SYSTEMS AND METHODS FOR PREDICTING FUEL CONSUMPTION EFFICIENCY

1 . A system for predicting fuel consumption efficiency, the 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:

receiving, via a network, vehicle telematics data from a client device, wherein the client device is associated with a vehicle operated by a user and comprises one or more sensors and a display unit, wherein the one or more sensors include at least one of a GPS sensor or an accelerometer and are configured to collect the vehicle telematics data during one or more past vehicle trips made by the user of the vehicle, and wherein the vehicle telematics data includes at least one of acceleration data, braking data, or location data;

analyzing historical trip data including past commute patterns and vehicle route data based on the vehicle telematics data to:

determine one or more routes for one or more future vehicle trips to be made by the user during a predetermined future period of time; and

modify the one or more routes based on receiving one or more user inputs to update information for the one or more future vehicle trips;

analyzing, using a trained machine learning model, the vehicle telematics data to determine one or more past user driving features, wherein:

the trained machine learning model is trained by at least:

analyzing, by a machine learning model, training data included in one or more sets of training data to determine one or more training driving features associated with past fuel consumption, wherein the analyzing the training data includes performing at least one of feature extraction or pattern recognition to identify patterns in the at least one of the acceleration data, the braking data, or the location data, and wherein the patterns include at least one of braking patterns, acceleration patterns, or cornering patterns;

generating, by the machine learning model, using weight values associated with layers of the machine learning model, an estimated past efficiency value related to the past fuel consumption based at least on the one or more training driving features determined from the training data included in the one or more sets of training data;

comparing, by the machine learning model, the estimated past efficiency value with an actual past efficiency value to determine an accuracy of the estimated past efficiency value using one or more of a loss function or a cost function; and

adjusting, by the machine learning model, one or more parameters of the machine learning model including the weight values associated with the layers of the machine learning model, based at least on the estimated past efficiency value with the actual past efficiency value, as compared, to reduce one or more magnitudes of one or more outputs of the one or more of the loss function or the cost function until the loss function or the cost function is minimized for the one or more sets of training data;

the one or more past user driving features are related to a past fuel consumption efficiency of the user;

the past fuel consumption efficiency of the user indicates how fuel was consumed by the user during the one or more past vehicle trips given the one or more past user driving features; and

the one or more past user driving features include one or more of braking, accelerating, cornering, speeding, lane changing, tailgating, idling, or timing of gear shifting;

receiving at least one of traffic congestion information, terrain information, or weather condition information for the one or more future vehicle trips;

predicting, using the trained machine learning model, a future fuel consumption efficiency of the user during the predetermined future period of time based at least on the information for the one or more future vehicle trips and the one or more past user driving features, and the at least one of the traffic congestion information, historical traffic data, the terrain information, or the weather condition information; and

transmitting, via the network, the future fuel consumption efficiency, as predicted, for the one or more routes, as determined and modified, to the client device for display on the display unit for the user to plan for the one or more future vehicle trips.

2 . The system of claim 1 , wherein the information for the one or more future vehicle trips includes:

vehicle information for the one or more future vehicle trips;

distance information for the one or more future vehicle trips; and

congestion information for the one or more future vehicle trips.

3 . The system of claim 1 , wherein the one or more sensors further comprise one or more of a gyroscope, a magnetometer, a speedometer, a steering-angle sensor, a brake sensor, a yaw-rate sensor, or a proximity detector.

4 . The system of claim 1 , wherein the operations further comprise collecting past user driving data in response to a triggering event comprising a sensor of the one or more sensors acquiring a threshold amount of sensor measurements.

5 . The system of claim 1 , wherein the one or more past user driving features include:

one or more first past user driving features that increase the past fuel consumption efficiency of the user; and

one or more second past user driving features that decrease the past fuel consumption efficiency of the user.

6 . The system of claim 5 , wherein:

the one or more first past user driving features correspond to one or more first importance levels respectively for increasing the past fuel consumption efficiency of the user; and

the one or more second past user driving features correspond to one or more second importance levels respectively for decreasing the past fuel consumption efficiency of the user, wherein the one or more first importance levels and the one or more second importance levels are determined by the trained machine learning model.

7 . The system of claim 1 , wherein the traffic congestion information is derived from an analysis of the historical traffic data.

8 . A computer-implemented method for predicting fuel consumption efficiency, the method comprising:

receiving, via a network, vehicle telematics data from a client device, wherein the client device is associated with a vehicle operated by a user and comprises one or more sensors and a display unit, wherein the one or more sensors include at least one of a GPS sensor or an accelerometer and are configured to collect the vehicle telematics data during one or more past vehicle trips made by the user of the vehicle, and wherein the vehicle telematics data includes at least one of acceleration data, braking data, or location data;

analyzing historical trip data including past commute patterns and vehicle route data based on the vehicle telematics data to:

determine one or more routes for one or more future vehicle trips to be made by the user during a predetermined future period of time; and

modify the one or more routes based on receiving one or more user inputs to update information for the one or more future vehicle trips;

analyzing, using a trained machine learning model, the vehicle telematics data to determine one or more past user driving features, wherein:

the trained machine learning model is trained by at least:

analyzing, by a machine learning model, training data included in one or more sets of training data to determine one or more training driving features associated with past fuel consumption, wherein the analyzing the training data includes performing at least one of feature extraction or pattern recognition to identify patterns in the at least one of the acceleration data, the braking data, or the location data, and wherein the patterns include at least one of braking patterns, acceleration patterns, or cornering patterns;

generating, by the machine learning model, using weight values associated with layers of the machine learning model, an estimated past efficiency value related to the past fuel consumption based at least on the one or more training driving features determined from the training data included in the one or more sets of training data;

comparing, by the machine learning model, the estimated past efficiency value with an actual past efficiency value to determine an accuracy of the estimated past efficiency value using one or more of a loss function or a cost function; and

adjusting, by the machine learning model, one or more parameters of the machine learning model including the weight values associated with the layers of the machine learning model, based at least on the estimated past efficiency value with the actual past efficiency value, as compared, to reduce one or more magnitudes of one or more outputs of the one or more of the loss function or the cost function until the loss function or the cost function is minimized for the one or more sets of training data;

the one or more past user driving features are related to a past fuel consumption efficiency of the user;

the past fuel consumption efficiency of the user indicates how fuel was consumed by the user during the one or more past vehicle trips given the one or more past user driving features; and

the one or more past user driving features include one or more of braking, accelerating, cornering, speeding, lane changing, tailgating, idling, or timing of gear shifting;

receiving at least one of traffic congestion information, terrain information, or weather condition information for the one or more future vehicle trips;

predicting, using the trained machine learning model, a future fuel consumption efficiency of the user during the predetermined future period of time based at least on the information for the one or more future vehicle trips and the one or more past user driving features, and the at least one of the traffic congestion information, historical traffic data, the terrain information, or the weather condition information; and

transmitting, via the network, the future fuel consumption efficiency, as predicted, for the one or more routes, as determined and modified, to the client device for display on the display unit for the user to plan for the one or more future vehicle trips.

9 . The computer-implemented method of claim 8 , wherein the information for the one or more future vehicle trips includes:

vehicle information for the one or more future vehicle trips;

distance information for the one or more future vehicle trips; and

congestion information for the one or more future vehicle trips.

10 . The computer-implemented method of claim 8 , wherein the one or more sensors further comprise one or more of a gyroscope, a magnetometer, a speedometer, a steering-angle sensor, a brake sensor, a yaw-rate sensor, or a proximity detector.

11 . The computer-implemented method of claim 8 , further comprising collecting past user driving data in response to a triggering event comprising that a sensor of the one or more sensors acquiring a threshold amount of sensor measurements.

12 . The computer-implemented method of claim 8 , wherein the one or more past user driving features include:

one or more first past user driving features that increase the past fuel consumption efficiency of the user; and

one or more second past user driving features that decrease the past fuel consumption efficiency of the user.

13 . The computer-implemented method of claim 12 , wherein:

the one or more first past user driving features correspond to one or more first importance levels respectively for increasing the past fuel consumption efficiency of the user; and

the one or more second past user driving features correspond to one or more second importance levels respectively for decreasing the past fuel consumption efficiency of the user, wherein the one or more first importance levels and the one or more second importance levels are determined by the trained machine learning model.

14 . The computer-implemented method of claim 8 , wherein the traffic congestion information is derived from the historical traffic data.

15 . A non-transitory computer-readable medium storing instructions for predicting fuel consumption efficiency, the instructions, when executed by one or more processors of a computing device, cause the computing device to perform operations comprising:

receiving, via a network, vehicle telematics data from a client device, wherein the client device is associated with a vehicle operated by a user and comprises one or more sensors and a display unit, wherein the one or more sensors include at least one of a GPS sensor or an accelerometer and are configured to collect the vehicle telematics data during one or more past vehicle trips made by the user of the vehicle, and wherein the vehicle telematics data includes at least one of acceleration data, braking data, or location data;

analyzing historical trip data including past commute patterns and vehicle route data based on the vehicle telematics data to:

determine one or more routes for one or more future vehicle trips to be made by the user during a predetermined future period of time; and

modify the one or more routes based on receiving one or more user inputs to update information for the one or more future vehicle trips;

analyzing, using a trained machine learning model, the vehicle telematics data to determine one or more past user driving features, wherein:

the trained machine learning model is trained by at least:

analyzing, by a machine learning model, training data included in one or more sets of training data to determine one or more training driving features associated with past fuel consumption, wherein the analyzing the training data includes performing at least one of feature extraction or pattern recognition to identify patterns in the at least one of the acceleration data, the braking data, or the location data, and wherein the patterns include at least one of braking patterns, acceleration patterns, or cornering patterns;

generating, by the machine learning model, using weight values associated with layers of the machine learning model, an estimated past efficiency value related to the past fuel consumption based at least on the one or more training driving features determined from the training data included in the one or more sets of training data;

comparing, by the machine learning model, the estimated past efficiency value with an actual past efficiency value to determine an accuracy of the estimated past efficiency value using one or more of a loss function or a cost function; and

adjusting, by the machine learning model, one or more parameters of the machine learning model including the weight values associated with the layers of the machine learning model, based at least on the estimated past efficiency value with the actual past efficiency value, as compared, to reduce one or more magnitudes of one or more outputs of the one or more of the loss function or the cost function until the loss function or the cost function is minimized for the one or more sets of training data;

the one or more past user driving features are related to a past fuel consumption efficiency of the user;

the past fuel consumption efficiency of the user indicates how fuel was consumed by the user during the one or more past vehicle trips given the one or more past user driving features; and

the one or more past user driving features include one or more of braking, accelerating, cornering, speeding, lane changing, tailgating, idling, or timing of gear shifting;

receiving at least one of traffic congestion information, terrain information, or weather condition information for the one or more future vehicle trips;

predicting, using the trained machine learning model, a future fuel consumption efficiency of the user during the predetermined future period of time based at least on the information for the one or more future vehicle trips and the one or more past user driving features, and the at least one of the traffic congestion information, historical traffic data, the terrain information, or the weather condition information; and

transmitting, via the network, the future fuel consumption efficiency, as predicted, for the one or more routes, as determined and modified, to the client device for display on the display unit for the user to plan for the one or more future vehicle trips.

16 . The non-transitory computer-readable medium of claim 15 , wherein the information for the one or more future vehicle trips includes:

vehicle information for the one or more future vehicle trips;

distance information for the one or more future vehicle trips; and

congestion information for the one or more future vehicle trips.

17 . The non-transitory computer-readable medium of claim 15 , wherein the one or more sensors further comprise one or more of a gyroscope, a magnetometer, a speedometer, a steering-angle sensor, a brake sensor, a yaw-rate sensor, or a proximity detector.

18 . The non-transitory computer-readable medium of claim 15 , wherein the operations further comprise collecting past user driving data in response to a triggering event comprising that a sensor of the one or more sensors acquiring a threshold amount of sensor measurements.

19 . The non-transitory computer-readable medium of claim 15 , wherein:

the one or more past user driving features include:

one or more first past user driving features that increase the past fuel consumption efficiency of the user; and

one or more second past user driving features that decrease the past fuel consumption efficiency of the user; and

the one or more first past user driving features correspond to one or more first importance levels respectively for increasing the past fuel consumption efficiency of the user; and

the one or more second past user driving features correspond to one or more second importance levels respectively for decreasing the past fuel consumption efficiency of the user, wherein the one or more first importance levels and the one or more second importance levels are determined by the trained machine learning model.

20 . The non-transitory computer-readable medium of claim 15 , wherein the traffic congestion information is derived from the historical traffic data.