High-performance serving of large-scale OpenDRIVE datasets ...
Transcript of High-performance serving of large-scale OpenDRIVE datasets ...
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High-performance serving of large-scale OpenDRIVE datasets using
standardized GIS technology
6th Symposium Driving Simulation, 2020-11-05, Virtual Event
Michael Scholz
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 1
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German Aerospace Center (DLR)
Institute of Transportation Systems
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German Aerospace Center
Research institutes
• Aeronautics
• Space
• Energy
• Transportation
• Security
• Digitalisation
Space administration
Project management agency
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Institute of Transportation Systems
Key facts
• In Berlin and Brunswick
• Around 220 employees
Research fields
• Automotive
• Railway systems
• Traffic management
• Multi-modal and public transport
Area of work
• Fundamental research
• Conception and strategy development
• Prototyping
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 4
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Our research infrastructure ...
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... and our Testbed of Lower Saxony
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 6
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Challenges
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 7
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Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.
Testbed Lower Saxony – Part 1
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 8
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Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.
KoMo:D
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 9
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Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.
PEGASUS
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 10
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What is desirable?
• Consistent management of such heterogeneous
datasets
• Fast access and data browsing for different users
• Simple interface/API
• Easy snippet extraction from whole datasets
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Initial scope of application Current and future trends
OpenDRIVE over time
• Fast prototyping of simulation tracks
→ Artificial/imaginary test data
• Restricted (small) spatial extent
• High modelling detail with visual properties
→ 3D rendering
• Simple, continuous geometry definition
→ Smooth road course
• Real-time processing capability
• Real-world data
• From motorways over
• inner cities to
• multi-level parking decks
• Data updates and network merging
• Increasing spatial extents
• “From simulation into the car”
→ Electronic horizon, rejection of styling elements
• Linkage to supplementary environmental data
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 12
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Extracting/editing
OpenDRIVE is bad for
• Because you need tools which are
• commercial (money, money, money),
• complicated,
• inflexible,
• ugly.
• Because there is no server-based solution “as a
service”
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 13
see ASAM OpenDRIVE Format Specification, v. 1.6.0
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Data exchange
OpenDRIVE is bad for
• Because of strong data model hierarchy
and element cross-references
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 14
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Data exchange
OpenDRIVE is bad for
• Because of strong data model hierarchy
and element cross-references
• Because small data snippets quickly
result in millions of lines of text
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 15
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> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 16
Subset extraction is not trivial
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Solving “the problem” in three steps
1. Make OpenDRIVE data GIS-able
2. Deploy GIS data in spatial database
3. Publish as RESTful web service
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 17
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GIS? Why?
• Well-established standards around for 15+ years (OGC)
• A super-huge community of which all automotive guys can just dream about
• They know how to handle huge data
• Broad tool support, also for free (open source software)
• Some components offer standardised services
• Native workflow with cadastral data, CAD,
Road2Simulation, Lanelet/2, …
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 18
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GIS? Why?
• Well-established standards around for 15+ years (OGC)
• A super-huge community of which all automotive guys can just dream about
• They know how to handle huge data
• Broad tool support, also for free (open source software)
• Some components offer standardised services
• Native workflow with cadastral data, CAD,
Road2Simulation, Lanelet/2, …
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 19
“Don’t re-invent the wheel”
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Solving “the problem” in three steps
1. Make OpenDRIVE data GIS-able
2. Deploy GIS data in spatial database
3. Publish as RESTful web service
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 20
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Make OpenDRIVE data GIS-able
Geometry basics
• Elements refer to an imaginary reference line
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 21
see OpenDRIVE Format Specification, Rev. 1.5
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Make OpenDRIVE data GIS-able
Geometry basics
• Elements refer to an imaginary reference line
• Road topography (3D) and topology
• continuous geometry definition
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 22
see OpenDRIVE Format Specification, Rev. 1.5
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Make OpenDRIVE data GIS-able
Geometry basics: types
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 23
see OpenDRIVE Format Specification, Rev. 1.5
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Make OpenDRIVE data GIS-able
Geometry basics: discrete anchor points
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 24
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Make OpenDRIVE data GIS-able
Geometry basics: continuous geometry evolution
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 25
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Make OpenDRIVE data GIS-able
Application-based discretisation (sampling)
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 26
“Everyone is doing this!”
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Make OpenDRIVE data GIS-able
Application-based discretisation
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 27
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Make OpenDRIVE data GIS-able
OGC Simple Feature primitives
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 28
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Make OpenDRIVE data GIS-able
OGC Simple Feature primitives
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 29
see „Vector Data Models“ in Geographic Information System Basics v1.0, CC BY-NC-SA 3.0
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Make OpenDRIVE great again
OGC Simple Feature primitives
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 30
Point(x y)
LineString(x1 y1, x2 y2, ..., xn yn)
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Make OpenDRIVE data GIS-able
Translation into Simple Feature model
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 31
LineString(
604944.1037 5792860.1272,
604752.81 5792819.10, ...)
LineString(
604935.03 5792856.5285,
604754.39 5792810.73, ...)
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Make OpenDRIVE data GIS-able
Data binding to Java
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 32
OpenDRIVE
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Make OpenDRIVE data GIS-able
Geometry discretisation into Simple Feature model
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 33
OpenDRIVE
custom data model
with Simple Features
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Solving “the problem” in three steps
1. Make OpenDRIVE data GIS-able
2. Deploy GIS data in spatial database
3. Publish as RESTful web service
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 34
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Deploy GIS data in spatial database
Persisting through JPA with Hibernate Spatial
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 35
OpenDRIVE
custom data model
with Simple Features
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Deploy GIS data in spatial database
Custom data model
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 36
custom data model
with Simple Features
Simple Feature type OpenDRIVE element
Point <signal>
LineString driving <lane> boundary,<roadMark>,linear <object> (e.g. guardrail)
MultiLineString road reference line <planView>
Polygon driving <lane>,<parkingSpace>
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Deploy GIS data in spatial database
Custom data model
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 37
Simple Feature type OpenDRIVE element
Point <signal>
LineString driving <lane> boundary,<roadMark>,linear <object> (e.g. guardrail)
MultiLineString road reference line <planView>
Polygon driving <lane>,<parkingSpace>
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Deploy GIS data in spatial database
Custom data model
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 38
Additional raw XML elements “attached”
• <road>
• <junction>
Simple Feature type OpenDRIVE element
Point <signal>
LineString driving <lane> boundary,<roadMark>,linear <object> (e.g. guardrail)
MultiLineString road reference line <planView>
Polygon driving <lane>,<parkingSpace>
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Solving “the problem” in three steps
1. Make OpenDRIVE data GIS-able
2. Deploy GIS data in spatial database
3. Publish as RESTful web service
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 39
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Publish as RESTful web service
Directly from database through GeoServer
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 40
custom data model
with Simple Features
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Publish as RESTful web service
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 41
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Publish as RESTful web service
The power of GeoServer
• Detailed user management
• Fine-grained data security/access policies
• OGC-standardised REST API (this “web thing”)
• Web Map Service (WMS)
• Web Feature Service (WFS)
• Web Processing Service (WPS)
• Data output as image, KML, GML, GeoJSON, CSV,
Shapefile, etc.
• Easy snippet extraction through custom extension
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 42
• Benefiting spatial indices on data → “fast like hell”
• Scalability
• Integration into most GIS tools
• Bla, bla, bla …
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Live demo
OpenDRIVE through GeoServer
OpenDRIVE in QGIS
OpenDRIVE subset/snippet extraction queries
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 43
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Conclusion: “Don’t re-invent the wheel”
• Geometry discretization should be based on OGC Simple Features
• Benefit from well-established tools in GIS domain:
• Free/open frameworks for Java, C++, Python, … and web development
• Super-easy ad hoc combination with arbitrary geo-data
• Direct conversion into 100+ other formats: KML, GML, GeoJSON, CSV, Shapefile, SQLite, XLSX, …
→ GDAL: “One library to rule them all”
• Standardized web service interfaces already implemented (OGC WMS, WFS, …)
This ecosystem enables fast, large-scale serving of OpenDRIVE
> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 44
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> 6th Symposium Driving Simulation > High-performance serving of large-scale OpenDRIVE datasets using GIS > Michael Scholz > 2020-11-05DLR.de • Chart 45
https://youtu.be/diEnIUT6HmA
Dipl.-Geoinf.
Michael Scholz
GermanAerospace Center
Institute ofTransportation Systems
Lilienthalplatz 738108 BraunschweigGermany
+49 531 [email protected]
www.DLR.de/ts/en
Spatial Data Processingand Engineering
TelephoneE-mail
Internet