Information Exchange between GIS and Geospatial ITS Databases Based on a Generic Model

This study aims to improve interoperability between Geographic Information Systems (GIS) and geospatial databases for Intelligent Transport Systems (ITS). Road authorities maintain authoritative information for legal and safe navigation in GIS databases. This information needs to be shared with ITS databases for route planning and navigation, and for use in combination with local knowledge from vehicle sensors. Current solutions for modelling and exchanging geospatial information in the domains of GIS and ITS have been studied and evaluated. Limitations have been pointed out related to usability in the GIS domain and flexibility for representing an evolving real world. A prototype for an improved information exchange model has been developed, based on ISO/TC 211 standards, Model Driven Architecture (MDA), and concepts from the studied solutions. The prototype contains generic models for feature catalogues and features, with implementation schemas in the Geography Markup Language (GML). Results from a case study indicated that the models could be implemented with feature catalogues from the ITS standard ISO 14825 Geographic Data Files (GDF) and the INSPIRE Transport Networks specification. The prototype can be a candidate solution for improved information exchange from GIS databases to ITS databases that are based on the Navigation Data Standard.

[1]  Andreas Engelsberg,et al.  Digital Maps for ADAS , 2016 .

[2]  Michael Scholz,et al.  Deploying guidelines and a simplified data model to provide real world geodata in driving simulators and driving automation , 2017, Transportation Research Part F: Traffic Psychology and Behaviour.

[3]  Árpád Barsi,et al.  Supporting autonomous vehicles by creating HD maps , 2017 .

[4]  Pignatelli Francesco,et al.  Improving accuracy in road safety data exchange for navigation systems: European Union Location Framework Transportation Pilot , 2016 .

[5]  Reese Plews,et al.  Standards—Making Geographic Information Discoverable, Accessible and Usable for Modern Cartography , 2019 .

[6]  Sha Zhi-ren,et al.  A Conceptual Multi-level Data Model for Road Networks , 2012, 2012 Fifth International Conference on Intelligent Computation Technology and Automation.

[7]  T. H. Kolbe,et al.  CITYGML AND THE STREETS OF NEW YORK -A PROPOSAL FOR DETAILED STREET SPACE MODELLING , 2017 .

[8]  Matthias Althoff,et al.  Automatic Conversion of Road Networks from OpenDRIVE to Lanelets , 2018, 2018 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI).

[9]  Hermann Winner,et al.  Handbook of Driver Assistance Systems: Basic Information, Components and Systems for Active Safety and Comfort , 2015 .

[10]  Goce Trajcevski,et al.  Accurate vehicle self-localization in high definition map dataset , 2017, AutonomousGIS@SIGSPATIAL.

[11]  P. Helmholz,et al.  OVERVIEW OF STANDARDS TOWARDS ROAD ASSET INFORMATION EXCHANGE , 2018, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[12]  Kun Jiang,et al.  Lane-level route planning based on a multi-layer map model , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[13]  Cyril Ray,et al.  Multi-scale and multi-modal GIS-T data model , 2011 .

[14]  Andreas Richter,et al.  Beyond OSM – Alternative Data Sources and Approaches Enhancing Generation of Road Networks for Traffic and Driving Simulations , 2016 .

[15]  Pavel Nedoma,et al.  Automated Driving from the View of Technical Standards , 2017 .

[16]  Marius Runde Strategies for Automated Updating of Road Networks for Driving Simulations , 2017 .

[17]  Jonathan E Campbell,et al.  Essentials of Geographic Information Systems , 2011 .

[18]  Katleen Janssen,et al.  The ITS Directive: More than a timeframe with privacy concerns and a means for access to public data for digital road maps? , 2012, Comput. Law Secur. Rev..

[19]  Doreen Böhnstedt,et al.  Dynamic Map Update Protocol for Highly Automated Driving Vehicles , 2017, VEHITS.

[20]  Wolfgang Nejdl,et al.  An Architecture to Process Massive Vehicular Traffic Data , 2015, 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC).

[21]  Xin Chen,et al.  High definition maps in urban context , 2018, SIGSPACIAL.