@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{SanchezMoralesBotschHuberetal.2019, author = {S{\´a}nchez Morales, Eduardo and Botsch, Michael and Huber, Bertold and Garc{\´i}a Higuera, Andr{\´e}s}, title = {High precision indoor navigation for autonomous vehicles}, booktitle = {2019 International Conference on Indoor Positioning and Indoor Navigation}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-1788-1}, doi = {https://doi.org/10.1109/IPIN.2019.8911780}, 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} } @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} } @inproceedings{NotomistaBotsch2015, author = {Notomista, Gennaro and Botsch, Michael}, title = {Maneuver segmentation for autonomous parking based on ensemble learning}, booktitle = {2015 International Joint Conference on Neural Networks (IJCNN)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-4799-1960-4}, issn = {2161-4407}, doi = {https://doi.org/10.1109/IJCNN.2015.7280546}, year = {2015}, 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} } @article{NotomistaSelvaggioSbrizzietal.2016, author = {Notomista, Gennaro and Selvaggio, Mario and Sbrizzi, Fiorentina and Di Maio, Gabriella and Grazioso, Stanislao and Botsch, Michael}, title = {A fast airplane boarding strategy using online seat assignment based on passenger classification}, volume = {2016}, journal = {Journal of Air Transport Management}, number = {53}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0969-6997}, doi = {https://doi.org/10.1016/j.jairtraman.2016.02.012}, pages = {140 -- 149}, year = {2016}, language = {en} } @article{FloresFernandezWurstSanchezMoralesetal.2022, author = {Flores Fern{\´a}ndez, Alberto and Wurst, Jonas and S{\´a}nchez Morales, Eduardo and Botsch, Michael and Facchi, Christian and Garc{\´i}a Higuera, Andr{\´e}s}, title = {Probabilistic Traffic Motion Labeling for Multi-Modal Vehicle Route Prediction}, volume = {22}, pages = {4498}, journal = {Sensors}, number = {12}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {https://doi.org/10.3390/s22124498}, year = {2022}, abstract = {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.}, 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} }