Vehicle resiliency, driving feedback and risk assessment using machine learning-based vehicle wear scoring
A machine learning model is manufactured by a process including retrieving training data, minimizing a loss function, wherein the training data may include labeled or unlabeled data, the machine learning model generating a prediction. A machine learning training/operation server includes a processor and a memory storing instructions that, when executed by the processor, cause the server to retrieve training data, input a training input, analyze the training input to generate a prediction, generate a loss score, and store the trained machine learning model. A method for training a machine learning model includes receiving training data, inputting a training input, analyzing the training input, generating a loss score, and storing the trained machine learning model.
1. A method implemented by one or more processors, the method comprising:
retrieving a machine learning model;
wherein the machine learning model is trained by a training dataset comprising a hash table until a loss score meets a predetermined criteria by at least:
analyzing a training input of the training dataset using the machine learning model to generate a training prediction;
generating, using a loss function, the loss score by comparing the training prediction to a corresponding label of the training dataset for the training input; and
modifying at least one of one or more weights of the machine learning model based at least in part upon the loss score; and
analyzing an operational input using the machine learning model, as trained, to obtain a prediction;
wherein:
the operational input includes a set of component wear scores of a vehicle; and
the prediction includes an aggregated wear score of the vehicle generated by analyzing the set of component wear scores.
2. The method of claim 1 , wherein:
the operational input includes telematics data corresponding to one or more vehicles operated by an operator of the vehicle, vehicle type data, or vehicle age; and
the prediction includes an estimated wear of the vehicle.
3. The method of claim 2 , wherein the telematics data includes data collected by one or more sensors, the one or more sensors including at least one selected from a group consisting of a speedometer sensor, a tire pressure sensor, a brake pad thickness sensor, a suspension ride height sensor, an accelerometer, a gyroscope, a magnetometer, and a position sensor.
4. The method of claim 3 , further comprising:
associating a vehicle operator with one or more telematics datasets in the telematics data; and
generating a vehicle operator profile based on the association.
5. The method of claim 1 , wherein the operational input is adjusted programmatically or by a user to simulate a partial effect.
6. The method of claim 1 ,
wherein the operational input includes telematics data, and
wherein the analyzing the operational input using the trained machine learning model, as trained, to obtain the prediction includes analyzing the telematics data to categorize collision risk of a vehicle operator of the vehicle.
7. The method of claim 6 , further comprising:
transmitting a feedback notification to the vehicle operator, the feedback notification including a warning in regard to the collision risk and a recommendation for reducing the collision risk.
8. The method of claim 7 , wherein the trained machine learning model, as trained, includes a first machine learning model to determine a vehicle wear score and a second machine learning model to generate a risk assessment.
9. A computing device comprising:
one or more processors; and
one or more memories storing instructions that, when executed by the one or more processors, cause the one or more processors to:
retrieve a machine learning model;
wherein the machine learning model is trained by a training dataset comprising a hash table until a loss score meets a predetermined criteria by at least:
analyzing a training input of the training dataset using the machine learning model to generate a training prediction;
generating, using a loss function, the loss score by comparing the training prediction to a corresponding label of the training dataset for the training input; and
modifying at least one of one or more weights of the machine learning model based at least in part upon the loss score; and
analyze an operational input using the machine learning model, as trained, to obtain a prediction;
wherein:
the operational input includes a set of component wear scores of a vehicle; and
the prediction includes an aggregated wear score of the vehicle generated by analyzing the set of component wear scores.
10. The computing device of claim 9 , wherein:
the operational input includes telematics data corresponding to one or more vehicles operated by an operator of the vehicle, vehicle type data, or vehicle age; and
the prediction includes an estimated wear of the vehicle.
11. The computing device of claim 10 , wherein the telematics data includes data collected by one or more sensors, the one or more sensors including at least one selected from a group consisting of a speedometer sensor, a tire pressure sensor, a brake pad thickness sensor, a suspension ride height sensor, an accelerometer, a gyroscope, a magnetometer, and a position sensor.
12. The computing device of claim 11 , wherein the instructions further cause the one or more processors to:
associate a vehicle operator with one or more telematics datasets in the telematics data; and
generate a vehicle operator profile based on the association.
13. The computing device of claim 9 , wherein the instructions further cause the one or more processors to:
receive telematics data corresponding to one or more vehicles operated by an operator of the vehicle, vehicle type data, or vehicle age; and
generate an estimated wear of the vehicle.
14. The computing device of claim 13 , wherein the operational input includes the telematics data, and
wherein the analyzing the operational input using the machine learning model, as trained, to obtain the prediction includes to analyze the telematics data to categorize collision risk of a vehicle operator of the vehicle.
15. The computing device of claim 14 , wherein the instructions further cause the one or more processors to:
transmit a feedback notification to the vehicle operator, the feedback notification including a warning in regard to the collision risk and a recommendation for reducing the collision risk.
16. The computing device of claim 15 , wherein the machine learning model, as trained, includes a first machine learning model to determine a vehicle wear score and a second machine learning model to generate a risk assessment.
17. A method implemented by one or more processors, the method comprising:
retrieving a machine learning model;
wherein the machine learning model is trained by a training dataset comprising a hash table until a loss score meets a predetermined criteria by at least:
analyzing a training input of the training dataset using the machine learning model to generate a training prediction;
generating, using a loss function, the loss score by comparing the training prediction to a corresponding label of the training dataset for the training input; and
modifying at least one of one or more weights of the machine learning model based at least in part upon the loss score;
analyzing an operational input using the trained machine learning model, as trained, to obtain a prediction;
wherein:
the operational input includes a set of component wear scores of a vehicle; and
the prediction includes an aggregated wear score of the vehicle generated by analyzing the set of component wear scores;
wherein the operational input further includes telematics data;
wherein the analyzing the operational input using the machine learning model, as trained, to obtain the prediction includes analyzing the telematics data to categorize collision risk of a vehicle operator of the vehicle; and
transmitting a feedback notification to the vehicle operator, the feedback notification including a warning in regard to the collision risk and a recommendation for reducing the collision risk.
18. The method of claim 17 , wherein:
the telematics data corresponds to one or more vehicles operated by an operator of the vehicle, vehicle type data, or vehicle age; and
the prediction includes an estimated wear of the vehicle.
19. The method of claim 18 , wherein the telematics data includes data collected by one or more sensors, the one or more sensors including at least one selected from a group consisting of a speedometer sensor, a tire pressure sensor, a brake pad thickness sensor, a suspension ride height sensor, an accelerometer, a gyroscope, a magnetometer, and a position sensor.
20. The method of claim 19 , further comprising:
associating the vehicle operator with one or more telematics datasets in the telematics data; and
generating a vehicle operator profile based on the association.
21. A system comprising:
a means for storing data thereon; and
a means for performing operations comprising:
retrieving a machine learning model;
wherein the machine learning model is trained by a training dataset comprising a hash table until a loss score meets a predetermined criteria by at least:
analyzing a training input of the training dataset using the machine learning model to generate a training prediction;
generating, using a loss function, the loss score by comparing the training prediction to a corresponding label of the training dataset for the training input; and
modifying at least one of one or more weights of the machine learning model based at least in part upon the loss score; and
analyzing an operational input using the machine learning model, as trained, to obtain a prediction;
wherein:
the operational input includes a set of component wear scores of a vehicle; and the prediction includes an aggregated wear score of the vehicle generated by analyzing the set of component wear scores.