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researcher

Andreas Geiger

15 papers hereh-index 111.6k citations19 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author12
  • last author3

Across the 15 of 15 papers where every author was matched, so the position is known.

fields
  • cs.CV8
  • cs.RO6
  • cs.LG1
same name
  • Andreas Geiger — 93 papers, h 95
  • Andreas Geiger — 10 papers, h 5
  • Andreas Geiger — 9 papers, h 3
  • Andreas Geiger — 8 papers, h 4
  • Andreas Geiger — 8 papers, h 6
  • Andreas Geiger — 7 papers, h 5

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedDriveLM: Driving with Graph Visual Question Answering

4 citations · 8 across the 14 of their papers we have counts for

collaborators
Showing 2024 · cs.CVShow all

4 papers · 2 filters

cs.CV2024★ 1 cited

Hidden Biases of End-to-End Driving Datasets

Julian Zimmerlin, Jens Beißwenger, Bernhard Jaeger +2

End-to-end driving systems have made rapid progress, but have so far not been applied to the challenging new CARLA Leaderboard 2.0. Further, while there is a large body of literatu…

cs.CV2024

NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking

Daniel Dauner, Marcel Hallgarten, Tianyu Li +9

Benchmarking vision-based driving policies is challenging. On one hand, open-loop evaluation with real data is easy, but these results do not reflect closed-loop performance. On th…

cs.CV2024★ 2 cited

Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability

Shenyuan Gao, Jiazhi Yang, Li Chen +5

World models can foresee the outcomes of different actions, which is of paramount importance for autonomous driving. Nevertheless, existing driving world models still have limitati…

cs.CV2024

GenAD: Generalized Predictive Model for Autonomous Driving

Jiazhi Yang, Shenyuan Gao, Yihang Qiu +11

In this paper, we introduce the first large-scale video prediction model in the autonomous driving discipline. To eliminate the restriction of high-cost data collection and empower…

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