E2E Parking Dataset: An Open Benchmark for End-to-End Autonomous Parking
This provides a benchmark for researchers in autonomous driving, but it is incremental as it builds on prior work by releasing the dataset.
The authors tackled the lack of publicly available datasets for end-to-end autonomous parking by creating and open-sourcing a high-quality dataset, achieving an 85.16% success rate with errors of 0.24 meters and 0.34 degrees using an existing model.
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).