Method and system for determining traffic-related characteristics

• US 10,304,329 B2
• Filed: 06/28/2018
• Issued: 05/28/2019
• Est. Priority Date: 06/28/2017
• Status: Active Grant

• Current Assignee: Zendrive, Inc.
• Original Assignee: Zendrive, Inc.
• Inventors: Matus, Jonathan A., Risbood, Pankaj
• Primary Examiner(s): Beaulieu, Yonel
• Application Number:
  US16/022,184
• Publication Number:
  US 20190005812A1
• Time in Patent Office: 334 Days
• CPC Class Codes: G06V 20/56 exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G06V 20/584 of vehicle lights or traffic lights
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/012 from other sources than vehicle or roadside beacons, e.g. mobile networks
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0133 for classifying traffic situation
• 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 1/096775 where the origin of the information is a central station
• CPC Class Codes: H04W 4/027 using movement velocity, acceleration information

Abstract

A method for traffic characterization associated with a vehicle including collecting a movement dataset sampled at least at one of a location sensor and a motion sensor associated with the vehicle, during a driving session associated with movement of the vehicle; extracting a set of features from the movement dataset associated with movement of the vehicle during the driving session; and determining one or more traffic-related characteristics associated with the vehicle based on the set of features.

Claims

1. A method for traffic compliance characterization with a mobile computing device located within a vehicle, comprising:

collecting a first location dataset at a GPS sensor of the mobile computing device during a driving session defining a first time period of movement of the vehicle, wherein the mobile computing device is associated with a user;

collecting a first motion dataset at an accelerometer of the mobile computing device during the driving session;

extracting a vehicle motion characteristic from at least one of the location dataset and the motion dataset;

extracting a vehicle location from the location dataset;

inferring a traffic law associated with the vehicle location based on a set of historical movement datasets collected at a set of mobile computing devices associated with historical driving sessions intersecting the vehicle location, wherein the historical driving sessions occur before the first time period;

wherein inferring the traffic law is performed at a remote computing system in communication with the mobile computing device;

receiving the inferred traffic law at the mobile computing device contemporaneously with the driving session;

at the mobile computing device, determining a traffic compliance parameter associated with the vehicle motion characteristic based on a comparison between the vehicle motion characteristic and the traffic law; and

in response to determining the traffic compliance parameter, initiating a traffic-related action.

2. The method of claim 1, wherein the traffic compliance parameter indicates the vehicle motion characteristic as noncompliant with the traffic law, and wherein the traffic-related action comprises generating a notification indicative of noncompliance and a corrective driver action, and providing the notification to the user at the mobile computing device.

3. The method of claim 2, wherein providing the notification to the user comprises rendering the notification at a display of the mobile computing device.

4. The method of claim 1, wherein the traffic-related action comprises storing the traffic compliance parameter in association with a user account of the user at the remote computing system.

5. The method of claim 1, wherein the traffic-related action comprises transmitting the traffic compliance parameter to an insurance entity associated with the user.

6. The method of claim 1, wherein the vehicle motion characteristic comprises a vehicle speed, wherein the traffic law comprises a speed limit, wherein comparing the vehicle motion characteristic to the traffic law comprises comparing the vehicle speed to the speed limit, and further comprising determining a risk parameter based on comparing the vehicle speed to the speed limit.

7. The method of claim 1, further comprising:

identifying a set of user devices corresponding to a set of secondary vehicles driving proximal the vehicle location during the driving session;

determining a traffic-related characteristic associated with the movement based on a set of proximal movement datasets collected by the set of user devices contemporaneously with the driving session; and

wherein determining the traffic compliance parameter is based on a comparison between the traffic-related characteristic and the vehicle motion characteristic in combination with the comparison between the vehicle motion characteristic and the traffic law.

8. The method of claim 1, further comprising:

collecting an image dataset at an image sensor of the mobile computing device, wherein the image sensor of the mobile computing device is arranged within the vehicle to image a spatial region forward of the vehicle;

extracting a visual traffic indicator from the image dataset;

validating the traffic law based on the visual traffic indicator at the mobile computing device;

determining the traffic compliance parameter in response to validating the traffic law; and

updating a traffic map stored at the remote computing system based on validating the traffic law.

9. The method of claim 1, further comprising:

determining an indicator type associated with a visual traffic indicator, based on the vehicle motion characteristic, wherein the vehicle motion characteristic characterizes a vehicle interaction with the visual traffic indicator;

associating the indicator type with the vehicle location; and

storing the indicator type in association with the vehicle location at a traffic map stored at the remote computing system.

10. The method of claim 1, wherein the traffic law defines a prohibition against executing a first traffic maneuver at the vehicle location, wherein determining the vehicle motion characteristic comprises determining a second traffic maneuver executed by the vehicle during the driving session at the vehicle location, and wherein determining the traffic compliance parameter is based on a comparison between the first traffic maneuver and the second traffic maneuver.

11. The method of claim 10, further comprising determining a time of day corresponding to the second traffic maneuver, wherein the traffic law defines a prohibition against executing the first traffic maneuver at the vehicle location between a first time of day and a second time of day, and wherein determining the traffic compliance parameter is based on determining that the time of day is between the first time of day and the second time of day.

12. A method for traffic compliance characterization with a mobile computing device located within a vehicle, comprising:

collecting a first location dataset at a GPS sensor of the mobile computing device during a driving session defining a first time period of movement of the vehicle, wherein the mobile computing device is associated with a user;

collecting a first motion dataset at an accelerometer of the mobile computing device during the driving session;

extracting a vehicle motion characteristic from at least one of the location dataset and the motion dataset;

extracting a vehicle location from the location dataset;

identifying a set of user devices corresponding to a set of geographically proximal vehicles driving proximal the vehicle location during the driving session;

determining a traffic-related characteristic associated with the movement based on a set of movement datasets collected by the set of user devices contemporaneously with the driving session, wherein each of the set of movement datasets comprises a corresponding location dataset collected at a GPS sensor at a corresponding user device of the set of user devices and a corresponding motion dataset at a corresponding accelerometer of the corresponding user device;

determining a traffic compliance parameter associated with the vehicle motion characteristic based on a comparison between the traffic-related characteristic and the vehicle motion characteristic; and

in response to determining the traffic compliance parameter, initiating a traffic-related action.

13. The method of claim 12, wherein the vehicle motion characteristic comprises a current vehicle speed, wherein the traffic-related characteristic comprises an average vehicle speed derived from the set of movement datasets collected by the set of user devices, and further comprising determining the traffic compliance parameter based on comparing the current vehicle speed to the average vehicle speed, and initiating the traffic-related action based on the traffic compliance parameter.

14. The method of claim 13, wherein the traffic-related action comprises storing the traffic compliance parameter in association with a user account of the user at a remote computing system.

15. The method of claim 12, further comprising predicting, at a processor of the mobile computing device, a traffic law associated with the vehicle location, based on the set of movement datasets collected by the set of user devices associated with the set of geographically proximal vehicles.

16. The method of claim 15, further comprising storing the predicted traffic law in association with the vehicle location at a remote computing system in communication with the mobile computing device.

17. The method of claim 16, further comprising:

determining an indicator type associated with a visual traffic indicator, based on the vehicle motion characteristic, wherein the vehicle motion characteristic characterizes a vehicle interaction with the visual traffic indicator;

associating the indicator type with the vehicle location; and

storing the indicator type in association with the vehicle location at a traffic map stored at the remote computing system.

18. The method of claim 12, wherein the traffic-related characteristic defines a first traffic maneuver at the vehicle location, wherein determining the vehicle motion characteristic comprises determining a second traffic maneuver executed by the vehicle during the driving session at the vehicle location, and wherein determining the traffic compliance parameter is based on a comparison between the first traffic maneuver and the second traffic maneuver.

19. The method of claim 12, further comprising:

inferring a traffic law associated with the vehicle location based on a set of historical motion datasets collected at a set of mobile computing devices associated with historical driving sessions intersecting the vehicle location, wherein the historical driving sessions occur before the first time period;

wherein inferring the traffic law is performed at a remote computing system in communication with the mobile computing device;

receiving the inferred traffic law at the mobile computing device contemporaneously with the driving session; and

determining the traffic compliance parameter based on a comparison between the vehicle motion characteristic and the traffic law performed at the mobile computing device in combination with the comparison between the traffic-related characteristic and the vehicle motion characteristic.

20. The method of claim 19, further comprising determining a risk parameter based on the traffic compliance parameter, and associating the risk parameter with a user account of the user at the remote computing system.