| 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 | 62 | 2018 |
| 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 | 61 | 2016 |
| 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 | 40 | 2020 |
| 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 | 37 | 2010 |
| 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 | 34 | 2019 |
| 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), 15-24, 2018 | 25 | 2018 |
| 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 7 (4), 243-255, 2017 | 18 | 2017 |
| Variational autoencoder-based vehicle trajectory prediction with an interpretable latent space M Neumeier, M Botsch, A Tollkühn, T Berberich 2021 IEEE International Intelligent Transportation Systems Conference (ITSC …, 2021 | 17 | 2021 |
| 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 | 17 | 2016 |
| 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 | 17 | 2016 |
| 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 | 17 | 2011 |
| 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 | 16 | 2017 |
| Maneuver segmentation for autonomous parking based on ensemble learning G Notomista, M Botsch 2015 International Joint Conference on Neural Networks (IJCNN), 1-8, 2015 | 16 | 2015 |
| 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 | 15 | 2018 |
| Machine learning architectures for the estimation of predicted occupancy grids in road traffic P Nadarajan, M Botsch, S Sardina Journal of Advances in Information Technology 9 (1), 1-9, 2018 | 15 | 2018 |
| 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 | 15 | 2016 |
| Vehicle position estimation with aerial imagery from unmanned aerial vehicles F Kruber, ES Morales, S Chakraborty, M Botsch 2020 IEEE Intelligent Vehicles Symposium (IV), 2089-2096, 2020 | 14 | 2020 |
| Interpretable feature generation using deep neural networks and its application to lane change detection O Gallitz, O De Candido, M Botsch, W Utschick 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 3405-3411, 2019 | 14 | 2019 |
| 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 | 14 | 2019 |
| 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 | 14 | 2007 |
