Method and system for determining and providing a personalized ETA with privacy preservation

• US 10,072,938 B2
• Filed: 04/29/2016
• Issued: 09/11/2018
• Est. Priority Date: 04/29/2016
• Status: Active Grant

• Current Assignee: Bayerische Motoren Werke AG
• Original Assignee: Bayerische Motoren Werke AG
• Inventors: Tian, Jilei, Nallappa Soundararajan, Kasthuri Rengan, Cao, Yang, Yang, Xiaogang, Chin, Alvin, Woker, Clint
• Primary Examiner(s): Badii, Behrang
• Assistant Examiner(s): Testardi, David A
• Application Number:
  US15/142,264
• Publication Number:
  US 20170314950A1
• Time in Patent Office: 865 Days
• Field of Search: 701465
• 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 21/365 Guidance using head up displays or projectors, e.g. virtual vehicles or arrows projected on the windscreen or on the road itself
• CPC Class Codes: G01C 21/3697 Output of additional, non-guidance related information, e.g. low fuel level G01C21/3679 takes precedence
• CPC Class Codes: G06F 21/6218 to a system of files or objects, e.g. local or distributed file system or database
• CPC Class Codes: G06F 21/6245 Protecting personal data, e.g. for financial or medical purposes
• CPC Class Codes: G07C 5/0825 using optical means
• CPC Class Codes: G07C 5/0841 Registering performance data recording measured values G01D; information storage G11B

Abstract

The disclosure is directed to improving driving experience by determining and providing a personalized estimated time of arrival (ETA). For example, using at least one computing device, driving data associated with a user may be collected and stored in memory. The at least one computing device may also be used to receive ETA-related data including one or more real time ETA estimations, perform analysis on one or more of the driving data and the ETA-related data so as to determine a set of personalized parameters of a mapping function that correspond to a training set. The at least one computing device may also be used to determine the personalized ETA based at least in part on the determined set of personalized parameters and provided to the user.

Claims

1. A method for determining and providing a personalized estimated time of arrival (ETA), the method comprising the acts of:

collecting, using at least one computing device, driving data associated with a user including one or more actual driving times from a first geographical location to a second geographical location via a route;

storing, using the at least one computing device, the driving data in memory;

sending, using the at least one computing device, at least user identification information to at least one third-party network in order to receive ETA-related data associated with the user, wherein a proxy server anonymizes the user identification information before the at least one third-party network receives the user identification information;

receiving, using the at least one computing device, the ETA-related data from the at least one third-party network based on the anonymized user identification information, the ETA-related data including one or more real time ETA estimations for the user;

performing, using the at least one computing device, analysis on the driving data and the ETA-related data;

determining, using the at least one computing device, the personalized ETA based at least in part on the analyzed driving data and the ETA-related data and using a non-linear mapping function that includes personal parameters that have been fit to training data collected by the at least one computing device; and

providing, by the at least one computing device, the personalized ETA to the user.

2. The method of claim 1, wherein the at least one computing device includes one or more of the following:

(i) an in-vehicle computer, (ii) a mobile computer, and (iii) a server computer.

3. The method of claim 1, wherein the act of providing the personalized ETA comprises outputting, using the at least one computing device, personalized ETA to the user via a display.

4. The method of claim 3, wherein the display is a head-up display.

5. The method of claim 1, wherein the driving data associated with the user further includes one or more of the following:

(i) a driving tendency of the user, (ii) a driving habit of the user, (iii) a driving preference of the user, and (iv) a vehicle condition.

6. The method of claim 5, wherein the driving data is acquired via one or more sensors of a vehicle.

7. The method of claim 6, wherein the driving preference includes the user driving in a particular lane on more than a threshold number of occasions, and wherein the vehicle condition includes the vehicle in a drive position or the vehicle in a park position.

8. The method of claim 1, wherein the ETA-related data further includes one or more of the following:

(i) static data, (ii) dynamic data, and (iii) crowd sourced data.

9. The method of claim 8, wherein the static data includes information related to the route, a distance of the route, one or more speed limits associated with the route, geometry of the route, and traffic light positions.

10. The method of claim 8, wherein the dynamic data includes information related to potential incidents, weather, change of traffic lights, and traffic flow.

11. The method of claim 8, wherein the crowd sourced data is from a plurality of other drivers and passengers.

12. A non-transitory computer-readable medium comprising a set of executable instructions, the set of executable instructions when executed by at least one processor causes the at least one processor to perform a method for determining and providing a personalized estimated time of arrival (ETA), the method comprising the acts of:

collecting driving data associated with a user including one or more actual driving times from a first geographical location to a second geographical location via a route;

storing the driving data in memory;

sending at least user identification information to at least one third-party network in order to receive ETA-related data associated with the user, wherein a proxy server anonymizes the user identification information before the at least one third-party network receives the anonymized user identification information;

receiving the ETA-related data from the at least one third-party network based on the anonymized user identification information, the ETA-related data including one or more real time ETA estimations for the user;

performing analysis on the driving data and the ETA-related data;

determining the personalized ETA based at least in part on the analyzed driving data and the ETA-related data and using a non-linear mapping function that includes personal parameters that have been fit to training data collected by at least one computing device; and

providing the personalized ETA to the user.

13. A system for determining and providing a personalized estimated time of arrival (ETA), the system comprising:

memory;

at least one computing device executing stored program instructions to;

collect driving data associated with a user including one or more actual driving times from a first geographical location to a second geographical location via a route;

store the driving data in the memory;

send at least user identification information to at least one third-party network in order to receive ETA-related data associated with the user, wherein a proxy server anonymizes the user identification information before the at least one third-party network receives the user identification information;

receive the ETA-related data from the at least one third-party network based on the anonymized user identification information, the ETA-related data including one or more real time ETA estimations for the user;

perform analysis on the driving data and the ETA-related data;

determine the personalized ETA based at least in part on the analyzed driving data and the ETA-related data and using a non-linear mapping function that includes personal parameters that have been fit to training data collected by the at least one computing device; and

provide the personalized ETA to the user.