A team hailing from the Innovation Center for Intelligent Connected Electric Vehicles at Shanghai Jiao Tong University made a significant contribution to the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) by presenting their research paper, "AVP Scene Graph: Hierarchical Visual Language Mapping and Navigation for Autonomous Valet Parking". This paper introduces an innovative navigation framework tailored for Autonomous Valet Parking (AVP) scenarios, leveraging hierarchical visual language mapping. By creating a scene graph structure, the system is adept at deciphering semantic information and spatial relationships within parking environments. This capability facilitates accurate vehicle localization and path planning, even in intricate scenarios. The research notably tackles the shortcomings of conventional methods, which often struggle with dynamic obstacles, multimodal data fusion, and real-time processing. As such, it offers a groundbreaking approach to low-speed autonomous driving scenarios.