Emperror: A Flexible Generative Perception Error Model for Probing Self-Driving Planners

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 typically assume a setting in which the ground-truth world state is available as input. However, when deployed, planning ne...

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Vydané v:	IEEE robotics and automation letters Ročník 10; číslo 6; s. 5807 - 5814
Hlavní autori:	Hanselmann, Niklas, Doll, Simon, Cordts, Marius, Lensch, Hendrik P.A., Geiger, Andreas
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.06.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	autonomous agents Collision rates Complexity theory Decoding Deep learning methods Detectors Failure modes Heavily-tailed distribution Noise Noise measurement object detection Perception Planning segmentation and categorization Three-dimensional displays Trajectory Transformers
ISSN:	2377-3766, 2377-3766
On-line prístup:	Získať plný text
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