Accident reconstruction implementing unmanned aerial vehicles (UAVs)

• US 10,145,684 B1
• Filed: 09/18/2015
• Issued: 12/04/2018
• Est. Priority Date: 09/22/2014
• Status: Active Grant

• Current Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Original Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Inventors: Tofte, Nathan L., Ryan, Timothy W., Baumann, Nathan W., Jacob, Michael Shawn, Lillie, Joshua David, Harvey, Brian N., Lyons, Roxane, Grant, Rosemarie Geier
• Primary Examiner(s): APPLE, KIRSTEN SACHWITZ
• Application Number:
  US14/858,038
• Time in Patent Office: 1,173 Days
• Field of Search: 703 38
• CPC Class Codes: B64C 39/024 of the remote controlled vehicle type, i.e. RPV
• CPC Class Codes: B64D 47/08 Arrangements of cameras
• CPC Class Codes: B64U 10/00 Type of UAV
• CPC Class Codes: B64U 10/14 with four distinct rotor axes, e.g. quadcopters
• CPC Class Codes: B64U 2101/00 UAVs specially adapted for particular uses or applications
• CPC Class Codes: B64U 2101/30 for imaging, photography or videography
• CPC Class Codes: G01C 11/02 Picture taking arrangements specially adapted for photogrammetry or photographic surveying, e.g. controlling overlapping of pictures
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G06T 11/60 Editing figures and text; Combining figures or text
• CPC Class Codes: G06T 17/05 Geographic models
• CPC Class Codes: G06T 2207/10032 Satellite or aerial image; Remote sensing
• CPC Class Codes: G06T 2207/30232 Surveillance
• CPC Class Codes: G06T 2207/30236 Traffic on road, railway or crossing
• CPC Class Codes: G06T 2207/30252 Vehicle exterior; Vicinity of vehicle
• CPC Class Codes: G06T 2215/16 Using real world measurements to influence rendering
• CPC Class Codes: G06T 7/00 Image analysis
• CPC Class Codes: G06T 7/20 Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G06T 7/246 using feature-based methods, e.g. the tracking of corners or segments
• CPC Class Codes: G06T 7/75 : involving models
• CPC Class Codes: G06V 10/42 : Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation
• CPC Class Codes: G06V 20/10 : Terrestrial scenes scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59
• CPC Class Codes: G06V 20/41 : Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items segmenting video sequences G06V20/49
• CPC Class Codes: G06V 20/44 : Event detection
• CPC Class Codes: H04N 5/44 : Receiver circuitry for the reception of television signals according to analogue transmission standards H04N5/14 takes precedence
• CPC Class Codes: H04N 7/185 : from a mobile camera, e.g. for remote control

Abstract

Unmanned aerial vehicles (UAVs) may facilitate the generation of a virtual reconstruction model of a vehicle collision. UAVs may collect data (including images) related to the vehicle collision, such as with the insured'"'"'s permission, which may be received by an external computing device associated with the insurer and utilized to perform a photogrammetric analysis of the images to determine vehicle impact points, the road layout at the scene of the collision, the speeds and directions of vehicles, etc. This data may be used to generate a virtual reconstruction model of the vehicle collision. An insurer may use the virtual reconstruction model to perform various insurance-related tasks, such as allocating fault to drivers or autonomous vehicles involved in the vehicle collision, and adjustment of insurance pricing based upon the fault allocation. As a result, innocent drivers or vehicles may not be unfairly penalized for vehicle collisions not their fault.

Claims

1. A computer-implemented method of vehicle collision or crash reconstruction using drone data, comprising:

receiving, by one or more processors, drone data via wireless communications, the drone data being generated by one or more drones and including vehicle collision or crash images of (i) vehicles involved in a vehicular collision or crash, and (ii) a scene of the vehicle collision or crash,

performing, by one or more processors, a photogrammetric analysis of the vehicle collision or crash images, the photogrammetric analysis including calculating respective speeds and respective directions of the vehicles before, during, and after the vehicle collision or crash;

generating, by one or more processors, a virtual accident reconstruction model of the vehicle collision or crash based upon the photogrammetric analysis of the vehicle collision or crash images, the virtual accident reconstruction model generated by overlaying respective movements of the vehicles, as indicated by the respective speeds and respective directions of the vehicles before, during, and after the vehicle collision or crash, onto respective locations of a model of a road on which the vehicle crash or collision occurred; and

assigning, by one or more processors, a percentage of fault to each driver and vehicle involved in the vehicle collision or crash based upon an analysis of the virtual accident reconstruction model of the vehicle collision or crash.

2. The computer-implemented method of claim 1, further comprising:

calculating, by one or more processors, pricing related to an insurance policy for a driver of an insured vehicle involved in the vehicle collision or crash based upon the percentage of fault assigned to the driver of the insured vehicle, or to the insured vehicle.

3. The computer-implemented method of claim 2, further comprising:

transmitting, by one or more processors, the pricing related to the insurance policy for the driver of the insured vehicle involved in the vehicle collision or crash to a mobile computing device associated with the insured driver.

4. The computer-implemented method of claim 1, wherein calculating the respective speeds and the respective directions of the vehicles before, during, and after the vehicle collision or crash further comprises:

identifying, by one or more processors, respective one or more impact points of each of the vehicles by analyzing the vehicle collision or crash images of the vehicles involved in the vehicular collision or crash;

measuring, by one or more processors, a respective structural crush distance of each of the respective one or more impact points of each of the vehicles; and

calculating, by one or more processors, a respective speed and a respective direction of each of the vehicles before, during, and after the vehicle collision or crash based upon a respective location and the respective structural crush distance of each of the respective one or more impact points of each of the vehicles.

5. The computer-implemented method of claim 1, wherein the model of the road comprises:

a scaled road layout corresponding to the scene of the vehicle collision or crash, the scaled road layout generated by analyzing the images of the scene of the vehicle collision or crash.

6. The computer-implemented method of claim 1, further comprising:

receiving, by one or more processors, telematics data from one or more of the vehicles involved in the vehicular collision or crash; and

verifying, by one or more processors, the virtual accident reconstruction model with the telematics data.

7. The computer-implemented method of claim 1, wherein drone data further comprises:

surveillance data collected of the scene prior to the vehicle collision or crash.

8. An external computing device associated with an insurance provider, comprising:

a communication unit configured to receive drone data via wireless communications,

wherein the drone data is generated by one or more drones and includes vehicle collision or crash images of (i) vehicles involved in a vehicular collision or crash, and (ii) a scene of the vehicle collision or crash; and

a processor configured to;

perform a photogrammetric analysis of the vehicle collision or crash images, the photogrammetric analysis including calculating respective speeds and respective directions of the vehicles before, during, and after the vehicle collision or crash;

generate a virtual accident reconstruction model of the vehicle collision or crash based upon the photogrammetric analysis of the vehicle collision or crash images, the virtual accident reconstruction model generated by overlaying respective movements of the vehicles, as indicated by the respective speeds and respective directions of the vehicles before, during, and after the vehicle collision or crash, onto respective locations of a model of a road on which the vehicle crash or collision occurred; and

assigning a percentage of fault to each driver involved in the vehicle collision or crash based upon an analysis of the virtual accident reconstruction model of the vehicle collision or crash.

9. The external computing device of claim 8, wherein the processor is further configured to calculate pricing related to an insurance policy for a driver of an insured vehicle involved in the vehicle collision or crash based upon the percentage of fault assigned to the driver of the insured vehicle.

10. The external computing device of claim 9, wherein the communication unit is further configured to transmit the pricing related to the insurance policy for the driver of the insured vehicle involved in the vehicle collision or crash to a mobile computing device associated with the insured driver.

12. The external computing device of claim 8, wherein the model of the road comprises:

a scaled road layout corresponding to the scene of the vehicle collision or crash, the scaled road layout generated by analyzing the images of the scene of the vehicle collision or crash.

13. The external computing device of claim 8, wherein the communication unit is further configured to receive telematics data from one or more of the vehicles involved in the vehicular collision or crash, and

wherein the processor is further configured to verify the virtual accident reconstruction model with the telematics data.

14. The external computing device of claim 8, wherein drone data further comprises:

surveillance data collected of the scene prior to the vehicle collision or crash.

11. The external computing device of 8, wherein the calculation of the respective speeds and the respective directions of the vehicles before, during, and after the vehicle collision or crash includes (i) a respective identification of a respective one or more impact points of each of the vehicles by analyzing the images of the vehicles involved in the vehicular collision or crash, (ii) a respective measurement of a respective structural crush distance of each of the respective one or more impact points of each of the vehicles, and (iii) the respective speed and the respective direction of each of the vehicles before, during, and after the vehicle collision or crash based upon a respective location and the respective structural crush distance of each of the respective one or more impact points of each of the vehicles.

15. A non-transitory computer readable media having instructions stored thereon that, when executed by a processor, cause the processor to:

receive drone data via wireless communications, the drone data being generated by one or more drones and including vehicle collision or crash images of (i) vehicles involved in a vehicular collision or crash, and (ii) a scene of the vehicle collision or crash;

perform a photogrammetric analysis of the vehicle collision or crash images to thereby calculate respective speeds and respective directions of the vehicles before, during, and after the vehicle collision or crash;

generate a virtual accident reconstruction model of the vehicle collision or crash based upon the photogrammetric analysis of the vehicle collision or crash images, the virtual accident reconstruction model generated by overlaying respective movements of the vehicles, as indicated by the respective speeds and respective directions of the vehicles before, during, and after the vehicle collision or crash, onto respective locations of a model of a road on which the vehicle crash or collision occurred; and

assigning a percentage of fault to each driver involved in the vehicle collision or crash based upon an analysis of the virtual accident reconstruction model of the vehicle collision or crash.

16. The non-transitory computer readable media of claim 15, further including instructions that, when executed by the processor, cause the processor to calculate pricing related to an insurance policy for a driver of an insured vehicle involved in the vehicle collision or crash based upon the percentage of fault assigned to the driver of the insured vehicle.

17. The non-transitory computer readable media of claim 16, further including instructions that, when executed by the processor, cause the processor to transmit the pricing related to the insurance policy for the driver of the insured vehicle involved in the vehicle collision or crash to a mobile computing device associated with the insured driver.

18. The non-transitory computer readable media of claim 15, wherein the instructions to perform the photogrammetric analysis of the vehicle collision or crash image to thereby calculate the respective speeds and the respective direction of the vehicles before, during, and after the vehicle collision or crash include instructions that, when executed by the processor, cause the processor to (i) identify respective one or more impact points of each of the vehicles by analyzing the images of the vehicles involved in the vehicular collision or crash, (ii) measure a respective structural crush distance of each of the respective one or more impact points of each of the vehicles, and (iii) calculate a respective speed and a respective direction of each of the vehicles before, during, and after the vehicle collision or crash based upon a respective location and the respective structural crush distance of each of the one or more impact points of each of the vehicles.

19. The non-transitory computer readable media of claim 15, wherein the model of the road comprises a scaled road layout corresponding to the scene of the vehicle collision or crash, the scaled road layout generated by analyzing the images of the scene of the vehicle collision or crash.

20. The non-transitory computer readable media of claim 15, further including instructions that, when executed by the processor, cause the processor to (i) receive telematics data from one or more of the vehicles involved in the vehicular collision or crash, and (ii) verify the virtual accident reconstruction model with the telematics data.

21. The non-transitory computer readable media of claim 15, wherein drone data further comprises:

surveillance data collected of the scene prior to the vehicle collision or crash.