MULTI-DIMENSIONAL MODELING OF DRIVER AND ENVIRONMENT CHARACTERISTICS
The disclosed embodiments provide techniques for scoring a driver or vehicle. In one embodiment, a method is disclosed comprising receiving metrics associated with a vehicle; generating a deviation vector based on the metrics and a plurality of aggregated values corresponding to the metrics; computing a driver update value based on the deviation vector and a plurality of model parameters, each of the plurality of model parameters corresponding to the metrics; and computing a driver score based on the driver update value, a previous score, and a learning rate.
1 . A method comprising:
receiving metrics associated with a vehicle;
generating a deviation vector based on the metrics and a plurality of aggregated values corresponding to the metrics;
computing a driver update value based on the deviation vector and a plurality of model parameters, each of the plurality of model parameters corresponding to the metrics; and
computing a driver score based on the driver update value, a previous score, and a learning rate.
2 . The method of claim 1 , further comprising generating the aggregated values by:
receiving, for a plurality of road segments, corresponding metrics from a plurality of drivers; and
aggregating, for each of the plurality of road segments, the corresponding metrics.
3 . The method of claim 2 , wherein aggregating the corresponding metrics further comprises averaging the corresponding metrics.
4 . The method of claim 2 , wherein computing a driver update value based on the deviation vector comprises computing deviation values for each of the metrics, the deviation value computed by subtracting a corresponding aggregated value from the corresponding metric.
5 . The method of claim 4 , wherein computing deviation values for each of the metrics comprises:
selecting a plurality of road segments;
computing deviation values for the metric for each of the plurality of road segments; and
summing the deviations values to generate the deviation value for the metric.
6 . The method of claim 1 , further comprising calculating the model parameters via a statistical learning methodology.
7 . The method of claim 6 , wherein the statistical learning methodology is trained using a combination of video, telematics, and externally-obtained data.
8 . A non-transitory computer-readable storage medium for tangibly storing computer program instructions capable of being executed by a computer processor, the computer program instructions defining steps of:
receiving metrics associated with a vehicle;
generating a deviation vector based on the metrics and a plurality of aggregated values corresponding to the metrics;
computing a driver update value based on the deviation vector and a plurality of model parameters, each of the plurality of model parameters corresponding to the metrics; and
computing a driver score based on the driver update value, a previous score, and a learning rate.
9 . The medium of claim 8 , the computer program instructions defining the step of: generating the aggregated values by:
receiving, for a plurality of road segments, corresponding metrics from a plurality of drivers; and
aggregating, for each of the plurality of road segments, the corresponding metrics.
10 . The medium of claim 9 , wherein aggregating the corresponding metrics further comprises averaging the corresponding metrics.
11 . The medium of claim 9 , wherein computing a driver update value based on the deviation vector comprises computing deviation values for each of the metrics, the deviation value computed by subtracting a corresponding aggregated value from the corresponding metric.
12 . The medium of claim 11 , wherein computing deviation values for each of the metrics comprises:
selecting a plurality of road segments;
computing deviation values for the metric for each of the plurality of road segments; and
summing the deviations values to generate the deviation value for the metric.
13 . The medium of claim 8 , the computer program instructions defining the step of calculating the model parameters via a statistical learning methodology.
14 . The medium of claim 13 , wherein the statistical learning methodology is trained using a combination of video, telematics, and externally-obtained data.
15 . A device comprising:
a processor configured to:
receive metrics associated with a vehicle;
generate a deviation vector based on the metrics and a plurality of aggregated values corresponding to the metrics;
compute a driver update value based on the deviation vector and a plurality of model parameters, each of the plurality of model parameters corresponding to the metrics; and
compute a driver score based on the driver update value, a previous score, and a learning rate.
16 . The device of claim 15 , the processor further configured to generate the aggregated values by:
receiving, for a plurality of road segments, corresponding metrics from a plurality of drivers; and
aggregating, for each of the plurality of road segments, the corresponding metrics.
17 . The device of claim 16 , wherein aggregating the corresponding metrics further comprises averaging the corresponding metrics.
18 . The device of claim 16 , wherein computing a driver update value based on the deviation vector comprises computing deviation values for each of the metrics, the deviation value computed by subtracting a corresponding aggregated value from the corresponding metric.
19 . The device of claim 18 , wherein computing deviation values for each of the metrics comprises:
selecting a plurality of road segments;
computing deviation values for the metric for each of the plurality of road segments; and
summing the deviations values to generate the deviation value for the metric.
20 . The device of claim 15 , the processor further configured to calculate the model parameters via a statistical learning methodology.