Systems and Methods for Estimating Local Traffic Flow

• US 20120078507A1
• Filed: 09/27/2010
• Published: 03/29/2012
• Est. Priority Date: 09/27/2010
• Status: Active Grant

• Current Assignee: Toyota Jidosha Kabushiki Kaisha (Toyota Motor Corporation)
• Original Assignee: Toyota Motor Engineering & Manufacturing North America Incorporated (Toyota Motor Corporation)
• Inventors: Caveney, Derek Stanley, McNew, John Michael
• Granted Patent: US 8,897,948 B2
• Time in Patent Office: Days
• US Class Current: 701/414
• CPC Class Codes: G08G 1/0104 Measuring and analyzing of parameters relative to traffic conditions
• CPC Class Codes: G08G 1/096716 where the received information does not generate an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096725 where the received information generates an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096791 where the origin of the information is another vehicle
• CPC Class Codes: G08G 1/162 event-triggered

Abstract

Systems and methods for estimating local traffic flow are described. One embodiment of a method includes determining a driving habit of a user from historical data, determining a current location of a vehicle that the user is driving, and determining a current driving condition for the vehicle. Some embodiments include predicting a desired driving condition from the driving habit and the current location, comparing the desired driving condition with the current driving condition to determine a traffic congestion level, and sending a signal that indicates the traffic congestion level.

Claims

1. A method for estimating local traffic flow, comprising steps of:

determining a driving habit of a user from historical data;

determining a current location of a vehicle that the user is driving;

determining a current driving condition for the vehicle;

predicting a desired driving condition from the driving habit and the current location;

comparing, by the vehicle, the desired driving condition with the current driving condition to determine a traffic congestion level; and

sending a signal that indicates the traffic congestion level.

2. The method of claim 1, wherein the driving habit includes at least one of the following:

a speed the user prefers to drive, a headway gap the user prefers, and a lateral gap the user prefers in order to change lanes.

3. The method of claim 1, wherein the current driving condition of the vehicle includes at least one of the following:

a current vehicle speed, a current headway gap, a current lateral gap.

4. The method of claim 1, wherein comparing the desired driving condition with the current driving condition includes:

determining whether the current driving condition is different than the desired driving condition;

in response to determining that the current driving condition is different than the desired driving condition, determining an amount that the current driving condition is different than the desired driving condition; and

comparing the amount that the current driving condition is different than the desired driving condition to a predetermined threshold to determine the traffic congestion level.

5. The method of claim 1, wherein determining the current driving condition includes calculating a lateral mobility factor.

6. The method of claim 1, wherein determining the current driving condition includes calculating a longitudinal mobility factor.

7. The method of claim 1, wherein determining the traffic congestion level includes:

calculating a lateral mobility factor;

calculating a longitudinal mobility factor; and

determining the traffic congestion level from a comparison of the lateral mobility factor and the longitudinal mobility factor.

8. A system for estimating local traffic flow, comprising:

a memory component, at a vehicle that a user is driving, that stores vehicle environment logic that, when executed, causes a vehicle computing device to perform at least the following;

determine a driving habit of the user from historical data;

determine a current location of the vehicle;

determine a current driving condition for the vehicle;

predict a desired driving condition from driving habit and the current location;

compare the desired driving condition with the current driving condition to determine a traffic congestion level; and

send a signal that indicates the traffic congestion level.

9. The system of claim 8, wherein the driving habit includes at least one of the following:

a speed the user prefers to drive, a headway gap the user prefers, and a lateral gap the user prefers in order to change lanes.

10. The system of claim 8, wherein the current driving condition of the vehicle includes at least one of the following:

a current vehicle speed, a current headway gap, and a current lateral gap.

11. The system of claim 8, wherein comparing the desired driving condition with the current driving condition includes:

determining whether the current driving condition is different than the desired driving condition;

in response to determining that the current driving condition is different than the desired driving condition, determining an amount that the current driving condition is different than the desired driving condition; and

comparing the amount that the current driving condition is different than the desired driving condition to a predetermined threshold to determine the traffic congestion level.

12. The system of claim 8, wherein determining the current driving condition includes calculating a lateral mobility factor.

13. The system of claim 8, wherein determining the current driving condition includes calculating a longitudinal mobility factor.

14. A non-transitory computer-readable medium for estimating local traffic flow, the non-transitory computer-readable medium storing a program that, when executed by a vehicle computing device at a vehicle a user is driving, causes the vehicle computing device to perform at least the following:

determine a driving habit of the user from historical data;

determine a current location of the vehicle;

determine a current driving condition for the vehicle;

predict a desired driving condition from the driving habit and the current location;

compare the desired driving condition with the current driving condition to determine a traffic congestion level; and

send a signal that indicates the traffic congestion level.

15. The non-transitory computer-readable medium of claim 14, wherein the driving habit includes at least one of the following:

a speed the user prefers to drive, a headway gap the user prefers, and a lateral gap the user prefers in order to change lanes.

16. The non-transitory computer-readable medium of claim 14, wherein the current driving condition of the vehicle includes at least one of the following:

a current vehicle speed, a current headway gap, and a current lateral gap.

17. The non-transitory computer-readable medium of claim 14, wherein comparing the desired driving condition with the current driving condition includes:

determining whether the current driving condition is different than the desired driving condition;

in response to determining that the current driving condition is different than the desired driving condition, determining an amount that the current driving condition is different than the desired driving condition; and

comparing the amount that the current driving condition is different than the desired driving condition to a predetermined threshold to determine the traffic congestion level.

18. The non-transitory computer-readable medium of claim 14, wherein determining the current driving condition includes calculating a lateral mobility factor.

19. The non-transitory computer-readable medium of claim 14, wherein determining the current driving condition includes calculating a longitudinal mobility factor.

20. The non-transitory computer-readable medium of claim 14, wherein determining the traffic congestion level includes:

calculating a lateral mobility factor;

calculating a longitudinal mobility factor; and

determining the traffic congestion level from a comparison of the lateral mobility factor and the longitudinal mobility factor.