SYSTEMS AND METHODS FOR TRAFFIC LEARNING

• US 20170120890A1
• Filed: 10/30/2015
• Published: 05/04/2017
• Est. Priority Date: 10/30/2015
• 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: Payne, Joshua D.
• Granted Patent: US 10,118,603 B2
• Time in Patent Office: Days
• CPC Class Codes: B60K 2360/169 Remaining operating distance or charge
• CPC Class Codes: B60K 2370/169 Remaining operating distance or charge
• CPC Class Codes: B60K 35/28 characterised by the type of the output information, e.g. video entertainment or vehicle dynamics information; characterised by the purpose of the output information, e.g. for attracting the attention of the driver
• CPC Class Codes: B60K 6/20 the prime-movers consisting of electric motors and internal combustion engines, e.g. HEVs
• CPC Class Codes: B60W 20/10 Controlling the power contribution of each of the prime movers to meet required power demand
• CPC Class Codes: B60W 20/12 using control strategies taking into account route information estimation or calculation of non-directly measurable driving parameters B60W40/00
• CPC Class Codes: B60W 2050/146 Display means
• CPC Class Codes: B60W 2510/244 Charge state
• CPC Class Codes: B60W 2540/30 Driving style
• CPC Class Codes: B60W 2552/00 Input parameters relating to infrastructure
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2554/406 Traffic density
• CPC Class Codes: B60W 2556/10 Historical data
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: B60Y 2200/10 Road Vehicles
• CPC Class Codes: B60Y 2300/182 Selecting between different operative modes, e.g. comfort and performance modes
• CPC Class Codes: B60Y 2300/244 Adaptation to traffic conditions
• CPC Class Codes: B60Y 2400/61 Arrangements of controllers for electric machines, e.g. inverters
• CPC Class Codes: G01C 21/3469 Fuel consumption; Energy use; Emission aspects
• CPC Class Codes: G01C 21/3492 : employing speed data or traffic data, e.g. real-time or historical traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968
• CPC Class Codes: G05G 1/00 : Controlling members, e.g. knobs or handles; Assemblies or arrangements thereof; Indicating position of controlling members means for preventing, limiting or returning the movements of parts of a control mechanism G05G5/00; providing feel, e.g. means to create a counterforce G05G5/03; specially adapted for programme control G05G21/00; vibration damping G05G25/02; joysticks G05G9/04; steering wheels for motor vehicles B62D
• CPC Class Codes: G08G 1/096833 : where different aspects are considered when computing the route
• CPC Class Codes: Y02T 10/84 : Data processing systems or methods, management, administration
• CPC Class Codes: Y10S 903/93 : Conjoint control of different elements

Abstract

Systems and methods for traffic learning are described. One embodiment of a method includes predicting a vehicle route of a vehicle, determining historical traffic data related to at least one segment along the vehicle route, and determining an energy traffic budget for electric power consumption of the vehicle along the vehicle route, where the energy traffic budget is learned from the historical traffic data. Some embodiments include creating a schedule for transitioning between gasoline power and electric power for the vehicle route, implementing the schedule as the vehicle traverses the vehicle route, and in response to encountering traffic along the vehicle route, implementing the energy traffic budget.

Claims

1. A method for traffic learning comprising:

predicting, by a computing device, a vehicle route of a vehicle;

determining, by the computing device, historical traffic data related to at least one segment along the vehicle route;

determining, by the computing device, an energy traffic budget for electric power consumption of the vehicle along the vehicle route, wherein the energy traffic budget is learned from the historical traffic data;

creating, by the computing device, a schedule for transitioning between gasoline power and electric power for the vehicle route;

implementing, by the computing device, the schedule as the vehicle traverses the vehicle route; and

in response to encountering traffic along the vehicle route, implementing the energy traffic budget.

2. The method of claim 1, wherein the energy traffic budget is created utilizing at least one of the following:

current electrical power reserves, and driving habits of a driver that is driving the vehicle.

3. The method of claim 1, further comprising providing an option to alter at least one of the following;

the energy traffic budget, the schedule, and the vehicle route.

4. The method of claim 1, further comprising:

determining a change in the determined historical traffic data;

determining whether the change affects the energy traffic budget;

determining a revised energy traffic budget; and

implementing the revised energy traffic budget in place of the energy traffic budget.

5. The method of claim 4, further comprising in response to determining that the vehicle has passed the previously encountered traffic, resuming implementation of the schedule.

6. The method of claim 1, wherein the vehicle route is determined from at least one of the following:

a user input and historical data.

7. The method of claim 1, wherein implementing the schedule comprises switching the vehicle from between electric vehicle (EV) mode and a gasoline mode according to the schedule.

8. A system for traffic learning comprising:

a vehicle computing device that comprises a memory component that stores logic that, when executed by a processor, causes the system to perform at least the following;

predict a vehicle route of a vehicle, wherein the vehicle is configured to operate in an electric vehicle (EV) mode and a gasoline mode;

determine at least one segment along the vehicle route;

receive historical traffic data related to the at least one segment along the vehicle route;

determine an energy traffic budget for electric power consumption of the vehicle along the vehicle route, wherein the energy traffic budget is created based on the historical traffic data;

create a schedule for transitioning between the gasoline mode and the EV mode for the vehicle route; and

in response to encountering traffic along the vehicle route, implement the energy traffic budget.

9. The system of claim 8, wherein the logic further causes the system to perform at least the following:

determine a change in the historical traffic data;

determine whether the change affects the energy traffic budget;

determine a revised energy traffic budget; and

implement the revised energy traffic budget in place of the energy traffic budget.

10. The system of claim 8, wherein the logic further causes the system to implement the schedule as the vehicle traverses the vehicle route.

11. The system of claim 8, wherein the logic further causes the system to perform at least the following:

determine that the vehicle can traverse the vehicle route according to the schedule with extra energy; and

alter the schedule to utilize at least a portion of the extra energy along the vehicle route.

12. The system of claim 8, wherein the energy traffic budget is created utilizing at least one of the following:

current electrical power reserves and driving habits of a driver that is driving the vehicle.

13. The system of claim 8, wherein the vehicle route is determined from at least one of the following:

a user input and historical data.

14. The system of claim 8, wherein implementing the schedule comprises switching the vehicle from between the EV mode and the gasoline mode according to the schedule.

15. A computing device for traffic learning comprising:

a processor; and

a memory component that stores logic that, when executed by the processor, causes the computing device to perform at least the following;

predict a vehicle route of a vehicle, wherein the vehicle is configured to operate in an electric vehicle (EV) mode and a gasoline mode, and wherein the vehicle is configured to automatically change between the EV mode and the gasoline mode in response to a change command;

determine at least one segment along the vehicle route;

determine historical traffic data related to the at least one segment along the vehicle route;

determine an energy traffic budget for electric power consumption of the vehicle along the at least one segment, wherein the energy traffic budget is created based on the historical traffic data;

create a schedule for transitioning between the gasoline mode and the EV mode for the vehicle route;

implement the schedule as the vehicle traverses the vehicle route, wherein implementing the schedule comprises sending the change command to change between the EV mode and the gasoline mode according to the schedule; and

in response to encountering traffic along the vehicle route, implement the energy traffic budget.

16. The computing device of claim 15, wherein the logic further causes the computing device to perform at least the following:

determine a change in the traffic data;

determine whether the change affects the energy traffic budget;

in response to determining that the change affects the energy traffic budget, determine a revised energy traffic budget; and

implement the revised energy traffic budget in place of the energy traffic budget.

17. The computing device of claim 15, wherein the logic further causes the computing device to resume implementation of the schedule, in response to determining that the vehicle has passed the previously encountered traffic.

18. The computing device of claim 15, wherein the energy traffic budget is created utilizing at least one of the following:

current electrical power reserves, the traffic data, historical traffic data, and driving habits of a driver that is driving the vehicle.

19. The computing device of claim 15, wherein the vehicle route is determined from at least one of the following:

a user input and historical data.

20. The computing device of claim 15, where in the logic further causes the computing device to send the energy traffic budget to a remote computing device.