Telematics-based driver training and credentialing

A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising:

• capturing telematics data, using one or more sensors of an electronic device of a user, wherein the one or more sensors comprise one or more of a Global Positioning System (GPS), a camera, or an accelerometer;
• assessing driving behavior of the user based on the telematics data;
• generating a personalized digital training program for the user based at least on the driving behavior of the user;
• generating one or more driving scores and a driving credential for the user based on performance of the user on the personalized digital training program, wherein the driving credential is based on the one or more driving scores and comprises an authentic verification that the user possesses a threshold level of driving skills;
• storing data of the one or more driving scores and the driving credential in a secure database having access controls operated by the user;
• and
• transmitting one or more of the one or more driving scores or the driving credential to a third party to verify that the user possesses the threshold level of driving skills,
• wherein:
• the personalized digital training program is generated using one or more machine learning models that analyze the telematics data to identify driving skill weaknesses of the user;
• the one or more machine learning models comprise one or more of decision trees, KNN, or neural networks;
• the one or more machine learning models are trained using supervised, semi-supervised, or unsupervised learning;
• and
• training data for the one or more machine learning models is collected from historical input and output data and updated periodically to re-train the one or more machine learning models.

One or more non-transitory computer-readable media storing computing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• capturing telematics data, using one or more sensors of an electronic device of a user, wherein the one or more sensors comprise one or more of a Global Positioning System (GPS), a camera, or an accelerometer;
• assessing driving behavior of the user based on the telematics data;
• generating a personalized digital training program for the user based at least on the driving behavior of the user;
• generating one or more driving scores and a driving credential for the user based on performance of the user on the personalized digital training program, wherein the driving credential is based on the one or more driving scores and comprises an authentic verification that the user possesses a threshold level of driving skills;
• storing data of the one or more driving scores and the driving credential in a secure database having access controls operated by the user;
• and
• transmitting one or more of the one or more driving scores or the driving credential to a third party to verify that the user possesses the threshold level of driving skills,
• wherein:
• the personalized digital training program is generated using one or more machine learning models that analyze the telematics data to identify driving skill weaknesses of the user;
• the one or more machine learning models comprise one or more of decision trees, KNN, or neural networks;
• the one or more machine learning models are trained using supervised, semi-supervised, or unsupervised learning;
• and
• training data for the one or more machine learning models is collected from historical input and output data and updated periodically to re-train the one or more machine learning models.

A system comprising:

• first means for capturing telematics data, using one or more sensors of an electronic device of a user, wherein the one or more sensors comprise one or more of a Global Positioning System (GPS), a camera, or an accelerometer;
• second means for assessing driving behavior of the user based on the telematics data;
• third means for generating a personalized digital training program for the user based at least on the driving behavior of the user;
• fourth means for generating one or more driving scores and a driving credential for the user based on performance of the user on the personalized digital training program, wherein the driving credential is based on the one or more driving scores and comprises an authentic verification that the user possesses a threshold level of driving skills;
• fifth means for storing data of the one or more driving scores and the driving credential in a secure database having access controls operated by the user;
• and
• sixth means for transmitting one or more of the one or more driving scores or the driving credential to a third party to verify that the user possesses the threshold level of driving skills,
• wherein:
• the personalized digital training program is generated using one or more machine learning models that analyze the telematics data to identify driving skill weaknesses of the user;
• the one or more machine learning models comprise one or more of decision trees, KNN, or neural networks;
• the one or more machine learning models are trained using supervised, semi-supervised, or unsupervised learning;
• and
• training data for the one or more machine learning models is collected from historical input and output data and updated periodically to re-train the one or more machine learning models.