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 - 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 - TY - CHAP A1 - Botsch, Michael A1 - Lauer, Christoph T1 - Complexity reduction using the Random Forest classifier in a collision detection algorithm T2 - 2010 IEEE Intelligent Vehicles Symposium UR - https://doi.org/10.1109/IVS.2010.5548044 Y1 - 2010 UR - https://doi.org/10.1109/IVS.2010.5548044 SN - 978-1-4244-7868-2 SN - 978-1-4244-7866-8 SN - 1931-0587 SP - 1228 EP - 1235 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Dirndorfer, Tobias A1 - Botsch, Michael A1 - Knoll, Alois T1 - Model-based analysis of sensor-noise in predictive passive safety algorithms T2 - The 22nd ESV Conference Proceedings Y1 - 2011 UR - https://www-esv.nhtsa.dot.gov/Proceedings/22/isv7/main.htm PB - NHTSA CY - Washington, D.C. ER - TY - CHAP A1 - Notomista, Gennaro A1 - Botsch, Michael T1 - Maneuver segmentation for autonomous parking based on ensemble learning T2 - 2015 International Joint Conference on Neural Networks (IJCNN) UR - https://doi.org/10.1109/IJCNN.2015.7280546 KW - vehicles KW - microcontrollers KW - simultaneous localization and mapping KW - Jacobian matrices KW - covariance matrices Y1 - 2015 UR - https://doi.org/10.1109/IJCNN.2015.7280546 SN - 978-1-4799-1960-4 SN - 2161-4407 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Herrmann, Stephan A1 - Utschick, Wolfgang A1 - Botsch, Michael A1 - Keck, Frank T1 - Supervised learning via optimal control labeling for criticality classification in vehicle active safety T2 - Proceedings: 2015 IEEE 18th International Conference on Intelligent Transportation Systems UR - https://doi.org/10.1109/ITSC.2015.328 KW - vehicles KW - acceleration KW - trajectory KW - collision avoidance KW - tires KW - force KW - optimal control Y1 - 2015 UR - https://doi.org/10.1109/ITSC.2015.328 SN - 978-1-4673-6596-3 SN - 2153-0017 SP - 2024 EP - 2031 PB - IEEE CY - Los Alamitos ER - TY - JOUR A1 - Notomista, Gennaro A1 - Selvaggio, Mario A1 - Sbrizzi, Fiorentina A1 - Di Maio, Gabriella A1 - Grazioso, Stanislao A1 - Botsch, Michael T1 - A fast airplane boarding strategy using online seat assignment based on passenger classification JF - Journal of Air Transport Management UR - https://doi.org/10.1016/j.jairtraman.2016.02.012 KW - Turnaround time KW - Boarding KW - Seat allocation algorithm KW - Agility coefficient KW - Cost saving KW - State feedback Y1 - 2016 UR - https://doi.org/10.1016/j.jairtraman.2016.02.012 SN - 0969-6997 VL - 2016 IS - 53 SP - 140 EP - 149 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Flores Fernández, Alberto A1 - Wurst, Jonas A1 - Sánchez Morales, Eduardo A1 - Botsch, Michael A1 - Facchi, Christian A1 - García Higuera, Andrés T1 - Probabilistic Traffic Motion Labeling for Multi-Modal Vehicle Route Prediction JF - Sensors N2 - The prediction of the motion of traffic participants is a crucial aspect for the research and development of Automated Driving Systems (ADSs). Recent approaches are based on multi-modal motion prediction, which requires the assignment of a probability score to each of the multiple predicted motion hypotheses. However, there is a lack of ground truth for this probability score in the existing datasets. This implies that current Machine Learning (ML) models evaluate the multiple predictions by comparing them with the single real trajectory labeled in the dataset. In this work, a novel data-based method named Probabilistic Traffic Motion Labeling (PROMOTING) is introduced in order to (a) generate probable future routes and (b) estimate their probabilities. PROMOTING is presented with the focus on urban intersections. The generation of probable future routes is (a) based on a real traffic dataset and consists of two steps: first, a clustering of intersections with similar road topology, and second, a clustering of similar routes that are driven in each cluster from the first step. The estimation of the route probabilities is (b) based on a frequentist approach that considers how traffic participants will move in the future given their motion history. PROMOTING is evaluated with the publicly available Lyft database. The results show that PROMOTING is an appropriate approach to estimate the probabilities of the future motion of traffic participants in urban intersections. In this regard, PROMOTING can be used as a labeling approach for the generation of a labeled dataset that provides a probability score for probable future routes. Such a labeled dataset currently does not exist and would be highly valuable for ML approaches with the task of multi-modal motion prediction. The code is made open source. UR - https://doi.org/10.3390/s22124498 KW - PROMOTING KW - automated driving systems KW - autonomous vehicles KW - multi-modal KW - motion prediction KW - route prediction KW - machine learning KW - real traffic data Y1 - 2022 UR - https://doi.org/10.3390/s22124498 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-24341 SN - 1424-8220 VL - 22 IS - 12 PB - MDPI CY - Basel ER - TY - CHAP A1 - Chaulwar, Amit A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - A Hybrid Machine Learning Approach for Planning Safe Trajectories in Complex Traffic-Scenarios T2 - 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) UR - https://doi.org/10.1109/ICMLA.2016.0095 KW - Hybrid learning algorithms KW - Trajectory Planning in Road Traffic KW - 3D-ConvNets Y1 - 2016 UR - https://doi.org/10.1109/ICMLA.2016.0095 SN - 978-1-5090-6167-9 SP - 540 EP - 546 PB - IEEE CY - Los Alamitos ER - TY - CHAP A1 - Chaulwar, Amit A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - A machine learning based biased-sampling approach for planning safe trajectories in complex, dynamic traffic-scenarios T2 - 2017 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IVS.2017.7995735 KW - Acceleration KW - Trajectory KW - Heuristic algorithms KW - Vehicle dynamics KW - Planning KW - Prediction algorithms KW - Roads Y1 - 2017 UR - https://doi.org/10.1109/IVS.2017.7995735 SN - 978-1-5090-4804-5 SP - 297 EP - 303 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Müller, Marcus A1 - Botsch, Michael A1 - Böhmländer, Dennis A1 - Utschick, Wolfgang ED - Klaffke, Werner T1 - A Simulation Framework for Vehicle Safety Testing T2 - Aktive Sicherheit und Automatisieres Fahren : 3. Interdisziplinärer Expertendialog (IEDAS) T2 - Ein Simulationsframework für die Absicherung von Fahrzeugsicherheitsfunktionen Y1 - 2017 SN - 978-3-8169-3405-9 SP - 147 EP - 167 PB - expert Verlag CY - Renningen ER - TY - JOUR A1 - Müller, Marcus A1 - Botsch, Michael A1 - Böhmländer, Dennis A1 - Utschick, Wolfgang T1 - Machine Learning Based Prediction of Crash Severity Distributions for Mitigation Strategies JF - Journal of Advances in Information Technology N2 - 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. UR - https://doi.org/10.12720/jait.9.1.15-24 KW - crash severity KW - vehicle safety KW - reliable prediction KW - machine learning Y1 - 2018 UR - https://doi.org/10.12720/jait.9.1.15-24 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-24644 SN - 1798-2340 VL - 9 (2018) IS - 1 SP - 15 EP - 24 PB - Engineering and Technology Publishing CY - Rowland Heights (CA) ER - TY - CHAP A1 - Cañas, Valentin A1 - Sánchez Morales, Eduardo A1 - Botsch, Michael A1 - García Higuera, Andres T1 - Wireless Communication System for the Validation of Autonomous Driving Functions on Full-Scale Vehicles T2 - 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES) UR - https://doi.org/10.1109/ICVES.2018.8519492 KW - Vehicle safety KW - wireless communication KW - autonomous driving KW - Intelligent Transport Systems Y1 - 2018 UR - https://doi.org/10.1109/ICVES.2018.8519492 SN - 978-1-5386-3543-8 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Chaulwar, Amit A1 - Botsch, Michael A1 - Utschick, Wolfgang ED - Kurková, Vera ED - Manolopoulos, Yannis ED - Hammer, Barbara ED - Iliadis, Lazaros ED - Maglogiannis, Ilias T1 - Generation of Reference Trajectories for Safe Trajectory Planning T2 - Artificial Neural Networks and Machine Learning – ICANN 2018 : 27th International Conference on Artificial Neural Networks,Rhodes, Greece, October 4–7, 2018 : Proceedings, Part I UR - https://doi.org/10.1007/978-3-030-01418-6_42 KW - Safe trajectory planning KW - Hybrid machine learning KW - Variational autoencoder Y1 - 2018 UR - https://doi.org/10.1007/978-3-030-01418-6_42 SN - 978-3-030-01418-6 SN - 978-3-030-01417-9 SP - 423 EP - 434 PB - Springer CY - Cham ER -