Dual-Stage LLM Framework for Scenario-Centric Semantic Interpretation in Driving Assistance
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. Deterministic, temporally bounded scenario windows are constructed from multimodal driving data and evaluated under fixed prompt constraints and a closed numeric risk schema, ensuring structured and comparable outputs across models. Experiments on a curated near-people scenario set compare two te...
| Authors | Jean Douglas Carvalho, Hugo Taciro Kenji, Ahmad Mohammad Saber et al. |
|---|---|
| Primary category | cs.AI |
| Published | 29 March 2026 |
| Last updated at source | 29 March 2026 |
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