Methods and systems for sensor-based driving data collection

• US 10,217,055 B2
• Filed: 01/10/2018
• Issued: 02/26/2019
• Est. Priority Date: 05/07/2015
• Status: Active Grant

• Current Assignee: Cambridge Mobile Telematics, Inc.
• Original Assignee: Truemotion, Inc.
• Inventors: Cordova, Brad, Adelmann, Joe
• Primary Examiner(s): Wong, Yuen H
• Application Number:
  US15/866,949
• Publication Number:
  US 20180197101A1
• Time in Patent Office: 412 Days
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083
• CPC Class Codes: G07C 11/00 Arrangements, systems or apparatus for checking, e.g. the occurrence of a condition, not provided for elsewhere
• CPC Class Codes: G08G 1/005 including pedestrian guidance indicator
• 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 1/0141 for traffic information dissemination
• CPC Class Codes: H04W 4/00 Services specially adapted for wireless communication networks; Facilities therefor
• CPC Class Codes: H04W 4/02 Services making use of location information
• CPC Class Codes: H04W 4/024 Guidance services
• CPC Class Codes: H04W 4/027 using movement velocity, acceleration information
• CPC Class Codes: H04W 4/38 for collecting sensor information
• CPC Class Codes: H04W 4/40 for vehicles, e.g. vehicle-to-pedestrians [V2P]

Abstract

A system for collecting vehicle data includes a mobile device comprising a plurality of sensors, a memory, and a processor coupled to the memory. The processor is configured to perform operations including obtaining a plurality of movement measurements from at least one of the plurality of sensors in the mobile device, determining a plurality of transportation modes using the plurality of movement measurements, and determining a mode transition using the plurality of transportation modes. The operations also include determining a transition probability using the mode transition. The operations further include creating a transition model using the transition probabilities, determining that the transition model indicates that a probability that the second transportation mode comprises driving is above a threshold, obtaining a plurality of driving movement measurements from at least one of the plurality of sensors, and determining the vehicle data using the driving movement measurements.

Claims

1. A system for collecting vehicle data, the system comprising:

a mobile device of a user comprising a plurality of sensors;

a memory; and

a processor coupled to the memory, wherein the processor is configured to perform operations including;

obtaining a plurality of movement measurements from at least one of the plurality of sensors in the mobile device;

determining a plurality of transportation modes using the plurality of movement measurements, wherein determining includes;

assigning a plurality of first movement measurements of the plurality of movement measurements to a first transportation mode of the plurality of transportation modes, wherein the first transportation mode includes driving by the user of the mobile device; and

assigning a plurality of second movement measurements of the plurality of movement measurements to a second transportation mode of the plurality of transportation modes;

identifying a plurality of first features of the first transportation mode using the plurality of first movement measurements;

identifying a plurality of second features of the second transportation mode using the plurality of second movement measurements;

obtaining a plurality of new movement measurements from the at least one of the plurality of sensors in the mobile device;

extracting a plurality of new features from the plurality of new movement measurements;

associating the first transportation mode with the plurality of new movement measurements based on a comparison of the plurality of new features to the plurality of first features and the plurality of second features; and

operating the at least one of the plurality of sensors at an increased frequency of sampling of the vehicle data based on the association of the first transportation mode with the plurality of new movement measurements.

2. The system of claim 1, wherein the memory and the processor are located on a server in a cloud.

3. The system of claim 1, wherein the memory and the processor are located in the mobile device.

4. The system of claim 1, wherein the plurality of sensors includes a gyroscope, an accelerometer, and a GPS.

5. The system of claim 1, wherein determining the plurality of transportation modes includes determining a Fourier frequency of a subset of the plurality of movement measurements.

6. The system of claim 1, wherein the plurality of first features includes at least one of frequency features, statistical features, principle component analysis (PCA) features, and derived kinematic quantity features.

7. The system of claim 6, wherein the frequency features include at least one of fast Fourier transform (FFT) coefficients and wavelet coefficients.

8. The system of claim 6, wherein the statistical features include at least one of a mean, a median, a standard deviation, a crest factor, and a root mean square.

9. The system of claim 6, wherein the derived kinematic quantity features include at least one of a gravity vector, a rotation angle around gravity vector, and a measure of acceleration around gravity vector.

10. A method for collecting vehicle data, the method comprising:

obtaining, by a processor, a plurality of movement measurements from at least one of a plurality of sensors in a mobile device;

determining, by the processor, a plurality of transportation modes using the plurality of movement measurements, wherein determining includes;

assigning a plurality of first movement measurements of the plurality of movement measurements to a first transportation mode of the plurality of transportation modes, wherein the first transportation mode includes driving by a user of the mobile device; and

assigning a plurality of second movement measurements of the plurality of movement measurements to a second transportation mode of the plurality of transportation modes;

identifying, by the processor, a plurality of first features of the first transportation mode using the plurality of first movement measurements;

identifying, by the processor, a plurality of second features of the second transportation mode using the plurality of second movement measurements;

obtaining, by the processor, a plurality of new movement measurements from the at least one of the plurality of sensors in the mobile device;

extracting, by the processor, a plurality of new features from the plurality of new movement measurements;

associating, by the processor, the first transportation mode with the plurality of new movement measurements based on a comparison of the plurality of new features to the plurality of first features and the plurality of second features; and

operating, by the processor, the at least one of the plurality of sensors at an increased frequency of sampling of the vehicle data based on the association of the first transportation mode with the plurality of new movement measurements.

11. The method of claim 10, wherein assigning the plurality of second movement measurements of the plurality of movement measurements to the second transportation mode is based on contextual data.

12. The method of claim 11, wherein the contextual data includes at least one of schedules and route data for trains and buses.

13. The method of claim 10, wherein the plurality of sensors includes a gyroscope, an accelerometer, and a GPS.

14. The method of claim 10, wherein determining the plurality of transportation modes includes determining a Fourier frequency of a subset of the plurality of movement measurements.

15. The method of claim 10, wherein the plurality of first features includes at least one of frequency features, statistical features, principle component analysis (PCA) features, and derived kinematic quantity features.

16. The method of claim 15, wherein the frequency features include at least one of fast Fourier transform (FFT) coefficients and wavelet coefficients.

17. The method of claim 15, wherein the statistical features include at least one of a mean, a median, a standard deviation, a crest factor, and a root mean square.

18. The method of claim 15, wherein the derived kinematic quantity features include at least one of a gravity vector, a rotation angle around gravity vector, and a measure of acceleration around gravity vector.