Validation of Location Referencing -Methods

Transcription

1 Validation of Location Referencing -Methods Networks for Mobility Stuttgart, September 28, 2012 Rainer Schützle, M.Sc. Institute of Engineering Geodesy University of Stuttgart

2 Contents Driver assistance systems Intro: Cooperative Driver Assistance Systems Location Referencing Introduction OpenLR Experiences from the EU-Project ROSATTE Evaluation of Location Referencing Definition of quality parameters Results Conclusions and Outlook Rainer Schützle: Valididation of Location Referencing Methods Networks for Mobility, Stuttgart,

3 Cooperative Driver assistance systems Infrastructure Source: VOLKSWAGEN AG Communication Road side IR-OnBoard Unit Vehicle-System Vehicle-Antenna Source: Rainer Schützle: Valididation of Location Referencing Methods Networks for Mobility, Stuttgart,

4 Cooperative Driver assistance systems Example Active control of traffic lights by vehicles with special privileges Free traffic flow Accident avoidance Source: Rainer Schützle: Valididation of Location Referencing Methods Networks for Mobility, Stuttgart,

5 Cooperative Driver assistance systems In future: assistance systems will communicate with Systems in other vehicles The Infrastructure (e.g. traffic control centers) Examples for Cooperative Driver assistance systems Traffic jam (detected from speed profile) Accident location (detected with onboard Radar-Systems) Slippery road (detected with gyroscopes and odometers/esp) Such messages (e.g. detected traffic jams) have to be georeferenced Allocation of traffic jam message to a road link in the vehicle s digital road map Such georeferenced information can be shared with other systems (vehicle or infrastructure-based) Rainer Schützle: Valididation of Location Referencing Methods Networks for Mobility, Stuttgart,

6 Geo-Referencing of traffic information Base map (road network) Object to be referenced (e.g. speed limit) HOWEVER: it is not that simple (unfortunately) Rainer Schützle: Valididation of Location Referencing Methods Networks for Mobility, Stuttgart,

7 Map deviations Map deviations caused by Different map versions (Updating of maps) Different map vendors e.g. TomTom, Nokia, OpenStreetMap, Source: Heinz-Werner Pfeiffer, BOSCH Rainer Schützle: Valididation of Location Referencing Methods Networks for Mobility, Stuttgart,

8 Problem identified?! Information objects can be easily exchanged between different systems, if the same base map is used! Problem: Many different infrastructure- and vehicle systems Many different road maps in use No common map base!! Necessity of a map-based but map-independent georeferencing Rainer Schützle: Valididation of Location Referencing Methods Networks for Mobility, Stuttgart,

9 Objective: Location Referencing Source map Object to be referenced (e.g. speed limit) Target map Transmitted speed limit Rainer Schützle: Valididation of Location Referencing Methods Networks for Mobility, Stuttgart,

10 Dynamic Location Referencing Search for additional location information resp. references in digital road maps, e.g.: Street name National road class (e.g. Federal-, State-, County road) with respective number Road category (e.g. 10 categories in GDF) Physical road type (e.g. motorway, single- or multiple carriageway, circle drive, pedestrian zone, pavement, etc.) Driving direction (one-way or both ways) Enrichment of existing location information (ID) with redundant information from the source map Identification of the probably corresponding road element in the receiving map Dynamic Location Referencing Thus: performing a Map to Map Matching, in principle BUT: These attributes are not necessarily present in every map Attributes are probably are modeled differently (e.g. due to different classification) Rainer Schützle: Valididation of Location Referencing Methods Networks for Mobility, Stuttgart,

11 OpenLR Based on shortest path principle 1. Original object in source map is to be divided into parts, represented by a shortest path 2. Resulting location points will be enriched with over-determined location information in the source map 3. Corresponding location points to be identified in the receiving map 4. Compute shortest paths between location points in the receiving map Identification of corresponding Geometry in the receiving map Developed by TomTom Open Source Sample implementation in JAVA available Rainer Schützle: Valididation of Location Referencing Methods Networks for Mobility, Stuttgart,

12 OpenLR - Example Road Line Location Network Shortest Path Location Reference Point TomTom International B.V. Rainer Schützle: Valididation of Location Referencing Methods Networks for Mobility, Stuttgart,

13 EU-project ROSATTE Road authority Rainer Schützle: Valididation of Location Referencing Methods Networks for Mobility, Stuttgart,

14 ROSATTE: Test and validation Quality parameters (selection): Completeness Of single Update-Objects Of datasets (% in receiving map decoded objects) Attributive correctness Attribute-matching correctly done? Topological correctness Topological reference of the test objects to the road network correctly decoded in receiving map? Accuracy Distance difference of start-/endpoints to the next reference point: At intersections: Ref.-pt. = intersection point Apart from intersections: Ref.-pt. = perpendicular to road edge Rainer Schützle: Valididation of Location Referencing Methods Networks for Mobility, Stuttgart,

15 Example topological correctness Topological correct: Rainer Schützle: Valididation of Location Referencing Methods Networks for Mobility, Stuttgart,

16 Example topological correctness Topological incorrect: Rainer Schützle: Valididation of Location Referencing Methods Networks for Mobility, Stuttgart,

17 Example Accuracy At intersections: Rainer Schützle: Valididation of Location Referencing Methods Networks for Mobility, Stuttgart,

18 Example Accuracy Apart from intersections: Rainer Schützle: Valididation of Location Referencing Methods Networks for Mobility, Stuttgart,

19 Geometry ROSATTE: Results Bavaria Used LR: OpenLR Test site Bavaria Map vendor 1 Map vendor 2 Total number of features considered for validation % % Integrated features % 21 96% Topologically correct features % 14 64% mean distance [m] reference point to integrated point features with accuracy worse than 50 m 26 17% 6 27% features within accuracy tolerance of 50 m 27 18% 1 5% features within accuracy tolerance of 20 m 21 14% 0 0% features within accuracy tolerance of 10 m 24 16% 2 9% features within accuracy tolerance of 5 m 38 26% 12 55% overall correct integrated features (with accuracy better than 50 m) Source: ROSATTE, project deliverable D5.4 Aggregated test report, % 11 50% Location Referencing has biggest influence on results Big potential of improving LR Methods Rainer Schützle: Valididation of Location Referencing Methods Networks for Mobility, Stuttgart,

20 ROSATTE.. and now? Evaluation methodology Does a representative dataset even exist? Or is it rather depending on the respective project / problem? LR quality = LR methodology influence + LR implementation influence evaluate separately!! Examine different approaches Automate Evaluation methods Sequence: 1. Insertion of test objects w.r.t. source map 2. Encoding of the test objects with the help of a LR method (e.g. OpenLR, AGORA) 3. Decoding of the dataset on the target map 4. Analyse test objects by comparison of status prior/after Location Referencing Possible to process bigger datasets More frequent tests Incremental quality gain achievable using LR methods Rainer Schützle: Valididation of Location Referencing Methods Networks for Mobility, Stuttgart,

21 Outlook Location Referencing Pointidentification in the target road network Potentials exist Shortest-Path Routing in the target network Routing Algorithmen is well known But: is the shortest path in two networks always the same? Search for alternative possibilities Map linkage Attribute matching Transistions of modeling Big potential surely does exist here! However: the linkage always is specific to the used map database! Validation of LR Methods Automation of Validation How to replace the human perception by software? Rainer Schützle: Valididation of Location Referencing Methods Networks for Mobility, Stuttgart,

22 Thanks a lot for your attention! Contact: rainer.schuetzle@ingeo.uni-stuttgart.de Rainer Schützle: Valididation of Location Referencing Methods Networks for Mobility, Stuttgart,