Dino-Diffusionモジュラー設計による自動駐車のクロスドメインギャップの橋渡し
Dino-Diffusion Modular Designs Bridge the Cross-Domain Gap in Autonomous Parking
視覚基盤モデルと拡散ベースの計画を組み合わせ、天候や照明の変化などドメインシフト下でも90%以上の成功率で駐車できる自動駐車パイプラインを提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Zixuan Wu, Hengyuan Zhang, Ting-Hsuan Chen, Yuliang Guo, David Paz, Xinyu Huang, Liu Ren
分類: cs.RO, cs.CV
原文アブストラクト
Parking is a critical pillar of driving safety. While recent end-to-end (E2E) approaches have achieved promising in-domain results, robustness under domain shifts (e.g., weather and lighting changes) remains a key challenge. Rather than relying on additional data, in this paper, we propose Dino-Diffusion Parking (DDP), a domain-agnostic autonomous parking pipeline that integrates visual foundation models with diffusion-based planning to enable generalized perception and robust motion planning under distribution shifts. We train our pipeline in CARLA at regular setting and transfer it to more adversarial settings in a zero-shot fashion. Our model consistently achieves a parking success rate above 90% across all tested out-of-distribution (OOD) scenarios, with ablation studies confirming that both the network architecture and algorithmic design significantly enhance cross-domain performance over existing baselines. Furthermore, testing in a 3D Gaussian splatting (3DGS) environment reconstructed from a real-world parking lot demonstrates promising sim-to-real transfer.
