Toyota Jidosha Kabushiki Kaisha

Satoshi Takamoto

Yohsuke Hashimoto

Harufumi Muto

Akihiro Katayama

Masatomo Yoshihara

Satoshi Masuda

Tatsuhisa Yokoi

Akio Matsunaga

Masakazu Aoki

Tatsunobu Murai

Atsushi Nagai

Matsuei Ueda

Kohei Mori

Makoto Nagaoka

Takato Ikedo

Hitoshi Hosaki

A vehicle control device includes: a processor configured to execute automated acceleration control for automatically controlling acceleration of a vehicle; and one or more sensors configured to detect an operation of a driver related to acceleration/deceleration of the vehicle. The processor is configured to detect, during a specific acceleration period in which a specific condition is satisfied and the vehicle is accelerated by the automated acceleration control, an acceleration change operation of the driver that intervenes to change a set acceleration set by the automated acceleration control. The specific condition indicates that a vehicle-to-vehicle distance or a vehicle-to-vehicle time between the vehicle and a preceding vehicle is equal to or greater than a threshold value or that there is no preceding vehicle. The processor is also configured to change the set acceleration for future acceleration based on a history of the detected acceleration change operation.

Vehicle control device

A control device for a vehicle controls a subject vehicle configured to switch between manual driving by a driver and automated driving. The control device includes a processor and a memory device. The processor is configured to execute learning control that learns vehicle-to-vehicle information being vehicle-to-vehicle time or vehicle-to-vehicle distance of the subject vehicle with respect to a preceding vehicle during traveling with the manual driving, and to reflect a learning result of the vehicle-to-vehicle information by the learning control in a control of the vehicle-to-vehicle information during the automated driving. The memory device is configured to store the vehicle-to-vehicle information as learning data during the manual driving. In the learning control, the processor determines, based on a type of the preceding vehicle, whether or not the vehicle-to-vehicle information for the preceding vehicle is caused to be stored in the memory device as the learning data.

Control device for vehicle

A control device for a vehicle controls a subject vehicle configured to switch between manual driving and automated driving. The control device includes a processor and a memory device. The processor is configured to execute learning control that learns vehicle-to-vehicle distance when the driver stops the subject vehicle during the manual driving, and to reflect a learning result of the vehicle-to-vehicle distance by the learning control in a control of the vehicle-to-vehicle distance during the automated driving. The memory device is configured to store the vehicle-to-vehicle distance as learning data during the manual driving. In the learning control, the processor is configured to determine, based on vehicle operation information by the driver during a series of periods from a pre-stop period to a post-stop period of the subject vehicle, whether or not the vehicle-to-vehicle distance is caused to be stored in the memory device as the learning data.

A data generation unit generates second abnormal sound data indicating a change in loudness of a sound each time a rotation state changes by a certain amount, based on first abnormal sound data that is data of an abnormal sound generated due to a rotary motion of a rotation device mounted on an object, and rotation data that is data relating to the rotary motion of the rotation device. The second abnormal sound data is input to a sound source estimation unit, and a certainty factor of being a sound source of the abnormal sound indicated by the second abnormal sound data is output to each of a plurality of parts mounted on a vehicle.

Sound source estimation server, sound source estimation system, sound source estimation device, and sound source estimation method

A misfire detection device for an internal combustion engine is provided. A mapping takes time series data of instantaneous speed parameters as inputs. Each instantaneous speed parameter corresponds to one of a plurality of successive second intervals in a first interval. The instantaneous speed parameters correspond to the rotational speed of the crankshaft. The first interval is a rotational angular interval of the crankshaft in which compression top dead center occurs. The second interval is smaller than an interval between compression top dead center positions. The mapping outputs a probability that a misfire has occurred in at least one cylinder that reaches compression top dead center in the first interval. The mapping data defining the mapping has been learned by machine learning.

Misfire detection device for internal combustion engine, misfire detection system for internal combustion engine, data analysis device, and controller for internal combustion engine

A CPU calculates probability models by inputting input variables to a neural network. Next, the CPU determines whether a maximum value among the calculated probability models is larger than an upper limit value of a permissible range. When the maximum value is larger than the upper limit value of the permissible range, the CPU calculates a difference between the maximum value and a first reference value within the permissible range, and subtracts the difference from all the probability models. Then, the CPU calculates a probability of misfire in each cylinder by inputting each of the calculated probability models to a softmax function of mapping.

Internal combustion engine condition determination apparatus, internal combustion engine condition determination system, and data analyzing apparatus

A catalyst state estimation apparatus includes a first sensor, a memory and a processor. The first sensor is configured to acquire information about a catalyst that removes a toxic substance in an exhaust gas, the first sensor being provided in a main passage into which the exhaust gas flows from an internal combustion engine. The memory is configured to previously store a catalyst state estimation model that includes at least one mathematical model. The processor is configured to estimate a removal performance of the catalyst by applying the information about the catalyst acquired by the first sensor to the catalyst state estimation model.

Catalyst state estimation apparatus, catalyst state estimation method with information processing apparatus, and non-transitory recording medium in which program is stored

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Miyoshi, Japan

Satoshi Takamoto