Grounding LLMs via node-wise anchors in a traffic scenario taxonomy improves law-scenario matching by 29.1% and derived requirement accuracy by 36.9-38.2% on Chinese laws and 5,897 scenarios, enabling a compliance layer and real-time monitor for AVs.
An ethi- cal trajectory planning algorithm for autonomous vehicles.Nature Machine Intelligence, 5(2):137–144
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Towards Lawful Autonomous Driving: Deriving Scenario-Aware Driving Requirements from Traffic Laws and Regulations
Grounding LLMs via node-wise anchors in a traffic scenario taxonomy improves law-scenario matching by 29.1% and derived requirement accuracy by 36.9-38.2% on Chinese laws and 5,897 scenarios, enabling a compliance layer and real-time monitor for AVs.