TY - CHAP A1 - Sánchez Morales, Eduardo A1 - Botsch, Michael A1 - Huber, Bertold A1 - García Higuera, Andrés T1 - High precision indoor navigation for autonomous vehicles T2 - 2019 International Conference on Indoor Positioning and Indoor Navigation UR - https://doi.org/10.1109/IPIN.2019.8911780 KW - Sensors KW - Automobiles KW - State estimation KW - Autonomous vehicles KW - Testing KW - Receivers KW - Discrete Fourier transforms Y1 - 2019 UR - https://doi.org/10.1109/IPIN.2019.8911780 SN - 978-1-7281-1788-1 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Gallitz, Oliver A1 - de Candido, Oliver A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - Interpretable feature generation using deep neural networks and its application to lane change detection T2 - 2019 IEEE Intelligent Transportation Systems Conference (ITSC) UR - https://doi.org/10.1109/ITSC.2019.8917524 KW - Time series analysis KW - Kernel KW - Convolution KW - Feature extraction KW - Finite impulse response filters KW - Neural networks KW - Heating systems Y1 - 2019 UR - https://doi.org/10.1109/ITSC.2019.8917524 SN - 978-1-5386-7024-8 SP - 3405 EP - 3411 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Botsch, Michael A1 - Dietl, Guido A1 - Utschick, Wolfgang T1 - Iterative Multi-User Detection Using Reduced-Complexity Equalization T2 - TURBO – CODING – 2006: 4th International Symposium on Turbo Codes & Related Topics, 6th International ITG-Conference on Source and Channel Coding KW - Decoding Y1 - 2006 UR - https://www.vde-verlag.de/proceedings-de/442947088.html SN - 978-3-8007-2947-0 PB - VDE CY - Berlin ER - TY - CHAP A1 - Balasubramanian, Lakshman A1 - Wurst, Jonas A1 - Egolf, Robin A1 - Botsch, Michael A1 - Utschick, Wolfgang A1 - Deng, Ke T1 - ExAgt: Expert-guided Augmentation for Representation Learning of Traffic Scenarios T2 - 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC55140.2022.9922453 KW - Representation learning KW - Visualization KW - Codes KW - Self-supervised learning KW - Prediction methods KW - Distortion KW - Stability analysis Y1 - 2022 UR - https://doi.org/10.1109/ITSC55140.2022.9922453 SN - 978-1-6654-6880-0 SP - 1471 EP - 1478 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Neumeier, Marion A1 - Tollkühn, Andreas A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction T2 - 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC55140.2022.9922419 KW - Road transportation KW - Computational modeling KW - Computer architecture KW - Predictive models KW - Network architecture KW - Trajectory KW - Decoding Y1 - 2022 UR - https://doi.org/10.1109/ITSC55140.2022.9922419 SN - 978-1-6654-6880-0 SP - 687 EP - 694 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Kruber, Friedrich A1 - Sánchez Morales, Eduardo A1 - Egolf, Robin A1 - Wurst, Jonas A1 - Chakraborty, Samarjit A1 - Botsch, Michael T1 - Micro- and Macroscopic Road Traffic Analysis using Drone Image Data JF - Leibniz Transactions on Embedded Systems N2 - 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. UR - https://doi.org/10.4230/LITES.8.1.2 KW - traffic data analysis KW - trajectory data KW - drone image data Y1 - 2022 UR - https://doi.org/10.4230/LITES.8.1.2 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-29704 SN - 2199-2002 VL - 8 IS - 1 SP - 02:1 EP - 02:27 PB - Schloss Dagstuhl CY - Wadern ER - TY - BOOK A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - Fahrzeugsicherheit und automatisiertes Fahren BT - Methoden der Signalverarbeitung und des maschinellen Lernens UR - https://doi.org/10.3139/9783446468047 Y1 - 2020 UR - https://doi.org/10.3139/9783446468047 SN - 978-3-446-46804-7 SN - 978-3-446-45326-5 PB - Hanser CY - München ER - TY - CHAP A1 - Botsch, Michael A1 - Nossek, Josef A. T1 - Feature Selection for Change Detection in Multivariate Time-Series T2 - 2007 IEEE Symposium on Computational Intelligence and Data Mining UR - https://doi.org/10.1109/CIDM.2007.368929 KW - machine learning KW - sequences KW - circuit theory KW - signal processing KW - radio frequency KW - signal processing algorithms KW - stochastic processes KW - feature extraction KW - computational intelligence KW - data mining Y1 - 2007 UR - https://doi.org/10.1109/CIDM.2007.368929 SN - 1-4244-0705-2 SP - 590 EP - 597 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Botsch, Michael A1 - Nossek, Josef A. T1 - Construction of interpretable Radial Basis Function classifiers based on the Random Forest kernel T2 - The 2008 IEEE International Joint Conference on Neural Networks (IJCNN 2008) UR - https://doi.org/10.1109/IJCNN.2008.4633793 KW - Radio frequency KW - Classification algorithms KW - Kernel KW - Support vector machine classification KW - Training KW - Classification tree analysis KW - Artificial neural networks Y1 - 2008 UR - https://doi.org/10.1109/IJCNN.2008.4633793 SN - 978-1-4244-1820-6 SN - 2161-4393 SN - 2161-4407 SP - 220 EP - 227 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Bergmiller, Peter A1 - Botsch, Michael A1 - Speth, Johannes A1 - Hofmann, Ulrich T1 - Vehicle rear detection in images with Generalized Radial-Basis-Function classifiers T2 - 2008 IEEE Intelligent Vehicles Symposium UR - https://doi.org/10.1109/IVS.2008.4621273 KW - driver information systems KW - expert systems KW - image classification KW - radial basis function networks Y1 - 2008 UR - https://doi.org/10.1109/IVS.2008.4621273 SN - 978-1-4244-2568-6 SN - 1931-0587 SP - 226 EP - 233 PB - IEEE CY - Piscataway ER - TY - BOOK A1 - Botsch, Michael T1 - Machine Learning Techniques for Time Series Classification Y1 - 2009 UR - https://cuvillier.de/de/shop/publications/1092-machine-learning-techniques-for-time-series-classification SN - 978-3-86727-950-5 SN - 978-3-7369-2950-0 N1 - Dissertation, Technische Universität München, 2009 PB - Cuvillier Verlag CY - Göttingen ER -