Prof. Dr.-Ing. Michael Botsch

Wiss. Leiter Institut AImotion Bavaria; Studiengangleiter Automatisiertes Fahren und Fahrzeugsicherheit (Master)

Raum: K209
Lehrgebiet: Fahrzeugsicherheit und Signalverarbeitung
Fakultät: Fakultät E

Forschung

  • Statistische Signalverarbeitung und maschinelles Lernen
  • Aktive und integrale Fahrzeugsicherheit
  • Algorithmen für das automatisierte Fahren
  • Wissenschaftliche Leitung des Instituts AImotion Bavaria

Vita

  • Seit 2013 an der THI
  • 2008 - 2013: Entwicklungsingenieur bei Audi AG im Bereich aktive Fahrzeugsicherheit
  • 2009: Promotion an der Technischen Universität München
  • 1999 - 2005: Studium der Elektro- und Informationstechnik an der Technischen Universität München

Veröffentlichungen

  • Buch: Fahrzeugsicherheit und automatisiertes Fahren: Methoden der Signalverarbeitung und des maschinellen Lernens. Michael Botsch und Wolfgang Utschick. Carl Hanser Verlag. Juni 2020.
  • Hier finden Sie eine Liste der Veröffentlichungen von Prof. Dr.-Ing. Michael Botsch.

Auszeichnungen und Mitgliedschaften

  • Best Paper Award
    • IEEE CIDM 2007
    • IEEE ITSC 2010
  • Best Poster Award: IJCNN 2017
  • Rotary Forschungspreis 2023 des Rotary Club Ingolstadt
  • Mitglied bei IEEE, VDE

Publikationen

2024
FERTIG, Alexander, Lakshman BALASUBRAMANIAN und Michael BOTSCH, 2024. Clustering and Anomaly Detection in Embedding Spaces for the Validation of Automotive Sensors. In: 2024 IEEE Intelligent Vehicles Symposium (IV). Piscataway: IEEE, S. 1076-1083. ISBN 979-8-3503-4881-1. Verfügbar unter: https://doi.org/10.1109/IV55156.2024.10588817
NADARAJAN, Parthasarathy, Michael BOTSCH und Sebastian SARDINA, 2024. Continuous Probabilistic Motion Prediction based on Latent Space Interpolation. In: 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). Piscataway: IEEE, S. 3796-3803. ISBN 979-8-3503-9946-2. Verfügbar unter: https://doi.org/10.1109/ITSC57777.2023.10422685
CHANDRA SEKARAN, Karthikeyan, Lakshman BALASUBRAMANIAN, Michael BOTSCH und Wolfgang UTSCHICK, 2024. Open-Set Object Detection for the Identification and Localization of Dissimilar Novel Classes by means of Infrastructure Sensors. In: 2024 IEEE Intelligent Vehicles Symposium (IV). Piscataway: IEEE, S. 1643-1650. ISBN 979-8-3503-4881-1. Verfügbar unter: https://doi.org/10.1109/IV55156.2024.10588872
NEUMEIER, Marion, Sebastian DORN, Michael BOTSCH und Wolfgang UTSCHICK, 2024. Prediction and Interpretation of Vehicle Trajectories in the Graph Spectral Domain. In: 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). Piscataway: IEEE, S. 1172-1179. ISBN 979-8-3503-9946-2. Verfügbar unter: https://doi.org/10.1109/ITSC57777.2023.10422530
BALASUBRAMANIAN, Lakshman, Jonas WURST, Robin EGOLF, Michael BOTSCH, Wolfgang UTSCHICK und Ke DENG, 2024. SceneDiffusion: Conditioned Latent Diffusion Models for Traffic Scene Prediction. In: 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). Piscataway: IEEE, S. 3914-3921. ISBN 979-8-3503-9946-2. Verfügbar unter: https://doi.org/10.1109/ITSC57777.2023.10422482
2023
BOTSCH, Michael, Werner HUBER, Lakshman BALASUBRAMANIAN, Alberto FLORES FERNANDEZ, Markus GEISLER, Christian GUDERA, Mauricio Rene MORALES GOMEZ, Peter RIEGL, Eduardo SÁNCHEZ MORALES, Karthikeyan CHANDRA SEKARAN und Michael WEINZIERL, 2023. Data Collection and Safety Use Cases in Smart Infrastructures. In: 15th International ACM Conference on Automotive User Interfaces: Adjunct Conference Proceedings. New York: ACM, S. 333-336. ISBN 979-8-4007-0112-2. Verfügbar unter: https://doi.org/10.1145/3581961.3609858
CHANDRA SEKARAN, Karthikeyan, Lakshman BALASUBRAMANIAN, Michael BOTSCH und Wolfgang UTSCHICK, 2023. Metric Learning Based Class Specific Experts for Open-Set Recognition of Traffic Participants in Urban Areas Using Infrastructure Sensors. In: IEEE IV 2023 Symposium Proceedings. Piscataway: IEEE. ISBN 979-8-3503-4691-6. Verfügbar unter: https://doi.org/10.1109/IV55152.2023.10186527
BALASUBRAMANIAN, Lakshman, Jonas WURST, Michael BOTSCH und Ke DENG, 2023. Open-World Learning for Traffic Scenarios Categorisation. IEEE Transactions on Intelligent Vehicles, 8(5), 3506-3521. ISSN 2379-8904. Verfügbar unter: https://doi.org/10.1109/TIV.2023.3260270
NEUMEIER, Marion, Andreas TOLLKÜHN, Sebastian DORN, Michael BOTSCH und Wolfgang UTSCHICK, 2023. Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applications. In: IEEE IV 2023 Symposium Proceedings. Piscataway: IEEE. ISBN 979-8-3503-4691-6. Verfügbar unter: https://doi.org/10.1109/IV55152.2023.10186536
KRUBER, Friedrich, Jonas WURST, Michael BOTSCH, Samarjit CHAKRABORTY, Vipin Kumar KUKKALA und Sudeep PASRICHA, 2023. Unsupervised Random Forest Learning for Traffic Scenario Categorization. In: KUKKALA, Vipin Kumar und Sudeep PASRICHA , Hrsg. Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems. Cham: Springer, S. 565-590. ISBN 978-3-031-28016-0. Verfügbar unter: https://doi.org/10.1007/978-3-031-28016-0_20
2022
NEUMEIER, Marion, Andreas TOLLKÜHN, Michael BOTSCH und Wolfgang UTSCHICK, 2022. A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction. In: 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). Piscataway: IEEE, S. 687-694. ISBN 978-1-6654-6880-0. Verfügbar unter: https://doi.org/10.1109/ITSC55140.2022.9922419
BALASUBRAMANIAN, Lakshman, Jonas WURST, Robin EGOLF, Michael BOTSCH, Wolfgang UTSCHICK und Ke DENG, 2022. ExAgt: Expert-guided Augmentation for Representation Learning of Traffic Scenarios. In: 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). Piscataway: IEEE, S. 1471-1478. ISBN 978-1-6654-6880-0. Verfügbar unter: https://doi.org/10.1109/ITSC55140.2022.9922453
WURST, Jonas, Lakshman BALASUBRAMANIAN, Michael BOTSCH und Wolfgang UTSCHICK, 2022. Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios. In: 2022 IEEE Intelligent Vehicles Symposium (IV). Piscataway: IEEE, S. 484-491. ISBN 978-1-6654-8821-1. Verfügbar unter: https://doi.org/10.1109/IV51971.2022.9827187
FLORES FERNANDEZ, Alberto, Eduardo SÁNCHEZ MORALES, Michael BOTSCH, Christian FACCHI und Andrés GARCÍA HIGUERA, 2022. Generation of Correction Data for Autonomous Driving by Means of Machine Learning and On-Board Diagnostics. Sensors, 23(1), 159. ISSN 1424-8220. Verfügbar unter: https://doi.org/10.3390/s23010159
ELTER, Tim, Tobias DIRNDORFER, Michael BOTSCH und Wolfgang UTSCHICK, 2022. Interaction-aware Prediction of Occupancy Regions based on a POMDP Framework. In: 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). Piscataway: IEEE, S. 980-987. ISBN 978-1-6654-6880-0. Verfügbar unter: https://doi.org/10.1109/ITSC55140.2022.9922127
KRUBER, Friedrich, Eduardo SÁNCHEZ MORALES, Robin EGOLF, Jonas WURST, Samarjit CHAKRABORTY und Michael BOTSCH, 2022. Micro- and Macroscopic Road Traffic Analysis using Drone Image Data. Leibniz Transactions on Embedded Systems, 8(1), 2-2. ISSN 2199-2002. Verfügbar unter: https://doi.org/10.4230/LITES.8.1.2
FLORES FERNANDEZ, Alberto, Jonas WURST, Eduardo SÁNCHEZ MORALES, Michael BOTSCH, Christian FACCHI und Andrés GARCÍA HIGUERA, 2022. Probabilistic Traffic Motion Labeling for Multi-Modal Vehicle Route Prediction. Sensors, 22(12), 4498. ISSN 1424-8220. Verfügbar unter: https://doi.org/10.3390/s22124498
2021
SÁNCHEZ MORALES, Eduardo, Friedrich KRUBER, Michael BOTSCH, Bertold HUBER und Andrés GARCÍA HIGUERA, 2021. Accuracy characterization of the vehicle state estimation from aerial imagery. In: 2020 IEEE Intelligent Vehicles Symposium (IV). Piscataway: IEEE, S. 2081-2088. ISBN 978-1-7281-6673-5. Verfügbar unter: https://doi.org/10.1109/IV47402.2020.9304705
WURST, Jonas, Alberto FLORES FERNANDEZ, Michael BOTSCH und Wolfgang UTSCHICK, 2021. An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images. In: 2020 IEEE Intelligent Vehicles Symposium (IV). Piscataway: IEEE, S. 1436-1443. ISBN 978-1-7281-6673-5. Verfügbar unter: https://doi.org/10.1109/IV47402.2020.9304733
SÁNCHEZ MORALES, Eduardo, Julian DAUTH, Bertold HUBER, Andrés GARCÍA HIGUERA und Michael BOTSCH, 2021. High precision outdoor and indoor reference state estimation for testing autonomous vehicles. Sensors, 21(4), 1131. ISSN 1424-8220. Verfügbar unter: https://doi.org/10.3390/s21041131
GALLITZ, Oliver, Oliver DE CANDIDO, Michael BOTSCH und Wolfgang UTSCHICK, 2021. Interpretable Early Prediction of Lane Changes Using a Constrained Neural Network Architecture. In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC). Piscataway (NJ): IEEE, S. 493-499. ISBN 978-1-7281-9142-3. Verfügbar unter: https://doi.org/10.1109/ITSC48978.2021.9564555
WURST, Jonas, Lakshman BALASUBRAMANIAN, Michael BOTSCH und Wolfgang UTSCHICK, 2021. Novelty detection and analysis of traffic scenario infrastructures in the latent space of a vision transformer-based triplet autoencoder. In: 2021 IEEE Intelligent Vehicles Symposium (IV). Piscataway: IEEE, S. 1304-1311. Verfügbar unter: https://doi.org/10.1109/IV48863.2021.9575730
BALASUBRAMANIAN, Lakshman, Friedrich KRUBER, Michael BOTSCH und Ke DENG, 2021. Open-Set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios. In: 2021 IEEE Intelligent Vehicles Symposium (IV). Piscataway: IEEE, S. 674-681. ISBN 978-1-7281-5394-0. Verfügbar unter: https://doi.org/10.1109/IV48863.2021.9575433
CHAULWAR, Amit, Hussein AL-HASHIMI, Michael BOTSCH und Wolfgang UTSCHICK, 2021. Sampling Algorithms Combination with Machine Learning for Effcient Safe Trajectory Planning. International Journal of Machine Learning and Computing, 11(1). ISSN 2010-3700. Verfügbar unter: https://doi.org/10.18178/ijmlc.2021.11.1.1007
BALASUBRAMANIAN, Lakshman, Jonas WURST, Michael BOTSCH und Ke DENG, 2021. Traffic scenario clustering by iterative optimisation of self-supervised networks using a random forest activation pattern similarity. In: 2021 IEEE Intelligent Vehicles Symposium (IV). Piscataway: IEEE, S. 682-689. ISBN 978-1-7281-5394-0. Verfügbar unter: https://doi.org/10.1109/IV48863.2021.9575615
NEUMEIER, Marion, Michael BOTSCH, Andreas TOLLKÜHN und Thomas BERBERICH, 2021. Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Space. In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC). Piscataway: IEEE, S. 820-827. ISBN 978-1-7281-9142-3. Verfügbar unter: https://doi.org/10.1109/ITSC48978.2021.9565120
KRUBER, Friedrich, Eduardo SÁNCHEZ MORALES, Samarjit CHAKRABORTY und Michael BOTSCH, 2021. Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles. In: 2020 IEEE Intelligent Vehicles Symposium (IV). Piscataway: IEEE, S. 2089-2096. ISBN 978-1-7281-6673-5. Verfügbar unter: https://doi.org/10.1109/IV47402.2020.9304794
2020
BOTSCH, Michael und Wolfgang UTSCHICK, 2020. Fahrzeugsicherheit und automatisiertes Fahren: Methoden der Signalverarbeitung und des maschinellen Lernens. München: Hanser. ISBN 978-3-446-46804-7. Verfügbar unter: https://doi.org/10.3139/9783446468047
GALLITZ, Oliver, Oliver DE CANDIDO, Michael BOTSCH, Ron MELZ und Wolfgang UTSCHICK, 2020. Interpretable Machine Learning Structure for an Early Prediction of Lane Changes. In: Artificial Neural Networks and Machine Learning – ICANN 2020: 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I. Cham: Springer, S. 337-349. ISBN 978-3-030-61609-0. Verfügbar unter: https://doi.org/10.1007/978-3-030-61609-0_27
DE CANDIDO, Oliver, Michael KOLLER, Oliver GALLITZ, Ron MELZ, Michael BOTSCH und Wolfgang UTSCHICK, 2020. Towards feature validation in time to lane change classification using deep neural networks. In: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). Piscataway: IEEE. ISBN 978-1-7281-4149-7. Verfügbar unter: https://doi.org/10.1109/ITSC45102.2020.9294555
2019
CHAULWAR, Amit, Hussein AL-HASHIMI, Michael BOTSCH und Wolfgang UTSCHICK, 2019. Efficient hybrid machine learning algorithm for trajectory planning in critical traffic-scenarios. In: The 4th International Conference on Intelligent Transportation Engineering, ICITE 2019. Piscataway: IEEE, S. 196-202. ISBN 978-1-7281-4553-2. Verfügbar unter: https://doi.org/10.1109/ICITE.2019.8880266
SÁNCHEZ MORALES, Eduardo, Michael BOTSCH, Bertold HUBER und Andrés GARCÍA HIGUERA, 2019. High precision indoor navigation for autonomous vehicles. In: 2019 International Conference on Indoor Positioning and Indoor Navigation. Piscataway: IEEE. ISBN 978-1-7281-1788-1. Verfügbar unter: https://doi.org/10.1109/IPIN.2019.8911780
SÁNCHEZ MORALES, Eduardo, Michael BOTSCH, Bertold HUBER und Andrés GARCÍA HIGUERA, 2019. High precision indoor positioning by means of LiDAR. In: 2019 DGON Inertial Sensors and Systems (ISS), Proceedings. Piscataway: IEEE. ISBN 978-1-7281-1935-9. Verfügbar unter: https://doi.org/10.1109/ISS46986.2019.8943731
GALLITZ, Oliver, Oliver DE CANDIDO, Michael BOTSCH und Wolfgang UTSCHICK, 2019. Interpretable feature generation using deep neural networks and its application to lane change detection. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC). Piscataway: IEEE, S. 3405-3411. ISBN 978-1-5386-7024-8. Verfügbar unter: https://doi.org/10.1109/ITSC.2019.8917524
SÁNCHEZ MORALES, Eduardo, Richard MEMBARTH, Andreas GAULL, Philipp SLUSALLEK, Tobias DIRNDORFER, Alexander KAMMENHUBER, Christoph LAUER und Michael BOTSCH, 2019. Parallel multi-hypothesis algorithm for criticality estimation in traffic and collision avoidance. In: 2019 IEEE Intelligent Vehicles Symposium (IV). Piscataway: IEEE, S. 2164-2171. ISBN 978-1-7281-0560-4. Verfügbar unter: https://doi.org/10.1109/IVS.2019.8814015
KRUBER, Friedrich, Eduardo SÁNCHEZ MORALES, Michael BOTSCH und Samarjit CHAKRABORTY, 2019. Unsupervised and Supervised Learning with the Random Forest Algorithm for Traffic Scenario Clustering and Classification. In: 2019 IEEE Intelligent Vehicles Symposium (IV). Piscataway: IEEE, S. 2463-2470. ISBN 978-1-7281-0560-4. Verfügbar unter: https://doi.org/10.1109/IVS.2019.8813994
2018
KRUBER, Friedrich, Jonas WURST und Michael BOTSCH, 2018. An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization. In: 2018 IEEE Intelligent Transportation Systems Conference. Piscataway: IEEE, S. 2811-2818. ISBN 978-1-7281-0323-5. Verfügbar unter: https://doi.org/10.1109/ITSC.2018.8569682
CHAULWAR, Amit, Michael BOTSCH, Wolfgang UTSCHICK, Vera KURKOVA, Yannis MANOLOPOULOS, Barbara HAMMER, Lazaros ILIADIS und Ilias MAGLOGIANNIS, 2018. Generation of Reference Trajectories for Safe Trajectory Planning. In: KURKOVA, Vera , Yannis MANOLOPOULOS , Barbara HAMMER , Lazaros ILIADIS und Ilias MAGLOGIANNIS , Hrsg. Artificial Neural Networks and Machine Learning – ICANN 2018 : 27th International Conference on Artificial Neural Networks,Rhodes, Greece, October 4–7, 2018 : Proceedings, Part I. Cham: Springer, S. 423-434. ISBN 978-3-030-01418-6. Verfügbar unter: https://doi.org/10.1007/978-3-030-01418-6_42
NADARAJAN, Parthasarathy, Michael BOTSCH und Sebastian SARDINA, 2018. Machine Learning Architectures for the Estimation of Predicted Occupancy Grids in Road Traffic. Journal of Advances in Information Technology, 9(1), 1-9. ISSN 1798-2340. Verfügbar unter: https://doi.org/10.12720/jait.9.1.1-9
MÜLLER, Marcus, Michael BOTSCH, Dennis BÖHMLÄNDER und Wolfgang UTSCHICK, 2018. Machine Learning Based Prediction of Crash Severity Distributions for Mitigation Strategies. Journal of Advances in Information Technology, 9 (2018)(1), 15-24. ISSN 1798-2340. Verfügbar unter: https://doi.org/10.12720/jait.9.1.15-24
MÜLLER, Marcus, Xing LONG, Michael BOTSCH, Dennis BÖHMLÄNDER und Wolfgang UTSCHICK, 2018. Real-Time Crash Severity Estimation with Machine Learning and 2D Mass-Spring-Damper Model. In: 2018 IEEE Intelligent Transportation Systems Conference. Piscataway: IEEE, S. 2036-2043. ISBN 978-1-7281-0323-5. Verfügbar unter: https://doi.org/10.1109/ITSC.2018.8569471
GALLITZ, Oliver, Michael BOTSCH, Oliver DE CANDIDO und Wolfgang UTSCHICK, 2018. Validation of Machine Learning Algorithms through Visualization Methods. In: ELIV-MarketPlace 2018. Düsseldorf: VDI Verlag, S. 29-46. ISBN 978-3-18-092338-3. Verfügbar unter: https://doi.org/10.51202/9783181023389-29
CAÑAS, Valentin, Eduardo SÁNCHEZ MORALES, Michael BOTSCH und Andrés GARCÍA HIGUERA, 2018. Wireless Communication System for the Validation of Autonomous Driving Functions on Full-Scale Vehicles. In: 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES). Piscataway: IEEE. ISBN 978-1-5386-3543-8. Verfügbar unter: https://doi.org/10.1109/ICVES.2018.8519492
2017
NOTOMISTA, Gennaro und Michael BOTSCH, 2017. A Machine Learning Approach for the Segmentation of Driving Maneuvers and its Application in Autonomous Parking. Journal of Artificial Intelligence and Soft Computing Research (JAISCR), 7(4), 243-255. ISSN 2449-6499. Verfügbar unter: https://doi.org/10.1515/jaiscr-2017-0017
CHAULWAR, Amit, Michael BOTSCH und Wolfgang UTSCHICK, 2017. A machine learning based biased-sampling approach for planning safe trajectories in complex, dynamic traffic-scenarios. In: 2017 IEEE Intelligent Vehicles Symposium (IV). Piscataway: IEEE, S. 297-303. ISBN 978-1-5090-4804-5. Verfügbar unter: https://doi.org/10.1109/IVS.2017.7995735
MÜLLER, Marcus, Michael BOTSCH, Dennis BÖHMLÄNDER, Wolfgang UTSCHICK und Werner KLAFFKE, 2017. A Simulation Framework for Vehicle Safety Testing. In: KLAFFKE, Werner , Hrsg. Aktive Sicherheit und Automatisieres Fahren : 3. Interdisziplinärer Expertendialog (IEDAS). Renningen: expert Verlag, S. 147-167. ISBN 978-3-8169-3405-9.
NADARAJAN, Parthasarathy, Michael BOTSCH und Sebastian SARDINA, 2017. Predicted-occupancy grids for vehicle safety applications based on autoencoders and the Random Forest algorithm. In: 2017 International Joint Conference on Neural Networks (IJCNN). Piscataway: IEEE, S. 1244-1251. ISBN 978-1-5090-6182-2. Verfügbar unter: https://doi.org/10.1109/IJCNN.2017.7965995
2016
NOTOMISTA, Gennaro, Mario SELVAGGIO, Fiorentina SBRIZZI, Gabriella DI MAIO, Stanislao GRAZIOSO und Michael BOTSCH, 2016. A fast airplane boarding strategy using online seat assignment based on passenger classification. Journal of Air Transport Management, 2016(53), 140-149. ISSN 0969-6997. Verfügbar unter: https://doi.org/10.1016/j.jairtraman.2016.02.012
CHAULWAR, Amit, Michael BOTSCH und Wolfgang UTSCHICK, 2016. A Hybrid Machine Learning Approach for Planning Safe Trajectories in Complex Traffic-Scenarios. In: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). Los Alamitos: IEEE, S. 540-546. ISBN 978-1-5090-6167-9. Verfügbar unter: https://doi.org/10.1109/ICMLA.2016.0095
MÜLLER, Marcus, Parthasarathy NADARAJAN, Michael BOTSCH, Wolfgang UTSCHICK, Dennis BÖHMLÄNDER und Stefan KATZENBOGEN, 2016. A statistical learning approach for estimating the reliability of crash severity predictions. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). Piscataway: IEEE, S. 2199-2206. ISBN 978-1-5090-1889-5. Verfügbar unter: https://doi.org/10.1109/ITSC.2016.7795911
NADARAJAN, Parthasarathy und Michael BOTSCH, 2016. Probability estimation for Predicted-Occupancy Grids in vehicle safety applications based on machine learning. In: 2016 IEEE Intelligent Vehicles Symposium (IV). Piscataway: IEEE, S. 1285-1292. ISBN 978-1-5090-1821-5. Verfügbar unter: https://doi.org/10.1109/IVS.2016.7535556
NOTOMISTA, Gennaro, Alexander KAMMENHUBER, Parthasarathy NADARAJAN, Michael BOTSCH und Mario SELVAGGIO, 2016. Relative Motion Estimation Based on Sensor Eigenfusion Using a Stereoscopic Vision System and Adaptive Statistical Filtering. In: Proceedings of ISR 2016: 47st International Symposium on Robotics. Berlin: VDE Verlag, S. 604-609. ISBN 978-3-8007-4231-8.
2015
NOTOMISTA, Gennaro und Michael BOTSCH, 2015. Maneuver segmentation for autonomous parking based on ensemble learning. In: 2015 International Joint Conference on Neural Networks (IJCNN). Piscataway: IEEE. ISBN 978-1-4799-1960-4. Verfügbar unter: https://doi.org/10.1109/IJCNN.2015.7280546
HERRMANN, Stephan, Wolfgang UTSCHICK, Michael BOTSCH und Frank KECK, 2015. Supervised learning via optimal control labeling for criticality classification in vehicle active safety. In: Proceedings: 2015 IEEE 18th International Conference on Intelligent Transportation Systems. Los Alamitos: IEEE, S. 2024-2031. ISBN 978-1-4673-6596-3. Verfügbar unter: https://doi.org/10.1109/ITSC.2015.328
2011
DIRNDORFER, Tobias, Michael BOTSCH und Alois KNOLL, 2011. Model-based analysis of sensor-noise in predictive passive safety algorithms. In: The 22nd ESV Conference Proceedings. Washington, D.C.: NHTSA. Verfügbar unter: https://www-esv.nhtsa.dot.gov/Proceedings/22/isv7/main.htm
2010
BOTSCH, Michael und Christoph LAUER, 2010. Complexity reduction using the Random Forest classifier in a collision detection algorithm. In: 2010 IEEE Intelligent Vehicles Symposium. Piscataway: IEEE, S. 1228-1235. ISBN 978-1-4244-7868-2. Verfügbar unter: https://doi.org/10.1109/IVS.2010.5548044
REICHEL, Michael, Michael BOTSCH, Robert RAUSCHECKER, Karl-Heinz SIEDESBERGER und Markus MAURER, 2010. Situation aspect modelling and classification using the scenario based random forest algorithm for convoy merging situations. In: 2010 13th International IEEE Annual Conference on Intelligent Transportation Systems. Piscataway: IEEE, S. 360-366. ISBN 978-1-4244-7659-6. Verfügbar unter: https://doi.org/10.1109/ITSC.2010.5625213
2009
BOTSCH, Michael, 2009. Machine Learning Techniques for Time Series Classification. Göttingen: Cuvillier Verlag. ISBN 978-3-86727-950-5. Verfügbar unter: https://cuvillier.de/de/shop/publications/1092-machine-learning-techniques-for-time-series-classification
2008
BOTSCH, Michael und Josef A. NOSSEK, 2008. Construction of interpretable Radial Basis Function classifiers based on the Random Forest kernel. In: The 2008 IEEE International Joint Conference on Neural Networks (IJCNN 2008). Piscataway: IEEE, S. 220-227. ISBN 978-1-4244-1820-6. Verfügbar unter: https://doi.org/10.1109/IJCNN.2008.4633793
BERGMILLER, Peter, Michael BOTSCH, Johannes SPETH und Ulrich HOFMANN, 2008. Vehicle rear detection in images with Generalized Radial-Basis-Function classifiers. In: 2008 IEEE Intelligent Vehicles Symposium. Piscataway: IEEE, S. 226-233. ISBN 978-1-4244-2568-6. Verfügbar unter: https://doi.org/10.1109/IVS.2008.4621273
2007
BOTSCH, Michael und Josef A. NOSSEK, 2007. Feature Selection for Change Detection in Multivariate Time-Series. In: 2007 IEEE Symposium on Computational Intelligence and Data Mining. Piscataway: IEEE, S. 590-597. ISBN 1-4244-0705-2. Verfügbar unter: https://doi.org/10.1109/CIDM.2007.368929
2006
BOTSCH, Michael, Guido DIETL und Wolfgang UTSCHICK, 2006. Iterative Multi-User Detection Using Reduced-Complexity Equalization. In: TURBO – CODING – 2006: 4th International Symposium on Turbo Codes & Related Topics, 6th International ITG-Conference on Source and Channel Coding. Berlin: VDE. ISBN 978-3-8007-2947-0. Verfügbar unter: https://www.vde-verlag.de/proceedings-de/442947088.html
2005
DIETL, Guido, Michael BOTSCH, F. A. DIETRICH und Wolfgang UTSCHICK, 2005. Robust and reduced-rank matrix Wiener filter based on the conjugate gradient algorithm. In: 2005 IEEE 6th Workshop on Signal Processing Advances in Wireless Communications. Piscataway: IEEE, S. 555-559. ISBN 0-7803-8867-4. Verfügbar unter: https://doi.org/10.1109/SPAWC.2005.1506201