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Scene Graph-based Driving Scenario Extraction for Automotive Egocentric Datasets

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

Extracting scenarios from unlabelled real-world sensor data streams is a critical but challenging task in the development process of automated driving systems (ADS). Automatically sifting through large datasets to spatially and temporally locate critical scenarios can enable scenario-based coverage analysis of ADS datasets. In this paper, we present a method for extracting scenarios from egocentric datasets using scene graphs and Linear Temporal Logic (LTL). We first process egocentric sensor data and HD maps to generate a sequence of scene graphs representing a driving scenario. Next, we use

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