Systems and methods for identifying a vehicle platform using machine learning on vehicle bus data
Embodiments of the invention include a vehicle telematics system that obtains vehicle bus data for a time period, determines identification information regarding a vehicle platform using a machine learning process on the vehicle bus data, and obtains a set of communication data for communicating with at least one vehicle module on the vehicle bus based on the identified vehicle platform.
1. A method of identifying a vehicle platform using vehicle bus data, comprising:
obtaining vehicle bus data for a time period from a vehicle, wherein the vehicle bus data comprises data communicated via a vehicle bus of the vehicle during the time period;
identifying a vehicle platform using a machine learning process based on the vehicle bus data, wherein the vehicle platform specifies a set of configuration settings for a set of vehicle modules for the vehicle, wherein the set of configuration settings comprises On-Board Diagnostic Parameter IDs (OBD-II PIDs); and
obtaining a set of communication data for communicating with at least one vehicle module on the vehicle bus based on the identified vehicle platform;
wherein identifying the vehicle platform using the machine learning process comprises:
extracting data from a data-field from the vehicle bus data, wherein the data-field comprises vehicle module ID;
determining frequency information for each vehicle module ID, wherein the frequency information comprises a number of occurrences for each vehicle module ID in the vehicle bus data during the time period; and
providing the extracted data and the frequency information to a trained machine learning model that classifies the extracted data and the frequency information to a label indicative of the identified vehicle platform.
2. The method of claim 1 , wherein the vehicle bus is a Controller Area Network (CAN) vehicle bus and the communication data is a set of On-board Diagnostic Parameter IDs (OBD-II PIDs).
3. The method of claim 2 , further comprising:
obtaining information regarding a year, make, and model (YMM) of the identified vehicle platform; and
using the YMM information to obtain a set of OBD-II PIDs for the vehicle.
4. The method of claim 1 , wherein the machine learning process is a supervised neural network model that has been trained on a set of vehicle bus data obtained from a plurality of different vehicles with different YMMs.
5. The method off claim 1 , wherein the machine learning process is a unsupervised machine learning process that performs cluster analysis on vehicle bus data obtained from a plurality of vehicles to group the vehicle bus data.
6. A vehicle telematics device, comprising:
a processor and a memory storing a vehicle telematics application; and
a communication interface for communicating with a remote server system and a plurality of vehicle modules on a vehicle bus of the vehicle;
wherein the processor of the telematics device, on reading the vehicle telematics application, is directed to:
obtain vehicle bus data for a time period, wherein the vehicle bus data comprises data communicated via the vehicle bus during the time period;
identify a vehicle platform using a machine learning process on the vehicle bus data, wherein the vehicle platform specifies a set of configuration settings for a set of vehicle modules for the vehicle, wherein the set of configuration settings comprises On-Board Diagnostic Parameter IDs (OBD-II PIDs); and
obtain a set of communication data for communicating with at least one vehicle module on the vehicle bus based on the identified vehicle platform;
wherein to identify the vehicle platform using the machine learning process comprises to:
extract data from a data-field from the vehicle bus data, wherein the data-field comprises vehicle module ID;
determine frequency information for each vehicle module ID, wherein the frequency information comprises a number of occurrences for each vehicle module ID in the vehicle bus data during the time period; and
provide the extracted data and the frequency information to a trained machine learning model that classifies the extracted data and the frequency information to a label indicative of the identified vehicle platform.
7. The vehicle telematics device of claim 6 , wherein the extracted data and the frequency information are provided to a remote server system that performs a machine learning model on the extracted data and the frequency information.
8. The vehicle telematics device of claim 6 , wherein the vehicle bus is a Controller Area Network (CAN) vehicle bus and the communication data is a set of On-board Diagnostic Parameter IDs (OBD-II PIDs).
9. The vehicle telematics device of claim 8 , wherein the processor of the telematics device, on reading the vehicle telematics application, is further directed to:
obtaining information regarding a year, make, and model (YMM) of the identified vehicle platform; and
using the YMM information to obtain a set of OBD-II PIDs for the vehicle.
10. The vehicle telematics device of claim 6 , wherein the machine learning process is a supervised neural network model that has been trained on a set of vehicle bus data obtained from a plurality of different vehicles with different YMMs.
11. The vehicle telematics device of claim 6 , wherein the machine learning process is a unsupervised machine learning process that performs cluster analysis on vehicle bus data obtained from a plurality of vehicles to group the vehicle bus data.
12. The vehicle telematics device of claim 6 , wherein the time period is dynamically adjusted and determined based on an accuracy of the machine learning process on a set of collected bus data.