Collision avoidance ar application

Abstract

To provide a technology of realizing an AR APPLICATION for avoiding collision with a moving body.SOLUTION: AR glasses comprise one or more processors, which execute: signal-reception processing for receiving, from an external devise, collision prediction information including position information about a risk area as an area where it is predicted that a user collides with a moving body and information about a trajectory of the moving body; determination processing for determining a risk degree according to a distance of the user by deriving the risk area and the distance, based on the collision prediction information; production processing for producing content showing that risk avoidance is urged, according to the risk degree; and display control processing for displaying the content on a display.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to an AR application for collision avoidance with moving objects. [Background technology]

[0002] In recent years, technologies have been developed that detect the possibility of a collision with a moving object and provide information related to the detection. For example, Patent Document 1 discloses a collision possibility determination device that determines the possibility of a collision between moving objects, such as moving vehicles, and notifies the determination result to a communication terminal device worn by a user using one of the moving objects. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2023-091191 Summary of the Invention [Problem to be solved by the invention]

[0004] According to the technology disclosed in the above document, a user of one mobile object can be made aware of the possibility of a collision with another mobile object, thereby enabling collisions between the mobile objects to be avoided. However, this technology requires each mobile object to periodically transmit its own location information to a collision possibility determination device at a predetermined frequency, and therefore this technology cannot be applied to mobile objects that do not transmit location information. Therefore, there is a need for a technology that can detect collisions between mobile objects without using location information and provide information related to the detection.

[0005] For example, if a walking user (pedestrian) is at an intersection and there is a possibility that the user will come into contact with a bicycle traveling from a direction outside the user's field of vision, a system that determines the possibility of a collision without using the position information of the user and bicycle is useful because it can be used regardless of whether the bicycle or the user has a terminal device that can acquire position information.Here, if the user wears a see-through display that allows them to see what is behind them, and information about the possibility of a collision or danger is presented on the see-through display as an Augmented Reality (AR) application, the user can confirm the information in their own field of vision, which is even more useful.

[0006] In view of the above-mentioned problems, the present disclosure aims to provide a technique for realizing an AR application for avoiding collisions with moving objects. [Means for solving the problem]

[0007] The AR glasses according to one embodiment of the present disclosure include one or more processors, and at least one of the one or more processors executes a reception process for receiving collision prediction information from an external device, the collision prediction information including location information of a danger area, which is an area where a user is predicted to collide with a moving object, and information about the trajectory of the moving object; a determination process for deriving the distance between the danger area and the user based on the collision prediction information and determining a level of danger according to the distance; a generation process for generating content encouraging the user to avoid danger based on the level of danger; and a display control process for displaying the content on a display.

[0008] A control method according to one aspect of the present disclosure is a control method executed by a communication device, and includes receiving collision prediction information from an external device, the collision prediction information including location information of a danger area, which is an area where a user wearing AR glasses is predicted to collide with a moving object, and information regarding the trajectory of the moving object; deriving the distance between the danger area and the user based on the collision prediction information, and determining a level of danger according to the distance; generating content that encourages danger avoidance according to the level of danger; and displaying the content on a display of the AR glasses.

[0009] A communication system according to one embodiment of the present disclosure is a communication system having a first device, a second device, and AR glasses, wherein the first device, the second device, and the AR glasses each have one or more processors, and at least one of the one or more processors executes the following processes: the first device detects a moving object, generates collision prediction information including location information of a danger area, which is an area where a user wearing the AR glasses is predicted to collide with the moving object, and information about the trajectory of the moving object, and transmits the collision prediction information to the second device; the second device receives the collision prediction information from the first device, generates AR collision prediction information based on the collision prediction information, and transmits the information to the AR glasses; and the AR glasses receives the AR collision prediction information from the second device, derives the distance between the danger area and the user based on the AR collision prediction information, generates content that encourages risk avoidance according to the risk level determined according to the distance, and displays the content on a display. [Effects of the Invention]

[0010] According to the technology of the present disclosure, a technology for realizing an AR application for avoiding collisions with moving objects is provided. The above-mentioned objects, aspects, and advantages of the present invention, as well as other objects, aspects, and advantages of the present invention not described above, will be understood by those skilled in the art from the following detailed description of the invention by referring to the accompanying drawings and the claims. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 shows an example of the configuration of a communication system according to an embodiment. [Figure 2A] FIG. 2A shows an example of an image displayed on the AR glasses. [Figure 2B] FIG. 2B shows another example of an image displayed on the AR glasses. [Figure 2C] FIG. 2C shows another example of an image displayed on the AR glasses. [Figure 2D] FIG. 2D shows another example of an image displayed on the AR glasses. [Figure 3] FIG. 3 is a conceptual diagram showing the relationship between the risk level and the image displayed on the AR glasses. [Figure 4] FIG. 4 shows a flowchart of the processing performed in the communication system according to the embodiment. [Figure 5] FIG. 5 illustrates an example of the functional configuration of an object recognition device according to an embodiment. [Figure 6] FIG. 6 illustrates an example of a functional configuration of an information providing device according to an embodiment. [Figure 7] FIG. 7 illustrates an example of the functional configuration of the AR glasses according to the embodiment. [Figure 8] FIG. 8 illustrates an example of a hardware configuration of a communication device according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Among the components disclosed below, components having the same functions are designated by the same reference numerals, and their description will be omitted. Note that the embodiment disclosed below is one form of the present disclosure, and should be appropriately modified or changed depending on the configuration of the device and various conditions, and is not limited to the following embodiment. Furthermore, not all of the combinations of features described in the present embodiment are necessarily essential to solving the above-mentioned problems.

[0013] In the embodiments disclosed below, a network (5G network) conforming to the fifth generation (5G) standardized by the Third Generation Partnership Project (3GPP (registered trademark)) is assumed as a network to which the technology according to the present disclosure is applied. Note that the network here also includes user equipment. Note that the technology according to the present disclosure may also be applied to networks other than 5G networks.

[0014] [Communication system configuration] 1 shows an example of the configuration of a communication system 1 according to this embodiment. The communication system 1 includes an object recognition device 10, an information providing device 11, an RSU (Road Side Unit) 12, a wireless base station 13, user equipment (UE) 14, and AR glasses 15.

[0015] The object recognition device 10 is a server device (edge server) for edge computing (MEC (Multi-Access Edge Computing) in this embodiment) and is configured to be able to communicate with the information providing device 11 and the RSU 12. The information providing device 11 is a server device (edge server) for edge computing (MEC in this embodiment) and is configured to be able to communicate with the object recognition device 10 and a wireless base station 13. The RSU 12 is a roadside communication device installed around the road and is configured to be able to communicate with the object recognition device 10 via a wired network 16. Note that in this embodiment, the RSU 12 is configured to be able to communicate with the object recognition device 10 via the wired network 16, but it may also be configured to be able to communicate with the object recognition device 10 via a wireless network via a wireless base station (not shown) or the like. The wireless base station 13 is , and is configured to be able to communicate with the information providing device 11 and the UE 14. The UE 14 is a communication device having a communication function compliant with 5G, and is configured to be able to communicate with the wireless base station 13 and the AR glasses 15. The AR glasses 15 have a see-through display that allows the user 100 to see what is behind them, are worn by the user 100, and are configured to be able to communicate with the UE 14. The user 100 can view an image displayed by the AR glasses 15 (hereinafter also referred to as an AR image) while viewing the real world. Specifically, the field of view of the user 100, i.e., the AR image, includes an area of the real world that is not the AR content displayed by the AR glasses 15, and an area of the AR content displayed by the AR glasses 15. Note that the configuration of the system 1 shown in FIG. 1 is an example, and the system 1 may include multiple RSUs, or other devices may be configured to be able to communicate with the illustrated device.

[0016] In this embodiment, a moving object (moving body) that the user 100 may collide with, such as a vehicle or another user, is referred to as a collision object 101. In the example embodiment, the collision object 101 will be described using a bicycle (hereinafter, the collision object 101 will also be referred to as the bicycle 101). In this embodiment, a situation is assumed in which a bicycle 101 is traveling from a direction outside the field of view of the user 100. For example, the user 100 is traveling on a road with a poorly visible intersection in the near future, and the bicycle 101 is traveling on a different road from the user 100, toward the intersection. The bicycle 101 is in the blind spot of the user 100, and the bicycle 101 and the user 100 cannot recognize each other's positions. If the user 100 and the bicycle 101 arrive at the intersection at the same time, the bicycle 101 and the user 100 may collide at the intersection.

[0017] In such a situation, the RSU 12 that detects the bicycle 101 predicts the trajectory of the bicycle 101 and also predicts the predicted arrival point of the bicycle 101 at a predetermined time. The RSU 12 predicts the trajectory of the bicycle 101 and the predicted arrival point after a predetermined time using, for example, LiDAR (Light Detection and Ranging) or an existing object recognition technology. LiDAR is a technology that emits laser light and measures the distance to an object, the shape of the object, etc. based on information about the reflected light. The RSU 12 derives various parameters (detection data related to object detection) based on the predicted trajectory and the predicted arrival point, and transmits them to the object recognition device 10 via the wired network 16. Furthermore, the RSU 12 may predict the trajectory of the user 100 (or the UE 14 or the AR glasses 15) and the predicted arrival point after a predetermined time, derive various parameters based on the trajectory of the user 100 and the predicted arrival start point, and transmit them to the object recognition device 10. In this embodiment, the RSU 12 is configured to transmit various parameters to the object recognition device 10, but it may be configured to derive and transmit data based on the predicted trajectory and predicted arrival point.

[0018] The object recognition device 10 receives various parameters from the RSU 12, predicts (detects) the possibility of a collision, and derives information regarding the possibility of a collision (hereinafter also referred to as collision prediction information). In this embodiment, the object recognition device 10 derives (determines) coordinates (hereinafter also referred to as predicted risk position coordinates) of a location of an area 17 predicted to be dangerous (hereinafter also referred to as danger area 17) in a predetermined coordinate space of the map information, from map information and the various received parameters. The danger area 17 may be an area having a certain area (predicted collision area), or may be defined as a certain point (predicted collision point). For example, in FIG. 1, the danger area 17 is illustrated as a predicted collision area, which is an area where a collision between the bicycle 101 and the user 100 is predicted. Furthermore, the object recognition device 10 derives information based on the trajectory of the bicycle 101 (hereinafter also referred to as object information) from the map information and the various received parameters. The object information may be coordinate data of the trajectory of the bicycle 101 in the coordinate space. Here, the coordinate data may be coordinate data of the tip position of the bicycle 101. Furthermore, the coordinate data may be coordinate data with time information attached. Furthermore, the coordinate data may be data based on the current position of the bicycle 101 and a predicted trajectory to the danger area 17. Furthermore, the coordinate data may be replaced with other types of information indicating a position. The object recognition device 10 includes the predicted danger position coordinates and object information in the collision prediction information. Note that the object recognition device 10 may derive other information based on parameters received from the RSU 12 and include the other information in the collision prediction information.

[0019] Next, the object recognition device 10 transmits (push-transmits) the collision prediction information to the information providing device 11. In this embodiment, the object recognition device 10 transmits the predicted danger position coordinates and object information to the information providing device 11. The information is transmitted, for example, via a private network using 5G. The information may also be transmitted using a request using a POST method in HTTP (HyperText Transfer Protocol). The request may include parameters and data in the body portion. The data format is, for example, JSON format.

[0020] The information providing device 11 receives collision prediction information (hazard predicted position coordinates and object information) from the object recognition device 10. The information can be received using a Representational State Transfer Application Programming Interface (REST API). The information providing device 11 transmits the received collision prediction information to the UE 14 via the wireless base station 13. The information can be transmitted using a WebSocket API.

[0021] The information providing device 11 may generate collision prediction information (AR collision prediction information) to be transmitted to the AR glasses 15 via the UE 14 based on the collision prediction information received from the object recognition device 10, and transmit the generated collision prediction information to the UE 14. For example, when the collision prediction information received from the object recognition device 10 needs to be converted into a format that can be processed by the AR glasses 15, or when the received collision prediction information includes unnecessary or redundant information, the information providing device 11 may generate AR collision prediction information and transmit the generated collision prediction information to the UE 14.

[0022] Furthermore, the information providing device 11 may determine whether to transmit collision prediction information (or AR collision prediction information; the same applies to the description of the information providing device 11) to the AR glasses 15 via the UE 14, and if it is determined to transmit the collision prediction information, transmit the collision prediction information to the UE 14. Here, for example, the information providing device may acquire user attributes (user characteristics) of the user 100 wearing the AR glasses 15 from the UE 14, and determine whether to transmit the collision prediction information to the AR glasses 15 based on the user attributes. The user attributes of the user 100 are fact attributes (information on fact attributes) about the UE 14 or the user 100, and are fact-based attributes that are actually or objectively obtained from the UE 14 or the user 100. The information providing device 11 can acquire the user attributes directly from the UE 14, for example. Furthermore, the information providing device 11 can acquire the user attributes as information registered in a predetermined web service by the user 100 of the UE 14. The user attributes of the user 100 may include at least one of the demographic information of the user 100 and service usage information for the one or more web services.

[0023] Demographic information is information that indicates demographic user attributes such as gender, age, residential area, occupation, family structure, etc. Service usage information includes a service usage history by user 100 in a web service. For example, if an item (goods or service) is purchased in a web service, the service usage history includes information about the purchased item (item name, genre, seller, etc.) and information about the purchase date and time.

[0024] As an example of determining whether to transmit collision prediction information to the AR glasses 15 based on the user attributes acquired by the information providing device 11, it is possible to use demographic information included in the user attributes of the user 100. For example, if the age included in the user attributes of the user 100 is old (e.g., 65 years old or older), the information providing device 11 may decide to transmit the collision prediction information to the AR glasses 15 via the UE 14. This is based on the assumption that the older a person is, the more likely their physical and mental functions and cognitive functions are to decline. Conversely, in this case, if the age included in the user attributes of the user 100 is not old (e.g., under 65 years old), the information providing device 11 may decide not to transmit the collision prediction information to the AR glasses 15 via the UE 14. Alternatively, the information providing device 11 may use service usage information included in the user attributes of the user 100 to determine whether to transmit the collision prediction information to the AR glasses 15. For example, when the information providing device 11 determines from the service usage information that the user 100 is elderly, the information providing device 11 may decide to transmit the collision prediction information to the AR glasses 15 via the UE 14.

[0025] The UE 14 receives collision prediction information (including predicted danger position coordinates and object information) from the information providing device 11 and transmits it to the AR glasses 15. Here, the UE 14 may transmit the information to the AR glasses 15 using a tethering function. For example, the UE 14 establishes a communication link with the AR glasses 15 according to Bluetooth (registered trademark) and transmits the information to the AR glasses 15 via the communication link. The UE 14 may also generate AR content to be displayed on the AR glasses 15 and transmit it to the AR glasses 15.

[0026] The AR glasses 15 generate AR content or receive AR content from the UE 14 and display it on the see-through display. The AR content may include 3D content. The AR glasses 15 also execute an AR application for collision avoidance (a collision avoidance AR application) to achieve the following functions: First, the AR glasses 15 receive collision prediction information (including predicted danger position coordinates and object information) from the UE 14. Based on the received information, the AR glasses 15 use a Visual Positioning System (VPS) to determine whether the user 100 is approaching a danger area 17 indicated by the predicted danger position coordinates. The VPS is a technology that identifies the location and orientation of a mobile terminal (the AR glasses 15 or the UE 14 in this embodiment) by performing real-world positioning using image recognition technology or the like. For example, the AR glasses 15 use Immersal as the VPS and point cloud data (PCD) data to identify the location of the user 100 and determine that the user 100 is approaching the danger area 17 (i.e., that the user 100 is traveling toward the danger area 17).

[0027] When the AR glasses 15 determine that the user 100 is approaching the danger area 17, they generate AR content encouraging the user 100 to avoid danger and display it on the transparent display (i.e., display an alert). In this embodiment, the AR glasses 15 derive the distance from the user 100 to the danger area 17 from the positions of the danger area 17 and the user 100 (the AR glasses 15 or the UE 14), and determine a risk level according to that distance. The risk level indicates the possibility of a collision between the user 100 and the bicycle 101, which is a moving object. The AR glasses 15 then generate AR content according to the determined risk level and display it on the transparent display. In this embodiment, the risk level is expressed as a percentage.

[0028] 2A to 2D show examples of AR images including AR content. FIG. 2A shows an AR image 20 when it is determined that the user 100 is not approaching the dangerous area 17. For example, when the user 100 performs a predetermined operation on the AR glasses 15 or the UE 14, a standard phrase corresponding to the operation is displayed. For example, "Hello. Need help?" is displayed. Fig. 2B is an AR image 21 including AR content generated when it is determined that the user 100 is approaching the dangerous area 17. Fig. 2C is an AR image 32 including AR content generated when it is determined that the user 100 is closer to the dangerous area 17 than in Fig. 2B. Fig. 2D is an AR image 23 including AR content generated when it is determined that the user 100 is closer to the dangerous area 17 than in Fig. 2C. The closer the distance between the dangerous area 17 and the user 100, the higher the level of danger is determined to be, and therefore, the images displayed on the transparent display are displayed in the order of AR images 21, 22, and 23 according to the increasing level of danger.

[0029] FIG. 3 is a conceptual diagram showing the relationship between the risk level and AR content. The AR glasses 15 can generate AR content according to the determined risk level. If the UE 100 and the bicycle 101 are not close enough to collide immediately and the urgency is relatively low, the risk level is determined to be low. On the other hand, if the user 100 and the bicycle 101 are likely to collide immediately and the urgency is high, the risk level is determined to be high. Therefore, as the risk level increases, the AR glasses 15 generate AR content that more strongly reminds the user 100 of the risk and how to avoid the risk, and display the AR content on the transparent display. In other words, as the risk level increases, a more explicit alert is displayed. As an example, if the risk level is less than a predetermined first threshold, the AR glasses 15 determine that there is no risk and do not display an explicit alert (i.e., do not display anything related to risk avoidance). When the danger level is equal to or greater than the first threshold and less than a predetermined second threshold, the AR glasses 15 generate and display AR content that encourages the user 100 to gently change their behavior (for example, to divert their attention and stop walking) and move away from the collision object 101, rather than displaying an explicit alert. Furthermore, when the danger level is equal to or greater than the second threshold, the AR glasses 15 display an explicit alert to notify the user 100 that the collision object 101 is approaching. A danger level less than the first threshold corresponds to the distance between the danger area 17 and the user 100 being equal to or greater than a predetermined first distance, a danger level greater than or equal to the first threshold and less than the second threshold corresponds to the distance between the danger area 17 and the user 100 being equal to or greater than a predetermined second distance (second distance<first distance) and less than the first distance, and a danger level greater than or equal to the second threshold corresponds to the distance between the danger area 17 and the user 100 being less than the second distance. The first threshold and the second threshold may be set in advance in the collision avoidance AR application, or may be set by other means.

[0030] In FIG. 3, AR images 30, 31, and 32 including AR content indicating a recommendation to avoid danger are AR images including AR content generated when the level of danger is equal to or greater than a first threshold and less than a predetermined second threshold. As the level of danger increases, the AR image more explicitly indicates staying away from danger. AR image 33 including AR content indicating a requirement to avoid danger is an AR image including AR content generated when the level of danger is equal to or greater than a second threshold. As the level of danger increases, the AR content may use colors that are more visually appealing to user 100. AR content 34 indicates AR content according to the level of danger.

[0031] [System processing flow] 4 is a flowchart of the process executed in the communication system according to this embodiment. This process is roughly divided into information processing for object recognition S40 and information processing for AR S41. In S401 of the information processing S40 for object recognition, the RSU 12 detects an object. In this embodiment, the RSU 12 detects a bicycle 101, which is a moving object. When the object is detected, the RSU 12 derives detection data related to the object detection and transmits it to the object recognition device 10. Next, in S402, the object recognition device 10 derives collision prediction information based on the detection data from the RSU 12. The collision prediction information includes predicted risk position coordinates and object information. In S411 of the subsequent AR information processing S41, the information providing device 11 transmits the collision prediction information acquired from the object recognition device 10 to the AR glasses 15 via the UE 14. In S411, the AR glasses 15 determine that the user is approaching the predicted collision point and display AR content encouraging the user to avoid danger on the transparent display. This determination can be made using the VPS. By this processing, after the RSU 12 detects the bicycle 101, AR content prompting the driver to avoid danger is displayed on the AR glasses 15, realizing real-time notification of the collision object 101 and how to avoid the collision. By using the MEC server (the object recognition device 10 and the information providing device 11), which is an edge server, it is possible to more quickly perform the processing from detecting the bicycle 101 to displaying the AR content.

[0032] [Example of functional configuration of object recognition device] 5 shows an example of the functional configuration of the object recognition device 10. As an example of the functional configuration, the object recognition device 10 includes a collision possibility detection unit 51 and a collision prediction information generation unit 52. The collision possibility detection unit 51 detects the possibility of a collision from various parameters received from the RSU 12. The collision prediction information generation unit 52 generates collision prediction information including predicted risk position coordinates and object information.

[0033] [Example of functional configuration of information provision device] FIG. 6 shows an example of a functional configuration of the information providing device 10. The object recognition device 10 includes, as an example of a functional configuration, a collision prediction information generation unit 61, a user attribute acquisition unit 62, and a transmission determination unit 63. The collision prediction information generation unit 61 generates collision prediction information for the AR glasses 15 (AR collision prediction information) based on the collision prediction information received from the object recognition device 10. The user attribute acquisition unit 62 acquires user attributes of the user 100 of the UE 14 from the UE 14 that is capable of communicating via the information providing device 10. The transmission determination unit 63 determines whether to transmit the collision prediction information (or AR collision prediction information) to the UE 14 based on the user attributes of the user 100 acquired by the user attribute acquisition unit 62. When the transmission determination unit 63 determines to transmit the collision prediction information (or AR collision prediction information) to the UE 14, the information is transmitted to the UE 14.

[0034] [Example of AR glasses function configuration] FIG. 7 shows an example of the functional configuration of the AR glasses 15. The example functional configuration of the AR glasses 15 shown in FIG. 7 corresponds to at least functions included in a collision avoidance AR application. The AR glasses 15 include, as an example of a functional configuration, a risk determination unit 71, an AR content generation unit 72, and a display control unit 73. The risk determination unit 71 determines the collision risk between the user 100 and a moving object (a bicycle 101 in this embodiment). To achieve this, the risk determination unit 71 first determines whether the user 100 is approaching a danger area 17 indicated by the danger prediction position coordinates based on collision prediction information (including danger prediction position coordinates and object information) received from the UE 14. When the risk determination unit 71 determines that the user 100 is approaching the danger area 17, it derives the distance to the danger area 17 from the positions of the danger area 17 and the user 100 (the AR glasses 15 or the UE 14) and determines the risk level according to the distance. The AR content generation unit 72 generates AR content to be displayed on the transparent display. The AR content generation unit 72 can generate AR content according to the risk level determined by the risk level determination unit 71. The AR content generation unit 72 may acquire pre-generated AR content stored in the storage unit, or may generate AR content by combining information stored in the storage unit. The display control unit 73 displays the AR content generated by the AR content generation unit 72 on the transparent display. When the AR content generation unit 72 generates parallax images (images for the left eye and right eye with different parallaxes), the display control unit 73 may control the transparent display to display each parallax image in an area for projecting the parallax image on the transparent display.

[0035] In the present embodiment, it is assumed that the collision avoidance AR application is executed by the AR glasses 15, but at least some of the functions of the application may be executed by the UE 14. For example, the UE 14 held by the user 100 wearing the AR glasses 15 may be configured to control the AR glasses 15 so as to determine the level of danger described in the present embodiment, generate AR content, and display it on the transparent display of the AR glasses 15.

[0036] [Hardware configuration of communication device] 8 shows an example of a hardware configuration of the communication device (object recognition device 10, information providing device 11, UE 14, and AR glasses 15) described in this embodiment. The communication device includes, as an example of the hardware configuration, a CPU (Central Processing Unit) 81, a ROM (Read Only Memory) 82, a RAM (Random Access Memory) 83, an HDD (Hard Disk Drive) 84, an input unit 85, a display unit 86, and a communication unit 87.

[0037] The CPU (Central Processing Unit) 81 is configured with one or more processors and controls the overall operation of the communication device. The CPU 81 may be replaced with one or more processors such as an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), a DSP (Digital Signal Processor), or a GPU (Graphics Processing Unit).

[0038] The ROM (Read Only Memory) 82 is a non-volatile memory that stores control programs and the like required for the CPU 81 to execute processing. Note that the programs may be stored in a non-volatile memory such as an HDD 84 or an SSD (Solid State Drive) or an external memory such as a removable storage medium (not shown). The RAM (Random Access Memory) 83 is a volatile memory and functions as the main memory, work area, etc. of the CPU 81. That is, when executing a process, the CPU 81 loads necessary programs, etc. from the ROM 82 into the RAM 83 and executes the programs, etc. to realize various functional operations.

[0039] The HDD 84 stores, for example, various data and information required when the CPU 81 performs processing using a program. The HDD 84 also stores, for example, various data and information obtained when the CPU 81 performs processing using a program. Note that this storage may be performed using an external memory such as a nonvolatile memory such as an SSD or a removable storage medium together with the HDD 84 or instead of the HDD 84.

[0040] The input unit 85 is configured to be able to accept operations by the user. For example, the input unit 85 can accept operations on another communication device (for example, a smartphone) configured to be able to communicate with the communication device, operations by gestures, and operations by voice. The display unit 86 is configured to be able to display various types of information. When the communication device is the AR glasses 15, the display unit 86 is a transmissive display. The type of the transmissive display is not particularly limited, and may be a transmissive organic EL display, a transmissive inorganic EL display, a transmissive LCD (liquid crystal display), or the like. The communication unit 87 is an interface that controls communication between the communication device and an external device.

[0041] Although specific embodiments have been described above, these embodiments are merely examples and are not intended to limit the scope of the present disclosure. The devices and methods described herein may be embodied in forms other than those described above. Furthermore, appropriate omissions, substitutions, and modifications may be made to the above-described embodiments without departing from the scope of the present disclosure. Such omissions, substitutions, and modifications are included within the scope of the claims and their equivalents, and belong to the technical scope of the present disclosure.

[0042] (Embodiments of the present disclosure) The present disclosure includes the following embodiments. [1] AR glasses worn by a user, comprising one or more processors, wherein at least one of the one or more processors executes a reception process for receiving collision prediction information from an external device, the collision prediction information including location information of a danger area, which is an area where the user is at risk of colliding with a moving object, and information regarding the trajectory of the moving object; a determination process for deriving the distance between the danger area and the user based on the collision prediction information and determining a level of danger according to the distance; a generation process for generating content encouraging danger avoidance according to the level of danger; and a display control process for displaying the content on a display.

[0043] [2] The AR glasses described in [1], wherein the determination process includes determining the degree of danger so that the degree of danger increases as the distance becomes shorter.

[0044] [3] The AR glasses described in [1] or [2], wherein the generation process includes generating content that makes the user more aware of danger and the need to avoid danger as the level of danger increases.

[0045] [4] The AR glasses described in any one of [1] to [3], wherein the generation process includes generating the content indicating that the user should move away from the moving object if the degree of danger is greater than or equal to a first threshold and less than a second threshold, and generating the content indicating that the moving object is approaching the user if the degree of danger is greater than or equal to the second threshold.

[0046] [5] A control method executed by a communication device, comprising: receiving, from an external device, collision prediction information including location information of a danger area, which is an area where a user wearing AR glasses is at risk of colliding with a moving object, and information regarding the trajectory of the moving object; deriving a distance between the danger area and the user based on the collision prediction information; and determining a risk level according to the distance; A control method including: generating content encouraging danger avoidance according to the danger level; and displaying the content on a display of the AR glasses.

[0047] [6] A communication system having a first device, a second device, and AR glasses, wherein the first device, the second device, and the AR glasses each have one or more processors, and at least one of the one or more processors executes the following processes: the first device detects a moving object, generates collision prediction information including position information of a danger area, which is an area where a user wearing the AR glasses is at risk of colliding with the moving object, and information about the trajectory of the moving object, and transmits the collision prediction information to the second device; the second device receives the collision prediction information from the first device, generates AR collision prediction information based on the collision prediction information, and transmits the information to the AR glasses; and the AR glasses receives the AR collision prediction information from the second device, derives the distance between the danger area and the user based on the AR collision prediction information, generates content that encourages the user to avoid danger according to the level of danger determined according to the distance, and displays the content on a display.

[0048] [7] The communication system described in [6], wherein the second device further acquires user attributes of the user, determines whether to send the AR collision prediction information to the AR glasses based on the user attributes, and if it is decided to send the AR collision prediction information, further executes a process of sending the AR collision prediction information to the AR glasses.

[0049] [8] The communication system described in [7], wherein the user attributes include at least one of the user's demographic information and the user's service usage information via a web service.

[0050] [9] A communication system according to any one of [6] to [8], wherein the first device and the second device are MEC servers. [Explanation of symbols]

[0051] 10: Object recognition device, 11: Information providing device, 12: RSU (Road Side Unit), 13: Wireless base station, 14: UE (User Equipment), 15: AR glasses, 16: Wired network, 17: Danger area, 100: User, 101: Bicycle

Claims

1. one or more processors; by at least one of the one or more processors, a receiving process for receiving, from an external device, collision prediction information including position information of a danger area, which is an area where a user is predicted to collide with a moving object, and information about a trajectory of the moving object; a determination process of deriving a distance between the dangerous area and the user based on the collision prediction information and determining a risk level according to the distance; a generation process for generating content that prompts the user to avoid danger according to the degree of danger; a display control process for displaying the content on a display; AR glasses worn by the user, in which the above is executed.

2. The AR glasses according to claim 1 , wherein the determination process includes determining the degree of risk such that the shorter the distance, the higher the degree of risk.

3. The AR glasses according to claim 1 , wherein the generation process includes generating the content such that the user is more reminded of danger and the need to avoid danger as the level of danger increases.

4. The AR glasses of claim 3, wherein the generation process includes generating the content indicating that the user should move away from the moving object when the degree of danger is greater than or equal to a first threshold and less than a second threshold, and generating the content indicating that the moving object is approaching the user when the degree of danger is greater than or equal to the second threshold.

5. A control method executed by a communication device, comprising: receiving, from an external device, collision prediction information including position information of a danger area, which is an area where a user wearing the AR glasses is predicted to collide with a moving object, and information about a trajectory of the moving object; deriving a distance between the dangerous area and the user based on the collision prediction information, and determining a risk level according to the distance; generating content that prompts the user to avoid danger according to the degree of danger; Displaying the content on a display of the AR glasses; A control method comprising:

6. A communication system having a first device, a second device, and AR glasses, wherein the first device, the second device, and the AR glasses each include one or more processors, and at least one of the one or more processors: A process in which the first device detects a moving object, generates collision prediction information including position information of a danger area, which is an area in which a user wearing the AR glasses is predicted to collide with the moving object, and information about the trajectory of the moving object, and transmits the collision prediction information to the second device; a process of receiving, by the second device, the collision prediction information from the first device, generating AR collision prediction information based on the collision prediction information, and transmitting the AR collision prediction information to the AR glasses; a process of receiving the AR collision prediction information from the second device by the AR glasses, deriving a distance between the danger area and the user based on the AR collision prediction information, generating content that encourages the user to avoid danger according to a risk level determined according to the distance, and displaying the content on a display; A communication system in which

7. The communication system described in claim 6, wherein the second device further acquires user attributes of the user, determines whether to send the AR collision prediction information to the AR glasses based on the user attributes, and if it decides to send the AR collision prediction information, further executes a process of sending the AR collision prediction information to the AR glasses.

8. The communication system according to claim 7 , wherein the user attributes include at least one of demographic information of the user and service usage information of the user via a web service.

9. The communication system of claim 6 , wherein the first device and the second device are MEC servers.

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