Abstract: The following relates generally to determining effectiveness of an update to a vehicle feature. In some embodiments, information indicating an update to a vehicle feature, and accident record information may be received. A first dataset from before the update was implemented in the vehicle, and a second dataset from after the update was implemented in the vehicle may then be constructed. An effectiveness score may then be calculated based upon the first and second datasets.

Abstract: The following relates generally to determining effectiveness of an update to a vehicle feature. In some embodiments, information indicating an update to a vehicle feature, and accident record information may be received. A first dataset from before the update was implemented in the vehicle, and a second dataset from after the update was implemented in the vehicle may then be constructed. An effectiveness score may then be calculated based upon the first and second datasets.

Abstract: Systems and methods for determining the effectiveness of vehicle safety features are provided. Vehicle data is obtained for vehicles having various smart safety features, and a list of translated vehicle build records is generated from the obtained data applying OEM-agnostic terminology for the smart safety features. A machine learning algorithm may be trained to generate an effectiveness score associated with one or more smart safety features, at least by analyzing a plurality of translated vehicle build records, vehicle telematics data, and vehicle accident records associated with each of the plurality of vehicles.

Abstract: Systems and methods for building a vehicle data repository (VDR) are provided. In some embodiments, the VDR is constructed by adding standardized build sheets to it. The standardized build sheets may be constructed by selecting data from various data sources (e.g., original equipment manufacturer (OEM) databases, National Highway Traffic Safety Administration (NHTSA) databases, Highway Loss Data Institute (HLDI) databases, and/or Insurance Institute for Highway Safety (IIHS) databases). Furthermore, a common ontology may be created and applied to the various data sources to aide in the selection of data between the data sources to construct the standardized build sheets.

Abstract: Systems and methods for determining a reparability of a vehicle are provided. In some embodiments, vehicle data is obtained, and a list of variables is generated from the obtained data. A machine learning algorithm may then be trained to generate a reparability metric by: (i) generating correlation metrics between the variables and costs to repair the vehicle, (ii) removing variables with correlation metrics below a threshold, and (iii) training the machine learning algorithm based upon unremoved variables.

Abstract: The following relates generally to determining effectiveness of an update to a vehicle feature. In some embodiments, information indicating an update to a vehicle feature, and accident record information may be received. A first dataset from before the update was implemented in the vehicle, and a second dataset from after the update was implemented in the vehicle may then be constructed. An effectiveness score may then be calculated based upon the first and second datasets.

Abstract: The following relates generally to tracking effectiveness of an update to a vehicle feature using a distributed ledger. In some embodiments, a distributed ledger including a vehicle feature is added to or constructed. Information indicating an update to the vehicle feature, and accident record information may then be received. A first dataset from before the update was implemented in the vehicle, and a second dataset from after the update was implemented in the vehicle may then be constructed. An effectiveness score may then be calculated based upon the first and second datasets, and added to the distributed ledger.