Generating visual information associated with traffic

• US 9,640,073 B2
• Filed: 07/08/2015
• Issued: 05/02/2017
• Est. Priority Date: 03/05/2002
• Status: Active Grant

• Current Assignee: Uber Technologies, Inc.
• Original Assignee: Pelmorex Canada Inc.
• Inventors: Gueziec, Andre
• Primary Examiner(s): NGUYEN, TAI T
• Application Number:
  US14/793,879
• Publication Number:
  US 20150325123A1
• Time in Patent Office: 664 Days
• Field of Search: 304905-907, 304944, 304988, 30499524, 30499527
• CPC Class Codes: G01C 21/30 Map- or contour-matching
• CPC Class Codes: G01C 21/3407 specially adapted for specific applications
• 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
• CPC Class Codes: G01C 21/3694 Output thereof on a road map
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G08G 1/0125 Traffic data processing
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/093 Data selection, e.g. prioritizing information, managing message queues, selecting the information to be output
• CPC Class Codes: G08G 1/0967 Systems involving transmission of highway information, e.g. weather, speed limits transmission of navigation instructions to the vehicle G08G1/0968
• CPC Class Codes: G08G 1/096716 where the received information does not generate an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096741 where the source of the transmitted information selects which information to transmit to each vehicle
• CPC Class Codes: G08G 1/09675 where a selection from the received information takes place in the vehicle
• CPC Class Codes: G08G 1/096775 where the origin of the information is a central station
• CPC Class Codes: G08G 1/096816 where the complete route is transmitted to the vehicle at once
• CPC Class Codes: G08G 1/096822 where the segments of the route are transmitted to the vehicle at different locations and times
• CPC Class Codes: G08G 1/096827 where the route is computed onboard
• CPC Class Codes: G08G 1/096866 where the complete route is shown to the driver
• CPC Class Codes: G08G 1/096888 where input information is obtained using learning systems, e.g. history databases
• CPC Class Codes: H04B 1/3822 specially adapted for use in vehicles H04B1/3827 takes precedence
• CPC Class Codes: H04W 4/025 : using location based information parameters
• CPC Class Codes: H04W 4/029 : Location-based management or tracking services

Abstract

Disclosed herein is a traveler information monitoring and dissemination system. The system disclosed herein provides real time information to a traveler, wherein the real time information may be pre-selected by the traveler. The system ensures consistent and quality data are produced and issued to the traveler.

Claims

1. A method for generating visual information associated with traffic, the method comprising:

accessing a past database, wherein the database includes information relating to previously obtained real-time traffic conditions associated for one or more segments of a road or highway;

identifying a real-world traffic route, wherein the real-world traffic route includes an initial starting point and a destination;

identifying a future period of time corresponding to a desired time period to travel, and

generating a prediction traffic model of the real-world traffic route during the identified future period of time, the generated prediction traffic model based on information stored in the past database.

2. The method of claim 1, wherein the generated traffic model comprises one or more of the following:

a color coded traffic route,

at least one thoroughfare image,

at least one thoroughfare identifier,

at least one structure identifier when at least one structure is present on the real-world traffic route,

a visual representation of at least one vehicle, or

a visual representation of a landscape.

3. The method of claim 2, wherein the visual representation of the landscape includes textured topography.

4. The method of claim 2, wherein the visual representation of the landscape is a three-dimensional representation.

5. The method of claim 2, wherein the visual representation of the landscape includes satellite imagery.

6. The method of claim 2, wherein the visual representation of the landscape includes aerial imagery.

7. The method of claim 2, wherein the visual representation of the landscape includes at least one three-dimensional landmark.

8. The method of claim 7, wherein the at least one landmark is a three-dimensional landmark.

9. The method of claim 1, wherein the generated prediction traffic model is further based on weather forecasts.

10. The method of claim 9, wherein the generated prediction traffic model includes a visual representation of the weather forecast.

11. The method of claim 9, wherein the weather forecasts includes information regarding wind, ice, or snow.

12. The method of claim 1, wherein the generated prediction traffic model is further based on planned events.

13. The method of claim 12, wherein the generated prediction traffic model includes a visual representation for the planned event.

14. The method of claim 12, wherein the planned event includes scheduled construction.

15. The method of claim 12, wherein the planned event includes scheduled sporting events.

16. The method of claim 12, wherein the planned event includes scheduled road closures.

17. The method of claim 12, wherein the planned event includes holiday traffic.