@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} } @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} } @phdthesis{Kress2022, author = {Kreß, Viktor}, title = {Posenbasierte Intentionserkennung von ungesch{\"u}tzten Verkehrsteilnehmern aus einem Fahrzeug}, school = {Technische Hochschule Aschaffenburg}, year = {2022}, subject = {Fahrerassistenzsystem}, language = {de} } @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} }