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cs.RO2026
What Matters for Scalable and Robust Learning in End-to-End Driving Planners?
David Holtz, Niklas Hanselmann, Simon Doll +2
End-to-end autonomous driving has gained significant attention for its potential to learn robust behavior in interactive scenarios and scale with data. Popular architectures often…
cs.RO2025
EMPERROR: A Flexible Generative Perception Error Model for Probing Self-Driving Planners
Niklas Hanselmann, Simon Doll, Marius Cordts +2
To handle the complexities of real-world traffic, learning planners for self-driving from data is a promising direction. While recent approaches have shown great progress, they typ…