Research

• Usage of infrastructure sensors for motion prediction of traffic participants using deep neural networks
• Unceratainty estimation for motion prediction of traffic participants using deep neural networks
• Software-in-the-loop simulation for testing motion planning schemes, using deep trajectory prediction networks
• EU-funded project ICT4CART

Teaching

•  Exercises for the lecture Introduction to Deep Learning

List of publications

Conference articles

1. J. Strohbeck, M. Herrmann, J. Müller, M. Buchholz, „An Extension Proposal for the Collective Perception Service to Avoid Transformation Errors and Include Object Predictions,” Accepted for publication at the 2021 IEEE Vehicular Networking Conference (VNC), 2021.
2. J. Strohbeck*, J. Müller*, A. Holzbock, M. Buchholz, "DeepSIL: A Software-in-the-Loop Framework for Evaluating Motion Planning Schemes Using Multiple Trajectory Prediction Networks," 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), 2021
3. M. Buchholz, J Müller, M. Herrmann, J. Strohbeck, B. Völz, M. Maier, J. Paczia, O. Stein, H. Rehborn,R.-W. Henn, "Handling Occlusions in Automated Driving Using a Multiaccess Edge Computing Server-Based Environment Model From Infrastructure Sensors," in IEEE Intell. Transp. Syst., 2021.
   doi: https://doi.org/10.1109/MITS.2021.3089743
   URL: https://ieeexplore.ieee.org/document/9481097
4. J. Strohbeck, V. Belagiannis, J. Müller, M. Schreiber, M. Herrmann, D. Wolf and M. Buchholz, "Multiple Trajectory Prediction with Deep Temporal and Spatial Convolutional Neural Networks," Accepted for publication at the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020), LV, 2020
5. M. Herrmann, A. Piroli, J. Strohbeck, J. Müller and M. Buchholz, "LMB Filter Based Tracking Allowing for Multiple Hypotheses in Object Reference Point Association," 2020 IEEE International Conference on Multisensor Fusion and Integration (MFI 2020), 2020
6. M. Herrmann, J. Müller, J. Strohbeck and M. Buchholz, "Environment Modeling Based on Generic Infrastructure Sensor Interfaces Using a Centralized Labeled-Multi-Bernoulli Filter *," 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 2019, pp. 2414-2420.
   doi: 10.1109/ITSC.2019.8916923
   URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8916923&isnumber=8916833
7. J. Müller, M. Herrmann, J. Strohbeck, V. Belagiannis and M. Buchholz, "LACI: Low-effort Automatic Calibration of Infrastructure Sensors," 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 2019, pp. 3928-3933.
   doi: 10.1109/ITSC.2019.8917310
   URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8917310&isnumber=8916833