AI RESEARCH
Dual-Stage LLM Framework for Scenario-Centric Semantic Interpretation in Driving Assistance
arXiv CS.AI
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ArXi:2603.27536v1 Announce Type: new Advanced Driver Assistance Systems (ADAS) increasingly rely on learning-based perception, yet safety-relevant failures often arise without component malfunction, driven instead by partial observability and semantic ambiguity in how risk is interpreted and communicated. This paper presents a scenario-centric framework for reproducible auditing of LLM-based risk reasoning in urban driving contexts.