Core Insights - The article discusses advancements in autonomous parking systems, particularly focusing on a modular approach that combines visual foundation models and diffusion models to enhance cross-domain generalization capabilities [8][33]. Group 1: Autonomous Driving Technology - Autonomous driving technology has rapidly developed, with nearly 60% of new cars globally equipped with some form of autonomous driving features [6]. - Parking-related accidents account for 20% of all vehicle accidents in the U.S., with 91% occurring during reverse maneuvers, highlighting the need for precise perception, planning, and control [6]. Group 2: Proposed System and Methodology - The proposed Dino-Diffusion Parking (DDP) system integrates a robust perception module based on the DINOv2 model and a diffusion model for trajectory planning, enhancing the system's ability to generalize across different domains [8][9]. - The DDP system includes several modules: robust perception using DINOv2, target fusion through re-labeling, trajectory planning via diffusion models, and precise tracking using a Stanley controller [9][10][14]. Group 3: Experimental Results - The system was tested in the CARLA simulator with 800 expert trajectories across various weather conditions, demonstrating significant improvements in success rates and reduced errors compared to existing methods [20][27]. - The combination of the diffusion model and Stanley controller improved success rates by 16% under severe domain shifts, showcasing the system's robustness in complex environments [27]. Group 4: Future Directions - Future work includes integrating video world models to further bridge the gap between simulation and reality, collecting human demonstration data in 3DGS environments, and deploying the system in real vehicles to validate performance across diverse scenarios [34].