LLM-Assisted Tool for Joint Generation of Formulas and Functions in Rule-Based Verification of Map Transformations
For developers of autonomous driving systems, this work offers a semi-automated approach to generating verification rules, though the evaluation is limited to synthetic scenarios and the method is incremental.
The paper presents an LLM-assisted pipeline that jointly generates logical formulas and executable predicates for rule-based verification of high-definition map transformations, reducing manual engineering effort while preserving correctness in synthetic bridge and slope scenarios.
High-definition map transformations are essential in autonomous driving systems, enabling interoperability across tools. Ensuring their semantic correctness is challenging, since existing rule-based frameworks rely on manually written formulas and domain-specific functions, limiting scalability. In this paper, We present an LLM-assisted pipeline that jointly generates logical formulas and corresponding executable predicates within a computational FOL framework, extending the map verifier in CommonRoad scenario designer with elevation support. The pipeline leverages prompt-based LLM generation to produce grammar-compliant rules and predicates that integrate directly into the existing system. We implemented a prototype and evaluated it on synthetic bridge and slope scenarios. The results indicate reduced manual engineering effort while preserving correctness, demonstrating the feasibility of a scalable, semi-automated human-in-the-loop approach to map-transformation verification.