AVKG: An interlinked Cross-domain Knowledge Graph for Smart Autonomous Vehicle Decision Making
Huihai Wang, Junfeng Jiao, Rui Zhu, Gengchen Mai
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Problems Identified (4)
AV urban data integration demand: Autonomous vehicle deployments require timely, integrated urban data to support routing and higher-level mobility applications.
GIS information silos: Dynamic urban knowledge reasoning and discovery over conventional GIS databases is challenging because heterogeneous sources, formats, and standards create information silos.
AV urban data integration demand: Autonomous vehicle deployments require timely, integrated urban data to support routing and higher-level mobility applications.
GIS information silos: Dynamic urban knowledge reasoning and discovery over conventional GIS databases is challenging because heterogeneous sources, formats, and standards create information silos.
Proposed Solutions (5)
Autonomous Vehicle Knowledge Graph: The paper develops AVKG, a machine-readable semantically structured graph repository that integrates diverse urban contextual data for AV operation.
AVKG schema ontology: The paper develops an AV knowledge graph schema/ontology to capture semantic relations among key AVKG concepts.
Domain subgraph organization: AVKG is organized into five domain subgraphs covering road networks, incidents, activities, points of interest, and AV operators.
SPARQL GeoSPARQL triple store: AVKG is stored in GraphDB and supports SPARQL and GeoSPARQL querying for spatiotemporal semantic reasoning.
Autonomous Vehicle Knowledge Graph: The paper develops AVKG, a machine-readable semantically structured graph repository that integrates diverse urban contextual data for AV operation.
Results (3)
Competency question demonstration:
Urban awareness AV platform:
Competency question demonstration:
Research Domain
Autonomous vehicle urban knowledge graphs and GIS data integration