Journey Learning System

• US 20130158855A1
• Filed: 12/16/2011
• Published: 06/20/2013
• Est. Priority Date: 12/16/2011
• Status: Active Grant

• Current Assignee: Toyota Jidosha Kabushiki Kaisha (Toyota Motor Corporation)
• Original Assignee: Toyota Infotechnology Center Co., Ltd. (Toyota Motor Corporation), Toyota Jidosha Kabushiki Kaisha (Toyota Motor Corporation)
• Inventors: Weir, David Frank Russell, Melen, Roger, Oguchi, Kentaro
• Granted Patent: US 8,892,350 B2
• Time in Patent Office: Days
• US Class Current: 701/400
• CPC Class Codes: G01C 21/3617 using user history, behaviour, conditions or preferences, e.g. predicted or inferred from previous use or current movement
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06V 20/10 Terrestrial scenes scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0962 having an indicator mounted inside the vehicle, e.g. giving voice messages
• CPC Class Codes: G09B 29/00 Maps; Plans; Charts; Diagrams, e.g. route diagram star maps G09B27/04; devices for holding or supporting maps A47B97/02; for computing purposes G06G1/14, G09B1/16; display boards G09F
• CPC Class Codes: G09B 29/10 Map spot or coordinate position indicators; Map reading aids optical projection apparatus per se G03B
• CPC Class Codes: G09B 29/106 using electronic means
• CPC Class Codes: H04W 4/029 Location-based management or tracking services

Abstract

A system and method for estimating journey destinations is disclosed. The system comprises a conversion module, a frequency module, a metric module, a quality module and a summary module. The conversion module converts a set of driver history data to a set of learning parameters. The frequency module analyzes the set of learning parameters and current journey data to generate estimated journey data describing one or more potential journeys. The metric module analyzes the estimated journey data and the set of current status data to determine one or more metrics associated with the estimated journey data. The quality module determines one or more quality scores associated with the estimated journey data. The summary module determines one or more status summaries and one or more estimate summaries. The summary module associates the one or more status summaries and the one or more estimate summaries with the estimated journey data.

Claims

1. A method comprising:

converting a set of driver history data to a set of learning parameters;

analyzing the set of learning parameters and current journey data to generate estimated journey data describing one or more potential journeys;

retrieving a set of current status data;

analyzing the estimated journey data and the set of current status data to determine one or more metrics associated with the estimated journey data;

determining one or more quality scores associated with the estimated journey data based at least in part on the one or more metrics;

determining one or more status summaries and one or more estimate summaries;

associating the one or more status summaries and the one or more estimate summaries with the estimated journey data;

outputting display data for depicting the estimated journey data, the one or more status summaries and the one or more estimate summaries on a display; and

storing the current journey data as additional driver history data.

2. The method of claim 1, wherein the set of driver history data describes one or more past destinations and one or more estimated destinations, and the estimated journey data is created corresponding to the one or more estimated destinations.

3. The method of claim 1, wherein one of the one or more metrics is related to an estimated abnormal condition associated with one of the one or more potential journeys.

4. The method of claim 3 further comprising outputting a navigational recommendation related to the estimated abnormal condition.

5. The method of claim 1, wherein the set of learning parameters are arranged in a learning table.

6. The method of claim 1, wherein the estimated journey data describes three potential journeys.

7. The method of claim 1, wherein different quality scores are associated with different journeys.

8. The method of claim 1, wherein the display is a component of a navigation system.

9. The method of claim 1 further comprising:

retrieving, from a driver history repository, the set of driver history data;

detecting that a present journey has ended;

collecting data describing the present journey; and

storing the collected data in the driver history repository.

10. A computer program product comprising a non-transitory computer readable medium encoding instructions that, in response to execution by a computing device, cause the computing device to perform operations comprising:

converting a set of driver history data to a set of learning parameters;

analyzing the set of learning parameters and current journey data to generate estimated journey data describing one or more potential journeys;

retrieving a set of current status data;

analyzing the estimated journey data and the set of current status data to determine one or more metrics associated with the estimated journey data;

determining one or more quality scores associated with the estimated journey data based at least in part on the one or more metrics;

determining one or more status summaries and one or more estimate summaries;

associating the one or more status summaries and the one or more estimate summaries with the estimated journey data;

outputting display data for depicting the estimated journey data, the one or more status summaries and the one or more estimate summaries on a display; and

storing the current journey data as additional driver history data.

11. The computer program product of claim 10, wherein the set of driver history data describes one or more past destinations and one or more estimated destinations, and the estimated journey data is created corresponding to the one or more estimated destinations.

12. The computer program product of claim 10, wherein one of the one or more metrics is related to an estimated abnormal condition associated with one of the one or more potential journeys.

13. The computer program product of claim 12, wherein the instructions cause the computing device to perform operations further comprising outputting a navigational recommendation related to the estimated abnormal condition.

14. The computer program product of claim 10, wherein the set of learning parameters are arranged in a learning table.

15. The computer program product of claim 10, wherein the estimated journey data describes three potential journeys.

16. The computer program product of claim 10, wherein different quality scores are associated with different journeys.

17. The computer program product of claim 10, wherein the display is a component of a navigation system.

18. The computer program product of claim 10, wherein the instructions cause the computing device to perform operations further comprising:

retrieving, from a driver history repository, the set of driver history data;

detecting that a present journey has ended;

collecting data describing the present journey; and

storing the collected data in the driver history repository.

19. A system comprising:

a conversion module converting a set of driver history data to a set of learning parameters;

a frequency module communicatively coupled to the conversion module, the frequency module analyzing the set of learning parameters and current journey data to generate estimated journey data describing one or more potential journeys, the frequency module storing the current journey data as additional driver history data;

a metric module communicatively coupled to the frequency module, the metric module retrieving a set of current status data, the metric module analyzing the estimated journey data and the set of current status data to determine one or more metrics associated with the estimated journey data;

a quality module communicatively coupled to the metric module, the quality module determining one or more quality scores associated with the estimated journey data based at least in part on the one or more metrics;

a summary module communicatively coupled to the quality module and the frequency module, the summary module determining one or more status summaries and one or more estimate summaries, the summary module associating the one or more status summaries and the one or more estimate summaries with the estimated journey data; and

an output module communicatively coupled to the frequency module, the quality module and the summary module, the output module outputting display data for depicting the estimated journey data, the one or more status summaries and the one or more estimate summaries on a display.

20. The system of claim 19, wherein the set of driver history data describes one or more past destinations and one or more estimated destinations, and the estimated journey data is created corresponding to the one or more estimated destinations.

21. The system of claim 19, wherein one of the one or more metrics is related to an estimated abnormal condition associated with one of the one or more potential journeys.

22. The system of claim 21, wherein the output module is further configured to output a navigational recommendation related to the estimated abnormal condition.

23. The system of claim 19, wherein the set of learning parameters are arranged in a learning table.

24. The system of claim 19, wherein the estimated journey data describes three potential journeys.

25. The system of claim 19, wherein different quality scores are associated with different journeys.

26. The system of claim 19, wherein the display is a component of a navigation system.

27. The system of claim 19 further comprising:

a driving history module communicatively coupled to the conversion module, the driving history module retrieving, from a driver history repository, the set of driver history data; and

a history module communicatively coupled to the driving history module, the history module detecting that a present journey has ended, the history module collecting data describing the present journey, the history module storing the collected data in the driver history repository.