开源首个端到端自动驾驶泊车公开数据集，助力模型复现与评测
E2E Parking Dataset: An Open Benchmark for End-to-End Autonomous Parking
- 基于真实车辆数据构建端到端泊车数据集
- 模型实现85.16%成功率，位置误差低于0.24米
- 适合自动驾驶算法研发与基准测试人员
端到端学习在自动驾驶泊车中展现出巨大潜力，但公开可用的数据集缺乏限制了可复现性与基准测试。尽管先前工作提出了基于视觉的泊车模型及数据生成、训练与闭环测试流程，但数据集本身并未公开。为填补这一空白，我们创建并开源了一个高质量的端到端自动驾驶泊车数据集。使用原始模型，在该数据集上实现了85.16%的整体成功率，平均位置误差为0.24米，方向误差为0.34度。
原文摘要 · Abstract (English)
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).
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