用视觉大模型+扩散规划实现跨域泊车，零样本迁移成功率超90%
Dino-Diffusion Modular Designs Bridge the Cross-Domain Gap in Autonomous Parking
- 融合视觉基础模型与扩散规划，构建抗域偏移的泊车系统
- 零样本迁移下在所有测试场景中成功率均超90%
- 适合关注自动驾驶泛化能力与真实场景部署的研究者
泊车是驾驶安全的关键环节。尽管近期端到端方法在同域环境下表现良好，但在领域偏移（如天气、光照变化）下的鲁棒性仍是主要挑战。本文提出Dino-Diffusion Parking（DDP）——一种无需额外数据的域无关自主泊车流程，通过整合视觉基础模型与基于扩散的规划，实现泛化感知与稳健运动规划。模型在CARLA中常规设置下训练，并以零样本方式迁移到更具挑战性的设定。实验表明，该模型在所有测试的域外（OOD）场景中均保持超过90%的泊车成功率；消融研究证实，网络结构与算法设计对跨域性能提升显著。此外，在由真实停车场重建的3D高斯泼溅（3DGS）环境中测试，展现出良好的仿真到现实迁移潜力。
原文摘要 · Abstract (English)
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.
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