  Traffic rules formalization is crucial for verifying the compliance and
safety of autonomous vehicles (AVs). However, manual translation of natural
language traffic rules as formal specification requires domain knowledge and
logic expertise, which limits its adaptation. This paper introduces TR2MTL, a
framework that employs large language models (LLMs) to automatically translate
traffic rules (TR) into metric temporal logic (MTL). It is envisioned as a
human-in-loop system for AV rule formalization. It utilizes a chain-of-thought
in-context learning approach to guide the LLM in step-by-step translation and
generating valid and grammatically correct MTL formulas. It can be extended to
various forms of temporal logic and rules. We evaluated the framework on a
challenging dataset of traffic rules we created from various sources and
compared it against LLMs using different in-context learning methods. Results
show that TR2MTL is domain-agnostic, achieving high accuracy and generalization
capability even with a small dataset. Moreover, the method effectively predicts
formulas with varying degrees of logical and semantic structure in unstructured
traffic rules.
