Abstract: A control device includes: a control unit that controls a vehicle. Further, the control unit determines, when the vehicle travels in a section including a plurality of lanes, whether a negative emotion associated with a lane change is detected in a preceding vehicle on a same lane as the vehicle, and causes, in a case of determining that the negative emotion is detected, a notification device of the vehicle to give notification of instruction information giving an instruction for the lane change.

Abstract: A driving support apparatus including a processor, where the processor acquires operation information for an own vehicle by a driver at the time of a driving assistance intervention for keeping a lane of the own vehicle, environmental information including another vehicle around the own vehicle, and biometric information of the driver, estimates emotion of the driver based on the operation information, the environmental information, and the biometric information, estimates control parameters related to an amount of intervention at the time of driving assistance intervention based on the estimation result of emotion of the driver, and controls the own vehicle based on the control parameters.

Abstract: A control device includes: a controller controlling a first vehicle, the controller being configured to estimate a degree of concentration of a driver in the first vehicle during traveling of the first vehicle based on a first distance between the first vehicle and a second vehicle traveling in front of the first vehicle, a second distance between the first vehicle and a third vehicle traveling behind the first vehicle, and a relationship between a vehicle speed of the first vehicle and a predetermined vehicle speed, and execute a vehicle control of recovering the concentration of the driver based on an estimation result.

Abstract: A control device includes a processor that acquires travel data of a target vehicle, determines whether the target vehicle obeys a traffic rule, and adjusts a usage form of the travel data in a case of determining that the target vehicle does not obey the traffic rule.

Abstract: A machine learning device includes a processor which performs, when performing learning of a model with image data, the learning of the model while excluding the image data of an animal having a feature amount common with a feature amount of a human.

Abstract: An information processing system includes: a vehicle that captures an image of a periphery by an in-vehicle camera; and an information processing device including a processor. Further, the processor stores acquired image data without performing privacy processing in a case where a predetermined specific event is occurred therein, and stores the acquired image data after performing the privacy processing in a case where the specific event is not occurred therein.

Abstract: A machine learning device training a learning model unique to a vehicle is provided with: a processor configured to use training data sets including values of state parameters detected by detectors provided at the vehicle, to train the learning model; and if an abnormality occurs in values of a state parameter detected by a detector, acquire values of the state parameter, where an abnormality has occurred, detected by another vehicle under conditions matching detection conditions when the values of the state parameter included in the training data sets were detected by the detector. If an abnormality occurs in values of the state parameter detected by the detector, the training part uses training data sets including values acquired from another vehicle by the parameter value acquiring part, instead of the values of the state parameter where an abnormality has occurred detected by the detector, to train the leaning model.

Abstract: A hybrid vehicle includes an internal combustion engine, electric motors, an electric supercharger, a battery, and a controller. The controller determines whether to increase a total electric power supply amount to the electric motor and the electric supercharger from the battery based on a state of charge of the battery. When so determined, electric power supply promotion control is performed to increase the total electric power supply amount from the battery. In the electric power supply promotion control, the controller controls the electric power supply amount to the electric motors and the electric supercharger so as to maximize fuel save rate, which is the percentage of decrease of the fuel consumption of an internal combustion engine relative to an increase in the total electric power supply amount from the battery.

Abstract: A machine learning device training a learning model unique to a vehicle is provided with: a processor configured to use training data sets including values of state parameters detected by detectors provided at the vehicle, to train the learning model; and if an abnormality occurs in values of a state parameter detected by a detector, acquire values of the state parameter, where an abnormality has occurred, detected by another vehicle under conditions matching detection conditions when the values of the state parameter included in the training data sets were detected by the detector. If an abnormality occurs in values of the state parameter detected by the detector, the training part uses training data sets including values acquired from another vehicle by the parameter value acquiring part, instead of the values of the state parameter where an abnormality has occurred detected by the detector, to train the leaning model.

Abstract: A vehicle allocation device includes a processor including hardware, the processor being configured to: determine, for each vehicle, a necessity of suppressing lowering of performance of an internal combustion engine based on vehicle information corresponding to the vehicle; determine a reservation as a first reservation when reservation information corresponding to the reservation of renting the vehicle satisfies a first condition under which suppressing of lowering of the performance is expected; and allocate the vehicle to the reservation based on the reservation information, wherein the processor is configured to allocate, in a case where the reservation is the first reservation, the vehicle satisfying a second condition indicating a high necessity of the suppression of lowering of the performance or the vehicle having a higher necessity of the suppression of lowering of the performance than the other vehicle to the reservation.

Abstract: A vehicle includes a first communication device configured to communicate with a server that correlatively stores trained models of other vehicles and information relating to training conditions of the models of the other vehicles, and a first control device. The first control device is configured to transmit information relating to training conditions of a model of an own vehicle to the server. The first control device is configured to receive part of a model of a particular vehicle of which training conditions are closest to those of the model of the own vehicle, which has been selected from the models of the other vehicles stored in the server, as transfer-learning data. The first control device is configured to reuse part of the received model of the particular vehicle and perform training of the model of the own vehicle.

Abstract: A vehicle allocation device for allocating a vehicle in response to a vehicle allocation request from a terminal of a user includes a vehicle selection unit configured to select, from a plurality of vehicles learning a relationship between input parameters and an output parameter related to traveling, a vehicle having relatively large learning progress in the relationship between the input parameters and the output parameter, and output a vehicle allocation instruction to the selected vehicle in a case where the vehicle allocation request is received.

Abstract: A vehicle allocation device allocates a vehicle in response to a vehicle allocation request from a terminal of a user. The vehicle allocation device includes a vehicle selection unit configured to select a vehicle having relatively small learning progress of an input/output relationship of parameters depending on a scheduled traveling area where the user is traveling, from a plurality of vehicles learning an input/output relationship of parameters depending on a predetermined area, and output a vehicle allocation instruction to the selected vehicle in a case where the vehicle allocation request is received.

Abstract: A vehicle allocation device allocates a vehicle in response to a vehicle allocation request from a terminal of a user, and includes a vehicle selector configured to, when acquiring the vehicle allocation request, select a vehicle having a relatively large degree of progress in learning of a relation between input and output of a parameter depending on an area to be traveled by the user from a plurality of vehicles learning a relation between input and output of a parameter depending on a predetermined area, and output a vehicle allocation instruction to the selected vehicle.

Abstract: A machine learning device of an amount of unburned fuel using a neural network in which the correlation functions showing correlations between parameters and an amount of unburned fuel are found for parameters relating to operation of an internal combustion engine, and the parameters with strong degrees of correlation with the amount of unburned fuel exhausted from the engine are selected from among the parameters based on the correlation functions. The amount of unburned fuel is learned by using the neural network from the selected parameters and amount of unburned fuel.

Abstract: A learning use data set showing relationships among an engine speed, an engine load rate, an air-fuel ratio of the engine, an ignition timing of the engine, an HC or CO concentration of exhaust gas flowing into an exhaust purification catalyst and a temperature of the exhaust purification catalyst is acquired. The acquired engine speed, engine load rate, air-fuel ratio of the engine, ignition timing of the engine, and HC or CO concentration of the exhaust gas flowing into the exhaust purification catalyst are used as input parameters of a neural network and the acquired temperature of the exhaust purification catalyst is used as training data to learn a weight of the neural network. The learned neural network is used to estimate the temperature of the exhaust purification catalyst.

Abstract: A learning use data set showing relationships among an engine speed, an engine load rate, an air-fuel ratio of the engine, an ignition timing of the engine, an HC or CO concentration of exhaust gas flowing into an exhaust purification catalyst and a temperature of the exhaust purification catalyst is acquired. The acquired engine speed, engine load rate, air-fuel ratio of the engine, ignition timing of the engine, and HC or CO concentration of the exhaust gas flowing into the exhaust purification catalyst are used as input parameters of a neural network and the acquired temperature of the exhaust purification catalyst is used as training data to learn a weight of the neural network. The learned neural network is used to estimate the temperature of the exhaust purification catalyst.

Abstract: A control support device which supports control of an apparatus by using a learned model by machine learning, includes: a data acquisition unit acquiring an input/output data set which are data relating to an internal or external state of the apparatus and including input parameters and an output parameter of the learned model; a learning unit generating a learned model by performing the machine learning using the input/output data set acquired by the data acquisition unit; and a determination unit determining a learned model which is to be used for the control from among a plurality of learned models, which include a learned model having been completely generated and can be used for the control, based on the input parameters acquired by the data acquisition unit and a learning situation of the machine learning in the learning unit.

Abstract: A machine learning device of an amount of unburned fuel using a neural network in which the correlation functions showing correlations between parameters and an amount of unburned fuel are found for parameters relating to operation of an internal combustion engine, and the parameters with strong degrees of correlation with the amount of unburned fuel exhausted from the engine are selected from among the parameters based on the correlation functions. The amount of unburned fuel is learned by using the neural network from the selected parameters and amount of unburned fuel.

Abstract: A learning use data set showing relationships among an engine speed, an engine load rate, an air-fuel ratio of the engine, an ignition timing of the engine, an HC or CO concentration of exhaust gas flowing into an exhaust purification catalyst and a temperature of the exhaust purification catalyst is acquired. The acquired engine speed, engine load rate, air-fuel ratio of the engine, ignition timing of the engine, and HC or CO concentration of the exhaust gas flowing into the exhaust purification catalyst are used as input parameters of a neural network and the acquired temperature of the exhaust purification catalyst is used as training data to learn a weight of the neural network. The learned neural network is used to estimate the temperature of the exhaust purification catalyst.