mlsanityData to 14 Oct 2026

E2E Parking Dataset: An Open Benchmark for End-to-End Autonomous Parking

arXiv 2504.10812 ↗First posted 15 Apr 2025cs.RO cs.AIcited by 1

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Abstract (arXiv)

End-to-end learning has shown great potential in autonomous parking, yet the lack of publicly available datasets limits reproducibility and benchmarking. While prior work introduced a visual-based parking model and a pipeline for data generation, training, and close-loop test, the dataset itself was not released. To bridge this gap, we create and open-source a high-quality dataset for end-to-end autonomous parking. Using the original model, we achieve an overall success rate of 85.16% with lower average position and orientation errors (0.24 meters and 0.34 degrees).

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 26 references that are in the map. First posted on arXiv 15 Apr 2025.

Builds on

22 of 26 references.

References · 22

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10 more
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Citing papers per year: 2026: 1.

1 paper.