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

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).

@article{gao2025_2504.10812,
  title={ E2E Parking Dataset: An Open Benchmark for End-to-End Autonomous Parking },
  author={ Kejia Gao and Liguo Zhou and Mingjun Liu and Alois Knoll },
  journal={arXiv preprint arXiv:2504.10812},
  year={ 2025 }
}