Vehicle recommendations based on driving habits
Disclosed embodiments provide techniques for providing vehicular recommendations based on driver habits. Embodiments utilize a variety of input data, including, but not limited to, static vehicular data, dynamic vehicular data, and/or environmental data. In embodiments, empirical rules are used to adjust recommended maintenance schedules based on the input conditions. Additionally, the adjusted recommendations along with unscheduled maintenance data are input to a machine learning system, such as a neural network. The machine learning system is used to further revise the maintenance schedule, estimate end of life of the vehicle, and issue recommendations for when to sell a vehicle and recommendations on attributes of a new vehicle for acquisition.
1. A computer-implemented method for generating a vehicle report for a vehicle, comprising:
obtaining static vehicular data for the vehicle;
obtaining dynamic vehicular data for the vehicle;
obtaining environmental data associated with the vehicle;
computing a maintenance score from a summation of a number of parameter based calculations, each of the parameter based calculations utilizing a function that is specific to a particular parameter of the number of parameters based on the static vehicular data, dynamic vehicular data, and environmental data;
obtaining a maintenance procedure for the vehicle, the maintenance procedure having a static recommendation point for performing the maintenance procedure, the static recommendation point including at least one of a fixed time interval or a fixed interval of elapsed vehicle miles;
adjusting an interval associated with the maintenance procedure based on the maintenance score; and
providing the adjusted interval in the vehicle report.
2. The method of claim 1 , wherein the obtaining of the environmental data includes obtaining ambient temperature data.
3. The method of claim 1 , wherein the obtaining of the environmental data includes obtaining ambient humidity data.
4. The method of claim 1 , wherein the obtaining of the environmental data includes obtaining airborne salinity data.
5. The method of claim 1 , wherein the obtaining of the environmental data includes obtaining terrain data, the terrain data including crowdsourced road data regarding road conditions.
6. The method of claim 1 , wherein the obtaining of the environmental data includes obtaining altitude data.
7. The method of claim 1 , wherein the obtaining of the environmental data includes obtaining storage data.
8. The method of claim 1 , wherein the obtaining of the dynamic vehicle data includes obtaining suspension deflection data from a device onboard the vehicle that collects data pertaining to how much a suspension is compressed during operation of the vehicle by a user.
9. The method of claim 8 , wherein the obtaining of the suspension deflection data includes obtaining average suspension deflection data.
10. The method of claim 8 , wherein the obtaining of the suspension deflection data includes obtaining significant deflection event data.
11. The method of claim 1 , wherein the obtaining of the dynamic vehicle data includes obtaining engine data.
12. The method of claim 11 , wherein the obtaining of the engine data includes obtaining average revolutions-per-minute data.
13. The method of claim 11 , wherein the obtaining of the engine data includes obtaining average idle time data.
14. The method of claim 1 , further comprising:
obtaining unscheduled maintenance data;
inputting unscheduled maintenance data into a machine learning system as training data;
inputting the static vehicular data, dynamic vehicular data, and environmental data for the vehicle into the machine learning system; and
generating a vehicular recommendation for the vehicle for by the machine learning system.
15. The method of claim 14 , wherein the vehicular recommendation includes a maintenance schedule.
16. The method of claim 14 , wherein the vehicular recommendation includes a vehicle sell recommendation.
17. The method of claim 14 , wherein the vehicular recommendation includes a vehicle purchase recommendation.
18. An electronic computing device comprising:
a processor;
a memory coupled to the processor, the memory containing instructions, that when executed by the processor, perform the steps of:
obtaining static vehicular data for a vehicle;
obtaining dynamic vehicular data for the vehicle;
obtaining environmental data associated with the vehicle;
computing a maintenance score from a summation of a number of parameter based calculations, each of the parameter based calculations utilizing a function that is specific to a particular parameter of the number of parameters based on the static vehicular data, dynamic vehicular data, and environmental data;
obtaining a maintenance procedure for the vehicle, the maintenance procedure having a static recommendation point for performing the maintenance procedure, the static recommendation point including at least one of a fixed time interval or a fixed interval of elapsed vehicle miles;
adjusting an interval associated with the maintenance procedure based on the maintenance score;
obtaining unscheduled maintenance data;
inputting the unscheduled maintenance data into a machine learning system as training data;
inputting the static vehicular data, dynamic vehicular data, and environmental data for the vehicle into the machine learning system; and
generating a vehicular recommendation for the vehicle from the machine learning system.
19. The electronic computing device of claim 18 , wherein the memory further comprises instructions, that when executed by the processor, perform the step of including a vehicle sell recommendation in the vehicular recommendation.
20. A computer program product for an electronic computing device comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the electronic computing device to:
obtain static vehicular data for a vehicle;
obtain dynamic vehicular data for the vehicle;
obtain environmental data associated with the vehicle;
compute a maintenance score from a summation of a number of parameter based calculations, each of the parameter based calculations utilizing a function that is specific to a particular parameter of the number of parameters based on the static vehicular data, dynamic vehicular data, and environmental data;
obtain a maintenance procedure for the vehicle, the maintenance procedure having a static recommendation point for performing the maintenance procedure, the static recommendation point including at least one of a fixed time interval or a fixed interval of elapsed vehicle miles;
adjust an interval associated with the maintenance procedure based on the maintenance score;
obtain unscheduled maintenance data;
input the unscheduled maintenance data into a machine learning system as training data;
input the static vehicular data, dynamic vehicular data, and environmental data for the vehicle into the machine learning system; and
generate a vehicular recommendation for the vehicle from the machine learning system.