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20232025
most citedParting with Misconceptions about Learning-based Vehicle Motion Planning

15 citations · 25 across the 11 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

cs.RO2023★ 2 cited

Conditional Unscented Autoencoders for Trajectory Prediction

Faris Janjoš, Marcel Hallgarten, Anthony Knittel +3

The CVAE is one of the most widely-used models in trajectory prediction for AD. It captures the interplay between a driving context and its ground-truth future into a probabilistic…

cs.RO2023★ 6 cited

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…

cs.RO2023★ 15 cited

Parting with Misconceptions about Learning-based Vehicle Motion Planning

Daniel Dauner, Marcel Hallgarten, Andreas Geiger +1

The release of nuPlan marks a new era in vehicle motion planning research, offering the first large-scale real-world dataset and evaluation schemes requiring both precise short-ter…

cs.RO2023

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…

cs.RO2023★ 1 cited

From Prediction to Planning With Goal Conditioned Lane Graph Traversals

Marcel Hallgarten, Martin Stoll, Andreas Zell

The field of motion prediction for automated driving has seen tremendous progress recently, bearing ever-more mighty neural network architectures. Leveraging these powerful models…