Method of constructing a digital model of a route

• US 8,510,085 B2
• Filed: 10/22/2007
• Issued: 08/13/2013
• Est. Priority Date: 10/23/2006
• Status: Expired due to Fees

• Current Assignee: Nodbox Co.
• Original Assignee: Nodbox Co.
• Inventors: Fargas, Thierry, Clarac, Dominique
• Primary Examiner(s): Rivas, Omar Fernandez
• Assistant Examiner(s): CALLE, ANGEL J.
• Application Number:
  US12/446,660
• Publication Number:
  US 20100004903A1
• Time in Patent Office: 2,122 Days
• Field of Search: 703/6, 701/59, 701/400
• US Class Current: 703/6
• 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, said method comprising:

using an implementation device comprised of a differential global positioning system device and a hybridised inertial unit with a global positioning system (GPS) to execute the steps of

acquiring, with the differential global positioning system device at a first refresh frequency, a first set of data relating to the geometry of the road, the differential global positioning system device (DGPS) being relatively precise as compared to the hybridised inertial unit in absolute terms as to a specific point,

acquiring, with the hybridised inertial unit at a second refresh frequency which is greater than the first refresh frequency, a second set of data relating to the geometry of the road, the hybridised inertial unit being relatively precise as compared to the differential global positioning system device in relative terms between two consecutive points,

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

eliminating the values of 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, and

aggregating data of said first and second sets of corrected data in order to obtain the digital model of the road whose data refresh frequency is equal to the second refresh frequency.

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

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

5. The method according to claim 4, in which

the acquisition of additional road characterization data comprises a video capture of at least one video signal, and

the at least one video signal is processed so as to extract multidimensional characteristics of the road chosen from:

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

6. The method according to claim 1, further comprising obtaining data issuing from road management centre databases and incorporating the obtained data in the digital model in addition to the data of the first and second sets of corrected data.

7. The 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.

8. A method of determining at least one adaptive localized road algorithm (ALA) for a given application chosen among cartography and the management of roads, the method comprising:

using an implementation device comprised of a differential global positioning system device and a hybridised inertial unit with a global positioning system (GPS) to execute

step a) of constructing a digital model of a road using a construction method comprising;

acquiring, at a first refresh frequency, a first set of data relating to geometry of the road by means of the differential global positioning system (DGPS) device precise in absolute terms as to a specific point,

acquiring, at a second refresh frequency which is greater than the first refresh frequency, a second set of data relating to geometry of the road by means of the hybridised inertial unit with the global positioning system (GPS) precise in relative terms between two consecutive points,

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

eliminating values of the 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 sets of corrected data in order to obtain the digital model of the road whose data refresh frequency is equal to the second refresh frequency;

step (b) of choosing influencing variables according to the given application;

step (c) of indexing the variables and then aggregating the indexed variables with the data of the digital model;

step (d) of determining a confidence level for the values, variables and data of the digital model; and

step (e) of constructing the adaptive localized road algorithm (ALA) algorithm when the confidence level and availability of the variables are above predefined thresholds.

9. The method according to claim 8, further comprising:

constructing plural adaptive localized road algorithms (ALA), each adaptive localized road algorithm (ALA) constructed from an execution of the construction process, wherein executions of the construction process are performed in parallel, and

applying predefined precision constraint rules to the plural adaptive localized road algorithms (ALA) and choosing one adaptive localized road algorithm (ALA) from among the plural each adaptive localized road algorithms (ALA) according to results of the application of the predefined precision constraint rules.

10. The method according to claim 8, in which, when the confidence level is below the predefined threshold, additional variables are determined in order to reach a threshold and steps b, c, d are reiterated, incorporating said additional variables.

11. The method according to claim 9, in which a further adaptive localized road algorithm (ALA) is created by associating the plural adaptive localized road algorithms (ALA).

12. The method according to claim 8, wherein said steps b, c, and d are performed by implementing one of:

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

13. The method according to claim 8, further comprising using the adaptive localized road algorithm (ALA) application for determining one or more of the following items of information:

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

14. The method according to claim 8, in which the adaptive localized road algorithm (ALA) is determined by means of a circuit of the in-situ programmable gate array type for processing parallel data.

15. The method according to claim 8, in which the adaptive localized road algorithm (ALA) is determined by a formal neural network.

16. The method according to claim 8, in which the adaptive localized road algorithm (ALA) is determined by genetic algorithms.

17. The method according to claim 8, in which the adaptive localized road algorithm (ALA) is determined by fuzzy logic algorithms.

18. The method according to claim 9, in which, when the confidence level is below the predefined threshold, additional variables are determined in order to reach a threshold and steps b, c, d are reiterated, incorporating said additional variables.

19. The method according to claim 9, wherein steps b, c, and d are performed by implementing one of:

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

20. A method of determining at least one adaptive localized road algorithm (ALA) for a given application chosen among cartography and the management of roads, the method comprising:

using an implementation device comprised of an implementation device comprised of a differential global positioning system device and a hybridised inertial unit with a global positioning system (GPS) to execute

step a) of constructing a digital model of a road using a construction method comprising;

acquiring a first set of data, at a first refresh frequency, a relating to geometry of the road by means of a differential global positioning system (DGPS) precise in absolute terms as to specific individual points,

acquiring a second set of data, at a second refresh frequency which is greater than the first refresh frequency, relating to geometry of the road by means of a hybridised inertial unit with a global positioning system (GPS) precise in relative terms between consecutive points,

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

eliminating values of the 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 sets of corrected data in order to obtain the digital model of the road whose data refresh frequency is equal to the second refresh frequency; and

step (b) of choosing influencing variables according to the given application;

step (c) of indexing the variables and then aggregating the indexed variables with the data of the digital model;

step (d) of determining a confidence level for the values, variables and data of the digital model; and

step (e) of constructing the adaptive localized road algorithms (ALA) when the confidence level and the availability of the variables are above predefined thresholds, comprising;

constructing plural adaptive localized road algorithms (ALA), each adaptive localized road algorithm (ALA) constructed from an execution of the construction process, wherein executions of the construction processes are performed in parallel;

applying predefined precision constraint rules to the plural adaptive localized road algorithms (ALA) and choosing one adaptive localized road algorithm (ALA) from among the plural each adaptive localized road algorithms (ALA) according to results of the application of the predefined precision constraint rules;

wherein when the confidence level is below the predefined threshold, additional variables are determined in order to reach a threshold and steps b, c, d are reiterated, incorporating said additional variables.