Traffic Regulation-aware Path Planning with Regulation Databases and Vision-Language Models
Journal:
arXiv
Published Date:
Mar 12, 2025
Abstract
This paper introduces and tests a framework integrating traffic regulation
compliance into automated driving systems (ADS). The framework enables ADS to
follow traffic laws and make informed decisions based on the driving
environment. Using RGB camera inputs and a vision-language model (VLM), the
system generates descriptive text to support a regulation-aware decision-making
process, ensuring legal and safe driving practices. This information is
combined with a machine-readable ADS regulation database to guide future
driving plans within legal constraints. Key features include: 1) a regulation
database supporting ADS decision-making, 2) an automated process using sensor
input for regulation-aware path planning, and 3) validation in both simulated
and real-world environments. Particularly, the real-world vehicle tests not
only assess the framework's performance but also evaluate the potential and
challenges of VLMs to solve complex driving problems by integrating detection,
reasoning, and planning. This work enhances the legality, safety, and public
trust in ADS, representing a significant step forward in the field.
