Control device, resource management method, and resource management program

Abstract

A control device (3) is provided with: an automatic driving container (33) for operating, on a host operating system (30), an automatic driving application (330) for executing automatic driving processing of a vehicle (2); a user container (34) for operating, on the host operating system (30), a user application (340) designated by a user; and a management hardware resource (12) for the automatic driving application (330) and the user application (340) and a container management layer (32) for dispensing. The container management layer (32) allocates, to the user application (340), the remaining hardware resources (12) corresponding to the hardware resources (12) required to continue the operation of the automatic driving application (330) when a low-load condition in which the operation load of the automatic driving application (330) falls within a set range is satisfied.

Description

Technical Field

[0001] Cross - Reference to Related Applications

[0002] This application is based on Japanese Patent Application No. 2022 - 189434 filed on November 28, 2022, and incorporates the entire contents of the basic application by reference. Background Art

[0003] The present disclosure relates to a container - type virtualization technology that enables multiple containers to share hardware resources and a host OS.

[0004] Prior Art Documents

[0005] Patent Documents

[0006] Patent Document 1: U.S. Patent No. 10099630 Specification.

[0007] Generally, in a control device, high - performance hardware resources for executing the autonomous driving process of a vehicle are statically allocated to the execution of an autonomous driving application. Therefore, when the action load decreases, such as when the autonomous driving application is not operating, idle time of the hardware resources may occur. Summary of the Invention

[0008] An object of the present disclosure is to provide a control device that effectively utilizes high - performance hardware resources capable of executing an autonomous driving process.

[0009] A control device according to a first aspect of the present disclosure enables multiple containers to share hardware resources and a host operating system, and includes:

[0010] An autonomous driving container that causes an autonomous driving application for executing the autonomous driving process of a vehicle to operate on the host operating system;

[0011] A user container that causes a user - specified user application to operate on the host operating system; and

[0012] A container management layer that manages the allocation of hardware resources to the autonomous driving application and the user application,

[0013] When a low - load condition is established in which the action load of the autonomous driving application decreases to within a set range, the container management layer allocates the remaining hardware resources corresponding to the hardware resources required for the continuous operation of the autonomous driving application to the user application.

[0014] A resource management method according to a second aspect of the present disclosure is executed by a processor to enable multiple containers to share hardware resources and a host operating system, and includes the following:

[0015] Cause an autonomous driving application that performs autonomous driving processing of a vehicle to operate on a host operating system;

[0016] Cause a user application specified by a user to operate on the host operating system; and

[0017] Manage the allocation of hardware resources to the autonomous driving application and the user application,

[0018] Managing the allocation of hardware resources includes the following: When a low-load condition in which the operation load of the autonomous driving application is reduced to within a set range is satisfied, allocate the remaining hardware resources corresponding to the hardware resources required for the continuous operation of the autonomous driving application to the user application.

[0019] A resource management program according to the third aspect of the present disclosure is stored in a storage medium and includes commands executed by a processor to enable multiple containers to share hardware resources and a host operating system,

[0020] The commands include the following:

[0021] Cause an autonomous driving application that performs autonomous driving processing of a vehicle to operate on the host operating system;

[0022] Cause a user application specified by a user to operate on the host operating system; and

[0023] Manage the allocation of hardware resources to the autonomous driving application and the user application,

[0024] Managing the allocation of hardware resources includes the following: When a low-load condition in which the operation load of the autonomous driving application is reduced to within a set range is satisfied, allocate the remaining hardware resources corresponding to the hardware resources required for the continuous operation of the autonomous driving application to the user application.

[0025] Thus, according to the first to third aspects of the present disclosure, when a low-load condition in which the operation load of the autonomous driving application performing autonomous driving processing is reduced to within a set range is satisfied, the remaining hardware resources corresponding to the hardware resources required for the continuous operation of the autonomous driving application are allocated to the user application. Thereby, it is possible to allocate the hardware resources released as the operation load of the autonomous driving application decreases to a user-oriented user application independent of the autonomous driving processing. Therefore, it is possible to effectively utilize high-performance hardware resources capable of performing autonomous driving processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a schematic diagram showing a vehicle equipped with a control device according to the first embodiment of the present disclosure.

[0027] Figure 2It is a schematic diagram showing a control device according to the first embodiment of the present disclosure.

[0028] Figure 3 It is a flowchart showing a resource management process according to the first embodiment of the present disclosure.

[0029] Figure 4 It is a schematic diagram showing a control device according to the second embodiment of the present disclosure.

[0030] Figure 5 It is a flowchart showing a resource management process according to the second embodiment of the present disclosure.

[0031] Figure 6 It is a schematic diagram showing a control device according to the third embodiment of the present disclosure.

[0032] Figure 7 It is a flowchart showing a resource management process according to the third embodiment of the present disclosure.

[0033] Figure 8 It is a schematic diagram showing a control device according to the fourth embodiment of the present disclosure.

[0034] Figure 9 It is a flowchart showing a resource management process according to the fourth embodiment of the present disclosure.

[0035] Figure 10 It is a schematic diagram showing a control device according to the fifth embodiment of the present disclosure.

[0036] Figure 11 It is a flowchart showing a resource management process according to the fifth embodiment of the present disclosure.

[0037] Figure 12 It is a schematic diagram showing a control device according to the sixth embodiment of the present disclosure.

[0038] Figure 13 It is a flowchart showing a resource management process according to the sixth embodiment of the present disclosure.

[0039] Figure 14 It is a schematic diagram showing a control device according to the seventh embodiment of the present disclosure.

[0040] Figure 15 It is a flowchart showing a resource management process according to the seventh embodiment of the present disclosure.

[0041] Figure 16 It is a schematic diagram showing a control device according to the eighth embodiment of the present disclosure.

[0042] Figure 17 It is a flowchart showing a resource management process according to the eighth embodiment of the present disclosure.

[0043] Figure 18 It is a schematic diagram showing a control device according to a ninth embodiment of the present disclosure.

[0044] Figure 19 It is a flowchart showing a resource management process according to a ninth embodiment of the present disclosure.

[0045] Figure 20 It is a schematic diagram showing a control device according to a tenth embodiment of the present disclosure.

[0046] Figure 21 It is a flowchart showing a resource management process according to a tenth embodiment of the present disclosure.

[0047] Figure 22 It is a schematic diagram showing a control device according to a modification of the second embodiment.

[0048] Figure 23 It is a flowchart showing a resource management process according to a modification of the second embodiment. Detailed Embodiments

[0049] Hereinafter, based on the drawings, a plurality of embodiments of the present disclosure will be described. In addition, redundant explanations may be omitted by assigning the same reference numerals to corresponding components in each embodiment. In addition, when only a part of the structure is described in each embodiment, for the other parts of the structure, the structure of other previously described embodiments can be applied. Moreover, in addition to the combinations of structures explicitly shown in the description of each embodiment, as long as there is no particular obstacle to the combination, the structures of multiple embodiments can be partially combined with each other even if not explicitly stated.

[0050] (First Embodiment)

[0051] Figure 1 , 2 The control device 3 of the first embodiment shown in FIG. enables multiple containers to share the hardware resource 12 and the host OS 30. In the vehicle 2, levels are divided according to the degree of manual intervention of the occupant in the driving task, and an autonomous driving mode is assigned. The autonomous driving mode can be achieved by autonomous driving control in which the system performs all driving tasks during operation, such as conditional driving automation, highly automated driving, or fully automated driving. The autonomous driving mode can be achieved by highly assisted driving control in which the occupant performs part or all of the driving tasks, such as driving assistance or partial driving automation. The autonomous driving mode can be achieved by any one, combination, or switching of these autonomous driving controls and highly assisted driving controls. The autonomous driving mode is achieved through driving control based on the autonomous driving process described later.

[0052] AsFigure 1 As shown, a sensor system 4, an application program designated terminal 5, and a control device 3 are mounted on a vehicle 2. The sensor system 4 obtains sensor information that can be utilized by the control device 3 through detection of the outside and inside of the vehicle 2. Therefore, the sensor system 4 is configured to include an outside sensor 40 and an inside sensor 42. In addition, in Figures 1 - 23 , for the convenience of illustration, "application program" is abbreviated as "application".

[0053] The outside sensor 40 obtains information about the outside, which is the surrounding environment of the vehicle 2. The outside sensor 40 can obtain outside information by detecting objects existing outside the vehicle 2. The object detection type outside sensor 40 is, for example, at least one type among a camera, LiDAR (Light Detection and Ranging / Laser Imaging Detection and Ranging), radar, and sonar. The outside sensor 40 can also obtain outside information by receiving positioning signals from artificial satellites of the GNSS (Global Navigation Satellite System) existing outside the vehicle 2. The positioning type outside sensor 40 is, for example, a GNSS receiver. The outside sensor 40 can also obtain outside information by transmitting and receiving communication signals with a V2X system existing outside the vehicle 2. The communication type outside sensor 40 is, for example, at least one type among a DSRC (Dedicated Short Range Communications) communicator, a cellular V2X (C-V2X) communicator, a Bluetooth (registered trademark) device, a Wi-Fi (registered trademark) device, and an infrared communication device.

[0054] The inside sensor 42 obtains sensor information about the inside, which is the internal environment of the vehicle 2. The inside sensor 42 can obtain inside information by detecting a specific motion physical quantity inside the vehicle 2. The physical quantity detection type inside sensor 42 is, for example, at least one type among a traveling speed sensor, an acceleration sensor, and a gyro sensor. The inside sensor 42 can also obtain inside information by detecting a specific state of an occupant inside the vehicle 2. The occupant detection type inside sensor 42 is, for example, at least one type among a driver status monitor (registered trademark), a biosensor, a seating sensor, an actuator sensor, and an in-vehicle device sensor.

[0055] Here, the actuator sensor detects one type among, for example, the operation state of the accelerator pedal, the operation state of the brake pedal, the operation state of the parking brake, the steering state of the steering wheel, the on / off state of the start switch, the gear position of the vehicle 2, the charging state of the vehicle 2, etc., as the indication state of the driver for the driving actuator of the vehicle 2. The in-vehicle device sensor detects at least one type among, for example, the operation state of the on / off switch, the operation state of the touch panel, and the gestures that can be non-contact recognized, etc., as the indication state of the driver and other passengers for the in-vehicle devices.

[0056] The application specifying terminal 5 is, for example, a central display screen that can accept operations such as touch operations by the user. The application specifying terminal 5 displays applications that can be downloaded from Figure 2 the external container image server 6 shown. The user who is a passenger of the vehicle 2 can specify the application to be executed by the control device 3 as the user application 340 by selecting the application displayed on the application specifying terminal 5.

[0057] Here, the container image server 6 is, for example, a Docker Hub (Docker Hub) repository that stores various container images. Each container image contains an application and middleware and libraries required to execute the application, etc., as container configuration files.

[0058] As Figure 1 , 2 shown, the control device 3 is connected to the sensor system 4 and the application specifying terminal 5 via at least one type among, for example, a LAN (Local Area Network) line, a wiring harness, an internal bus, and a wireless communication line, etc. The control device 3 is configured to include at least one dedicated computer.

[0059] The dedicated computer constituting the control device 3 can be an integrated ECU (Electronic Control Unit) that integrates the driving control of the vehicle 2. The dedicated computer constituting the control device 3 can be a judgment ECU that judges the driving tasks in the driving control of the vehicle 2. The dedicated computer constituting the control device 3 can be a monitoring ECU that monitors the driving control of the vehicle 2. The dedicated computer constituting the control device 3 can be an evaluation ECU that evaluates the driving control of the vehicle 2.

[0060] As Figure 1As shown, the dedicated computer that constitutes the control device 3 has at least one memory 10 and one processor 11 as the hardware resources 12 of the control device 3. The memory 10 is a non-transitory tangible storage medium, such as at least one type among semiconductor memories, magnetic media, and optical media, that non-temporarily stores programs, data, etc. that can be read by a computer. To perform high-load autonomous driving processing, the processor 11 includes, as a core, a high-processing-capability component such as a GPU (Graphics Processing Unit). Moreover, the processor 11 may further include, as a core, at least one type among, for example, a CPU (Central Processing Unit), a RISC (Reduced Instruction Set Computer)-CPU, a DFP (Data Flow Processor), and a GSP (Graph Streaming Processor).

[0061] In addition to the software such as the host OS (Operating System) 30, the container engine 31, and a resource manager (not shown) that operate on the processor 11 as shown, the memory 10 also stores an autonomous driving container image and a user container image for creating the autonomous driving container 33 and the user container 34 shown in the same figure. The host OS 30 may be at least one type among, for example, a real-time OS, Linux (registered trademark), and UNIX (registered trademark). Figure 2 As shown, the dedicated computer that constitutes the control device 3 has at least one memory 10 and one processor 11 as the hardware resources 12 of the control device 3. The memory 10 is a non-transitory tangible storage medium, such as at least one type among semiconductor memories, magnetic media, and optical media, that non-temporarily stores programs, data, etc. that can be read by a computer. To perform high-load autonomous driving processing, the processor 11 includes, as a core, a high-processing-capability component such as a GPU (Graphics Processing Unit). Moreover, the processor 11 may further include, as a core, at least one type among, for example, a CPU (Central Processing Unit), a RISC (Reduced Instruction Set Computer)-CPU, a DFP (Data Flow Processor), and a GSP (Graph Streaming Processor).

[0062] The processor 11 executes a plurality of commands included in the resource manager stored in the memory 10 to enable multiple containers to share the hardware resources 12 and the host OS 30. As a result, the control device 3 creates at least one layer of a container management layer 32 for enabling multiple containers to share the hardware resources 12 and the host OS 30. At the same time, the control device 3 creates the autonomous driving container 33 and the user container 34 based on the autonomous driving container image and the user container image.

[0063] The container engine 31 is software such as Docker (Docker) that integrates the environments for executing the application programs of each container. The container engine 31 causes the autonomous driving application program 330 included in the autonomous driving container 33 and the user application program 340 included in the user container 34 to operate on the host OS 30.

[0064] The autonomous driving container 33 includes an autonomous driving application 330 and middleware (illustration omitted). The autonomous driving application 330 performs autonomous driving processing of the vehicle 2 based on the sensor information acquired by the sensor system 4. In the autonomous driving processing performed by the autonomous driving application 330, high-load processing such as, for example, external recognition processing and driving plan processing is included.

[0065] The user container 34 shares the host OS 30 and the hardware resources 12 of the control device 3 with the autonomous driving container 33. The user container 34 includes a user-specified user application 340 and middleware (illustration omitted). Here, the user application 340 can be a high-load application that requires high-performance hardware resources 12 during execution.

[0066] The user application 340 can be a machine learning application that updates the parameters of a machine learning model related to the autonomous driving of the vehicle 2. The user application 340 in the case of a machine learning application can feedback the parameters of the machine learning model to an external center or directly update the parameters themselves.

[0067] The user application 340 can be a leasing application that leases the hardware resources 12 of the control device 3 to an external entity such as a server. The user application 340 in the case of a leasing application can lease the hardware resources 12 as, for example, IaaS (Infrastructure As A Service) or PaaS (Platform As A Service), or lease the hardware resources 12 for virtual currency mining.

[0068] The user application 340 can cause a pre-release application related to the vehicle 2 to perform a test operation in a virtual environment. The user application 340 can also be an update application that updates software through an OTA (On The Air) release from an external source to the vehicle 2. The user application 340 can also be a sensor pre-processing application that uploads the pre-processing of the sensor information acquired by the sensor system 4 and the data obtained through this pre-processing to an external entity such as a server.

[0069] The user application 340 can also be a media application that plays video content such as movies. The user application 340 can also be an encoding application that encodes video data and / or audio data. The user application 340 can also be an upscaling application that upscales video data and / or still image data. The user application 340 can also be a simulation application that performs, for example, numerical simulation or new drug discovery simulation.

[0070] The container management layer 32 manages the hardware resources 12 allocated to the autonomous driving application 330 and the user application 340 respectively based on the sensor information acquired by the sensor system 4. Specifically, the container management layer 32 determines whether a low load condition in which the operation load of the autonomous driving application 330 has decreased to within a set range is established. Here, the set range is set to a range in which the hardware resources 12 required for the operation of the user application 340 can be ensured by the remaining hardware resources 12 corresponding to the hardware resources 12 required for the continuous operation of the autonomous driving application 330. When the low load condition is established, the container management layer 32 allocates the remaining hardware resources 12 corresponding to the hardware resources 12 required for the continuous operation of the autonomous driving application 330 to the user application 340. On the other hand, when the low load condition is not established, the container management layer 32 stops the execution of the user application 340 by aborting the allocation of the hardware resources 12 to the user application 340. The low load condition in the first embodiment and the second to eighth embodiments described later is established when the vehicle 2 is in the parking mode. In particular, the low load condition in the first embodiment is established when the parking brake of the vehicle 2 is effective.

[0071] When the user designates the user application 340 by operating the application specifying terminal 5, the container management layer 32 confirms whether the container image including the user application 340 exists in the memory 10. When the container image including the user application 340 does not exist in the memory 10, the container management layer 32 acquires the container image by downloading from the container image server 6, and creates the user container 34 based on the acquired container image.

[0072] The container management layer 32 prohibits the user application 340 from accessing the driving-related part of the vehicle 2. As the access prohibition to the driving-related part, the container management layer 32 may prohibit the user application 340 from accessing the autonomous driving container 33. As the access prohibition to the driving-related part, the container management layer 32 may also prohibit the user application 340 from accessing outside the hardware resources 12.

[0073] Hereinafter, Figure 3 , the process of the resource management method based on the control device 3 described above will be described. In addition, each "S" in this process represents a plurality of steps executed by a plurality of commands included in the resource management program. This resource management process starts at each control cycle of the control device 3.

[0074] In S101, the container management layer 32 acquires sensor information from the sensor system 4. At this time, the container management layer 32 based on the first embodiment acquires at least the operation state of the parking brake in the vehicle 2 as sensor information.

[0075] In S102 following S101, the container management layer 32 determines whether a low-load condition is satisfied based on the sensor information obtained in S101. At this time, the container management layer 32 determines whether the parking brake is effective. If an affirmative determination is made in S102, the resource management process proceeds to S103. On the other hand, if a negative determination is made in S102, the resource management process proceeds to S104.

[0076] In S103 when the low-load condition is satisfied, the container management layer 32 allocates the remaining hardware resources 12 of the hardware resources 12 required for the continuous operation of the autonomous driving application program 330 to the user application program 340.

[0077] On the other hand, in S104 when the low-load condition is not satisfied, the container management layer 32 aborts the allocation of the hardware resources 12 to the user application program 340.

[0078] (Function and effect)

[0079] Hereinafter, the function and effect of the first embodiment described above will be described.

[0080] Based on the first embodiment, when the low-load condition is satisfied in which the operation load of the autonomous driving application program 330 that executes autonomous driving processing is reduced to within a set range, the container management layer 32 allocates the remaining hardware resources 12 of the hardware resources 12 required for the continuous operation of the autonomous driving application program 330 to the user application program 340. Thereby, the hardware resources 12 released as the operation load of the autonomous driving application program 330 is reduced can be allocated to the user application program 340 for users independent of the autonomous driving processing. Therefore, the high-performance hardware resources 12 capable of executing autonomous driving processing can be effectively utilized.

[0081] According to the first embodiment, the low-load condition is satisfied when the vehicle 2 is in the parking mode. Thereby, in the parking mode in which the operation load of the autonomous driving application program 330 is reduced, the hardware resources 12 can be allocated to the user application program 340. Therefore, as the operation load of the autonomous driving application program 330 is reduced, the high-performance hardware resources 12 capable of executing autonomous driving processing can be effectively utilized.

[0082] According to the first embodiment, the low-load condition is satisfied when the parking brake becomes effective. Thereby, the allocation of the hardware resources 12 can be performed in coordination with the recognition that the vehicle 2 is in the parking mode based on the operation state of the parking brake. Therefore, the high-performance hardware resources 12 capable of executing autonomous driving processing can be effectively utilized in a timely manner as the operation load of the autonomous driving application program 330 is reduced.

[0083] According to the first embodiment, the user application 340 can be a machine learning application that updates the parameters of a machine learning model related to vehicle driving. In this case, it is possible to effectively utilize the high-performance hardware resource 12 capable of performing autonomous driving processing to update the parameters of the machine learning model as the operation load of the autonomous driving application 330 decreases.

[0084] According to the first embodiment, the user application 340 can be a rental application that lends out the hardware resource 12 of the control device 3 to the outside. In this case, it is possible to effectively utilize the high-performance hardware resource 12 capable of performing autonomous driving processing to reduce the computing load of an external server or the like as the operation load of the autonomous driving application 330 decreases.

[0085] According to the first embodiment, the user application 340 can cause the application before release to perform a test operation in a virtual environment. In this case, it is possible to effectively utilize the high-performance hardware resource 12 capable of performing autonomous driving processing to conduct a real machine test of the application before formal operation as the operation load of the autonomous driving application 330 decreases.

[0086] Based on the container management layer 32 of the first embodiment, access by the user application 340 to the autonomous driving container 33 can be prohibited. In this case, the influence of the operation of the user application 340 on the autonomous driving process can be suppressed. Therefore, on the basis of effectively utilizing the high-performance hardware resource 12 capable of performing autonomous driving processing, the safety of the vehicle 2 can be improved.

[0087] Based on the container management layer 32 of the first embodiment, access by the user application 340 outside the hardware resource 12 can also be prohibited. In this case, the influence of the operation of the user application 340 on the vehicle driving operation can be suppressed. Therefore, the high-performance hardware resource 12 capable of performing autonomous driving processing can be safely and effectively utilized.

[0088] Based on the container management layer 32 of the first embodiment, when the low load condition is not satisfied, the allocation of the hardware resource 12 to the user application 340 is aborted. Thereby, it is possible to appropriately allocate the hardware resource 12 when the operation load of the autonomous driving application 330 has not decreased to the autonomous driving process. Therefore, on the basis of effectively utilizing the high-performance hardware resource 12 capable of performing autonomous driving processing, the safety of the vehicle 2 can be improved.

[0089] (Second Embodiment)

[0090] Figure 4 、 5The second embodiment shown is a modification of the first embodiment that can be adopted when the vehicle 2 can be charged. In the second embodiment, the low-load condition in which the operation load of the autonomous driving application program 330 is reduced to within a set range is different from the low-load condition of the first embodiment.

[0091] The low-load condition of the second embodiment is established when the parking brake of the vehicle 2 is effective and the vehicle 2 is being charged. When such a low-load condition is established, the container management layer 32a allocates the remaining hardware resources 12 corresponding to the hardware resources 12 required for the continuous operation of the autonomous driving application program 330 to the user application program 340.

[0092] Hereinafter, with reference to Figure 5 the flowchart shown, the resource management process of the control device 3 based on the second embodiment will be described. This resource management process starts at the control cycle of each control device 3.

[0093] In the resource management process of the second embodiment, in S201 that replaces S101, the container management layer 32a acquires at least the operation state of the parking brake in the vehicle 2 and the charging state in the vehicle 2 as sensor information from the sensor system 4.

[0094] In the resource management process of the second embodiment, in S202 that replaces S102, the container management layer 32a determines whether the low-load condition is established based on the sensor information acquired in S201. At this time, the container management layer 32a determines whether the parking brake of the vehicle 2 is effective and the vehicle 2 is being charged. If an affirmative determination is made in S202, the resource management process proceeds to S103. On the other hand, if a negative determination is made in S202, the resource management process proceeds to S104.

[0095] According to such a second embodiment, the low-load condition is established when the parking brake is effective and the vehicle 2 is being charged. As a result, it is possible to allocate the hardware resources 12 in cooperation with identifying that the vehicle 2 has entered the parking mode based not only on the operation state of the parking brake but also on the charging state of the vehicle 2. Therefore, it is possible to effectively utilize the high-performance hardware resources 12 capable of executing autonomous driving processing in a timely manner according to the reduction of the operation load of the autonomous driving application program 330.

[0096] (Third Embodiment)

[0097] Figure 6 、 7 The third embodiment shown is a modification of the first embodiment. In the third embodiment, the low-load condition in which the operation load of the autonomous driving application program 330 is reduced to within a set range is different from the low-load condition of the first embodiment.

[0098] The low-load condition in the third embodiment is established when a certain period of time has elapsed since the parking brake of the vehicle 2 became effective. When such a low-load condition is established, the container management layer 32b allocates the remaining hardware resources 12 of the hardware resources 12 required for the continuous operation of the autonomous driving application program 330 to the user application program 340.

[0099] Hereinafter, with reference to Figure 7 the flowchart of, the resource management process of the control device 3 based on the third embodiment will be described. This resource management process starts at the control cycle of each control device 3.

[0100] In the resource management process of the third embodiment, in S302 which replaces S102 following S101, the container management layer 32b determines whether the low-load condition is established based on the sensor information acquired in S101 and the internal clock information of the control device 3. At this time, the container management layer 32b determines whether a certain period of time has elapsed since the parking brake of the vehicle 2 became effective. When an affirmative determination is made in S302, the resource management process proceeds to S103. On the other hand, when a negative determination is made in S302, the resource management process proceeds to S104.

[0101] According to such a third embodiment, the low-load condition is established when a certain period of time has elapsed since the parking brake became effective. Thereby, it is possible to allocate the hardware resources 12 in cooperation with the identification that the vehicle 2 has entered the parking mode based on the elapsed time since the parking brake became effective. Therefore, it is possible to effectively utilize the high-performance hardware resources 12 capable of executing autonomous driving processing in a timely manner according to the reduction of the operation load of the autonomous driving application program 330.

[0102] In addition, S302 can also be considered as whether the reduction time of the operation load within the set range has continued for more than the set time. In this case, the low-load condition in the third embodiment is established when the effective state of the parking brake of the vehicle 2 has continued for more than the set time.

[0103] (Fourth Embodiment)

[0104] Figure 8 and 9 The fourth embodiment shown is a modification of the first embodiment. In the fourth embodiment, the low-load condition in which the operation load of the autonomous driving application program 330 is reduced to within the set range is different from the low-load condition in the first embodiment.

[0105] The low-load condition in the fourth embodiment is established when the parking brake of the vehicle 2 is effective and the start switch of the vehicle 2 is turned off. When such a low-load condition is established, the container management layer 32c allocates the remaining hardware resources 12 of the hardware resources 12 required for the continuous operation of the autonomous driving application 330 to the user application 340.

[0106] Hereinafter, with reference to Figure 9 the flowchart of, the resource management process of the control device 3 based on the fourth embodiment will be described. This resource management process starts at the control cycle of each control device 3.

[0107] In S401 that replaces S101 in the resource management process of the fourth embodiment, the container management layer 32c acquires at least the operation state of the parking brake and the on / off state of the start switch from the sensor system 4 as sensor information.

[0108] In S402 that replaces S102 in the resource management process of the fourth embodiment, the container management layer 32c determines whether the low-load condition is established based on the sensor information acquired in S401. At this time, the container management layer 32c determines whether the parking brake of the vehicle 2 is effective and the start switch of the vehicle 2 is turned off. If an affirmative determination is made in S402, the resource management process proceeds to S103. On the other hand, if a negative determination is made in S402, the resource management process proceeds to S104.

[0109] Thus, according to the fourth embodiment, the low-load condition is established when the parking brake is effective and the start switch of the vehicle 2 is turned off. Thereby, it is possible to allocate the hardware resources 12 in accordance with the recognition that the vehicle 2 has entered the parking mode based not only on the operation state of the parking brake but also on the state of the start switch of the vehicle 2. Therefore, it is possible to effectively utilize the high-performance hardware resources 12 capable of executing autonomous driving processing in a timely manner in accordance with the reduction of the operation load of the autonomous driving application 330.

[0110] (Fifth Embodiment)

[0111] Figure 10 、 11 The fifth embodiment shown is a modification of the first embodiment. In the fifth embodiment, the low-load condition in which the operation load of the autonomous driving application 330 is reduced to within a set range is different from the low-load condition of the first embodiment.

[0112] The low load condition of the fifth embodiment is established when the parking brake of the vehicle 2 is effective and the gear position of the vehicle 2 becomes the parking gear. When such a low load condition is established, the container management layer 32d allocates the remaining hardware resources 12 of the hardware resources 12 corresponding to the actions of the autonomous driving application 330 to the user application 340.

[0113] Hereinafter, with reference to Figure 11 the flowchart of, the resource management process of the control device 3 based on the fifth embodiment will be described. This resource management process starts at the control cycle of each control device 3.

[0114] In S501 that replaces S101 in the resource management process of the fifth embodiment, the container management layer 32d acquires at least the operation state of the parking brake and the gear position as sensor information from the sensor system 4.

[0115] In S502 that replaces S102 in the resource management process of the fifth embodiment, the container management layer 32d determines whether the low load condition is established based on the sensor information acquired in S501. At this time, the container management layer 32d determines whether the parking brake of the vehicle 2 is effective and the gear position of the vehicle 2 becomes the parking gear. If an affirmative determination is made in S502, the resource management process proceeds to S103. On the other hand, if a negative determination is made in S502, the resource management process proceeds to S104.

[0116] Thus, according to the fifth embodiment, the low load condition is established when the parking brake is effective and the gear position of the vehicle 2 becomes the parking gear. As a result, the allocation of the hardware resources 12 can be performed in cooperation with the recognition that the vehicle 2 has entered the parking mode based on not only the operation state of the parking brake but also the gear position of the vehicle 2. Therefore, the high-performance hardware resources 12 capable of executing the autonomous driving process can be effectively utilized in a timely manner according to the reduction of the operation load of the autonomous driving application 330.

[0117] (Sixth Embodiment)

[0118] Figure 12 、 13 The sixth embodiment shown in is a modification of the first embodiment. In the sixth embodiment, the low load condition in which the operation load of the autonomous driving application 330 is reduced to within a set range is different from the low load condition of the first embodiment.

[0119] The low load condition of the sixth embodiment is established when the parking brake of the vehicle 2 is effective and the brake pedal of the vehicle 2 is not depressed. When such a low load condition is established, the container management layer 32e allocates the remaining hardware resources 12 of the hardware resources 12 corresponding to the actions of the autonomous driving application 330 to the user application 340.

[0120] Hereinafter, with reference to Figure 13 the flowchart of Figure 13 , the resource management process of the control device 3 based on the sixth embodiment will be described. This resource management process starts at the control cycle of each control device 3.

[0121] In S601 which replaces S101 in the resource management process of the sixth embodiment, the container management layer 32e acquires at least the operation state of the parking brake and the operation state of the brake pedal as sensor information from the sensor system 4.

[0122] In S602 which replaces S102 in the resource management process of the sixth embodiment, the container management layer 32e determines whether the low load condition is satisfied based on the sensor information acquired in S601. At this time, the container management layer 32e determines whether the parking brake of the vehicle 2 is effective and the brake pedal is not depressed. If an affirmative determination is made in S602, the resource management process proceeds to S103. On the other hand, if a negative determination is made in S602, the resource management process proceeds to S104.

[0123] Thus, according to the sixth embodiment, the low load condition is satisfied when the parking brake is effective and the brake pedal is not depressed. Thereby, it is possible to allocate the hardware resource 12 in accordance with the recognition that the vehicle 2 has entered the parking mode based not only on the operation state of the parking brake but also on the operation state of the brake pedal. Therefore, it is possible to effectively utilize the high-performance hardware resource 12 capable of executing the autonomous driving process in a timely manner as the operation load of the autonomous driving application program 330 decreases.

[0124] (Seventh Embodiment)

[0125] Figure 14 , 15 The seventh embodiment shown in 15 is a modification of the first embodiment. In the seventh embodiment, the low load condition in which the operation load of the autonomous driving application program 330 is reduced to within the set range is different from the low load condition of the first embodiment.

[0126] The low load condition of the seventh embodiment is satisfied when the parking brake of the vehicle 2 is effective and the current position of the vehicle 2 is within the parking lot. The current position of the vehicle 2 can be identified based on the GNSS information acquired from the sensor system 4 and the map information stored in the memory 10, etc. When such a low load condition is satisfied, the container management layer 32f allocates the remaining hardware resource 12 corresponding to the hardware resource 12 required for the continuous operation of the autonomous driving application program 330 to the user application program 340.

[0127] Hereinafter, with reference to Figure 15The flowchart of the resource management process of the control device 3 based on the seventh embodiment will be described. This resource management process starts at the control cycle of each control device 3.

[0128] In S701 which replaces S101 in the resource management process of the seventh embodiment, the container management layer 32f acquires at least the operation state of the parking brake and GNSS information as sensor information from the sensor system 4. Moreover, in S701, the container management layer 32f acquires map information from the memory 10 in the hardware resource 12.

[0129] In S702 which replaces S102 in the resource management process of the seventh embodiment, the container management layer 32f determines whether the low-load condition is satisfied based on the sensor information and map information acquired in S701. At this time, the container management layer 32f determines whether the parking brake of the vehicle 2 is effective and the current position of the vehicle 2 is within the parking lot. When an affirmative determination is made in S702, the resource management process proceeds to S103. On the other hand, when a negative determination is made in S702, the resource management process proceeds to S104.

[0130] In addition, in S701, the container management layer 32f may acquire the operation state of the parking brake as sensor information and acquire the current position of the vehicle 2 from a locator (not shown). In this case, in S702, the container management layer 32f may determine whether the low-load condition is satisfied based on the sensor information acquired from the sensor system 4 and the current position acquired from the locator.

[0131] In S701, the container management layer 32f may also acquire the operation state of the parking brake as sensor information and acquire the current position of the vehicle 2 as a result of the autonomous driving process from the autonomous driving container 33. In this case, in S702, the container management layer 32f may also determine whether the low-load condition is satisfied based on the sensor information acquired from the sensor system 4 and the current position acquired from the autonomous driving container 33.

[0132] Thus, according to the seventh embodiment, the low-load condition is satisfied when the parking brake is effective and the current position of the vehicle 2 is within the parking lot. Thereby, the allocation of the hardware resource 12 can be performed in cooperation with the recognition that the vehicle 2 has entered the parking mode based not only on the operation state of the parking brake but also on the current position of the vehicle 2. Therefore, the high-performance hardware resource 12 capable of executing the autonomous driving process can be effectively utilized in a timely manner according to the reduction of the operation load of the autonomous driving application program 330.

[0133] (Eighth Embodiment)

[0134] Figure 16 、 17The eighth embodiment shown is a modification of the first embodiment. In the eighth embodiment, the operating load of the autonomous driving application 330 is reduced to a low load condition within a set range, which is different from the low load condition of the first embodiment.

[0135] The low load condition of the eighth embodiment is established when the parking brake of the vehicle 2 is effective and the distance between the unlocking unit for unlocking the vehicle 2 and the vehicle 2 increases beyond the conditional range. The unlocking unit includes an electronic key such as an IC (Integrated Circuit) key and a mobile terminal that can unlock the vehicle 2 and communicate with the sensor system 4 of the vehicle 2. The distance between the unlocking unit and the vehicle 2 is identified based on the sensor information from the communication type external sensor 40 in the sensor system 4. Therefore, the conditional range related to the distance from the unlocking unit is set to be, for example, several meters to several tens of meters, etc., and is a distance range for determining that the user carrying the unlocking unit has moved away from the vehicle 2 to a far distance. When the low load condition including such a distance condition from the unlocking unit is established, the container management layer 32g allocates the remaining hardware resources 12 corresponding to the hardware resources 12 required for the continuous operation of the autonomous driving application 330 to the user application 340.

[0136] Hereinafter, with reference to Figure 17 the flowchart, the resource management process of the control device 3 based on the eighth embodiment will be described. This resource management process starts at the control cycle of each control device 3.

[0137] In S801 that replaces S101 in the resource management process of the eighth embodiment, the container management layer 32g acquires at least the operation state of the parking brake and the distance between the unlocking unit and the vehicle 2 as sensor information from the sensor system 4.

[0138] In S802 that replaces S102 in the resource management process of the eighth embodiment, the container management layer 32g determines whether the low load condition is established based on the sensor information acquired in S801. At this time, the container management layer 32g determines whether the parking brake of the vehicle 2 is effective and the distance between the unlocking unit for unlocking the vehicle 2 and the vehicle 2 has increased beyond the conditional range. If a negative determination is made in S802, the resource management process proceeds to S103. On the other hand, if an affirmative determination is made in S802, the resource management process proceeds to S104.

[0139] Thus, according to the eighth embodiment, a low load condition is established when the parking brake is effective and the distance between the unlocking unit that unlocks the vehicle 2 and the vehicle 2 increases beyond the conditional range. Thereby, it is possible to allocate the hardware resources 12 in cooperation with identifying that the vehicle 2 has entered the parking mode based not only on the operating state of the parking brake but also on the distance information between the unlocking unit and the vehicle 2. Therefore, it is possible to effectively utilize the high-performance hardware resources 12 capable of executing the autonomous driving process in a timely manner according to the reduction of the operation load of the autonomous driving application 330.

[0140] (Ninth Embodiment)

[0141] Figure 18 、 19 The ninth embodiment shown is a modification of the first embodiment. In the ninth embodiment, the low load condition in which the operation load of the autonomous driving application 330 is reduced to within the set range is different from the low load condition of the first embodiment.

[0142] The low load conditions of the ninth embodiment and the tenth embodiment described later are established when the vehicle 2 is in the manual driving mode. Here, the manual driving mode means, for example, a driving mode in which at least a part of the driving operations are performed by the user among the autonomous driving levels 0 to 3 defined by the Society of Automotive Engineers (SAE) in the United States. In particular, the low load condition of the ninth embodiment is established when the automatic auxiliary driving (Auto pilot) function of the vehicle 2 executed by the autonomous driving process of the autonomous driving application 330 is invalid. When such a low load condition is established, the container management layer 32h allocates the remaining hardware resources 12 corresponding to the hardware resources 12 required for the continuous operation of the autonomous driving application 330 to the user application 340.

[0143] Hereinafter, with reference to Figure 9 the flowchart, the resource management process of the control device 3 based on the ninth embodiment will be described. This resource management process starts at the control cycle of each control device 3.

[0144] In S901 that replaces S101 in the resource management process of the ninth embodiment, the container management layer 32h acquires at least the on / off state of the automatic auxiliary driving function as a result of the autonomous driving process from the autonomous driving container 33.

[0145] In S902, which replaces S102 in the resource management process of the ninth embodiment, the container management layer 32h determines whether the low-load condition is satisfied based on the automated driving state obtained in S901. At this time, the container management layer 32h determines whether the automated driving function of the vehicle 2 executed by the automated driving process is turned off. If an affirmative determination is made in S902, the resource management process proceeds to S103. On the other hand, if a negative determination is made in S902, the resource management process proceeds to S104.

[0146] Thus, according to the ninth embodiment, the low-load condition is satisfied when the vehicle 2 is in the manual driving mode. As a result, in the manual driving mode where the operation load of the automated driving application 330 is reduced, the hardware resource 12 can be allocated to the user application 340. Therefore, the high-performance hardware resource 12 capable of executing the automated driving process can be effectively utilized as the operation load of the automated driving application 330 decreases.

[0147] Moreover, according to the ninth embodiment, the low-load condition is satisfied when the automated driving function of the vehicle 2 executed by the automated driving process becomes invalid. As a result, the allocation of the hardware resource 12 can be performed in coordination with the recognition that the vehicle 2 is in the manual driving mode based on the state of the automated driving function. Therefore, the high-performance hardware resource 12 capable of executing the automated driving process can be effectively utilized in a timely manner as the operation load of the automated driving application 330 decreases.

[0148] (Tenth Embodiment)

[0149] Figure 20 、 21 The tenth embodiment shown in FIG. is a modification of the ninth embodiment. In the tenth embodiment, the low-load condition in which the operation load of the automated driving application 330 is reduced to within a set range is different from the low-load condition of the ninth embodiment.

[0150] The low-load condition of the tenth embodiment is satisfied when the vehicle is outside the operational design domain (ODD) set by the automated driving process of the automated driving application. Here, the ODD represents the driving area where the driving environment conditions that are the premise of the automated driving process are satisfied. For example, the ODD is set to a dedicated vehicle driving area such as a highway lane during driving, a driving area with good driving visibility, and a driving area where position-related information such as map information or GNSS information can be appropriately obtained. When the low-load condition including such ODD conditions is satisfied, the container management layer 32i allocates the remaining hardware resource 12 corresponding to the hardware resource 12 required for the continuous operation of the automated driving application 330 to the user application 340.

[0151] Hereinafter, with reference to Figure 21 the flowchart of Figure 21 , the resource management process of the control device 3 based on the tenth embodiment will be described. This resource management process starts at the control cycle of each control device 3.

[0152] In S1001 which replaces S101 in the resource management process of the tenth embodiment, the container management layer 32i obtains, from the autonomous driving container 33, ODD information indicating whether the vehicle 2 is outside the ODD set by the autonomous driving process as a result of the autonomous driving process.

[0153] In S1002 which replaces S102 in the resource management process of the tenth embodiment, the container management layer 32i determines whether the low-load condition is satisfied based on the ODD information obtained in S1001. At this time, the container management layer 32i determines whether it has become outside the ODD set by the autonomous driving process. If an affirmative determination is made in S1002, the resource management process proceeds to S103. On the other hand, if a negative determination is made in S1002, the resource management process proceeds to S104.

[0154] Thus, according to the tenth embodiment, the low-load condition is satisfied when it has become outside the ODD set by the autonomous driving process. Thereby, it is possible to allocate the hardware resource 12 in coordination with the recognition that the vehicle 2 has entered the manual driving mode based on the ODD information. Therefore, it is possible to effectively utilize in a timely manner the high-performance hardware resource 12 capable of executing the autonomous driving process in accordance with the reduction of the operation load of the autonomous driving application 330.

[0155] (Other Embodiments)

[0156] As described above, multiple embodiments of the present disclosure have been described, but the present disclosure is not limited to being construed as these embodiments, and can be applied to various embodiments and combinations without departing from the gist of the present disclosure.

[0157] In a modified example, the dedicated computer constituting the control device 3 may have at least one of a digital circuit and an analog circuit as a processor. Here, the digital circuit is, for example, at least one type among an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), a SOC (System on a Chip), a PGA (Programmable Gate Array), and a CPLD (Complex Programmable Logic Device). In addition, such a digital circuit may have a memory storing a program.

[0158] In a modified example of the first to tenth embodiments, a part or the whole of the functions of the container management layer may be deployed as functions of the container engine 31.

[0159] In a modified example of the first to tenth embodiments, the prohibited access range of the user application 340 may be changed according to the type of the user application 340.

[0160] In a modified example of the first to tenth embodiments, the application specifying terminal 5 may be a terminal other than the terminal mounted on the vehicle 2. For example, the application specifying terminal 5 may be a user-portable terminal such as a smartphone or a tablet that can communicate with the control device 3 of the vehicle.

[0161] The low-load condition based on the modified examples of the second, fourth to tenth embodiments may be established when the reduction time during which the operation load of the autonomous driving application 330 is reduced to within a set range continues for a set time or more. Hereinafter, with reference to Figure 22 、 23 , a modified example of the second embodiment will be specifically described.

[0162] In the second embodiment, the condition for determining that the operation load of the autonomous driving application 330 is reduced to within a set range is that the parking brake of the vehicle 2 is effective and the vehicle 2 is being charged. Therefore, the low-load condition based on the modified example of the second embodiment is established when the state in which the parking brake of the vehicle 2 is effective and the vehicle 2 is being charged continues for a set time or more. When such a low-load condition is established, the container management layer 32j allocates the remaining hardware resources 12 corresponding to the hardware resources 12 required for the continuous operation of the autonomous driving application 330 to the user application 340.

[0163] In Figure 23In S1102 which replaces S202 in the resource management process of the illustrated modification example, the container management layer 32j determines whether a low load condition is satisfied based on the sensor information acquired in S201 and the internal clock information of the control device 3. If an affirmative determination is made in S1102, the resource management process proceeds to S103. On the other hand, if a negative determination is made in S1102, the resource management process proceeds to S104.

[0164] According to such a modification example, the low load condition is satisfied when the reduction time for reducing to within the set range of the operating load continues for a set time or longer. Thereby, it is possible to perform the allocation of the hardware resource 12 in coordination with the identification of the idle state of the hardware resource 12 based on the duration of the low load state of the autonomous driving application 330. Therefore, it is possible to effectively utilize the high-performance hardware resource 12 capable of executing the autonomous driving process in a timely manner according to the reduction of the operating load of the autonomous driving application 330.

[0165] (Disclosure of Technical Ideas)

[0166] This specification discloses a plurality of technical ideas described in the following listed items. There may be cases where a plurality of items are described in a multiple dependent form in which a subsequent item alternatively refers to a preceding item. Moreover, there may be cases where some items are described in a multiple dependent form that refers to another multiple dependent form. The items described in these multiple dependent forms define a plurality of technical ideas.

[0167] (Technical Idea 1)

[0168] A control device that enables a plurality of containers (33, 34) to share a hardware resource (12) and a host operating system (30), the control device (3) comprising:

[0169] An autonomous driving container (33) that causes an autonomous driving application (330) for executing an autonomous driving process of a vehicle to operate on the host operating system;

[0170] A user container (34) that causes a user application (340) specified by a user to operate on the host operating system; and

[0171] A container management layer (32) that manages the allocation of the hardware resource to the autonomous driving application and the user application,

[0172] When the low-load condition is established where the action load of the autonomous driving application program is reduced to within a set range, the container management layer allocates the remaining hardware resources corresponding to the hardware resources required for the continuous action of the autonomous driving application program to the user application program.

[0173] (Technical idea 2)

[0174] In the control device described in Technical idea 1,

[0175] The low-load condition is established when the vehicle is in the parking mode.

[0176] (Technical idea 3)

[0177] In the control device described in Technical idea 2,

[0178] The low-load condition is established when the parking brake of the vehicle becomes effective.

[0179] (Technical idea 4)

[0180] In the control device described in Technical idea 3,

[0181] The low-load condition is established when the parking brake is effective and the vehicle is being charged.

[0182] (Technical idea 5)

[0183] In the control device described in Technical idea 3,

[0184] The low-load condition is established when a certain period of time has passed since the parking brake became effective.

[0185] (Technical idea 6)

[0186] In the control device described in Technical idea 3,

[0187] The low-load condition is established when the parking brake is effective and the start switch of the vehicle is turned off.

[0188] (Technical idea 7)

[0189] In the control device described in Technical idea 3,

[0190] The low-load condition is established when the parking brake is effective and the gear of the vehicle is in the park position.

[0191] (Technical idea 8)

[0192] In the control device described in Technical idea 3,

[0193] The low load condition is established when the parking brake is effective and the brake pedal of the vehicle is not depressed.

[0194] (Technical idea 9)

[0195] In the control device described in Technical idea 3,

[0196] The low load condition is established when the parking brake is effective and the current position of the vehicle is within a parking lot.

[0197] (Technical idea 10)

[0198] In the control device described in Technical idea 3,

[0199] The low load condition is established when the parking brake is effective and the distance between the unlocking unit that unlocks the vehicle and the vehicle increases beyond the conditional range.

[0200] (Technical idea 11)

[0201] In the control device described in Technical idea 1,

[0202] The low load condition is established when the vehicle is in the manual driving mode.

[0203] (Technical idea 12)

[0204] In the control device described in Technical idea 11,

[0205] The low load condition is established when the automatic assistance driving function of the vehicle executed by the automatic driving process becomes invalid.

[0206] (Technical idea 13)

[0207] In the control device described in Technical idea 11,

[0208] The low load condition is established when it is outside the operation design area set by the automatic driving process.

[0209] (Technical idea 14)

[0210] In the control device described in Technical idea 3,

[0211] The low load condition is established when the reduction time during which the operation load is reduced to within the set range continues for a set time or more.

[0212] (Technical idea 15)

[0213] In the control device described in any one of Technical ideas 1 to 14,

[0214] The user application is a machine learning application for updating parameters of a machine learning model related to the autonomous driving process.

[0215] (Technical idea 16)

[0216] In the control device described in any one of Technical ideas 1 to 14,

[0217] The user application is a rental application for lending out the hardware resources to the outside.

[0218] (Technical idea 17)

[0219] In the control device described in any one of Technical ideas 1 to 14,

[0220] The user application is an application for making a pre-release application related to the vehicle perform a test operation in a virtual environment.

[0221] (Technical idea 18)

[0222] In the control device described in any one of Technical ideas 1 to 17,

[0223] The container management layer prohibits the user application from accessing the autonomous driving container.

[0224] (Technical idea 19)

[0225] In the control device described in any one of Technical ideas 1 to 17,

[0226] The container management layer prohibits the user application from accessing outside the hardware resources.

[0227] (Technical idea 20)

[0228] In the control device described in any one of Technical ideas 1 to 19,

[0229] In the case where the low load condition is not satisfied, the container management layer suspends the allocation of the hardware resources to the user application.

[0230] (Technical idea 21)

[0231] A resource management method is executed by a processor (11) to enable multiple containers (33, 34) to share hardware resources (12) and a host operating system (30), and includes the following:

[0232] Make an autonomous driving application program (330) that executes the autonomous driving process of the vehicle (2) operate on the host operating system;

[0233] Cause a user application (340) specified by a user to operate on the host operating system; and

[0234] Manage the allocation of the hardware resources to the autonomous driving application and the user application,

[0235] Managing the allocation of the hardware resources includes the following: when a low load condition in which the operation load of the autonomous driving application is reduced to within a set range is satisfied, allocate the remaining hardware resources corresponding to the hardware resources required for the continuous operation of the autonomous driving application to the user application.

[0236] (Technical idea 22)

[0237] A resource management program, stored in a storage medium (10), includes commands executed by a processor (11) for enabling a plurality of containers (33, 34) to share hardware resources (12) and a host operating system (30),

[0238] The commands include the following:

[0239] Cause an autonomous driving application (330) that performs autonomous driving processing of a vehicle (2) to operate on the host operating system;

[0240] Cause a user application (340) specified by a user to operate on the host operating system; and

[0241] Manage the allocation of the hardware resources to the autonomous driving application and the user application,

[0242] Managing the allocation of the hardware resources includes the following: when a low load condition in which the operation load of the autonomous driving application is reduced to within a set range is satisfied, allocate the remaining hardware resources corresponding to the hardware resources required for the continuous operation of the autonomous driving application to the user application.

Claims

1. A control device that enables multiple containers (33, 34) to share hardware resources (12) and a host operating system (30), the control device (3) being characterized by comprising: An autonomous driving container (33) that causes an autonomous driving application program (330) for executing the autonomous driving process of a vehicle to operate on the host operating system; A user container (34) that causes a user application program (340) specified by a user to operate on the host operating system; and A container management layer (32) that manages the allocation of the hardware resources to the autonomous driving application program and the user application program, When a low-load condition in which the operation load of the autonomous driving application program is reduced to within a set range is satisfied, the container management layer allocates the remaining hardware resources corresponding to the hardware resources required for the continuous operation of the autonomous driving application program to the user application program.

2. The control device according to claim 1, wherein The low-load condition is satisfied when the vehicle becomes a parking mode.

3. The control device according to claim 2, wherein The low-load condition is satisfied when the parking brake of the vehicle becomes effective.

4. The control device according to claim 3, wherein The low-load condition is satisfied when the parking brake is effective and the vehicle is being charged.

5. The control device according to claim 3, wherein The low-load condition is satisfied when a certain time or more has elapsed since the parking brake became effective.

6. The control device according to claim 3, wherein The low-load condition is satisfied when the parking brake is effective and the start switch of the vehicle is turned off.

7. The control device according to claim 3, wherein The low-load condition is satisfied when the parking brake is effective and the gear position of the vehicle becomes the parking position.

8. The control device according to claim 3, wherein The low-load condition is satisfied when the parking brake is effective and the brake pedal of the vehicle is not depressed.

9. The control device according to claim 3, wherein The low-load condition is satisfied when the parking brake is effective and the current position of the vehicle is within a parking lot.

10. The control device according to claim 3, wherein The low-load condition is satisfied when the parking brake is effective and the distance between the unlocking unit for unlocking the vehicle and the vehicle increases beyond a conditional range.

11. The control device according to claim 1, wherein The low-load condition is satisfied when the vehicle becomes a manual driving mode.

12. The control device according to claim 11, wherein The low-load condition is satisfied when the automatic auxiliary driving function of the vehicle executed by the autonomous driving process becomes invalid.

13. The control device according to claim 11, wherein The low load condition holds when it is outside the operating design domain set by the autonomous driving process.

14. The control device according to claim 3, characterized in that The low load condition holds when the reduction time during which the operation load is reduced to within the set range continues for a set time or more.

15. The control device according to claim 1, characterized in that The user application is a machine learning application for updating parameters of a machine learning model related to the autonomous driving process.

16. The control device according to claim 1, characterized in that The user application is a rental application for lending out the hardware resources to the outside.

17. The control device according to claim 1, characterized in that The user application is an application for causing a pre-release application related to the vehicle to perform a test operation in a virtual environment.

18. The control device according to claim 1, characterized in that The container management layer prohibits the user application from accessing the autonomous driving container.

19. The control device according to claim 1, characterized in that The container management layer prohibits the user application from accessing outside the hardware resources.

20. The control device according to claim 1, characterized in that When the low load condition does not hold, the container management layer aborts the allocation of the hardware resources to the user application.

21. A resource management method, executed by a processor (11) to enable multiple containers (33, 34) to share hardware resources (12) and a host operating system (30), characterized in that, including the following: Causing an autonomous driving application (330) that executes the autonomous driving process of the vehicle (2) to operate on the host operating system; Causing a user application (340) specified by the user to operate on the host operating system; and Managing the allocation of the hardware resources to the autonomous driving application and the user application, Managing the allocation of the hardware resources includes the following: When the low load condition that the operation load of the autonomous driving application is reduced to within the set range holds, allocating the remaining hardware resources corresponding to the hardware resources required for the continuous operation of the autonomous driving application to the user application.

22. A resource management program stored in a storage medium (10), including commands executed by a processor (11) for enabling a plurality of containers (33, 34) to share hardware resources (12) and a host operating system (30), characterized in that The commands include the following: Causing an autonomous driving application (330) that executes the autonomous driving process of the vehicle (2) to operate on the host operating system; Causing a user application (340) specified by the user to operate on the host operating system; and Managing the allocation of the hardware resources to the autonomous driving application and the user application, Managing the allocation of the hardware resources includes the following: When the low load condition that the operation load of the autonomous driving application is reduced to within the set range holds, allocating the remaining hardware resources corresponding to the hardware resources required for the continuous operation of the autonomous driving application to the user application.

Images