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.

翻译：泊车是驾驶安全的关键支柱。尽管近期的端到端方法已在域内取得了有希望的结果，但在域偏移（如天气和光照变化）下的鲁棒性仍然是一个关键挑战。本文不依赖于额外数据，而是提出了Dino-Diffusion泊车，一种域无关的自主泊车流程。该流程将视觉基础模型与基于扩散的规划相结合，以实现分布偏移下的泛化感知与鲁棒运动规划。我们在常规设置下的CARLA中训练该流程，并以零样本方式将其迁移至更具对抗性的设置中。我们的模型在所有测试的分布外场景中，持续实现了90%以上的泊车成功率，消融研究证实，网络架构和算法设计均显著提升了跨域性能，超越了现有基线。此外，在一个从真实世界停车场重建的3D高斯泼溅环境中的测试，展示了有希望的仿真到现实迁移潜力。