| Device and method for training a variational autoencoder F Janjos, L Rosenbaum, M Dolgov US Patent App. 18/465,627, 2024 | | 2024 |
| Method for Temporal Correction of Multimodal Data C Glaeser, F Timm, F Drews, M Ulrich, F Faion, L Rosenbaum US Patent App. 18/337,153, 2023 | | 2023 |
| Method for Training and Operating Movement Estimation of Objects C Glaeser, F Timm, F Drews, M Ulrich, F Faion, L Rosenbaum US Patent App. 18/337,111, 2023 | | 2023 |
| Method for monitoring surroundings of a first sensor system S Muenzner, AP Condurache, C Glaeser, F Timm, F Drews, F Faion, ... US Patent App. 18/246,144, 2023 | | 2023 |
| Method and Control Device for Training an Object Detector C Glaeser, F Timm, F Drews, M Ulrich, F Faion, L Rosenbaum US Patent App. 18/157,544, 2023 | | 2023 |
| Unscented autoencoder F Janjos, L Rosenbaum, M Dolgov, JM Zöllner International Conference on Machine Learning, 14758-14779, 2023 | 2 | 2023 |
| Deepfusion: A robust and modular 3d object detector for lidars, cameras and radars F Drews, D Feng, F Faion, L Rosenbaum, M Ulrich, C Gläser 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2022 | 21 | 2022 |
| Method, device, computer program, and machine-readable storage medium for the detection of an object F Faion, AP Condurache, C Glaeser, F Drews, J Ebert, L Rosenbaum, ... US Patent 11,455,791, 2022 | | 2022 |
| Method, Computer Program, Storage Medium and Apparatus for Creating a Training, Validation and Test Dataset for an AI Module M Schoene, AP Condurache, C Glaeser, F Faion, F Drews, J Ebert, ... US Patent App. 17/475,500, 2022 | | 2022 |
| Labels are not perfect: Inferring spatial uncertainty in object detection D Feng, Z Wang, Y Zhou, L Rosenbaum, F Timm, K Dietmayer, ... IEEE Transactions on Intelligent Transportation Systems 23 (8), 9981-9994, 2021 | 19 | 2021 |
| Inferring spatial uncertainty in object detection Z Wang, D Feng, Y Zhou, L Rosenbaum, F Timm, K Dietmayer, ... 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020 | 30 | 2020 |
| Leveraging uncertainties for deep multi-modal object detection in autonomous driving D Feng, Y Cao, L Rosenbaum, F Timm, K Dietmayer 2020 IEEE Intelligent Vehicles Symposium (IV), 877-884, 2020 | 29 | 2020 |
| Labels are not perfect: Improving probabilistic object detection via label uncertainty D Feng, L Rosenbaum, F Timm, K Dietmayer arXiv preprint arXiv:2008.04168, 2020 | 7 | 2020 |
| Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges D Feng, C Haase-Schütz, L Rosenbaum, H Hertlein, C Glaeser, F Timm, ... IEEE Transactions on Intelligent Transportation Systems 22 (3), 1341-1360, 2020 | 1036 | 2020 |
| Can we trust you? on calibration of a probabilistic object detector for autonomous driving D Feng, L Rosenbaum, C Glaeser, F Timm, K Dietmayer arXiv preprint arXiv:1909.12358, 2019 | 45 | 2019 |
| Fix-net: pure fixed-point representation of deep neural networks L Enderich, F Timm, L Rosenbaum, W Burgard | 1 | 2019 |
| Learning multimodal fixed-point weights using gradient descent L Enderich, F Timm, L Rosenbaum, W Burgard arXiv preprint arXiv:1907.07220, 2019 | 9 | 2019 |
| Leveraging heteroscedastic aleatoric uncertainties for robust real-time lidar 3d object detection D Feng, L Rosenbaum, F Timm, K Dietmayer 2019 IEEE Intelligent Vehicles Symposium (IV), 1280-1287, 2019 | 77 | 2019 |
| Deep active learning for efficient training of a lidar 3d object detector D Feng, X Wei, L Rosenbaum, A Maki, K Dietmayer 2019 IEEE Intelligent Vehicles Symposium (IV), 667-674, 2019 | 87 | 2019 |
| Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets D Feng, C Haase-Schuetz, L Rosenbaum, H Hertlein, C Gläser, F Timm, ... Methods, and Challenges, 2019 | 18 | 2019 |
