Industrial vehicle, speaker sound volume estimation system, and sound volume estimation program

Abstract

To provide an industrial vehicle capable of preventing a person from falling due to being surprised by notification sound.SOLUTION: An industrial vehicle 1 includes a speaker that outputs notification sound to a person H, a learned model including a sound volume estimation unit, and a sound volume change unit. The sound volume estimation unit performs machine learning in advance using training data using a notification distance from the speaker to the person H and a sound volume of the notification sound as input data and a degree of surprise of the notified person as output data. When the sound volume estimation unit receives an input of the notification distance, the sound volume estimation unit estimates a sound volume with which the degree of surprise of the notified person becomes equal to or less than a predetermined degree of surprise. The sound volume change unit changes a sound volume of the speaker to equal to or less than the estimated sound volume.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a speaker volume estimation system for use in an industrial vehicle, an industrial vehicle including the system, and a volume estimation program. [Background technology]

[0002] In recent years, the proportion of workers aged 60 or older among those killed or injured in industrial accidents has been increasing. One of the major workplace accidents involving elderly people is falling while working. Therefore, it is necessary to create a work environment that reduces the number of falls among elderly people.

[0003] Industrial vehicles such as forklifts are equipped with speakers that warn of their approach while driving, and the warning sound helps to avoid contact with pedestrians (see, for example, Patent Documents 1 and 2). However, many elderly people have hearing impairments and tend to have difficulty hearing the warning sound. As a result, it is possible to make the warning sound louder, but if the warning sound is too loud, it may startle pedestrians and cause them to fall over. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 6-149373 [Patent Document 2] Patent Publication No. 2022-139495 Summary of the Invention [Problem to be solved by the invention]

[0005] Therefore, an object of the present invention is to provide an industrial vehicle that can prevent a person from being startled by an alarm sound and falling over. [Means for solving the problem]

[0006] In order to solve the above problems, the industrial vehicle according to the present invention comprises: A speaker that outputs an alarm sound to a person; A trained model having a volume estimation unit; A volume change unit, the volume estimation unit performs machine learning in advance using teacher data in which the notification distance from the speaker to a person and the volume of the notification sound are input data and the surprise level of the person who is notified is output data, and when the notification distance is input, the volume estimation unit estimates a volume at which the surprise level of the person who is notified is equal to or lower than a predetermined level; The volume change unit changes the volume of the speaker to be equal to or lower than the estimated volume.

[0007] The industrial vehicle preferably has Further comprising a camera for photographing a person and generating a person image, The trained model further includes a distance estimation unit, The distance estimation unit performs machine learning in advance using teacher data in which a human image is input data and the reported distance from the speaker to the person is output data, and when a human image is input, it estimates the reported distance from the speaker to the person.

[0008] The industrial vehicle preferably has Further comprising a camera for photographing a person and generating a person image, The trained model further includes a posture estimation unit, The posture estimation unit performs machine learning in advance using training data in which a human image is input data and the type of posture of the person is output data, and when a human image is input, the posture estimation unit estimates the type of posture of the person, When the type of posture of the person is input, the volume estimation unit estimates a volume that will be equal to or lower than a predetermined surprise level based on the type of posture of the person.

[0009] The industrial vehicle preferably has Further comprising a camera for photographing a person and generating a person image, The trained model further includes an orientation estimation unit, The orientation estimation unit performs machine learning in advance using teacher data in which a human image is input data and the orientation of the human face is output data, and when a human image is input, the orientation estimation unit estimates the orientation of the human face, When the direction of the person's face is input, the volume estimation unit estimates a volume that will be equal to or lower than a predetermined surprise level based on the direction of the person's face.

[0010] The industrial vehicle preferably has An environmental sound acquisition unit for acquiring environmental sound is further provided, When the environmental sound is input, the volume estimation unit estimates a volume that will be equal to or lower than a predetermined surprise level based on the environmental sound.

[0011] The industrial vehicle preferably has Further comprising a camera for photographing a person and generating a person image, The trained model further includes a surprise level estimation unit, the surprise level estimation unit performs machine learning in advance using teacher data in which the image of the notified person is input data and the surprise level is output data, and when the image of the person notified by the alarm sound is input, the surprise level estimation unit estimates the surprise level; The volume estimation unit re-learns the volume that is equal to or less than a predetermined surprise level based on the image of the person before the notification, the notification distance, the volume of the notification sound when the notification is made, and the surprise level of the person who is notified.

[0012] The industrial vehicle preferably has Further comprising an irradiation unit that irradiates light with directionality, The illumination unit emits light to warn of the approach of an industrial vehicle.

[0013] In order to solve the above problems, a speaker volume estimation system according to the present invention includes: A speaker volume estimation system for use in an industrial vehicle having a speaker that outputs an alarm sound to a person, A trained model having a volume estimation unit; A volume change unit, the volume estimation unit performs machine learning in advance using teacher data in which the notification distance from the speaker to a person and the volume of the notification sound are input data and the surprise level of the person who is notified is output data, and when the notification distance is input, the volume estimation unit estimates a volume at which the surprise level of the person who is notified is equal to or lower than a predetermined level; The volume change unit changes the volume of the speaker to be equal to or lower than the estimated volume.

[0014] In order to solve the above problems, a volume estimation program according to the present invention includes: A volume estimation program for use in a speaker volume estimation system, comprising: The speaker volume estimation system is a system used in an industrial vehicle having a speaker that outputs an alarm sound to a person, the system comprising: a computer; The volume estimation program is The volume estimation unit performs machine learning in advance using teacher data in which the notification distance from the speaker to a person and the volume of the notification sound are input data and the degree of surprise of the person who is notified is output data, and when the notification distance is input, it estimates the volume at which the person who is notified will be at or below a predetermined level of surprise. Effect of the Invention

[0015] The industrial vehicle according to the present invention can prevent a person from falling over due to being startled by the alarm sound while still achieving the effect of the alarm sound. [Brief description of the drawings]

[0016] [Figure 1] 1 is a schematic diagram showing an industrial vehicle according to an embodiment of the present invention and its surroundings. [Diagram 2] FIG. 2 is a block diagram of a control unit of the industrial vehicle. [Diagram 3] A diagram illustrating the operation of each estimation unit of a trained model. [Figure 4] FIG. 1 is a diagram showing examples of human postures. [Diagram 5] FIG. 13 is a diagram illustrating machine learning of a volume estimation unit. [Figure 6] FIG. 11 is a diagram illustrating an operation of a volume estimation unit. [Figure 7] FIG. 13 is a diagram showing the state where an irradiating unit irradiates light. [Figure 8] FIG. 11 is a flow chart showing an operation of the industrial vehicle to notify a person of its approach. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, an embodiment of an industrial vehicle, a speaker volume estimation system, and a volume estimation program according to the present invention will be described with reference to the accompanying drawings.

[0018] Fig. 1 is a schematic diagram showing an industrial vehicle 1 according to one embodiment of the present invention and its surroundings. As shown in Fig. 1, the industrial vehicle 1 is a forklift truck moving forward along an aisle R. In front of the industrial vehicle 1, there is a pedestrian H1 (hereinafter sometimes referred to as "person H"), and next to the aisle R, a worker H2 is working.

[0019] The industrial vehicle 1 is an autonomously traveling unmanned guided vehicle, but this is merely an example, and the industrial vehicle according to the present invention may be a manned industrial vehicle. The industrial vehicle 1 does not have to be a forklift, and may be, for example, a yard transport vehicle without a fork, a yard towing vehicle, a straddle carrier, or the like, and is not particularly limited.

[0020] As shown in Fig. 1, the industrial vehicle 1 includes a vehicle body 10 and a fork 11. The fork 11 is configured to be capable of ascending and descending, thereby lifting and lowering a load.

[0021] As shown in FIG. 2, the industrial vehicle 1 includes a speaker 12, a camera 13, an irradiation unit 14, and a control unit 18.

[0022] The speaker 12 outputs a directional alarm sound to the person H. The speaker 12 is composed of a plurality of parametric speakers, and is configured to be able to expand and contract the range of directivity according to the distance from the person H. This makes it possible to prevent the speaker 12 from outputting the alarm sound to anyone other than the target person H. Therefore, it is possible to prevent the alarm sound from being output to a worker H2 or the like who is not located in the traveling direction of the industrial vehicle 1, and to prevent the worker H2 from being startled. Since the industrial vehicle 1 moves forward and backward, the speaker 12 is configured to be able to output the alarm sound at least in the forward and backward directions of the industrial vehicle 1. In addition, the number of speakers 12 and the configuration of the directionality of the speaker 12 are not particularly limited.

[0023] The specific sound of the notification sound is not particularly limited. For example, the notification sound may be a buzzer sound, a specific melody, or a human voice.

[0024] The speaker 12 may not output an alarm sound unless the person H is detected by the industrial vehicle 1. Alternatively, the speaker 12 may be configured to output an alarm sound at a predetermined volume only at an intersection of the passage R, even if the person H is not detected. In this embodiment, the speaker 12 is configured to output an alarm sound when the person H is detected by the camera 13 (i.e., when the person H is photographed).

[0025] Moreover, the industrial vehicle 1 further includes a melody output unit (not shown) that outputs a melody around the vehicle while it is traveling. The volume of the melody is set to a volume that will not startle the person H.

[0026] The camera 13 is configured to be able to capture an image of the periphery of the vehicle body 10, captures an image of a person H, and generates a person image HI. The camera 13 is made up of a plurality of cameras 13, and is configured to be able to capture images of at least the front and rear directions of the industrial vehicle 1.

[0027] The irradiation unit 14 is configured to be capable of emitting directional light L. The light L emitted from the irradiation unit 14 may be of multiple colors, or may be configured to be switchable between the multiple colors. The irradiation unit 14 may be configured to emit the light L by blinking it. The irradiation unit 14 is disposed at the front and rear of the vehicle body 10, and is configured to be capable of emitting the light L at least in the forward and backward directions of the industrial vehicle 1.

[0028] The control unit 18 has a computer having a storage device, a memory, and a calculation device, and the storage device stores a volume estimation program. The volume estimation program causes the computer to execute the trained model 20 and the volume change unit 26, which will be described later.

[0029] <Configuration of trained model> 2, the control unit 18 has, as its functional configuration, a trained model 20 and a volume change unit 26. The trained model 20 has a surprise level estimation unit 21, a distance estimation unit 22, a posture estimation unit 23, a direction estimation unit 24, and a volume estimation unit 25.

[0030] The surprise degree estimation unit 21 is, for example, a neural network, and previously learns the correlation by machine learning using teacher data having the human image HI of the notified person H as input data and the surprise degree Q as output data. The machine learning method may be, for example, a machine learning method that learns the correlation between the difference between the human image HI of the person H before being notified and the human image HI of the person H after being notified, and the surprise degree Q. Alternatively, the surprise degree estimation unit 21 may learn by machine learning a face image expressing an emotion such as surprise or impatience as teacher data, and may be configured to estimate the surprise degree Q of the notified person H when the human image HI of the notified person H is input. By such machine learning, the surprise degree estimation unit 21 estimates the surprise degree Q of the notified person H when the human image HI of the person H notified by the alarm sound is input, as shown in FIG. 3A. The surprise degree Q may be output as a numerical value between 0.01 and 1.0, for example.

[0031] The "predetermined surprise level RQ" in the present invention is a surprise level Q that can prevent a user from falling over, dropping something they are holding, or making a mistake in their work due to being startled. This predetermined surprise level RQ may be a value of the surprise level Q, such as 0.7.

[0032] The distance estimation unit 22 is, for example, a neural network, and has previously learned the correlation between these by machine learning using teacher data in which the human image HI is input data and the notification distance DN from the speaker 12 to the person H is output data. As a result, when the human image HI is input, the distance estimation unit 22 estimates the notification distance DN from the speaker 12 to the person H, as shown in FIG. 3B. The estimated notification distance DN is output to the volume estimation unit 25. Note that when the human image HI is not input, the distance estimation unit 22 does not output the notification distance DN to the volume estimation unit 25.

[0033] The posture estimation unit 23 is, for example, a neural network, and has previously learned the correlation between these by machine learning using teacher data having the human image HI as input data and the posture type AP of the person H as output data. As a result, when the posture estimation unit 23 receives the human image HI as input data, it estimates the posture type AP of the person H (hereinafter, may be simply referred to as the "posture AP of the person H") as shown in FIG. 3C. The estimated posture AP of the person H is output to the volume estimation unit 25.

[0034] Fig. 4 is a diagram showing examples of postures AP of a person H. As shown in Fig. 4, types of postures AP of the person H include, for example, a standing posture AP (see Fig. 4A), a posture AP carrying a load (see Fig. 4B), a walking posture AP (see Fig. 4C), and a posture AP doing work (see Fig. 4D).

[0035] The direction estimation unit 24 is, for example, a neural network, and is machine-learned in advance using teacher data in which the human image HI is input data and the facial direction DF of the person H is output data. As a result, when the human image HI is input, the direction estimation unit 24 estimates the facial direction DF of the person H, as shown in FIG. 3D. The facial direction DF of the person H is, for example, the direction of the industrial vehicle 1, the opposite direction to the industrial vehicle 1, the direction of the load that the person H is carrying, the direction of the work tool when working, etc. The estimated facial direction DF of the person H is output to the volume estimation unit 25 and the irradiation unit 14.

[0036] The volume estimation unit 25 is, for example, a neural network, and as shown in Fig. 5, the notification distance DN, the posture AP of the person H, the facial direction DF of the person H, and the volume of the notification sound (hereinafter referred to as "notification volume") db are input as input data, and the correlation between them is machine-learned in advance using teacher data having the surprise level Q as output data. As a result, when the notification distance DN, the posture AP of the person H, and the facial direction DF estimated by the distance estimation unit 22, the direction estimation unit 24, and the posture estimation unit 23 are input, the volume estimation unit 25 estimates the notification volume db that is equal to or less than a predetermined surprise level RQ, as shown in Fig. 6. The estimated notification volume db is output to the volume change unit 26.

[0037] In this embodiment, the volume estimation unit 25 does not output the notification volume db to the volume change unit 26 unless the notification distance DN is input (i.e., unless the person H is detected). Note that the volume estimation unit 25 may be configured to estimate the notification volume db that is equal to or less than a predetermined surprise level RQ as zero when the notification distance is equal to or less than a predetermined notification distance DN. This makes it possible to prevent the notification sound from being output when the distance between the industrial vehicle 1 and the person H is too close.

[0038] The volume change unit 26 changes the notification volume db of the speaker 12 to be equal to or lower than the notification volume db estimated by the volume estimation unit 25. The volume change unit 26 makes the speaker 12 output the notification sound at a volume equal to or lower than the estimated notification volume db, thereby preventing the person H from being startled by the notification sound and falling over.

[0039] Furthermore, when the alarm sound is actually output, the volume estimation unit 25 uses the alarm distance DN, the posture AP of the person H, the facial orientation DF of the person H, the actually output alarm sound volume db, and the surprise degree Q estimated by the surprise degree estimation unit 21 at that time as teacher data, and re-learns the correlations among them. That is, the volume estimation unit 25 in this embodiment uses the alarm distance DN, the posture AP of the person H, the facial orientation DF, and the actually output alarm sound volume db estimated by the distance estimation unit 22, the orientation estimation unit 24, and the posture estimation unit 23 as input data, and uses the surprise degree Q estimated by the surprise degree estimation unit 21 as teacher data, and re-learns the correlations among them, thereby making it possible to make more accurate estimation from the next time.

[0040] The illuminating unit 14 irradiates light L in the direction in which the face of the person H is facing based on the facial direction DF estimated by the direction estimating unit 24, thereby alerting the person H of the approach of the industrial vehicle 1. FIG. 7 shows the light L being irradiated to the load when the person H is facing the direction of the load he is carrying. In this way, if the alarm sound is output at a high volume when the person H is facing the load he is carrying, the person H may be startled. Therefore, by reducing the volume of the alarm sound and irradiating light L to the load, the approach of the industrial vehicle 1 can be alerted without startling the person H. The direction in which the irradiating unit 14 irradiates light L may be, for example, the road surface.

[0041] A system including the above-mentioned camera 13, the trained model 20, and the volume change unit 26 corresponds to the speaker volume estimation system of the present invention. In this embodiment, the speaker volume estimation system further includes an irradiation unit 14.

[0042] <Industrial vehicle operation> Next, the operation of the industrial vehicle 1 will be described again with reference to FIG.

[0043] (1) While traveling, the industrial vehicle 1 outputs a melody by the melody output unit (see S1 (step 1) in FIG. 8). In the industrial vehicle 1, the notification volume db is set to zero until the person H is photographed by the camera 13 (see S2 in FIG. 8).

[0044] (2) When the industrial vehicle 1 photographs a person H using the camera 13 (i.e., when it detects person H) (Yes in S3 of Figure 8), the industrial vehicle 1 estimates the warning distance DN, the posture AP of person H, and the facial direction DF based on the human image HI using the learned model 20 (see S4 of Figure 8).

[0045] (3) Next, the industrial vehicle 1 estimates the notification volume db that is equal to or lower than a predetermined surprise level RQ based on the estimated notification distance DN, the posture AP of the person H, and the facial direction DF using the trained model 20 (see S5 in FIG. 8).

[0046] (4) Next, the industrial vehicle 1 changes the notification volume db of the speaker 12 by the volume changer 26 so that the notification volume is equal to or less than the estimated notification volume db (see S6 in FIG. 8).

[0047] (5) Next, the industrial vehicle 1 outputs an alarm sound from the speaker 12 and irradiates light L from the irradiation unit 14 (see S7 in FIG. 8).

[0048] While the industrial vehicle 1 is photographing the person H with the camera 13 (i.e., while the person H is continuously detected), it repeats S4 to S7 in Fig. 8, and automatically changes the notification volume db as the notification distance DN, the posture AP of the person H, and the facial direction DF change. Then, when the camera 13 no longer photographs the person H (i.e., when the person H is no longer detected) (No in S3 in Fig. 8), the notification volume db is changed to zero again (see S2 in Fig. 8).

[0049] The industrial vehicle 1, having the above configuration, can prevent the person H from being startled by the alarm sound and falling over. Furthermore, the industrial vehicle 1 learns not only the correlation between the alarm distance DN, the alarm volume db, and the surprise level Q, but also the correlation between the posture AP and facial direction DF of the person H, using the trained model 20, so it is possible to prevent the alarm volume db being output too loud to a person H concentrating on work or a person H looking in the opposite direction from the industrial vehicle 1, startling the person H and causing him to fall over. Moreover, since the industrial vehicle 1 estimates the alarm distance DN, the posture AP, and the facial direction DF of the person H based on the human image HI, there is no need to add special sensors.

[0050] Although an embodiment of the industrial vehicle, the speaker volume estimation system, and the volume estimation program of the present invention has been described above, the present invention is not limited to the above embodiment. For example, the industrial vehicle, the speaker volume estimation system, and the volume estimation program of the present invention may be implemented by appropriately combining the following modified examples.

[0051] <Modification> The industrial vehicle 1 may be equipped with a known sensor capable of detecting the direction of the person H and the notification distance DN. In this case, the sound volume estimation unit 25 acquires the notification distance DN from the sensor, and the trained model 20 does not need to include the distance estimation unit 22.

[0052] The human image HI when each component of the trained model 20 undergoes machine learning and the human image HI when each estimation is performed may be input data consisting of a plurality of human images HI. In other words, the human image HI according to the present invention is a concept that includes a moving image.

[0053] The irradiating unit 14 may irradiate the light L not in the direction of the face DF of the person H, but, for example, toward the ground in the direction in which the person H is facing.

[0054] The input data of the teacher data when the volume estimation unit 25 performs machine learning may include at least the notification distance DN and the notification volume db. In this case, when the notification distance DN is input, the volume estimation unit 25 estimates the notification volume db that is equal to or less than a predetermined surprise level RQ.

[0055] The industrial vehicle 1 may further include an environmental sound acquisition unit that acquires environmental sound. In this case, the volume estimation unit 25 may further learn correlations between the environmental sound, the notification distance DN, the posture AP of the person H, the facial direction DF of the person H, and the notification volume db by machine learning in advance using teacher data having the environmental sound, the notification distance DN, the posture AP of the person H, and the facial direction DF as output data, and estimate the notification volume db that is equal to or less than a predetermined surprise level RQ when the environmental sound, the notification distance DN, the posture AP, and the facial direction DF of the person H are input. In this case, the volume estimation unit 25 may learn correlations between the environmental sound, the notification distance DN, and the notification volume db by machine learning in advance using teacher data having the surprise level Q as output data, and estimate the notification volume db that is equal to or less than a predetermined surprise level RQ when the environmental sound and the notification distance DN are input.

[0056] The speaker volume estimation system may further include a server computer capable of communicating with the industrial vehicle 1, and the trained model 20 may be configured by the server computer. In this case, the industrial vehicle 1 receives an alarm volume db that is equal to or lower than a predetermined surprise level RQ by communicating with the server computer, and outputs an alarm sound at this alarm volume db. [Explanation of symbols]

[0057] 1. Industrial vehicles (forklifts) 10. Body 11. Fork 12 Speaker 13. Camera 14 Irradiation unit 18 Control Unit 20 Pre-trained models 21 Surprise Level Estimation Unit 22 Distance Estimation Unit 23 Posture estimation section 24 Orientation Estimation Unit 25 Volume Estimation Unit 26 Volume change section Types of AP posture DF Face Direction DN broadcast distance H People (pedestrians, workers) HI People Images L light Q Surprise Level R aisle RQ Predetermined surprise level

Claims

1. A speaker that outputs an alarm sound to a person; A trained model having a volume estimation unit; A volume change unit, the volume estimation unit performs machine learning in advance using teacher data in which the notification distance from the speaker to the person and the volume of the notification sound are input data and the surprise level of the person when the notification is received is output data, and when the notification distance is input, estimates a volume at which the surprise level of the person when the notification is received is equal to or lower than a predetermined level; The volume change unit of the industrial vehicle changes the volume of the speaker to a volume equal to or lower than the estimated volume.

2. Further comprising a camera for photographing the person and generating a person image; The trained model further includes a distance estimation unit, 2. The industrial vehicle according to claim 1, wherein the distance estimation unit performs machine learning in advance using teacher data in which the human image is input data and the reporting distance from the speaker to the person is output data, and when the human image is input, estimates the reporting distance from the speaker to the person.

3. Further comprising a camera for photographing a person and generating a person image, The trained model further includes a posture estimation unit, the posture estimation unit performs machine learning in advance using teacher data in which the human image is input data and a type of posture of the person is output data, and when the human image is input, the posture estimation unit estimates a type of posture of the person; The industrial vehicle according to claim 1 , wherein the volume estimating unit, when receiving an input of the type of posture of the person, estimates a volume that will be equal to or lower than the predetermined surprise level based on the type of posture of the person.

4. Further comprising a camera for photographing a person and generating a person image, The trained model further includes an orientation estimation unit, the orientation estimation unit performs machine learning in advance using teacher data in which the human image is input data and the orientation of the face of the person is output data, and when the human image is input, estimates the orientation of the face of the person; The industrial vehicle according to claim 1 , wherein the volume estimating unit, upon receiving the direction of the person's face, estimates a volume that will be equal to or lower than the predetermined surprise level based on the direction of the person's face.

5. An environmental sound acquisition unit for acquiring environmental sound is further provided, The industrial vehicle according to any one of claims 1 to 4, wherein the volume estimation unit, when receiving the environmental sound, estimates a volume that will be equal to or lower than the predetermined surprise level based on the environmental sound.

6. Further comprising a camera for photographing the person and generating a person image; The trained model further includes a surprise level estimation unit, the surprise level estimation unit performs machine learning in advance using teacher data in which the human image of the notified person is input data and a surprise level is output data, and when the human image of the person notified by the alarm sound is input, the surprise level estimation unit estimates the surprise level; The industrial vehicle according to claim 1, wherein the volume estimation unit re-learns a volume that is below the predetermined surprise level based on the human image of the person before the alert, the alert distance, the volume of the alert sound when the alert is made, and the surprise level of the person who is alerted.

7. Further comprising an irradiation unit that irradiates light with directionality, The industrial vehicle according to claim 1 , wherein the illuminating unit irradiates the light to notify an approach of the industrial vehicle.

8. A speaker volume estimation system for use in an industrial vehicle having a speaker that outputs an alarm sound to a person, A trained model having a volume estimation unit; A volume change unit, the volume estimation unit performs machine learning in advance using teacher data in which the notification distance from the speaker to the person and the volume of the notification sound are input data and the surprise level of the person when the notification is received is output data, and when the notification distance is input, estimates a volume at which the surprise level of the person when the notification is received will be equal to or lower than a predetermined level; The volume change unit changes the volume of the speaker to a volume equal to or lower than the estimated volume.

9. A volume estimation program for use in a speaker volume estimation system, comprising: The speaker volume estimation system is a system used in an industrial vehicle having a speaker that outputs an alarm sound to a person, the system including a computer, The volume estimation program includes: A volume estimation program that performs machine learning in advance using teacher data in which the notification distance from the speaker to the person and the volume of the notification sound are input data and the level of surprise of the person when notified is output data, and that executes the volume estimation program as a volume estimation unit that, when the notification distance is input, estimates the volume at which the level of surprise of the person when notified will be below a predetermined level.

Images