Vehicle manufacturing system and vehicle manufacturing method

Abstract

The present invention provides a vehicle manufacturing system and a vehicle manufacturing method that enable rapid vehicle control. [Solution] The vehicle manufacturing system 50 according to this embodiment is a vehicle manufacturing system that controls multiple vehicles to travel in a convoy during the manufacturing process or transport process, and comprises a vibration sensor 150 provided on the vehicle 100 for detecting vibrations received by the vehicle, a comparison unit 151 for comparing the vibration pattern of the vibration detected by the vibration sensor 150 with a preset reference pattern, and a control unit for controlling the vehicle's movement based on the comparison result.

Description

Technical Field

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[0001] The present disclosure relates to a vehicle manufacturing system and a vehicle manufacturing method.

Background Art

[0002] Patent Document 1 discloses a vehicle manufacturing system. The vehicle travels within a system for manufacturing a vehicle by autonomous control or remote control.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a vehicle manufacturing factory, a plurality of vehicles autonomously travel along a conveyance path and are sequentially manufactured. Therefore, productivity can be improved. On the other hand, it is also desired to control the vehicle from the outside. For example, in an emergency, an operator may want to stop or decelerate the vehicle. In such a case, operating devices such as switches and buttons are installed near the manufacturing line so that the operator can easily operate. However, installing devices in the conveyance path will interfere with the running of the vehicle and the work of the operator. Therefore, there is a problem that an operator working cannot quickly stop the vehicle.

[0005] Therefore, an object of the present disclosure is to provide a vehicle manufacturing system and a vehicle manufacturing method that enable an operator to quickly control a vehicle.

Means for Solving the Problems

[0006] The vehicle manufacturing system according to this disclosure is a vehicle manufacturing system that controls a plurality of vehicles to travel in a convoy during a manufacturing process or a transport process, and comprises: a sensor provided on the vehicle for detecting vibrations received by the vehicle; a comparison unit for comparing the vibration pattern of the vibrations detected by the sensor with a preset reference pattern; and a vehicle control unit for controlling the vehicle based on the comparison result between the vibration pattern and the reference pattern.

[0007] The vehicle manufacturing method according to this disclosure is a vehicle manufacturing method that controls a plurality of vehicles to travel in a convoy during a manufacturing process or a transport process, comprising the steps of: detecting vibrations received by the vehicles using vibration sensors provided on the vehicles; comparing the vibration pattern of the vibrations detected by the vibration sensors with a preset reference pattern; and controlling the movement of the vehicles based on the comparison result between the vibration pattern and the reference pattern. [Effects of the Invention]

[0008] This disclosure provides a vehicle manufacturing system and a vehicle manufacturing method that enable workers to quickly control the vehicle. [Brief explanation of the drawing]

[0009] [Figure 1] This is a schematic diagram showing the overall configuration of the vehicle manufacturing system. [Figure 2] This is a schematic diagram showing a part of the vehicle manufacturing system. [Figure 3] This is a schematic plan view showing a portion of the track layout of the vehicle manufacturing system. [Figure 4] This is a schematic side view showing a portion of the track layout of a vehicle manufacturing system. [Figure 5] This is a block diagram of the control system for a vehicle manufacturing system. [Figure 6] This is a flowchart showing the vehicle manufacturing process. [Figure 7] This is a diagram illustrating the vehicle's driving control. [Figure 8]This is a control block diagram illustrating example 1 of the driving control system. [Figure 9] This is a flowchart to explain example 1 of the driving control system. [Figure 10] This is a control block diagram illustrating example 2 of the driving control system. [Figure 11] This is a flowchart to explain example 2 of the driving control system. [Modes for carrying out the invention]

[0010] Embodiments of the present invention will be described below with reference to the drawings. However, the invention claimed is not limited to the following embodiments. Furthermore, not all of the configurations described in the embodiments are necessarily essential for solving the problem. For clarity of explanation, the following descriptions and drawings have been omitted and simplified as appropriate. In each drawing, the same elements are denoted by the same reference numerals, and redundant explanations have been omitted where necessary.

[0011] Embodiment 1 Vehicle manufacturing system The vehicle manufacturing system 50 according to this embodiment will be described with reference to Figures 1 and 2. Figure 1 is a schematic diagram showing the configuration of the vehicle manufacturing system 50. Figure 2 is a schematic diagram showing two vehicles 100 in motion. Note that in Figure 1, an XY Cartesian coordinate system is shown for illustrative purposes.

[0012] The vehicle manufacturing system (also simply called the system) 50 is used in a vehicle manufacturing plant to manufacture vehicles 100. Alternatively, the vehicle manufacturing system 50 is also used at transport locations where transport processes such as transport to yards or loading onto ships are carried out. As shown in Figure 1, the vehicle manufacturing system 50 includes a server 200, a sensor 300, and a robot 600. Multiple vehicles 100 are self-propelled vehicles that can move on their own during the manufacturing process. The vehicle manufacturing system 50 controls the multiple vehicles 100 to move in a convoy.

[0013] The sensor 300 includes a communication device 330 that transmits and receives data to and from the server 200. The server 200 includes a communication device 230 that transmits and receives data to and from the sensor 300. Further, as shown in FIG. 2, the communication device 230 has a function of transmitting and receiving data to and from the vehicle 100. Also, the vehicle 100 includes a communication device 130 that receives data from the server 200. Each vehicle 100 includes the communication device 130.

[0014] The communication device 130, the communication device 230, and the communication device 330 may each be a general-purpose device such as a network hub or a router device. The communication device 130, the communication device 230, and the communication device 330 use, for example, general-purpose wireless communication such as WiFi (registered trademark). An address for identifying a communication partner is set for each of the communication device 130, the communication device 230, and the communication device 330. The communication address is, for example, an IP (Internet Protocol) address.

[0015] Each vehicle 100 is an unfinished vehicle. As shown in FIG. 1, the vehicle 100 travels along a preset running path TR. As the vehicle 100 travels along the running path, the vehicle 100 is manufactured. Specifically, while the vehicle 100 is running on the running path, an operator W or a robot 600 or the like executes operations such as component assembly, switch operation, welding, and inspection. Thereby, the operations of each manufacturing process are executed. And the vehicle 100 is manufactured by performing the operations of each manufacturing process in a predetermined order.

[0016] A plurality of vehicles 100 travel in a formation. Specifically, the vehicles 100 travel at a constant speed so that the vehicle distance remains constant at a predetermined distance. Further, the speeds of the plurality of vehicles 100 are the same. Also, the running path TR has a straight-ahead area TR1 where the vehicle 100 travels straight and a turning area TR2 where the vehicle 100 turns. In the straight-ahead area TR1, the running path TR is linear.

[0017] The turning area TR2 is the location where the vehicle 100 changes direction. In the turning area TR2, the vehicle 100 makes a U-turn. In the turning area TR2, for example, the traveling path TR is in the shape of an arc having a predetermined radius of curvature. In the turning area TR2, the traveling path TR is a semi-circle. The turning areas TR2 are provided at both ends of the straight traveling area TR1. For example, when the vehicle 100 advances in the +X direction in the straight traveling area TR1, it reaches the turning area TR2. When the vehicle 100 makes a 180-degree turn in the turning area TR2, it advances in the -X direction in the straight traveling area TR1. Conversely, when the vehicle 100 advances in the -X direction in the straight traveling area TR1, it reaches the turning area TR2. When the vehicle 100 makes a 180-degree turn in the turning area TR2, it advances in the +X direction in the straight traveling area TR1. In this way, the vehicle 100 is sequentially manufactured by passing through the straight traveling area TR1 and the turning area TR2 alternately.

[0018] The sensor 300 is a camera that images the moving or stationary vehicle 100. The sensor 300 images one or a plurality of vehicles 100. The sensor 300 is provided for detecting the inter-vehicle distance. Based on the image captured by the sensor 300, the server 200 can detect the position of the vehicle 100 in the factory. For example, it is installed on the wall surface, columns, ceiling, etc. of the factory and images the vehicle 100 from an obliquely upward direction. The sensor 300 images an image with an angle of view including two or more vehicles 100 forming a queue. The sensor 300 is set at the same height as the vehicle 100 and may image two or more vehicles 100 from the side.

[0019] The communication device 330 transmits the captured image captured by the sensor 300 to the server 200. The communication device 330 may transmit not only the captured image but also the information obtained from the captured image to the server 200. That is, the communication device 330 transmits the detection result detected by the sensor 300. Note that the communication device 330 may be built into the sensor 300 or may be a separate body. Also, the communication device 330 may be shared by a plurality of sensors 300. That is, when a plurality of sensors 300 are installed, one communication device 330 may transmit data to the server 200.

[0020] In this manner, when the sensor 300 captures an image of the vehicle 100, the communication device 330 transmits the captured image and other data to the server 200. The communication device 230 receives the captured image data from the sensor 300. The server 200 can estimate the distance between vehicles by performing predetermined image processing on the image captured by the sensor 300. For example, the server 200 calculates the distance between vehicles in a convoy of multiple vehicles 100. The number of vehicles in the convoy is not particularly limited; it must be three or more.

[0021] Furthermore, the sensor 300 for detecting the distance between vehicles is not limited to a camera. The sensor for detecting the distance between vehicles may be various types of sensors such as an RGB camera, a far-infrared camera, or LiDAR. The sensor 300 is not limited to an optical sensor; it may also be radar. Of course, two or more sensors 300 may be installed, or two or more types of sensors 300 may be used in combination. For example, the sensor 300 may include both LiDAR and a camera.

[0022] The communication device 330 transmits the detection result to the server 200. As described above, the detection result transmitted by the sensor 300 may be an captured image or information extracted from the image. For example, if the sensor 300 has an image processing function, the sensor 300 transmits information extracted by image processing to the server 200.

[0023] Furthermore, the sensor 300 may be mounted on the vehicle 100, as shown in Figure 2. For example, an on-board camera, LiDAR, or radar can be the sensor 300. If the sensor 300 is an on-board camera, the sensor 300 will capture an image of the vehicle 100 in front. If the sensor 300 is an on-board LiDAR, the sensor 300 will measure the distance to the vehicle 100 in front. The communication device 130 will transmit the image and measurement results to the server 200.

[0024] The server 200 controls the vehicle 100 so that it moves along the track TR. Furthermore, the server 200 controls multiple vehicles 100 so that they travel in a convoy. For example, the vehicles 100 travel in a single file along the track TR. The server 200 transmits control signals to each vehicle 100 via the communication device 230.

[0025] Furthermore, the detailed configuration of the track TR and its surrounding equipment will be explained using Figures 3 and 4. Figure 3 is a schematic plan view showing a vehicle traveling on the track TR and workers W in its vicinity. Figures 3 and 4 show three vehicles 100 autonomously traveling along the track TR.

[0026] Here, the area in which vehicle 100 travels is defined as the travel area A. The travel area A includes the track TR. In this XY plan view, since the track TR is a straight line along the X direction, the travel area A is a strip-shaped area along the X direction. The travel area A is a strip-shaped area with a width approximately the same as the width of vehicle 100.

[0027] As shown in Figures 3 and 4, each vehicle 100 is equipped with a vibration sensor 150. The vibration sensor 150 detects vibrations experienced by the vehicle 100. The vibration sensor 150 is, for example, a sensor for airbags. In other words, the vibration sensor 150 is a satellite sensor for collision detection. After the vehicle 100 is completed, if the vehicle 100 is subjected to a large impact, the vibration sensor 150 detects the impact. The vibration sensor 150 then outputs a signal to deploy the airbags.

[0028] For example, vibration sensors 150 are mounted on the front, rear, left, and right sides of vehicle 100. As shown in the central vehicle 100 in Figures 3 and 4, the vibration sensors 150 located on the front, rear, left, and right sides of vehicle 100 are designated as vibration sensors 150F, 150B, 150L, ​​and 150R, respectively. Vibration sensor 150F is installed on the front of vehicle 100, vibration sensor 150B is installed on the rear of vehicle 100, vibration sensor 150L is installed on the left side of vehicle 100, and vibration sensor 150R is installed on the right side of vehicle 100.

[0029] The vibration sensors 150 are mounted on the left and right doors, front and rear bumpers, etc. The vibration sensors 150 may be sensors other than airbag sensors. The vibration sensors 150 may also detect pressure and acceleration. The vibration sensors 150 output a detection signal to the ECU or other device corresponding to the detected vibration. The detection signal indicates the change in vibration over time. Note that there may be one or more vibration sensors 150 mounted on a single vehicle 100. Also, the placement of the vibration sensors 150 is not limited to the four locations of front, rear, left, and right.

[0030] A worker W is present around vehicle 100. Worker W is performing tasks on vehicle 100. While vehicle 100 is traveling in travel area A, worker W performs tasks such as assembling parts, operating switches, welding, and inspection. This completes the tasks for each manufacturing process. Vehicle 100 is manufactured when the tasks for each manufacturing process are performed in a predetermined order. Worker W may be walking at a distance from vehicle 100. Alternatively, worker W may be walking towards vehicle 100 carrying parts or other items.

[0031] Furthermore, control equipment 30 is provided around the travel area A. The control equipment 30 transmits a control signal to the server 200 to stop the vehicle 100. The control equipment 30 is installed, for example, on the ceiling or walls of the factory building. The installation location of the control equipment 30 may be movable. In this case, control equipment 30 is provided on both sides of the travel area A. That is, control equipment 30 is provided on both the +Y side and the -Y side of the travel area A. Of course, the installation location and number of control equipment 30 are not particularly limited.

[0032] A string 31 is connected to the control device 30. The control device 30 and the string 31 are installed at a height that does not interfere with the worker W or the vehicle 100. Furthermore, a suspension string 32 is attached to the string 31. The suspension string 32 is suspended at a height that can be reached by the worker W. When the worker W pulls the suspension string 32, the control device 30 turns on and transmits a control signal. As a result, the vehicle 100 stops.

[0033] When worker W detects an anomaly or trouble, they can pull the suspension cord 32 to bring the vehicle 100 to an emergency stop. The suspension cord 32 functions as a stop switch to stop the vehicle 100. In other words, when worker W pulls the suspension cord 32, which is the stop switch, the control device 30 turns on and sends a control signal to the server 200. As a result, the vehicle 100 stops. The control device 30, the cord 31, and the suspension cord 32 function as emergency stop devices.

[0034] Here, the suspension cord 32 is positioned outside the travel area A so as not to interfere with the vehicle 100. If the suspension cord 32, which is operated by the worker W, is installed in the travel area A, it may come into contact with the moving vehicle 100. There may be cases where the worker W, who is near the vehicle 100, wants to stop the vehicle 100 immediately. Therefore, the suspension cords 32 are installed on both sides of the travel area A. In other words, the suspension cords 32 are installed outside the travel area A.

[0035] Furthermore, if worker W, who is in the travel area A, wants to stop the vehicle 100 using the suspension cord 32, worker W will need to move to the suspension cord 32 and pull it. Therefore, if the distance from worker W to the suspension cord 32 is far, it becomes difficult to quickly pull the suspension cord 32. For this reason, in this embodiment, the vehicle 100 can be controlled based on the detection result of the vibration sensor 150.

[0036] The control of the vehicle 100 during the manufacturing or transport process will be explained below using Figure 5. Figure 5 is a block diagram showing the configuration of the control system of the vehicle manufacturing system 50. As shown in Figure 5, the server 200 is equipped with a remote control unit 210. Although Figure 5 shows one vehicle 100 and one sensor 300, multiple vehicles 100 and sensors 300 are provided, as shown in Figure 1.

[0037] The vehicle 100 includes a vehicle control unit 115, an actuator group 120, a communication device 130, a vibration sensor 150, and a comparison unit 151. Although only one vibration sensor 150 is shown in Figure 5 for the vehicle 100, the vehicle 100 may be equipped with multiple vibration sensors 150, as shown in Figures 3 and 4.

[0038] The sensor 300 is equipped with a communication device 130. The server 200 is not limited to a single physical device, but may be distributed. For example, the database may be a separate storage device or cloud server located independently of the processor.

[0039] The communication device 230 includes a receiver 231 and a transmitter 232. The receiver 231 receives various signals and data from the sensor 300 and the vehicle 100. For example, the receiver 231 receives data indicating the detection result from the sensor 300. The data received from the sensor 300 may be image data or data extracted from image data.

[0040] The transmitter 232 transmits various signals and data to the sensor 300 and the vehicle 100. For example, the transmitter 232 transmits control instruction values ​​to the vehicle 100. Of course, the server 200 may also send and receive data other than those mentioned above. For communication between the receiver 231 and the transmitter 232, it is possible to use processing in accordance with general-purpose communication standards such as WiFi (registered trademark).

[0041] The communication device 130 of vehicle 100 is a wireless terminal device for wireless communication with server 200. The communication device 130 is configured with an IP (Internet Protocol) address, etc. When the communication device 130 of vehicle 100 receives a control instruction value, vehicle 100 moves according to the control instruction value. The actuator group 120 includes wheel motors for driving the wheels, steering motors for controlling the steering angle, brakes for stopping the vehicle, etc. The vehicle control unit 115 generates control signals to control the actuator group 120 according to the control instruction. The vehicle control unit 115 may be composed of an ECU (Electronic Control Unit). This allows vehicle 100 to move along the track TR.

[0042] Sensor 300 is provided to detect the distance between vehicles. For example, the server 200 can determine the distance between the preceding and succeeding vehicles 100 by performing image processing on the image from sensor 300. The remote control unit 210 of the server 200 generates a control signal based on the vehicle distance. The control signal is, for example, a signal to control the speed of the vehicles. The server 200 generates a control signal and transmits it to the vehicles 100 so that the distance between each vehicle 100 remains constant. As a result, the vehicles 100 travel at the desired speed.

[0043] The vibration sensor 150 detects vibrations experienced by the vehicle 100. The vibration sensor 150 outputs a detection signal corresponding to the detected vibration to the comparison unit 151. The voltage of the detection signal changes over time. Here, the vibration indicated by the detection signal is called the vibration pattern. The vibration pattern may be the detection signal itself, or it may be a feature vector extracted from the detection signal.

[0044] The comparison unit 151 compares the vibration pattern indicated by the detection signal with a reference pattern. The comparison unit 151 has a reference pattern pre-set. The comparison unit 151 outputs the comparison result to the vehicle control unit 115. The vehicle control unit 115 controls the vehicle 100 based on the comparison result between the vibration pattern and the reference pattern.

[0045] The following describes an example of driving control according to the vibration pattern. Here, we describe an example of control to stop the vehicle 100. The comparison unit 151 outputs a match signal to the vehicle control unit 115 if the vibration pattern matches the reference pattern. When the vehicle control unit 115 receives the match signal, it stops the vehicle 100. For example, the vehicle control unit 115 operates the brakes of the actuator group. As a result, the vehicle 100 comes to a quick stop.

[0046] The comparison unit 151 stores the vibrations applied to the vehicle 100 by the worker W in advance as a reference pattern. The reference pattern represents, for example, the vibrations when the worker W strikes the vehicle 100 twice in a row. When the worker W detects an abnormality, they strike the vehicle 100 twice in a row. The vibration pattern then becomes similar to the reference pattern, and the comparison unit 151 determines that the vibration pattern and the reference pattern match. If it is determined that the vibration pattern and the reference pattern match, the vehicle control unit 115 stops the vehicle 100. This allows the vehicle 100 to be stopped quickly when an abnormality is detected. If the worker W wants to control the vehicle 100, they strike the same spot on the vehicle 100 repeatedly. This causes the vehicle 100 to stop quickly.

[0047] The comparison unit 151 performs pattern matching processing between the vibration pattern and the reference pattern. The comparison unit 151 can use pattern matching processing for audio signals, etc. For example, the comparison unit 151 extracts feature vectors from the vibration pattern. Then, the comparison unit 151 calculates the distance between the feature vectors of the vibration pattern and the feature vectors of the reference pattern. The comparison unit 151 can determine, for example, whether the reference pattern and the vibration pattern match based on the distance between the feature vectors.

[0048] Furthermore, the comparison unit 151 may have multiple reference patterns set. In this case, control content may be assigned to each of the multiple reference patterns. The control content may include one or more of the following: emergency stop, temporary stop, deceleration, acceleration, and start. Here, it is assumed that the comparison unit 151 has first to fifth reference patterns set. The first to fifth reference patterns are all distinguishable patterns. For example, the number of times the vehicle 100 is struck and the force of the strikes may differ for each reference pattern.

[0049] For example, if the vibration pattern matches the first reference pattern, the vehicle will make an emergency stop. If the vibration pattern matches the second reference pattern, the vehicle 100 will temporarily stop. If the vibration pattern matches the third reference pattern, the vehicle 100 will decelerate. If the vibration pattern matches the fourth reference pattern, the vehicle 100 will accelerate. If the vibration pattern matches the fifth reference pattern, the vehicle 100 will start moving. In this way, the comparison unit 151 may be configured to have multiple reference patterns, and the control content may be changed according to the reference pattern. This allows the operator W to control the vehicle 100 appropriately with simple operation.

[0050] Furthermore, it is preferable for the comparison unit 151 to compare the vibration patterns detected by the multiple vibration sensors 150 with a reference pattern. For example, the comparison unit 151 may have a reference pattern representing vibration set for each vibration sensor 150. Specifically, if the vehicle 100 is equipped with four vibration sensors 150F, 150B, 150L, ​​and 150R, the first reference pattern consists of four sub-patterns for the front, rear, left, and right directions as one set. The sub-patterns will be different for each reference pattern. The comparison unit 151 determines whether the vibration patterns of all vibration sensors 150F, 150B, 150L, ​​and 150R match the sub-patterns. For example, if all vibration patterns match the reference pattern, the comparison unit 151 outputs a match signal. This improves the accuracy of the determination.

[0051] Furthermore, by using multiple vibration sensors 150, the number of reference patterns can be increased. Therefore, the number of control parameters can be increased. For example, the comparison unit 151 can estimate the position and direction of vibration applied to the vehicle 100 by comparing multiple vibration patterns. Specifically, when worker W strikes the front of the vehicle 100, the vibration detected by vibration sensor 150F will be greater and detected earlier than the vibrations detected by the other vibration sensors 150B, 150L, ​​and 150R. When worker W strikes the front of the vehicle 100, the vibration detected by vibration sensor 150B will be the smallest and detected later.

[0052] Therefore, by mounting vibration sensors 150 at different locations on the vehicle 100, the comparison unit 151 can identify the location and direction of vibration. Depending on at least one of the location and direction in which vibration is applied to the vehicle 100, the comparison unit 151 can determine whether the vibration pattern matches a reference pattern. This improves the accuracy of the determination.

[0053] Specifically, the vibration when worker W strikes vehicle 100 twice from the right side is designated as the first reference pattern. The vibration when worker W strikes vehicle 100 once from the left or right side, and then once from the front, is designated as the second reference pattern. Furthermore, the vibration when worker W strikes vehicle 100 three times from the front is designated as the third reference pattern. The vibration when worker W strikes vehicle 100 once from the front, and then twice from the rear, is designated as the fourth reference pattern. The vibration when worker W strikes vehicle 100 twice from the left side, and then twice from the right side, is designated as the fifth reference pattern. In this way, the number of reference patterns can be increased.

[0054] Furthermore, for highly urgent control actions such as emergency stops, it is preferable to use vibrations that can be easily applied by the operator W as the reference pattern. This allows the operator W to quickly stop the vehicle 100 in the event of an abnormality. For example, for emergency stops, the vibration pattern obtained by striking the same spot is used as the reference pattern. On the other hand, for control actions such as acceleration and starting, urgency is not required. Moreover, if the vehicle 100 accelerates or starts unintentionally, there is a risk that the vehicle 100 may come into contact with the operator W. Therefore, the difficulty of applying the vibration may be changed depending on the urgency of the control action. For example, for acceleration or starting, the vibration pattern obtained by striking multiple times in different locations may be used as the reference pattern.

[0055] Furthermore, the comparison processing by the comparison unit 151 may also use a machine learning model. By inputting the vibration pattern into a model utilizing artificial intelligence, the comparison unit 151 can perform the comparison processing. Examples of machine learning models include deep learning models having multiple hidden layers. As this machine learning model, for example, a neural network trained by supervised learning using a training dataset can be used. The training dataset, for example, has detection signals with correct labels. When training the neural network, it is preferable to update the network parameters by backpropagation to reduce the error between the model's output and the correct labels.

[0056] Here, the machine learning model may be a classification model that takes detection signals from multiple vibration sensors 150 as input. For example, the vibration patterns shown by the four detection signals from vibration sensors 150F, 150B, 150L, ​​and 150R become the input data for the classification model. The classification model outputs a classification result indicating whether the vibration pattern matches a reference pattern. Furthermore, it outputs a classification result indicating which reference pattern the vibration pattern matches. The machine learning model can be a classification model such as k-nearest neighbors or a decision tree.

[0057] For example, when preparing training data for a machine learning model, the detection signals when worker W applies vibrations shown in the first to fifth reference patterns are labeled with the correct answer. Alternatively, the detection signals when applying vibrations other than those shown in the reference patterns are labeled with the correct answer. By performing supervised machine learning using the training data with correct answers labeled, a classification model is constructed. For example, the classification model outputs the probability of matching with each reference pattern. The classification model determines that if the highest probability of matching exceeds a threshold, it matches the reference pattern corresponding to that probability. The classification model determines that if the highest probability of matching is below the threshold, it does not match any of the reference patterns. In this way, the classification accuracy can be improved. Therefore, the vehicle 100 can be controlled appropriately.

[0058] Alternatively, the machine learning model may be a model that takes one vibration sensor 150 as input. In this case, the machine learning model determines whether the vibration pattern of each vibration sensor 150 matches the reference pattern. The machine learning model then determines that there is a match if the vibration pattern of each vibration sensor 150 matches a subpattern of the reference pattern. Alternatively, the machine learning model may output a match probability for each subpattern. In this case, the comparison unit 151 determines whether the reference pattern and the vibration pattern match based on the four match probabilities.

[0059] In this way, the worker W can control the vehicle 100 by applying a predetermined vibration to it. Therefore, the worker W can quickly stop the vehicle 100. In other words, the worker W, who is in the travel area A, can stop the vehicle 100 without having to move to the suspension rope 32. When the worker W detects an abnormality, the worker W can quickly stop the vehicle 100 by applying vibration to it.

[0060] Furthermore, when worker W pulls the suspension cord 32 (see Figures 3 and 4), the control device 30 sends a signal to the server 200 to stop the vehicle 100. The server 200 then sends a stop signal to the vehicle 100 to stop it, as described above. This allows worker W, who is in the non-detection area DA2, to stop the vehicle quickly. Alternatively, the control device 30 may directly send a stop signal to the vehicle 100.

[0061] Furthermore, the reference pattern may differ for each work process. For example, suppose multiple work processes are performed sequentially on a moving vehicle. In this case, the reference pattern may be set according to the work process. For example, suppose worker W performs different tasks depending on the work process. In this case, it is preferable to set the reference pattern so that the vibrations applied to the vehicle 100 by the work do not match the reference pattern. Doing so can improve the accuracy of the determination. In other words, it is possible to prevent the comparison unit 151 from mistakenly determining that the vibration pattern of the vibrations applied by the work matches the reference pattern. Thus, it is possible to prevent the vehicle 100 from being controlled incorrectly.

[0062] The following example describes the process of attaching a part to the rear of vehicle 100. In this case, worker W attaches the part to vehicle 100 using a tool such as a screwdriver. This attachment process applies vibration to vehicle 100 from the rear. Therefore, it is preferable that the reference pattern be the pattern when vibration is applied to vehicle 100 from a direction other than the rear. For example, the vibration when vehicle 100 is struck from the side or from the front can be used as the reference pattern. In this way, the comparison unit 151 can set an appropriate reference pattern for each work process, thereby preventing misjudgments.

[0063] For example, it is preferable that the direction and location of vibration in the reference pattern differ from the location and direction of vibration applied in the work process. Alternatively, the reference pattern may be a vibration applied from a location where a worker is expected to be present and its surroundings during the work process.

[0064] As described above, the order of work processes is defined in the manufacturing process. In this case, the location where each work process is performed within the factory is defined. In other words, the XY coordinates within the factory are associated with the work processes. Therefore, a reference pattern can be set according to the position coordinates of the vehicle 100 as it travels. Alternatively, when the vibration sensor 150 detects vibrations applied during a work process, a setting pattern corresponding to the vibrations of the work may be selected. In this case, it is preferable to measure the pattern of vibrations applied during the work in advance.

[0065] In the comparison unit 151, the reference pattern may be set in advance for each work process. The comparison unit 151 then selects the appropriate reference pattern according to the work process. This improves the accuracy of the judgment, allowing the operator W to control the vehicle 100 more appropriately.

[0066] Furthermore, based on the comparison results, the controlled vehicle 100 is not limited to the vehicle 100 that has been subjected to vibrations matching the reference pattern. For example, the vehicle 100 that has been subjected to vibrations matching the reference pattern and the surrounding vehicles 100 may be controlled. For example, if the vehicle 100 that has been subjected to vibrations stops, the system 50 may control subsequent vehicles 100 to stop.

[0067] Specifically, when the comparison unit 151 outputs a matching signal to the vehicle control unit 115, the vehicle control unit 115 generates a driving control signal to stop the vehicle 100. The communication device 130 transmits the driving control signal, including the vehicle ID of its own vehicle, to the server 200. When the receiver 231 receives the driving control signal from the vehicle 100, the remote control unit 210 determines which vehicle 100 to control based on the driving control signal. The remote control unit 210 generates a remote control signal including the vehicle ID of the determined vehicle 100. The transmitter 232 transmits the remote control signal to the vehicle 100. This makes it possible to control multiple vehicles 100.

[0068] For example, if only the vehicle 100 that is subjected to vibration comes to a complete stop, the distance between it and the next vehicle 100 will gradually decrease. Therefore, it is preferable to have the vehicle 100 that comes to a complete stop, as well as the vehicles 100 that come to a complete stop after it. Also, if the vehicle 100 that is subjected to vibration comes to an emergency stop, there is a possibility that a problem has occurred with that vehicle 100. For this reason, it is preferable for the vehicle control unit 115 to also bring the vehicles 100 in front of or behind that vehicle 100 to an emergency stop. In this way, the number and range of vehicles 100 to be controlled may be set according to the control content. In this manner, multiple vehicles 100 in the vicinity can be controlled based on vibrations applied to one vehicle 100.

[0069] When controlling a plurality of vehicles 100, the driving control signal and the remote control signal may include information indicating the vehicle 100 to be controlled. For example, the driving control signal may include the ID of the vehicle 100 to be controlled. In this case, the vehicle control unit 115 and the communication device 130 generate a driving control signal including the ID of the vehicle to be controlled. The vehicle 100 to be controlled may be determined by the position of the vehicle 100 and the work process.

[0070] Referring to FIG. 6, a vehicle manufacturing method will be described. FIG. 6 is a flowchart showing the vehicle manufacturing method according to the present embodiment.

[0071] First, the vibration sensor 150 detects the vibration applied to the vehicle 100 (S11). The comparison unit 151 compares the vibration pattern detected by the vibration sensor 150 with the reference pattern. (S12). If the vibration pattern does not match the reference pattern (NO in S13), the process ends. The

[0072] If the vibration pattern matches the reference pattern (YES in S13), the vehicle control unit 115 controls the vehicle (S14). As a result, the actuator group 120 operates and the vehicle 100 stops. The vehicle 100 can be controlled promptly.

[0073] Then, the above process is executed for each vehicle 100. Further, the vehicle 100 repeats the above process.

[0074] Hereinafter, a driving control example for controlling the driving of the vehicle 100 in the system will be described.

[0075] <A. Driving Control Example 1> FIG. 7 is a conceptual diagram showing the configuration of the system 50 in Driving Control Example 1. The system 50 includes a plurality of vehicles 100 as moving bodies, a server 200, and one or more sensors 300.

[0076] Furthermore, if the moving object is not a vehicle, the terms "vehicle" and "car" in this disclosure may be replaced with "moving object" as appropriate, and the term "driving" may be replaced with "moving" as appropriate.

[0077] Vehicle 100 is configured to operate autonomously. "Autonomous operation" means operation without the operation of a passenger. Operation refers to operations related to at least one of the following: "going," "turning," or "stopping" of vehicle 100. Autonomous operation is achieved by automatic or manual remote control using a device located outside vehicle 100, or by autonomous control of vehicle 100. Vehicle 100 operating autonomously may have passengers on board who do not perform operation. Passengers who do not perform operation include, for example, people simply sitting in the seats of vehicle 100, or people performing tasks other than operation, such as assembly, inspection, or operating switches, while on board vehicle 100. Operation by a passenger is sometimes called "manned operation."

[0078] In this specification, "remote control" includes "fully remote control," in which all operations of the vehicle 100 are completely determined from outside the vehicle 100, and "partial remote control," in which some operations of the vehicle 100 are determined from outside the vehicle 100. Furthermore, "autonomous control" includes "fully autonomous control," in which the vehicle 100 autonomously controls its own operations without receiving any information from external devices, and "partial autonomous control," in which the vehicle 100 autonomously controls its own operations using information received from external devices.

[0079] In this embodiment, system 50 is used in a factory FC where vehicle 100 is manufactured. The reference coordinate system of the factory FC is the global coordinate system GC. That is, any position within the factory FC is represented by X, Y, Z coordinates in the global coordinate system GC. The factory FC comprises a first location PL1 and a second location PL2. The first location PL1 and the second location PL2 are connected by a track TR on which vehicle 100 can travel. Multiple sensors 300 are installed in the factory FC along the track TR. The position of each sensor 300 in the factory FC is pre-adjusted. Vehicle 100 moves from the first location PL1 to the second location PL2 via the track TR by unmanned operation.

[0080] Figure 8 is a block diagram showing the configuration of system 50. The vehicle 100 includes a vehicle control device 110 for controlling various parts of the vehicle 100, an actuator group 120 including one or more actuators driven under the control of the vehicle control device 110, and a communication device 130 for communicating wirelessly with external devices such as a server 200. The actuator group 120 includes actuators for a drive system to accelerate the vehicle 100, actuators for a steering system to change the direction of travel of the vehicle 100, and actuators for a braking system to decelerate the vehicle 100.

[0081] The vehicle control device 110 is composed of a computer comprising a processor 111, a memory 112, an input / output interface 113, and an internal bus 114. The processor 111, the memory 112, and the input / output interface 113 are connected via the internal bus 114 to enable bidirectional communication. The input / output interface 113 is connected to an actuator group 120 and a communication device 130. The processor 111 implements various functions, including those of a vehicle control unit 115, by executing PG1 stored in the memory 112.

[0082] The vehicle control unit 115 drives the vehicle 100 by controlling the actuator group 120. The vehicle control unit 115 can drive the vehicle 100 by controlling the actuator group 120 using the driving control signal received from the server 200. The driving control signal is a control signal for driving the vehicle 100. In this embodiment, the driving control signal includes the acceleration and steering angle of the vehicle 100 as parameters. In other embodiments, the driving control signal may include the speed of the vehicle 100 as a parameter instead of, or in addition to, the acceleration of the vehicle 100.

[0083] The server 200 is composed of a computer comprising a processor 201, memory 202, an input / output interface 203, and an internal bus 204. The processor 201, memory 202, and input / output interface 203 are connected via the internal bus 204 to enable bidirectional communication. A communication device 205 for communicating with various devices outside the server 200 is connected to the input / output interface 203. The communication device 205 can communicate with the vehicle 100 via wireless communication and can communicate with each sensor 300 via wired or wireless communication. The processor 201 implements various functions, including those of a remote control unit 210, by executing PG2 stored in memory 202.

[0084] The remote control unit 210 acquires detection results from sensors, generates a driving control signal to control the actuator group 120 of the vehicle 100 using the detection results, and transmits the driving control signal to the vehicle 100, thereby driving the vehicle 100 by remote control. In addition to the driving control signal, the remote control unit 210 may also generate and output control signals to control various auxiliary equipment and actuators that operate various devices such as wipers, power windows, and lamps, which are provided on the vehicle 100. In other words, the remote control unit 210 may operate these various devices and auxiliary equipment by remote control.

[0085] Sensor 300 is a sensor located outside of vehicle 100. In this embodiment, sensor 300 is a sensor that detects vehicle 100 from outside of vehicle 100. Sensor 300 is equipped with a communication device (not shown) and can communicate with other devices such as server 200 via wired or wireless communication.

[0086] Specifically, the sensor 300 is comprised of a camera. The camera, as part of the sensor 300, captures an image including the vehicle 100 and outputs the captured image as the detection result.

[0087] Figure 9 is a flowchart showing the processing procedure for vehicle 100's driving control in an example of driving control. In the processing procedure shown in Figure 9, the processor 201 of the server 200 functions as a remote control unit 210 by executing program PG2. The processor 111 of the vehicle 100 functions as a vehicle control unit 115 by executing program PG1.

[0088] In step S110, the processor 201 of the server 200 acquires vehicle position information of the vehicle 100 using the detection result output from the sensor 300. The vehicle position information is the position information that forms the basis for generating the driving control signal. In this embodiment, the vehicle position information includes the position and orientation of the vehicle 100 in the global coordinate system GC of the factory FC. Specifically, in step S110, the processor 201 acquires vehicle position information using the captured image acquired from the camera, which is the sensor 300.

[0089] In detail, in step S110, the processor 201 detects the outline of the vehicle 100 from the captured image, calculates the coordinates of the vehicle 100's positioning point in the coordinate system of the captured image, i.e., the local coordinate system, and obtains the position of the vehicle 100 by converting the calculated coordinates to coordinates in the global coordinate system GC. The outline of the vehicle 100 included in the captured image can be detected, for example, by inputting the captured image into a detection model DM that utilizes artificial intelligence. The detection model DM is prepared, for example, within or outside the system 50 and pre-stored in the memory 202 of the server 200. Examples of the detection model DM include a pre-trained machine learning model that has been trained to implement either semantic segmentation or instance segmentation. As this machine learning model, for example, a convolutional neural network (CNN) trained by supervised learning using a training dataset can be used. The training dataset includes, for example, multiple training images containing vehicle 100, and labels indicating whether each region in the training images represents vehicle 100 or something other than vehicle 100. During CNN training, it is preferable to update the CNN parameters using backpropagation to reduce the error between the output result of the detection model DM and the labels. Furthermore, the processor 201 can obtain the orientation of vehicle 100 by, for example, using the optical flow method, estimating it based on the direction of the vehicle 100's movement vector calculated from the positional changes of the vehicle 100's feature points between frames of the captured images.

[0090] In step S120, the processor 201 of the server 200 determines the next target location that the vehicle 100 should head to. In this embodiment, the target location is represented by X, Y, Z coordinates in the global coordinate system GC. The memory 202 of the server 200 pre-stores a reference route RR, which is the path that the vehicle 100 should travel. The route is represented by a node indicating the starting point, nodes indicating waypoints, a node indicating the destination, and links connecting each node. The processor 201 uses the vehicle position information and the reference route RR to determine the next target location that the vehicle 100 should head to. The processor 201 determines the target location on the reference route RR beyond the vehicle 100's current location.

[0091] In step S130, the processor 201 of the server 200 generates a driving control signal to drive the vehicle 100 toward the determined target position. The processor 201 calculates the vehicle's speed from the change in the vehicle's position and compares the calculated speed with the target speed. Overall, the processor 201 determines the acceleration so that the vehicle 100 accelerates if the speed is lower than the target speed, and determines the acceleration so that the vehicle 100 decelerates if the speed is higher than the target speed. Furthermore, if the vehicle 100 is located on the reference path RR, the processor 201 determines the steering angle and acceleration so that the vehicle 100 does not deviate from the reference path RR, and if the vehicle 100 is not located on the reference path RR, in other words, if the vehicle 100 has deviated from the reference path RR, the processor 201 determines the steering angle and acceleration so that the vehicle 100 returns to the reference path RR.

[0092] In step S140, the processor 201 of the server 200 transmits the generated driving control signal to the vehicle 100. The processor 201 repeats the process of acquiring the position of the vehicle 100, determining the target position, generating the driving control signal, and transmitting the driving control signal at predetermined intervals.

[0093] In step S150, the processor 111 of the vehicle 100 receives a driving control signal transmitted from the server 200. In step S160, the processor 111 of the vehicle 100 controls the actuator group 120 using the received driving control signal, thereby driving the vehicle 100 at the acceleration and steering angle represented by the driving control signal. The processor 111 repeats the reception of the driving control signal and the control of the actuator group 120 at a predetermined cycle. According to the system 50 in this example, the vehicle 100 can be driven by remote control, and the vehicle 100 can be moved without using conveying equipment such as a crane or a conveyor.

[0094] <B:Driving Control Example 2> FIG. 10 is an explanatory diagram showing a schematic configuration of the system 50v in driving control example 2. In this example, the system 50v is different from driving control example 1 in that it does not include the server 200. Also, the vehicle 100v in the configuration can travel by autonomous control of the vehicle 100v. For other configurations, unless otherwise particularly described, they are the same as above.

[0095] In this example, the processor 111v of the vehicle control device 110v functions as the vehicle control unit 115v by executing the program PG1 stored in the memory 112v. The vehicle control unit 115v acquires the output result from the sensor, generates a driving control signal using the output result, outputs the generated driving control signal, and operates the actuator group 120, thereby enabling the vehicle 100v to travel by autonomous control. In this example, in addition to the program PG1, a detection model DM and a reference route RR are stored in the memory 112v in advance.

[0096] FIG. 11 is a flowchart showing the processing procedure of the driving control of the vehicle 100v in Example 2. In the processing procedure of FIG. 11, the processor 111v of the vehicle 100v functions as the vehicle control unit 115v by executing the program PG1.

[0097] In step S210, the processor 111v of the vehicle control device 110v acquires vehicle position information using the detection result output from the camera, which is the sensor 300. In step S220, the processor 111v determines the target position to which the vehicle 100v should next go. In step S230, the processor 111v generates a driving control signal to drive the vehicle 100v toward the determined target position. In step S240, the processor 111v controls the actuator group 120 using the generated driving control signal to drive the vehicle 100v according to the parameters expressed in the driving control signal. The processor 111v repeats the acquisition of vehicle position information, determination of the target position, generation of the driving control signal, and control of the actuators at a predetermined cycle. According to the system 50v in this example, the vehicle 100v can be driven by autonomous control of the vehicle 100v without remote control of the vehicle 100v by the server 200.

[0098] YY: Other examples of driving control (YY1) In the above example, sensor 300 is a camera. However, sensor 300 does not have to be a camera; for example, it could be LiDAR (Light Detection And Ranging). In this case, the detection result output by sensor 300 may be 3D point cloud data representing vehicle 100. In this case, the server 200 and vehicle 100 may acquire vehicle position information by template matching using the 3D point cloud data as the detection result and pre-prepared reference point cloud data.

[0099] In (YY2) Driving control example 1, the server 200 performs the processing from acquiring vehicle position information to generating driving control signals. In contrast, the vehicle 100 may perform at least a part of the processing from acquiring vehicle position information to generating driving control signals. For example, the following forms (1) to (3) may be used.

[0100] (1) The server 200 may acquire vehicle location information, determine the next target location that vehicle 100 should head to, and generate a route from the vehicle 100's current location, as shown in the acquired vehicle location information, to the target location. The server 200 may generate a route to the target location between the current location and the destination, or it may generate a route to the destination. The server 200 may transmit the generated route to vehicle 100. Vehicle 100 may generate a driving control signal so that vehicle 100 travels along the route received from the server 200, and may use the generated driving control signal to control the actuator group 120.

[0101] (2) The server 200 may acquire vehicle location information and transmit the acquired vehicle location information to the vehicle 100. The vehicle 100 may determine the next target location to which the vehicle 100 should go, generate a route from the vehicle 100's current location shown in the received vehicle location information to the target location, generate a driving control signal so that the vehicle 100 travels along the generated route, and control the actuator group 120 using the generated driving control signal.

[0102] (3) In the embodiments of (1) and (2) above, the vehicle 100 is equipped with internal sensors, and the detection results output from the internal sensors may be used in at least one of the generation of a route and the generation of a driving control signal. The internal sensors are sensors mounted on the vehicle 100. The internal sensors may include, for example, sensors that detect the motion state of the vehicle 100, sensors that detect the operating state of each part of the vehicle 100, and sensors that detect the environment around the vehicle 100. Specifically, the internal sensors may include, for example, cameras, LiDAR, millimeter-wave radar, ultrasonic sensors, GPS sensors, acceleration sensors, gyroscopes, etc. For example, in the embodiment of (1) above, the server 200 may acquire the detection results of the internal sensors and reflect the detection results of the internal sensors in the route when generating a route. In the embodiment of (1) above, the vehicle 100 may acquire the detection results of the internal sensors and reflect the detection results of the internal sensors in the driving control signal when generating a driving control signal. In the embodiment of (2) above, the vehicle 100 may acquire the detection results of the internal sensors and reflect the detection results of the internal sensors in the route when generating a route. In the embodiment described in (2) above, the vehicle 100 may acquire the detection results of the internal sensors and reflect the detection results of the internal sensors in the driving control signal when generating the driving control signal.

[0103] (YY3) In the driving control example 2, the vehicle 100v is equipped with an internal sensor, and the detection result output from the internal sensor may be used in at least one of the generation of the route and the generation of the driving control signal. For example, the vehicle 100v may acquire the detection result from the internal sensor and reflect the detection result from the internal sensor in the route when generating the route. The vehicle 100v may acquire the detection result from the internal sensor and reflect the detection result from the internal sensor in the driving control signal when generating the driving control signal.

[0104] (YY4) In driving control example 2, vehicle 100v acquires vehicle position information using the detection results of sensor 300. Alternatively, vehicle 100v may be equipped with an internal sensor, which may acquire vehicle position information using the detection results of the internal sensor, determine the next target location to which vehicle 100v should go, generate a route from vehicle 100v's current location to the target location as shown in the acquired vehicle position information, generate a driving control signal for driving along the generated route, and control the actuator group 120 using the generated driving control signal. In this case, vehicle 100v can drive without using the detection results of sensor 300 at all. Vehicle 100v may also acquire target arrival time and congestion information from outside vehicle 100v and reflect the target arrival time and congestion information in at least one of the route and the driving control signal. Furthermore, all the functional configurations of system 50v may be provided in vehicle 100v. That is, the processing realized by system 50v in this disclosure may be realized by vehicle 100v alone. For example, the leading vehicle 100V may transmit a control instruction value to the following vehicle 100.

[0105] (YY5) In the driving control example 1, the server 200 automatically generates a driving control signal to be transmitted to the vehicle 100. Alternatively, the server 200 may generate a driving control signal to be transmitted to the vehicle 100 in accordance with the operation of an external operator located outside the vehicle 100. For example, the external operator may operate a control device that includes a display for displaying captured images output from the sensor 300, a steering wheel for remotely controlling the vehicle 100, an accelerator pedal, a brake pedal, and a communication device for communicating with the server 200 via wired or wireless communication, and the server 200 may generate a driving control signal in accordance with the operation applied to the control device.

[0106] (YY6) In each of the above driving control examples, the vehicle 100 only needs to have a configuration that allows it to move by unmanned operation, and may be in the form of a platform having the configuration described below. Specifically, in order for the vehicle 100 to perform the three functions of "driving," "turning," and "stopping" by unmanned operation, it only needs to be equipped with at least a vehicle control device 110 and an actuator group 120. When the vehicle 100 acquires information from the outside for unmanned operation, the vehicle 100 may further be equipped with a communication device 130. That is, the vehicle 100 that can move by unmanned operation does not need to have at least some of the interior parts such as the driver's seat and dashboard installed, at least some of the exterior parts such as the bumper and fender installed, and does not need to have a body shell installed. In this case, the remaining parts such as the body shell may be attached to the vehicle 100 before it is shipped from the factory FC, or the remaining parts such as the body shell may be attached to the vehicle 100 after it has been shipped from the factory FC, while the remaining parts such as the body shell are not attached to the vehicle 100. Each part may be attached from any direction, such as the top, bottom, front, rear, right, or left side of the vehicle 100, and each part may be attached from the same direction or from different directions. The positioning of the platform can also be determined in the same way as the vehicle 100 in the first embodiment.

[0107] (YY7) Vehicle 100 may be manufactured by combining multiple modules. A module means a unit composed of multiple parts grouped together according to the part or function of the vehicle 100. For example, the platform of vehicle 100 may be manufactured by combining a front module that constitutes the front part of the platform, a central module that constitutes the central part of the platform, and a rear module that constitutes the rear part of the platform. The number of modules that constitute the platform is not limited to three, but may be two or fewer, or four or more. In addition to, or instead of, the parts that constitute the platform may be modularized, as well as parts that constitute parts of the vehicle 100 that are different from the platform. Various modules may also include any exterior parts such as bumpers and grilles, or any interior parts such as seats and consoles. Furthermore, not limited to vehicle 100, any type of mobile body may be manufactured by combining multiple modules. Such modules may be manufactured, for example, by joining multiple parts by welding or fasteners, or by integrally molding at least a part of the parts that constitute the module as a single part by casting. A molding technique for integrally molding a single component, especially a relatively large component, is also called gigacast or megacast. For example, the front module, central module, and rear module mentioned above may be manufactured using gigacast.

[0108] (YY8) Transporting vehicle 100 using the unmanned operation of the vehicle 100 is also called "autonomous transport." The configuration for realizing autonomous transport is also called a "vehicle remote control autonomous driving transport system." Furthermore, a production method that uses autonomous transport to produce vehicle 100 is also called "autonomous production." In autonomous production, for example, at a factory fuel cell (FC) that manufactures vehicle 100, at least a portion of the transport of vehicle 100 is realized by autonomous transport.

[0109] (YY9) In each of the above driving control examples, some or all of the functions and processes implemented in software may be implemented in hardware. Also, some or all of the functions and processes implemented in hardware may be implemented in software. As hardware for implementing the various functions in each of the above embodiments, various circuits such as integrated circuits and discrete circuits may be used.

[0110] In the above driving control examples 1 and 2, the driving control shown in Figures 3 to 6 can also be applied. For example, the vehicle control unit 115 shown in Figures 8 and 11 performs driving control based on vibration. In addition, in Figures 9 to 11, driving control using the suspension string 32 and control equipment 30 shown in Figures 3 and 4 may also be applied.

[0111] Furthermore, some or all of the processing in the aforementioned sensor 300, vehicle 100, server 200, sensor 300, robot 600, etc., can be implemented as computer programs. Such programs can be stored using various types of non-temporary computer-readable media and supplied to a computer. Non-temporary computer-readable media include various types of tangible recording media. Examples of non-temporary computer-readable media include magnetic recording media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (e.g., magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-R / Ws, and semiconductor memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, RAMs (Random Access Memory)). Programs may also be supplied to a computer using various types of temporary computer-readable media. Examples of temporary computer-readable media include electrical signals, optical signals, and electromagnetic waves. Temporary computer-readable media can be supplied to a computer via wired communication channels such as electric wires and optical fibers, or via wireless communication channels.

[0112] It should be noted that the present invention is not limited to the embodiments described above, and can be modified as appropriate without departing from the spirit of the invention. [Explanation of symbols]

[0113] 30 Control equipment 31 string 32 Hanging string 50 Vehicle Manufacturing Systems 100 vehicles 115 Vehicle Control Unit 120 Actuator Group 130 Communication equipment 150 Vibration Sensor 151 Comparison Section 200 servers 210 Remote Control Unit 230 Communication equipment 231 Receiver 232 Transmitter 257 Driving Control Unit 300 sensors 330 Communication equipment A Driving area

Claims

1. A vehicle manufacturing system that controls multiple vehicles to travel in a convoy during the manufacturing or transport process, A sensor provided on the vehicle for detecting vibrations experienced by the vehicle, A comparison unit compares the vibration pattern of the vibration detected by the sensor with a preset reference pattern. A vehicle manufacturing system comprising: a vehicle control unit that controls the vehicle based on the results of comparing the vibration pattern with a reference pattern.

2. Multiple work processes are sequentially performed on the aforementioned vehicle. The vehicle manufacturing system according to claim 1, wherein the reference pattern is set according to the work process.

3. A vehicle manufacturing system according to claim 1, which determines whether the vibration pattern matches the reference pattern depending on at least one of the position and direction in which the vibration is applied to the vehicle.

4. The vehicle manufacturing system according to any one of claims 1 to 3, wherein the sensor is a sensor for the vehicle's airbag.

5. A vehicle manufacturing method that controls multiple vehicles to travel in a convoy during the manufacturing process or the transport process, The steps include detecting vibrations experienced by the vehicle using a vibration sensor provided on the vehicle, The steps include comparing the vibration pattern of the vibration detected by the vibration sensor with a preset reference pattern, A vehicle manufacturing method comprising the step of controlling the vehicle's movement based on the result of comparing the vibration pattern with a reference pattern.

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