6 papers
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…
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…
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…
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…