VEHICLE CLASSIFICATION BASED ON TELEMATICS DATA

• US 20190311289A1
• Filed: 04/04/2019
• Published: 10/10/2019
• Est. Priority Date: 04/09/2018
• Status: Active Application

• Current Assignee: Cambridge Mobile Telematics, Inc.
• Original Assignee: Cambridge Mobile Telematics, Inc.
• Inventors: Nguyen, Linh Vuong
• Application Number:
  US16/375,170
• Publication Number:
  US 20190311289A1
• Time in Patent Office: Days
• CPC Class Codes: B60L 2200/12 Bikes
• CPC Class Codes: B60L 2200/24 Personal mobility vehicles
• CPC Class Codes: B60L 3/12 Recording operating variables ; Monitoring of operating variables
• CPC Class Codes: B60L 50/20 using propulsion power generated by humans or animals
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/20 Ensemble learning
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G07C 5/02 Registering or indicating driving, working, idle, or waiting time only apparatus forming part of taximeters G07B13/00
• CPC Class Codes: G07C 5/08 Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
• CPC Class Codes: G07C 5/0816 Indicating performance data, e.g. occurrence of a malfunction

Abstract

Among other things, motion data is acquired from a device in a vehicle during a trip. The motion data is applied to a trained classifier to produce a commercial classification of the vehicle.

Claims

1. A method comprising

acquiring motion data from a device in a vehicle during a trip,

applying the motion data to a trained classifier to produce a commercial classification of the vehicle.

2. The method of claim 1 in which the motion data comprises at least one of acceleration, location, and elevation.

3. The method of claim 1 in which the commercial classification comprises vehicle type.

4. The method of claim 1 in which the commercial classification comprises vehicle model.

5. The method of claim 1 in which the commercial classification comprises vehicle make.

6. The method of claim 1 in which the device comprises a sensor.

7. The method of claim 6 in which the sensor comprises one of an accelerometer, a GPS component, a gyroscope, a barometer, and a magnetometer.

8. The method of claim 1 in which the device comprises a tag.

9. The method of claim 1 in which the device comprises a smart phone.

10. The method of claim 1 comprising building the classifier based on vehicle type using motion data of trips, each trip being labeled with the commercial classification of the vehicle used on the trip.

11. The method of claim 1 comprising applying heuristics to an output of the trained classifier to correct classification of the trip.

12. The method of claim 1 comprising extracting features from the motion data for use by the trained classifier.

13. The method of claim 12 in which the features comprise statistical features.

14. The method of claim 12 in which the features comprise time-dependent features.

15. The method of claim 14 in which the time-dependent features comprise autocorrelation coefficients of a vertical acceleration.

16. The method of claim 12 in which the features comprise event-based features.

17. The method of claim 12 in which the features comprise one or a combination of two or more of suspension response, power to weight ratio, and aerodynamics and longitudinal friction.

18. The method of claim 12 in which the features comprise lateral dynamics.

19. The method of claim 12 in which the features comprise hard acceleration or hard deacceleration.

20. The method of claim 12 in which the features comprise spectral features.

21. The method of claim 20 in which the spectral features are associated with engine vibration.

22. The method of claim 20 in which the spectral features are derived from gyroscope fluctuations.

23. The method of claim 12 in which the features comprise metadata features.

24. The method of claim 23 in which the metadata features comprise one or more of:

time of day, trip duration, or type of road.

25. The method of claim 1 in which the classifier produces a probability distribution over different commercial classifications of the vehicle.

26. The method of claim 11 in which the heuristics comprise taking account of two consecutive matching trips.

27. The method of claim 11 in which the heuristics comprise taking account of two trips for which the trajectories match.

28. The method of claim 12 in which the features implicitly contain driver input.

29. The method of claim 1 in which the classifier takes account of driver usage patterns.

30. The method of claim 1 comprising determining a driving score for a driver of the vehicle based on the motion data and the commercial classification of the vehicle.