Paper Detail

Fail2Drive: Benchmarking Closed-Loop Driving Generalization

Simon Gerstenecker, Andreas Geiger, Katrin Renz

arxiv Score 6.8

Published 2026-04-09 · First seen 2026-04-10

General AI

Abstract

Generalization under distribution shift remains a central bottleneck for closed-loop autonomous driving. Although simulators like CARLA enable safe and scalable testing, existing benchmarks rarely measure true generalization: they typically reuse training scenarios at test time. Success can therefore reflect memorization rather than robust driving behavior. We introduce Fail2Drive, the first paired-route benchmark for closed-loop generalization in CARLA, with 200 routes and 17 new scenario classes spanning appearance, layout, behavioral, and robustness shifts. Each shifted route is matched with an in-distribution counterpart, isolating the effect of the shift and turning qualitative failures into quantitative diagnostics. Evaluating multiple state-of-the-art models reveals consistent degradation, with an average success-rate drop of 22.8\%. Our analysis uncovers unexpected failure modes, such as ignoring objects clearly visible in the LiDAR and failing to learn the fundamental concepts of free and occupied space. To accelerate follow-up work, Fail2Drive includes an open-source toolbox for creating new scenarios and validating solvability via a privileged expert policy. Together, these components establish a reproducible foundation for benchmarking and improving closed-loop driving generalization. We open-source all code, data, and tools at https://github.com/autonomousvision/fail2drive .

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BibTeX

@article{gerstenecker2026fail2drive,
  title = {Fail2Drive: Benchmarking Closed-Loop Driving Generalization},
  author = {Simon Gerstenecker and Andreas Geiger and Katrin Renz},
  year = {2026},
  abstract = {Generalization under distribution shift remains a central bottleneck for closed-loop autonomous driving. Although simulators like CARLA enable safe and scalable testing, existing benchmarks rarely measure true generalization: they typically reuse training scenarios at test time. Success can therefore reflect memorization rather than robust driving behavior. We introduce Fail2Drive, the first paired-route benchmark for closed-loop generalization in CARLA, with 200 routes and 17 new scenario class},
  url = {https://arxiv.org/abs/2604.08535},
  keywords = {cs.RO, cs.CV},
  eprint = {2604.08535},
  archiveprefix = {arXiv},
}

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