@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} } @article{KruberSanchezMoralesEgolfetal.2022, author = {Kruber, Friedrich and S{\´a}nchez Morales, Eduardo and Egolf, Robin and Wurst, Jonas and Chakraborty, Samarjit and Botsch, Michael}, title = {Micro- and Macroscopic Road Traffic Analysis using Drone Image Data}, volume = {8}, journal = {Leibniz Transactions on Embedded Systems}, number = {1}, publisher = {Schloss Dagstuhl}, address = {Wadern}, issn = {2199-2002}, doi = {https://doi.org/10.4230/LITES.8.1.2}, pages = {02:1 -- 02:27}, year = {2022}, abstract = {The current development in the drone technology, alongside with machine learning based image processing, open new possibilities for various applications. Thus, the market volume is expected to grow rapidly over the next years. The goal of this paper is to demonstrate the capabilities and limitations of drone based image data processing for the purpose of road traffic analysis. In the first part a method for generating microscopic traffic data is proposed. More precisely, the state of vehicles and the resulting trajectories are estimated. The method is validated by conducting experiments with reference sensors and proofs to achieve precise vehicle state estimation results. It is also shown, how the computational effort can be reduced by incorporating the tracking information into a neural network. A discussion on current limitations supplements the findings. By collecting a large number of vehicle trajectories, macroscopic statistics, such as traffic flow and density can be obtained from the data. In the second part, a publicly available drone based data set is analyzed to evaluate the suitability for macroscopic traffic modeling. The results show that the method is well suited for gaining detailed information about macroscopic statistics, such as traffic flow dependent time headway or lane change occurrences. In conclusion, this paper presents methods to exploit the remarkable opportunities of drone based image processing for joint macro- and microscopic traffic analysis.}, 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{BotschNossek2007, author = {Botsch, Michael and Nossek, Josef A.}, title = {Feature Selection for Change Detection in Multivariate Time-Series}, booktitle = {2007 IEEE Symposium on Computational Intelligence and Data Mining}, publisher = {IEEE}, address = {Piscataway}, isbn = {1-4244-0705-2}, doi = {https://doi.org/10.1109/CIDM.2007.368929}, pages = {590 -- 597}, year = {2007}, language = {en} } @inproceedings{BotschNossek2008, author = {Botsch, Michael and Nossek, Josef A.}, title = {Construction of interpretable Radial Basis Function classifiers based on the Random Forest kernel}, booktitle = {The 2008 IEEE International Joint Conference on Neural Networks (IJCNN 2008)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-4244-1820-6}, issn = {2161-4393}, doi = {https://doi.org/10.1109/IJCNN.2008.4633793}, pages = {220 -- 227}, year = {2008}, language = {en} } @inproceedings{BergmillerBotschSpethetal.2008, author = {Bergmiller, Peter and Botsch, Michael and Speth, Johannes and Hofmann, Ulrich}, title = {Vehicle rear detection in images with Generalized Radial-Basis-Function classifiers}, booktitle = {2008 IEEE Intelligent Vehicles Symposium}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-4244-2568-6}, issn = {1931-0587}, doi = {https://doi.org/10.1109/IVS.2008.4621273}, pages = {226 -- 233}, year = {2008}, language = {en} } @book{Botsch2009, author = {Botsch, Michael}, title = {Machine Learning Techniques for Time Series Classification}, publisher = {Cuvillier Verlag}, address = {G{\"o}ttingen}, isbn = {978-3-86727-950-5}, pages = {216}, year = {2009}, language = {en} } @inproceedings{BotschLauer2010, author = {Botsch, Michael and Lauer, Christoph}, title = {Complexity reduction using the Random Forest classifier in a collision detection algorithm}, booktitle = {2010 IEEE Intelligent Vehicles Symposium}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-4244-7868-2}, issn = {1931-0587}, doi = {https://doi.org/10.1109/IVS.2010.5548044}, pages = {1228 -- 1235}, year = {2010}, language = {en} } @inproceedings{DirndorferBotschKnoll2011, author = {Dirndorfer, Tobias and Botsch, Michael and Knoll, Alois}, title = {Model-based analysis of sensor-noise in predictive passive safety algorithms}, pages = {11-0251}, booktitle = {The 22nd ESV Conference Proceedings}, publisher = {NHTSA}, address = {Washington, D.C.}, url = {https://www-esv.nhtsa.dot.gov/Proceedings/22/isv7/main.htm}, year = {2011}, language = {en} }