@unpublished{KalyanasundaramChandraSekaranStaeuberetal.2025, author = {Kalyanasundaram, Abinav and Chandra Sekaran, Karthikeyan and St{\"a}uber, Philipp and Lange, Michael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{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} } @book{BotschUtschick2020, author = {Botsch, Michael and Utschick, Wolfgang}, title = {Fahrzeugsicherheit und automatisiertes Fahren}, subtitle = {Methoden der Signalverarbeitung und des maschinellen Lernens}, publisher = {Hanser}, address = {M{\"u}nchen}, isbn = {978-3-446-46804-7}, doi = {https://doi.org/10.3139/9783446468047}, pages = {448}, year = {2020}, language = {de} } @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{HerrmannUtschickBotschetal.2015, author = {Herrmann, Stephan and Utschick, Wolfgang and Botsch, Michael and Keck, Frank}, title = {Supervised learning via optimal control labeling for criticality classification in vehicle active safety}, booktitle = {Proceedings: 2015 IEEE 18th International Conference on Intelligent Transportation Systems}, publisher = {IEEE}, address = {Los Alamitos}, isbn = {978-1-4673-6596-3}, issn = {2153-0017}, doi = {https://doi.org/10.1109/ITSC.2015.328}, pages = {2024 -- 2031}, year = {2015}, language = {en} } @inproceedings{ChaulwarBotschUtschick2016, author = {Chaulwar, Amit and Botsch, Michael and Utschick, Wolfgang}, title = {A Hybrid Machine Learning Approach for Planning Safe Trajectories in Complex Traffic-Scenarios}, booktitle = {2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)}, publisher = {IEEE}, address = {Los Alamitos}, isbn = {978-1-5090-6167-9}, doi = {https://doi.org/10.1109/ICMLA.2016.0095}, pages = {540 -- 546}, year = {2016}, language = {en} } @inproceedings{ChaulwarBotschUtschick2017, author = {Chaulwar, Amit and Botsch, Michael and Utschick, Wolfgang}, title = {A machine learning based biased-sampling approach for planning safe trajectories in complex, dynamic traffic-scenarios}, booktitle = {2017 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5090-4804-5}, doi = {https://doi.org/10.1109/IVS.2017.7995735}, pages = {297 -- 303}, year = {2017}, language = {en} } @inproceedings{MuellerBotschBoehmlaenderetal.2017, author = {M{\"u}ller, Marcus and Botsch, Michael and B{\"o}hml{\"a}nder, Dennis and Utschick, Wolfgang}, title = {A Simulation Framework for Vehicle Safety Testing}, booktitle = {Aktive Sicherheit und Automatisieres Fahren : 3. Interdisziplin{\"a}rer Expertendialog (IEDAS)}, editor = {Klaffke, Werner}, publisher = {expert Verlag}, address = {Renningen}, isbn = {978-3-8169-3405-9}, pages = {147 -- 167}, year = {2017}, language = {en} } @article{MuellerBotschBoehmlaenderetal.2018, author = {M{\"u}ller, Marcus and Botsch, Michael and B{\"o}hml{\"a}nder, Dennis and Utschick, Wolfgang}, title = {Machine Learning Based Prediction of Crash Severity Distributions for Mitigation Strategies}, volume = {9 (2018)}, journal = {Journal of Advances in Information Technology}, number = {1}, publisher = {Engineering and Technology Publishing}, address = {Rowland Heights (CA)}, issn = {1798-2340}, doi = {https://doi.org/10.12720/jait.9.1.15-24}, pages = {15 -- 24}, year = {2018}, abstract = {In road traffic, critical situations pass by as quickly as they appear. Within the blink of an eye, one has to come to a decision, which can make the difference between a low severity, high severity or fatal crash. Because time is important, a machine learning driven Crash Severity Predictor (CSP) is presented which provides the estimated crash severity distribution of an imminent crash in less than 0.2ms. This is 63⋅ 103 times faster compared to predicting the same distribution through computationally expensive numerical simulations. With the proposed method, even very complex crash data, like the results of Finite Element Method (FEM) simulations, can be made available ahead of a collision. Knowledge, which can be used to prepare occupants and vehicle to an imminent crash, activate and adjust safety measures like airbags or belt tensioners before of a collision or let self-driving vehicles go for the maneuver with the lowest crash severity. Using a real-world crash test it is shown that significant safety potential is left unused if instead of the CSP-proposed driving maneuver, no or the wrong actions are taken.}, language = {en} } @inproceedings{ChaulwarBotschUtschick2018, author = {Chaulwar, Amit and Botsch, Michael and Utschick, Wolfgang}, title = {Generation of Reference Trajectories for Safe Trajectory Planning}, booktitle = {Artificial Neural Networks and Machine Learning - ICANN 2018 : 27th International Conference on Artificial Neural Networks,Rhodes, Greece, October 4-7, 2018 : Proceedings, Part I}, editor = {Kurkov{\´a}, Vera and Manolopoulos, Yannis and Hammer, Barbara and Iliadis, Lazaros and Maglogiannis, Ilias}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-01418-6}, doi = {https://doi.org/10.1007/978-3-030-01418-6_42}, pages = {423 -- 434}, year = {2018}, language = {en} } @inproceedings{GallitzBotschdeCandidoetal.2018, author = {Gallitz, Oliver and Botsch, Michael and de Candido, Oliver and Utschick, Wolfgang}, title = {Validation of Machine Learning Algorithms through Visualization Methods}, booktitle = {ELIV-MarketPlace 2018}, publisher = {VDI Verlag}, address = {D{\"u}sseldorf}, isbn = {978-3-18-092338-3}, doi = {https://doi.org/10.51202/9783181023389-29}, pages = {29 -- 46}, year = {2018}, language = {en} } @inproceedings{MuellerLongBotschetal.2018, author = {M{\"u}ller, Marcus and Long, Xing and Botsch, Michael and B{\"o}hml{\"a}nder, Dennis and Utschick, Wolfgang}, title = {Real-Time Crash Severity Estimation with Machine Learning and 2D Mass-Spring-Damper Model}, booktitle = {2018 IEEE Intelligent Transportation Systems Conference}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-0323-5}, doi = {https://doi.org/10.1109/ITSC.2018.8569471}, pages = {2036 -- 2043}, year = {2018}, language = {en} } @inproceedings{NeumeierTollkuehnDornetal.2023, author = {Neumeier, Marion and Tollk{\"u}hn, Andreas and Dorn, Sebastian and Botsch, Michael and Utschick, Wolfgang}, title = {Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applications}, booktitle = {IEEE IV 2023 Symposium Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-4691-6}, issn = {2642-7214}, doi = {https://doi.org/10.1109/IV55152.2023.10186536}, year = {2023}, language = {en} } @inproceedings{ChandraSekaranBalasubramanianBotschetal.2023, author = {Chandra Sekaran, Karthikeyan and Balasubramanian, Lakshman and Botsch, Michael and Utschick, Wolfgang}, title = {Metric Learning Based Class Specific Experts for Open-Set Recognition of Traffic Participants in Urban Areas Using Infrastructure Sensors}, booktitle = {IEEE IV 2023 Symposium Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-4691-6}, issn = {2642-7214}, doi = {https://doi.org/10.1109/IV55152.2023.10186527}, year = {2023}, language = {en} } @inproceedings{GallitzdeCandidoBotschetal.2020, author = {Gallitz, Oliver and de Candido, Oliver and Botsch, Michael and Melz, Ron and Utschick, Wolfgang}, title = {Interpretable Machine Learning Structure for an Early Prediction of Lane Changes}, booktitle = {Artificial Neural Networks and Machine Learning - ICANN 2020: 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15-18, 2020, Proceedings, Part I}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-61609-0}, doi = {https://doi.org/10.1007/978-3-030-61609-0_27}, pages = {337 -- 349}, year = {2020}, language = {en} } @article{ChaulwarAlHashimiBotschetal.2021, author = {Chaulwar, Amit and Al-Hashimi, Hussein and Botsch, Michael and Utschick, Wolfgang}, title = {Sampling Algorithms Combination with Machine Learning for Effcient Safe Trajectory Planning}, volume = {11}, journal = {International Journal of Machine Learning and Computing}, number = {1}, publisher = {IACSIT Press}, address = {Singapur}, issn = {2010-3700}, doi = {https://doi.org/10.18178/ijmlc.2021.11.1.1007}, year = {2021}, abstract = {The planning of safe trajectories in critical traffic scenarios using model-based algorithms is a very computationally intensive task. Recently proposed algorithms, namely Hybrid Augmented CL-RRT, Hybrid Augmented CL-RRT+ and GATE-ARRT+, reduce the computation time for safe trajectory planning drastically using a combination of a deep learning algorithm 3D-ConvNet with a vehicle dynamic model. An efficient embedded implementation of these algorithms is required as the vehicle on-board micro-controller resources are limited. This work proposes methodologies for replacing the computationally intensive modules of these trajectory planning algorithms using different efficient machine learning and analytical methods. The required computational resources are measured by downloading and running the algorithms on various hardware platforms. The results show significant reduction in computational resources and the potential of proposed algorithms to run in real time. Also, alternative architectures for 3D-ConvNet are presented for further reduction of required computational resources.}, language = {en} } @inproceedings{BalasubramanianWurstEgolfetal.2024, author = {Balasubramanian, Lakshman and Wurst, Jonas and Egolf, Robin and Botsch, Michael and Utschick, Wolfgang and Deng, Ke}, title = {SceneDiffusion: Conditioned Latent Diffusion Models for Traffic Scene Prediction}, booktitle = {2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-9946-2}, doi = {https://doi.org/10.1109/ITSC57777.2023.10422482}, pages = {3914 -- 3921}, year = {2024}, language = {en} } @unpublished{NeumeierDornBotschetal.2024, author = {Neumeier, Marion and Dorn, Sebastian and Botsch, Michael and Utschick, Wolfgang}, title = {Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2405.14384}, year = {2024}, language = {en} } @inproceedings{ChandraSekaranBalasubramanianBotschetal.2024, author = {Chandra Sekaran, Karthikeyan and Balasubramanian, Lakshman and Botsch, Michael and Utschick, Wolfgang}, title = {Open-Set Object Detection for the Identification and Localization of Dissimilar Novel Classes by means of Infrastructure Sensors}, booktitle = {2024 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-4881-1}, doi = {https://doi.org/10.1109/IV55156.2024.10588872}, pages = {1643 -- 1650}, year = {2024}, language = {en} } @inproceedings{NeumeierDornBotschetal.2024, author = {Neumeier, Marion and Dorn, Sebastian and Botsch, Michael and Utschick, Wolfgang}, title = {Prediction and Interpretation of Vehicle Trajectories in the Graph Spectral Domain}, booktitle = {2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-9946-2}, doi = {https://doi.org/10.1109/ITSC57777.2023.10422530}, pages = {1172 -- 1179}, year = {2024}, language = {en} } @unpublished{ChandraSekaranGeislerRoessleetal.2025, author = {Chandra Sekaran, Karthikeyan and Geisler, Markus and R{\"o}ßle, Dominik and Mohan, Adithya and Cremers, Daniel and Utschick, Wolfgang and Botsch, Michael and Huber, Werner and Sch{\"o}n, Torsten}, title = {UrbanIng-V2X: A Large-Scale Multi-Vehicle, Multi-Infrastructure Dataset Across Multiple Intersections for Cooperative Perception}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2510.23478}, year = {2025}, language = {en} } @inproceedings{ElterDirndorferBotschetal.2025, author = {Elter, Tim and Dirndorfer, Tobias and Botsch, Michael and Utschick, Wolfgang}, title = {Validation of a POMDP Framework for Interaction-aware Trajectory Prediction in Vehicle Safety}, booktitle = {2025 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3315-3803-3}, doi = {https://doi.org/10.1109/IV64158.2025.11097498}, pages = {663 -- 670}, year = {2025}, language = {en} } @inproceedings{MuellerNadarajanBotschetal.2016, author = {M{\"u}ller, Marcus and Nadarajan, Parthasarathy and Botsch, Michael and Utschick, Wolfgang and B{\"o}hml{\"a}nder, Dennis and Katzenbogen, Stefan}, title = {A statistical learning approach for estimating the reliability of crash severity predictions}, booktitle = {2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5090-1889-5}, issn = {2153-0017}, doi = {https://doi.org/10.1109/ITSC.2016.7795911}, pages = {2199 -- 2206}, year = {2016}, language = {en} } @inproceedings{WurstFloresFernandezBotschetal.2021, author = {Wurst, Jonas and Flores Fernandez, Alberto and Botsch, Michael and Utschick, Wolfgang}, title = {An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images}, booktitle = {2020 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-6673-5}, doi = {https://doi.org/10.1109/IV47402.2020.9304733}, pages = {1436 -- 1443}, year = {2021}, language = {en} } @unpublished{WurstFloresFernandezBotschetal.2020, author = {Wurst, Jonas and Flores Fernandez, Alberto and Botsch, Michael and Utschick, Wolfgang}, title = {An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2005.13288}, year = {2020}, language = {en} } @inproceedings{WurstBalasubramanianBotschetal.2021, author = {Wurst, Jonas and Balasubramanian, Lakshman and Botsch, Michael and Utschick, Wolfgang}, title = {Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder}, booktitle = {2021 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-5394-0}, doi = {https://doi.org/10.1109/IV48863.2021.9575730}, pages = {1304 -- 1311}, year = {2021}, language = {en} } @unpublished{WurstBalasubramanianBotschetal.2021, author = {Wurst, Jonas and Balasubramanian, Lakshman and Botsch, Michael and Utschick, Wolfgang}, title = {Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2105.01924}, year = {2021}, language = {en} }