RSG-Search Plus:An Advanced Traffic Scene Retrieval Methods based on Road Scene Graph

Currently, with the rapid growth of training datasets for autonomous driving systems, we are faced with a challenge: how to efficiently retrieve specific traffic scenes from massive amount of scene in multiple datasets. This challenge primarily stems from the heterogeneity of existing datasets, meaning these datasets contain different types of data, follow different data formats, and use different sensors for data collection. To address this issue, we present RSG-Search Plus, a universal traffic scene searching method based on Road Scene Graph and Large Language Models (LLMs). Our approach first transform datasets into scene graphs to exclude irrelevant details, then efficiently retrieving specific configurations among thousands of traffic scenes by matching isomorphic sub-graphs between input graph and road scene graph. Experimental results demonstrate that our graph searching method can accurately match the scenes described by input condition. Additionally, this method is easily adaptable to different datasets, significantly simplifying the scene search process.