Automatic generation of optimized aggregation metrics for usage based insurance

A system for automatic generation of optimized aggregation metrics for UBI, comprising:

• a data archive including a plurality of signals from vehicles and corresponding UBI effects;
• and
• one or more computing devices configured to:
• perform a multi-dimensional Bayesian optimization on the data archive, wherein the multi-dimensional Bayesian optimization includes performing testing on weighted groups of the plurality of signals using one or more aggregation functions, wherein the multi-dimensional Bayesian optimization is repeated until a convergence criterion is met and/or until an iteration budget is exhausted;
• transmit the one or more aggregation functions to a vehicle for execution by an on-vehicle processor of the vehicle, to cause the vehicle to provide aggregated signals generated from the plurality of signals without providing the plurality of signals, the aggregated signals being for input to a UBI model to predict a UBI rate for the vehicle;
• receive the aggregated signals from the vehicle, thereby reducing data transfer from the vehicle as compared to the vehicle providing the plurality of signals;
• add the aggregated signals to the data archive to update the data archive;
• and
• repeat performing the multi-dimensional Bayesian optimization and the transmit of the one or more aggregation functions to the vehicle based on the data archive as updated.

A non-transitory computer-readable medium comprising instructions for automatic generation of optimized aggregation metrics for UBI that, when executed by one or more computing devices, cause the one or more computing devices to perform operations including to:

• perform a multi-dimensional Bayesian optimization on a data archive, the data archive including a plurality of signals from vehicles and corresponding UBI effects, wherein the multi-dimensional Bayesian optimization includes performing testing on weighted groups of the plurality of signals using one or more aggregation functions, wherein the multi-dimensional Bayesian optimization is repeated until a convergence criterion is met and/or until an iteration budget is exhausted, wherein the multi-dimensional Bayesian optimization includes to:
• train a surrogate model using input-output pairs to approximate system behavior,
• formulate an acquisition function based on predictions of the surrogate model, the acquisition function being used to determine additional query points for the system behavior,
• optimize the acquisition function to obtain an input trigger,
• evaluate a UBI model, using the obtained input trigger to obtain an actual UBI-relatedness output associated with the input trigger, thereby creating a new input-output pair, and
• add the new input-output pair to the data archive for further iterations;
• transmit the one or more aggregation functions to a vehicle for execution by an on-vehicle processor of the vehicle, to cause the vehicle to provide aggregated signals generated from the plurality of signals without providing the plurality of signals, the aggregated signals being for input to the UBI model to predict a UBI rate for the vehicle;
• receive the aggregated signals from the vehicle, thereby reducing data transfer from the vehicle as compared to the vehicle providing the plurality of signals;
• add the aggregated signals to the data archive to update the data archive;
• and
• repeat performing the multi-dimensional Bayesian optimization and the transmit of the one or more aggregation functions to the vehicle based on the data archive as updated.