Accident reconstruction implementing unmanned aerial vehicles (UAVs)
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 state of the traffic light 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.
1. A system comprising:
an unmanned aerial vehicle (UAV);
a server in communication with the UAV via a network, the server including:
a processor; and
a non-transitory computer-readable memory storing instructions thereon that, when executed by the processor, cause the server to:
receive a request to inspect a vehicle involved in a vehicle collision, the request including a location of the vehicle;
instruct the UAV to travel to the location of the vehicle;
instruct the UAV to collect damage information associated with the vehicle using one or more sensors of the UAV;
receive traffic light information regarding a state of a traffic light at the location of the vehicle;
determine that at least one vehicle involved in the vehicle collision committed a traffic violation based upon the traffic light information; and
determine an amount of fault for each vehicle involved in the vehicle collision based upon the damage information collected by the UAV.
2. The system of claim 1 , wherein the damage information further 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.
3. The system of claim 2 , wherein the instructions further cause the server to:
perform a photogrammetric analysis of the vehicle collision or crash images; and
generate a virtual reconstruction model of the vehicle collision or crash based upon (i) the photogrammetric analysis of the vehicle collision or crash images and (ii) the traffic light information regarding the state of the traffic light at the location of the vehicle,
wherein the amount of fault for each vehicle involved in the vehicle collision is further determined based upon an analysis of the virtual reconstruction model of the vehicle collision or crash.
4. The system of claim 3 , wherein to perform the photogrammetric analysis of the vehicle collision or crash images, the instructions cause the server to:
identify one or more impact points of vehicles involved in the vehicle collision or crash by analyzing the images of vehicles involved in the vehicular collision or crash;
measure a structural crush distance for each of the one or more impact points for each of the vehicles; and
calculate a speed and direction of each of the vehicles involved in the vehicular collision or crash before, during, and after the vehicle collision or crash based upon a location of each of the one or more impact points and the structural crush distance for each of the vehicles involved in the vehicle collision or crash.
5. The system of claim 4 , wherein to generate the virtual reconstruction model of the vehicle collision or crash, the instructions cause the server to:
generate the virtual reconstruction model of the vehicle collision or crash to indicate the calculated speed and direction of each of the vehicles involved in the vehicular collision or crash before, during, and after the vehicle collision or crash.
6. The system of claim 5 , wherein to perform the photogrammetric analysis of the vehicle collision or crash images, the instructions cause the server to:
generate a scaled road layout corresponding to the scene of the vehicle collision or crash by analyzing the images of the scene of the vehicle collision or crash, and
generate the virtual reconstruction model of the vehicle collision or crash to indicate the calculated speed and direction of each of the vehicles involved in the vehicular collision or crash before, during, and after the vehicle collision or crash overlaid onto the scaled road layout.
7. The system of claim 3 , wherein the instructions further cause the server to:
receive telematics data from one or more of the vehicles involved in the vehicular collision or crash; and
verify the virtual reconstruction model with the telematics data.
8. A computer-implemented method for determining an amount of fault for each vehicle involved in a vehicle collision, the method comprising:
receiving, by one or more processors, a request to inspect a vehicle involved in a vehicle collision, the request including a location of the vehicle;
instructing, by the one or more processors, an unmanned aerial vehicle (UAV) to travel to the location of the vehicle;
instructing, by the one or more processors, the UAV to collect damage information associated with the vehicle using one or more sensors of the UAV;
receiving, by the one or more processors, traffic light information regarding a state of a traffic light at the location of the vehicle;
determining, by the one or more processors, that at least one vehicle involved in the vehicle collision committed a traffic violation based upon the traffic light information; and
determining, by the one or more processors, an amount of fault for each vehicle involved in the vehicle collision based upon the damage information collected by the UAV.
9. The computer-implemented method of claim 8 , wherein the damage information further 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.
10. The computer-implemented method of claim 9 , further comprising:
performing, by the one or more processors, a photogrammetric analysis of the vehicle collision or crash images; and
generating, by the one or more processors, a virtual reconstruction model of the vehicle collision or crash based upon (i) the photogrammetric analysis of the vehicle collision or crash images and (ii) the traffic light information regarding the state of the traffic light at the location of the vehicle,
wherein the amount of fault for each vehicle involved in the vehicle collision is further determined based upon an analysis of the virtual reconstruction model of the vehicle collision or crash.
11. The computer-implemented method of claim 10 , wherein performing the photogrammetric analysis of the vehicle collision or crash images includes
identifying, by the one or more processors, one or more impact points of vehicles involved in the vehicle collision or crash by analyzing the images of vehicles involved in the vehicular collision or crash;
measuring, by the one or more processors, a structural crush distance for each of the one or more impact points for each of the vehicles; and
calculating, by the one or more processors, a speed and direction of each of the vehicles involved in the vehicular collision or crash before, during, and after the vehicle collision or crash based upon a location of each of the one or more impact points and the structural crush distance for each of the vehicles involved in the vehicle collision or crash.
12. The computer-implemented method of claim 11 , wherein generating the virtual reconstruction model of the vehicle collision or crash includes:
generating, by the one or more processors, the virtual reconstruction model of the vehicle collision or crash to indicate the calculated speed and direction of each of the vehicles involved in the vehicular collision or crash before, during, and after the vehicle collision or crash.
13. The computer-implemented method of claim 12 , wherein performing the photogrammetric analysis of the vehicle collision or crash images includes:
generating, by the one or more processors, a scaled road layout corresponding to the scene of the vehicle collision or crash by analyzing the images of the scene of the vehicle collision or crash, and
generating, by the one or more processors, the virtual reconstruction model of the vehicle collision or crash to indicate the calculated speed and direction of each of the vehicles involved in the vehicular collision or crash before, during, and after the vehicle collision or crash overlaid onto the scaled road layout.
14. The computer-implemented method of claim 10 , further comprising:
receiving, by the one or more processors, telematics data from one or more of the vehicles involved in the vehicular collision or crash; and
verifying, by the one or more processors, the virtual reconstruction model with the telematics data.
15. A non-transitory computer readable media having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to:
receive a request to inspect a vehicle involved in a vehicle collision, the request including a location of the vehicle;
instruct an unmanned aerial vehicle (UAV) to travel to the location of the vehicle;
instruct the UAV to collect damage information associated with the vehicle using one or more sensors of the UAV;
receive traffic light information regarding a state of a traffic light at the location of the vehicle;
determine that at least one vehicle involved in the vehicle collision committed a traffic violation based upon the traffic light information; and
determine an amount of fault for each vehicle involved in the vehicle collision based upon the damage information collected by the UAV.
16. The non-transitory computer readable media of claim 15 , wherein the damage information further 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.
17. The non-transitory computer readable media of claim 16 , wherein the instructions further cause the one or more processors to:
perform a photogrammetric analysis of the vehicle collision or crash images; and
generate a virtual reconstruction model of the vehicle collision or crash based upon (i) the photogrammetric analysis of the vehicle collision or crash images and (ii) the traffic light information regarding the state of the traffic light at the location of the vehicle,
wherein the amount of fault for each vehicle involved in the vehicle collision is further determined based upon an analysis of the virtual reconstruction model of the vehicle collision or crash.
18. The non-transitory computer readable media of claim 17 , wherein to perform the photogrammetric analysis of the vehicle collision or crash images, the instructions cause the one or more processors to:
identify one or more impact points of vehicles involved in the vehicle collision or crash by analyzing the images of vehicles involved in the vehicular collision or crash;
measure a structural crush distance for each of the one or more impact points for each of the vehicles; and
calculate a speed and direction of each of the vehicles involved in the vehicular collision or crash before, during, and after the vehicle collision or crash based upon a location of each of the one or more impact points and the structural crush distance for each of the vehicles involved in the vehicle collision or crash.
19. The non-transitory computer readable media of claim 18 , wherein to generate the virtual reconstruction model of the vehicle collision or crash, the instructions cause the one or more processors to:
generate the virtual reconstruction model of the vehicle collision or crash to indicate the calculated speed and direction of each of the vehicles involved in the vehicular collision or crash before, during, and after the vehicle collision or crash.
20. The non-transitory computer readable media of claim 19 , wherein to perform the photogrammetric analysis of the vehicle collision or crash images, the instructions cause the one or more processors to:
generate a scaled road layout corresponding to the scene of the vehicle collision or crash by analyzing the images of the scene of the vehicle collision or crash, and
generate the virtual reconstruction model of the vehicle collision or crash to indicate the calculated speed and direction of each of the vehicles involved in the vehicular collision or crash before, during, and after the vehicle collision or crash overlaid onto the scaled road layout.