Methods of Discretizing data captured at event data recorders

• US 20080147267A1
• Filed: 12/13/2006
• Published: 06/19/2008
• Est. Priority Date: 12/13/2006
• Status: Abandoned Application

• Current Assignee: SmartDrive Systems, Inc. (Solera Holdings Incorporated)
• Original Assignee: SmartDrive Systems, Inc. (Solera Holdings Incorporated)
• Inventors: Kasavaraju, Ramesh, Nickerson, Andrew, Plante, James, Mauro, Gregory
• Application Number:
  US11/637,755
• Publication Number:
  US 20080147267A1
• Time in Patent Office: Days
• US Class Current: 701/35
• CPC Class Codes: B60R 2021/0027 Post collision measures, e.g. notifying emergency services
• CPC Class Codes: G07C 2205/02 using a vehicle scan tool
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/085 using electronic data carriers
• CPC Class Codes: G07C 5/0891 Video recorder in combination with video camera

Abstract

Exception event recorders and analysis systems include: vehicle mounted sensors arranged as a vehicle event recorder to capture both discrete and non-discrete data; a discretization facility; a database; and an analysis server all coupled together as a computer network. Motor vehicles with video cameras and onboard diagnostic systems capture data when the vehicle is involved in a crash or other anomaly (an ‘event’). In station where interpretation of non-discrete data is rendered, i.e. a discretization facility, captured data is used as a basis for production of supplemental discrete data to further characterize the event. Such interpreted data is joined to captured data and inserted into a database in a structure which is searchable and which supports logical or mathematical analysis by automated machines. A coupled analysis server is arranged to test stored data for prescribed conditions and upon finding such, to initiate further actions appropriate for the detected condition.

Claims

1. ) Methods of characterizing an exception event associated with vehicle use comprising the steps:

detecting the exception event;

recording data associated with said exception event;

converting non-discrete data into discrete data; and

associating so converted discrete data with other recorded data to form a complete event dataset.

2. ) Methods of characterizing an exception event of claim 1, said ‘

detecting the exception event’

step is characterized as sensing a physical signal and comparing to a prescribed threshold value whereby when the physical signal exceeds the threshold value an exception event is declared.

3. ) Methods of characterizing an exception event of claim 1, said ‘

recording data associated with said exception event’

step is characterized as capturing data both discrete and non-discrete in nature including video, audio, acceleration, and system parameter values, as a compound data set.

4. ) Methods of characterizing an exception event of claim 1, said ‘

converting non-discrete data into discrete data’

step is characterized as applying machine algorithms on non-discrete data to produce discrete outputs.

5. ) Methods of characterizing an exception event of claim 1, said ‘

converting non-discrete data into discrete data’

step is characterized as processing non-discrete data via a human interpretation process executed in conjunction with a multi-media player and specially configured graphical user interface elements well coupled to database fields of a particular event record.

6. ) Methods of characterizing an exception event of claim 1, said ‘

converting non-discrete data into discrete data’

step further comprises the steps;

playing video;

audio; and

acceleration data at a multimedia player interpreting images, sounds and waveforms;

setting state values of graphical user interface controls in accordance with the interpretation step; and

inserting state values for recording in a database record comprising data from the same event.

7. ) Methods of characterizing an exception event of claim 1, said ‘

associating so converted discrete data’

step including inserting so converted discrete data into a database record having a unique index and being connected with precisely one event, the database record comprising discrete data directly from a video event recorder, so converted discrete data, and non-discrete data.

8. ) Methods of characterizing an exception event of claim 5, further comprising the steps:

conveying non-discrete data captured at a vehicle in association with a declared exception event to a discretization facility;

playing non-discrete data at a media player arranged to present time synchronized data for viewing by a human operator; and

forming a dataset of discrete data to represent interpretations of information presented at the media player;

conveying said dataset of discrete data to a database in an ‘

insert’

operation whereby the dataset of discrete data is combined with data captured in the declared exception event.

9. ) Methods of characterizing an exception event of claim 1, further comprising the steps running an analysis against complete event dataset including so converted discrete data.

10. ) Methods of characterizing an exception event of claim 8, said ‘

playing non-discrete data at a media player’

step is further characterized as synchronously displaying a time dependant image series (video), a graphical representation of an audio signal, and a graphical representation of an acceleration signal.

11. ) Methods of characterizing an exception event of claim 8, said ‘

forming a dataset of discrete data’

step is further characterized as using ‘

point-and-click’

actions in conjunction with a computer pointing apparatus to manipulate the value states of a computer graphical interface control elements.

12. ) Methods of characterizing an exception event of claim 4, said machine executed algorithms include those characterized as ‘

artificial intelligence’

type processes.

13. ) Methods of characterizing an exception event of claim 4, said machine executed algorithms include those characterized as ‘

fuzzy logic’

type processes.

14. ) Methods of characterizing an exception event of claim 4, said machine executed algorithms include those characterized as image recognition type processes.

15. ) Methods of characterizing an exception event of claim 8, said ‘

playing non-discrete data at a media player’

step is preformed simultaneously with presentation of a graphical user interface having control elements with adjustable value states therein.

16. ) Methods of characterizing an exception event of claim 15, at least one control element further includes a timeline stamp in addition to its value state.