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.
RSG-Search Plus:An Advanced Traffic Scene Retrieval Methods based on Road Scene Graph
2024 IEEE Intelligent Vehicles Symposium (IV) ; 1171-1178
2024-06-02
2137487 byte
Conference paper
Electronic Resource
English
Road Traffic Scene Acquisition
Springer Verlag | 2018
|Road Traffic Scene Acquisition
TIBKAT | 2019
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