VEHICLE EVENT ASSESSMENT

• US 20170050599A1
• Filed: 02/11/2015
• Published: 02/23/2017
• Est. Priority Date: 02/12/2014
• Status: Abandoned Application

• Current Assignee: Risk Telematics UK Limited
• Original Assignee: Risk Telematics UK Limited
• Inventors: Gilbert, Wayne, Cowper, Steve
• Application Number:
  US15/119,982
• Publication Number:
  US 20170050599A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B60R 21/01332 by frequency or waveform analysis
• CPC Class Codes: B60R 21/01334 using Fourier analysis
• CPC Class Codes: B60R 21/01336 using filtering
• CPC Class Codes: G01H 17/00 Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups see provisionally also G01H1/00
• CPC Class Codes: G01P 15/001 by measuring acceleration changes by making use of a triple differentiation of a displacement signal
• CPC Class Codes: G01P 15/18 in two or more dimensions
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C

Abstract

The disclosure relates to apparatus (300) and automated methods (100, 200) for generating a library of templates (304) corresponding to different known types of motor vehicle event and discriminating between types of event on a motor vehicle. The apparatus (300) comprises the template library (304) and a pattern matching processor (302).

Claims

1. Apparatus for discriminating between types of event on a motor vehicle, comprising:

a template library storing a plurality of different templates, each template corresponding to an event type; and

a pattern matching processor configured to (i) receive motion sensor data from one or more motion sensors on the motor vehicle, (ii) apply a wavelet transformation to the motion sensor data in order to identify features of transformed motion sensor data, (iii) compare at least one of the identified features of the transformed motion sensor data with templates in the template library and (iv) determine an event type based on the comparison.

2. The apparatus of claim 1 wherein the one or more identified features are coefficients of the transformed motion sensor data, wherein a plurality of coefficients associated with at least one template is provided in the template library, and wherein the pattern matching processor is configured to compare each coefficient of the transformed motion sensor data with the at least one template of a corresponding coefficient provided in the template library.

3. The apparatus of claim 2, wherein the pattern matching processor is configured to match a scale and translation value of each of the coefficients of the transformed motion sensor data with a scale and translation value of the at least one template of the corresponding coefficient provided in the template library.

4. The apparatus of claim 1 comprising the at least one motion sensor.

5. The apparatus of claim 4 comprising only a single type of motion sensor.

6. The apparatus of claim 5 comprising only a single motion sensor.

7. The apparatus of claim 4 wherein the at least one motion sensor comprise one of an accelerometer and a three-dimensional accelerometer.

9. The apparatus of claim 7 wherein the accelerometer is configured to be mounted in the vehicle with an axis of acceleration normal to the ground.

10. The apparatus of claim 1 wherein the at least one motion sensor comprise a vibration sensor.

12. The apparatus of claim 1 wherein the events are impact events.

13. The apparatus of claim 1 wherein the events are non-impact events.

14. The apparatus of claim 1 wherein the type of event is an acceleration event.

15. The apparatus of claim 1 wherein the pattern matching processor is configured to classify a type of vehicle behaviour based on a number of determinations of acceleration event types, the classification based on the occurrence of each type of acceleration event.

16. The apparatus of claim 13 wherein one or more of the templates in the template library are associated with a risk weighting for the corresponding event type and wherein the classification is also based on the risk weighting of each type of acceleration event.

17. The apparatus of claim 1 wherein at least one of the templates in the template library is each associated with a type of braking event.

20. The automated method of claim 17 wherein the motion sensor data comprises a plurality of examples of each different type of motor vehicle event.

8. (canceled)

11. (canceled)

18. An automated method for discriminating between types of event on a motor vehicle, comprising:

receiving motion sensor data from at least one motion sensor on the motor vehicle;

retrieving a plurality of different templates from a template library, each template corresponding to an event type;

applying a wavelet transformation to the motion sensor data in order to identify features of transformed motion sensor data;

comparing at least one of the identified features of the transformed motion sensor data with the plurality of different templates; and

determining an event type based on the comparison.

21. The automated method of claim 18 wherein identifying features of the motion sensor data comprises generating a matrix of coefficients using a discrete wavelet transformation, each coefficient having an element associated with one of the plurality of examples of each different type of motor vehicle event.

19. An automated method for generating a library of templates corresponding to different known types of motor vehicle event, comprising:

receiving motion sensor data representative of the different types of motor vehicle event;

applying a wavelet transformation to the motion sensor data in order to identify features of transformed motion sensor data;

for at least some of the different types of motor vehicle event, determining values of at least one indicative feature, each value corresponding with a particular type of motor vehicle event;

providing the library of templates comprising the indicative features and an identifier of the particular type of motor vehicle event with which each value corresponds.

22. The automated method of claim 19 wherein determining values of the at least one indicative feature comprises performing cluster analysis on the elements of each coefficient and identifying at least one coefficient that provides a separate cluster for each different type of motor vehicle event, and wherein each template comprises a description of a cluster.

23-30. -30. (canceled)