Systems and methods for vehicle performance optimization using machine learning
AssigneeToyota Jidosha Kabushiki Kaisha
InventorDaisuke Hashimoto
Life of this patent
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Abstract
Provided are a method, system, and device for optimizing performance of a vehicle. The method may include, receiving vehicle condition data prior to operating the vehicle; generating, based on the vehicle condition data, input data for a machine learning (ML) model, wherein the ML model is configured to output at least one of predicted vehicle performance and vehicle parameters based on the vehicle condition data; suggesting, by the ML model, optimized vehicle parameters based on the input data; and sending, based on the optimized vehicle parameters, instructions to tune the vehicle.
Claims18
A method for optimizing performance of a vehicle, the method comprising:
- receiving vehicle condition data prior to operating the vehicle;
- generating, based on the vehicle condition data, input data for a machine learning (ML) model, wherein the ML model is configured to output at least one of predicted vehicle performance or vehicle parameters based on the vehicle condition data;
- obtaining, based on the ML model, vehicle parameters based on the input data;
- and
- sending, based on the vehicle parameters, instructions to tune the vehicle, wherein training the ML model further comprises:
- receiving vehicle performance data after operating the vehicle;
- generating, based on the vehicle performance data, a Requirements as Code (RaC) file using a Large Language Model (LLM);
- generating, based on the RaC file, simulated vehicle data;
- and
- training the ML model based on simulated vehicle data.
An apparatus for optimizing performance of a vehicle, the apparatus comprising:
- at least one memory storing computer-executable instructions;
- and
- at least one processor configured to execute the computer-executable instructions to:
- receive vehicle condition data prior to operating the vehicle;
- generate, based on the vehicle condition data, input data for a machine learning (ML) model, wherein the ML model is configured to output at least one of predicted vehicle performance or vehicle parameters based on the vehicle condition data;
- obtain, based on the ML model, vehicle parameters based on the input data;
- and
- send, based on the vehicle parameters, instructions to tune the vehicle, wherein to train the ML model the at least one processor configured to execute the computer-executable instructions to:
- receive vehicle performance data after operating the vehicle;
- generate, based on the vehicle performance data, a Requirements as Code (RaC) file using a Large Language Model (LLM);
- generate, based on the RaC file, simulated vehicle data;
- and
- train the ML model based on simulated vehicle data.
A non-transitory computer-readable recording medium having recorded thereon instructions executable by at least one processor to cause the at least one processor to perform a method comprising:
- receiving vehicle condition data prior to operating the vehicle;
- generating, based on the vehicle condition data, input data for a machine learning (ML) model, wherein the ML model is configured to output at least one of predicted vehicle performance or vehicle parameters based on the vehicle condition data;
- obtaining, based on the ML model, vehicle parameters based on the input data;
- and
- sending, based on the vehicle parameters, instructions to tune the vehicle, wherein training the ML model further comprises:
- receiving vehicle performance data after operating the vehicle;
- generating, based on the vehicle performance data, a Requirements as Code (RaC) file using a Large Language Model (LLM);
- generating, based on the RaC file, simulated vehicle data;
- and
- training the ML model based on simulated vehicle data.
Dependencies are inferred from the claim text.