Learning automated vehicle

• US 9,097,549 B1
• Filed: 03/17/2014
• Issued: 08/04/2015
• Est. Priority Date: 03/17/2014
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Rao, Manoharprasad K., Prakah-Asante, Kwaku O.
• Primary Examiner(s): Cheung, Mary
• Assistant Examiner(s): Trivedi, Atul
• Application Number:
  US14/215,350
• Time in Patent Office: 505 Days
• Field of Search: 701 23- 26, 701410-411, 701420-421
• US Class Current: 1/1
• CPC Class Codes: G01C 21/3461 Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types, segments such as motorways, toll roads, ferries
• CPC Class Codes: G01C 21/3484 Personalized, e.g. from learned user behaviour or user-defined profiles

Abstract

A vehicle includes a navigation system and a processing device. The navigation system determines a route from a current location to a selected destination. The processing device applies a template to the route. The template represents driving trends associated with a geographic region. The processing device adjusts the route based on the template prior to the vehicle embarking on the route.

Claims

1. A vehicle comprising:

a navigation system configured to determine a route from a current location to a selected destination; and

a processing device programmed to apply a template to the route, wherein the template represents a plurality of driving trends associated with avoiding areas of a geographic region along the route, and wherein the processing device is programmed to adjust the route based on the template prior to the vehicle embarking on the route.

2. The vehicle of claim 1, wherein the navigation system is programmed to determine the route based on at least one user preference, and wherein the processing device is programmed to ignore the template if the template is inconsistent with the user preference.

3. The vehicle of claim 2, wherein the user preference is based on at least one of fuel efficiency and travel time to the selected destination.

4. The vehicle of claim 1, further comprising a predictive learning system configured to select the template.

5. The vehicle of claim 4, wherein the predictive learning system is configured to select the template based on a geographic region associated with the current location.

6. The vehicle of claim 4, wherein the predictive learning system is configured to receive the template from a remote server.

7. The vehicle of claim 1, wherein the template defines at least one of safety information, infrastructure information, convenience information, and traffic flow information associated with avoiding areas of the geographic region.

8. The vehicle of claim 1, wherein the processing device is incorporated into an autonomous mode controller configured to operate the vehicle in an autonomous mode.

9. The vehicle of claim 1, wherein the processing device is configured to update the template based at least in part on a user input.

10. A vehicle comprising:

an autonomous driving sensor;

an autonomous mode controller configured to control at least one vehicle subsystem in accordance with signals output by the autonomous driving sensor when operating in an autonomous mode; and

a navigation system configured to determine a route from a current location to a selected destination;

wherein the autonomous mode controller includes a processing device programmed to apply a template to the route, wherein the template represents a plurality of driving trends associated with avoiding areas of a geographic region, and wherein the processing device is programmed to adjust the route based on the template prior to the autonomous mode controller operating in the autonomous mode.

11. The vehicle of claim 10, wherein the navigation system is programmed to determine the route based on at least one user preference, and wherein the processing device is programmed to ignore the template if the template is inconsistent with the user preference.

12. The vehicle of claim 11, wherein the user preference is based on at least one of fuel efficiency and travel time to the selected destination.

13. The vehicle of claim 10, further comprising a predictive learning system configured to select the template.

14. The vehicle of claim 13, wherein the predictive learning system is configured to select the template based on a geographic region associated with the current location.

15. The vehicle of claim 13, wherein the predictive learning system is configured to receive the template from a remote server.

16. The vehicle of claim 10, wherein the template defines at least one of safety information, infrastructure information, convenience information, and traffic flow information associated with avoiding areas of the geographic region.

17. The vehicle of claim 10, wherein the processing device is configured to update the template based at least in part on a user input.

18. A method comprising:

determining a current location of a vehicle;

determining a destination of the vehicle;

generating a route based at least in part on the current location and the destination;

applying a template to the route, the template representing a plurality of driving trends associated with avoiding areas of a geographic region; and

adjusting the route based on the template prior to the vehicle embarking on the route.

19. The method of claim 18, further comprising selecting the template based at least in part on the geographic region.

20. The method of claim 18, further comprising requesting the template from a remote server.