TY - CHAP A1 - Serfling, Benjamin A1 - Reichert, Hannes A1 - Bayerlein, Lorenzo A1 - Doll, Konrad A1 - Radkhah-Lens, Kati T1 - LiDAR Based Semantic Perception for Forklifts in Outdoor Environments T2 - 2025 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI) KW - Gabelstapler KW - Autonomes Fahrzeug Y1 - 2025 U6 - https://doi.org/https://doi.org/10.1109/SOLI66289.2025.11396313 SP - 40 EP - 45 PB - IEEE ER - TY - CHAP A1 - Reichert, Hannes T1 - Sensor Equivariance for Active Road User Safety BT - Doctoral Dissertation Colloquium 2023 T2 - Organic Computing KW - Autonomes Fahrzeug KW - Bildverarbeitung Y1 - 2024 VL - 2024 SP - 241 EP - 252 PB - Kassel University Press CY - Kassel ER - TY - CHAP A1 - Hetzel, Manuel A1 - Reichert, Hannes A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Smart Infrastructure: A Research Junction T2 - 2021 IEEE International Smart Cities Conference (ISC2) KW - Autonomes Fahrzeug KW - Kreuzung Y1 - 2021 U6 - https://doi.org/https://doi.org/10.1109/ISC253183.2021.9562809 SP - 1 EP - 4 PB - IEEE ER - TY - CHAP A1 - Reichert, Hannes A1 - Lang, Lukas A1 - Rosch, Kevin A1 - Bogdoll, Daniel A1 - Doll, Konrad A1 - Sick, Bernhard A1 - Rellss, Hans-Christian A1 - Stiller, Christoph A1 - Zollner, J. Marius T1 - Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems T2 - 2021 IEEE International Smart Cities Conference (ISC2) KW - Autonomes Fahrzeug Y1 - 2021 U6 - https://doi.org/https://doi.org/10.1109/ISC253183.2021.9562912 SP - 1 EP - 4 PB - IEEE ER - TY - CHAP A1 - Zernetsch, Stefan A1 - Reichert, Hannes A1 - Kress, Viktor A1 - Doll, Konrad A1 - Sick, Bernhard T1 - A Holistic View on Probabilistic Trajectory Forecasting – Case Study. Cyclist Intention Detection T2 - 2022 IEEE Intelligent Vehicles Symposium (IV) KW - Autonomes Fahrzeug KW - Radfahrer Y1 - 2022 U6 - https://doi.org/https://doi.org/10.1109/IV51971.2022.9827220 SP - 265 EP - 272 PB - IEEE ER - TY - CHAP A1 - Hetzel, Manuel A1 - Reichert, Hannes A1 - Reitberger, Günther A1 - Fuchs, Erich A1 - Doll, Konrad A1 - Sick, Bernhard T1 - The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset T2 - 2023 IEEE Intelligent Vehicles Symposium (IV) KW - Autonomes Fahrzeug KW - Sensortechnik Y1 - 2023 U6 - https://doi.org/https://doi.org/10.1109/IV55152.2023.10186776 SP - 1 EP - 7 PB - IEEE ER - TY - CHAP A1 - Schreck, Steven A1 - Reichert, Hannes A1 - Hetzel, Manuel A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Height Change Feature Based Free Space Detection T2 - 2023 11th International Conference on Control, Mechatronics and Automation (ICCMA) KW - Gabelstapler KW - Autonomes Fahrzeug Y1 - 2023 U6 - https://doi.org/https://doi.org/10.1109/ICCMA59762.2023.10374705 SP - 171 EP - 176 PB - IEEE ER - TY - CHAP A1 - Zernetsch, Stefan A1 - Reichert, Hannes A1 - Kreß, Viktor A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks T2 - 2019 IEEE Intelligent Vehicles Symposium (IV) N2 - 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. KW - Fahrerassistenzsystem KW - Fahrrad KW - Autonomes Fahrzeug Y1 - 2019 U6 - https://doi.org/10.1109/IVS.2019.8814258 PB - IEEE CY - Paris, France ER -