@unpublished{KalyanasundaramChandraSekaranStaeuberetal.2025, author = {Kalyanasundaram, Abinav and Chandra Sekaran, Karthikeyan and St{\"a}uber, Philipp and Lange, Michael and Utschick, Wolfgang and Botsch, Michael}, title = {Uncertainty-Aware Hybrid Machine Learning in Virtual Sensors for Vehicle Sideslip Angle Estimation}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2504.06105}, year = {2025}, abstract = {Precise vehicle state estimation is crucial for safe and reliable autonomous driving. The number of measurable states and their precision offered by the onboard vehicle sensor system are often constrained by cost. For instance, measuring critical quantities such as the Vehicle Sideslip Angle (VSA) poses significant commercial challenges using current optical sensors. This paper addresses these limitations by focusing on the development of high-performance virtual sensors to enhance vehicle state estimation for active safety. The proposed Uncertainty-Aware Hybrid Learning (UAHL) architecture integrates a machine learning model with vehicle motion models to estimate VSA directly from onboard sensor data. A key aspect of the UAHL architecture is its focus on uncertainty quantification for individual model estimates and hybrid fusion. These mechanisms enable the dynamic weighting of uncertainty-aware predictions from machine learning and vehicle motion models to produce accurate and reliable hybrid VSA estimates. This work also presents a novel dataset named Real-world Vehicle State Estimation Dataset (ReV-StED), comprising synchronized measurements from advanced vehicle dynamic sensors. The experimental results demonstrate the superior performance of the proposed method for VSA estimation, highlighting UAHL as a promising architecture for advancing virtual sensors and enhancing active safety in autonomous vehicles.}, language = {en} } @inproceedings{deCandidoKollerGallitzetal.2020, author = {de Candido, Oliver and Koller, Michael and Gallitz, Oliver and Melz, Ron and Botsch, Michael and Utschick, Wolfgang}, title = {Towards feature validation in time to lane change classification using deep neural networks}, booktitle = {2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-4149-7}, doi = {https://doi.org/10.1109/ITSC45102.2020.9294555}, year = {2020}, language = {en} } @inproceedings{DietlBotschDietrichetal.2005, author = {Dietl, Guido and Botsch, Michael and Dietrich, F. A. and Utschick, Wolfgang}, title = {Robust and reduced-rank matrix Wiener filter based on the conjugate gradient algorithm}, booktitle = {2005 IEEE 6th Workshop on Signal Processing Advances in Wireless Communications}, publisher = {IEEE}, address = {Piscataway}, isbn = {0-7803-8867-4}, doi = {https://doi.org/10.1109/SPAWC.2005.1506201}, pages = {555 -- 559}, year = {2005}, language = {en} } @inproceedings{WurstBalasubramanianBotschetal.2022, author = {Wurst, Jonas and Balasubramanian, Lakshman and Botsch, Michael and Utschick, Wolfgang}, title = {Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios}, booktitle = {2022 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-8821-1}, doi = {https://doi.org/10.1109/IV51971.2022.9827187}, pages = {484 -- 491}, year = {2022}, language = {en} } @inproceedings{ElterDirndorferBotschetal.2022, author = {Elter, Tim and Dirndorfer, Tobias and Botsch, Michael and Utschick, Wolfgang}, title = {Interaction-aware Prediction of Occupancy Regions based on a POMDP Framework}, booktitle = {2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-6880-0}, doi = {https://doi.org/10.1109/ITSC55140.2022.9922127}, pages = {980 -- 987}, year = {2022}, language = {en} } @unpublished{NeumeierTollkuehnDornetal.2023, author = {Neumeier, Marion and Tollk{\"u}hn, Andreas and Dorn, Sebastian and Botsch, Michael and Utschick, Wolfgang}, title = {Gradient Derivation for Learnable Parameters in Graph Attention Networks}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2304.10939}, year = {2023}, language = {en} } @inproceedings{GallitzdeCandidoBotschetal.2021, author = {Gallitz, Oliver and de Candido, Oliver and Botsch, Michael and Utschick, Wolfgang}, title = {Interpretable Early Prediction of Lane Changes Using a Constrained Neural Network Architecture}, booktitle = {2021 IEEE International Intelligent Transportation Systems Conference (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-9142-3}, doi = {https://doi.org/10.1109/ITSC48978.2021.9564555}, pages = {493 -- 499}, year = {2021}, language = {en} } @inproceedings{ChaulwarAlHashimiBotschetal.2019, author = {Chaulwar, Amit and Al-Hashimi, Hussein and Botsch, Michael and Utschick, Wolfgang}, title = {Efficient hybrid machine learning algorithm for trajectory planning in critical traffic-scenarios}, booktitle = {The 4th International Conference on Intelligent Transportation Engineering, ICITE 2019}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-4553-2}, doi = {https://doi.org/10.1109/ICITE.2019.8880266}, pages = {196 -- 202}, year = {2019}, language = {en} } @inproceedings{GallitzdeCandidoBotschetal.2019, author = {Gallitz, Oliver and de Candido, Oliver and Botsch, Michael and Utschick, Wolfgang}, title = {Interpretable feature generation using deep neural networks and its application to lane change detection}, booktitle = {2019 IEEE Intelligent Transportation Systems Conference (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5386-7024-8}, doi = {https://doi.org/10.1109/ITSC.2019.8917524}, pages = {3405 -- 3411}, year = {2019}, language = {en} } @inproceedings{BotschDietlUtschick2006, author = {Botsch, Michael and Dietl, Guido and Utschick, Wolfgang}, title = {Iterative Multi-User Detection Using Reduced-Complexity Equalization}, pages = {88}, booktitle = {TURBO - CODING - 2006: 4th International Symposium on Turbo Codes \& Related Topics, 6th International ITG-Conference on Source and Channel Coding}, publisher = {VDE}, address = {Berlin}, isbn = {978-3-8007-2947-0}, url = {https://www.vde-verlag.de/proceedings-de/442947088.html}, year = {2006}, language = {en} } @inproceedings{BalasubramanianWurstEgolfetal.2022, author = {Balasubramanian, Lakshman and Wurst, Jonas and Egolf, Robin and Botsch, Michael and Utschick, Wolfgang and Deng, Ke}, title = {ExAgt: Expert-guided Augmentation for Representation Learning of Traffic Scenarios}, booktitle = {2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-6880-0}, doi = {https://doi.org/10.1109/ITSC55140.2022.9922453}, pages = {1471 -- 1478}, year = {2022}, language = {en} } @inproceedings{NeumeierTollkuehnBotschetal.2022, author = {Neumeier, Marion and Tollk{\"u}hn, Andreas and Botsch, Michael and Utschick, Wolfgang}, title = {A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction}, booktitle = {2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-6880-0}, doi = {https://doi.org/10.1109/ITSC55140.2022.9922419}, pages = {687 -- 694}, year = {2022}, language = {en} }