@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{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{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} }