Patent Publication Number: US-11377110-B2

Title: Machine learning device

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to Japanese Patent Application No. 2020-171862 filed on Oct. 12, 2020, the entire contents of which are herein incorporated by reference. 
     FIELD 
     The present disclosure relates to a machine learning device for training a learning model unique to a vehicle. 
     BACKGROUND 
     It is known to acquire values of state parameters relating to a vehicle and train a learning model by using the acquired values of the state parameters as training data sets (for example, JP 2019-183698 A). In particular, in JP 2019-183698 A, it is proposed to train a learning model outputting a temperature of an exhaust purification catalyst, based on training data sets including values of an engine rotational speed, engine load factor, temperature of the exhaust purification catalyst, etc. 
     SUMMARY 
     For example, in the case where the values of state parameters which are parts of the training data sets are detected by sensors, if an abnormality occurs in a sensor, it would not be possible to accurately detect the values of that state parameter. In the case where that in this way an abnormality occurs in the values of a state parameter which are parts of training data sets, if training the learning model by using such training data sets, it would not be possible to suitably train the learning model. 
     In consideration of the above problem, an object of the present disclosure is to provide a machine learning device able to suitably train a learning model even if an abnormality occurs in values of a state parameter which are parts of training data sets for training the learning model. 
     The present disclosure has as its gist the following. 
     (1) A machine learning device training a learning model unique to a vehicle, the machine learning device comprising: 
     a training part which uses training data sets including values of state parameters detected by detectors provided at the vehicle, to train the learning model; and 
     a parameter value acquiring part which, if an abnormality occurs in values of a state parameter detected by a detector, acquires 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, 
     wherein 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. 
     (2) The machine learning device according to above (1), wherein 
     the machine learning device is provided in the vehicle, and 
     the parameter value acquiring part acquires values of the state parameter, where the abnormality occurs, detected by the other vehicle by vehicle-vehicle communication with the other vehicle or by communication with a server able to communicate with the other vehicle. 
     (3) The machine learning device according to above (1), wherein 
     the machine learning device is provided at a server able to communicate with the vehicle and the other vehicle, and further comprises a model transmitting part transmitting information of the learning model trained by the training part to the vehicle, 
     the parameter value acquiring part acquires, from the other vehicle, values of the state parameter where the abnormality occurs detected by the other vehicle, by communication between the other vehicle and the server, and 
     the training part uses training data sets including values of a state parameter where the abnormality occurs acquired from the other vehicle and values of other state parameter relating to the vehicle, besides the state parameter where the abnormality occurs, transmitted from the vehicle by communication between the vehicle and the server, to train the learning model. 
     (4) The machine learning device according to any one of above (1) to (3), wherein the state parameter where the abnormality occurs is a physical quantity with a small difference between vehicles than other state parameters used in the training data sets, under the same detection conditions. 
     (5) The machine learning device according to any one of above (1) to (4), wherein the state parameter where the abnormality occurs is a state parameter relating to an external environment of the vehicle. 
     (6) The machine learning device according to above (5), wherein the state parameter where the abnormality occurs includes at least one of a temperature, humidity, and air pressure of an atmosphere of the surroundings of the vehicle. 
     (7) The machine learning device according to any one of above (1) to (6), wherein the detection conditions include a time and place where values of the state parameter constituting parts of the training data sets of the vehicle were detected by the detector. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a schematic view of the configuration of a machine learning system according to a first embodiment. 
         FIG. 2  is a view schematically showing a hardware configuration of a vehicle. 
         FIG. 3  is a functional block diagram of a processor of an ego vehicle. 
         FIG. 4  is a view schematically showing a hardware configuration of a server. 
         FIG. 5  is a functional block diagram of a processor of the server. 
         FIG. 6  shows one example of an NN model having a simple configuration. 
         FIG. 7  is an operation sequence diagram of training processing performed by a machine learning system. 
         FIG. 8  is a functional block diagram of a processor of an ego vehicle. 
         FIG. 9  is a functional block diagram of a processor of a server. 
         FIG. 10  is an operation sequence diagram of training processing performed by a machine learning system. 
     
    
    
     DETAILED DESCRIPTION 
     Below, referring to the drawings, embodiments of the present disclosure will be explained in detail. Note that, in the following explanation, similar component elements will be assigned the same reference notations. 
     First Embodiment 
     Configuration of Machine Learning System 
     First, referring to  FIGS. 1 to 7 , a machine learning system  1  according to a first embodiment will be explained.  FIG. 1  is a schematic view of the configuration of the machine learning system  1  according to the first embodiment. The machine learning system  1  trains a learning model unique to each vehicle using state parameters showing states of the vehicle. 
     As shown in  FIG. 1 , the machine learning system  1  is provided with a plurality of vehicles  2  and a server  3  able to communicate with each other. Each of the plurality of vehicles  2  and the server  3  are configured to be able to communicate with each other through a communication network  4  configured by optical communication lines, etc., and a wireless base station  5  connected with the communication network  4  through a gateway (not shown). The communication between the vehicles  2  and wireless base station  5  is communication compliant with any communication protocol. Note that, in the following explanation, the one vehicle using a learning model trained by the machine learning system  1  among the vehicles  2  will be referred to as the “ego vehicle  2   a ”, and vehicles other than the ego vehicle  2   a  will be referred to as “other vehicles  2   b”.    
       FIG. 2  is a view schematically showing the hardware configuration of a vehicle  2 . As shown in  FIG. 2 , the vehicle  2  is provided with an electronic control unit (ECU)  11 . The ECU  11  has an internal vehicle communication interface  12 , a memory  13 , and a processor  14 . The internal vehicle communication interface  12  and the memory  13  are connected to the processor  14  through signal wires. Note that, in the present embodiment, the vehicle  2  is provided with a single ECU  11 , but it may also be provided with a plurality of ECUs divided for different functions. 
     The internal vehicle communication interface  12  has an interface circuit for connecting the ECU  11  to an internal vehicle network  15  compliant with the CAN (controller area network) or another standard. The ECU  11  communicates with other vehicle-mounted equipment through the internal vehicle communication interface  12 . 
     The memory  13  is one example of a storage part for storing data. The memory  13 , for example, has a volatile semiconductor memory (for example, RAM) and nonvolatile semiconductor memory (for example, ROM). The memory  13  stores computer programs for performing various processing at the processor  14  and various data used when various processing is performed by the processor  14 , etc. Therefore, the memory  13  stores a learning model. 
     The processor  14  has one or more CPUs (central processing units) and their peripheral circuits. The processor  14  may further have a GPU (graphics processing unit) or a processing circuit such as a logic unit or arithmetic unit. The processor  14  performs various processing based on computer programs stored in the memory  13 . Therefore, if values of input parameters of the learning model are input, the processor  14  performs processing according to the learning model and outputs a value of an output parameter. 
       FIG. 3  is a functional block diagram of the processor  14  of the ego vehicle  2   a . As shown in  FIG. 3 , the processor  14  is provided with a control part  141  using a learning model to control controlled equipment  22  of the ego vehicle  2   a , a data transmitting part  142  transmitting a training data set used for training the learning model to the server  3 , and a model updating part  143  updating the learning model used in the control part  141 . These functional blocks of the processor  14  are, for example, functional modules realized by computer programs operating on the processor  14 . Alternatively, these functional blocks of the processor  14  may be dedicated processing circuits provided at the processor  14 . Details of the functional blocks of the processor  14  of the ego vehicle  2   a  will be explained later. 
     Further, as shown in  FIG. 2 , the vehicle  2  is further provided with an external vehicle communication module  21 , a plurality of controlled equipment  22 , and a plurality of sensors  23 . The external vehicle communication module  21 , controlled equipment  22 , and sensors  23  are connected to the ECU  11  through the internal vehicle network  15 . 
     The external vehicle communication module  21  is one example of a communicating part for communicating with equipment outside the vehicle. The external vehicle communication module  21  is, for example, equipment for communicating with the server  3  and other vehicles  2   b . The external vehicle communication module  21 , for example, includes a data communication module (DCM). The data communication module communicates with the server  3  through a wireless base station  5  and communication network  4 . 
     The controlled equipment  22  is equipment for performing various control operations of a vehicle  2 . Specifically, the controlled equipment  22 , for example, includes a drive actuator of a throttle valve for adjusting an opening degree of a throttle valve provided in an intake passage of an internal combustion engine, an injector supplying fuel to a combustion chamber of an internal combustion engine, a drive actuator of an EGR valve controlling an EGR rate of the internal combustion engine, a blower of an air-conditioner, a drive actuator of an air mix door controlling a flow of air of the air-conditioner, etc. These controlled equipment  22  are connected to the ECU  11  through the internal vehicle network  15  and are made to operate in accordance with drive signals from the ECU  11 . 
     The sensors  23  are examples of detectors for detecting values of various state parameters (state quantities) relating to a vehicle  2 . The sensors  23  include, for example, an air flow sensor for detecting a flow of intake air supplied to the internal combustion engine, an injection pressure sensor for detecting a fuel injection pressure from an injector of the internal combustion engine, an exhaust temperature sensor for detecting a temperature of the exhaust gas, an input detector sensor for detecting input of a driver at, for example, a touch panel, a self-position sensor for detecting a self-position of the vehicle  2  (for example, GPS), etc. Furthermore, the sensors  23  include, for example, an outside air temperature sensor for detecting a temperature of the air in the surroundings of the vehicle  2  (outside air temperature), an outside air humidity sensor for detecting a humidity of the air in the surroundings of the vehicle  2  (outside air humidity), an atmospheric pressure sensor for detecting an atmospheric pressure in the surroundings of the vehicle  2 , an internal vehicle temperature sensor for detecting a temperature inside a cabin of the vehicle  2  (internal cabin temperature), an internal vehicle humidity sensor for detecting a humidity inside the cabin of the vehicle  2  (internal cabin humidity), a sunlight sensor for detecting an amount of sunlight, etc. These sensors  23  are connected to the ECU  11  through the internal vehicle network  15  and transmit output signals to the ECU  11 . 
     The server  3  is provided at the outside of the vehicle  2 , and communicates with the vehicle  2  while it travels through a specific region, through the communication network  4  and wireless base station  5 . The server  3  receives various information from the vehicle  2  while it travels through the specific region. 
       FIG. 4  is a view schematically showing a hardware configuration of the server  3 . The server  3 , as shown in  FIG. 4 , is provided with an external communication module  31 , storage device  32 , and processor  33 . Further, the server  3  may have an input device such as a keyboard or mouse, and an output device such as a display. 
     The external communication module  31  is one example of a communicating part for communicating with equipment other than the server  3 . The external communicating module  31  is provided with an interface circuit for connecting the server  3  to the communication network  4 . The external communicating module  31  is configured to be able to communicate respectively with the plurality of vehicles  2  through the communication network  4  and wireless base station  5 . 
     The storage device  32  is one example of a storage part for storing data. The storage device  32  is, for example, provided with a hard disk drive (HDD), solid state drive (SSD), or optical storage medium. The storage device  32  stores computer programs for the processor  33  to perform various processing and various data used when the various processing is performed by the processor  33 . 
     The processor  33  has one or more CPUs and their peripheral circuits. The processor  33  may further have a GPU or processing circuit such as a logic unit or arithmetic unit. The processor  33  performs various processing based on computer programs stored in the storage device  32 . In the present embodiment, the processor  33  of the server  3  functions as a machine learning device for training a learning model. 
       FIG. 5  is a functional block diagram of the processor  33  of the server  3 . As shown in  FIG. 5 , the processor  33  is provided with an abnormal parameter identifying part  331  for identifying a state parameter where an abnormality has occurred among the state parameters forming a training data set transmitted from the ego vehicle  2   a , a parameter value acquiring part  332  for acquiring the values of the state parameter where an abnormality has occurred from other vehicles  2   b  different from the ego vehicle  2   a , a training part  333  for training a learning model, and a model transmitting part  334  for transmitting the trained learning model to the ego vehicle  2   a . These functional blocks of the processor  33  are, for example, functional modules realized by computer programs operated on the processor  33 . Alternatively, these functional blocks of the processor  33  may be dedicated processing circuits provided at the processor  33 . Details of the various functional blocks of the processor  33  of the server  3  will be explained later. 
     Learning Model 
     In the present embodiment, in the control part  141  of the ego vehicle  2   a , when controlling controlled equipment  22  mounted in the ego vehicle  2   a , a learning model trained by machine learning is used. In the present embodiment, as the learning model, a neural network model (below, referred to as “NN model”) is used. Below, referring to  FIG. 6 , an outline of an NN model will be explained.  FIG. 6  shows one example of a NN model having a simple configuration. 
     The circle marks in  FIG. 6  show artificial neurons. The artificial neurons are usually called “nodes” or “units” (in the Description, referred to as “nodes”). In  FIG. 6 , L=1 shows the input layer, L=2 and L=3 show hidden layers (or intermediate layers), and L=4 shows the output layer. 
     In  FIG. 6 , x 1  and x 2  show the nodes of the input layer (L=1) and the output values from those nodes, while “y” shows the node of the output layer (L=4) and the output value of the same. Similarly, z 1   (L=2) , z 2   (L=2) , and z 3   (L=2)  show the nodes of the hidden layer (L=2) and the output values from those nodes, while z 1   (L=3)  and z 2   (L=3)  show the nodes of the hidden layer (L=3) and the output values from those nodes. 
     At the nodes of the input layer, the inputs are output as they are. On the other hand, at the nodes of the hidden layer (L=2), the output values x 1  and x 2  of the nodes of the input layer are input, while at the nodes of the hidden layer (L=2), the respectively corresponding weights “w” and biases “b” are used to calculate the sum input values “u”. For example, in  FIG. 6 , the sum input value u k   (L=2)  calculated at a node shown by z k   (L=2)  (k=1, 2, 3) in the hidden layer (L=2) is calculated by the following equation (M is the number of nodes of the input layer). 
     
       
         
           
             
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     Next, this sum input value u k   (L=2)  is converted by an activation function “f”, and is output from a node shown by z k   (L=2)  of the hidden layer (L=2) as an output value z 1   (L=2) (=f(u k   (L=2) )). On the other hand, at the nodes of the hidden layer (L=3), the output values z 1   (L=2) , z 2   (L=2) , and z 3   (L=2)  of the nodes of the hidden layer (L=2) are input. At the nodes of the hidden layer (L=3), the respectively corresponding weights “w” and biases “b” are used to calculate the sum input values “u” (=Σz·w+b). The sum input values “u” are similarly converted by an activation function and output from the nodes of the hidden layer (L=3) as the output values z 1   (L=3)  and z 2   (L=3) . This activation function is for example a ReLU function σ. 
     Further, the output values z 1   (L=3)  and z 2   (L=3)  of the nodes of the hidden layer (L=3) are input to the node of the output layer (L=4). At the node of the output layer, the respectively corresponding weight “w” and bias “b” are used to calculate the sum input value “u” (Σz·w+b), or only the respectively corresponding weight “w” is used to calculate the sum input value “u” (Σz·w). For example, at the node of the output layer, as an activation function, an identity function is used. In this case, the sum input value “u” calculated at the node of the output layer is output as is, as the output value “y” from the node of the output layer. 
     In this way, an NN model is provided with an input layer, hidden layers, and an output layer. If one or more input parameters are input to the input layer, one or more output parameters corresponding to the input parameters are output from the output layer. 
     In the present embodiment, as such a learning model, for example, a model which, when receiving an outside air temperature, amount of intake air, amount of fuel injection, timing of fuel injection, fuel injection pressure, or EGR rate as values of input parameters, outputs a temperature of the exhaust gas as a value of an output parameter, is used. In the control part  141  of the ego vehicle  2   a , the temperature of the exhaust gas is output by inputting values of various input parameters detected by the sensors  23  to such a learning model. The control part  141  controls the controlled equipment  22  relating to the internal combustion engine based on the output temperature of the exhaust gas. Here, there is a delay in response in the exhaust temperature sensor for detecting the temperature of the exhaust gas, therefore if controlling the internal combustion engine based on the output of the exhaust temperature sensor, it was not necessarily possible to suitably control the internal combustion engine. As opposed to this, no delay occurs in calculation of the temperature of the exhaust gas using the learning model, therefore it is possible to more suitably control the internal combustion engine by controlling the internal combustion engine using the temperature of the exhaust gas calculated by the learning model. 
     Alternatively, as such a learning model, for example, a model which outputs a target temperature of an air-conditioner as a value of the output parameter if an outside air temperature, inside vehicle temperature, inside vehicle humidity, and amount of sunlight are input as the values of the input parameters, may be used. In this case, in the control part  141  of the ego vehicle  2   a , by inputting values of the different input parameters detected by the sensors  23  into such a learning model, a target temperature of the air-conditioner is output. The ECU  11  controls the controlled equipment  22  relating to the air-conditioner so that the inside vehicle temperature becomes the target temperature output from the learning model. 
     Note that, various models can be used as the learning model. Therefore, the input parameters may include various state parameters such as the outside air temperature, outside humidity, atmospheric pressure, internal cabin temperature, internal cabin humidity, amount of sunlight, amount of intake air, intake temperature, fuel injection pressure, fuel injection timing, amount of fuel injection, air-fuel ratio, ignition timing, engine cooling water temperature, and supercharging pressure. Further, the output parameter may include various state parameters expressing states of the vehicle such as the temperature of the exhaust purification catalyst, concentration of the NOx in the exhaust gas, engine output torque, and internal cabin humidity. 
     Basic Training of Learning Model 
     Next, the machine learning of the above-such learning model (NN model) will be explained. To improve the precision of the NN model, it is necessary to train the NN model. Therefore, in the present embodiment, the training part  333  of the server  3  trains the NN model. First, the training technique of the NN model performed at the training part  333  will be briefly explained. 
     In training the NN model, training data sets including values of state parameters detected by the sensors  23  provided at the vehicle  2  are used. The training data sets are comprised of combinations of the plurality of measured values of the plurality of input parameters, and the plurality of measured values of at least one output parameter corresponding to these measured values (ground truth data). In the present embodiment, the measured values of the input parameters and the measured values of the output parameters are values detected by the sensors  23  of the ego vehicle  2   a  or control command values from the ECU  11  to the controlled equipment  22 . Further, in the present embodiment, in order for the server  3  to train the NN model, measured values used as training data sets are transmitted from the ego vehicle  2   a  to the server  3 . 
     The training part  333  of the server  3  preprocesses (normalizes, standardizes, etc.) the training data sets transmitted from the ego vehicle  2   a , then trains the NN model. In the training of the NN model, the training part  333 , for example, repeatedly updates the weights “w” and biases “b” in the NN model by known error backpropagation so that the difference between the output value of the NN model and the measured value of the output parameter included in the training data sets becomes smaller. As a result, the NN model is trained and a trained NN model is generated. Information of the trained NN model (structure, weights “w”, biases “b”, etc. of the model) are stored in the storage device  32  of the server and transmitted from the server  3  to the ego vehicle  2   a.    
     Training in Case where Abnormality Occurs in Value of State Parameter 
     In this regard, if for example a malfunction or other abnormality occurs in a sensor  23  detecting values of a state parameter constituting parts of the training data sets, error will occur in the output of the sensor  23  or the sensor  23  will no longer output a signal. If using the output of a sensor  23  where an abnormality has occurred in this way for training a learning model, it would not be possible to suitably train the learning model. 
     Therefore, in the present embodiment, if an abnormality has occurred in the values of a state parameter detected by a sensor  23 , the values of that state parameter are acquired from another vehicle  2   b  in the same region as the ego vehicle  2   a , and the learning model is trained by using training data sets including the values acquired from the other vehicle  2   b , instead of the values of the state parameter detected by the sensor  23 . Below, referring to  FIG. 7 , the training of a learning model in the case where an abnormality has occurred in the values of a state parameter detected by a sensor  23  will be specifically explained. 
       FIG. 7  is an operation sequence diagram of training processing performed by the machine learning system  1 . In particular,  FIG. 7  is an operation sequence when training a learning model which is used when controlling controlled equipment  2   a  of the ego vehicle  2   a , in the case where an abnormality has occurred in an outside air temperature sensor of the ego vehicle  2   a.    
     As shown in  FIG. 7 , other vehicles  2   b  periodically detect the values of state parameters including the outside air temperature and the detection conditions at that time (step S 11 ). The values of the state parameters are detected by the sensors  23  of the other vehicles  2   b . In particular, among the state parameters, the outside air temperature is detected by the outside air temperature sensors. Further, the detection conditions express the conditions when the values of state parameters are detected by the sensors  23 . In the present embodiment, they include the times and places where the values of state parameters are detected. The times when values of state parameters are detected, are acquired from the ECUs  11  of the other vehicles  2   b  functioning as clocks. Further, the places where the values of state parameters are detected are determined by self-position sensors. Note that, the detection conditions, for example, may also include amounts of rainfall detected by rain sensors, models of vehicles, and various other conditions. 
     The processors  14  of the other vehicles  2   b  store the values of the detected state parameters and the detection conditions at which the state parameters were detected, in their memories  13 . In addition, the data transmitting parts  142  of the processors  14  of the other vehicles  2   b  transmit the detection conditions and types of the detected state parameters stored in the memories  13 , through their external vehicle communication modules  21  to the server  3 , every predetermined time interval. The server  3  stores the received detection conditions and the types of the state parameters in the storage device  32 . 
     On the other hand, the ego vehicle  2   a  periodically detects the values of state parameters used for training the learning model and the detection conditions at that time (step S 12 ). The values of the state parameters are detected by the sensors  23  of the ego vehicle  2   a , while the detection conditions are acquired from the ECU  11  or detected by the sensors  23 . Note that, in the example shown in  FIG. 7 , an abnormality occurs in the outside air temperature sensor of the ego vehicle  2   a , therefore a large error arises in the outside air temperature detected by the outside air temperature sensor. 
     The processor  14  of the ego vehicle  2   a  stores time series values of the plurality of detected state parameters and the detection conditions under which the values of the state parameters were detected, in its memory  13 . In addition, if the number of the values of the state parameters required for training are detected and stored in the memory  13 , the data transmitting part  142  of the ego vehicle  2   a  assembles the time series values of the plurality of state parameters stored in the memory  13 , and transmits them as training data sets to the server  3 , and transmits the detection conditions of the values of the state parameters to the server  3 . The server  3  stores the received training data sets and detection conditions to the storage device  32 . Note that, the data transmitting part  142  of the ego vehicle  2   a  may also transmit the values of state parameters and detection conditions to the server  3  every time these are detected. 
     If the number of the values of the state parameters required for training are stored as training data sets in the storage device  32 , the abnormal parameter identifying part  331  of the server  3  judges if there is any state parameter where an abnormality has occurred in the training data sets and, if there is a state parameter where an abnormality has occurred, identifies that state parameter (step S 13 ). The abnormal parameter identifying part  331  judges that an abnormality has occurred in values of a state parameter, if the values of the state parameter are abnormal values which could never occur (for example, if it is detected that the outside air temperature is more than 50° C. in the winter), if the values of the state parameter are fixed at a certain value (for example, if it is detected that the outside air temperature is fixed to 25° C. throughout the day), and, further, if no signal is output from a sensor and therefore the values of the state parameter are devoid. If it is judged at the abnormal parameter identifying part  331  that there is no state parameter where an abnormality has occurred, the server  3  trains the learning model at the later explained step S 16 . 
     Note that, in the present embodiment, the abnormal parameter identifying part  331  of the server  3  identifies a parameter where an abnormality has occurred. However, a parameter where an abnormality has occurred may also be identified by another method. For example, if able to diagnose a malfunction of a sensor  23  mounted in the ego vehicle  2   a , the processor  14  of the ego vehicle  2   a  may also identify a state parameter detected by a sensor  23  diagnosed as malfunctioning, as a state parameter where an abnormality has occurred. In this case, the processor  14  of the ego vehicle  2   a  functions as the abnormal parameter identifying part  331 , and the data transmitting part  142  transmits the type of parameter where an abnormality has occurred to the server  3  in addition to the value of state parameter and detection conditions. 
     If the state parameter where an abnormality has occurred (below, referred to as an “abnormal state parameter”, and which is, in the example shown in  FIG. 7 , the outside air temperature) is identified by the abnormal parameter identifying part  331 , the parameter value acquiring part  332  of the server  3  acquires the values of the abnormal state parameter from another vehicle  2   b . In particular, in the present embodiment, the parameter value acquiring part  332  acquires the values of the abnormal state parameter detected by the other vehicle  2   b  from the other vehicle  2   b  under conditions matching with the detection conditions when the ego vehicle  2   a  detected the values of the state parameters constituting the training data sets. 
     For this reason, if the fact that an abnormality has occurred in a state parameter detected by a sensor  23  is identified, the parameter value acquiring part  332  of the server  3  identifies another vehicle  2   b  acquiring the values of the abnormal state parameter (step S 14 ). The parameter value acquiring part  332  searches for another vehicle  2   b  transmitting detection conditions matching the detection conditions when the ego vehicle  2   a  detected the values of the abnormal state parameter, among the large number of other vehicles  2   b  transmitting the detection conditions. Specifically, for example, another vehicle  2   b  which had detected a state parameter at a time and place with the highest match with the time and place where the values of the state parameter contained in the training data sets at the ego vehicle  2   a , is identified as another vehicle  2   b  acquiring the values of the abnormal state parameter. 
     If identifying another vehicle  2   b  for acquiring the values of the abnormal state parameter, the parameter value acquiring part  332  requests the other vehicle  2   b  to transmit the values of the abnormal state parameter (that is, the outside air temperature). In particular, the parameter value acquiring part  332  transmits to the other vehicle  2   b , through the external communication module  31 , a signal requesting it to transmit the values of the abnormal state parameter detected by the identified other vehicle  2   b  under detection conditions matching the detection conditions of the training data sets. 
     If transmission of the values of the abnormal state parameter is requested, the processor  14  of the other vehicle  2   b  acquires from its memory  13  the values of the abnormal state parameter detected under detection conditions matching the detection conditions of the training data sets (step S 15 ). Further, the processor  14  of the other vehicle  2   b  transmits the acquired values of the abnormal state parameter through the external vehicle communication module  21  to the server  3 . The parameter value acquiring part  332  of the server  3  acquires the values of the abnormal state parameter detected by the other vehicle  2   b  through communication between the other vehicle  2   b  and the server  3 . Therefore, if an abnormality has occurred in the values of a certain state parameter detected by a sensor  23  (abnormal state parameter), the parameter value acquiring part  332  of the server  3  acquires the values of the abnormal state parameter detected by the other vehicle  2   b  under conditions matching the detection conditions when the values of the abnormal state parameter included in the training data sets of the ego vehicle  2   a  were detected by the sensor  23 . 
     The training part  333  of the server  3  uses the training data sets prepared by the ego vehicle  2   a  and the values of the abnormal state parameter transmitted from the other vehicle  2   b  to train the learning model (step S 16 ). If it was judged at the abnormal parameter identifying part  331  that there was no state parameter where an abnormality occurred, the training part  333  uses only the training data sets transmitted from the ego vehicle  2   a  and trains the learning model by the above-mentioned training technique. On the other hand, if an abnormal state parameter is identified at the abnormal parameter identifying part  331 , the training part  333  uses training data sets including the values of the abnormal state parameter acquired by the parameter value acquiring part  332  from another vehicle  2   b , instead of the values of the abnormal state parameter detected by the sensor  23  of the ego vehicle  2   a , so as to train the learning model by the above-mentioned training technique. That is, the training part  333 , in this case, uses training data sets including the values of the abnormal state parameter acquired by the parameter value acquiring part  332  from the other vehicle  2   b  and the values of state parameters, other than the abnormal state parameter, detected by the sensors  23  of the ego vehicle  2   a , so as to train the learning model. Specifically, in the present embodiment, instead of the data of the atmospheric temperature detected by the atmospheric temperature sensor of the ego vehicle  2   a , the training data sets include the data of the atmospheric temperature acquired by the parameter value acquiring part  332  from another vehicle  2   b  and include data of other state parameters detected by other sensors of the ego vehicle  2   a  (for example, air flow sensor or injection pressure sensor) (for example, amount of intake air or fuel injection pressure) or data of other state parameters formed by control commands from the ECU  11  to the controlled equipment  22  (for example, amount of fuel injection or fuel injection timing). 
     When the training of the learning model has been completed, the training part  333  stores the information of the trained learning model in the storage device  32  of the server  3 . Further, when training of the learning model has been completed, the model transmitting part  334  of the server  3  transmits the information of the trained learning model (for example, the updated weights “w” and biases “b”) to the ego vehicle  2   a.    
     If receiving information of the trained learning model, the model updating part  143  of the ego vehicle  2   a  updates the information of the learning model stored in the memory  13  of the ego vehicle  2   a  (step S 17 ). Due to this, when later using the learning model to control the controlled equipment  22 , the control part  141  controls the controlled equipment  22  based on the updated learning model. 
     Advantageous Effects and Modifications 
     According to this embodiment, for example, if a malfunction or other abnormality occurs in a sensor  23  of the ego vehicle  2   a  and thereby an abnormality occurs in the values of a state parameter constituting parts of training data sets, training data sets including the values of the abnormal state parameter detected by another vehicle  2   b , instead of the values of the abnormal state parameter where the abnormality occurred, are used to train the learning model. As a result, according to this embodiment, even if there is an abnormality in values of a state parameter detected by a sensor  23  of the ego vehicle  2   a , training data sets with relatively accurate values of state parameters are used to train the learning model. Therefore, even if an abnormality occurs in values of a state parameter constituting part of training data sets for training a learning model, it is possible to suitably train the learning model. 
     Further, in the present embodiment, the learning model is trained in the server  3 . Therefore, there is no need for training with a high load of calculation to be performed at the ego vehicle  2   a , therefore there is no need for making the processing ability of the processor  14  of the ego vehicle  2   a  that high and accordingly the manufacturing cost of the ego vehicle  2   a  can be reduced. 
     Note that, in the above embodiment, the example is shown of training a learning model if an outside air temperature sensor malfunctions and an abnormality occurs in the outside air temperature detected by the outside air temperature sensor. However, the above-mentioned training technique of a learning model can also be applied to the case where a sensor  23  other than the outside air temperature sensor malfunctions and an abnormality occurs in values of a state parameter other than the outside air temperature. 
     In particular, the above-mentioned training technique of a learning model can be applied to the case where an abnormality arises in values of a state parameter which, under similar detection conditions, give similar values even if the vehicles  2  differ. Therefore, the above-mentioned training technique of a learning model can be applied when there is an abnormality in values detected by a sensor  23  of the ego vehicle  2   a  for a state parameter expressing a physical quantity with a smaller difference among vehicles than other state parameters under the same detection conditions among the state parameters relating to the ego vehicle  2   a . In particular, the external environment of the surroundings of the ego vehicle  2   a  is substantially the same between the ego vehicle  2   a  and other vehicles  2   b  in the surroundings of the ego vehicle  2   a , therefore the above-mentioned training technique of a learning model can be applied when there is an abnormality in values of a state parameter relating to the external environment in the surroundings of the ego vehicle  2   a  detected by a sensor  23  of the ego vehicle  2   a . Such a state parameter may include, in addition to the atmospheric temperature of the surroundings of the ego vehicle  2   a , for example, the humidity of the atmosphere and air pressure of the surroundings of the ego vehicle  2   a.    
     Further, in the above embodiment, the other vehicle  2   b  transmits the type of the state parameter and detection conditions detected at step S 11  to the server  3 . However, the other vehicle  2   b  may also transmit the values of the state parameter and detection conditions detected at step S 11  to the server  3 . In this case, the values of the state parameter detected at the other vehicle  2   b  are stored at the storage device  32  of the server  3 . Therefore, if at step S 14  another vehicle  2   b  is identified, the server  3  does not request the other vehicle  2   b  to transmit the values of the abnormal state parameter, but acquires the values of the abnormal state parameter detected at the other vehicle  2   b  from the storage device  32 . 
     In addition, the learning model trained in the server  3  may also be a machine learning model using a random forest, k-nearest neighbor, support vector machine, or other algorithm besides a neural network. 
     Second Embodiment 
     Next, referring to  FIGS. 8 to 10 , the machine learning system  1  according to a second embodiment will be explained. Below, the points of difference from the machine learning system according to the first embodiment will be mainly explained. 
     In the present embodiment, the processor  14  of the ego vehicle  2   a  functions as a machine learning device for training a learning model.  FIG. 8  is a functional block diagram of the processor  14  of the ego vehicle  2   a  according to the second embodiment. As shown in  FIG. 8 , the processor  14  is provided with a control part  141  using a learning model to control controlled equipment  22  of the ego vehicle  2   a , an abnormal parameter identifying part  144  identifying a state parameter where an abnormality occurs among the state parameters constituting the training data sets prepared by the ego vehicle  2   a , a parameter value acquiring part  145  acquiring the values of the state parameter where an abnormality occurs from another vehicle  2   b  different from the ego vehicle  2   a , a training part  146  training the learning model, and a model updating part  143  updating the learning model used at the control part  141 . 
       FIG. 9  is a functional block diagram of the processor  33  of the server  3  according to the second embodiment. As shown in  FIG. 9 , the processor  33  is provided with a vehicle identifying part  335  for identifying another vehicle  2   b  acquiring values of an abnormal parameter, and a parameter value transmitting part  336  transmitting values of an abnormal state parameter acquired from another vehicle  2   b  to the ego vehicle  2   a.    
       FIG. 10  is an operation sequence diagram of training processing performed by the machine learning system  1  according to the second embodiment. In particular,  FIG. 10  is an operation sequence diagram at the time when training a learning model used for control of controlled equipment  22  of the ego vehicle  2   a  if an abnormality occurs in an outside air temperature sensor of the ego vehicle  2   a.    
     As shown in  FIG. 10 , in the same way as step S 11  of  FIG. 7 , other vehicles  2   b  periodically detect values of state parameters including the outside air temperature and the detection conditions at that time, and stores them in their memories  13  (step S 21 ). Further, the data transmitting parts  142  of the processors  14  of the other vehicles  2   b  transmit the detection conditions and the detected types of the state parameters stored in their memories  13  every certain time interval through an external vehicle communication module  21  to the server  3 . 
     On the other hand, in the same way as step S 12  of  FIG. 7 , the ego vehicle  2   a  periodically detects values of state parameters used for training the learning model and the detection conditions at that time, and stores them in its memory  13  (step S 22 ). If the number of the values of the state parameters required for training are detected and stored in the memory  13 , the processor  14  of the ego vehicle  2   a  assembles the values of the plurality of state parameters stored in the memory  13  in time series to prepare training data sets. 
     If training data sets are prepared, the abnormal parameter identifying part  144  of the processor  14  of the ego vehicle  2   a , in the same way as step S 13  of  FIG. 7 , judges the presence of any state parameter where an abnormality has occurred in the training data sets (abnormal state parameter) and identifies the state parameter if there is an abnormal state parameter (in the present example, the outside air temperature) (step S 23 ). 
     If an abnormal state parameter is identified by the abnormal parameter identifying part  144 , the parameter value acquiring part  145  of the ego vehicle  2   a  acquires the values of the abnormal state parameter from another vehicle  2   b . In particular, in the present embodiment, the parameter value acquiring part  145  acquires from another vehicle  2   b  the values of the abnormal state parameter detected by the other vehicle  2   b  under conditions matching the detection conditions when the ego vehicle  2   a  detected the values of the state parameter constituting part of the training data sets. 
     Therefore, first, if a state parameter detected by a sensor  23  is identified as abnormal, the parameter value acquiring part  145  transmits the type and detection conditions of the abnormal state parameter to the server  3 . 
     If the type and detection conditions of the abnormal state parameter are transmitted from the ego vehicle  2   a , in the same way as step S 14  of  FIG. 7 , the vehicle identifying part  335  of the server  3  identifies another vehicle  2   b  acquiring values of the abnormal state parameter (step S 24 ). The vehicle identifying part  335  searches for another vehicle  2   b  transmitting detection conditions matching the detection conditions when the ego vehicle  2   a  was detecting the values of the abnormal state parameter, among the large number of other vehicles  2   b  transmitting detection conditions. If identifying another vehicle  2   b  acquiring values of the abnormal state parameter, the vehicle identifying part  335  requests the other vehicle  2   b  to transmit the values of the abnormal state parameter (that is, the outside air temperature). 
     If requested to transmit values of the abnormal state parameter, the processor  14  of the other vehicle  2   b , in the same way as step S 15  of  FIG. 7 , acquires the values of the abnormal state parameter detected under detection conditions complying with the detection conditions of the training data sets from the memory  13  (step S 25 ). Further, the processor  14  of the other vehicle  2   b  transmits the acquired values of the abnormal state parameter to the server  3 . If receiving the values of the abnormal state parameter from the other vehicle  2   b , the parameter value transmitting part  336  of the server  3  transmits the values of the abnormal state parameter to the ego vehicle  2   a . The parameter value acquiring part  145  of the ego vehicle  2   a  acquires the values of the abnormal state parameter detected by the other vehicle  2   b  in this way through communication between the server  3  able to communicate with the other vehicle  2   b  and the ego vehicle  2   a . Therefore, if an abnormality occurs in the values of a certain state parameter (abnormal state parameter) detected by a sensor  23 , the parameter value acquiring part  145  of the ego vehicle  2   a  acquires the values of the abnormal state parameter detected by another vehicle  2   b  under conditions matching the detection conditions when the values of the abnormal state parameter included in the training data sets of the ego vehicle  2   a  were detected by the sensor  23 . 
     The training part  146  of the ego vehicle  2   a , in the same way as step S 16  of  FIG. 7 , uses the training data sets prepared by the ego vehicle  2   a  and the values of the abnormal state parameter transmitted from the other vehicle  2   b , to train the learning model (step S 26 ). If it is judged at the abnormal parameter identifying part  144  that there is no abnormal state parameter, the training part  146  uses only the training data sets prepared by the ego vehicle  2   a  for training the learning model by the above-mentioned training technique. On the other hand, if an abnormal state parameter is identified in the abnormal parameter identifying part  144 , the training part  146  trains the learning model by the above-mentioned training technique using training data sets including the values of the abnormal state parameter acquired from the other vehicle  2   b  by the parameter value acquiring part  145 , instead of the values of the abnormal state parameter detected by the sensor  23  of the ego vehicle  2   a . That is, the training part  146 , in this case, trains the learning model using training data sets including values of the abnormal state parameter acquired by the parameter value acquiring part  145  from the other vehicle  2   b  and the values of other state parameters, besides the abnormal state parameter, detected by the sensors  23  of the ego vehicle  2   a.    
     If completing the training of the learning model, the model updating part  143  of the ego vehicle  2   a  updates the information of the learning model stored in the memory  13  of the ego vehicle  2   a  (for example, the weights “w” and biases “b”) (step S 27 ). Due to this, when later using the learning model to control the controlled equipment  22 , the control part  141  controls the controlled equipment  22  based on the updated learning model. 
     Advantageous Effects and Modifications 
     In the present embodiment, the ego vehicle  2   a  trains the learning model. If the training of the learning models of all of the vehicles  2  were performed by the server  3 , the load of calculation at the server  3  would become extremely high. In this regard, in the present embodiment, since the ego vehicle  2   a  trains the learning model, the load of calculation at the server  3  can be reduced. 
     Note that, in the above embodiment, the parameter value acquiring part  145  of the ego vehicle  2   a  acquires the values of the abnormal state parameter detected by another vehicle  2   b  through communication between the server  3  able to communicate with the other vehicle  2   b  and the ego vehicle  2   a . However, the parameter value acquiring part  145  of the ego vehicle  2   a  may also acquire the values of the abnormal state parameter detected by another vehicle  2   b  through vehicle-vehicle communication between the ego vehicle  2   a  and the other vehicle  2   b . In this case, if acquiring the values of the abnormal state parameter from the memory  13 , the processor  14  of the other vehicle  2   b  directly transmits the acquired values of the abnormal state parameter through vehicle-vehicle communication to the ego vehicle  2   a.    
     Above, embodiments according to the present disclosure were explained, but the present disclosure is not limited to these embodiments and can be corrected and changed in various ways within the language of the claims.