SYSTEMS AND METHODS OF DETERMINING EFFECTIVENESS OF VEHICLE SAFETY FEATURES
Systems and methods for determining the effectiveness of vehicle safety features are provided. Vehicle build information (VBI) for vehicles manufactured by a plurality of OEMs may be obtained. The VBI may contain OEM-specific terminology for smart safety features associated with each vehicle. The obtained VBI may be analyzed to generate an ontology model mapping each feature to any OEM-specific terminology associated with the feature. The ontology model may be applied to the VBI to generate translated VBI for each vehicle, such that the OEM-specific terminology associated with each feature is replaced with OEM-agnostic terminology for the feature. Vehicle accident record information may be obtained for each vehicle, including, e.g., the number, frequency, severity, etc. of accidents associated with each vehicle. Using the OEM-agnostic terminology for each feature associated with each vehicle and the vehicle accident information for each vehicle, an effectiveness score associated with each feature may be calculated.
1 . A computer-implemented method for analyzing a performance of vehicle safety features during a collision, the method comprising:
obtaining, by a processor, vehicle build information for a plurality of vehicles associated with a collision manufactured by a plurality of original equipment manufacturers (OEMs), the vehicle build information containing OEM-specific terminology associated with one or more safety features associated with each vehicle;
applying, by the processor, a machine learning model, trained to map each of the one or more safety features associated with each vehicle to any OEM-specific terminology associated with the safety feature for each OEM, to the vehicle build information to generate translated vehicle build information for each of the plurality of vehicles associated with the collision, such that the OEM-specific terminology associated with each safety feature is replaced with OEM-agnostic terminology for the safety feature; and
analyzing, by the processor, using the OEM-agnostic terminology for each safety feature and one or more of: vehicle telematics data associated with the collision or safety feature data associated with the collision, to determine one or more of (i) whether a performance of the safety feature was relevant to the collision, or (ii) whether the safety feature operated as intended one or more of: prior to, during, or after the collision.
2 . The computer-implemented method of claim 1 , the method further comprising:
assigning, via the one or more processors, based upon analyzing one or more of: vehicle telematics data associated with the collision or safety feature data associated with the collision, a percentage of fault for the collision to each safety feature.
3 . The computer-implemented method of claim 1 , the method further comprising:
determining, via the one or more processors, based upon analyzing one or more of: vehicle telematics data associated with the collision or safety feature data associated with the collision, a percentage of fault for the collision to each safety feature, one or more of an individual safety performance rating or a safety score for each safety feature.
4 . The computer-implemented method of claim 3 , the method further comprising generating a virtual report detailing the one or more of the individual safety performance rating or the safety score for each safety feature.
5 . The computer-implemented method of claim 1 , wherein the safety feature associated with the collision is one or more of safety feature configuration data, safety feature software data software, or safety feature usage data.
6 . The computer-implemented method of claim 1 , wherein the safety feature associated with the collision includes vehicle owner preferences for one or more of safety feature configuration or safety feature usage.
7 . The computer-implemented method of claim 1 , wherein the safety feature associated with the collision includes software version information of a current software version associated with the safety feature.
8 . The computer-implemented method of claim 1 , wherein the safety feature is an autonomous vehicle safety feature.
9 . The computer-implemented method of claim 1 , wherein the safety feature is an semi-autonomous vehicle safety feature.
10 . The computer-implemented method of claim 1 , wherein the vehicle telematics data associated with the collision includes one or more of: speed data, acceleration data, cornering data, braking data, location data, or time of day data.
11 . A computer system for analyzing a performance of vehicle safety features during a collision, comprising:
one or more processors; and
a non-transitory program memory communicatively coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, cause the computer system to:
obtain vehicle build information for a plurality of vehicles associated with a collision and manufactured by a plurality of original equipment manufacturers (OEMs), the vehicle build information containing OEM-specific terminology associated with one or more safety features associated with each vehicle;
apply a machine learning model, trained to map each of the one or more safety features associated with each vehicle to any OEM-specific terminology associated with the safety feature for each OEM, to the vehicle build information to generate translated vehicle build information for each of the plurality of vehicles associated with the collision, such that the OEM-specific terminology associated with each safety feature is replaced with OEM-agnostic terminology for the safety feature; and
analyze, using the OEM-agnostic terminology for each safety feature and one or more of: vehicle telematics data associated with the collision or safety feature data associated with the collision, to determine one or more of (i) whether a performance of the safety feature was relevant to the collision, or (ii) whether the safety feature operated as intended one or more of: prior to, during, or after the collision.
12 . The computer system of claim 11 , wherein the executable instructions, when executed by the one or more processors, further cause the computer system to:
assign, based upon analyzing one or more of: vehicle telematics data associated with the collision or safety feature data associated with the collision, a percentage of fault for the collision to each safety feature.
13 . The computer system of claim 11 , wherein the executable instructions, when executed by the one or more processors, further cause the computer system to:
determine based upon analyzing one or more of: vehicle telematics data associated with the collision or safety feature data associated with the collision, a percentage of fault for the collision to each safety feature, one or more of an individual safety performance rating or a safety score for each safety feature.
14 . The computer system of claim 13 , wherein the executable instructions, when executed by the one or more processors, further cause the computer system to:
generate a virtual report detailing the one or more of the individual safety performance rating or the safety score for each safety feature.
15 . The computer system of claim 11 , wherein the safety feature associated with the collision is one or more of safety feature configuration data, safety feature software data software, or safety feature usage data.
16 . The computer system of claim 11 , wherein the safety feature associated with the collision includes vehicle owner preferences for one or more of safety feature configuration or safety feature usage.
17 . The computer system of claim 11 , wherein the safety feature associated with the collision includes software version information of a current software version associated with the safety feature.
18 . The computer system of claim 11 , wherein the safety feature is an autonomous vehicle safety feature.
19 . The computer system of claim 11 , wherein the vehicle telematics data associated with the collision includes one or more of: speed data, acceleration data, cornering data, braking data, location data, or time of day data.
20 . A tangible, non-transitory computer-readable medium storing executable instructions for analyzing a performance of vehicle safety features during a collision that, when executed by at least one processor of a computer system, cause the computer system to:
obtain vehicle build information for a plurality of vehicles associated with a collision and manufactured by a plurality of original equipment manufacturers (OEMs), the vehicle build information containing OEM-specific terminology associated with one or more safety features associated with each vehicle;
apply a machine learning model, trained to map each of the one or more safety features associated with each vehicle to any OEM-specific terminology associated with the safety feature for each OEM, to the vehicle build information to generate translated vehicle build information for each of the plurality of vehicles associated with the collision, such that the OEM-specific terminology associated with each safety feature is replaced with OEM-agnostic terminology for the safety feature; and
analyze, using the OEM-agnostic terminology for each safety feature and one or more of: vehicle telematics data associated with the collision or safety feature data associated with the collision, to determine one or more of (i) whether a performance of the safety feature was relevant to the collision, or (ii) whether the safety feature operated as intended one or more of: prior to, during, or after the collision.