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September 22, 2025Open Access

Why Braking? Scenario Extraction and Reasoning Utilizing LLM

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The proposed method improves scenario retrieval for braking events in urban settings, enhancing safety assessments.
Experimental results indicate our novel approach outperforms traditional rule-based heuristics in categorizing driving scenarios.
By utilizing large language models, the framework effectively interprets both known and out-of-distribution scenarios.
Annotations were curated on the Argoverse 2 Sensor Dataset, providing a comprehensive evaluation basis for the method.

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Framework utilizes large language models to enhance scenario retrieval in braking events, indicating safer driving assessments.

Wu et al. (2025) studied this question.

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