35 citations · 180 across the 39 of their papers we have counts for
7 papers · 1 filter
The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review
Steffen Hagedorn, Marcel Hallgarten, Martin Stoll +1
Automated driving has the potential to revolutionize personal, public, and freight mobility. Beside accurately perceiving the environment, automated vehicles must plan a safe, comf…
Uncertainty Propagation of Initial Conditions in Thermal Models
Alexandra Bünger, Roland Herzog, Andreas Naumann +1
The operation of machine tools often demands a highly accurate knowledge of the tool center point's (TCP) position. The displacement of the TCP over time can be inferred from therm…
Stay on Track: A Frenet Wrapper to Overcome Off-road Trajectories in Vehicle Motion Prediction
Marcel Hallgarten, Ismail Kisa, Martin Stoll +1
Predicting the future motion of observed vehicles is a crucial enabler for safe autonomous driving. The field of motion prediction has seen large progress recently with state-of-th…
Scaling Planning for Automated Driving using Simplistic Synthetic Data
Martin Stoll, Markus Mazzola, Maxim Dolgov +2
We challenge the perceived consensus that the application of deep learning to solve the automated driving planning task necessarily requires huge amounts of real-world data or high…
Adaptive rational Krylov methods for exponential Runge--Kutta integrators
Kai Bergermann, Martin Stoll
We consider the solution of large stiff systems of ordinary differential equations with explicit exponential Runge--Kutta integrators. These problems arise from semi-discretized se…
A weighted subspace exponential kernel for support tensor machines
Kirandeep Kour, Sergey Dolgov, Peter Benner +2
High-dimensional data in the form of tensors are challenging for kernel classification methods. To both reduce the computational complexity and extract informative features, kernel…