PERSONALIZED ROUTE INDICES VIA CROWD-SOURCED DATA

• US 20160117921A1
• Filed: 10/22/2014
• Published: 04/28/2016
• Est. Priority Date: 10/22/2014
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: MICHELINI, John Ottavio, D'AMATO, Anthony Mario
• Granted Patent: US 10,037,695 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 3/0484 for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0133 for classifying traffic situation
• CPC Class Codes: G08G 1/0141 for traffic information dissemination
• CPC Class Codes: G08G 1/096716 where the received information does not generate an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096741 where the source of the transmitted information selects which information to transmit to each vehicle
• CPC Class Codes: G08G 1/096775 where the origin of the information is a central station

Abstract

Traffic incident information may be clustered to determine geographical locations in which a statistically-increased level of traffic incidents occur. Indices indicative of vehicle operating conditions within the locations may be generated according to crowd-sourced event information factors and an index function including coefficients defining contributions of the factors to the indices. The coefficients may be optimized to minimize error between the index-predicted statistically-increased level areas and the clustered traffic incident information.

Claims

1. A system comprising:

a processor configured to

cluster traffic incident information to determine geographical locations in which a statistically-increased level of traffic incidents occur;

generate traffic incident likelihood indices predictive of traffic incidents levels within the geographic locations according to crowd-sourced event information factors and an index function including coefficients defining contributions of the factors to the indices; and

optimize the coefficients to minimize error between the indices and the clustered traffic incident information.

2. The system of claim 1, wherein the crowd-sourced event information includes at least two of traffic data, environment data, road type data, human factor data, and vehicle parameter data.

3. The system of claim 1, wherein the factors include hard braking events and hard steering events.

4. The system of claim 1, wherein the processor is further configured to optimize the coefficients according to historical crowd-sourced event information of a specific vehicle driver to determine coefficients configured to generate indices optimized for the driver.

5. The system of claim 1, wherein the traffic incident information further includes traffic incidents identified according to the crowd-sourced event information.

6. The system of claim 1, wherein the processor is included in an index server device further configured to receive the crowd-sourced event information over a wide area network from a plurality of vehicles.

7. The system of claim 1, wherein the processor is further configured to:

receive a request for an index for a geographic location from a vehicle; and

provide the index to vehicle according to the index function and coefficients.

8. The system of claim 7, wherein the geographic location is a current geographic area in which the vehicle is located.

21. The system of claim 1, wherein the index function is of the form:

Ri(k)=f₁(geometry(k), a₁, . . . , a_n)+f₂(weather(k), a_n+1, . . . , a_m)+

f₃(crowd data(k), a_m+1, . . . , a_l),

where Ri represents the indices, k represents the geographic locations for which the indices Ri are being calculated, f₁ represents a function of road geometry information, f₂ represents a function of weather conditions, f₃ represent a function of the crowd-sourced event information, and a₁, . . . , a_l represent the coefficients defining contributions used to weight the factors in creation of the indices Ri.

9. A system comprising:

a processor of a vehicle configured to

send, to an index server, event information indicative of current driving conditions for the vehicle;

request, from the index server, an index of driving incident likelihood for a geographic location; and

receive, from the index server, the index as computed by the index server according to coefficients to an index function defining contributions of factors of the event information to the driving incident likelihood.

10. The system of claim 9, wherein the event information includes at least two of traffic data, environment data, road type data, human factor data, and vehicle parameter data.

11. The system of claim 9, wherein the factors include hard braking events and hard steering events.

12. The system of claim 9, wherein the index server is configured to optimize the coefficients according to historical crowd-sourced event information of a specific driver of the vehicle to determine coefficients optimized for the driver of the vehicle.

13. The system of claim 9, wherein the index server is configured to optimize the coefficients according to historical crowd-sourced event information of a plurality of drivers to determine coefficients optimized for the plurality of drivers.

14. The system of claim 9, wherein the geographic location is one or more of (i) a current geographic location in which the vehicle is located, and (ii) a potential geographic location though which the vehicle is routed.

15. A non-transitory computer-readable medium comprising instructions that, when executed by a processor of a vehicle, cause the processor to:

send, to an index server, event information indicative of current driving conditions for the vehicle;

request, from the index server, an index of driving incident likelihood for a geographic location; and

receive, from the index server, the index as computed by the index server according to coefficients to an index function defining contributions of factors of the event information to the driving incident likelihood.

16. The medium of claim 15, wherein the event information includes at least two of traffic data, environment data, road type data, human factor data, and vehicle parameter data.

17. The medium of claim 15, wherein the factors include hard braking events and hard steering events.

18. The medium of claim 15, wherein the index server is configured to optimize the coefficients according to historical crowd-sourced event information of a specific driver of the vehicle to determine coefficients optimized for the driver of the vehicle.

19. The medium of claim 15, wherein the index server is configured to optimize the coefficients according to historical crowd-sourced event information of a plurality of drivers to determine coefficients optimized for the plurality of drivers.

20. The medium of claim 15, wherein the geographic location is one or more of (i) a current geographic location in which the vehicle is located, and (ii) a potential geographic location though which the vehicle is routed.