System

Abstract

An object of a system according to an embodiment is to detect an obstacle or a dangerous situation during bicycle riding in real time and to appropriately avoid the obstacle or the dangerous situation.SOLUTION: A system includes a monitoring unit, a detection unit, a transmission unit, and an avoidance unit. The monitoring unit monitors a surrounding situation in real time. The detection unit detects an obstacle or a dangerous situation based on the information monitored by the monitoring unit. The transmission unit transmits information detected by the detection unit to the automatic bicycle. The avoidance unit performs a specific avoidance operation based on the information transmitted by the transmission unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to detect obstacles and dangerous situations in real time while riding a bicycle and to avoid them appropriately.

[0005] The system according to the embodiment aims to detect obstacles and dangerous situations in real time while riding a bicycle and to avoid them appropriately. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, a detection unit, a transmission unit, and an avoidance unit. The monitoring unit monitors the surrounding situation in real time. The detection unit detects obstacles and dangerous situations based on the information monitored by the monitoring unit. The transmission unit transmits the information detected by the detection unit to the bicycle. The avoidance unit performs specific avoidance operations based on the information transmitted by the transmission unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect obstacles and dangerous situations in real time while riding a bicycle and avoid them appropriately. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A bicycle accident avoidance system according to an embodiment of the present invention is a system equipped with an AI camera mounted on a helmet that monitors the surrounding environment in real time, detects obstacles and dangerous situations, and transmits the information to the automatic bicycle to take appropriate evasive action. For example, in a bicycle accident avoidance system, a helmet-mounted AI camera monitors the surrounding environment in real time. For example, it detects vehicles, pedestrians, and road obstacles ahead. The AI ​​camera then uses high-precision image recognition technology based on deep learning to identify the obstacle or dangerous situation. When the AI ​​camera detects an obstacle or dangerous situation, it transmits that information to the automatic bicycle. For example, if a vehicle ahead suddenly stops, the AI ​​camera detects the situation and sends an emergency stop command to the automatic bicycle. This information is transmitted in real time via Bluetooth (registered trademark) or Wi-Fi. The automatic bicycle then takes appropriate evasive action based on the received information. For example, if an obstacle is detected ahead, the automatic bicycle automatically slows down and selects a route to avoid the obstacle. Furthermore, if an emergency stop is necessary, the automatic bicycle automatically applies the brakes. This prevents accidents from occurring. Furthermore, by incorporating an AI camera in a helmet, it can be easily installed on existing bicycles, and widespread adoption is expected. This allows the bicycle accident avoidance system to significantly reduce bicycle accidents. In particular, by acquiring visual information in real time and responding immediately, it is possible to safely respond to situations that would be difficult with conventional manual operation. In addition, by equipping the helmet with an AI camera, it can be easily installed on existing bicycles, and widespread adoption is expected.

[0029] A bicycle accident avoidance system according to an embodiment includes a monitoring unit, a detection unit, a transmission unit, and an avoidance unit. The monitoring unit monitors the surrounding conditions in real time. Examples of the surrounding conditions include, but are not limited to, traffic conditions, weather, and pedestrian movement. The monitoring unit identifies obstacles and dangerous situations using, for example, high-precision image recognition technology based on deep learning. Examples of deep learning technology include, for example, convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The detection unit detects obstacles and dangerous situations based on the information monitored by the monitoring unit. Examples of obstacles include, but are not limited to, vehicles, pedestrians, and fixed objects. Examples of dangerous situations include, but are not limited to, sudden swerving and running red lights. The transmission unit transmits information detected by the detection unit to the motorized bicycle. For transmission, a communication protocol such as Bluetooth or Wi-Fi is used. The transmitter can use the latest communication technologies, such as Bluetooth 5.0 and Wi-Fi 6 (802.11ax). The avoidance unit performs appropriate avoidance actions based on the information transmitted by the transmitter. Avoidance actions include, but are not limited to, adjusting speed and changing course. For example, if there is an obstacle ahead, the avoidance unit automatically reduces speed and selects a route to avoid the obstacle. Furthermore, if an emergency stop is required, the avoidance unit automatically applies the brakes. As a result, the bicycle accident avoidance system according to the embodiment monitors the surrounding situation in real time, detects obstacles and dangerous situations, and transmits the information to the automatic bicycle to perform appropriate avoidance actions, thereby preventing bicycle accidents.

[0030] The monitoring unit can identify obstacles and dangerous situations using specific image recognition technologies that use deep learning. Deep learning technologies include, for example, convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Image recognition technologies include, for example, object detection and face recognition, but are not limited to these examples. The monitoring unit can use, for example, CNNs to detect vehicles and pedestrians ahead with high accuracy. The monitoring unit can also use RNNs to detect movement from consecutive image frames and identify dangerous situations. Furthermore, the monitoring unit can use object detection algorithms to identify obstacles on the road. As a result, the use of deep learning improves the accuracy of identifying obstacles and dangerous situations.

[0031] The transmitter can transmit information in real time using Bluetooth or Wi-Fi. For example, a communication protocol such as Bluetooth or Wi-Fi is used for transmission. Examples of Bluetooth include Bluetooth 5.0 and BLE (Bluetooth Low Energy). Examples of Wi-Fi include Wi-Fi 6 (802.11ax) and 802.11ac. For example, the transmitter transmits information in real time with low power consumption using Bluetooth 5.0. The transmitter can also transmit large amounts of data at high speed in real time using Wi-Fi 6. Furthermore, the transmitter can switch between Bluetooth and Wi-Fi depending on the communication environment. This makes it possible to transmit information in real time using Bluetooth or Wi-Fi.

[0032] The avoidance unit can automatically reduce speed and select a route to avoid an obstacle when an obstacle is detected ahead. Examples of obstacles include, but are not limited to, vehicles, pedestrians, and fixed objects. For example, when a vehicle ahead suddenly stops, the avoidance unit can automatically reduce speed and select a route to avoid the obstacle. Criteria such as a deceleration rate and a lower speed limit are used to reduce speed. For example, the avoidance unit can set the deceleration rate to 30% and the lower speed limit to 10 km / h. Criteria such as the shortest route and a safe route are used to select a route. For example, when selecting the shortest route, the avoidance unit can select a route that takes the shortest distance to avoid the obstacle. Furthermore, when selecting a safe route, the avoidance unit can also select the safest route to avoid the obstacle. As a result, when an obstacle is detected ahead, the avoidance unit can automatically reduce speed and avoid the obstacle, thereby preventing an accident.

[0033] The avoidance unit can automatically apply the brakes when a sudden stop is necessary. Criteria for sudden stops, such as braking strength and stopping distance, are used for the sudden stop. The avoidance unit can, for example, set the braking strength to maximum and the stopping distance to minimum. For example, when a sudden obstacle appears ahead, the avoidance unit automatically applies the brakes to bring the vehicle to an emergency stop. This makes it possible to prevent accidents by automatically applying the brakes when a sudden stop is necessary.

[0034] The monitoring unit is attached to the front of the helmet, and can ensure a wide forward field of view. To ensure a wide forward field of view, criteria such as the viewing angle and camera placement are used. For example, the monitoring unit can be set to have a viewing angle of 120 degrees, and the camera can be placed in the center front of the helmet. This allows a wide forward field of view to be ensured by attaching it to the front of the helmet.

[0035] The transmitter is built into the helmet and can transmit information smoothly. To transmit information smoothly, standards such as communication speed and data compression method are used. For example, the transmitter can set the communication speed to 1 Gbps and the data compression method to H.265. By building the transmitter into the helmet, this allows for smooth transmission of information.

[0036] The monitoring unit can switch monitoring modes depending on the weather and time of day. For example, the monitoring unit uses an infrared camera at night to ensure visibility. The monitoring unit can also detect water droplets when it is raining and automatically clear the camera lens. Furthermore, the monitoring unit can improve visibility by adjusting the exposure of the camera when it is under strong sunlight during the day. Examples of weather include sunny, rainy, and snowy weather. Examples of time periods include daytime, nighttime, and rush hour. This allows appropriate monitoring by switching the monitoring mode depending on the weather and time of day.

[0037] The monitoring unit can identify danger by using surrounding sound information in combination. The monitoring unit can, for example, detect the sound of a car horn and identify danger. The monitoring unit can also detect the voices or cries of pedestrians and alert them. Furthermore, the monitoring unit can detect the sound of an emergency vehicle siren and alert them to evasive action. Surrounding sound information includes, for example, the sound of a car engine and the voices of pedestrians. By using surrounding sound information in combination, the accuracy of identifying danger is improved.

[0038] The monitoring unit can optimize the monitoring algorithm by referring to past monitoring data. The monitoring unit can improve monitoring accuracy in specific situations, for example, based on past accident data. The monitoring unit can also analyze the user's past driving patterns and customize the monitoring algorithm. Furthermore, the monitoring unit can predict dangers at specific times or locations from past monitoring data and strengthen monitoring. Past monitoring data includes, for example, the database format and data storage period. This makes it possible to optimize the monitoring algorithm by referring to past monitoring data.

[0039] The monitoring unit can adjust the monitoring range according to the user's traveling speed. For example, when the user is traveling at high speed, the monitoring unit widens the forward monitoring range. Furthermore, when the user is traveling at low speed, the monitoring unit can widen the surrounding monitoring range. Furthermore, when the user is stopped, the monitoring unit can perform omnidirectional monitoring. The traveling speed is measured using criteria such as the type of speed sensor and the measurement frequency. This allows for appropriate monitoring by adjusting the monitoring range according to the user's traveling speed.

[0040] The monitoring unit can predict and monitor the movements of other traffic participants. For example, the monitoring unit can predict the movement of a vehicle ahead and strengthen monitoring. The monitoring unit can also predict the movement of pedestrians and warn them. Furthermore, the monitoring unit can predict the movement of bicycles and monitor them to avoid collisions. Other traffic participants include, for example, vehicles, pedestrians, and bicycles. To predict movement, for example, past movement patterns or machine learning techniques are used. This makes it possible to predict the movements of other traffic participants and perform appropriate monitoring.

[0041] The monitoring unit can customize the monitoring method by reflecting the user's past driving history. For example, the monitoring unit can strengthen monitoring in specific locations based on routes the user has taken in the past. The monitoring unit can also analyze the user's past driving patterns and optimize the monitoring algorithm. Furthermore, the monitoring unit can strengthen monitoring in specific situations based on the user's past accident history. Past driving history includes, for example, GPS data and driving logs. This makes it possible to customize the monitoring method by reflecting the user's past driving history.

[0042] The detection unit can apply different detection algorithms depending on the type of obstacle. For example, when detecting a vehicle, the detection unit uses an algorithm dedicated to vehicles. Furthermore, when detecting a pedestrian, the detection unit can also use an algorithm dedicated to pedestrians. Furthermore, when detecting an obstacle on the road, the detection unit can also use an algorithm dedicated to obstacles. Types of obstacles include, for example, vehicles, pedestrians, fixed objects, etc. Detection algorithms include, for example, object detection algorithms and motion detection algorithms. In this way, by applying different detection algorithms depending on the type of obstacle, detection accuracy is improved.

[0043] The detection unit can improve detection accuracy by predicting the movement of an obstacle. The detection unit can predict, for example, the movement of a vehicle, thereby improving detection accuracy. The detection unit can also predict the movement of a pedestrian, thereby improving detection accuracy. Furthermore, the detection unit can predict the movement of a bicycle, thereby improving detection accuracy. The movement of an obstacle can be based on, for example, past movement patterns and machine learning techniques. Thus, by predicting the movement of an obstacle, detection accuracy can be improved.

[0044] The detection unit can optimize the detection algorithm by referring to past detection data. The detection unit can improve detection accuracy in specific situations, for example, based on past accident data. The detection unit can also analyze the user's past driving patterns and customize the detection algorithm. Furthermore, the detection unit can predict dangers at specific times or locations based on past detection data and strengthen detection. Past detection data includes, for example, the database format and data storage period. This makes it possible to optimize the detection algorithm by referring to past detection data.

[0045] The detection unit can adjust the detection method according to the size and speed of the obstacle. For example, when detecting a large obstacle, the detection unit performs wide-area detection. Furthermore, when detecting a small obstacle, the detection unit can perform detailed detection. Furthermore, when detecting an obstacle moving at high speed, the detection unit can perform rapid detection. The size of the obstacle includes, for example, the dimensions and volume of the object. The speed of the obstacle includes, for example, the type of speed sensor and the measurement frequency. As a result, appropriate detection is possible by adjusting the detection method according to the size and speed of the obstacle.

[0046] The detection unit can identify obstacles by analyzing surrounding environmental sounds. For example, the detection unit can analyze the engine sound of a car to identify the vehicle. The detection unit can also analyze the footsteps of a pedestrian to identify the pedestrian. Furthermore, the detection unit can analyze the siren sound of an emergency vehicle to identify the emergency vehicle. Surrounding environmental sounds include, for example, the engine sound of a car, the voices of pedestrians, etc. Thus, by analyzing surrounding environmental sounds, the accuracy of identifying obstacles is improved.

[0047] The detection unit can predict the user's driving route and adjust the detection range. For example, if the user is driving a specific route, the detection unit performs detection along that route. In addition, if the user is heading toward a destination, the detection unit can also perform detection with an emphasis on that direction. Furthermore, if the user gets lost, the detection unit can perform omnidirectional detection. The driving route includes, for example, past driving data and map information. This makes it possible to appropriately adjust the detection range by predicting the user's driving route.

[0048] The transmitting unit can switch the transmission method depending on the communication environment. For example, when the communication environment is good, the transmitting unit transmits information using Wi-Fi. Furthermore, when the communication environment is unstable, the transmitting unit can also transmit information using Bluetooth. Furthermore, when the communication environment is very poor, the transmitting unit can compress and transmit data. The communication environment includes, for example, a Wi-Fi environment, a mobile network environment, etc. The transmission method includes, for example, Wi-Fi, Bluetooth, mobile data communication, etc. This makes it possible to transmit information appropriately by switching the transmission method depending on the communication environment.

[0049] The transmitting unit can set a transmission priority according to the importance of data. For example, the transmitting unit transmits emergency information with the highest priority. The transmitting unit can also transmit normal information with normal priority. Furthermore, the transmitting unit can transmit information of low importance later. The importance of data includes, for example, urgency and relevance. The transmission priority includes, for example, prioritization based on importance and time priority. Thus, by setting a transmission priority according to the importance of data, important information can be preferentially transmitted.

[0050] The transmitting unit can optimize the transmission algorithm by referring to past transmission data. The transmitting unit can, for example, set the optimal transmission timing based on the past transmission data. The transmitting unit can also analyze the past transmission data and optimize the transmission method. Furthermore, the transmitting unit can improve the transmission method in specific situations based on the past transmission data. The past transmission data includes, for example, the database format and the data storage period. This makes it possible to optimize the transmission algorithm by referring to the past transmission data.

[0051] The transmitter can adjust the transmission frequency according to the user's traveling speed. For example, the transmitter increases the transmission frequency when the user is traveling at a high speed. The transmitter can also return the transmission frequency to normal when the user is traveling at a low speed. Furthermore, the transmitter can also decrease the transmission frequency when the user is stopped. The traveling speed is measured using criteria such as the type of speed sensor and the measurement frequency. This allows appropriate information transmission by adjusting the transmission frequency according to the user's traveling speed.

[0052] The transmitter can predict the movements of other traffic participants and adjust the transmission data. For example, the transmitter can predict the movements of vehicles ahead and adjust the transmission data. The transmitter can also predict the movements of pedestrians and adjust the transmission data. Furthermore, the transmitter can predict the movements of bicycles and adjust the transmission data. Other traffic participants include, for example, vehicles, pedestrians, and bicycles. To predict movements, for example, past movement patterns or machine learning techniques are used. This makes it possible to predict the movements of other traffic participants and transmit appropriate information.

[0053] The transmission unit can customize the transmission method by reflecting the user's past driving history. For example, the transmission unit can optimize the transmission method in a specific location based on the route the user has taken in the past. The transmission unit can also analyze the user's past driving patterns and customize the transmission algorithm. Furthermore, the transmission unit can improve the transmission method in a specific situation based on the user's past accident history. Past driving history includes, for example, GPS data and driving logs. This makes it possible to customize the transmission method by reflecting the user's past driving history.

[0054] The avoidance unit can apply different avoidance algorithms depending on the type of obstacle. For example, when avoiding a vehicle, the avoidance unit uses an algorithm dedicated to vehicles. Furthermore, when avoiding a pedestrian, the avoidance unit can also use an algorithm dedicated to pedestrians. Furthermore, when avoiding an obstacle on the road, the avoidance unit can also use an algorithm dedicated to the obstacle. Types of obstacles include, for example, vehicles, pedestrians, fixed objects, etc. The avoidance algorithm includes, for example, a method for calculating an avoidance path and a method for controlling an avoidance operation. In this way, by applying different avoidance algorithms depending on the type of obstacle, avoidance accuracy is improved.

[0055] The avoidance unit can improve the accuracy of avoidance by predicting the movement of an obstacle. The avoidance unit can, for example, predict the movement of a vehicle and improve the accuracy of avoidance. The avoidance unit can also predict the movement of a pedestrian and improve the accuracy of avoidance. Furthermore, the avoidance unit can also predict the movement of a bicycle and improve the accuracy of avoidance. The movement of an obstacle includes, for example, past movement patterns and machine learning techniques. In this way, the accuracy of avoidance is improved by predicting the movement of an obstacle.

[0056] The avoidance unit can optimize the avoidance algorithm by referring to past avoidance data. The avoidance unit can improve avoidance accuracy in specific situations, for example, based on past accident data. The avoidance unit can also analyze the user's past driving patterns and customize the avoidance algorithm. Furthermore, the avoidance unit can predict dangers at specific times or locations from the past avoidance data and strengthen avoidance. The past avoidance data includes, for example, the database format and the data storage period. This makes it possible to optimize the avoidance algorithm by referring to the past avoidance data.

[0057] The avoidance unit can adjust the avoidance method according to the user's traveling speed. For example, when the user is traveling at a high speed, the avoidance unit performs a rapid avoidance operation. Furthermore, when the user is traveling at a low speed, the avoidance unit can also perform a normal avoidance operation. Furthermore, when the user is stopped, the avoidance unit can also suspend the avoidance operation. The traveling speed is measured using criteria such as the type of speed sensor and the measurement frequency. As a result, by adjusting the avoidance method according to the user's traveling speed, an appropriate avoidance operation can be performed.

[0058] The avoidance unit can predict the movements of other traffic participants and take avoidance action. The avoidance unit, for example, predicts the movement of a vehicle ahead and takes avoidance action. The avoidance unit can also predict the movement of a pedestrian and take avoidance action. Furthermore, the avoidance unit can predict the movement of a bicycle and take avoidance action. Other traffic participants include, for example, vehicles, pedestrians, bicycles, etc. To predict movement, for example, past movement patterns or machine learning techniques are used. This makes it possible to predict the movements of other traffic participants and take appropriate avoidance action.

[0059] The avoidance unit can customize the avoidance method by reflecting the user's past driving history. For example, the avoidance unit can optimize the avoidance method in a specific location based on routes the user has taken in the past. The avoidance unit can also analyze the user's past driving patterns and customize the avoidance algorithm. Furthermore, the avoidance unit can improve the avoidance method in a specific situation based on the user's past accident history. The past driving history includes, for example, GPS data and driving logs. This makes it possible to customize the avoidance method by reflecting the user's past driving history.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The monitoring unit can monitor the ambient temperature and humidity and adjust the monitoring accuracy according to the environmental conditions. For example, when the temperature is high, the monitoring unit adjusts the exposure of the camera to improve visibility. When the humidity is high, the monitoring unit can automatically clear the lens to prevent fogging. Furthermore, when the temperature is low, the monitoring unit can use an infrared camera to ensure visibility. This allows for appropriate monitoring by adjusting the monitoring accuracy according to the ambient environmental conditions.

[0062] The detection unit can monitor the amount of ambient light and adjust the detection sensitivity according to the amount of light. For example, when the amount of light is high, the detection unit sets the sensitivity low and acquires only the minimum amount of information necessary. When the amount of light is low, the detection unit can increase the sensitivity to acquire more detailed information. Furthermore, when the amount of light fluctuates, the detection unit can adjust the sensitivity in real time and acquire appropriate information. This allows for more appropriate detection by adjusting the detection sensitivity according to the amount of ambient light.

[0063] The transmitting unit can monitor fluctuations in the communication environment and automatically switch the transmission method depending on the environment. For example, if the communication environment is good, the transmitting unit can transmit information using Wi-Fi. If the communication environment is unstable, the transmitting unit can also transmit information using Bluetooth. Furthermore, if the communication environment is very poor, the transmitting unit can compress and transmit data. This makes it possible to transmit information appropriately by automatically switching the transmission method depending on the communication environment.

[0064] The avoidance unit can monitor road conditions and adjust the method of avoidance operation according to the conditions. For example, if the road is slippery, the avoidance unit will slow down and perform avoidance operation carefully. Also, if the road is dry, the avoidance unit can perform avoidance operation at a normal speed. Furthermore, if there are many obstacles on the road, the avoidance unit can also select a route to avoid the obstacles. In this way, by adjusting the method of avoidance operation according to the road conditions, appropriate avoidance operation is possible.

[0065] The monitoring unit can predict the movements of other traffic participants and adjust the accuracy of monitoring based on the predictions. For example, if there is a possibility that a vehicle ahead will suddenly stop, the monitoring unit can increase the accuracy of monitoring to obtain more detailed information. Also, if there is a possibility that a pedestrian will cross the street, the monitoring unit can concentrate its monitoring in that direction. Furthermore, if a bicycle is approaching, the monitoring unit can track its movement and obtain appropriate information. This makes it possible to appropriately monitor other traffic participants by predicting their movements.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The monitoring unit monitors the surrounding conditions in real time. The surrounding conditions include traffic conditions, weather, and pedestrian movement. The monitoring unit uses high-precision image recognition technology based on deep learning to identify obstacles and dangerous situations. Deep learning technologies include CNN (convolutional neural networks) and RNN (recurrent neural networks). Step 2: The detection unit detects obstacles and dangerous situations based on the information monitored by the monitoring unit. Obstacles include vehicles, pedestrians, and fixed objects, while dangerous situations include sudden jumps in front of the vehicle and ignoring traffic signals. Step 3: The transmitter transmits the information detected by the detector to the bicycle using a communication protocol such as Bluetooth or Wi-Fi, and the latest communication technologies such as Bluetooth 5.0 and Wi-Fi 6 (802.11ax) can be used. Step 4: The avoidance unit performs appropriate avoidance actions based on the information transmitted by the transmission unit. Avoidance actions include adjusting speed and changing course. For example, if there is an obstacle ahead, the vehicle will automatically slow down and select a route to avoid the obstacle. If an emergency stop is required, the vehicle will automatically apply the brakes.

[0068] (Example 2) A bicycle accident avoidance system according to an embodiment of the present invention is a system equipped with an AI camera mounted on a helmet that monitors the surrounding environment in real time, detects obstacles and dangerous situations, and transmits the information to the automatic bicycle to take appropriate evasive action. For example, in a bicycle accident avoidance system, a helmet-mounted AI camera monitors the surrounding environment in real time. For example, it detects vehicles, pedestrians, and road obstacles ahead. The AI ​​camera then uses high-precision image recognition technology based on deep learning to identify the obstacle or dangerous situation. When the AI ​​camera detects an obstacle or dangerous situation, it transmits that information to the automatic bicycle. For example, if a vehicle ahead suddenly stops, the AI ​​camera detects the situation and sends an emergency stop command to the automatic bicycle. This information is transmitted in real time via Bluetooth or Wi-Fi. The automatic bicycle then takes appropriate evasive action based on the received information. For example, if an obstacle is detected ahead, the automatic bicycle automatically slows down and selects a route to avoid the obstacle. Furthermore, if an emergency stop is necessary, the automatic bicycle automatically applies the brakes. This prevents accidents from occurring. Furthermore, by equipping the helmet with an AI camera, it can be easily installed on existing bicycles, and widespread adoption is expected. This allows the bicycle accident avoidance system to significantly reduce bicycle accidents. In particular, by acquiring visual information in real time and responding immediately, it is possible to safely respond to situations that would be difficult with conventional manual operation. In addition, by equipping the helmet with an AI camera, it can be easily installed on existing bicycles, and widespread adoption is expected.

[0069] A bicycle accident avoidance system according to an embodiment includes a monitoring unit, a detection unit, a transmission unit, and an avoidance unit. The monitoring unit monitors the surrounding conditions in real time. Examples of the surrounding conditions include, but are not limited to, traffic conditions, weather, and pedestrian movement. The monitoring unit identifies obstacles and dangerous situations using, for example, high-precision image recognition technology based on deep learning. Examples of deep learning technology include, for example, convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The detection unit detects obstacles and dangerous situations based on the information monitored by the monitoring unit. Examples of obstacles include, but are not limited to, vehicles, pedestrians, and fixed objects. Examples of dangerous situations include, but are not limited to, sudden swerving and running red lights. The transmission unit transmits information detected by the detection unit to the motorized bicycle. For transmission, a communication protocol such as Bluetooth or Wi-Fi is used. The transmitter can use the latest communication technologies, such as Bluetooth 5.0 and Wi-Fi 6 (802.11ax). The avoidance unit performs appropriate avoidance actions based on the information transmitted by the transmitter. Avoidance actions include, but are not limited to, adjusting speed and changing course. For example, if there is an obstacle ahead, the avoidance unit automatically reduces speed and selects a route to avoid the obstacle. Furthermore, if an emergency stop is required, the avoidance unit automatically applies the brakes. As a result, the bicycle accident avoidance system according to the embodiment monitors the surrounding situation in real time, detects obstacles and dangerous situations, and transmits the information to the automatic bicycle to perform appropriate avoidance actions, thereby preventing bicycle accidents.

[0070] The monitoring unit can identify obstacles and dangerous situations using specific image recognition technologies that use deep learning. Deep learning technologies include, for example, convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Image recognition technologies include, for example, object detection and face recognition, but are not limited to these examples. The monitoring unit can use, for example, CNNs to detect vehicles and pedestrians ahead with high accuracy. The monitoring unit can also use RNNs to detect movement from consecutive image frames and identify dangerous situations. Furthermore, the monitoring unit can use object detection algorithms to identify obstacles on the road. As a result, the use of deep learning improves the accuracy of identifying obstacles and dangerous situations.

[0071] The transmitter can transmit information in real time using Bluetooth or Wi-Fi. For example, a communication protocol such as Bluetooth or Wi-Fi is used for transmission. Examples of Bluetooth include Bluetooth 5.0 and BLE (Bluetooth Low Energy). Examples of Wi-Fi include Wi-Fi 6 (802.11ax) and 802.11ac. For example, the transmitter transmits information in real time with low power consumption using Bluetooth 5.0. The transmitter can also transmit large amounts of data at high speed in real time using Wi-Fi 6. Furthermore, the transmitter can switch between Bluetooth and Wi-Fi depending on the communication environment. This makes it possible to transmit information in real time using Bluetooth or Wi-Fi.

[0072] The avoidance unit can automatically reduce speed and select a route to avoid an obstacle when an obstacle is detected ahead. Examples of obstacles include, but are not limited to, vehicles, pedestrians, and fixed objects. For example, when a vehicle ahead suddenly stops, the avoidance unit can automatically reduce speed and select a route to avoid the obstacle. Criteria such as a deceleration rate and a lower speed limit are used to reduce speed. For example, the avoidance unit can set the deceleration rate to 30% and the lower speed limit to 10 km / h. Criteria such as the shortest route and a safe route are used to select a route. For example, when selecting the shortest route, the avoidance unit can select a route that takes the shortest distance to avoid the obstacle. Furthermore, when selecting a safe route, the avoidance unit can also select the safest route to avoid the obstacle. As a result, when an obstacle is detected ahead, the avoidance unit can automatically reduce speed and avoid the obstacle, thereby preventing an accident.

[0073] The avoidance unit can automatically apply the brakes when a sudden stop is necessary. Criteria for sudden stops, such as braking strength and stopping distance, are used for the sudden stop. The avoidance unit can, for example, set the braking strength to maximum and the stopping distance to minimum. For example, when a sudden obstacle appears ahead, the avoidance unit automatically applies the brakes to bring the vehicle to an emergency stop. This makes it possible to prevent accidents by automatically applying the brakes when a sudden stop is necessary.

[0074] The monitoring unit is attached to the front of the helmet, and can ensure a wide forward field of view. To ensure a wide forward field of view, criteria such as the viewing angle and camera placement are used. For example, the monitoring unit can be set to have a viewing angle of 120 degrees, and the camera can be placed in the center front of the helmet. This allows a wide forward field of view to be ensured by attaching it to the front of the helmet.

[0075] The transmitter is built into the helmet and can transmit information smoothly. To transmit information smoothly, standards such as communication speed and data compression method are used. For example, the transmitter can set the communication speed to 1 Gbps and the data compression method to H.265. By building the transmitter into the helmet, this allows for smooth transmission of information.

[0076] The monitoring unit can estimate the user's emotions and adjust the monitoring accuracy based on the estimated user's emotions. For example, if the user is nervous, the monitoring unit can increase the monitoring accuracy to obtain more detailed information. Furthermore, if the user is relaxed, the monitoring unit can return the monitoring accuracy to normal to reduce battery consumption. Furthermore, if the user is excited, the monitoring unit can adjust the monitoring accuracy to a medium level to obtain only the necessary information. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. This allows for more appropriate monitoring by adjusting the monitoring accuracy according to the user's emotions.

[0077] The monitoring unit can switch monitoring modes depending on the weather and time of day. For example, the monitoring unit uses an infrared camera at night to ensure visibility. The monitoring unit can also detect water droplets when it is raining and automatically clear the camera lens. Furthermore, the monitoring unit can improve visibility by adjusting the exposure of the camera when it is under strong sunlight during the day. Examples of weather include sunny, rainy, and snowy weather. Examples of time periods include daytime, nighttime, and rush hour. This allows appropriate monitoring by switching the monitoring mode depending on the weather and time of day.

[0078] The monitoring unit can identify danger by using surrounding sound information in combination. The monitoring unit can, for example, detect the sound of a car horn and identify danger. The monitoring unit can also detect the voices or cries of pedestrians and alert them. Furthermore, the monitoring unit can detect the sound of an emergency vehicle siren and alert them to evasive action. Surrounding sound information includes, for example, the sound of a car engine and the voices of pedestrians. By using surrounding sound information in combination, the accuracy of identifying danger is improved.

[0079] The monitoring unit can optimize the monitoring algorithm by referring to past monitoring data. The monitoring unit can improve monitoring accuracy in specific situations, for example, based on past accident data. The monitoring unit can also analyze the user's past driving patterns and customize the monitoring algorithm. Furthermore, the monitoring unit can predict dangers at specific times or locations from past monitoring data and strengthen monitoring. Past monitoring data includes, for example, the database format and data storage period. This makes it possible to optimize the monitoring algorithm by referring to past monitoring data.

[0080] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated user emotions. For example, if the user is nervous, the monitoring unit prioritizes monitoring the area ahead. Furthermore, if the user is relaxed, the monitoring unit can monitor all directions evenly. Furthermore, if the user is excited, the monitoring unit can focus monitoring on a specific direction. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. This allows for more appropriate monitoring by determining monitoring priorities according to the user's emotions.

[0081] The monitoring unit can adjust the monitoring range according to the user's traveling speed. For example, when the user is traveling at high speed, the monitoring unit widens the forward monitoring range. Furthermore, when the user is traveling at low speed, the monitoring unit can widen the surrounding monitoring range. Furthermore, when the user is stopped, the monitoring unit can perform omnidirectional monitoring. The traveling speed is measured using criteria such as the type of speed sensor and the measurement frequency. This allows for appropriate monitoring by adjusting the monitoring range according to the user's traveling speed.

[0082] The monitoring unit can predict and monitor the movements of other traffic participants. For example, the monitoring unit can predict the movement of a vehicle ahead and strengthen monitoring. The monitoring unit can also predict the movement of pedestrians and warn them. Furthermore, the monitoring unit can predict the movement of bicycles and monitor them to avoid collisions. Other traffic participants include, for example, vehicles, pedestrians, and bicycles. To predict movement, for example, past movement patterns or machine learning techniques are used. This makes it possible to predict the movements of other traffic participants and perform appropriate monitoring.

[0083] The monitoring unit can customize the monitoring method by reflecting the user's past driving history. For example, the monitoring unit can strengthen monitoring in specific locations based on routes the user has taken in the past. The monitoring unit can also analyze the user's past driving patterns and optimize the monitoring algorithm. Furthermore, the monitoring unit can strengthen monitoring in specific situations based on the user's past accident history. Past driving history includes, for example, GPS data and driving logs. This makes it possible to customize the monitoring method by reflecting the user's past driving history.

[0084] The detection unit can estimate the user's emotions and adjust the detection sensitivity based on the estimated user's emotions. For example, if the user is nervous, the detection unit can increase the detection sensitivity to obtain more detailed information. Furthermore, if the user is relaxed, the detection unit can return the detection sensitivity to normal to reduce battery consumption. Furthermore, if the user is excited, the detection unit can adjust the detection sensitivity to a medium level to obtain only the necessary information. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. This allows for more appropriate detection by adjusting the detection sensitivity according to the user's emotions.

[0085] The detection unit can apply different detection algorithms depending on the type of obstacle. For example, when detecting a vehicle, the detection unit uses an algorithm dedicated to vehicles. Furthermore, when detecting a pedestrian, the detection unit can also use an algorithm dedicated to pedestrians. Furthermore, when detecting an obstacle on the road, the detection unit can also use an algorithm dedicated to obstacles. Types of obstacles include, for example, vehicles, pedestrians, fixed objects, etc. Detection algorithms include, for example, object detection algorithms and motion detection algorithms. In this way, by applying different detection algorithms depending on the type of obstacle, detection accuracy is improved.

[0086] The detection unit can improve detection accuracy by predicting the movement of an obstacle. The detection unit can predict, for example, the movement of a vehicle, thereby improving detection accuracy. The detection unit can also predict the movement of a pedestrian, thereby improving detection accuracy. Furthermore, the detection unit can predict the movement of a bicycle, thereby improving detection accuracy. The movement of an obstacle can be based on, for example, past movement patterns and machine learning techniques. Thus, by predicting the movement of an obstacle, detection accuracy can be improved.

[0087] The detection unit can optimize the detection algorithm by referring to past detection data. The detection unit can improve detection accuracy in specific situations, for example, based on past accident data. The detection unit can also analyze the user's past driving patterns and customize the detection algorithm. Furthermore, the detection unit can predict dangers at specific times or locations based on past detection data and strengthen detection. Past detection data includes, for example, the database format and data storage period. This makes it possible to optimize the detection algorithm by referring to past detection data.

[0088] The detection unit can estimate the user's emotions and determine the detection priority based on the estimated user's emotions. For example, if the user is nervous, the detection unit prioritizes detection in front. Also, if the user is relaxed, the detection unit can perform detection evenly in all directions. Furthermore, if the user is excited, the detection unit can focus detection in a specific direction. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. This allows for more appropriate detection by determining the detection priority according to the user's emotions.

[0089] The detection unit can adjust the detection method according to the size and speed of the obstacle. For example, when detecting a large obstacle, the detection unit performs wide-area detection. Furthermore, when detecting a small obstacle, the detection unit can perform detailed detection. Furthermore, when detecting an obstacle moving at high speed, the detection unit can perform rapid detection. The size of the obstacle includes, for example, the dimensions and volume of the object. The speed of the obstacle includes, for example, the type of speed sensor and the measurement frequency. As a result, appropriate detection is possible by adjusting the detection method according to the size and speed of the obstacle.

[0090] The detection unit can identify obstacles by analyzing surrounding environmental sounds. For example, the detection unit can analyze the engine sound of a car to identify the vehicle. The detection unit can also analyze the footsteps of a pedestrian to identify the pedestrian. Furthermore, the detection unit can analyze the siren sound of an emergency vehicle to identify the emergency vehicle. Surrounding environmental sounds include, for example, the engine sound of a car, the voices of pedestrians, etc. Thus, by analyzing surrounding environmental sounds, the accuracy of identifying obstacles is improved.

[0091] The detection unit can predict the user's driving route and adjust the detection range. For example, if the user is driving a specific route, the detection unit performs detection along that route. In addition, if the user is heading toward a destination, the detection unit can also perform detection with an emphasis on that direction. Furthermore, if the user gets lost, the detection unit can perform omnidirectional detection. The driving route includes, for example, past driving data and map information. This makes it possible to appropriately adjust the detection range by predicting the user's driving route.

[0092] The transmitting unit can estimate the user's emotions and adjust the timing of transmission based on the estimated user's emotions. For example, if the user is nervous, the transmitting unit can transmit information immediately. Furthermore, if the user is relaxed, the transmitting unit can transmit information at a normal timing. Furthermore, if the user is excited, the transmitting unit can transmit information at an appropriate timing. The user's emotions are estimated using techniques such as facial expression recognition and voice analysis. This allows for more appropriate information transmission by adjusting the timing of transmission according to the user's emotions.

[0093] The transmitting unit can switch the transmission method depending on the communication environment. For example, when the communication environment is good, the transmitting unit transmits information using Wi-Fi. Furthermore, when the communication environment is unstable, the transmitting unit can also transmit information using Bluetooth. Furthermore, when the communication environment is very poor, the transmitting unit can compress and transmit data. The communication environment includes, for example, a Wi-Fi environment, a mobile network environment, etc. The transmission method includes, for example, Wi-Fi, Bluetooth, mobile data communication, etc. This makes it possible to transmit information appropriately by switching the transmission method depending on the communication environment.

[0094] The transmitting unit can set a transmission priority according to the importance of data. For example, the transmitting unit transmits emergency information with the highest priority. The transmitting unit can also transmit normal information with normal priority. Furthermore, the transmitting unit can transmit information of low importance later. The importance of data includes, for example, urgency and relevance. The transmission priority includes, for example, prioritization based on importance and time priority. Thus, by setting a transmission priority according to the importance of data, important information can be preferentially transmitted.

[0095] The transmitting unit can optimize the transmission algorithm by referring to past transmission data. The transmitting unit can, for example, set the optimal transmission timing based on the past transmission data. The transmitting unit can also analyze the past transmission data and optimize the transmission method. Furthermore, the transmitting unit can improve the transmission method in specific situations based on the past transmission data. The past transmission data includes, for example, the database format and the data storage period. This makes it possible to optimize the transmission algorithm by referring to the past transmission data.

[0096] The transmission unit can estimate the user's emotions and determine the priority of transmission data based on the estimated user's emotions. For example, if the user is nervous, the transmission unit can prioritize transmitting important data. Furthermore, if the user is relaxed, the transmission unit can prioritize transmitting normal data. Furthermore, if the user is excited, the transmission unit can prioritize transmitting moderate data. The user's emotions are estimated using, for example, techniques such as facial expression recognition and voice analysis. This allows for more appropriate information transmission by determining the priority of transmission data according to the user's emotions.

[0097] The transmitter can adjust the transmission frequency according to the user's traveling speed. For example, the transmitter increases the transmission frequency when the user is traveling at a high speed. The transmitter can also return the transmission frequency to normal when the user is traveling at a low speed. Furthermore, the transmitter can also decrease the transmission frequency when the user is stopped. The traveling speed is measured using criteria such as the type of speed sensor and the measurement frequency. This allows appropriate information transmission by adjusting the transmission frequency according to the user's traveling speed.

[0098] The transmitter can predict the movements of other traffic participants and adjust the transmission data. For example, the transmitter can predict the movements of vehicles ahead and adjust the transmission data. The transmitter can also predict the movements of pedestrians and adjust the transmission data. Furthermore, the transmitter can predict the movements of bicycles and adjust the transmission data. Other traffic participants include, for example, vehicles, pedestrians, and bicycles. To predict movements, for example, past movement patterns or machine learning techniques are used. This makes it possible to predict the movements of other traffic participants and transmit appropriate information.

[0099] The transmission unit can customize the transmission method by reflecting the user's past driving history. For example, the transmission unit can optimize the transmission method in a specific location based on the route the user has taken in the past. The transmission unit can also analyze the user's past driving patterns and customize the transmission algorithm. Furthermore, the transmission unit can improve the transmission method in a specific situation based on the user's past accident history. Past driving history includes, for example, GPS data and driving logs. This makes it possible to customize the transmission method by reflecting the user's past driving history.

[0100] The avoidance unit can estimate the user's emotions and adjust the method of the avoidance operation based on the estimated user's emotions. For example, if the user is nervous, the avoidance unit can quickly perform the avoidance operation. Furthermore, if the user is relaxed, the avoidance unit can also perform a normal avoidance operation. Furthermore, if the user is excited, the avoidance unit can also perform a moderate avoidance operation. The user's emotions are estimated using techniques such as facial expression recognition and voice analysis. This allows the method of the avoidance operation to be adjusted according to the user's emotions, thereby enabling a more appropriate avoidance operation.

[0101] The avoidance unit can apply different avoidance algorithms depending on the type of obstacle. For example, when avoiding a vehicle, the avoidance unit uses an algorithm dedicated to vehicles. Furthermore, when avoiding a pedestrian, the avoidance unit can also use an algorithm dedicated to pedestrians. Furthermore, when avoiding an obstacle on the road, the avoidance unit can also use an algorithm dedicated to the obstacle. Types of obstacles include, for example, vehicles, pedestrians, fixed objects, etc. The avoidance algorithm includes, for example, a method for calculating an avoidance path and a method for controlling an avoidance operation. In this way, by applying different avoidance algorithms depending on the type of obstacle, avoidance accuracy is improved.

[0102] The avoidance unit can improve the accuracy of avoidance by predicting the movement of an obstacle. The avoidance unit can, for example, predict the movement of a vehicle and improve the accuracy of avoidance. The avoidance unit can also predict the movement of a pedestrian and improve the accuracy of avoidance. Furthermore, the avoidance unit can also predict the movement of a bicycle and improve the accuracy of avoidance. The movement of an obstacle includes, for example, past movement patterns and machine learning techniques. In this way, the accuracy of avoidance is improved by predicting the movement of an obstacle.

[0103] The avoidance unit can optimize the avoidance algorithm by referring to past avoidance data. The avoidance unit can improve avoidance accuracy in specific situations, for example, based on past accident data. The avoidance unit can also analyze the user's past driving patterns and customize the avoidance algorithm. Furthermore, the avoidance unit can predict dangers at specific times or locations from the past avoidance data and strengthen avoidance. The past avoidance data includes, for example, the database format and the data storage period. This makes it possible to optimize the avoidance algorithm by referring to the past avoidance data.

[0104] The avoidance unit can estimate the user's emotions and determine the priority of avoidance operations based on the estimated user's emotions. For example, if the user is nervous, the avoidance unit prioritizes forward avoidance operations. Furthermore, if the user is relaxed, the avoidance unit can perform avoidance operations evenly in all directions. Furthermore, if the user is excited, the avoidance unit can focus avoidance operations in a specific direction. The user's emotions are estimated using techniques such as facial expression recognition and voice analysis. This allows for more appropriate avoidance operations by determining the priority of avoidance operations according to the user's emotions.

[0105] The avoidance unit can adjust the avoidance method according to the user's traveling speed. For example, when the user is traveling at a high speed, the avoidance unit performs a rapid avoidance operation. Furthermore, when the user is traveling at a low speed, the avoidance unit can also perform a normal avoidance operation. Furthermore, when the user is stopped, the avoidance unit can also suspend the avoidance operation. The traveling speed is measured using criteria such as the type of speed sensor and the measurement frequency. As a result, by adjusting the avoidance method according to the user's traveling speed, an appropriate avoidance operation can be performed.

[0106] The avoidance unit can predict the movements of other traffic participants and take avoidance action. The avoidance unit, for example, predicts the movement of a vehicle ahead and takes avoidance action. The avoidance unit can also predict the movement of a pedestrian and take avoidance action. Furthermore, the avoidance unit can predict the movement of a bicycle and take avoidance action. Other traffic participants include, for example, vehicles, pedestrians, bicycles, etc. To predict movement, for example, past movement patterns or machine learning techniques are used. This makes it possible to predict the movements of other traffic participants and take appropriate avoidance action.

[0107] The avoidance unit can customize the avoidance method by reflecting the user's past driving history. For example, the avoidance unit can optimize the avoidance method in a specific location based on routes the user has taken in the past. The avoidance unit can also analyze the user's past driving patterns and customize the avoidance algorithm. Furthermore, the avoidance unit can improve the avoidance method in a specific situation based on the user's past accident history. The past driving history includes, for example, GPS data and driving logs. This makes it possible to customize the avoidance method by reflecting the user's past driving history. === Hard Collateral 1-1 === Each of the multiple elements including the monitoring unit, detection unit, transmission unit, and avoidance unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the monitoring unit is realized by the camera 42 of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the detection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the transmission unit is realized by the communication I / F 44 of the smart device 14 or the communication I / F 26 of the data processing device 12. For example, the avoidance unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-described monitoring unit, detection unit, transmission unit, and avoidance unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the monitoring unit is realized by the camera 42 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the detection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the transmission unit is realized by the communication I / F 44 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. For example, the avoidance unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the monitoring unit, detection unit, transmission unit, and avoidance unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the monitoring unit is realized by the camera 42 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the detection unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the transmission unit is realized by the communication I / F 44 of the headset type terminal 314 or the communication I / F 26 of the data processing device 12. For example, the avoidance unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the monitoring unit, detection unit, transmission unit, and avoidance unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the monitoring unit is realized by the camera 42 of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the detection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the transmission unit is realized by the communication I / F 44 of the robot 414 or the communication I / F 26 of the data processing device 12. For example, the avoidance unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The monitoring unit monitors the user's heart rate and can adjust the monitoring accuracy based on heart rate fluctuations. For example, if the user's heart rate suddenly rises, the monitoring unit increases the monitoring accuracy to obtain more detailed information. If the heart rate is stable, the monitoring unit can maintain normal monitoring accuracy and reduce battery consumption. Furthermore, if the heart rate drops, the monitoring unit can set the monitoring accuracy to low and obtain only the minimum necessary information. This allows for more appropriate monitoring by adjusting the monitoring accuracy according to the user's heart rate.

[0110] The detection unit can analyze the user's breathing pattern and adjust the detection sensitivity based on the breathing rhythm. For example, if the user's breathing becomes faster, the detection unit can increase the detection sensitivity to obtain more detailed information. If the user's breathing is stable, the detection unit can maintain the normal detection sensitivity to reduce battery consumption. Furthermore, if the user's breathing becomes slower, the detection unit can set the detection sensitivity to a lower level to obtain only the minimum amount of information necessary. This allows for more appropriate detection by adjusting the detection sensitivity according to the user's breathing pattern.

[0111] The transmitting unit can monitor the user's body temperature and adjust the timing of transmission based on fluctuations in body temperature. For example, if the user's body temperature rises, the transmitting unit can immediately transmit information. If the body temperature is stable, the transmitting unit can also transmit information at the normal timing. Furthermore, if the body temperature drops, the transmitting unit can delay the timing of transmission and transmit only the minimum amount of information necessary. This allows for more appropriate information transmission by adjusting the timing of transmission according to the user's body temperature.

[0112] The avoidance unit can monitor the degree of muscle tension of the user and adjust the method of the avoidance operation based on the degree of tension. For example, when the user's muscles are tense, the avoidance unit can perform a quick avoidance operation. When the muscles are relaxed, the avoidance unit can also perform a normal avoidance operation. Furthermore, when the muscles are relaxed, the avoidance unit can also perform a moderate avoidance operation. In this way, by adjusting the method of the avoidance operation according to the degree of muscle tension of the user, a more appropriate avoidance operation can be performed.

[0113] The monitoring unit can monitor the degree of dilation of the user's pupils and determine monitoring priorities based on the degree of dilation. For example, if the user's pupils are dilated, monitoring of the front is prioritized. Also, if the pupils are normal, monitoring can be performed evenly in all directions. Furthermore, if the pupils are constricted, monitoring can be concentrated in a specific direction. This allows for more appropriate monitoring by determining monitoring priorities according to the degree of dilation of the user's pupils.

[0114] The monitoring unit can monitor the ambient temperature and humidity and adjust the monitoring accuracy according to the environmental conditions. For example, when the temperature is high, the monitoring unit adjusts the exposure of the camera to improve visibility. When the humidity is high, the monitoring unit can automatically clear the lens to prevent fogging. Furthermore, when the temperature is low, the monitoring unit can use an infrared camera to ensure visibility. This allows for appropriate monitoring by adjusting the monitoring accuracy according to the ambient environmental conditions.

[0115] The detection unit can monitor the amount of ambient light and adjust the detection sensitivity according to the amount of light. For example, when the amount of light is high, the detection unit sets the sensitivity low and acquires only the minimum amount of information necessary. When the amount of light is low, the detection unit can increase the sensitivity to acquire more detailed information. Furthermore, when the amount of light fluctuates, the detection unit can adjust the sensitivity in real time and acquire appropriate information. This allows for more appropriate detection by adjusting the detection sensitivity according to the amount of ambient light.

[0116] The transmitting unit can monitor fluctuations in the communication environment and automatically switch the transmission method depending on the environment. For example, if the communication environment is good, the transmitting unit can transmit information using Wi-Fi. If the communication environment is unstable, the transmitting unit can also transmit information using Bluetooth. Furthermore, if the communication environment is very poor, the transmitting unit can compress and transmit data. This makes it possible to transmit information appropriately by automatically switching the transmission method depending on the communication environment.

[0117] The avoidance unit can monitor road conditions and adjust the method of avoidance operation according to the conditions. For example, if the road is slippery, the avoidance unit will slow down and perform avoidance operation carefully. Also, if the road is dry, the avoidance unit can perform avoidance operation at a normal speed. Furthermore, if there are many obstacles on the road, the avoidance unit can also select a route to avoid the obstacles. In this way, by adjusting the method of avoidance operation according to the road conditions, appropriate avoidance operation is possible.

[0118] The monitoring unit can predict the movements of other traffic participants and adjust the accuracy of monitoring based on the predictions. For example, if there is a possibility that a vehicle ahead will suddenly stop, the monitoring unit can increase the accuracy of monitoring to obtain more detailed information. Also, if there is a possibility that a pedestrian will cross the street, the monitoring unit can concentrate its monitoring in that direction. Furthermore, if a bicycle is approaching, the monitoring unit can track its movement and obtain appropriate information. This makes it possible to appropriately monitor other traffic participants by predicting their movements.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The monitoring unit monitors the surrounding conditions in real time. The surrounding conditions include traffic conditions, weather, and pedestrian movement. The monitoring unit uses high-precision image recognition technology based on deep learning to identify obstacles and dangerous situations. Deep learning technologies include CNN (convolutional neural networks) and RNN (recurrent neural networks). Step 2: The detection unit detects obstacles and dangerous situations based on the information monitored by the monitoring unit. Obstacles include vehicles, pedestrians, and fixed objects, while dangerous situations include sudden jumps in front of the vehicle and ignoring traffic signals. Step 3: The transmitter transmits the information detected by the detector to the bicycle using a communication protocol such as Bluetooth or Wi-Fi, and the latest communication technologies such as Bluetooth 5.0 and Wi-Fi 6 (802.11ax) can be used. Step 4: The avoidance unit performs appropriate avoidance actions based on the information transmitted by the transmission unit. Avoidance actions include adjusting speed and changing course. For example, if there is an obstacle ahead, the vehicle will automatically slow down and select a route to avoid the obstacle. If an emergency stop is required, the vehicle will automatically apply the brakes.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0164] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0165] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0166] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0168] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0169] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0173] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0174] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0176] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0177] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0178] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0182] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0183] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0184] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0185] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0186] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0187] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0189] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0190] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0191] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0192] [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A monitoring unit that monitors the surrounding situation in real time; a detection unit that detects obstacles and dangerous situations based on the information monitored by the monitoring unit; a transmitter that transmits information detected by the detector to the bicycle; an avoidance unit that performs a specific avoidance operation based on the information transmitted by the transmission unit; A system characterized by:

2. The monitoring unit Uses specific image recognition techniques with deep learning to identify obstacles and dangerous situations 2. The system of claim 1.

3. The transmission unit Transmit information in real time using Bluetooth or Wi-Fi 2. The system of claim 1.

4. The avoidance unit is If there is an obstacle ahead, the car will automatically slow down and choose a route to avoid the obstacle.

2. The system of claim 1.

5. The avoidance unit is Automatically applies the brakes if an emergency stop is required 2. The system of claim 1.

6. The monitoring unit Attaches to the front of the helmet and provides a wide field of view 2. The system of claim 1.

7. The transmission unit Built into the helmet, it transmits information smoothly.

2. The system of claim 1.

8. The monitoring unit Estimate the user's emotions and adjust the accuracy of monitoring based on the estimated user emotions.

2. The system of claim 1.

Images