IP Library Granted Patent US 10,395,438
Granted Patent B2
US 10,395,438 · App. 15/241,517 · Granted Aug 27, 2019

Systems and methods for crash determination with noise filtering

Inventors: Jeff Jenkins (Irvine, CA); Greg Bayley (Irvine, CA); Peter Byrne (Fort Collins, CO); Gallin Chen (Irvine, CA)
Assignee: CalAmp Corp.
G07C5/008H04L67/12H04L67/42
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Quick Facts
Patent No.
US 10,395,438
App. No.
15/241,517
Granted
Aug 27, 2019
Kind
B2
Abstract

Embodiments of the invention include a vehicle telematics system including a telematics device and a remote server system, wherein the telematics device obtains sensor data from at least one sensor installed in a vehicle, calculates peak resultant data based on the sensor data, generates crash score data based on the peak resultant data and a set of crash curve data for the vehicle, and provides the obtained sensor data when the crash score data exceeds a crash threshold to the remote server system and the remote server system obtains vehicle sensor data and vehicle identification data from the vehicle telematics device, calculates resultant change data and absolute speed change data based on the obtained sensor data and/or the vehicle identification data, and generates crash occurred data when the resultant change data exceeds a first threshold value and when the absolute speed change data is below a second threshold value.

Claims (60)

1. A vehicle telematics system, comprising:

a telematics device comprising at least one sensor having an accelerometer, a processor and a memory storing a crash determination application; and

a remote server system comprising a processor and a memory coupled to the processor and storing a crash analysis application and a class of the vehicle;

wherein the processor of the telematics device, on reading the memory, is directed to:

obtain sensor data from the at least one sensor;

calculate peak resultant data based on the sensor data, wherein the peak resultant data describes the acceleration of the vehicle over a first time period;

dynamically determine a set of crash curve data based on the class of the vehicle;

generate crash score data based on the peak resultant data and the set of crash curve data for the vehicle, wherein the crash score data describes the likelihood that the vehicle was involved in a crash based on the class of the vehicle and the sensor data; and

provide the obtained sensor data when the crash score data exceeds a crash threshold to the remote server system; and

wherein the processor of the remote server system, on reading the crash analysis application, is directed to:

in response to the crash source data exceeding the crash threshold, receive the obtained sensor data from the vehicle telematics device;

obtain vehicle identification data from the vehicle telematics device;

calculate resultant velocity change based on the obtained sensor data and the vehicle identification data;

calculate absolute speed change data based on the obtained sensor data; and

generate crash occurred data when the resultant velocity change exceeds a first threshold value and when the absolute speed change is below a second threshold value; and

generate a false positive non-crash event when an oscillating event is detected that decreases in magnitude over time and storing the obtained sensor data from the vehicle telematics device.

2. The vehicle telematics system of claim 1 , wherein the at least one sensor comprises an accelerometer.

3. The vehicle telematics system of claim 1 , wherein the crash curve data is based on vehicle data comprising a vehicle identification number associated with the vehicle.

4. The vehicle telematics system of claim 1 , wherein the class of the vehicle is based on at least one of:

a height of the vehicle;

a length of the vehicle; or

a wheelbase of the vehicle.

5. The vehicle telematics system of claim 4 , wherein the at least one sensor comprises a weight sensor.

6. The vehicle telematics system of claim 1 , wherein:

the vehicle telematics system further comprises a communications interface; and

the vehicle telematics unit obtains the sensor data by using the communications interface.

7. The vehicle telematics system of claim 1 , wherein the set of crash curve data is provided by the remote server system.

8. The vehicle telematics system of claim 1 , wherein the absolute speed change data is calculated by taking the absolute value of an integral of the sensor data indicating acceleration data in a forward vector.

9. The vehicle telematics system of claim 1 , wherein the absolute speed change data is calculated by taking the absolute value of the integral of the sensor data indicating acceleration data in a lateral vector.

10. The vehicle telematics system of claim 1 , wherein the crash score data is generated by:

calculating peak resultant data based on the obtained sensor data; and

calculating the crash score data based on a duration of time in which the peak resultant data exceeds the crash curve data.

11. A method for determining if a vehicle has been involved in a crash, comprising:

obtaining sensor data from at least one sensor installed in the vehicle by using a vehicle telematics device, wherein the at least one sensor includes an accelerometer, and wherein the vehicle telematics device comprises a processor and a memory storing a class of the vehicle;

calculating peak resultant data based on the sensor data by using the vehicle telematics device, wherein the peak resultant data describes the acceleration of the vehicle over a first time period;

dynamically determining a set of crash curve data based on the class of the vehicle;

generating crash score data based on the peak resultant data and the set of crash curve data for the vehicle by using the vehicle telematics device, wherein the crash score data describes the likelihood that the vehicle was involved in a crash based on the class of the vehicle and the sensor data;

providing the obtained sensor data when the crash score data exceeds a crash threshold to a remote server system using the vehicle telematics device, wherein the remote server system comprises a processor and a memory;

obtaining vehicle sensor data from the vehicle telematics device by using the remote server system;

obtaining vehicle identification data from the vehicle telematics device by using the remote server system;

calculating resultant velocity change based on the obtained sensor data and the vehicle identification data by using the remote server system;

calculating an absolute speed change based on the obtained sensor data by using the remote server system; and

generating crash occurred data when the resultant velocity change exceeds a threshold value and when the absolute speed change is below a second threshold value by using the remote server system and;

generating a false-positive non-crash event when an oscillating event is detected that decreases in magnitude over time and storing the obtained vehicle sensor data from the vehicle telematics device.

12. The method of claim 11 , wherein the at least one sensor comprises an accelerometer.

13. The method of claim 11 , wherein the crash curve data is based on vehicle data comprising a vehicle identification number associated with the vehicle.

14. The method of claim 11 , wherein the class of the vehicle is based on at least one of:

a height of the vehicle;

a length of the vehicle; or

a wheelbase of the vehicle.

15. The method of claim 14 , wherein the at least one sensor comprises a weight sensor.

16. The method of claim 11 , wherein:

the vehicle telematics system further comprises a communications interface; and

the vehicle telematics unit obtains the sensor data by using the communications interface.

17. The method of claim 11 , wherein the set of crash curve data is provided by the remote server system.

18. The method of claim 11 , wherein the absolute speed change data is calculated by taking the absolute value of the integral of the sensor data indicating acceleration data in a forward vector.

19. The method of claim 11 , wherein the absolute speed change data is calculated by taking the absolute value of the integral of the sensor data indicating acceleration data in a lateral vector.

20. The method of claim 11 , wherein the crash score data is generated by:

calculating peak resultant data based on the obtained sensor data by using the vehicle telematics device; and

calculating the crash score data based on a duration of time in which the peak resultant data exceeds the crash curve data by using the vehicle telematics device.

Assignments (7)
RELEASE OF SECURITY INTEREST Recorded Aug 14, 2024
From: THE BANK OF NEW YORK MELLON TRUST COMPANY, N.A.
To: CALAMP CORP.; CALAMP WIRELESS NETWORKS CORPORATION; SYNOVIA SOLUTIONS LLC
Reel/Frame 068604/0284 →
PATENT SECURITY AGREEMENT Recorded Dec 18, 2023
From: CALAMP CORP.; CALAMP WIRELESS NETWORKS CORPORATION; SYNOVIA SOLUTIONS LLC
To: LYNROCK LAKE MASTER FUND LP [LYNROCK LAKE PARTNERS LLC, ITS GENERAL PARTNER]
Reel/Frame 066061/0946 →
PATENT SECURITY AGREEMENT Recorded Dec 18, 2023
From: CALAMP CORP.; CALAMP WIRELESS NETWORKS CORPORATION; SYNOVIA SOLUTIONS LLC
To: THE BANK OF NEW YORK MELLON TRUST COMPANY, N.A.
Reel/Frame 066062/0303 →
RELEASE OF SECURITY INTEREST Recorded Dec 18, 2023
From: PNC BANK, NATIONAL ASSOCIATION
To: CALAMP CORP
Reel/Frame 066059/0252 →
SECURITY INTEREST Recorded Jul 14, 2022
From: CALAMP CORP.; CALAMP WIRELESS NETWORKS CORPORATION; SYNOVIA SOLUTIONS LLC
To: PNC BANK, NATIONAL ASSOCIATION
Reel/Frame 060651/0651 →
SECURITY INTEREST Recorded Apr 4, 2018
From: CALAMP CORP.
To: JPMORGAN CHASE BANK, N.A.
Reel/Frame 045853/0023 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 4, 2016
From: JENKINS, JEFF; BAYLEY, GREG; BYRNE, PETER; CHEN, GALLIN
To: CALAMP CORP.
Reel/Frame 039932/0262 →
Continuity (1)
Related Publication 20180053354A1 · Feb 22, 2018
Cited By (3)
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