A Data Format Integration of Open-Street-Map and Lanelet2 Toward the Ontology Framework for Safety Automated driving systems
Year: 2025
Type: conference-paper
Abstract: This study proposes a framework that integrates OpenStreetMap (OSM) data with ontology-based systems to enhance automated driving.OSM provides static geographical data, while the Lanelet2 mapping framework incorporates lanelevel road information and topological relationships.This combination enables advanced testing of vehicle behavior in realistic environments.Ontology-based integration offers semantic representations of road elements and traffic rules, supporting the modeling of complex driving scenarios.By structuring spatial and semantic data, this approach ensures accurate simulation and testing, facilitating applications such as traffic analysis, route optimization, and automated driving decision-making.The framework enhances scalability, precision, and safety in autonomous vehicle development, enabling more effective testing and validation.This integration supports comprehensive, context-aware simulations that improve vehicle response to real-world driving conditions.
Authors: Obada Al Aama, Takahiro Koga, Tomoki Taniguchi, Davaanyam Jargal, Junya Oishi, Shigeru Nemoto, Wataru Mizushina, Kazuki Hirao, Hakaru Tamukoh, Hiroaki Wagatsuma
Institution: Kyushu Institute of Technology
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409913380