Self-learning Trajectory Prediction with Recurrent Neural Networks at Intelligent Intersections. - Julian Bock; Till Beemelmanns; Markus Klösges; Jens Kotte (VEHITS, 2017)

3D Point Cloud Compression with Recurrent Neural Network and Image Compression Methods. - Till Beemelmanns; Yuchen Tao; Bastian Lampe; Lennart Reiher; Raphael van Kempen; Timo Woopen; Lutz Eckstein (IV, 2022)

Enabling Connectivity for Automated Mobility: A Novel MQTT-based Interface Evaluated in a 5G Case Study on Edge-Cloud Lidar Object Detection. - Lennart Reiher; Bastian Lampe; Timo Woopen; Raphael van Kempen; Till Beemelmanns; Lutz Eckstein (CoRR, 2022)

Data-Driven Occupancy Grid Mapping using Synthetic and Real-World Data. - Raphael van Kempen; Bastian Lampe; Lennart Reiher; Timo Woopen; Till Beemelmanns; Lutz Eckstein (CoRR, 2022)

Explainable Multi-Camera 3D Object Detection with Transformer-Based Saliency Maps. - Till Beemelmanns; Wassim Zahr; Lutz Eckstein (CoRR, 2023)

MultiCorrupt: A Multi-Modal Robustness Dataset and Benchmark of LiDAR-Camera Fusion for 3D Object Detection. - Till Beemelmanns; Quan Zhang; Christian Geller; Lutz Eckstein (IV, 2024)

CARLOS: An Open, Modular, and Scalable Simulation Framework for the Development and Testing of Software for C-ITS. - Christian Geller; Benedikt Haas; Amarin Kloeker; Jona Hermens; Bastian Lampe; Till Beemelmanns; Lutz Eckstein (IV, 2024)

MultiCorrupt: A Multi-Modal Robustness Dataset and Benchmark of LiDAR-Camera Fusion for 3D Object Detection. - Till Beemelmanns; Quan Zhang; Lutz Eckstein (CoRR, 2024)

OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy Prediction. - Severin Heidrich; Till Beemelmanns; Alexey Nekrasov 001; Bastian Leibe; Lutz Eckstein (CoRR, 2025)