Information Processing Device, Vehicle System, Information Processing Method, and Storage Medium

Abstract

The present invention discloses an information processing device, a vehicle system, an information processing method, and a storage medium. The power consumption of a vehicle is reduced. The information processing device detects, for a predetermined vehicle, a case where a disconnection operation that is an operation for stopping a traveling system has been performed, and determines, based on a first location that is the location where the disconnection operation has been performed, an operation mode after the disconnection operation for at least any one of a plurality of electronic control units included in the vehicle.

Description

Technical Field

[0001] The present invention relates to the control of vehicles. Background Art

[0002] Systems in which in-vehicle computers perform wireless communication are becoming widespread. In association therewith, for example, Patent Document 1 discloses an invention related to an in-vehicle communication module that communicates with a server outside the vehicle.

[0003] Prior Art Documents

[0004] Patent Documents

[0005] Patent Document 1: Japanese Unexamined Patent Application Publication No. 2018-061190 Summary of the Invention

[0006] An object of the present disclosure is to reduce the power consumption of a vehicle.

[0007] One aspect of an embodiment of the present disclosure is an information processing apparatus having a control unit that executes: detecting, for a predetermined vehicle, a case where a disconnection operation that is an operation for stopping the driving system has been performed; and determining, for at least any one of a plurality of electronic control units included in the vehicle, an operation mode after the disconnection operation based on a first location that is a location where the disconnection operation has been performed.

[0008] One aspect of an embodiment of the present disclosure is a vehicle system including a vehicle and a server device, wherein the server device has a first control unit that executes: generating first data that is data for determining an operation mode of each of a plurality of electronic control units included in the vehicle based on position information of a first location that is a location where a disconnection operation that is an operation for stopping the driving system of the vehicle has been performed, the vehicle having a second control unit that executes: transmitting the position information of the first location to the server device; and determining, for each of the plurality of electronic control units included in the own vehicle, an operation mode after the disconnection operation based on the first data.

[0009] One aspect of an embodiment of the present disclosure is an information processing method including: a step of detecting a case where a disconnection operation that is an operation for stopping the driving system of a vehicle has been performed; and a step of determining, for at least any one of a plurality of electronic control units included in the vehicle, an operation mode after the disconnection operation based on a first location that is a location where the disconnection operation has been performed.

[0010] According to the present disclosure, it is possible to reduce the power consumption of a vehicle. Brief Description of the Drawings

[0011] Figure 1 It is a schematic diagram of the vehicle system of the first embodiment.

[0012] Figure 2 It is a diagram illustrating the components of the vehicle 1 of the first embodiment.

[0013] Figure 3 It is a schematic diagram illustrating the functional modules of the control unit and the data stored in the storage unit.

[0014] Figure 4 It is an example of the mode list stored in the storage unit.

[0015] Figure 5 It is a schematic diagram of the server device in the first embodiment.

[0016] Figure 6 It is a diagram illustrating the machine learning in the first embodiment.

[0017] Figure 7 It is a flowchart of the first stage in the first embodiment.

[0018] Figure 8 It is a flowchart of the second stage in the first embodiment.

[0019] Figure 9 It is a diagram illustrating the parking location model in the second embodiment.

[0020] Figure 10 It is a flowchart of the first stage in the second embodiment.

[0021] Figure 11 It is an example of the mode list in the second embodiment.

[0022] Figure 12 It is a flowchart of the second stage in the second embodiment.

[0023] Figure 13 It is a schematic diagram of the server device in the third embodiment.

[0024] Figure 14 It is a diagram illustrating the relationship between the actual operation performance data and the operation mode.

[0025] Figure 15 It is a diagram illustrating the power saving model in the third embodiment.

[0026] Figure 16 It is a flowchart of the first stage in the third embodiment.

[0027] Figure 17 It is a flowchart of the second stage in the third embodiment.

[0028] Symbol Explanation

[0029] 1: Vehicle; 100: DCM; 200: ECU; 400: Network bus; 101, 201: Control unit; 102, 202: Storage unit; 103, 203: Communication interface; 110: Antenna; 120: Communication module; 130: GPS antenna; 140: GPS module. Detailed Implementation Manner

[0030] Generally, the electronic control unit of a vehicle stops operating when the vehicle's driving system is turned off. However, with the improvement of vehicle functions in recent years, the number of electronic control units that continue to standby even when the vehicle is parked has increased. Such electronic control units include those providing safety functions, those providing remote control functions, and those providing functions for cooperation with smart homes.

[0031] However, when the number of electronic control units that continue to standby during parking increases, the power consumption increases, causing a problem of burdening the vehicle's battery.

[0032] The information processing device of the present disclosure solves this problem.

[0033] An information processing device according to one aspect of the present disclosure includes a control unit that performs: detecting, for a predetermined vehicle, a case where a disconnection operation that is an operation to stop the driving system is performed; and determining, based on a first location that is the location where the disconnection operation is performed, an operation mode after the disconnection operation for at least any one of a plurality of electronic control units included in the vehicle.

[0034] The disconnection operation is an operation to stop the vehicle's driving system. As the disconnection operation, for example, an operation of turning off the ignition of the vehicle, an operation of turning off the power for driving, an operation of stopping the engine, an operation of stopping the hybrid system, etc. can be exemplified. Performing the disconnection operation means that the vehicle changes from a state where it can travel to a parked state.

[0035] The control unit determines the operation mode after the disconnection operation (i.e., the operation mode after the vehicle starts parking) for each of the plurality of electronic control units included in the vehicle based on the place where the disconnection operation is performed.

[0036] During parking, whether it is preferable to operate the plurality of electronic control units included in the vehicle changes depending on the environment.

[0037] For example, when the vehicle is parked at a place other than the owner's own residence, there is no need to operate the electronic control unit for cooperation with the smart home. On the other hand, when the vehicle is in the parking lot of the owner's own residence, it is preferable to operate this electronic control unit.

[0038] Therefore, according to the location where the cutting operation is performed (i.e., the location where the vehicle is parked), the operation mode of each electronic control unit is determined, so that unnecessary operations of the electronic control unit can be suppressed and power consumption can be reduced.

[0039] As the operation mode, for example, modes such as "sleep mode" and "mode of continuing operation" can be exemplified. In addition, as long as the operation mode is a mode related to power consumption, it can also be other modes. For example, it can also be a "mode of reducing communication volume compared to normal" or a "mode of reducing power consumption compared to normal".

[0040] Hereinafter, specific embodiments of the present disclosure will be described with reference to the drawings. The hardware structure, module structure, functional structure, etc. described in each embodiment are not intended to limit the technical scope of the disclosure only to these unless otherwise specified.

[0041] (First Embodiment)

[0042] Refer to Figure 1 to describe the outline of the vehicle system of the first embodiment. The vehicle system of this embodiment is configured to include a vehicle 1 and a server device 300.

[0043] The vehicle 1 is a connected vehicle having a communication function with an external network. The vehicle 1 is configured to include a DCM (Data Communication Module) 100 and an electronic control unit 200 (also referred to as an ECU, Electronic Control Unit).

[0044] In addition, Figure 1 shows a single ECU 200 as an example, but the vehicle 1 may also include multiple ECUs 200.

[0045] The DCM 100 is a device for wireless communication with an external network. The DCM 100 functions as a gateway for connecting components (hereinafter referred to as vehicle components) of the vehicle 1 to an external network. For example, the DCM 100 provides access to the external network for the ECU 200 of the vehicle 1. Thus, multiple in-vehicle ECUs 200 can communicate with external devices connected to the network via the DCM 100.

[0046] The server device 300 is a device for providing information to the vehicle 1. In this embodiment, the server device 300 provides information for determining the operation mode of multiple ECUs 200 during parking to the vehicle 1. In addition, the server device 300 may also serve as a device for providing other information (such as traffic information, information related to infotainment, etc.) to the vehicle 1.

[0047] Figure 2 This is a diagram showing the components of the vehicle 1 according to this embodiment. The vehicle 1 according to this embodiment is configured to include a DCM 100 and a plurality of ECUs 200A, 200B... (hereinafter, collectively referred to as ECUs 200).

[0048] The ECU 200 may also include a plurality of ECUs that govern different vehicle components. As the plurality of ECUs, for example, a body ECU, an engine ECU, a hybrid ECU, a power train ECU, etc. can be exemplified. In addition, the ECU 200 may be divided by function. For example, it is also possible to make divisions such as an ECU that executes a safety function, an ECU that executes an automatic parking function, an ECU that executes a remote control function, and an ECU that executes an information entertainment function.

[0049] The DCM 100 is configured to have an antenna 110, a communication module 120, a GPS antenna 130, a GPS module 140, a control unit 101, a storage unit 102, and a communication interface 103.

[0050] The antenna 110 is an antenna element that inputs and outputs wireless signals. In this embodiment, the antenna 110 is suitable for mobile communication (for example, mobile communication such as 3G, LTE, 5G, etc.). In addition, the antenna 110 may be configured to include a plurality of physical antennas. For example, in the case of mobile communication using radio waves in high frequency bands such as microwaves and millimeter waves, in order to achieve stable communication, a plurality of antennas may be arranged dispersedly.

[0051] The communication module 120 is a communication module for performing mobile communication.

[0052] The GPS antenna 130 is an antenna that receives positioning signals transmitted from positioning satellites (also referred to as GNSS satellites).

[0053] The GPS module 140 is a module that calculates position information based on the signals received by the GPS antenna 130.

[0054] The control unit 101 is an arithmetic unit that realizes various functions of the DCM 100 by executing a predetermined program. The control unit 101 can also be realized by, for example, a CPU or the like.

[0055] The storage unit 102 is a memory device including a main storage device and an auxiliary storage device. An operating system (OS), various programs, various tables, etc. are stored in the auxiliary storage device, and by loading the programs stored here into the main storage device and executing them, various functions that meet a predetermined purpose as described later can be realized.

[0056] The control unit 101 performs the function of mediating communication between an external network and components (vehicle components) possessed by the vehicle 1. For example, when a certain vehicle component requires communication with the external network, the control unit 101 performs the function of relaying data sent from the vehicle component to the external network. In addition, it performs the function of receiving data sent from the external network and transferring the data to an appropriate vehicle component.

[0057] Furthermore, the control unit 101 can perform functions inherent to this device. For example, the control unit 101 is configured to be able to perform the monitoring function of the security system, the call function, and can perform security notifications, emergency notifications, etc. according to triggers generated inside the vehicle.

[0058] In addition, when this vehicle is parked, the control unit 101 determines the operation modes of the plurality of ECUs 200 based on its position, and performs control to switch the operation modes during parking. The detailed method will be described later.

[0059] The communication interface 103 is an interface unit for connecting the DCM 100 to the in-vehicle network. In the present embodiment, a plurality of vehicle components including electronic control units (ECUs 200) are interconnected via the bus 400 of the in-vehicle network. As a standard of the in-vehicle network, for example, CAN (Controller Area Network) can be exemplified. In addition, when multiple standards are used for the in-vehicle network, the communication interface 103 may also have a plurality of interface devices that match the standards of the communication destinations. As communication standards other than CAN, for example, Ethernet (registered trademark) etc. can be exemplified.

[0060] Next, the functions performed by the control unit 101 will be described. Figure 3 It is a schematic diagram showing the functional modules possessed by the control unit 101 and the data stored in the storage unit 102. The functional modules possessed by the control unit 101 can be realized by the control unit 101 executing programs stored in storage units such as ROM.

[0061] The data relay unit 1011 relays data transmitted and received between vehicle components. For example, it performs the following processing: receiving a message sent from a first device connected to the in-vehicle network, and transferring the message to a second device connected to the in-vehicle network as needed. The first and second devices may be ECUs 200 or other vehicle components.

[0062] In addition, when the data relay unit 1011 receives a message addressed to the external network from a vehicle component, it relays the message to the external network. In addition, it receives data sent from the external network and transfers the data to an appropriate vehicle component.

[0063] When an abnormal situation occurs in vehicle 1, the Emergency Notification Unit 1012 makes an emergency notification to the operator outside the vehicle. As an example of an abnormal situation, the occurrence of a traffic accident or a vehicle failure can be cited. The Emergency Notification Unit 1012 starts connecting to the operator, for example, when a predetermined trigger such as the pressing of a call button provided inside the vehicle or the deployment of an airbag occurs, enabling a call between the passengers of the vehicle and the operator. In addition, at the time of emergency notification, the Emergency Notification Unit 1012 can also send the position information of the vehicle to the operator. In this case, the Emergency Notification Unit 1012 can also obtain the position information from the GPS module 140.

[0064] The Security Management Unit 1013 performs security monitoring processing. The Security Management Unit 1013 detects, for example, the situation where the vehicle is unlocked based on the data received from the ECU 200 that controls the electronic lock of the vehicle, regardless of the normal order, and sends a security notification to a predetermined device. In addition, the security notification can also include the position information of the vehicle. In this case, the Security Management Unit 1013 can also obtain the position information from the GPS module 140. The Security Management Unit 1013 can also obtain the position information when it determines that there is a problem with the security of the vehicle itself, and periodically send the obtained position information to a pre-specified external device.

[0065] The Power Management Unit 1014 performs power-saving related control by determining the operation modes of the multiple ECUs 200 that vehicle 1 has. Specifically, when vehicle 1 is in a parked state, the location where vehicle 1 is parked (hereinafter referred to as the parking location) is obtained. In addition, based on the parking location, the operation mode is determined for each of the multiple ECUs 200, and the operation modes of the multiple ECUs 200 are switched.

[0066] The operation mode is a mode that causes the ECU 200 to operate, for example, it refers to "normal mode", "sleep mode", "power-saving mode", etc. The power-saving mode can also be divided into a mode that reduces the execution frequency of the processing performed by the ECU 200, a mode that reduces the execution time of the processing, a mode that reduces the communication frequency with the outside, a mode that reduces the standby current, etc.

[0067] By switching the multiple ECUs 200 to appropriate operation modes respectively, the power consumption of the entire vehicle system can be reduced. For example, in normal operation, when there is an ECU 200 that communicates with the outside every 30 seconds, the power consumption can be suppressed by changing the communication interval to 5 minutes.

[0068] In the present embodiment, as the operation modes, "normal" and "stop (sleep)" are exemplified.

[0069] The Storage Unit 102 stores the mode list 102A.

[0070] The mode list 102A is a list that records the operation modes of a plurality of ECUs 200 included in the vehicle 1. Figure 4 An example of the mode list 102A is shown.

[0071] The mode list 102A is data that defines the operation modes of a plurality of ECUs 200 when the vehicle 1 is parked at a specific location.

[0072] In the present embodiment, when the vehicle 1 is parked at a specific location (hereinafter referred to as the specific location), the power management unit 1014 changes the operation modes of the plurality of ECUs 200 included in the vehicle 1 in accordance with the mode list 102A. When the vehicle 1 is parked at a location other than the specific location, the plurality of ECUs 200 included in the vehicle 1 perform the same operations as normal.

[0073] Return to Figure 2 , and the ECU 200 will be described.

[0074] The ECU 200 is an electronic control unit that controls components included in the vehicle 1. There may be a plurality of ECUs 200 included in the vehicle 1. The plurality of ECUs 200 control components of different systems such as an engine system, an electrical equipment system, and a power transmission system, for example. The ECU 200 has a function of generating a predetermined message and periodically transmitting and receiving it via an in-vehicle network.

[0075] In addition, the ECU 200 communicates with an external network via the DCM 100, so that a predetermined service can be provided. As the predetermined service, for example, a remote service (e.g., a remote air conditioning service), a security monitoring service, a service in cooperation with a smart home, an automatic parking service (a service of automatically driving between a parking area and an entrance of a building), etc. can be cited.

[0076] In addition, the ECU 200 may also control an in-vehicle device (e.g., a car navigation device) that provides information to passengers of the vehicle. The in-vehicle device is a device that provides information to passengers of the vehicle, and is also referred to as a car navigation system, an infotainment system, a head unit. Thereby, navigation and entertainment can be provided to passengers of the vehicle. In addition, the ECU 200 may download traffic information, road map data, music, moving images, etc. via a home network.

[0077] Similar to the DCM 100, the ECU 200 can be configured as a computer having a processor such as a CPU and a GPU, a main storage device such as a RAM and a ROM, and an auxiliary storage device such as an EPROM, a disk drive, and a removable medium.

[0078] The ECU 200 is configured to include a control unit 201, a storage unit 202, and a communication interface 203.

[0079] The control unit 201 is an arithmetic unit (processor) that implements various functions of the ECU 200 by executing a predetermined program. The storage unit 202 is a memory device including a main storage device and an auxiliary storage device.

[0080] The communication interface 203 is an interface that connects the ECU 200 to the in-vehicle network (CAN bus). The communication interface 203 performs the process of sending a message in a predetermined format generated by the control unit 201 to the CAN bus and the process of sending the message received from the CAN bus to the control unit 201.

[0081] The network bus 400 is a communication bus that constitutes the in-vehicle network. In addition, in this example, one bus is illustrated, but the vehicle 1 may also have two or more communication buses. The multiple communication buses can also be interconnected by the DCM 100 and a gateway that aggregates the multiple communication buses.

[0082] Next, the server device 300 will be described. Figure 5 This is a schematic diagram of the server device 300 in the first embodiment.

[0083] The server device 300 is a device that classifies the location where the vehicle 1 parks (parking location) into a predetermined level.

[0084] In the first embodiment, as a classification result, the server device 300 determines whether the location where the vehicle 1 parks (parking location) is a specific location. In this embodiment, the specific location refers to the location where the parking time is the longest among the locations where the vehicle 1 has parked in the past. It can be speculated that the specific location is a location corresponding to the usage base point of the vehicle 1 (for example, the owner's own house) (for example, the home parking lot).

[0085] The server device 300 stores a machine learning model (parking location model 302A) that has learned the relationship between the parking location and the parking time, and the server device 300 uses the data output by this model to determine whether the parking location is a specific location.

[0086] The server device 300 can be composed of a general-purpose computer. That is, the server device 300 can be configured as a computer having a processor such as a CPU and a GPU, a main storage device such as a RAM and a ROM, and an auxiliary storage device such as an EPROM, a hard disk drive, and a removable medium.

[0087] The server device 300 is configured to have a control unit 301, a storage unit 302, and a communication unit 303.

[0088] The control unit 301 is an arithmetic device that manages the control performed by the server device 300. The control unit 301 can be implemented by an arithmetic processing device such as a CPU.

[0089] The control unit 301 is configured to have two functional modules, namely, a learning unit 3011 and a classification unit 3012. Each functional module can also be implemented by a CPU executing a stored program.

[0090] The learning unit 3011 performs learning of a machine learning model (parking location model 302A) for classifying parking locations. Figure 6 This is a diagram for explaining machine learning.

[0091] The learning unit 3011 executes a learning phase. Specifically, based on the position information corresponding to the parking location received from the vehicle 1 and the data indicating the parking time at the parking location (parking time data), learning of the parking location model 302A is performed. Thereby, when the position information of the parking location is input, a machine learning model that outputs the predicted parking time at the parking location can be obtained. In addition, the learning phase is executed in advance at a predetermined timing.

[0092] In addition, the parking location model 302A is constructed for each of the multiple vehicles 1 managed by the server device 300.

[0093] The classification unit 3012 executes a classification phase. Specifically, the position information received from the vehicle 1 (i.e., the position information corresponding to the parking location) is input to the parking location model 302A, and parking time data is obtained. The parking time data includes an expected value of the parking time at the parking location. The parking time data may also include data indicating the rank in the whole. The classification unit 3012 determines whether the parking location is a specific location based on the parking time data, and sends the result to the vehicle 1.

[0094] The storage unit 302 is configured to include a main storage device and an auxiliary storage device. The main storage device is a memory in which the programs executed by the control unit 301 and the data used by the control programs are expanded. The auxiliary storage device is a device that stores the programs executed in the control unit 301 and the data used by the control programs (including the aforementioned parking location model 302A).

[0095] The communication unit 303 is a communication interface for connecting the server device 300 to a network. The communication unit 303 is configured to include, for example, a network interface board and a wireless communication interface for wireless communication.

[0096] Next, the processing flow executed by the components included in the vehicle system of the present embodiment will be described.

[0097] In the vehicle system of this embodiment, the processes executed by the constituent elements are roughly divided into a stage in which the server device 300 learns the parking location model 302A and a stage in which the learned parking location model 302A is used to determine the operation mode of the ECU 200 included in the vehicle 1. The former is called the first stage, and the latter is called the second stage.

[0098] First, the first stage will be described. Figure 7 It is a flowchart of the process in which the server device 300 learns the parking location model 302A in the first stage.

[0099] First, in step S11, the DCM 100 (power management unit 1014) included in the vehicle 1 detects that the ignition of this vehicle is turned off. When the power management unit 1014 detects that the ignition of this vehicle is turned off, it starts counting the parking time.

[0100] In step S12, the DCM 100 (power management unit 1014) included in the vehicle 1 detects that the ignition of this vehicle is turned on. When the power management unit 1014 detects that the ignition of this vehicle is turned on, it stops counting the parking time. In this step, the power management unit 1014 acquires the position information via the GPS module 140, and sends the acquired position information and the data representing the counted parking time (parking time data) to the server device 300.

[0101] In step S13, the server device 300 (learning unit 3011) uses the position information and the parking time data received from the vehicle 1 to learn the parking location model 302A.

[0102] Next, the second stage will be described. Figure 8 It is a flowchart of the second stage in which the learned parking location model 302A is used to determine the operation mode of the ECU 200 included in the vehicle 1.

[0103] First, in step S21, the DCM 100 (power management unit 1014) included in the vehicle 1 detects that the ignition of this vehicle is turned off. When the power management unit 1014 detects that the ignition of this vehicle is turned off, it acquires the position information via the GPS module 140 and sends it to the server device 300.

[0104] Next, in step S22, the server device 300 (classification unit 3012) uses the parking location model to acquire the parking time data corresponding to the parking location. Then, it determines whether this parking location is a specific location (that is, whether it is the parking location with the longest parking time among multiple parking locations), and sends the determination result to the DCM 100.

[0105] Next, in step S23, the power management unit 1014 determines whether the parking location is a specific location. If the parking location is not a specific location, the process proceeds to step S24. If the parking location is a specific location, the process ends.

[0106] In step S24, the power management unit 1014 refers to the mode list 102A and generates commands for changing the operation modes of the respective ECUs 200. Commands for the number of target ECUs 200 can also be generated. The generated commands are sent to each of the target ECUs 200 via the in-vehicle network.

[0107] For example, when the specified operation mode is "stop", a command instructing the target ECU 200 to enter the sleep state is sent. Additionally, when a power consumption suppression mode is specified as the operation mode, a command instructing the target ECU 200 to shift to that mode is sent.

[0108] In step S25, each ECU 200 that has received the command changes its operation mode in accordance with the command.

[0109] In the first embodiment, when the parking location is not a specific location, that is, when the vehicle 1 is parked outside the home, as Figure 4 shown, the ECU 200 that collaborates with the smart home enters the stop state. Thereby, the consumption of unnecessary power caused by the ECU 200 can be suppressed.

[0110] When the DCM 100 (power management unit 1014) detects that the ignition of the vehicle has been turned on (step S26), the power management unit 1014 generates a command (restoration command) for returning the operation mode of the ECU 200 to the original state and sends it to the target ECU 200.

[0111] As described above, in the present embodiment, according to whether the parking location of the vehicle 1 is a specific location, a process for specifying the operation mode of each of the multiple ECUs 200 provided in the vehicle 1 is executed. Thereby, the operation modes of the multiple ECUs 200 can be changed at a parking location having a predetermined feature.

[0112] Furthermore, in the present embodiment, the parking location where the vehicle 1 has parked for the longest time (i.e., the base of the vehicle 1) is regarded as the specific location, but it is also possible to determine whether a certain parking location is a specific location based on other criteria. Additionally, this determination can also be made using data other than the position information obtained from the vehicle 1.

[0113] Moreover, in the present embodiment, when the parking location is not a specific location, the operation mode of the ECU 200 is changed, but it is also possible to change the operation mode of the ECU 200 when the parking location is a specific location.

[0114] In addition, in the present embodiment, a machine learning model is used to make a determination related to a specific location, but this determination can also be made using other means. Further, the learning results (for example, a list of specific locations, etc.) can be sent from the server device 300 to the DCM 100, and the determination related to the specific location can be made on the vehicle 1 side.

[0115] (Second Embodiment)

[0116] In the first embodiment, whether to switch the operation modes of the plurality of ECUs 200 is determined based on whether the location where the vehicle 1 stops is a specific location.

[0117] In contrast, the second embodiment is an embodiment in which the parking location is classified into a plurality of levels, and the operation modes of the respective ECUs 200 are individually specified according to the classification results.

[0118] In the second embodiment, the server device 300 classifies the parking location according to a plurality of predefined tags such as "own home" and "workplace".

[0119] For example, the server device 300 learns from the information collected from the vehicle 1 that a predetermined parking location is a location corresponding to the own home of the owner of the vehicle 1. Further, based on the learning result, the vehicle 1 starting to park is notified of a tag such as "own home". Thereby, the vehicle 1 can recognize that it is sufficient to operate the plurality of ECUs 200 in an operation mode corresponding to "own home".

[0120] In the second embodiment, the parking location model is a model learned using the position information of the parking location and a tag (hereinafter, category tag) indicating the category of the parking location. Figure 9 It is a diagram for explaining the parking location model in the second embodiment. The difference between the parking location model in the second embodiment and that in the first embodiment is that the training data is not parking time data but category tags.

[0121] Figure 10 It is a flowchart of the first stage in the second embodiment.

[0122] In the second embodiment, the server device 300 (learning unit 3011) learns the parking location model 302A based on the position information corresponding to the parking location received from the vehicle 1 and the category tag corresponding to the parking location. Thereby, when the position information of the parking location is input, a machine learning model that outputs the category tag corresponding to the parking location can be obtained.

[0123] In step S11A, the DCM100 (power management unit 1014) of the vehicle 1 detects the situation where the ignition of the vehicle is turned off, and obtains the position information of the parking location in the same manner as in the first embodiment.

[0124] In addition, the power management unit 1014 obtains the category label corresponding to the parking location, and sends it to the server device 300 together with the position information.

[0125] The category label can also be different for each user, such as "own home" and "workplace". In this case, the category label corresponding to the parking location can also be obtained from the user. For example, data related to the positions of the own home and the workplace can be obtained from the portable terminal carried by the user, and the category label can be generated using this data.

[0126] In addition, the category label can also be a general label such as "commercial facility" and "railway station". In this case, the category label can also be generated using the map data stored in the navigation device mounted on the vehicle 1, etc.

[0127] In step S12A, the learning unit 3011 uses the position information and the category label corresponding to the parking location to learn the parking location model 302A.

[0128] In the second embodiment, the DCM100 stores a mode list for each category of the parking location. Figure 11 This is an example of the mode list 102A in the second embodiment. In this example, different action modes are defined for "own home" and "workplace" respectively. In this example, for example, at the workplace, the ECU200 that cooperates with the smart home stops.

[0129] Figure 12 This is the flowchart of the second stage in the second embodiment. The same processing as in the first embodiment is indicated by a dotted line and the description is omitted.

[0130] In this embodiment, in step S22A, the server device 300 (classification unit 3012) uses the parking location model 302A to obtain the category label corresponding to the parking location. The obtained category label is sent to the DCM100.

[0131] In step S24A, the DCM100 (power management unit 1014) obtains the list of action modes corresponding to the category label, and generates commands for each ECU200. For example, in Figure 11 the example, when the category "workplace" is obtained, during parking, a command to only stop the ECU200D is generated. In addition, when the category corresponding to the parking location is not obtained, the multiple ECUs 200 of the vehicle 1 operate as usual.

[0132] Subsequent processing is the same as that in the first embodiment.

[0133] As described above, in the second embodiment, the parking locations are classified into multiple levels according to a predetermined criterion, and the operation modes of each ECU200 are specified for each classification result. Thus, more detailed power-saving control can be performed.

[0134] In addition, the category labels in this embodiment can also indicate the presence or absence of a predetermined facility such as a charging device. Thus, for example, it is possible to "operate the ECU200 for managing charging in a parking lot having a charging device".

[0135] Alternatively, the category labels can also be based on the presence or absence of network infrastructure. Thus, for example, it is possible to "operate the ECU200 that must access a predetermined network in a parking lot where the predetermined network can be accessed".

[0136] Alternatively, the category labels can also be based on the wireless communication environment. For example, it can be the electric field strength map of the wireless signals used in mobile communication stored in the server device 300, and the category labels are generated according to the electric field strength at each parking position. Thus, for example, communication can be suppressed in a place where the performance of mobile communication cannot be fully exerted.

[0137] (Third Embodiment)

[0138] In the first to second embodiments, the DCM100 stores a pre-made mode list. In contrast, the third embodiment is an embodiment in which the server device 300 learns the preferred operation mode of the ECU200 during the parking process based on the past operation results of multiple ECU200s during the parking process.

[0139] In the third embodiment, whenever the vehicle 1 parks, the server device 300 acquires the operation results of multiple ECU200s during the parking process, and learns the relationship between the parking location and the preferred operation mode of the ECU200 according to a machine learning model (referred to as a power-saving model).

[0140] In the third embodiment, the mode list 102A is not stored in the storage unit 102 of the DCM100. Instead, the power-saving model 302B is stored in the storage unit 302 of the server device 300. Figure 13 It is a schematic diagram of the server device 300 in the third embodiment.

[0141] In the third embodiment, the server device 300 (learning unit 3011) acquires data on the past operation actual results (operation actual result data) of a plurality of ECUs 200 at a predetermined parking location, determines the best operation mode of the corresponding ECU 200 based on the operation actual result data, and learns the power saving model.

[0142] For example, based on the information collected from the vehicle 1, the server device 300 determines at a predetermined parking location that "a certain ECU 200 requested communication but failed to use the mobile communication network". At such a parking location, there is a high possibility that the network cannot be used during subsequent parking. Therefore, the server device 300 learns the power saving model in such a way that the ECU 200 does not operate or the communication frequency of the ECU 200 is decreased at this parking location.

[0143] Figure 14 It is a diagram illustrating the relationship between the operation actual result data of a plurality of ECUs 200 and the best operation mode of each ECU 200. For example, for a certain ECU 200, although a communication request occurred during parking, in the case of communication timeout, it is considered that there is a problem with the network environment at the parking location, so a determination is made to the effect that the ECU 200 should be stopped (or the communication frequency should be decreased) at this parking location. On the contrary, in the case where there is a communication request and communication is performed normally, a determination is made to the effect that the ECU 200 should operate. Such determinations are made for each of the plurality of ECUs 200, so that a mode list describing the preferred operation modes of the plurality of ECUs 200 can be generated.

[0144] In addition, the server device 300 uses the generated mode list to learn the power saving model 302B. The power saving model 302B is a model that learns the relationship between the location information of the parking location and the mode list. Figure 15 It is a diagram explaining the power saving model in the third embodiment. In the present embodiment, the server device 300 generates a mode list based on the operation actual result data and uses this mode list to learn the power saving model 302B. As a result, when the location information of the parking location is input, a machine learning model that outputs a mode list corresponding to the parking location (that is, a list of the preferred operation modes of a plurality of ECUs 200) can be obtained.

[0145] Figure 16 It is a flowchart of the first stage in the third embodiment.

[0146] First, in step S31, the DCM 100 (power management unit 1014) of vehicle 1 detects that the ignition of this vehicle is turned off. When the power management unit 1014 detects that the ignition of this vehicle is turned off, it starts monitoring the operation status of multiple ECUs 200 (step S32). The monitoring results (operation logs and communication logs) are accumulated in the storage unit 102 at any time during the parking process.

[0147] In step S33, the DCM 100 (power management unit 1014) of vehicle 1 detects that the ignition of this vehicle is turned on. When the power management unit 1014 detects that the ignition of this vehicle is turned on, it stops the monitoring of the ECU 200. In this step, the power management unit 1014 acquires location information via the GPS module 140, and sends the acquired location information and the data representing the operation status of the ECU 200 (operation actual achievement data) accumulated during the parking process to the server device 300.

[0148] In step S34, the server device 300 (learning unit 3011) generates a pattern list based on the operation actual achievement data.

[0149] Then, in step S35, using the received location information and the generated pattern list, the power saving model 302B is learned.

[0150] Figure 17 It is a flowchart of the second stage in the third embodiment. The same processing as in the first embodiment is indicated by a dotted line and the description is omitted.

[0151] In this embodiment, in step S22B, the server device 300 (classification unit 3012) uses the power saving model 302B to acquire a pattern list corresponding to the parking location. The acquired pattern list is sent to the DCM 100.

[0152] In step S24B, the DCM 100 (power management unit 1014) generates commands for each ECU 200 according to the acquired pattern list. For example, in the Figure 14 case of the example, commands to stop the ECU 200A and 200B are generated.

[0153] The subsequent processing is the same as that in the first embodiment.

[0154] As described above, in the third embodiment, according to the operation actual achievements of the ECU 200 during the parking process, for each predetermined parking location, the preferred operation pattern of the ECU 200 is learned. Thus, even without generating a pattern list in advance, the operation pattern of the ECU 200 can be determined.

[0155] In addition, in the present embodiment, the mode list generated based on the actual operation performance data is used as training data to learn the power saving model 302B. However, the actual operation performance data may also be used as training data for such learning. In this case, in step S22B, the mode list may also be generated based on the output of the power saving model 302B.

[0156] (Modification example)

[0157] The above-described embodiment is merely an example, and the present disclosure can be appropriately modified and implemented without departing from its gist.

[0158] For example, the processes and units described in the present disclosure can be freely combined and implemented as long as there is no technical contradiction.

[0159] In addition, in the first and second embodiments, the server device 300 classifies the parking location. However, the server device 300 may not be used, and the process may be completed in the DCM 100.

[0160] In addition, in the description of the embodiment, the operation mode of the ECU 200 is determined according to the parking location. However, further conditions may also be used to determine the operation mode of the ECU 200. For example, when the parking location is a parking lot where the vehicle 1 can be charged, conditions such as "when the vehicle is in the charging process, cause a predetermined ECU 200 to operate" and "when the vehicle is not in the charging process, cause the ECU to stop" may be added.

[0161] In addition, the processes described as being performed by one device may also be executed by multiple devices in a shared manner. Or, the processes described as being performed by different devices may also be executed by one device. In a computer system, it is possible to flexibly change the hardware structure (server structure) by which each function is implemented.

[0162] The present disclosure can also be implemented by providing a computer program installed with the functions described in the above embodiment to a computer, and one or more processors of the computer read the program and execute it. Such a computer program can be provided to the computer either through a non-temporary computer-readable storage medium connectable to the system bus of the computer or via a network. Non-temporary computer-readable storage media include, for example, any type of disk such as a magnetic disk (e.g., a floppy (registered trademark) disk, a hard disk drive (HDD), etc.), an optical disk (e.g., a CD-ROM, a DVD disk, a Blu-ray disk, etc.), a read-only memory (ROM), a random access memory (RAM), an EPROM, an EEPROM, a magnetic card, a flash memory, an optical card, and any type of medium suitable for storing electronic commands.

Claims

1. An information processing device having a control unit that executes: Detecting, for a predetermined vehicle, a case where a disconnection operation, which is an operation for stopping the driving system, has been performed; Transmitting position information of a first location, which is the location where the disconnection operation has been performed, to a server device; Receiving, from the server device, first data that uses a machine learning model and outputs, for each of a plurality of electronic control units of the vehicle, an operation mode corresponding to the position information of the first location; and Determining, based on the received first data, an operation mode after the disconnection operation for at least any one of the plurality of electronic control units, The machine learning model is obtained by: the server device generating, based on operation actual result data indicating past operation actual results of each of the plurality of electronic control units at a predetermined parking location, a pattern list including operation modes for determining whether each of the plurality of electronic control units performs normal operation or halts at the location where the vehicle is located, using the generated pattern list as learning data, and performing machine learning on the relationship between the position information of the location where the vehicle is located and the operation modes preferred for each of the plurality of electronic control units.

2. The information processing device according to claim 1, wherein The operation mode is at least any one of a first mode and a second mode in which the execution frequency or execution time of processing is lower than that of the first mode.

3. The information processing device according to claim 1, wherein The operation mode is at least any one of a first mode allowing operation and a second mode prohibiting operation, and the control unit puts the electronic control unit in the second mode into a sleep state.

4. A vehicle system including a vehicle and a server device, wherein The server device has a first control unit, and the vehicle has a second control unit, The second control unit transmits position information of a first location, which is the location where an operation for stopping the driving system of the vehicle, i.e., a disconnection operation, has been performed, to the server device, The first control unit generates, based on operation actual result data indicating past operation actual results of each of the plurality of electronic control units of the vehicle at a predetermined parking location, a pattern list including operation modes for determining whether each of the plurality of electronic control units performs normal operation or halts at the location where the vehicle is located, uses the generated pattern list as learning data, and performs machine learning on the relationship between the position information of the location where the vehicle is located and the operation modes preferred for each of the plurality of electronic control units, and obtains a machine learning model, When the first control unit receives the position information of the first location from the vehicle, the first control unit uses the machine learning model to send first data specifying the operation mode of each of the plurality of electronic control units corresponding to the position information of the first location to the vehicle, The second control unit determines an operation mode after the shutoff operation for each of a plurality of electronic control units included in the vehicle based on the received first data.

5. The vehicle system according to claim 4, wherein: The operation mode is at least one of a first mode and a second mode in which the execution frequency or execution time of a process is reduced compared to the first mode.

6. The vehicle system according to claim 4, wherein: The operation mode is at least one of a first mode in which the operation is permitted and a second mode in which the operation is prohibited, and the second control unit causes the electronic control unit to sleep in the second mode.

7. An information processing method, comprising: a step of detecting that a shutoff operation is performed as an operation to stop a travel system of the vehicle; transmitting position information of a first location where the cutting operation is performed to a server device, receiving, from the server device, first data specifying an operation mode of each of a plurality of electronic control units included in the vehicle, which is output by the server device using a machine learning model and corresponds to position information of the first location; as well as The step of determining, for at least one of the plurality of electronic control units, an operation mode after the cut-off operation based on the received first data, The machine learning model is obtained as follows: the server device generates a pattern list containing action modes for each of the multiple electronic control units to determine whether to perform normal action or stop at the location where the vehicle is located, based on actual action performance data representing the past actual action performance of each of the multiple electronic control units at a predetermined parking location; the server device uses the generated pattern list as learning data to perform machine learning on the relationship between position information of the location where the vehicle is located and the preferred action mode for each of the multiple electronic control units.

8. A non-transitory storage medium having a program recorded thereon, the program being used to cause a computer to execute the information processing method according to claim 7.

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