Toyota Jidosha Kabushiki Kaisha

Hideaki Bunazawa

Atsushi Tabata

Koichi Okuda

Kota Fujii

Ken Imamura

Keita Sasaki

Kenichi Yamaguchi

Kei Yuasa

Tetsuya Yoshikawa

Rikako Zenibana

Shintaro Mukogawa

Yuji Hattori

Kenji Matsuo

Akihide Itoh

Kunio Suzuki

Keisuke Ninomiya

Yoshitaka Tomiyama

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.

A first relative frequency distribution in original data is calculated for each of feature amounts. Data segments are cut out from the original data using time windows. A second relative frequency in extracted data is calculated for each of the feature amounts. An error between the first relative frequency distribution and the second relative frequency distribution is calculated. A trial from determination of a setting of time windows to calculation of the error is repeatedly executed while changing the setting of the time windows. One or more settings of the time windows in which the error is less than a threshold value are selected and output.

Information processing method

An abnormality determination device is applied to a vehicle provided with a transmission configured to transmit power by rotation of a shaft. The abnormality determination device includes a processor and a memory. The memory configured to store mapping data that is data that defines mapping learned by machine learning. The processor is configured to execute an acquisition process and a determination process. The acquisition process is a process of acquiring a variable indicating a time-series data of a rotation speed of the shaft and using the variable as a value of an input variable of the mapping. The determination process is a process of determining whether an abnormality has occurred in the transmission based on a value of the output variable acquired using the value of the input variable and the mapping.

Abnormality determination device

A vehicle control device includes a processor and memory. The memory stores relation-defining data for defining a relation between a state of a vehicle and an action variable that is a variable relating to operations of a transmission installed in the vehicle. The processor is configured to execute acquisition processing, operation processing, reward calculation processing, updating processing, counting processing, and limiting processing. The processor is configured to output the relation-defining data updated so that an expected income is increased when the transmission is operated following the relation-defining data, based on an updating map.

Vehicle control device, vehicle control system, vehicle learning device, and vehicle learning method

A degradation estimation device includes a storage device and an execution device. The storage device is configured to store mapping data that defines a map that outputs an output variable indicating a degree of degradation of a seal member when an input variable is input. The map includes a thermal history variable and a load history variable as the input variable. The thermal history variable is a variable indicating a period during which the seal member has been exposed to a temperature within a predetermined temperature range. The load history variable is a variable indicating the number of times a load has acted on the seal member. The execution device is configured to perform an acquisition process of acquiring the input variable and a calculation process of outputting a value of the output variable by inputting the input variable acquired in the acquisition process to the map.

Degradation estimation device, degradation estimation method, and non-transitory storage medium

An anomaly determination apparatus for a vehicle that includes a transmission includes an execution device and a memory device. The memory device is configured to store map data that defines pre-trained map, which has been trained through machine learning. When a variable representing time-series data of a rotation speed of a gear is fed to the map as an input variable, the map outputs a state variable representing a state of the gear as an output variable. The execution device is configured to execute: an obtaining process that obtains the variable representing the time-series data as a value of the input variable; and a determination process that determines whether there is an anomaly in the transmission based on a value of the output variable that is output by the map when the value of the input variable is fed to the map.

Anomaly determination apparatus

A storage device stores a mapping data entry that specifies a mapping and includes data learned through machine learning. A detection value of an oil temperature sensor is an oil temperature detection value. When oil temperature relation data, which is data corresponding to time series data of the oil temperature detection value, is input to the mapping as an input variable, the mapping outputs an output variable that determines whether a frictional engagement element has an anomaly. Processing circuitry executes an obtainment process that obtains the input variable and an anomaly determination process that determines whether the frictional engagement element has an anomaly based on the output variable that is output from the mapping as a result of inputting the input variable obtained in the obtainment process into the mapping.

Anomaly determination device for power transmission device

A diagnostic apparatus for a vehicle provided with a continuously-variable transmission including a primary pulley, a secondary pulley and a belt looped over the primary and secondary pulleys, such that each of the primary and secondary pulleys includes a hydraulic actuator to which a working fluid is to be supplied. The diagnostic apparatus is configured, in event of occurrence of slippage of the belt on at least one of the primary and secondary pulleys, to infer a factor causing the slippage of the belt, based on at least one of a hydraulic pressure of the working fluid supplied to the hydraulic actuator of the primary pulley and a hydraulic pressure of the working fluid supplied to the hydraulic actuator of the secondary pulley, upon the occurrence of the slippage of the belt.

Diagnostic apparatus for vehicle

A hydraulic pressure calculation apparatus is applied to a gear shifting system including a transmission configured to switch between a connected state and a disconnected state of a friction engagement element depending on a hydraulic pressure supplied from a hydraulic circuit, and a hydraulic controller configured to control the hydraulic circuit. The hydraulic pressure calculation apparatus includes a memory and a processor. The memory stores pieces of mapping data of a plurality of phases obtained by dividing a period from a start to an end of switching between the connected state and the disconnected state of the friction engagement element. Each piece of the mapping data defines a mapping. The processor is configured to output, as an output variable, an estimated hydraulic pressure variable indicating an estimated value of an actual hydraulic pressure supplied from the hydraulic circuit to the friction engagement element.

Hydraulic pressure calculation apparatus

A heat load estimation device for a friction engaging element that estimates a heat load when at least one of a temperature, a heat generation amount, and presence or absence of seizure in the friction engaging element at a time of shifting of the transmission is regarded as the heat load, includes a storage device and an execution device. The storage device stores mapping data defining mapping. The mapping includes, as an input variable, a speed variable that is a variable indicating a relative rotation speed of members of the friction engaging element and a hydraulic pressure variable that is a variable indicating hydraulic pressure during the shifting of the transmission, and includes, as an output variable, the heat load. The execution device executes a calculation process for calculating a value of the output variable and a change process for changing an execution mode of the calculation process.

Heat load estimation device and heat load estimation method for friction engaging element

A control device serving as a torque estimation device includes a storage device and a processing circuit. The storage device stores data of a trained neural network. The trained neural network is trained using training data including data of an actually-measured torque that is measured, data of an accelerator operation amount in a period of a predetermined length up to a time point of measurement of the actually-measured torque, and data of an acceleration of a vehicle from the time point of measurement of the actually-measured torque onward. The processing circuit inputs, to the trained neural network stored in the storage device, input data including the data of the accelerator operation amount and the data of the acceleration of the vehicle, to estimate a torque generated in a power transmission member.

Torque estimation device

An oil pressure learning method of an automatic transmission includes acquiring a state of a vehicle in a state where relational regulation data is stored, supplying oil to the automatic transmission such that the value of the oil pressure is set to an oil pressure command value, calculating, as a specific variable, a variable representing an amount in which a detected input rotation speed exceeds a target input rotation speed, or the like, calculating a reward in a manner in which it has a larger value when the specific variable satisfies a criterion than when it does not satisfy the criterion, updating the relational regulation data by inputting, to an update mapping, the reward and the oil pressure command value, and calculating a torque variable having a value that is increased as an amount of change in an input torque is increased.

Oil pressure learning method of automatic transmission, control device thereof, and control system thereof

A vehicle control device includes: a storage device that stores relation-defining data that is data for defining a relation between a state of a vehicle and an action variable; and an executing device configured to acquire the state, operate a drivetrain device based on a value of the action variable, derive a reward such that the reward is larger when the state of the drivetrain device based on the acquired state satisfies a predetermined criterion, perform an updating of the relation-defining data using an updating map, and restrict the updating of the relation-defining data such that an updating amount of the relation-defining data is smaller when the drivetrain device is subject to a predetermined restriction.

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Nagoya, Japan

Hideaki Bunazawa