Toyota Jidosha Kabushiki Kaisha

Shintaro Mukogawa

Hideaki Bunazawa

Rikako Zenibana

A vehicle evaluation system includes processing circuitry and a memory device. The memory device stores data of a pretrained model. The pretrained model is a model trained by supervised learning to generate operation data from training sound data. The operation data is data indicating changes in operation information in a measurement operation pattern. The processing circuitry is configured to perform a generation process and an evaluation process. The generation process is a process of outputting generated data which is data of operation information generated by inputting the evaluation data to the pretrained model. The evaluation data includes evaluation sound data recorded while operating a target vehicle in the measurement operation pattern. The evaluation process is a process of comparing the operation data with the generated data and evaluating the target vehicle in accordance with the magnitude of a difference therebetween.

Vehicle evaluation system

In a vehicle diagnostic system, a storage device stores data of a learned model and probability distribution data obtained by generating pieces of generated data by a learned model using pieces of sound data recorded using vehicles with identified types of anomalies and by obtaining, for each of the types of anomalies, a probability distribution of a loss variable. Processing circuitry is configured to execute a loss calculation process that generates the pieces of the generated data using pieces of diagnostic sound data and obtains data of a probability distribution of a loss variable in the pieces of the generated data as sample data and a diagnostic process that determines which probability distribution of an anomaly the sample data matches and outputs a diagnosis result indicating that a type of anomaly corresponding to the probability distribution determined to match the sample data has occurred in the target vehicle.

Vehicle diagnostic system

A vehicle diagnostic system includes processing circuitry and a storage device. The storage device stores data of a learned model and cluster data created by inputting, to the learned model, pieces of sound data recorded using vehicles in which types of anomalies are different from each other and identified, outputting the pieces of generated data, and clustering, for each of the types of anomalies, a loss variable indicating a magnitude of an error in each of the variables in each piece of the generated data. The processing circuitry is configured to execute a loss calculation process that inputs diagnostic sound data of a target vehicle to the learned model and calculates the loss variable in the generated data and a diagnostic process that checks data of the loss variable calculated through the loss calculation process against the cluster data and outputs a diagnosis result of the target vehicle.

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Nagoya, Japan

Shintaro Mukogawa