10 citations · 12 across the 10 of their papers we have counts for
10 papers
Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment
Till Beemelmanns, Shayan Sharifi, Manas Mehrotra +2
Deep Neural Networks have become the dominant solution for Autonomous Driving perception, but their opacity conflicts with emerging Trustworthy AI guidelines and complicates safety…
Query2Uncertainty: Robust Uncertainty Quantification and Calibration for 3D Object Detection under Distribution Shift
Till Beemelmanns, Alexey Nekrasov, Stefan Vilceanu +4
Reliable uncertainty estimation for 3D object detection is critical for deploying safe autonomous systems, yet modern detectors remain poorly calibrated, especially under distribut…
karl. -- A Research Vehicle for Automated and Connected Driving
Jean-Pierre Busch, Lukas Ostendorf, Guido Linden +5
As highly automated driving is transitioning from single-vehicle closed-access testing to commercial deployments of public ride-hailing in selected areas (e.g., Waymo), automated d…
OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy Prediction
Severin Heidrich, Till Beemelmanns, Alexey Nekrasov +2
Autonomous driving has the potential to significantly enhance productivity and provide numerous societal benefits. Ensuring robustness in these safety-critical systems is essential…
CARLOS: An Open, Modular, and Scalable Simulation Framework for the Development and Testing of Software for C-ITS
Christian Geller, Benedikt Haas, Amarin Kloeker +4
Future mobility systems and their components are increasingly defined by their software. The complexity of these cooperative intelligent transport systems (C-ITS) and the everchang…
3D Point Cloud Compression with Recurrent Neural Network and Image Compression Methods
Till Beemelmanns, Yuchen Tao, Bastian Lampe +4
Storing and transmitting LiDAR point cloud data is essential for many AV applications, such as training data collection, remote control, cloud services or SLAM. However, due to the…
MultiCorrupt: A Multi-Modal Robustness Dataset and Benchmark of LiDAR-Camera Fusion for 3D Object Detection
Till Beemelmanns, Quan Zhang, Christian Geller +1
Multi-modal 3D object detection models for automated driving have demonstrated exceptional performance on computer vision benchmarks like nuScenes. However, their reliance on dense…
Explainable Multi-Camera 3D Object Detection with Transformer-Based Saliency Maps
Till Beemelmanns, Wassim Zahr, Lutz Eckstein
Vision Transformers (ViTs) have achieved state-of-the-art results on various computer vision tasks, including 3D object detection. However, their end-to-end implementation also mak…
Data-Driven Occupancy Grid Mapping using Synthetic and Real-World Data
Raphael van Kempen, Bastian Lampe, Lennart Reiher +3
In perception tasks of automated vehicles (AVs) data-driven have often outperformed conventional approaches. This motivated us to develop a data-driven methodology to compute occup…
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 +3
Enabling secure and reliable high-bandwidth lowlatency connectivity between automated vehicles and external servers, intelligent infrastructure, and other road users is a central s…