Using a Distributed Ledger to Determine Fault in Subrogation

Abstract

Systems and methods are disclosed with respect to using a blockchain for managing the subrogation claim process related to a vehicle accident, in particular, determining fault as part of the subrogation process. An exemplary embodiment may include receiving an electronic notification of a vehicle collision; receiving sensor data (such as telematics, image, audio, vehicle operational, or other sensor data) related to the vehicle collision; determining a percentage of fault of the vehicle collision for one or more vehicles, vehicle systems, and/or drivers based upon, at least in part, analysis of the sensor data collected; and creating a blockchain for the vehicle collision with one or more links to the sensor image data and an indication of the percentage of fault(s) determined to facilitate blockchain-based claim handling.

What is claimed: 
     
         1 . A computer-implemented method of handling vehicle collision-related data via a blockchain maintained by a plurality of nodes connected via a network, the method comprising:
 receiving, at one or more processors of a first node of the plurality of nodes, a first transaction broadcast to the blockchain by a source and including sensor data;   verifying, at the one or more processors, an identity of the source using a cryptographic proof-of-identity included in the first transaction;   determining, at the one or more processors, that a vehicle collision involving a vehicle occurred based upon, at least in part, analysis of the sensor data;   determining, at the one or more processors, an operational mode for the vehicle, the operational mode indicative of whether a human driver, the vehicle, or a vehicle system was in control of the vehicle at the time of the vehicle collision, based upon, at least in part, analysis of the sensor data;   determining, at the one or more processors, a percentage of fault for the vehicle collision attributable to at least one of the human driver, the vehicle, or the vehicle system, based upon, at least in part, analysis of the sensor data, and the operational mode for the vehicle at the time of the vehicle collision;   generating, at the one or more processors, a second transaction including a cryptographic proof-of-identity of an entity associated with the first node, an indication of the operational mode for the vehicle at the time of the vehicle collision, and an indication of the percentage of fault; and   broadcasting, by the one or more processors, the second transaction to at least one other node of the plurality of nodes.   
     
     
         2 . The computer-implemented method of  claim 1 , further comprising:
 receiving, at the one or more processors, an electronic arbitration demand associated with the vehicle collision; and   generating, at the one or more processors, an electronic recommendation based upon, at least in part, analysis of the first percentage of fault, the second percentage of fault, and the electronic arbitration demand.   
     
     
         3 . The computer-implemented method of  claim 2 , further comprising:
 generating, at the one or more processors, a new block including the electronic recommendation or a link thereto or a hash thereof, and   adding, at the one or more processors, the new block to the blockchain.   
     
     
         4 . The computer-implemented method of  claim 1 , wherein the sensor data includes data generated by smart infrastructure or by a vehicle not involved in the vehicle collision in the vicinity of the vehicle collision. 
     
     
         5 . The computer-implemented method of  claim 1 , wherein the sensor data includes telematics data collected by the vehicle, a mobile device traveling within the vehicle, another vehicle involved in the vehicle collision, another vehicle in the vicinity of the vehicle collision not involved in the vehicle collision, or combinations thereof. 
     
     
         6 . The computer-implemented method of  claim 1 , wherein determining the first percentage of fault and the second percentage of fault includes inputting the sensor data collected into a machine learning programmed trained to identify a percentage of fault attributable to one or more factors based upon sensor data. 
     
     
         7 . The computer-implemented method of  claim 1 , wherein determining a vehicle collision occurred based upon, at least in part, analysis of the sensor data collected includes inputting the sensor data collected into a machine learning programmed trained to identify that vehicle collision occurred based upon sensor data. 
     
     
         8 . The computer-implemented method of  claim 1 , further comprising:
 assigning, at the one or more processors, liability to a manufacturer of the vehicle or the human driver based upon whom had control before, during, or after the vehicle collision.   
     
     
         9 . The computer-implemented method of  claim 1 , further comprising:
 determining, at the one or more processors, which vehicle or human driver had the last clear chance to avoid the vehicle collision based upon, at least in part, analysis of the sensor data collected.   
     
     
         10 . The computer-implemented method of  claim 9 , wherein determining which vehicle or human driver had the last clear chance to avoid the vehicle collision based upon, at least in part, analysis of the sensor data collected further comprises:
 inputting, at the one or more processors, the sensor image data into a machine learning program trained to identify a party or vehicle that had the last clear chance to avoid the vehicle collision using sensor data.   
     
     
         11 . A tangible, non-transitory computer-readable medium storing instructions for handling vehicle collision-related data via a blockchain maintained by a plurality of nodes connected via a network that, when executed by one or more processors of a first node of the plurality of nodes, cause the one or more processors to:
 receive a first transaction broadcast to the blockchain by a source and including sensor data;   verify an identity of the source using a cryptographic proof-of-identity included in the first transaction;   determine that a vehicle collision involving a vehicle occurred based upon, at least in part, analysis of the sensor data;   determine an operational mode for the vehicle, the operational mode indicative of whether a human driver, the vehicle, or a vehicle system was in control of the vehicle at the time of the vehicle collision, based upon, at least in part, analysis of the sensor data;   determine a percentage of fault for the vehicle collision attributable to at least one of the human driver, the vehicle, or the vehicle system, based upon, at least in part, analysis of the sensor data, and the operational mode for the vehicle at the time of the vehicle collision;   generate a second transaction including a cryptographic proof-of-identity of an entity associated with the first node, an indication of the operational mode for the vehicle at the time of the vehicle collision, and an indication of the percentage of fault; and   broadcast the second transaction to at least one other node of the plurality of nodes.   
     
     
         12 . The computer-readable medium of  claim 11 , wherein the sensor data includes data generated by smart infrastructure or by a vehicle not involved in the vehicle collision in the vicinity of the vehicle collision. 
     
     
         13 . The computer-readable medium of  claim 11 , wherein the sensor data includes telematics data collected by the vehicle, a mobile device traveling within the vehicle, another vehicle involved in the vehicle collision, another vehicle in the vicinity of the vehicle collision not involved in the vehicle collision, or combinations thereof. 
     
     
         14 . The computer-readable medium of  claim 11 , wherein the instructions cause the one or more processors to determine the first percentage of fault and the second percentage of fault by inputting the sensor data collected into a machine learning programmed trained to identify a percentage of fault attributable to one or more factors based upon sensor data. 
     
     
         15 . A computer system for handling vehicle collision-related data via a blockchain maintained by a plurality of nodes connected via a network, the system comprising:
 a network interface configured to interface with a processor of a first node of the plurality of nodes;   one or more sensors;   a memory configured to store non-transitory computer executable instructions and configured to interface with the processor; and   the processor configured to interface with the memory, wherein the processor is configured to execute the non-transitory computer executable instructions to cause the processor to:
 receive a first transaction broadcast to the blockchain by a source and including sensor data; 
 verify an identity of the source using a cryptographic proof-of-identity included in the first transaction; 
 determine that a vehicle collision involving a vehicle occurred based upon, at least in part, analysis of the sensor data; 
 determine an operational mode for the vehicle, the operational mode indicative of whether a human driver, the vehicle, or a vehicle system was in control of the vehicle at the time of the vehicle collision, based upon, at least in part, analysis of the sensor data; 
 determine a percentage of fault for the vehicle collision attributable to at least one of the human driver, the vehicle, or the vehicle system, based upon, at least in part, analysis of the sensor data, and the operational mode for the vehicle at the time of the vehicle collision; 
 generate a second transaction including a cryptographic proof-of-identity of an entity associated with the first node, an indication of the operational mode for the vehicle at the time of the vehicle collision, and an indication of the percentage of fault; and 
 broadcast the second transaction to at least one other node of the plurality of nodes. 
   
     
     
         16 . The system of  claim 15 , wherein the sensor data includes data generated by smart infrastructure or by a vehicle not involved in the vehicle collision in the vicinity of the vehicle collision. 
     
     
         17 . The system of  claim 15 , wherein the sensor data includes telematics data collected by the vehicle, a mobile device traveling within the vehicle, another vehicle involved in the vehicle collision, another vehicle in the vicinity of the vehicle collision not involved in the vehicle collision, or combinations thereof. 
     
     
         18 . The system of  claim 15 , wherein to determine the first percentage of fault and the second percentage of fault, the processor is further configured to execute the non-transitory computer executable instructions to cause the processor to:
 input the sensor data collected into a machine learning programmed trained to identify a percentage of fault attributable to one or more factors based upon sensor data.   
     
     
         19 . The system of  claim 15 , wherein the processor is further configured to execute the non-transitory computer executable instructions to cause the processor to:
 determine which vehicle or human driver had the last clear chance to avoid the vehicle collision based upon, at least in part, analysis of the sensor data collected.   
     
     
         20 . The system of  claim 15 , wherein to determine which vehicle or human driver had the last clear chance to avoid the vehicle collision, the processor is further configured to execute the non-transitory computer executable instructions to cause the processor to:
 input the sensor image data into a machine learning program trained to identify a party or vehicle that had the last clear chance to avoid the vehicle collision using sensor data.