Abstract
Local dynamic map (LDM) is a key component in the future of autonomous and connected vehicles. An LDM serves as a local database with the necessary tools to have a common reference system for both static data (i.e., map information) and dynamic data (vehicles, pedestrians, etc.). The LDM should have a common and well-defined input system in order to be interoperable across multiple data sources such as sensor detections or V2X communications. In this work, we present an interoperable graph-based LDM (iLDM) using Neo4j as our database engine and OpenLABEL as a common data format. An analysis on data insertion and querying time to the iLDM is reported, including a vehicle discovery service function in order to test the capabilities of our work and a comparative analysis with other LDM implementations showing that our proposed iLDM outperformed in several relevant features, furthering its practical utilisation in advanced driver assistance system development.
Highlights
Graph-Based Local Dynamic Map.The implementation of a standardised, interoperable, and efficient local dynamic map (LDM) component is one of the actual challenges in the context of cooperative intelligent transport systems (C-ITS)
LDM implementations, such as software solutions, respond to this need, aiming to provide a centralised database of road scene information including static and dynamic elements that are relevant to supporting the integration of advanced driver assistance systems (ADAS)
We propose the interoperable graph-based LDM (iLDM) as a novel graphical database based on LDM implementation and present an analysis of the feasibility for its utilisation in real-time ADAS
Summary
Information about the local environment is essential for automated vehicles, and with the constant developments achieved in object detection tasks for perception systems, such as cameras or LiDAR sensors, vehicles are able to perceive their environment with more precision and detail This data must be properly structured and stored within the ego-vehicle for its posterior usage in different kinds of ADAS functions or at roadside units (RSUs) for wider-area local dynamic maps in cooperative driving contexts. Despite LDM being a well-known concept in C-ITS domains, standard-compliant LDM implementations have been largely ignored by the community and industry; much research is yet to come to provide functional data models, real-time databases, interoperable interfaces and general-purpose applicability. Numerical analysis of the iLDM’s performance for data insertion and retrieval
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