Patent Publication Number: US-2023142401-A1

Title: Vehicle resiliency, driving feedback and risk assessment using machine learning-based vehicle wear scoring

Description:
FIELD OF THE DISCLOSURE 
     The present disclosure is generally directed to modeling aspects of vehicle operation, and more particularly, for measuring and predicting vehicle resiliency, providing driving feedback, and performing risk profiling using machine learning-based techniques. 
     BACKGROUND 
     The operation of vehicles increasingly generates telematics data. Telematics data include data that represents various aspects of vehicle operation, including the state of vehicle systems (e.g., a braking system, a suspension system, a coolant system, etc.), in addition to vehicle positional and physical information (e.g., vehicle location, course, speed, etc.). 
     It has recently become possible to derive high quality telematics data via mobile computing devices. However, conventional techniques do not include using telematics data obtained from mobile computing devices for determining vehicle resiliency, for engaging users via driving feedback or for determining risk. Telematics data on its own, without interpretation, is voluminous and resists interpretation by humans. 
     BRIEF SUMMARY 
     In one aspect, a machine learning model is stored on a non-transitory computer readable medium, wherein the machine learning model is manufactured by a process comprising retrieving a training data set and training the machine learning model, until a loss score meets a predetermined criteria. The training may include inputting a training input and corresponding label, analyzing the training input using the machine learning model to generate a prediction, and generating the loss score by comparing the prediction to the label using a loss function. The non-transitory computer readable medium may be further manufactured by storing the trained machine learning model on the non-transitory computer readable medium. 
     In another aspect, a machine learning training and operation server includes one or more processors and a memory storing instructions. When executed by the one or more processors, the instructions may cause the server to retrieve a training data set; input a training input and corresponding label, analyze the training input to generate a prediction, and generate a loss score by comparing the prediction to the label using a loss function; and store, when the loss score meets a predetermined criteria, the trained machine learning model. 
     In yet another aspect, a computer-implemented method for training a machine learning model includes receiving, via one or more processors, a training data set, inputting to the machine learning model a training input and corresponding label, analyzing the training input using the machine learning model to generate a prediction, and generating a loss score by comparing the prediction to the label using a loss function. The method may include storing, when the loss score meets a predetermined criteria, the trained machine learning model. 
     Depending upon the embodiment, one or more benefits may be achieved. These benefits and various additional objects, features and advantages of the present disclosure can be fully appreciated with reference to the detailed description and accompanying drawings that follow. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The figures described below depict various aspects of the system and methods disclosed therein. It should be understood that each figure depicts one embodiment of a particular aspect of the disclosed system and methods, and that each of the figures is intended to accord with a possible embodiment thereof. Further, wherever possible, the following description refers to the reference numerals included in the following figures, in which features depicted in multiple figures are designated with consistent reference numerals. 
         FIG.  1    depicts an exemplary computing environment in which techniques for computing a vehicle wear score using machine learning, and for using the vehicle wear score to model vehicle warranty and vehicle reinsurance, are depicted, 
         FIG.  2    depicts an exemplary artificial neural network, according to one embodiment. 
         FIG.  3    depicts an exemplary neuron of the artificial neural network of  FIG.  2   , according to one embodiment and scenario. 
         FIG.  4    depicts an exemplary computer-implemented method, according to one embodiment and scenario. 
     
    
    
     The figures depict preferred embodiments for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the systems and methods illustrated herein may be employed without departing from the principles of the present disclosure. 
     DETAILED DESCRIPTION 
     The embodiments described herein relate to, inter alia, methods and systems for measuring and predicting vehicle resiliency, providing driving feedback, and performing risk profiling using one or more machine learning (ML) models. In some embodiments, vehicle telematics data may be generated by a stationary telematics system within a vehicle, and/or via a mobile computing device. The present techniques include measuring and predicting vehicle resiliency by training an ML model to rank vehicles according to the vehicles&#39; respective susceptibility to wear and tear. 
     The present techniques include simulating wear and tear according to vehicle operator behavior and providing feedback to the vehicle operator. The present techniques include providing risk assessment, by showing how driving behavior is correlated to risk over time. 
     Inputs to the one or more ML models include vehicle type, vehicle operation behavior, and vehicle age (as expressed as length of ownership and/or odometer). In an embodiment, vehicle type, vehicle operation behavior, and vehicle age are manually adjusted by an operator to simulate partial effects. 
     In some embodiments, the ML models may analyze historical driving data to simulate how vehicle operation behavior would affect wear and tear of a vehicle the driver has not yet driven, or how present driving behavior would have affected a vehicle driven in the past for which no telematics data are available. 
     In yet further embodiments, an autoencoder may be used to train a deep learning model that is able to analyze all vehicles in existence. 
     Exemplary Computing Environment 
       FIG.  1    depicts an example environment  100  for implementing methods and/or systems for measuring and predicting vehicle resiliency, providing driving feedback, and performing risk profiling using one or more machine learning (ML) models. The environment  100  may include a vehicle  102  associated with a telematics system  104 , a network  106 , and a server  108 . 
     The vehicle  102  and the telematics system  104  are remote from the server  108  and are coupled to the server  108  via the network  106 . The network  106  may include any suitable combination of wired and/or wireless communication networks, such as one or more local area networks (LANs), metropolitan area networks (MANs), and/or wide area network (WANs). As just one specific example, the network  106  may include a cellular network, the Internet, and a server-side LAN. As another example, the network  106  may support a cellular (e.g., 4G) connection to a mobile computing device of a user and an IEEE 802.11 connection to the mobile computing device. While referred to herein as a “server,” the server  108  may, in some implementations, include multiple servers and/or other computing devices. Moreover, the server  108  may include multiple servers and/or other computing devices distributed over a large geographic area (e.g., including devices at one or more data centers), and any of the operations, computations, etc., described below may be performed in by remote computing devices in a distributed manner. 
     The telematics system  104  may include hardware and software components implemented in one or more devices permanently and/or temporarily affixed to, or otherwise carried on or within, the vehicle  102 . For example, some or all of the components of the telematics system  104  may be built into the dash of the vehicle  102  or affixed elsewhere within the vehicle  102  (e.g., via an onboard diagnostics port of the vehicle  102 ). In an embodiment, the telematics system  104  may be implemented using a mobile computing device (e.g., a smart phone of the user). The telematics system  104  may include specialized hardware (e.g., one or more sensors) and computer-executable instructions for retrieving/receiving vehicle telematics data from the vehicle  102 . In some cases, the telematics system  104  may be implemented using components of the vehicle  102  and a mobile computing device. For example, a vehicle telematics generation module may be included in the vehicle  102  and a vehicle telematics collection module may be included in a mobile computing device of the user, wherein the vehicle telematics collection module receives and/or retrieves a telematics data set from the vehicle telematics generation module. In some embodiments, some or all of the telematics system  104  may be provided by a vehicle rental company, or another operator of a fleet of vehicles (e.g., a ride-sharing company). 
     In an embodiment, a telematics system in the vehicle  102  may collect a first data set and transmit the first data set to the server  108 , while a second telematics system in the mobile computing device of the user may collect and transmit a second data set to the server  108 . While  FIG.  1    depicts only a single vehicle  102 , the vehicle  102  may be in communication with numerous other vehicles  102  similar to the vehicle  102  via the network  106  and/or other networks. The telematics system  104  may include a processor  120 , a memory  122 , a display  124 , a network interface  126 , and a global positioning system (GPS) unit  128 . The processor  120  may be a single processor (e.g.; a central processing unit (CPU)), or may include a set of processors (e.g., a CPU and a graphics processing unit (GPU)). The telematics system  104  may further include a sensor  140  and a database  150 . 
     It will be appreciated that the present techniques advantageously provide for the collection, processing and analysis of telematics data collected from the mobile device of the vehicle operator, Specifically, in some embodiments, the telematics system  104  is implemented using a mobile device of the user that is carried into the vehicle by the vehicle operator or a passenger. Therefore, the present techniques describe a system architecture that does not need the telematics system  104  to be physically coupled to the vehicle  102 . This mobility represents an improvement over conventional computing systems at least because the server  108  is able to receive/retrieve data from the telematics system  104  even when the vehicle is not in an operational state, and the instructions in the memory  122  of the telematics system  104  may be upgraded and/or modified independent of the vehicle itself. 
     The memory  122  may be a computer-readable, non-transitory storage unit or device, or collection of units/devices, that includes persistent (e.g., hard disk) and/or non-persistent memory components. The memory  122  may store instructions that are executable on the processor  120  to perform various operations, including the instructions of various software applications and data generated and/or used by such applications. In the example implementation of  FIG.  1   , the memory  122  stores at least a telematics data collection module  130  and a telematics data processing module  132 . 
     Generally, the collection module  130  is executed by the processor  120  to facilitate collection of telematics data from the vehicle  102  and the processing module  132  is executed by the processor  120  to facilitate the bidirectional transmission of telematics data between the telematics system  104  and the server  108  via the network  106  (e.g., sending telematics data collected from the vehicle  102  to the server  108 , receiving requests and responses relating to telematics data from the server  108 , etc.). The processing module  132  may encode and/or otherwise manipulate (e.g., compress, normalize, filter, etc.) the telematics data. In some embodiments, the collection module  130  may include instructions for converting sensor data to telematics data, or for joining sensor data with telematics data. For example, the collection module  130  may merge data retrieved from a sensor of the vehicle  102  remote from the telematics system  104  with a sensor located locally with respect to the telematics system  104 . 
     The display  124  includes hardware, firmware and/or software configured to enable a user to interact with (i.e., both provide inputs to and perceive outputs of) the telematics system  104 . For example, the display  124  may include a touchscreen with both display and manual input capabilities, Alternatively, or in addition, the display  124  may include a keyboard for accepting user inputs, and/or a microphone (with associated processing components) that provides voice control/input capabilities to the user. In some embodiments, the telematics system  104  may include multiple different implementations of the display  124  (e.g., a first display  124  associated with the vehicle  102  and a second display  124  associated with a mobile computing device of the user). 
     The network interface  126  includes hardware, firmware and/or software configured to enable the telematics system  104  to wirelessly exchange electronic data with the server  108  via the network  106 , For example, network interface  126  may include a cellular communication transceiver, a WiFi transceiver, and/or transceivers for one or more other wireless communication technologies (e.g., 4G). 
     The GPS unit  128  includes hardware, firmware and/or software configured to enable the telematics system  104  to self-locate using GPS technology (alone, or in combination with the services of server  108  and/or another server not shown in  FIG.  1   ). Alternatively, or in addition, the telematics system  104  may include a unit configured to self-locate, or configured to cooperate with a remote server or other device(s) to self-locate, using other, non-GPS technologies (e.g., IP-based geolocation). 
     In some embodiments, the collection module  130  (or other software stored in the memory  122 ) provides functionality for collecting telematics data from the vehicle  102 . If the telematics system  104  is a unit integrated in the vehicle (e.g., a head unit providing vehicle dashboard integrated navigation technology), for example, the telematics system  104  may include a hardwired connection (e.g., via a Controller Area Network (CAN) bus) to one or more other units of the vehicle  102 . As another example, if the telematics system  104  is a smartphone (or smart watch, etc.), the telematics system  104  may couple to one or more units of the vehicle via a wired connection (e.g., an onboard diagnostics connection) or a wireless connection (e.g., Bluetooth, WiFi, etc.). The processing module  132  provides functionality for processing telematics data from the vehicle  102 . The processing module  132  may retrieve/receive data from the collection module  132  and/or the database  150 . The collection module  130  may collect data from the sensor  140  and may store collected data in the database  150 . 
     The sensor  140  may include one or more sensors associated with the vehicle  102  (e.g., a speedometer sensor, a tire pressure sensor, a brake pad thickness sensor, a suspension ride height sensor, etc.) and/or a mobile device of the user (e.g., an accelerometer in a smartphone), The sensor may provide data (e.g., sensor readings) to one or more applications (e.g., the collection module  130 ). Many types of sensors may be used, in some embodiments, such as cameras, video cameras, and/or microphones. In an embodiment, the sensor  140  includes the sensors found in a smart phone (e.g., an accelerometer, a gyroscope, a magnetometer, and/or location services data). In some embodiments the sensor  140  may transmit sensor data to one or more mobile computing devices. 
     The database  150  may be any suitable database (e.g., a structured query language (SQL) database, a flat file database, a key/value data store, etc.). The database  150  may include a plurality of database tables for storing data according to data storage schema. The database  150  may include relational linkages between tables (e.g., one or more foreign keys, primary keys, etc.), and may allow complex data types such as time series data to be stored and queried. The database  150  may include one or more indices. 
     The server  108  includes a network interface controller (MC)  160 , a memory  162 , a ML training module  164 , a ML operation module  166 , a profile module  168 , a feedback module  170  an input device  180 , an output device  182  and a database  190 . The MC  160  includes hardware, firmware and/or software configured to enable the server  108  to exchange electronic data (e.g., telematics data) with the telematics system  104  via network  106 . For example, NIC  160  may include a wired or wireless router, switch, model, etc. The network architecture of the environment  100  is simplified for explanatory purposes. However, in some embodiments, the network architecture of environment  100  may include other components and/or configurations. 
     The memory  162  is a computer-readable, non-transitory storage unit or device, or collection of such units/devices, that may include persistent (e.g., hard disk) and/or non-persistent memory components. The memory  162  may store data generated and/or used by the ML training module  164  and/or the ML operation module  166 , for example. 
     The ML training module  164  is generally configured to train one or more machine learning models. The memory  162  may include a module for transmitting ML outputs of the ML training module  162  to the telematics system  104  for display. In some embodiments, the ML model may execute in the telematics system  104 . The ML training module  164  may provide one or more inputs as training parameters to the ML models. 
     The ML training module  164  may initialize, train, and/or store any type of machine learning model, including supervised models and/or unsupervised models. For example, the ML training module  164  may train any suitable type of artificial neural network, such as a convolutional neural network, recurrent neural network, generative adversarial network or feed-forward neural network, for example. The neural network may include a number (e.g., hundreds or thousands) of nodes (i.e., neurons) arranged in multiple layers, with each node processing one or more inputs (e.g., from the preceding layer, if any) to generate a decision, prediction, or other output. In some embodiments, the machine learning model may be tree-based. 
     The ML training module  164  may retrieve historical data, such as claims data of an insurer. The claims data may represent electronic insurance claims filed by insurance policyholders, and may include information relating to insured assets, such as vehicle types, makes, models, etc. The historical claims data may include data related to outcomes (e.g., a collision, property damage, an injury, etc.), The ML training module  164  may use the historical data to train the one or more models. 
     The ML operation module  166  may be configured for loading, initializing, executing and receiving output from the one or more ML models trained by the ML training module  164 , or by other training modules/programs. The ML operation module  166  may be located in the telematics system  104 , in some embodiments. Locating the operation module  166  in the telematics system  104  advantageously allows the environment  100  to offload machine learning model operation and data processing to edge/consumer devices, and allows inputs (e.g., operational inputs) and outputs (e.g., operational outputs) of the ML model operated by the ML operation module  166  to be processed and displayed to the user with decreased latency and decreased use of server-side resources. Locating the operation module  166  in the telematics system further advantageously allows use of the trained ML model when the telematics system  104  is offline (i.e., when the telematics system  104  cannot communicate with the network  106 ). 
     The vehicle operator profile module  168  may be configured to generate vehicle operator profiles. Specifically, the vehicle operator profile may associate an operator with one or more vehicles and telematics data sets. For example, the vehicle operator profile module  168  may maintain an association between a particular vehicle operator (e.g., John), one or more vehicles (e.g., a 2018 Ford Explorer) and telematics data for a time period (e.g.,  2019 ). The vehicle operator profile module  168  may store the associated data in a database such as the database  190 , In this way, another module (e.g., the ML training module  164 ) may query the database  190  to retrieve a specific slice of telematics data corresponding to a particular vehicle operator&#39;s operation of a particular vehicle. 
     The feedback module  170  may include instructions for generating one or more notifications for display in a mobile computing device of the vehicle operator, such as the display  124  of  FIG.  1   . The feedback module  170  may include additional instructions for transmitting the notification and for receiving inputs from the vehicle operator (e.g., an acknowledgement message, an input parameter, etc.). The feedback module  170  may process the inputs from the vehicle operator. 
     The input device  180  and the output device  182  include hardware, firmware and/or software configured to respectively enable the user to provide inputs to and perceive outputs of the telematics system  104 . In an embodiment, the input device  180  and output device  182  may be combined and implemented as a touchscreen with both display and manual input capabilities, Alternatively, or in addition, the input device  180  may include a keyboard for accepting user inputs, and/or a microphone (with associated processing components) that provides voice control/input capabilities to the user. The output device  182  may include one or more display screens. In some embodiments, the server  108  may include multiple different implementations of the input device  180  and the output device  182 . 
     The database  190  may be any suitable database (e.g., a structured query language (SQL) database, a flat file database, a key/value data store, etc), The database  190  may include a plurality of database tables for storing data according to data storage schema. The database  190  may include relational linkages between tables (e.g., one or more foreign keys, primary keys, etc.), and may allow complex data types such as time series data to be stored and queried. The database  190  may include one or more indices. The database  190  may store profile information, training data, trained ML models/weights. 
     Exemplary Machine Learning Model Training 
     Training of various ML models will now be discussed with respect to the environment  100  of  FIG.  1   . The ML training module  164  may include computer-executable instructions for training one or more ML model using telematics data. In general, the ML module  164  may train one or more ML models by establishing a network architecture, or topology, and adding layers that may be associated with one or more activation functions (e.g., a rectified linear unit, softmax, etc.), loss functions and/or optimization functions. One or more types of artificial neural networks may be employed, including without limitation, recurrent neural networks, convolutional neural networks, and/or deep learning neural networks. Data sets used to train the artificial neural network(s) may be divided into training, validation, and testing subsets, and these subsets may be encoded in an N-dimensional tensor, array, matrix, or other suitable data structures. In supervised learning, training may be performed by iteratively training the network using labeled training samples. The labels may represent desired (e.g., labeled) outputs for inputs that are similar to the labeled training samples. In this way, the network is able to learn to make predictions or identify features of de novo inputs that were not used for training. 
     Training of the artificial neural network may produce byproduct weights, or parameters which may be initialized to random values, and/or manually adjusted. The weights may be modified as the network is iteratively trained, by using one of several gradient descent algorithms, to reduce loss and to cause the values output by the network to converge to expected, or “learned”, values. In an embodiment, a regression neural network may be selected which lacks an activation function, wherein input data may be normalized by mean centering, to determine loss and quantify the accuracy of outputs. Such normalization may use a mean squared error loss function and mean absolute error. The artificial neural network model may be validated and cross-validated using standard techniques such as hold-out, K-fold, etc. In some embodiments, multiple artificial neural networks may be separately trained and operated, and/or separately trained and operated in conjunction. In another embodiment, for example, a Bayesian model may be used to train the ML model. 
       FIG.  2    depicts an example ANN  200 . The ANN  200  may be initialized (i.e., created) and trained by the machine learning training module  164  of  FIG.  1   , in some embodiments. The ANN  200  may execute in the memory of a computing device (e.g., the server  108 ) and may analyze one or more data set. The data sets may be labeled or unlabeled. For example, a data set may correspond to labeled telematics data or unlabeled telematics data. The ANN  200  may be operated by the ML operation module  164  of  FIG.  1   , for example. 
     The ANN  200  includes an input layer  204 , one or more hidden layers  206 , and an output layer  208 . Each of the layers in the example neural network  200  may include an arbitrary number of neurons. For example, the input layer  204  is depicted as comprising three neurons, however, any number of neurons may be included in the input layer  204 . Each of the one or more hidden layers  206  may respectively include any number of neurons. The respective neurons comprising the hidden layers  206  may be configured in different ways. For example, the neurons may be chained together linearly and pass output from one to the next, or may be networked together such that the neurons communicate input and output in a non-linear way. In general, it should be understood that many configurations and/or connections different from those shown in  FIG.  2    are possible. 
     The ANN  200 , or another model, may be trained to compute various information. For example, the ML training module  164  may train a first ML model to calculate a vehicle wear score, a second ML model to simulate wear and tear according to vehicle operator behavior, a third ML model to generate feedback for the vehicle operator, and a fourth ML model to generate a risk assessment demonstrating showing how driving behavior is correlated to risk over time. 
     The input layer  204  may correspond to a large number of input parameters (e.g., one million inputs), in some embodiments, and may be analyzed serially or in parallel. Further, various neurons and/or neuron connections within the neural network  200  may be initialized with any number of weights and/or other training parameters, e.g., as depicted in  FIG.  3    (discussed further below). Each of the neurons in the hidden layers  206  may analyze one or more of the input parameters from the input layer  204 , and/or one or more outputs from a previous one or more of the hidden layers, to generate a decision or other output. In some embodiments, multiple ANNs  200  may be connected together to form an ensemble ANN. In yet further embodiments, an ANN of a different type (e.g., a convolutional neural network, or CNN) may be coupled to the ANN  200 , for example, to analyze image-based input. 
     The output layer  208  may include one or more outputs  210 , each indicating a prediction or result. In some embodiments and/or scenarios, the output layer  208  includes only a single output (e.g., a number predicted to be the vehicle wear score). In some embodiments, feedback from a subsequent or previous neuron may be used to identify neurons that are of lesser relevance to the determination of the trained outputs of the neural network  200 . Further, once the neural network  200  is trained, some useless (or less useful) neurons may be bypassed entirely to optimize the resource consumption of the neural network  200  and/or to improve the predictive capabilities of the neural network  200 . 
       FIG.  3    depicts an example neuron  220  that may correspond to one of the neurons in the hidden layers  206  of  FIG.  2   , in an embodiment. For example, the neuron  220  may correspond to the first neuron of the input layer  204  of  FIG.  2   . Each of the inputs to the neuron  220  may be weighted according to a set of weights W 1  through Wi, determined during the training process (for example, if the neural network  200  is a recurrent neural network) and then applied to a node  222  that performs an operation α. The operation a may include computing a sum, a difference, a multiple, or a different operation. In some cases, the initial weights may be manually adjusted. 
     In some embodiments, weights are not determined for some inputs, notwithstanding the fact that  FIG.  3    depicts all inputs X 1  through Xn as being associated with a weight. Further, the neuron  220  may not consider some inputs as relevant to the determination of outputs, and may thus ignore them (e.g., by setting the respective weight to zero). The sum of the weighted inputs, r 1 , may be input to a function  224 , labeled in  FIG.  3 B  as f1, 1(r1) which may represent any suitable functional operation on r 1 . As depicted in  FIG.  3   , the output of the function  224  may be provided to a number of neurons of a subsequent layer or as the output  210  of the neural network  200 . 
     It should be appreciated that in other embodiments or configurations, the neuron  220  may be arranged differently than the depiction in  FIG.  3   . For example, the node  222  may be omitted and/or the function  224  may work directly with the inputs X 1  through Xn. There may be a lack of any weighting, and the operation a may comprise a transforming function, such as taking an absolute value or conversion to a natural number, for example. The exact manner in which the neural network  200  constitutes and uses layers, and neurons within the layers, may vary depending on the nature of the input data and/or the desired output (e.g., ground truth), The structure of the individual layers and/or neurons, including without limitation the type, number, weightings, and so on, may affect the manner in which the overall neural network  200  functions and the purpose for the network. 
     For example, in some embodiments, vehicles may be grouped by vehicle identifiers (e.g., a key comprising the vehicle year, the vehicle make, and the vehicle model). In other embodiments, vehicles may be grouped according to other characteristics. For example, in an embodiment, the ML training module  164  may train an unsupervised learning model to perform cluster analysis, wherein the cluster analysis includes grouping vehicles according to vehicle features/attributes (e.g., engine type, square footage, vehicle tonnage, stopping speed, etc.). Such groupings may be further analyzed. 
     It will be appreciated by those of skill in the art that the present techniques may include the application of machine learning techniques other than artificial neural networks. For example, in one embodiment, a tree-based machine learning model is used that does not include any weights. Such a machine learning model may be a decision tree (e.g., a classification tree, a regression tree, a boosted tree, a random forest classifier, etc.). In still further embodiments, other techniques may be used, such as support vector machines, regression, Bayesian modeling, and/or genetic algorithms. As noted above, the machine learning modeling may be supervised or unsupervised, and other types of learning may be implemented, such as reinforcement learning, self-learning, etc. Specifically, the ML training module  164  may train one or more machine learning models in addition to, or instead of, the ANN  200 . 
     Exemplary Vehicle Wear Score Modeling Embodiment 
     In operation, the present techniques may train and operate one or more supervised machine learning models to accept a target (e.g., losses attributable to wear and tear) and map features (e.g., special interaction between type of vehicle, driving behavior, length of ownership, etc.) to that target. Specifically, the one or more neurons of the input layer  204  may correspond respectively to input parameters such as values measured from one or more sensors of a vehicle. For example, the collection module  130  may obtain telematics data and/or sensor data, and the processing module  132  may extract from collected data a set of sensor values of the vehicle. The input parameters may be analyzed by the one or more hidden layers  206  and a wear score generated as the output  210 . The wear score may be an integer value or a real number (e.g., a value from 0.0 to 1.0). Training an ML model to generate a wear score may include analyzing labeled wear scores corresponding to other vehicles. 
     For example, a training data set may include a data structure (e.g., a hash table), wherein the key of the hash table is a vehicle identification number (VIN) of the vehicle, a first data value is a set of vehicle scores (e.g., by system or by individual component), and a second data value is a vehicle wear score corresponding to the aggregate vehicle wear score of the vehicle. To train a wear score ML model, the ML training module  164  may, for example, iterate over the hash table, inputting each of the sets of vehicle component scores as training parameters, and each respective vehicle wear score as a label parameter to the wear sore ML model, until a predetermined accuracy is achieved. The ML training module  164  may determine the accuracy of the model by minimizing a loss function. 
     Once the wear score ML model is trained, a module (e.g., the ML operation module  166 ) may provide a de novo set of individual vehicle component scores to the trained wear score ML model (i.e., operational inputs) to obtain a wear score corresponding to the vehicle from which the collection module  130  collected the vehicle component scores. The wear score predicted by the wear score ML model may be stored, in association with the VIN in an electronic database, such as the database  190 , and/or provided to another component (e.g., another ML model for training/operation). 
     The present techniques include several advantageous aspects, including that the present techniques work on every vehicle, using a solution that does not need configuration for each specific vehicle. Further, by combining key data elements, the present techniques generate outputs (e.g., resiliency of vehicle, feedback on driving behaviors, and how actions over time contribute to increase in risk of the vehicle over time). These outputs are unique to the modeling approaches disclosed herein and are not available using conventional techniques. 
     Exemplary Wear and Tear Simulation Embodiment 
     For example, the ML training module  164  may retrieve a training data set from a database associated with the server  108 . The training data set may include a plurality of individual records, wherein each record includes a vehicle type, a set of vehicle operation behaviors, and a vehicle age (as expressed as length of ownership and/or odometer). Each of the training data set records may correspond to a particular vehicle operator, and may be associated with a profile record, also stored in the database. 
     The ML training module  164  may analyze the training data set to build a model for analyzing a vehicle operator&#39;s vehicle operation behavior with respect to a first vehicle to simulate the vehicle operator&#39;s vehicle operation behavior with respect to a second vehicle. The ML training module may use the simulated vehicle operation behavior to predict wear and tear with respect to the second vehicle. The present techniques advantageously improve prior systems because the simulated vehicle operation behavior is determined without needing sensors in the second vehicle. 
     In some embodiments, the ML models may analyze historical driving data to simulate how vehicle operation behavior would affect wear and tear of a vehicle the driver has not yet driven, or how present driving behavior would have affected a vehicle driven in the past for which no telematics data are available. 
     Exemplary Vehicle Operator Feedback Embodiment 
     In an embodiment, the present techniques are used to train an ML model may to accept telematics data corresponding to a vehicle operator (e.g., braking events) and output a braking profile based at least in part upon analyzing the braking information, wherein the ML model is trained using training data corresponding to the braking behavior of other vehicle operators. 
     For example, an unsupervised ML model may be used to group a set of vehicle operators according to respective vehicle operation behaviors. The unsupervised ML model may quantify vehicle operation behaviors such as hard braking, speed, and acceleration. The unsupervised ML model may, in some embodiments, determine whether the vehicle operator is speeding by analyzing mapping data. Once the unsupervised ML model has grouped the vehicle operators into categories, the ML operation module  166  may operate a second ML model to analyze telematics data of a vehicle operator not included in the training data set to determine which category the vehicle operator not included in the training data set most closely resembles. In this way, the present techniques are able to gauge the risk level of the vehicle operator, relative to other vehicle operators. 
     The feedback module  170  may include instructions for providing feedback to the vehicle operator in response to the vehicle operator&#39;s categorization. For example, when the vehicle operator is found to be in a higher risk vehicle operation behavior category, the feedback module  170  may generate a notification and transmit the notification to the telematics system  104  for display. The notification may 
     Exemplary Risk Assessment Embodiment 
     In an embodiment, transfer learning may be used to simulate vehicle operation behavior and wear outcomes. For example, each of a plurality of vehicle operators may operate a respective vehicle. A first vehicle operator in the plurality of vehicle operators may operate a first vehicle lightly as measured by, for example, mileage, braking, acceleration, and overall wear. A second vehicle operator in the plurality of vehicle operators may operate the second vehicle so as to cause dramatically more wear by, for example, more mileage, more braking, more acceleration, and generally more wear-causing behaviors. 
     In some embodiments, mileage may be determined by reference to third party data sources (e.g., an application programming interface (API)) or an electronic database. 
     Respective instances of the telematics system  104  of  FIG.  1    may be embodied in the respective mobile devices of the plurality of vehicle operators, for example. The respective telematics systems  104  collect telematics data of the two vehicle operators and store the telematics data in association with user profiles of the two vehicle operators. For example, the two vehicle operators may be customers of an insurer who download a computer application embodying the telematics system  104  into their respective smart phones. 
     The ML training module  164  may train a transfer ML model using the vehicle operation behavior and wear outcomes of the plurality of vehicle operators. Specifically, the ML training module  164  may train the transfer ML model to predict the wear to a vehicle by a vehicle operator not in the plurality of vehicle operators, based at least in part upon the similarity of the vehicle operator&#39;s vehicle operation behavior to that of the plurality of vehicle operators. The transfer ML model may also predict the wear that one of the plurality of vehicle operators will cause to a new vehicle that the vehicle operator has not operated previously, based at least in part upon the collected wear outcomes. Therefore, the transfer model may predict or estimate wear and tear (e.g., likelihood of brakes being worn thin) on first set of identical cars based at least in part upon difference in wear/tear. In some embodiments, other conditions regarding the plurality of vehicle operators may be used as training inputs to the transfer ML model, such as accident information, which allows the link between driving behavior to collisions to be seen. 
     The present techniques may analyze the plurality of vehicle operators further to determine whether risk is due to negative vehicle operation behavior or contributions from wear and tear. Specifically, by analyzing the length of ownership of vehicles within the plurality of vehicle operators, the present techniques may determine that in general, vehicle operators with a longer length of ownership are involved in more collisions. An ML model may be trained that normalizes the plurality of vehicle operators according to length of ownership, to determine the partial effect of vehicle operation behavior on wear and tear, and how the partial effect is correlated to losses. The trained model may accept as inputs vehicle operation behavior, length of ownership (e.g., odometer), and vehicle information and output an estimate of wear and tear to the vehicle. For example, the wear may be a vehicle wear score, a set of wear scores respective to particular components (e.g., brakes) or systems (e.g., cooling system). In particular, in some embodiments, a generalized linear model (GLM) may be used to manually specify interactions and to multiply factors together, to estimate how each factor is correlated to a loss (e.g., the likelihood of a collision). 
     A user may adjust parameters of inputs to the trained model to assist the user in reliability engineering. For example, with the trained model, the user may change the value of the length of ownership from one year to ten years, to see how the longer ownership duration influences predicted losses. The user may modify the vehicle type to determine how the probability of collision may increase or decrease. Each vehicle in existence may be ranked for comparative analyses. The user may also input driving behaviors, to determine how wear and tear, and thus the probability of collision, changes. The user may program simulations to automatically provide inputs to the trained ML model, for determining the unique wear/tear to vehicles by varying length of ownership, for measuring the resilience of vehicle types to wear/tear by changing their types. Feedback may be provided to users based at least in part upon the effect of modifying vehicle operation behaviors. For example, the message may include a note to the user that that by avoiding hard braking, wear and tear to the brake pads of the vehicle will be decreased by, for example, 60%. In yet further embodiments, an autoencoder may be used to train a deep learning model that is able to analyze all vehicles in existence. Specifically, a deep learning model may be trained to encode vehicle information into a smaller universe (e.g., into N-digit encoding). In this way, the N-digit encoding can express any possible vehicle that may exist. 
     Exemplary Computer-Implemented Methods 
       FIG.  4    depicts and exemplary method  400  for training a machine learning model. The method  400  may include retrieving/receiving a training data set (block  402 ). The training data set may correspond to telematics data, profile data, and/or vehicle data. For example, the training data set may be received/retrieved from the database  190 . The method  400  may include inputting the training data set into a machine learning model (block  404 ). In some embodiments, the method  400  may include inputting labeled data. 
     The method  400  may include analyzing the training input to generate a prediction (block  406 ). The method  400  may include generating the loss score by comparing the prediction to the label using a loss function (block  408 ). In embodiments wherein the machine learning model is an artificial neural network, the method  400  may include backpropagating the loss score to update the set of weights, wherein the method  400  trains the model repeatedly to adjust a set of weights of the machine learning model, until a loss score meets a predetermined criteria. Training, discussed in further detail above, may be carried out by the ML training module  164  of Figure. It should be appreciated that in some cases, the machine learning model may be trained without using any labeled information (e.g., in unsupervised learning). Further, in some embodiments, the method  400  may include storing a set of weights as the weights of the trained machine learning model. The method  400  may include storing the trained machine learning model once the loss score meets a predetermined criteria (block  410 ). Specifically, the ML training module  164  may serialize and store the weights of the network in an electronic database or on a disk (e.g., the memory  162 ) as discussed above. Another module (e.g., the ML operation module  168 ) may operate the machine learning model (e.g., using the stored weights and/or other parameterization/initialization data, such as hyperparameters). 
     The method  400  may train one or more ML model for a variety of tasks, including calculating a vehicle wear score, simulating wear and tear according to vehicle operator behavior, generating feedback for the vehicle operator, and generating a risk assessment demonstrating showing how driving behavior is correlated to risk over time. Once the method  400  has trained the machine learning model, the method  400  may include receiving and processing operational telematics information (e.g., from a mobile device of a user) as discussed above. 
     For example, historical data may demonstrate that a given percentage of collisions are due to vehicle maintenance problems (mechanical failure due to wear and tear). Further, wear and tear may be substantially affected by individual vehicle operation behaviors. Thus, vehicle operation behaviors may be used to train an ML model to predict risk pricing. A target variable of such an ML model is whether the vehicle will be involved in a collision due to a failure attributable to wear and tear. It should be appreciated that in an embodiment, telematics data are not used to train such a model. Rather, an encoding may include a type of the vehicle (e.g., year, make, and model), driving behaviors of the vehicle operator (e.g., braking events, and speeding) and the length of ownership (e.g., odometer or time). Such information may be used as features for the model to predict whether will be involved in a claim attributable to normal wear and tear. 
     Although specific embodiments of the present disclosure have been described, it will be understood by those of skill in the art that there are other embodiments that are equivalent to the described embodiments. Accordingly, it is to be understood that the present disclosure is not to be limited by the specific illustrated embodiments.