzelSchrecketal.2023, author = {Reichert, Hannes and Hetzel, Manuel and Schreck, Steven and Doll, Konrad and Sick, Bernhard}, title = {Sensor Equivariance by LiDAR Projection Images}, series = {2023 IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {2023 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, doi = {https://doi.org/10.1109/IV55152.2023.10186817}, pages = {1 -- 6}, year = {2023}, subject = {Bildverarbeitung}, language = {en} } @inproceedings{Reichert2024, author = {Reichert, Hannes}, title = {Sensor Equivariance for Active Road User Safety}, series = {Organic Computing}, volume = {2024}, booktitle = {Organic Computing}, publisher = {Kassel University Press}, address = {Kassel}, pages = {241 -- 252}, year = {2024}, subject = {Autonomes Fahrzeug}, language = {en} } @inproceedings{HetzelReichertReitbergeretal.2023, author = {Hetzel, Manuel and Reichert, Hannes and Reitberger, G{\"u}nther and Fuchs, Erich and Doll, Konrad and Sick, Bernhard}, title = {The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset}, series = {2023 IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {2023 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, doi = {https://doi.org/10.1109/IV55152.2023.10186776}, pages = {1 -- 7}, year = {2023}, subject = {Autonomes Fahrzeug}, language = {en} } @misc{ReichertDoll2023, author = {Reichert, Hannes and Doll, Konrad}, title = {An image encoding method for recording projection information of two-dimensional projections}, year = {2023}, subject = {Bildsignal}, language = {en} } @incollection{HetzelReichertDolletal.2025, author = {Hetzel, Manuel and Reichert, Hannes and Doll, Konrad and Sick, Bernhard}, title = {Reliable Probabilistic Human Trajectory Prediction for Autonomous Applications}, series = {Computer Vision - ECCV 2024 Workshops, Milan, Italy, September 29-October 4, 2024, Proceedings, Part XVII}, booktitle = {Computer Vision - ECCV 2024 Workshops, Milan, Italy, September 29-October 4, 2024, Proceedings, Part XVII}, publisher = {Springer Nature}, address = {Cham}, isbn = {9783031915840}, issn = {0302-9743}, doi = {https://doi.org/10.1007/978-3-031-91585-7_9}, pages = {135 -- 152}, year = {2025}, subject = {Autonomes System}, language = {en} } @inproceedings{HetzelReichertDolletal.2021, author = {Hetzel, Manuel and Reichert, Hannes and Doll, Konrad and Sick, Bernhard}, title = {Smart Infrastructure: A Research Junction}, series = {2021 IEEE International Smart Cities Conference (ISC2)}, booktitle = {2021 IEEE International Smart Cities Conference (ISC2)}, publisher = {IEEE}, doi = {https://doi.org/10.1109/ISC253183.2021.9562809}, pages = {1 -- 4}, year = {2021}, subject = {Autonomes Fahrzeug}, language = {en} } @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} } @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} } @inproceedings{ZernetschReichertKressetal.2019, author = {Zernetsch, Stefan and Reichert, Hannes and Kreß, Viktor and Doll, Konrad and Sick, Bernhard}, title = {Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks}, series = {2019 IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {2019 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Paris, France}, doi = {10.1109/IVS.2019.8814258}, year = {2019}, abstract = {In this article, we present an approach to forecast trajectories of vulnerable road users (VRUs) including a numerical quantification of the uncertainty of the forecast. The uncertainty estimates are modeled as normal distributions by means of neural networks. Additionally, we present a method to evaluate the reliability of the forecasted uncertainty estimates, where we utilize quantile-quantile (Q-Q) plots, a graphical method to compare two distributions widely used in statistics. The positional accuracy is evaluated using Euclidean distances, in specific we use the average Euclidean error (AEE) and the average specific AEE (ASAEE). The model is trained and tested using a large dataset of 1311 cyclist trajectories, recorded at an urban intersection in real world traffic. Using this method, we achieve a similar positional accuracy compared to our previous work, where only positions are forecasted. The method is able to produce reliable uncertainty estimates for the motion types start, stop, turn left, and turn rightand produces underconfident uncertainty estimates for the motion types waitand move straight. Since uncertainties are not underestimated, the method can be used as a basis for trajectory planing in automated vehicles.}, subject = {Fahrerassistenzsystem}, language = {en} } @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{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} }