Prof. Dr.-Ing. Michael Botsch


Stellv. wissenschaftliche Leitung CARISSMA


Tel.: +49 841 9348-2721
E-Mail:
Raum: H024
Lehrgebiet: Fahrzeugsicherheit und Signalverarbeitung

Forschung


  • Statistische Signalverarbeitung und maschinelles Lernen
  • Aktive und integrale Fahrzeugsicherheit
  • Algorithmen für die Trajektorienplanung

Vita


  • 2013 – heute:
    Professor für Fahrzeugsicherheit und Signalverarbeitung an der Technischen Hochschule Ingolstadt
  • 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 und an der University of Illinois at Urbana-Champaign

Veröffentlichungen


  • G. Dietl, M. Botsch, F.A. Dietrich und W. Utschick, “Robust and reduced-rank matrix Wiener filter based on the conjugate gradient algorithm”, IEEE Workshop on Signal Processing Advances in Wireless Communications, pp. 555 – 559,  2005
  • M. Botsch, G. Dietl, W. Utschick, “Iterative Multi-User Detection Using Reduced-Complexity Equalization”, International ITG-Conference on Source and Channel Coding (TURBOCODING), pp. 1-6, 2006
  • M. Botsch und J. A. Nossek, „Feature Selection for Change Detection in Multivariate Time-Series“, IEEE Symposium on Computational Intelligence and Data Mining, pp. 590 - 597, 2007.
  • M. Botsch und J. A. Nossek, „Construction of interpretable Radial Basis Function classifiers based on the Random Forest kernel,“ IEEE World Congress on Computational Intelligence, pp. 220 - 227, 2008.
  • P. Bergmiller, M. Botsch, J. Speth und U. Hofmann, „Vehicle rear detection in images with Generalized Radial-Basis-Function classifiers,“ IEEE Symposium Intelligent Vehicles, pp. 226 - 233, 2008.
  • M. Reichel, M. Botsch, R. Rauschecker, K. Siedersberger und M. Maurer, „Situation aspect modelling and classification using the Scenario Based Random Forest algorithm for convoy merging situations,“ IEEE International Conference on Intelligent Transportation Systems, pp. 360 - 366, 2010.
  • M. Botsch und C. Lauer, "Complexity reduction using the Random Forest classifier in a collision detection algorithm, " IEEE Intelligent Vehicles Symposium, pp. 1228-1235, 2010
  • T. Dirndorfer, M. Botsch und A. Knoll, „Model-based analysis of sensor-noise in predictive passive safety algorithms,“ Conference on Enhanced Safety of Vehicles, 2011.
  • M. Botsch und J. Stoll, "Analysis of the Aperture Angle of Exteroceptive Sensors for Automotive Safety Applications in Traffic-Scenarios with Crossing Objects," European Congress on Computational Methods in Applied Sciences and Engineering, 2012
  • G. Notomista und M. Botsch, "Maneuver Segmentation for Autonomous Parking Based on Ensemble Learning", International Joint Conference on Neural Networks, 2015
  • S. Herrmann, W. Utschick, M. Botsch und F. Keck, "Supervised Learning via Optimal Control Labeling for Criticality Classification in Vehicle Active Safety", IEEE International Conference on Intelligent Transportation Systems, 2015
  • G. Notomista, M. Selvaggio, F. Sbrizzi, G. Di Maio, S. Grazioso, M. Botsch, “A fast airplane boarding strategy using online seat assignment based on passenger classification“, Journal of Air Transport Management, pp. 140-149, 2016
  • Chaulwar, M. Botsch, T. Krueger und T. Miehling, “Planning of safe trajectories in dynamic multi-object traffic-scenarios”, Journal of Traffic and Logistics Engineering, Vol.4, no.2, 2016
  • P. Nadarajan; M. Botsch, “Probability Estimation for Predicted-Occupancy Grids in Vehicle Safety Applications Based on Machine Learning”, IEEE Intelligent Vehicles Symposium, 2016.
  • G. Notomista, A. Kammenhuber, P. Nadarajan, M. Botsch, M. Selvaggio, “Relative Motion Estimation Based on Sensor Eigenfusion Using a Stereoscopic Vision System and Adaptive Statistical Filtering”, International Symposium on Robotics, 2016.
  • M. Müller, P. Nadarajan, M. Botsch, W. Utschick, D. Böhmländer, S. Katzenbogen, “A Statistical Learning Approach for Estimating the Reliability of Crash Severity Predictions”, IEEE Intelligent Transportation Systems Society Conference, 2016.
  • A. Chaulwar, M. Botsch, W. Utschick, “A Hybrid Machine Learning Approach for Planning Safe Trajectories in Complex Traffic-Scenarios”, IEEE International Conference on Machine Learning and Applications, 2016
  • P. Nadarajan, M. Botsch, S. Sardina, “Predicted-Occupancy Grids for Vehicle Safety Applications based on Auotencoders and the Random Forest Algorithm”, International Joint Conference on Neural Networks, 2017
  • A. Chaulwar, M. Botsch, W. Utschick, “A Machine Learning based Biased-Sampling Approach for Planning Safe Trajectories in Complex, Dynamic Traffic-Scenarios”, IEEE Intelligent Vehicles Symposium, 2017
  • G. Notomista, M. Botsch, "A Machine Learning Approach for the Segmentation of Driving Maneuvers and its Application in Autonomous Parking", Journal of Artificial Intelligence and Soft Computing Research, Volume 7, Issue 4, pp. 243-255, 2017.
  • M. Müller, M. Botsch, D. Böhmländer, W. Utschick, „A Simulation Framework for Vehicle Safety Testing / Ein Simulationsframework für die Absicherung von Fahrzeugsicherheitsfunktionen“, Fachbuch/Tagung "Aktive Sicherheit und Automatisiertes Fahren", expert Verlag, pp. 135-155, ISBN: 978-3-8169-3405-9, 2017.
  • M. Müller, M. Botsch, D. Böhmländer, W. Utschick, "Machine Learning Based Prediction of Crash Severity Distributions for Mitigation Strategies", Journal of Advances in Information Technology, Vol. 9, No. 1, pp. 15-24, February 2018. doi: 10.12720/jait.9.1.15-24.
  • P. Nadarajan, M. Botsch, and S. Sardina, "Machine Learning Architectures for the Estimation of Predicted Occupancy Grids in Road Traffic", Journal of Advances in Information Technology, Vol. 9, No. 1, pp. 1-9, February 2018. doi: 10.12720/jait.9.1.1-9.