Adaptive and Personalized Navigation System

• US 20090192705A1
• Filed: 03/30/2009
• Published: 07/30/2009
• Est. Priority Date: 11/02/2006
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Rasmussen, Jens Eilstrup, Golding, Andrew R.
• Granted Patent: US 8,682,574 B2
• Time in Patent Office: Days
• US Class Current: 701/201
• CPC Class Codes: G01C 21/3484 Personalized, e.g. from learned user behaviour or user-defined profiles
• 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: G01C 21/36 Input/output arrangements for on-board computers

Abstract

Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user'"'"'s personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver'"'"'s personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user'"'"'s personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.

Claims

1. A machine-readable storage medium encoded with instructions that, when executed by one or more processors, cause the processor to carry out a process for generating directions for use in navigation during a current driving session, the process comprising:

receiving a target destination;

generating at least one candidate route;

probabilistically determining that one of a plurality of conditional variant models associated with a target attribute corresponds to a condition of the target attribute, the plurality conditional variant models learned from previous user driving sessions; and

scoring the at least one candidate route using the determined conditional variant model; and

providing a scored route to a user.

2. The machine-readable storage medium of claim 1, the process further comprising:

determining a driver ID for the current driving session; and

restricting the plurality of conditional variant models associated with the target attribute to include only conditional variant models associated with the driver ID.

3. The machine-readable storage medium of claim 2, wherein determining a driver ID for the current driving session comprises at least one selected from a group consisting of:

receiving login criteria associated with the driver ID;

detecting an ignition key associated with the driver ID;

detecting a preferred seat setting associated with the driver ID;

detecting a preferred mirror setting associated with the driver ID; and

detecting a driver weight associated with the driver ID.

4. The machine-readable storage medium of claim 1 wherein the target attribute is a road familiarity attribute.

5. The machine-readable storage medium of claim 4, the process further comprising:

providing summarized directions for the scored route in response to the road familiarity attribute indicating user familiarity with the scored route.

6. The machine-readable storage medium of claim 5, the process further comprising:

providing expanded directions for the scored route in response to a user request for additional details of the summarized directions.

7. The machine-readable storage medium of claim 4, the process further comprising providing a visual indicator of a route familiarity level to the user.

8. The machine-readable storage medium of claim 1 wherein the target attribute is a road preference attribute.

9. The machine-readable storage medium of claim 8, the process further comprising:

increasing the computed score for the candidate route in response to the road preference attribute indicating a favored route,.

10. The machine-readable storage medium of claim 8, the process further comprising:

decreasing the computed score for the candidate route in response to the road preference attribute indicating a disfavored route,.

11. The machine-readable storage medium of claim 1 wherein the target attribute is a road speed attribute.

12. The machine-readable storage medium of claim 11, wherein probabilistically determining that one of a plurality of conditional variant models associated with the road speed attribute corresponds to a condition of the road speed attribute comprises at least one selected from a group consisting of:

determining a level of user hurriedness based on one or more detected driving behaviors;

determining a current environmental condition;

determining a current level of traffic; and

determining a type of a road segment included in the candidate route.

13. The machine-readable storage medium of claim 1 wherein the target attribute is a road hazard attribute.

14. The machine-readable medium of claim 13, wherein probabilistically determining that one of a plurality of conditional variant models associated with the road hazard attribute corresponds to a condition of the road hazard attribute comprises at least one selected from a group consisting of:

detecting an avoidance maneuver undertaken by the user;

detecting a vehicular acceleration in a vertical or horizontal axis;

detecting a current environmental condition; and

detecting a current level of traffic.

15. The machine-readable storage medium of claim 13, the process further comprising:

in response to detecting a hazardous condition, reporting the hazardous condition to the user.

16. The machine-readable storage medium of claim 13, the process further comprising:

in response to detecting a hazardous condition, reporting the hazardous condition to a management agency.

17. The machine-readable storage medium of claim 1, the process further comprising:

detecting a change in the condition of the target attribute; and

providing an altered route to the user based on the detected change;

18. The machine-readable storage medium of claim 17, wherein detecting a change in the condition of the target attribute comprises:

recomputing probabilities for the conditional variant models associated with the target attribute periodically during the current driving session using subsequently observed data of the current driving session; and

probabilistically determining that a second conditional variant model currently corresponds to the condition of the target attribute.

19. A computer-implemented method for generating directions for use in navigation during a current driving session, comprising:

receiving a target destination;

generating at least one candidate route;

probabilistically determining that one of a plurality of conditional variant models associated with a target attribute corresponds to a condition of the target attribute, the plurality conditional variant models learned from previous user driving sessions; and

scoring the at least one candidate route using the determined conditional variant model; and

providing a scored route to a user.

20. The computer-implemented method of claim 19, the method further comprising:

determining a driver ID for the current driving session; and

restricting the plurality of conditional variant models associated with the target attribute to include only conditional variant models associated with the driver ID.

21. The computer-implemented method of claim 20, wherein determining a driver ID for the current driving session comprises at least one selected from a group consisting of:

receiving login criteria associated with the driver ID;

detecting an ignition key associated with the driver ID;

detecting a preferred seat setting associated with the driver ID;

detecting a preferred mirror setting associated with the driver ID; and

detecting a driver weight associated with the driver ID.

22. The computer-implemented method of claim 19 wherein the target attribute is a road familiarity attribute.

23. The computer-implemented method of claim 22, the method further comprising:

providing summarized directions for the scored route in response to the road familiarity attribute indicating user familiarity with the scored route.

24. The computer-implemented method of claim 23, the method further comprising:

providing expanded directions for the scored route in response to a user request for additional details of the summarized directions.

25. The computer-implemented method of claim 22, the method further comprising providing a visual indicator of a route familiarity level to the user.

26. The computer-implemented method of claim 19 wherein the target attribute is a road preference attribute.

27. The computer-implemented method of claim 26, the method further comprising:

increasing the computed score for the candidate route in response to the road preference attribute indicating a favored route,.

28. The computer-implemented method of claim 26, the method further comprising:

decreasing the computed score for the candidate route in response to the road preference attribute indicating a disfavored route,.

29. The computer-implemented method of claim 19 wherein the target attribute is a road speed attribute.

30. The computer-implemented method of claim 29, wherein probabilistically determining that one of a plurality of conditional variant models associated with the road speed attribute corresponds to a condition of the road speed attribute comprises at least one selected from a group consisting of:

determining a level of user hurriedness based on one or more detected driving behaviors;

determining a current environmental condition;

determining a current level of traffic; and

determining a type of a road segment included in the candidate route.

31. The computer-implemented method of claim 19 wherein the target attribute is a road hazard attribute.

32. The computer-implemented method of claim 31 wherein probabilistically determining that one of a plurality of conditional variant models associated with the road hazard attribute corresponds to a condition of the road hazard attribute comprises at least one selected from a group consisting of:

detecting an avoidance maneuver undertaken by the user;

detecting a vehicular acceleration in a vertical or horizontal axis;

detecting a current environmental condition; and

detecting a current level of traffic.

33. The computer-implemented method of claim 31, the method further comprising:

in response to detecting a hazardous condition, reporting the hazardous condition to the user.

34. The computer-implemented method of claim 31, the method further comprising:

in response to detecting a hazardous condition, reporting the hazardous condition to a management agency.

35. The computer-implemented method of claim 19, the method further comprising:

detecting a change in the condition of the target attribute; and

providing an altered route to the user based on the detected change;

36. The computer-implemented method of claim 35, wherein detecting a change in the condition of the target attribute comprises:

recomputing probabilities for the conditional variant models associated with the target attribute periodically during the current driving session using subsequently observed data of the current driving session; and

probabilistically determining that a second conditional variant model currently corresponds to the condition of the target attribute.