IP Library Granted Patent US 11,869,282
Granted Patent B2
US 11,869,282 · App. 17/826,574 · Granted Jan 9, 2024

Systems and methods for crash determination with noise filtering

Inventors: Jeff Jenkins (Invine, CA); Greg Bayley (Irvine, CA); Peter Byrne (Fort Collins, CO); Gallin Chen (Irvine, CA)
Assignee: CalAmp Corp.
G07C5/008B60R21/013B60R21/01332H04L67/01H04L67/12B60R2021/01304
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Quick Facts
Patent No.
US 11,869,282
App. No.
17/826,574
Granted
Jan 9, 2024
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 (41)

1. A vehicle telematics device comprising a processor and a memory storing a crash determination application, the vehicle telematics device coupled to at least one sensor having an accelerometer, wherein the processor of the vehicle telematics device, on reading the crash determination application, is directed to:

obtain sensor data from the at least one sensor installed in a vehicle, wherein the sensor data is indicative of acceleration data in a forward vector or acceleration data in a lateral vector;

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

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

calculate absolute speed change data based on the sensor data;

identify a false positive non-crash event when an oscillating event is detected in the sensor data that decreases in magnitude over time and that crosses zero in the sensor data; and

provide the obtained sensor data to a remote server system when the crash score data exceeds a crash threshold and when a false positive non-crash event is not identified.

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

3. The vehicle telematics device of claim 1 , wherein the crash curve data is dynamically determined based on vehicle data describing a weight of the vehicle.

4. The vehicle telematics device of claim 3 , wherein the at least one sensor comprises a weight sensor.

5. The vehicle telematics device of claim 1 , further comprising a communications interface, wherein the vehicle telematics device obtains the sensor data using the communications interface.

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

7. The vehicle telematics device 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 forward vector.

8. The vehicle telematics device 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.

9. The vehicle telematics device 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.

10. The vehicle telematics device of claim 1 , wherein the processor of the vehicle telematics device, on reading the crash determination application, is further directed to:

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

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

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

obtaining, by a telematics device, sensor data from the at least one sensor installed in a vehicle, wherein the at least one sensor has an accelerometer, and wherein the sensor data is indicative of acceleration data in a forward vector or acceleration data in a lateral vector;

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

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

calculating, by the telematics device, absolute speed change data based on the sensor data;

identifying, by the telematics device, a false positive non-crash event when an oscillating event is detected in the sensor data that decreases in magnitude over time and crosses zero in the sensor data; and

providing, by the telematics device, the obtained sensor data to a remote server system when the crash score data exceeds a crash threshold and when a false positive non-crash event is not identified.

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

13. The method of claim 11 , further comprising dynamically determining, by the telematics device, the crash curve data based on vehicle data describing a weight of the vehicle.

14. The method of claim 13 , wherein the at least one sensor comprises a weight sensor.

15. The method of claim 11 , further comprising

obtaining, by the telematics device, the sensor data using a communications interface of the telematics device.

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

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

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

19. The method of claim 11 , wherein generating the crash score data comprises:

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.

20. The method of claim 11 , further comprising:

calculating, by the telematics device, resultant velocity change based on the obtained sensor data and the vehicle identification data; and

generating, by the telematics device, crash occurred data when the resultant velocity change exceeds a first threshold value and when the absolute speed change data is below a second threshold value.

Assignments (4)
RELEASE OF SECURITY INTEREST Recorded Aug 15, 2024
From: THE BANK OF NEW YORK MELLON TRUST COMPANY, N.A.
To: CALAMP CORP.; CALAMP WIRELESS NETWORKS CORPORATION; SYNOVIA SOLUTIONS LLC
Reel/Frame 068655/0542 →
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 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 4, 2023
From: JENKINS, JEFF; BAYLEY, GREG; BYRNE, PETER; CHEN, GALLIN
To: CALAMP CORP.
Reel/Frame 065115/0481 →