In-vehicle systems

Abstract

To provide an on-vehicle system capable of preventing an occupant who does not grasp the behavior of a vehicle from becoming uncomfortable.SOLUTION: An on-vehicle system includes: a control device; a monitoring device that monitors whether each of a plurality of occupants in a vehicle grasps a behavior of the vehicle; and a storage device that stores a learned model for emotion prediction. The control device comprises: determining, based on a monitoring result of the monitoring device, whether there is an occupant who does not grasp the behavior of the vehicle in the plurality of occupants; determining, based on a result of the determination, a target occupant whose emotion is to be predicted, from the plurality of occupants; predicting the emotion of the determined target occupant by using the learned model; and executing vehicle control in accordance with a prediction result of the emotion of the target occupant.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to an in-vehicle system. [Background technology]

[0002] Patent Document 1 discloses a technique for generating driving advice based on the difference in emotions between the driver and passengers. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-098779 Summary of the Invention [Problem to be solved by the invention]

[0004] If the occupants are not aware of the vehicle's behavior, such as acceleration / deceleration, turning left or right, and bumps, they are likely to experience unpleasant feelings such as car sickness.

[0005] The present invention has been made in consideration of the above-mentioned problems, and its purpose is to provide an in-vehicle system that can prevent occupants who are not aware of the vehicle's behavior from feeling uncomfortable. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems and achieve the objectives, the in-vehicle system of the present invention is an in-vehicle system comprising a control device, a monitoring device that monitors whether each of multiple occupants in the vehicle is aware of the vehicle's behavior, and a storage device that stores a trained model for emotion estimation, wherein the control device is configured to determine, based on the monitoring results of the monitoring device, whether there is any occupant among the multiple occupants who is not aware of the vehicle's behavior, and based on the determination result, determine a target occupant from the multiple occupants whose emotion is to be estimated, use the trained model to estimate the emotion of the determined target occupant, and execute vehicle control according to the estimation result of the emotion of the target occupant.

[0007] As a result, in the vehicle system according to the present invention, vehicle control is performed by prioritizing the feelings of occupants who are not aware of the vehicle's behavior, thereby preventing the occupants from feeling uncomfortable.

[0008] In addition, in the above, determining the target occupant based on the judgment result may include determining the target occupant from one or more occupants when there are one or more occupants among the multiple occupants who do not understand the behavior of the vehicle.

[0009] This makes it possible to determine the target occupant from among a plurality of occupants, one or more of whom are not aware of the vehicle's behavior.

[0010] In addition, in the above, the vehicle may further include an imaging device positioned in a position capable of capturing images of the faces of the multiple occupants, and the trained model is generated by machine learning to derive an estimated result of a person's emotions from image data showing the person's facial expression, and estimating the emotion of the target occupant using the trained model may be configured by providing the trained model with image data showing the facial expression of the target occupant obtained by the imaging device, and performing calculation processing of the trained model to obtain an estimated result of the emotion of the target occupant from the trained model.

[0011] This makes it possible to estimate the emotion of the target occupant from the facial expression captured by the imaging device.

[0012] In addition, in the above, the monitoring device may be configured to be composed of the imaging device, and determining whether or not there is an occupant who is not aware of the behavior of the vehicle may be configured to determine whether or not there is an occupant among the multiple occupants who is not aware of the behavior of the vehicle based on image data obtained by the imaging device.

[0013] This makes it possible to determine whether an occupant is unaware of the vehicle's behavior based on image data obtained by the imaging device.

[0014] In addition, in the above, executing the vehicle control may include executing vehicle control that limits the range of acceleration of the vehicle in accordance with the estimated emotion of the target occupant when it is determined that there is an occupant who does not understand the behavior of the vehicle.

[0015] This prevents occupants who are not aware of the vehicle's behavior from feeling uncomfortable due to sudden acceleration or deceleration. [Effects of the Invention]

[0016] The in-vehicle system according to the present invention has an effect of preventing occupants who are not aware of the behavior of the vehicle from feeling uncomfortable. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a diagram showing an outline of a vehicle equipped with an in-vehicle system according to a first embodiment. [Figure 2] 4 is a flowchart showing an example of control performed by a control device. [Figure 3] FIG. 10 is a diagram showing an outline of a vehicle equipped with an in-vehicle system according to a second embodiment. [Figure 4]FIG. 10 is a diagram showing an outline of a vehicle equipped with an in-vehicle system according to a third embodiment. [Figure 5] FIG. 10 is a diagram showing an outline of a vehicle equipped with an in-vehicle system according to a fourth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0018] (Embodiment 1) A first embodiment of an in-vehicle system according to the present invention will be described below, although the present invention is not limited to this embodiment.

[0019] FIG. 1 is a diagram showing an outline of a vehicle 1 equipped with an in-vehicle system 2 according to the first embodiment.

[0020] 1, a vehicle 1 according to the embodiment includes an in-vehicle system 2, a steering wheel 4, front seats 31 and 32, and a rear seat 33. An arrow A in FIG. 1 indicates the traveling direction of the vehicle 1.

[0021] Occupants 10A, 10B, and 10C are seated in the front seats 31, 32 and the rear seat 33, respectively. Occupant 10A, seated in the front seat 31 opposite the steering wheel 4, is the driver of vehicle 1. Arrow LS1 in FIG. 1 indicates the direction of the line of sight of occupant 10B. Arrow LS2 in FIG. 1 indicates the direction of the line of sight of occupant 10C. In the following description, when there is no need to distinguish between occupants 10A, 10B, and 10C, they will simply be referred to as occupants 10.

[0022] The in-vehicle system 2 is configured with a control device 21, a storage device 22, an in-vehicle camera 23, and the like.

[0023] The control device 21 is configured by, for example, an integrated circuit including a CPU (Central Processing Unit). The control device 21 executes programs stored in the storage device 22. The control device 21 also acquires image data from, for example, an in-vehicle camera 23.

[0024] The storage device 22 includes, for example, at least one of a ROM (Read Only Memory), a RAM (Random Access Memory), an SSD (Solid State Drive), and an HDD (Hard Disk Drive). The storage device 30 does not need to be a single physical element, but may include multiple elements that are physically separated from one another. The storage device 22 stores programs executed by the control device 21, etc. The storage device 22 also stores various data used when executing programs such as trained machine learning models described below, such as a trained model for determining whether the behavior of the vehicle 1 is understood, a trained model for emotion estimation, and a trained model for vehicle control.

[0025] 1, the in-vehicle camera 23 is an imaging device placed in a position inside the vehicle where it can capture images of the faces of multiple occupants 10A, 10B, and 10C. Image data captured by the in-vehicle camera 23 is transmitted to the control device 21 and temporarily stored in the storage device 22. The in-vehicle camera 23 also functions as a monitoring device for monitoring whether each of the multiple occupants 10A, 10B, and 10C of the vehicle 1 is aware of the behavior of the vehicle 1.

[0026] The control device 21 can detect the facial direction, line of sight, and facial expression of the occupant 10 based on image data captured by the in-vehicle camera 23. Furthermore, the control device 21 can determine, using AI (Artificial Intelligence) with a machine-learned trained model, whether or not there is an occupant 10 who does not understand the behavior of the vehicle 1, and the emotions of the occupant 10 based on the facial expression of the occupant 10, based on image data of the facial expression of the person captured by the in-vehicle camera 23. Note that determining whether or not there is an occupant 10 who does not understand the behavior of the vehicle 1 is performed by determining whether or not there is an occupant 10 who does not understand the behavior of the vehicle 1, based on image data obtained by the in-vehicle camera 3. Furthermore, the control device 21 can determine the content of vehicle control based on the emotions of the occupant 10 with AI with a machine-learned trained model.

[0027] The trained model for determining whether the behavior of the vehicle 1 is understood is a trained machine learning model, which has been trained by supervised learning, for example, according to a neural network model, to output an estimated emotion result from input data. The trained model for determination is generated by repeatedly executing a learning process using a training dataset, which is a combination of input and result data. The training dataset includes, for example, multiple pieces of training data, in which input data, such as image data showing a person's face, such as the gaze of the occupant 10, is labeled as output indicating whether the behavior of the vehicle 1 is understood. The labeling of the input data as indicating whether the behavior of the vehicle 1 is understood is performed, for example, by a person skilled in the art. In this way, when the trained model for determination, trained using the training dataset, receives input data, it executes the arithmetic processing of the trained model to output whether the behavior of the vehicle 1 is understood.

[0028] The trained model for emotion estimation is a trained machine learning model that has been trained, for example, by supervised learning according to a neural network model, to output an emotion estimation result from input data. The training dataset in the trained model for emotion estimation includes, for example, a plurality of training data in which input data, such as image data showing a person's facial expression, such as the facial expression of the occupant 10, is labeled with the emotion of the occupant 10 to be output. The labeling of the emotion of the occupant 10 to the input data is performed, for example, by a person skilled in the art. In this way, the trained model for emotion estimation trained using the training dataset receives input data and outputs an estimation result of the emotion of the occupant 10 by executing arithmetic processing of the trained model.

[0029] The data used to determine whether the occupant 10 is aware of the vehicle's behavior and the data used to estimate the emotions of the occupant 10 who is not aware of the vehicle's behavior may be the same or different.

[0030] The trained model for vehicle control is a trained machine learning model that has been trained, for example, through supervised learning according to a neural network model, to output vehicle control results from input data. The training dataset in the trained model for vehicle control includes multiple training data in which input data, such as an estimated emotion of the occupant 10, is labeled with output vehicle control details. The labeling of the vehicle control details for the input data is performed, for example, by a person skilled in the art. In this way, the trained model for vehicle control trained using the training dataset receives input data and outputs vehicle control details by executing arithmetic processing of the trained model. Examples of vehicle control details include limiting the range of acceleration or ensuring that the steering angle and lateral G are below a threshold.

[0031] In addition, when determining the content of vehicle control, the control device 21 may determine the content of vehicle control from the emotions of the occupant 10 based on rules that associate the emotions of the occupant 10 with the content of vehicle control, rather than using a learned model for vehicle control.

[0032] In the in-vehicle system 2 according to the embodiment, the control device 21 executes vehicle control based on the emotion of the occupant 10. Here, if there are multiple occupants 10A, 10B, and 10C inside the vehicle 1, it may be unclear who should be the target of emotion estimation by the AI of the control device 21. Therefore, the control device 21 prioritizes, among the multiple occupants 10A, 10B, and 10C, the occupant 10 whose behavior of the vehicle 1, such as deceleration, acceleration, or turning, is not known, as the target of emotion estimation.

[0033] The control device 21 determines whether the occupant 10 is aware of the behavior of the vehicle 1, for example, from the line of sight of the occupant 10. That is, as shown in Fig. 1, occupant 10B, who is looking forward from the front seat 32 and looking at the scenery outside the vehicle, has a line of sight LS1 in the same direction as the traveling direction A of the vehicle 1, and therefore is determined to be aware of the behavior of the vehicle 1, such as acceleration / deceleration, right / left turns, and bumps. On the other hand, occupant 10C, who is looking sideways from the rear seat 33 and looking at the scenery outside the vehicle, has a line of sight LS2 in a direction different from the traveling direction A of the vehicle 1, and therefore is determined not to be aware of the behavior of the vehicle 1, such as acceleration / deceleration, right / left turns, and bumps.

[0034] The occupant 10A, who is the driver of the vehicle 1, drives the vehicle 1 while looking at the traveling direction A of the vehicle 1, and since he or she accelerates or decelerates the vehicle 1 by his or her own operation, he or she is aware of the behavior of the vehicle 1. A However, when the vehicle 1 is traveling in an autonomous driving mode, the occupant 10A may also be included in the determination of whether or not the occupant 10A is aware of the behavior of the vehicle 1.

[0035] If the occupant 10 does not understand the behavior of the vehicle 1, the occupant 10 may feel uncomfortable because he or she cannot predict the behavior of the vehicle 1, such as acceleration / deceleration, turning right or left, and bumps. Therefore, the control device 21 executes vehicle control to limit the acceleration / deceleration of the vehicle 1 in order to reduce the discomfort. In this way, the execution of vehicle control by the control device 21 includes executing vehicle control to limit the range of acceleration of the vehicle 1, etc., in accordance with the estimation result of the emotion of the target occupant, when it is determined that there is an occupant 10 who does not understand the behavior of the vehicle 1.

[0036] Furthermore, when the control device 21 determines that there are no occupants 10 who do not understand the behavior of the vehicle 1, the control device 21 selects any occupant 10 as the target of emotion estimation. For example, the control device 21 selects the occupant 10C seated in the rear seat 33, which has a poor view of the scenery ahead of the vehicle 1 and is more susceptible to car sickness than the front seats 31, 32, as the target of emotion estimation. Furthermore, when the control device 21 determines that there are no occupants 10 who do not understand the behavior of the vehicle 1, the control device 21 may, for example, prioritize the driver as the target of emotion estimation. In other words, when the control device 21 determines that there are no occupants 10 who do not understand the behavior of the vehicle 1, the control device 21 may prioritize the occupant 10 as the target of emotion estimation based on criteria other than the understanding of the behavior of the vehicle 1.

[0037] FIG. 2 is a flowchart showing an example of the control performed by the control device 21.

[0038] First, in step S1, the control device 21 monitors whether each of the multiple occupants 10 aboard the vehicle 1 understands the behavior of the vehicle 1. Next, in step S2, the control device 21 determines whether any of the multiple occupants 10 does not understand the behavior of the vehicle 1. If the control device 21 determines that no occupant 10 does not understand the behavior of the vehicle 1 exists, the control device 21 determines No in step S2 and ends the series of controls. On the other hand, if the control device 21 determines that any occupant 10 does not understand the behavior of the vehicle 1 exists, the control device 21 determines Yes in step S2 and proceeds to step S3. In step S3, the control device 21 determines a target occupant whose emotion is to be estimated. Note that determining the target occupant based on the determination result that there is any occupant 10 who does not understand the behavior of the vehicle 1 includes determining the target occupant from one or more occupants 10 when one or more occupants 10 who do not understand the behavior of the vehicle 1 exist among the multiple occupants 10. Next, in step S4, the control device 21 estimates the emotion of the determined target occupant using the trained model for emotion estimation. Next, in step S5, the control device 21 determines the content of vehicle control using the trained model for vehicle control based on the estimated emotion of the target occupant. Next, in step S6, the control device 21 executes vehicle control based on the determined content of vehicle control. Thereafter, the control device 21 ends the series of controls.

[0039] The in-vehicle system 2 according to the first embodiment executes vehicle control by prioritizing the feelings of the occupant 10 who does not understand the behavior of the vehicle 1, thereby making it possible to prevent the occupant 10 from feeling uncomfortable.

[0040] (Embodiment 2) Hereinafter, a second embodiment of the in-vehicle system according to the present invention will be described. Note that the description of the second embodiment common to the first embodiment will be omitted as appropriate.

[0041] FIG. 3 is a diagram showing an outline of a vehicle 1 equipped with an in-vehicle system 2 according to the second embodiment.

[0042] As shown in FIG. 3, the vehicle 1 according to the second embodiment has a display 24, which is a display device for AV equipment such as a DVD player, attached to the back of the front seat 31. Also, in FIG. 3, an occupant 10C seated in a rear seat 33 behind the front seat 31 is watching a video displayed on the display 24. Based on image data captured by the in-vehicle camera 23, the control device 21 uses a trained model for determining whether or not the occupant 10C understands the behavior of the vehicle 1, and determines that the occupant 10C, who is watching the display 24 with his / her line of sight LS2 directed toward the display 24, does not understand the behavior of the vehicle 1. The training dataset in the trained model for determination includes, for example, a plurality of pieces of training data in which input data, such as whether or not the occupant 10 is watching the display 24, is labeled with an output indicating whether or not the occupant 10 understands the behavior of the vehicle 1.

[0043] Occupant 10C, who is watching the video displayed on display 24 and does not understand the behavior of vehicle 1, becomes uncomfortable because he or she cannot predict the behavior of vehicle 1, such as acceleration / deceleration, turning right or left, and bumps. Therefore, control device 21 preferentially estimates the emotion of occupant 10C, who does not understand the behavior of vehicle 1, using a trained model for emotion estimation based on the facial expression of occupant 10C, based on image data captured by in-vehicle camera 23. Then, control device 21 executes vehicle control that limits acceleration, etc., using a trained model for vehicle control based on the emotion estimation result.

[0044] Furthermore, in the in-vehicle system 2 according to the second embodiment, the display 24 may be used as a monitoring device that monitors whether each of the multiple occupants 10 inside the vehicle is aware of the behavior of the vehicle 1. That is, the control device 21 may determine whether the occupant 10C is aware of the behavior of the vehicle 1, for example, by detecting the power state of the display 24. If the power of the display 24 is on, the control device 21 determines that the occupant 10C is watching the display 24 and that the occupant 10C is not aware of the behavior of the vehicle 1.

[0045] (Embodiment 3) Hereinafter, an in-vehicle system according to a third embodiment of the present invention will be described. Note that the description of the contents common to the first embodiment of the third embodiment will be omitted as appropriate.

[0046] FIG. 4 is a diagram showing an outline of a vehicle 1 equipped with an in-vehicle system 2 according to the third embodiment.

[0047] In FIG. 4, occupant 10B seated in front seat 32 is asleep. Based on image data of occupant 10 captured by in-vehicle camera 23, control device 21 determines that sleeping occupant 10B does not understand the behavior of vehicle 1, using a trained model for determining whether or not the behavior of vehicle 1 is understood. Note that the training data set in the trained model for determination includes, for example, multiple pieces of training data in which input data such as the facial expression of occupant 10 is given as input and labeled with whether or not the behavior of vehicle 1 is understood, which is the output.

[0048] Furthermore, because the control device 21 cannot estimate the emotion of the sleeping occupant 10B from his facial expression, it prioritizes estimating the emotions of the other occupants 10A and 10C. When the sleeping occupant 10B wakes up, the control device 21 prioritizes estimating the emotion of the occupant 10B using a trained model for estimating the emotion from the facial expression of the occupant 10B based on image data captured by the in-vehicle camera 23. Then, based on the emotion estimation result, the control device 21 executes vehicle control to limit acceleration, etc., using a trained model for vehicle control.

[0049] Furthermore, in the in-vehicle system 2 according to the third embodiment, a wearable device may be used as a monitoring device for monitoring whether each of the multiple occupants 10 in the vehicle is aware of the behavior of the vehicle 1. For example, as shown in FIG. 4 , an occupant 10B seated in a front seat 32 wears a wearable device 25. The wearable device 25 detects activity information, such as the movement and direction of the wearable device 25, using, for example, a triaxial acceleration sensor provided within the device. The control device 21 acquires the activity information from the wearable device 25 via wireless communication or the like, and determines that the occupant 10B is asleep if the occupant 10B has not engaged in strenuous activity for a certain period of time. The control device 21 then determines that the sleeping occupant 10B is not aware of the behavior of the vehicle 1. Note that the wearable device 25 may determine the sleeping state of the occupant 10B based on the activity information and transmit the determination result to the control device 21 via wireless communication or the like.

[0050] (Embodiment 4) Hereinafter, a fourth embodiment of the in-vehicle system according to the present invention will be described. Note that the description of the fourth embodiment common to the first embodiment will be omitted as appropriate.

[0051] FIG. 5 is a diagram showing an outline of a vehicle 1 equipped with an in-vehicle system 2 according to the fourth embodiment.

[0052] 5, in the vehicle 1 according to the fourth embodiment, the occupants 10B and 10C, other than the occupant 10A who is the driver seated in the front seat 31, wear visual cameras 26a and 26B, respectively. The visual cameras 26a and 26B are, for example, sensors worn on the heads of the occupants 10B and 10C to sense the viewpoints of the occupants 10B and 10C, such as head-mounted cameras. In the in-vehicle system 2 according to the fourth embodiment, the visual cameras 26a and 26B are used as monitoring devices that monitor whether the occupants 10B and 10C inside the vehicle are aware of the behavior of the vehicle 1.

[0053] The control device 21 can acquire image data captured by the visual cameras 26a and 26b via wireless communication with the visual cameras 26a and 26b. The control device 21 then senses the viewpoints of the occupants 10B and 10C based on the image data captured by the visual cameras 26a and 26b, and can detect the direction of the line of sight LS1 and LS2 of the occupants 10B and 10C based on the sensing results. The control device 21 determines whether the occupants 10B and 10C are aware of the behavior of the vehicle 1 from the direction of the line of sight LS1 and LS2 of the occupants 10B and 10C using a trained model for determining whether the behavior of the vehicle 1 is understood. That is, as shown in FIG. 5, the occupant 10B, who is looking forward from the front seat 32 and looking at the scenery outside the vehicle, has a line of sight LS1 in the same direction as the traveling direction A of the vehicle 1, and therefore is determined to be aware of the behavior of the vehicle 1. On the other hand, occupant 10C, who is looking sideways from the rear seat 33 and watching the scenery outside the vehicle, has a line of sight LS2 in a direction different from the traveling direction A of the vehicle 1, and is therefore determined to not understand the behavior of the vehicle 1. The learning dataset in the trained model for determination includes, for example, multiple pieces of learning data that are labeled with whether or not the behavior of the vehicle 1 is understood, which is the output, for input data such as the line of sight of occupant 10 given as input.

[0054] Thereafter, the control device 21 uses the image data captured by the visual camera 26b to preferentially estimate the emotion of the occupant 10C, who does not understand the behavior of the vehicle 1, using a trained model for emotion estimation from the facial expression of the occupant 10C. Then, the control device 21 executes vehicle control to limit acceleration and the like using a trained model for vehicle control based on the emotion estimation result.

[0055] In the in-vehicle system 2 according to the first to fourth embodiments described above, for example, when a child is seated in a child seat installed in the rear seat 33 of the vehicle 1, the child may be preferentially determined as an occupant 10 who is not aware of the behavior of the vehicle 1. For example, the in-vehicle system 2 determines whether a seat belt is fastened in the rear seat 33 corresponding to the location where the child seat is installed, based on the detection result of a seat belt sensor. Then, when the control device 21 determines that the occupant 10 in the rear seat 33 where the seat belt is installed is not fastened, the control device 21 determines that the occupant 10 is a child seated in a child seat. Then, when the control device 21 determines that the occupant 10 who is determined not to be aware of the behavior of the vehicle 1 is a child seated in a child seat, the control device 21 implements vehicle control with stricter restrictions (such as the range of acceleration G or lateral G below a threshold). Furthermore, when the control device 21 determines that the child is sleeping based on image data captured by the in-vehicle camera 23, the control device 21 may implement vehicle control with stricter restrictions. [Explanation of symbols]

[0056] 1 vehicle 2. In-vehicle systems 4 Handle 10, 10A, 10B, 10C crew 21 Control device 22 Storage device 23 In-car camera 24 displays 25 Wearable devices 26a, 26b Visual camera 31,32 Front seats 33 Back seat LS1,LS2 line of sight

Claims

1. a control device; a monitoring device that monitors whether each of a plurality of occupants in the vehicle is aware of the behavior of the vehicle; a storage device that stores a trained model for emotion estimation; An in-vehicle system comprising: The control device determining whether or not there is any occupant among the plurality of occupants who does not understand the behavior of the vehicle, excluding the occupant who is the driver of the vehicle, based on the monitoring result of the monitoring device; determining a target occupant from among the plurality of occupants whose emotion is to be estimated based on the determination result; Using the trained model, estimate the emotion of the determined target occupant; and and executing vehicle control in accordance with the estimation result of the emotion of the target occupant. It is structured as follows: When the control device determines that there is no occupant who does not understand the behavior of the vehicle, the control device targets emotion estimation of an occupant sitting in a rear seat of the vehicle. In-vehicle systems.

2. determining the target occupant based on the determination result includes, when one or more occupants who do not understand the behavior of the vehicle are present among the plurality of occupants, determining the target occupant from the one or more occupants. The in-vehicle system according to claim 1 .

3. The vehicle further includes an imaging device disposed in a position capable of capturing images of the faces of the plurality of occupants inside the vehicle, the trained model is generated by machine learning to derive a result of estimating a person's emotion from image data showing the person's facial expression, Estimating the emotion of the target occupant using the trained model includes: Image data showing the facial expression of the target occupant obtained by the imaging device is provided to the trained model, and Executing a calculation process of the trained model to obtain an estimation result of the emotion of the target occupant from the trained model; The in-vehicle system according to claim 1 , wherein the in-vehicle system comprises:

4. the monitoring device is configured by the imaging device, determining whether or not there is an occupant who does not understand the behavior of the vehicle by determining whether or not there is an occupant who does not understand the behavior of the vehicle among the plurality of occupants based on the image data obtained by the imaging device; The in-vehicle system according to claim 3 .

5. The vehicle control is executed when it is determined that there is an occupant who does not understand the behavior of the vehicle. and executing vehicle control to limit a range of acceleration of the vehicle in accordance with the estimation result of the emotion of the target occupant. The in-vehicle system according to any one of claims 1 to 4.

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