E2E駐車データセット：エンドツーエンド自動駐車のためのオープンベンチマーク
E2E Parking Dataset: An Open Benchmark for End-to-End Autonomous Parking
エンドツーエンド自動駐車の研究用に高品質なデータセットを構築しオープンソース化した。既存モデルで成功率85.16%を達成し、位置・姿勢誤差も低減した。
著者: Kejia Gao, Liguo Zhou, Mingjun Liu, Alois Knoll
分類: cs.RO, cs.AI
原文アブストラクト
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).
