3 papers
cs.RO2026
Rectify, Don't Regret: Avoiding Pitfalls of Differentiable Simulation in Trajectory Prediction
Harsh Yadav, Christian Bohn, Tobias Meisen
Current open-loop trajectory models struggle in real-world autonomous driving because minor initial deviations often cascade into compounding errors, pushing the agent into out-of-…
cs.CV2026
Faster Training, Fewer Labels: Self-Supervised Pretraining for Fine-Grained BEV Segmentation
Daniel Busch, Christian Bohn, Thomas Kurbiel +3
Dense Bird's Eye View (BEV) semantic maps are central to autonomous driving, yet current multi-camera methods depend on costly, inconsistently annotated BEV ground truth. We addres…
cs.CV2025
Efficient Inter-Task Attention for Multitask Transformer Models
Christian Bohn, Thomas Kurbiel, Klaus Friedrichs +2
In both Computer Vision and the wider Deep Learning field, the Transformer architecture is well-established as state-of-the-art for many applications. For Multitask Learning, howev…