Vehicle control device

Abstract

To provide a vehicle control device that can learn liking of a driver for inter-vehicle information on an own vehicle with respect to a preceding vehicle during running of the own vehicle by manual operation, at a proper timing.SOLUTION: A vehicle control device controls an own vehicle that enables a driver to switch between manual operation and automatic operation. The vehicle control device comprises a processor and a storing device. The processor executes learning control by which inter-vehicle information which is an inter-vehicle time or an inter-vehicle distance of the own vehicle with respect to a preceding vehicle is learnt during running of the own vehicle by the manual operation, and reflects a learnt result of the inter-vehicle information by the learning control on control of the inter-vehicle information during the automatic operation. The storing device stores the inter-vehicle information as learning data during the manual operation. In the learning control, the processor determines whether the storing device should be made to store the inter-vehicle information about the preceding vehicle as learning data or not, in accordance with a type of the preceding vehicle.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present disclosure relates to a vehicle control device capable of switching between manual driving and automatic driving. [Background technology]

[0002] Patent Document 1 discloses an automatic driving control device for a vehicle. The automatic driving control device learns the driving method of a driver based on the circumstances around the vehicle and the driving state of the vehicle. Through this learning, each driver's preferences regarding the vehicle driving method are learned. The automatic driving control device then controls the vehicle driving based on the results of this learning. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 07-108849 Summary of the Invention [Problem to be solved by the invention]

[0004] Incidentally, when learning driver preferences regarding vehicle-to-vehicle information (inter-vehicle time or inter-vehicle distance) between the host vehicle and a preceding vehicle while the vehicle is being driven manually, there are times when the vehicle is traveling that are suitable for such learning and times when the learning is not suitable. In this regard, the technology described in the above-mentioned Patent Document 1 does not clearly specify the timing of learning. Therefore, depending on the timing of learning, there is a risk that the driver's preferences may not be properly learned.

[0005] The present disclosure has been made in consideration of the above-mentioned problems, and aims to provide a vehicle control device that is capable of learning the driver's preferences regarding vehicle distance information between the vehicle and a preceding vehicle at an appropriate time while the vehicle is being manually driven. [Means for solving the problem]

[0006] A vehicle control device according to the present disclosure controls a host vehicle capable of switching between manual driving by a driver and automatic driving. The control device includes a processor and a storage device. The processor executes learning control to learn vehicle-to-vehicle information, which is the time or distance between the host vehicle and a preceding vehicle, while the vehicle is being driven manually, and reflects the learning results of the vehicle-to-vehicle information obtained through the learning control in the control of the vehicle-to-vehicle information during automatic driving. The storage device stores the vehicle-to-vehicle information as learning data during manual driving. In the learning control, the processor determines whether to store the vehicle-to-vehicle information for the preceding vehicle as learning data in the storage device, depending on the model of the preceding vehicle. Additionally, machine learning may be used to obtain the learning results of the vehicle-to-vehicle information obtained through the learning control. [Effects of the Invention]

[0007] According to the present disclosure, it is possible to learn the driver's preferences regarding inter-vehicle information of the host vehicle relative to a preceding vehicle at an appropriate timing while the vehicle is being manually driven. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a diagram illustrating an example of a configuration of a vehicle according to an embodiment. [Figure 2] FIG. 2 is a diagram for explaining inter-vehicle information used in the embodiment. [Figure 3] FIG. 2 is a diagram showing specific examples of vehicle types of preceding vehicles followed by the host vehicle. [Figure 4] 10 is a table showing an example of a list of learning conditions C according to the embodiment. [Figure 5] 4 is a flowchart illustrating an example of a process related to vehicle driving control according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] Embodiments of the present disclosure will be described with reference to the accompanying drawings.

[0010] 1. Vehicle configuration 1 is a diagram schematically illustrating an example of the configuration of a vehicle 1 according to an embodiment. The vehicle 1 is equipped with a vehicle control system 10. The vehicle control system 10 is mounted on the vehicle 1 and controls the driving of the vehicle 1. The vehicle control system 10 includes a vehicle state sensor 12, a recognition sensor 14, a position sensor 16, a communication device 18, a driving device 20, an electronic control unit (ECU) 22, and a driving selector switch 24.

[0011] The vehicle state sensor 12 detects the state of the vehicle 1. The vehicle state sensor 12 includes, for example, a vehicle speed sensor, an acceleration sensor, an accelerator pedal sensor, a brake pedal sensor, a steering angle sensor, and a turn signal sensor. The turn signal sensor detects the operation state of the turn signal. The recognition sensor 14 recognizes (detects) the situation around the vehicle 1. The recognition sensor 14 includes, for example, a camera. The position sensor 16 detects the position and orientation of the vehicle 1. The position sensor 16 includes, for example, a GNSS (Global Navigation Satellite System) receiver.

[0012] The communication device 18 communicates with the outside of the vehicle 1. The communication device 18 communicates with, for example, an external system to acquire various information. The information includes, for example, map information and traffic information. The map information includes road information such as road gradient. The traffic information includes, for example, congestion information, information on road surface conditions, and information on visibility such as fog or heavy rain. The communication device 18 may also include, for example, a vehicle-to-vehicle communication device that enables communication between the vehicle 1 and surrounding vehicles (i.e., vehicle-to-vehicle communication (V2V)).

[0013] The traveling device 20 is a device that operates the vehicle 1. For example, the traveling device 20 includes a drive device, a braking device, and a steering device. The drive device includes, for example, at least one of an electric motor and an internal combustion engine for driving (accelerating) the vehicle 1. The braking device includes a brake actuator for braking (deceleration) the vehicle 1. The steering device includes, for example, a steering motor for steering the vehicle 1.

[0014] The ECU 22 is a computer that controls the vehicle 1, and corresponds to an example of a "vehicle control device" according to the present disclosure. The ECU 22 includes a processor 26 and a storage device 28. The processor 26 executes various processes, including processes related to vehicle driving control, which will be described later. The storage device 28 stores various information necessary for the processes performed by the processor 26. The various processes performed by the ECU 22 are realized by the processor 26 executing a computer program. The computer program is stored in the storage device 28. Alternatively, the computer program may be recorded on a computer-readable recording medium. The ECU 22 may be configured by combining multiple ECUs.

[0015] The vehicle control system 10 is configured to be able to execute automatic driving control that controls the "automatic driving" of the vehicle 1. This automatic driving control has a follow-up driving function that controls the time T or distance D between the vehicle (host vehicle) 1 and a preceding vehicle while causing the vehicle 1 to follow the preceding vehicle. More specifically, the automatic driving referred to here corresponds to, for example, automatic driving of level 3 or higher as defined by the Society of Automotive Engineers (SAE) in the United States, but is not necessarily limited to automatic driving of level 3 or higher. In other words, the automatic driving control may be any automatic driving control that has the follow-up driving function described above, and may be, for example, adaptive cruise control (ACC). Known technologies are applied to such automatic driving control. Therefore, a detailed description of the automatic driving control will be omitted.

[0016] The driving mode changeover switch 24 is operated by the driver and is used to switch the driving mode of the vehicle 1 between manual driving by the driver and automatic driving. In other words, the vehicle 1 is configured to be switchable between manual driving and automatic driving. Manual driving is performed by the driver operating the accelerator pedal, brake pedal, and steering wheel at his / her own will.

[0017] 2. Vehicle driving control In this embodiment, when the driver selects automatic driving by operating the driving mode selector switch 24, the ECU 22 executes the above-described automatic driving control. On the other hand, when the driver selects manual driving, the ECU 22 executes the following "vehicle distance learning control."

[0018] 2-1. Vehicle distance learning control during following driving The inter-vehicle distance learning control (or simply learning control) according to this embodiment learns the "inter-vehicle information" of the host vehicle 1 relative to a preceding vehicle while the vehicle is being driven manually. The learning result of the inter-vehicle information by this learning control (for example, a learning value TL, which will be described later) is reflected in the control of the inter-vehicle information during automatic driving.

[0019] FIG. 2 is a diagram for explaining inter-vehicle information used in the embodiment. The inter-vehicle information here refers to the inter-vehicle time T or inter-vehicle distance D of the host vehicle 1 relative to the preceding vehicle, as shown in FIG. 2. The following explanation will be given using the inter-vehicle time T as an example, but the same applies to the inter-vehicle distance D. More specifically, the inter-vehicle time T is learned in association with the vehicle speed V of the host vehicle 1, as will be described in detail later with reference to FIG. 5. The same applies to the example of the inter-vehicle distance D.

[0020] The learning condition C is a condition for executing learning by learning control. In other words, the learning condition C is a condition for executing storage of the time headway T, which is learning data in the learning control. In order to accurately grasp the driver's preference regarding the time headway T when following a preceding vehicle, the learning condition C is set as follows. In other words, whether or not to store learning data (time headway T) for a certain preceding vehicle in the storage device 28 is determined (changed) depending on the type of the preceding vehicle.

[0021] Specifically, the determination of the learning target according to the type of the preceding vehicle is performed, for example, based on the height H of the preceding vehicle. Here, the inter-vehicle time T adjusted by the driver of the host vehicle 1 during manual driving is affected by the height H of the preceding vehicle. In other words, if the height H is large, the driver of the host vehicle 1 is more likely to have an obstructed forward visibility. For example, it becomes difficult to see traffic lights or signs ahead. For this reason, when following a preceding vehicle with a large height H, the driver is likely to ensure a longer inter-vehicle time T than when following a preceding vehicle with the same or smaller height H. In this way, the inter-vehicle time T adjusted by the driver during manual driving may vary depending on the height H of the preceding vehicle.

[0022] Therefore, in the learning control, when the height H of the preceding vehicle is less than a predetermined threshold value TH1, the ECU 22 Time T On the other hand, if the height H is equal to or greater than the threshold value TH1, the ECU 22 stores the distance H as learned data in the storage device 28. Time T is not stored in the storage device 28 as learning data.

[0023] 3(A) to 3(C) are diagrams showing specific examples of vehicle types of preceding vehicles followed by the host vehicle 1. In the example shown in FIG. 3(A), the preceding vehicle 2 has a height H1 equivalent to that of the host vehicle 1. On the other hand, in the example shown in FIG. 3(B), the preceding vehicle 3 has a height H2 greater than that of the host vehicle 1.

[0024] Specifically, the preceding vehicle 2 is an automobile, and more specifically, for example, a four-wheeled automobile. The preceding vehicle 2 corresponds to, for example, a standard automobile (a standard passenger car, a compact passenger car, or a light automobile) under the Road Traffic Act of Japan. In addition, the preceding vehicle 2 may be a private vehicle or a commercial vehicle. On the other hand, the preceding vehicle 3 is a larger automobile (such as a truck or a bus) than the preceding vehicle 2. More specifically, the preceding vehicle 3 corresponds to, for example, a medium-sized automobile, a medium-sized automobile, or a large automobile under the Road Traffic Act of Japan.

[0025] The threshold value TH1 is determined in advance as a value that can distinguish between the preceding vehicle 2 and the preceding vehicle 3. That is, the threshold value TH1 is determined based on the difference between the height H1 of the preceding vehicle 2 and the height H2 of the preceding vehicle 3. height In other words, the threshold value TH1 corresponds to a value that allows a standard-sized vehicle to be distinguished from a vehicle having a height H greater than that of a standard-sized vehicle.

[0026] In the example described with reference to preceding vehicles 2 and 3, it is assumed that host vehicle 1 is a standard automobile, like preceding vehicle 2. However, the vehicle type of host vehicle 1 is not necessarily limited to a standard automobile. In other words, the condition for excluding a preceding vehicle from the learning target in consideration of its vehicle type may be, for example, that the height H of the preceding vehicle is higher than the height of host vehicle 1, and that the relative height ΔH of the preceding vehicle with respect to host vehicle 1 is equal to or greater than a predetermined threshold.

[0027] Furthermore, in the example shown in FIG. 3(C), the preceding vehicle 4 is a two-wheeled vehicle (motorcycle). Here, when the host vehicle 1 is a four-wheeled vehicle, if the preceding vehicle is a two-wheeled vehicle, it is considered that the driver tends to have difficulty in grasping the appropriate inter-vehicle distance D due to the size and behavior of the preceding vehicle. For this reason, when following a two-wheeled vehicle, it is considered that the driver tends to try to maintain a longer inter-vehicle time T or the inter-vehicle time T becomes unstable compared to when following a four-wheeled vehicle such as a standard car. Therefore, the condition for excluding the preceding vehicle from the learning target in consideration of the vehicle type includes the preceding vehicle being a two-wheeled vehicle. In other words, when the preceding vehicle is a two-wheeled vehicle, the ECU 22 does not store the inter-vehicle time T as learning data in the storage device 28.

[0028] Furthermore, if learning control is executed continuously for the same preceding vehicle under the same environment while the vehicle is traveling, there is a possibility that a biased inter-vehicle time T may be continuously acquired for some reason. As a result, there is a possibility that the biased inter-vehicle time T may be mistakenly taken as the driver's preference and learned. The "same environment" here corresponds, for example, to a time when the host vehicle 1 is traveling on the same road at the same vehicle speed.

[0029] To avoid the above-described biased learning, the ECU 22 stops the learning control until a predetermined time TH2 has elapsed since the time headway T for calculating the learned value TL of the time headway T was stored as learning data in the storage device 28. In other words, storage of the next learning data for the time headway T is prohibited.

[0030] Furthermore, to avoid the above-described biased learning, the ECU 22 stops the learning control after storing the time headway T for calculating the learning value TL as learning data in the storage device 28 until the vehicle speed V of the host vehicle 1 changes by a predetermined amount TH3 or more. That is, storage of the next learned data for the time headway T is prohibited. In other words, for example, after the previous learning, if the vehicle speed V changes by a predetermined amount TH3 or more due to moving from a highway to an ordinary road while maintaining following the same preceding vehicle, the newly acquired time headway T is permitted to be stored as learning data in the storage device 28, even if the preceding vehicle is the same as that used in the previous learning. In addition, in an example in which learning data is stored for each vehicle speed range, the predetermined amount TH3 for the vehicle speed V is, for example, a value determined in advance as a value for determining whether the vehicle speed range has shifted to another vehicle speed range.

[0031] FIG. 4 is a table showing an example of a list of learning conditions C according to an embodiment. The learning conditions C in the example shown in FIG. 4 include the above-mentioned learning conditions based on the viewpoints of vehicle type and discontinuity of driving conditions. Specifically, in this table, the learning conditions C are classified into a prerequisite C0, a first condition C1 for following driving, and a second condition C2 for following driving. The first condition C1 is a learning condition that applies to the period from when the host vehicle 1 starts following the preceding vehicle until the host vehicle 1 enters a steady following driving state. The second condition C2 is a learning condition for a steady (in other words, stable) following driving state. The learning condition C is established when all of these conditions C0, C1, and C2 are satisfied.

[0032] The prerequisite C0 includes conditions C0-1 and C0-2, which are conditions based on the model of the preceding vehicle. That is, condition C0-1 is that the height H of the preceding vehicle is less than threshold TH1. This limits the learning targets to models that the driver of the host vehicle 1 can easily maintain visibility, thereby avoiding variations in the learning data of the inter-vehicle time T due to differences in model. Furthermore, condition C0-2 is that the preceding vehicle is not a two-wheeled vehicle. This is avoiding variations in the learning data of the inter-vehicle time T due to differences in the sense of proximity to the preceding vehicle due to differences in model. In addition, instead of conditions C0-1 and C0-2, for example, one of the prerequisites C0 may include that the model of the host vehicle 1 and the preceding vehicle is the same.

[0033] Furthermore, the prerequisite C0 includes, for example, conditions C0-3, C0-4, and C0-5. Condition C0-3 is that the road on which the vehicle 1 is traveling is not congested. Condition C0-4 is that the road surface and visibility conditions are good. Condition C0-5 is that the road is a public road that is not a narrow street. The reason for this is that when the road is congested, the road surface or visibility conditions are not good, or when traveling on a narrow street, the driver's adjustment of the inter-vehicle time T is likely to depend on the surrounding conditions, making it difficult to accurately grasp the driver's preferences.

[0034] The precondition C0 also includes, for example, that the absolute value of the road gradient is small (condition C0-6). The reason for this is that when the absolute value of the road gradient is large, such as on a steep downhill slope, the driver tends to increase the inter-vehicle time T due to factors such as increased braking distance, making it difficult to accurately grasp the driver's preferences. The precondition C0 also includes, for example, that the driving point of the host vehicle 1 is not at a junction of multiple roads (condition C0-7). The reason for this is that the driver's preferences are difficult to accurately grasp because the inter-vehicle time T with the preceding vehicle may increase due to the merging of other vehicles.

[0035] Next, the first condition C1 during following driving includes conditions C1-1 and C1-2 based on the viewpoint of discontinuity of the driving situation described above. TThe condition C1-2 is that a predetermined time TH2 has elapsed since the learning data was acquired (i.e., since the previous learning). T The vehicle speed V must have changed by a predetermined amount TH3 or more since the learning data was acquired (i.e., since the previous learning). This prevents continuous learning when the vehicle is continuously following the same preceding vehicle under the same environment, making it possible to prevent biased learning.

[0036] Condition C1 also includes the requirement that the vehicle continues following the same preceding vehicle immediately before the vehicle reaches a steady following state (condition C1-3). The reason for this is that, for example, if another preceding vehicle cuts in between the preceding vehicle intended by the driver and the vehicle 1 immediately before the vehicle reaches a steady following state, the inter-vehicle time T immediately after the other preceding vehicle is caught may differ from the driver's preference.

[0037] Next, the second condition C2 during following includes, for example, that the vehicle speed V is not extremely low or extremely high (condition C2-1). The reason for this is that when the vehicle is traveling at an extremely low or extremely high speed, the adjustment of the inter-vehicle time T tends to be dependent on the surrounding conditions, making it difficult to accurately grasp the driver's preferences. The second condition C2 also includes, for example, that the absolute value of the relative speed with respect to the preceding vehicle is smaller than a threshold (condition C2-2), that the vehicle speed V is constant (condition C2-3), that the absolute value of the steering angle of the host vehicle 1 is smaller than a threshold (condition C2-4), that the turn signal of the host vehicle 1 is not activated (condition C2-5), and that conditions C2-1 to C2-5 are continuously satisfied (condition C2-6). The reason for this is to grasp a stable following state.

[0038] 2-2. Processing flow 5 is a flowchart showing an example of processing related to vehicle driving control according to the embodiment. The processing of this flowchart is repeatedly executed while the vehicle control system 10 is running.

[0039] In step S100, the ECU 22 (processor 26) determines whether the vehicle 1 is being manually driven or automatically driven. This determination can be made based on the operation state of the driving mode changeover switch 24, for example.

[0040] If the vehicle 1 is being manually driven (step S100; Yes), the process proceeds to step S102. In step S102, the ECU 22 acquires various information necessary for executing learning control. The various information includes the current inter-vehicle time T. The current inter-vehicle time T is calculated, for example, based on the inter-vehicle distance D acquired using the recognition sensor 14 and the vehicle speed V detected using the vehicle state sensor 12. The various information also includes information necessary for determining whether the learning condition C (see, for example, FIG. 4) is met. The information includes the height H of the preceding vehicle. The height H is acquired, for example, using the recognition sensor 14 or vehicle-to-vehicle communication. Other information for determining the learning condition C is also acquired, for example, using information from the vehicle state sensor 12 and the recognition sensor 14, road information (map information), and traffic information.

[0041] In step S104 following step S102, the ECU 22 determines whether the learning condition C is satisfied. More specifically, it determines whether all of the above-mentioned conditions C0 to C2 are satisfied. If the learning condition C is satisfied, that is, if it is determined that the following driving situation is one in which the driver's preference is reflected in the inter-vehicle time T, the process proceeds to step S106.

[0042] On the other hand, if at least one of the conditions C0 to C2 is not satisfied in the process in which the host vehicle 1 reaches a steady following traveling state after starting following, the learning condition C is not established. If the learning condition C is not established, the process proceeds to return.

[0043] In step S106, the ECU 22 stores the time headway T acquired in step S102. That is, if the learning condition C is met, the time headway T is stored as learned data in the storage device 28. More specifically, the time headway T is learned in association with the vehicle speed V. For example, the time headway T is learned (stored) separately for each predetermined vehicle speed range. In addition, the learning condition C includes a second condition C2, i.e., a condition related to a steady following-up traveling state. Therefore, the data of the time headway T is stored in a steady following-up traveling state in which all of the above-mentioned conditions C0, C1, and C2 are satisfied as the learning condition C.

[0044] In step S108 following step S106, the ECU 22 determines whether a predetermined number of data sets for inter-vehicle time T have been accumulated. More specifically, it determines whether the number of data sets for inter-vehicle time T for the same vehicle speed range has reached a predetermined number. This predetermined number is, for example, 30. If the predetermined number of data sets for inter-vehicle time T have not yet been accumulated (step S108; No), the process proceeds to Return. On the other hand, if the predetermined number of data sets have been accumulated (step S108; Yes), the process proceeds to step S110.

[0045] In step S110, the ECU 22 calculates a learned value TL of the inter-vehicle time T. For example, the ECU 22 calculates the learned value TL as the average value of the accumulated data of the inter-vehicle time T. Thereafter, the process proceeds to step S112.

[0046] In step S112, the ECU 22 applies the learned value TL calculated in step S110 to the target inter-vehicle time Tt. The target inter-vehicle time Tt is used during follow-up driving during automatic driving. More specifically, the target inter-vehicle time Tt corresponding to the vehicle speed range associated with the learned value TL calculated in step S110 is updated by the currently calculated learned value TL.

[0047] On the other hand, if the vehicle 1 is in autonomous driving (step S100; No), the process proceeds to step S114. In step S114, the ECU 22 determines whether a predetermined condition for following the preceding vehicle is met, for example, using information obtained from the recognition sensor 14. As a result, if the following condition is met, the process proceeds to step S116. On the other hand, if the following condition is not met, the process proceeds to return.

[0048] In step S116, the ECU 22 controls the travel device 20 to control the (actual) inter-vehicle time T so as to realize the target inter-vehicle time Tt. More specifically, the (actual) inter-vehicle time T is controlled so as to approach the target inter-vehicle time Tt corresponding to the current vehicle speed range. In this way, by the processing of steps S112 to S116, the learning result (learned value TL) of the inter-vehicle time T through learning control is reflected in the control of the inter-vehicle time T during automatic driving.

[0049] In addition, acquisition of the learned value TL may be performed using, for example, a machine learning model instead of the process shown in Fig. 5. That is, this machine learning model is constructed using, for example, various predetermined parameters including the vehicle speed V as input and the learned value TL as output. The various parameters are a plurality of parameters related to the above-mentioned learning condition C. The learning of the machine learning model is performed, for example, using learning data acquired while the vehicle 1 is traveling (i.e., the above-mentioned various parameters as explanatory variables (input) and the inter-vehicle time T as the objective variable).

[0050] 3.Effects When learning inter-vehicle information (inter-vehicle time T or inter-vehicle distance D) during manual driving, if the vehicle model of the preceding vehicle is different, the driver's preferences for inter-vehicle information may change, which may result in variations in the learning results of the inter-vehicle information. According to the present embodiment described above, whether or not to store the inter-vehicle time T for the preceding vehicle as learning data in the storage device 28 is determined depending on the vehicle model of the preceding vehicle. That is, learning control is executed while limiting the vehicle models to be learned (in other words, learning scenarios). This reduces variations in learning results due to changes in preferences caused by differences in vehicle models. Therefore, it becomes possible to learn the driver's preferences for inter-vehicle information at an appropriate time. Then, during autonomous driving, the target inter-vehicle time Tt (or target inter-vehicle distance Dt) appropriately learned based on the driver's preferences can be used. This makes it possible to realize autonomous driving with reduced driver stress.

[0051] 4. Other examples of learning control Each of the above-described preconditions C0-3, C0-4, C0-5, and C0-6 may be used as follows, instead of limiting the driving scene to be learned as in the process of step S104. That is, for example, with respect to precondition C0-3, the learning control of this embodiment may be executed separately for when the road on which the host vehicle 1 is traveling is not congested and when the road is congested. The same applies to the other preconditions C0-4, C0-5, and C0-6. [Explanation of symbols]

[0052] 1 vehicle, 2, 3, 4 preceding vehicle, 10 vehicle control system, 12 vehicle state sensor, 14 recognition sensor, 16 position sensor, 18 communication device, 20 running device, 22 electronic control unit (ECU), 24 driving changeover switch, 26 processor, 28 storage device

Claims

1. A control device that controls a vehicle that can switch between manual driving by a driver and automatic driving, a processor that executes learning control to learn inter-vehicle information, which is an inter-vehicle time or inter-vehicle distance of the host vehicle relative to a preceding vehicle, while the vehicle is being driven manually, and that reflects a learning result of the inter-vehicle information obtained by the learning control in control of the inter-vehicle information during the automatic driving; a storage device that stores the inter-vehicle information as learned data during the manual driving; Equipped with In the learning control, the processor determines whether or not to store the inter-vehicle information regarding the preceding vehicle as the learning data in the storage device, depending on the vehicle type of the preceding vehicle; In the learning control, the processor If the height of the preceding vehicle is less than a threshold value, the vehicle-to-vehicle distance information is stored in the storage device as the learned data; If the height is equal to or greater than the threshold value, the inter-vehicle information is not stored in the storage device as the learned data. Vehicle control device.

2. In the learning control, the processor The next learning data is not stored until a predetermined time has elapsed since the vehicle-to-vehicle information was stored as the learning data in the storage device. The vehicle control device according to claim 1 .

3. In the learning control, the processor The storage of the next learned data is not executed until the vehicle speed of the host vehicle changes by a predetermined amount or more after the vehicle-to-vehicle distance information is stored in the storage device as the learned data. The vehicle control device according to claim 1 .

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