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Michael Botsch
Michael Botsch
Technische Hochschule Ingolstadt
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A fast airplane boarding strategy using online seat assignment based on passenger classification
G Notomista, M Selvaggio, F Sbrizzi, G Di Maio, S Grazioso, M Botsch
Journal of Air Transport Management 53, 140-149, 2016
552016
Method for associating a transmitter with a detected object in car-to-car communication and motor vehicle
S Engel, M Botsch, H Rößler
US Patent 10,650,674, 2020
402020
An unsupervised random forest clustering technique for automatic traffic scenario categorization
F Kruber, J Wurst, M Botsch
2018 21st International Conference on Intelligent Transportation Systems …, 2018
392018
Situation aspect modelling and classification using the scenario based random forest algorithm for convoy merging situations
M Reichel, M Botsch, R Rauschecker, KH Siedersberger, M Maurer
13th International IEEE Conference on Intelligent Transportation Systems …, 2010
332010
Unsupervised and supervised learning with the random forest algorithm for traffic scenario clustering and classification
F Kruber, J Wurst, ES Morales, S Chakraborty, M Botsch
2019 IEEE Intelligent Vehicles Symposium (IV), 2463-2470, 2019
182019
A machine learning approach for the segmentation of driving maneuvers and its application in autonomous parking
G Notomista, M Botsch
Journal of Artificial Intelligence and Soft Computing Research (JAISCR) 7 (4 …, 2017
162017
Machine learning based prediction of crash severity distributions for mitigation strategies
M Müller, M Botsch, D Böhmländer, W Utschick
Journal of Advances in Information Technology 9 (1), 2018
152018
A statistical learning approach for estimating the reliability of crash severity predictions
M Müller, P Nadarajan, M Botsch, W Utschick, D Böhmländer, ...
2016 IEEE 19th International Conference on Intelligent Transportation …, 2016
152016
Probability estimation for predicted-occupancy grids in vehicle safety applications based on machine learning
P Nadarajan, M Botsch
2016 IEEE Intelligent Vehicles Symposium (IV), 1285-1292, 2016
152016
A hybrid machine learning approach for planning safe trajectories in complex traffic-scenarios
A Chaulwar, M Botsch, W Utschick
2016 15th IEEE International Conference on Machine Learning and Applications …, 2016
142016
Model-based analysis of sensor-noise in predictive passive safety algorithms
T Dirndorfer, M Botsch, A Knoll
Proceedings of the 22nd Enhanced Safety of Vehicles Conference, 2011
142011
A machine learning based biased-sampling approach for planning safe trajectories in complex, dynamic traffic-scenarios
A Chaulwar, M Botsch, W Utschick
2017 IEEE Intelligent Vehicles Symposium (IV), 297-303, 2017
132017
Maneuver segmentation for autonomous parking based on ensemble learning
G Notomista, M Botsch
2015 International Joint Conference on Neural Networks (IJCNN), 1-8, 2015
122015
Vehicle rear detection in images with generalized radial-basis-function classifiers
P Bergmiller, M Botsch, J Speth, U Hofmann
2008 IEEE Intelligent Vehicles Symposium, 226-233, 2008
122008
Feature selection for change detection in multivariate time-series
M Botsch, JA Nossek
2007 IEEE Symposium on Computational Intelligence and Data Mining, 590-597, 2007
122007
Real-time crash severity estimation with machine learning and 2d mass-spring-damper model
M Müller, X Long, M Botsch, D Böhmländer, W Utschick
2018 21st International Conference on Intelligent Transportation Systems …, 2018
112018
Predicted-occupancy grids for vehicle safety applications based on autoencoders and the random forest algorithm
P Nadarajan, M Botsch, S Sardina
2017 International Joint Conference on Neural Networks (IJCNN), 1244-1251, 2017
112017
Planning of safe trajectories in dynamic multi-object traffic-scenarios
A Chaulwar, M Botsch, T Krüger, T Miehling
Journal of Traffic and Logistics Engineering, 2016
112016
Construction of interpretable radial basis function classifiers based on the random forest kernel
M Botsch, JA Nossek
2008 IEEE International Joint Conference on Neural Networks (IEEE World …, 2008
112008
Highway traffic data: macroscopic, microscopic and criticality analysis for capturing relevant traffic scenarios and traffic modeling based on the highD data set
F Kruber, J Wurst, S Chakraborty, M Botsch
arXiv preprint arXiv:1903.04249, 2019
102019
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Artikelen 1–20