1 citations · 1 across the 2 of their papers we have counts for
3 papers
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…
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…
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…