E2E Parking Dataset: An Open Benchmark for End-to-End Autonomous Parking
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909716520632320 |
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| author | Gao, Kejia Zhou, Liguo Liu, Mingjun Knoll, Alois |
| author_facet | Gao, Kejia Zhou, Liguo Liu, Mingjun Knoll, Alois |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10812 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | E2E Parking Dataset: An Open Benchmark for End-to-End Autonomous Parking Gao, Kejia Zhou, Liguo Liu, Mingjun Knoll, Alois Robotics Artificial Intelligence 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). |
| title | E2E Parking Dataset: An Open Benchmark for End-to-End Autonomous Parking |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2504.10812 |