Granted Patent B1

US 10,295,363 · App. 15/409,143 · Granted May 21, 2019

Inventors: Blake Konrardy (Bloomington, IL); Scott T. Christensen (Salem, OR); Gregory Hayward (Bloomington, IL); Scott Farris (Bloomington, IL)

Assignee: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

G01C21/3697G05D1/0088G07C5/008G08G1/20G06N20/00G06N99/005

Abstract

Claims (66)

1. A computer-implemented method for analyzing roadway suitability for autonomous or semi-autonomous vehicle operation, comprising:

receiving, at one or more processors, map data including indications of a plurality of road segments;

receiving, at the one or more processors, operating data from a plurality of vehicles having at least one autonomous operation feature, wherein the operating data includes location data associated with operation of the plurality of vehicles and wherein the operating data further includes data regarding accidents and data regarding hazardous events other than accidents, the hazardous events other than accidents including near collisions in which some of the plurality of vehicles came within threshold distances of other vehicles;

selecting, by the one or more processors, a road segment to analyze from the plurality of road segments;

associating, by the one or more processors, a subset of the operating data with the road segment based upon the location data;

determining, by the one or more processors, one or more risk levels associated with the road segment based upon the associated subset of the operating data;

determining, by the one or more processors, one or more suitability scores for the road segment based upon the one or more risk levels, each suitability score indicating a category of suitability for autonomous or semi-autonomous vehicle operation; and

storing, in a database, the one or more suitability scores, each suitability score further stored with an indication of the associated road segment.

2. The computer-implemented method of claim 1 , further comprising:

generating, by the one or more processors, one or more map tiles including visual representations of the one or more suitability scores.

3. The computer-implemented method of claim 2 , further comprising:

receiving, at the one or more processors, route information associated with a fleet of vehicles having autonomous operation features;

analyzing, by the one or more processors, the received route information using the one or more suitability scores;

identifying, by the one or more processors, one or more optimal routes associated with the fleet of vehicles; and

transmitting, to one or more additional processors associated with at least one vehicle of the fleet of vehicles, an indication of the one or more optimal routes.

4. The computer-implemented method of claim 3 , further comprising:

receiving, at the one or more processors, operating data from the fleet of vehicles, the operating data including location data indicating a plurality of locations of the vehicles of the fleet of vehicles; and

determining, by the one or more processors, an extent to which the fleet of vehicles follow the optimal routes.

5. The computer-implemented method of claim 1 , further comprising:

requesting, by the one or more processors, the map data from a third-party data source using an application program interface associated with the third-party data source.

6. The computer-implemented method of claim 1 , wherein the operating data includes usage information regarding one or more autonomous operation feature settings.

7. The computer-implemented method of claim 1 , wherein the operating data includes data regarding a plurality of types of environmental conditions associated with the plurality of vehicles.

8. The computer-implemented method of claim 1 , wherein the operating data for at least one of the plurality of vehicles is generated by a data application running on a mobile device associated with the vehicle.

9. The computer-implemented method of claim 1 ,

wherein the hazardous events other than accidents further include occurrences of (i) loss of control or (ii) hard braking.

10. The computer-implemented method of claim 1 , wherein

determining the one or more risk levels includes using machine learning techniques to automatically determine magnitudes of one or more risk factors associated with autonomous operation feature usage.

11. The computer-implemented method of claim 1 , wherein:

the map data includes one or more indications of location for each of the plurality of road segments, wherein the indications of location utilize a first format for indicating geospatial location;

the location data utilizes a second format distinct from the first format for indicating geospatial location; and

associating the subset of the operating data with the identified road segment includes converting one or both of the indication of location or the location data to a compatibility format for association.

12. The computer-implemented method of claim 1 , wherein the one or more suitability scores include a minimum safety rating for the road segment.

13. The computer-implemented method of claim 1 , wherein the one or more suitability scores indicate the suitability of the road segment for fully autonomous vehicle usage.

14. A computer system for analyzing roadway suitability for autonomous or semi-autonomous vehicle operation, comprising:

one or more processors;

a map database communicatively connected to the one or more processors; and

a program memory coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, cause the computer system to:

receive map data including indications of a plurality of road segments;

receive operating data from a plurality of vehicles having at least one autonomous operation feature, wherein the operating data includes location data associated with operation of the plurality of vehicles and wherein the operating data further includes data regarding accidents and data regarding hazardous events other than accidents, the hazardous events other than accidents including near collisions in which some of the plurality of vehicles came within threshold distances of other vehicles;

select a road segment to analyze from the plurality of road segments;

associate a subset of the operating data with the road segment based upon the location data;

determine one or more risk levels associated with the road segment based upon the associated subset of the operating data;

determine one or more suitability scores for the road segment based upon the one or more risk levels, each suitability score indicating a category of suitability for autonomous or semi-autonomous vehicle operation; and

store the one or more suitability scores in the map database, each suitability score further stored with an indication of the associated road segment.

15. The computer system of claim 14 , wherein the program memory further stores executable instructions that cause the computer system to:

generate one or more map tiles including visual representations of the one or more suitability scores.

16. The computer system of claim 15 , wherein the program memory further stores executable instructions that cause the computer system to:

receive route information associated with a fleet of vehicles having autonomous operation features;

analyze the received route information using the one or more suitability scores;

identify one or more optimal routes associated with the fleet of vehicles; and

transmit an indication of the one or more optimal routes to one or more additional processors associated with at least one vehicle of the fleet of vehicles.

17. The computer system of claim 14 , wherein:

the map data includes one or more indications of location for each of the plurality of road segments, wherein the indications of location utilize a first format for indicating geospatial location;

the location data utilizes a second format distinct from the first format for indicating geospatial location; and

associating the subset of the operating data with the identified road segment includes converting one or both of the indication of location or the location data to a compatibility format for association.

18. The computer system of claim 14 , wherein the operating data for at least one of the plurality of vehicles is generated by a data application running on a mobile device associated with the vehicle.

19. A tangible, non-transitory computer-readable medium storing instructions for analyzing roadway suitability for autonomous or semi-autonomous vehicle operation that, when executed by at least one processor of a computer system, cause the computer system to:

receive map data including indications of a plurality of road segments;

receive operating data from a plurality of vehicles having at least one autonomous operation feature, wherein the operating data includes location data associated with operation of the plurality of vehicles and wherein the operating data further includes data regarding accidents and data regarding hazardous events other than accidents, the hazardous events other than accidents including near collisions in which some of the plurality of vehicles came within threshold distances of other vehicles;

select a road segment to analyze from the plurality of road segments;

associate a subset of the operating data with the road segment based upon the location data;

determine one or more risk levels associated with the road segment based upon the associated subset of the operating data;

determine one or more suitability scores for the road segment based upon the one or more risk levels, each suitability score indicating a category of suitability for autonomous or semi-autonomous vehicle operation; and

store the one or more suitability scores in a map database, each suitability score further stored with an indication of the associated road segment.

20. The tangible, non-transitory computer-readable medium of claim 19 , further storing instructions that cause the computer system to:

generate one or more map tiles including visual representations of the one or more suitability scores.

Assignments (1)

Continuity (35)

Cited By (8)

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