7 citations · 12 across the 10 of their papers we have counts for
5 papers · 1 filter
Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment
Jean-Pierre Busch, Guido Linden, Jan Bergmann +1
Recent research in machine and deep learning has shown the potential of learningbased motion planning approaches to improve the driving behavior of automated vehicles, especially i…
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…
Robust Fusion of Object-Level V2X for Learned 3D Object Detection
Lukas Ostendorf, Lennart Reiher, Onn Haran +1
Perception for automated driving is largely based on onboard environmental sensors, such as cameras and radar, which are cost-effective but limited by line-of-sight and field-of-vi…
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…