METHOD OF CONSTRUCTING A DIGITAL MODEL OF A ROUTE

• US 20100004903A1
• Filed: 10/22/2007
• Published: 01/07/2010
• Est. Priority Date: 10/23/2006
• Status: Active Grant

• Current Assignee: Nodbox Co.
• Original Assignee: Nodbox Co.
• Inventors: Clarac, Dominique, Fargas, Thierry
• Granted Patent: US 8,510,085 B2
• Time in Patent Office: Days
• US Class Current: 703/1
• CPC Class Codes: G01C 21/26 specially adapted for navigation in a road network
• CPC Class Codes: G06T 17/05 Geographic models

Abstract

A method of constructing a digital model of a road, includes the steps of acquiring a first set of data relating to the geometry of the road via a differential global positioning system (DGPS), acquiring a second set of data relating to the geometry of the road via a hybridized inertial unit with a global positioning system (GPS), indexing the values of the first and second sets of data by determining their confidence level, discarding the values of the first and second sets of data below a predefined confidence level in order to obtain a first and second set of corrected data, and aggregating the data of the first and second sets of corrected data in order to obtain the digital model of the road. The applicable to the fields of road cartography for navigation and road management, for the creation of adaptive localized algorithms.

Claims

1. A method of constructing a digital model of a road, which comprises:

acquiring a first set of data relating to the geometry of the road by means of a differential global positioning system (DGPS),

acquiring a second set of data relating to the geometry of the road by means of a hybridised inertial unit with a global positioning system (GPS),

indexing the values of the said first and second steps of data by determining their confidence level,

eliminating the values of the said first and second sets of data below a predefined confidence threshold in order to obtain a first set and second set of corrected data,

aggregating the data of said first and second set of corrected data,

aggregating the data of said first and second sets of corrected data in order to obtain the digital model of the road.

2. A method according to claim 1, also comprising a refinement of the model obtained by aggregating data, the said refinement consisting of adding geometric reference points obtained by means of a topographical-precision differential global positioning system (TDGPS).

3. A method according to claim 1, in which the acquisition of the first and second sets of data takes place by the movement of a measuring vehicle on the road.

4. A method according to claim 3, in which, simultaneously with the acquisition of the second set of data, additional road characterisation data are acquired.

5. A method according to claim 4, in which

the acquisition of additional data comprises a video capture, and

the video signal or signals are processed so as to extract multidimensional characteristics of the road chosen from:

the width of the roadway, the height of edifices, road signs.

6. A method according to claim 1, in which data issuing from road management centre databases are incorporated in the digital model.

7. A method of determining an adaptive localised road algorithm ALA for an application to cartography or to the management of roads, in which:

a—

a digital model of a road is constructed using the construction method of the invention according to claim 1,

b—

a batch of influencing variables is chosen according to the application,

c—

the variables are indexed and then aggregated with the data of the digital model,

d—

a confidence level is determined for the values, variables and data of the digital model,

e—

the ALA algorithm is constructed if the confidence level and the availability of the variables is above predefined thresholds.

8. A method according to claim 7, in which:

preferably, steps b, c and d are performed in parallel using several ALA algorithm creation processes,

the result of the process that confers the best compromise between confidence and availability of variables is chosen applying predefined precision constraint rules.

9. A method according to claim 7, in which, if the confidence level is below the predefined threshold, the missing variables are determined in order to reach a threshold and steps b, c, d are reiterated, incorporating the said variables.

10. A method according to claim 7, in which a complex ALA algorithm is created by associating a plurality of ALA algorithms obtained by steps a, b, c, d, e.

11. A method according to claim 7, in which the ALA creation process used for steps b, c, d is chosen from:

knowledge interrogation, knowledge extraction, cloning of components, optimisation under constraints, the identification of processes, the reverse resolution of problems, the determination of precursors, the search for cause-and-effect links, the search for factors influencing consequences, analysis of the interaction between variables, analysis of correlation between variables, rejection of aberrations.

12. A method according to claim 7, in which the ALA algorithm application is the determination of one or more of the following items of information:

the localised adaptive coefficient of transverse friction, the localised adaptive braking distance, the localised adaptive acceleration distance, the localised adaptive approach distance, the adaptive positioning of the target paths of vehicles, the adaptive precursor of the adaptive location of the protection means, the adaptive localised information on the road geometry dangers, the adaptive localised information on the highway code dangers, the adaptive precursor for the creation of accident black spots, the adaptive water accumulation location, the adaptive visibility location.

13. A method according to claim 7, in which the ALA algorithm is determined by means of a circuit of the in-situ programmable gate array type for processing highly parallel data.

14. A method according to claim 7, in which the ALA algorithm is determined by a formal neural network.

15. A method according to claim 7, in which the ALA algorithm is determined by genetic algorithms.

16. A method according to claim 7, in which the ALA algorithm is determined by fuzzy logic algorithms.

17. A method according to claim 2, in which the acquisition of the first and second sets of data takes place by the movement of a measuring vehicle on the road.

18. A method according to claim 8, in which, if the confidence level is below the predefined threshold, the missing variables are determined in order to reach a threshold and steps b, c, d are reiterated, incorporating the said variables.

19. A method according to claim 8, in which a complex ALA algorithm is created by associating a plurality of ALA algorithms obtained by steps a, b, c, d, e.

20. A method according to claim 8, in which the ALA creation process used for steps b, c, d is chosen from: