5 citations · 9 across the 7 of their papers we have counts for
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cs.RO2024★ 1 cited
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…
cs.CV2024★ 1 cited
DualAD: Disentangling the Dynamic and Static World for End-to-End Driving
Simon Doll, Niklas Hanselmann, Lukas Schneider +4
State-of-the-art approaches for autonomous driving integrate multiple sub-tasks of the overall driving task into a single pipeline that can be trained in an end-to-end fashion by p…