System

Abstract

An object of a system according to an embodiment is to provide a driver with information on a road sign by voice.SOLUTION: A system includes an imaging unit, a transmission unit, an analysis unit, a reception unit, and a notification unit. The imaging unit captures an image of a road sign with an in-vehicle camera. The transmission unit transmits the image captured by the imaging unit. The analysis unit analyzes the image transmitted by the transmission unit. The reception unit receives a result analyzed by the analysis unit. The notification unit calls attention by voice on the basis of the result received by the reception 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 technologies do not adequately provide drivers with road sign information for areas that cannot be fully supported by car navigation systems, and there is room for improvement.

[0005] The system according to the embodiment aims to provide road sign information to the driver by voice. [Means for solving the problem]

[0006] The system according to the embodiment includes a photographing unit, a transmitting unit, an analyzing unit, a receiving unit, and a notifying unit. The photographing unit photographs road signs with an in-vehicle camera. The transmitting unit transmits the image photographed by the photographing unit. The analyzing unit analyzes the image transmitted by the transmitting unit. The receiving unit receives the results of the analysis by the analyzing unit. The notifying unit issues an audio warning based on the results received by the receiving unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide road sign information to the driver by voice. [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 system according to an embodiment of the present invention analyzes images of road signs captured by an onboard camera and provides audio warnings to the driver. In this system, the onboard camera captures road signs, AI analyzes the images, recognizes the content of the signs, and provides audio warnings to the driver. For example, the onboard camera captures a speed limit sign, the AI ​​recognizes the speed limit, and provides an audio warning to the driver, saying, "The speed limit is 50 km / h." Furthermore, when a stop sign is recognized, the AI ​​provides an audio warning, saying, "Please stop." This complements areas that cannot be fully supported by car navigation systems and supports safe driving. This complements areas that cannot be fully supported by car navigation systems and supports safe driving. For example, the system can recognize road signs outside the range of navigation systems based on map data and provide audio warnings to the driver. This supports safe driving and contributes to preventing traffic accidents.

[0029] A road sign recognition system according to an embodiment includes a photographing unit, a transmission unit, an analysis unit, a receiving unit, and a notification unit. The photographing unit photographs road signs using an onboard camera. For example, the photographing unit is installed in front of a vehicle and automatically photographs signs while the vehicle is traveling. The transmission unit transmits the image photographed by the photographing unit. For example, the transmission unit can transmit the image using a communication method such as Wi-Fi or Bluetooth (registered trademark). The analysis unit analyzes the image transmitted by the transmission unit. The analysis unit recognizes the content of the sign using image recognition technology with AI. For example, the analysis unit can recognize speed limit signs and identify the speed limit using deep learning. The analysis unit can also recognize stop signs and identify the stopping position indicated by the sign. The receiving unit receives the results of the analysis performed by the analysis unit. For example, the receiving unit can receive the analysis results using a communication method such as Wi-Fi or Bluetooth. The notification unit issues a voice alert based on the results received by the receiving unit. The notification unit issues a voice alert using AI and voice synthesis technology. For example, when the notification unit recognizes a speed limit sign, it notifies the driver by voice, saying, "The speed limit is 50 km / h." When the notification unit recognizes a stop sign, it notifies the driver by voice, saying, "Please stop." In this way, the road sign recognition system according to the embodiment can analyze the image of the road sign read by the on-board camera and alert the driver by voice.

[0030] The analysis unit can recognize the content of the sign using image recognition technology. Image recognition technology includes, for example, deep learning and pattern recognition. The analysis unit can recognize the content of the sign using, for example, deep learning. For example, the analysis unit can recognize a speed limit sign and identify the speed limit. The analysis unit can also recognize a stop sign and identify the stopping position indicated by the sign. Furthermore, the analysis unit can recognize the content of the sign using pattern recognition. For example, the analysis unit recognizes the content based on the shape and color of the sign. As a result, the content of the sign can be accurately recognized using image recognition technology.

[0031] The notification unit can issue a warning using a voice synthesis technology. Examples of voice synthesis technology include text-to-speech synthesis and voice sample-based synthesis. The notification unit issues a warning using, for example, text-to-speech synthesis. For example, when the notification unit recognizes a speed limit sign, it issues a voice warning saying, "The speed limit is 50 km / h." Furthermore, when the notification unit recognizes a stop sign, it can also issue a voice warning saying, "Please stop." Furthermore, the notification unit can also issue a warning using voice sample-based synthesis. For example, the notification unit issues a warning using a pre-recorded voice sample. In this way, by using voice synthesis technology, it is possible to effectively warn the driver.

[0032] The camera unit is installed in front of the vehicle and can automatically photograph signs while the vehicle is traveling. The front of the vehicle includes, for example, the top of the windshield and near the bumper. The camera unit is installed, for example, in the top of the windshield and can automatically photograph signs while the vehicle is traveling. For example, the camera unit photographs signs that contain important driving information, such as speed limit signs and stop signs. The camera unit can also be installed near the bumper and can automatically photograph signs while the vehicle is traveling. This reduces the burden on the driver by allowing the camera unit installed in front of the vehicle to automatically photograph signs.

[0033] The analysis unit can recognize speed limit signs and identify the speed limit. Speed ​​limit signs include, for example, speed limits on general roads and speed limits on expressways. The analysis unit can recognize, for example, speed limit signs on general roads and identify the speed limit. For example, the analysis unit can identify that the speed limit is 50 km / h. The analysis unit can also recognize speed limit signs on expressways and identify the speed limit. For example, the analysis unit can identify that the speed limit is 100 km / h. In this way, by recognizing speed limit signs and identifying the speed limit, it is possible to provide the driver with appropriate speed information.

[0034] The analysis unit can recognize a stop sign and identify the stop position indicated by the sign. Stop signs include, for example, stop signs at intersections and stop signs at railroad crossings. The analysis unit can recognize, for example, a stop sign at an intersection and identify the stop position indicated by the sign. For example, the analysis unit can identify the position of the stop line at the intersection. The analysis unit can also recognize a stop sign at a railroad crossing and identify the stop position indicated by the sign. For example, the analysis unit can identify the position of the stop line at the railroad crossing. In this way, by recognizing a stop sign and identifying its stop position, it is possible to provide the driver with appropriate stopping information.

[0035] The image capturing unit can automatically adjust a filter to accommodate different weather conditions. Different weather conditions include, for example, rain, snow, and fog. For example, the image capturing unit automatically applies a waterproof filter when it is raining to maintain the clarity of the captured image. For example, the image capturing unit applies a filter that removes raindrops to improve the visibility of signs. The image capturing unit can also automatically apply a filter that suppresses snow reflections on snowy days to improve the visibility of signs. For example, the image capturing unit applies a filter that reduces snow reflections to make the content of signs clear. The image capturing unit can also automatically apply a filter that penetrates fog when it is foggy to clearly capture signs. For example, the image capturing unit applies a filter that penetrates fog to ensure the visibility of signs. In this way, the clarity of the captured image can be maintained by automatically adjusting a filter that corresponds to different weather conditions.

[0036] The image capturing unit can also use an infrared camera to accurately capture signs even in low-light environments such as at night or inside a tunnel. Low-light environments include, for example, at night or inside a tunnel. The image capturing unit, for example, automatically activates the infrared camera when driving at night to clearly capture signs. For example, the image capturing unit uses the infrared camera to capture signs at night to improve visibility. The image capturing unit can also use the infrared camera when driving inside a tunnel to ensure sign visibility. For example, the image capturing unit uses the infrared camera in the low-light environment of a tunnel to clearly capture signs. The image capturing unit can also use the infrared camera when driving in a low-light environment to accurately capture signs. For example, the image capturing unit uses the infrared camera to capture signs in a low-light environment to improve visibility. This allows accurate capture of signs even in low-light environments to provide appropriate information to the driver.

[0037] The camera unit adjusts the capture frequency according to the vehicle speed, capturing images more frequently when the vehicle is traveling at high speeds. Examples of vehicle speeds include 30 km / h and 100 km / h. The camera unit increases the capture frequency when the vehicle is traveling on a highway to ensure that important signs are not overlooked. For example, the camera unit detects the vehicle's speed using a sensor and increases the capture frequency as the speed increases. The camera unit can also return the capture frequency to normal when traveling in urban areas and capture only necessary information. For example, the camera unit reduces the capture frequency when the vehicle is traveling at a low speed. The camera unit can also reduce the capture frequency during traffic jams to reduce the burden on the driver. For example, the camera unit minimizes the capture frequency when the vehicle is traveling at a very low speed. This allows the camera unit to adjust the capture frequency according to the vehicle's speed to ensure that important signs are not overlooked.

[0038] The image capturing unit can capture signs in all directions using multiple cameras that cover 360 degrees around the vehicle. Omnidirectional includes, for example, the front, sides, and rear. The image capturing unit can be installed with multiple cameras to capture signs not only in front but also on the sides and rear. For example, the image capturing unit can be installed with a front camera in front of the vehicle, side cameras on the sides, and a rear camera on the rear. The image capturing unit can also capture signs at intersections in all directions and provide necessary information to the driver. For example, the image capturing unit can be installed with cameras that cover intersections in all directions and capture signs. The image capturing unit can also capture signs in parking lots in all directions to provide parking support. For example, the image capturing unit can be installed with cameras that cover parking lots in all directions and capture signs. In this way, necessary information can be provided to the driver by capturing signs in all directions.

[0039] The camera unit can simultaneously capture the license plate of the vehicle and the movements of other vehicles to grasp the traffic situation. The traffic situation includes, for example, the movement of the vehicle and the recognition of the license plate. For example, the camera unit captures the license plate of another vehicle to grasp the traffic situation. For example, the camera unit recognizes the license plate of another vehicle using license plate recognition technology. The camera unit can also capture the movement of other vehicles and warn the driver. For example, the camera unit tracks the movement of other vehicles and warn the driver. The camera unit can also capture the traffic congestion situation and provide information to the driver in real time. For example, the camera unit captures the traffic congestion situation and provides information to the driver in real time. In this way, the traffic situation can be grasped and appropriate information can be provided to the driver.

[0040] The camera unit can track the driver's gaze and prioritize capturing images of signs that are in front of the driver's gaze. The driver's gaze includes, for example, an eye-tracking camera and an eye-gaze detection algorithm. The camera unit can, for example, track the driver's gaze and prioritize capturing images of signs that are in front of the driver's gaze. For example, the camera unit can detect the driver's gaze using an eye-tracking camera and capture images of signs that are in front of the driver's gaze. The camera unit can also use eye-tracking technology to capture and capture signs that are easy for the driver to miss. For example, the camera unit can analyze the driver's gaze movement using an eye-gaze detection algorithm to capture images of signs that are easy to miss. The camera unit can also analyze the driver's gaze movement and prioritize capturing images of important signs. For example, the camera unit can identify important signs based on the gaze movement and prioritize capturing images of them. In this way, by tracking the driver's gaze, it is possible to capture and capture images of signs that are easy to miss.

[0041] The transmitting unit can select an optimal transmission method depending on the communication environment. The communication environment includes, for example, Wi-Fi, 4G, 5G, etc. The transmitting unit transmits data at high speed in, for example, a Wi-Fi environment. For example, the transmitting unit transmits large amounts of data quickly using Wi-Fi. Furthermore, the transmitting unit can compress data and transmit it efficiently in a 4G environment. For example, the transmitting unit compresses data using 4G to improve the transmission speed. Furthermore, the transmitting unit can transmit large amounts of data quickly in a 5G environment. For example, the transmitting unit transmits high-resolution image data using 5G. In this way, data can be transmitted efficiently by selecting an optimal transmission method depending on the communication environment.

[0042] The transmitting unit can improve the transmission speed by using a data compression technique. Examples of data compression techniques include ZIP compression and JPEG compression. The transmitting unit, for example, compresses image data to improve the transmission speed. For example, the transmitting unit compresses image data using JPEG compression to improve the transmission speed. The transmitting unit can also compress video data to efficiently transmit the data. For example, the transmitting unit compresses video data using a video compression technique to improve the transmission speed. The transmitting unit can also compress text data to quickly transmit the data. For example, the transmitting unit compresses text data using ZIP compression to improve the transmission speed. In this way, the transmission speed can be improved by using data compression techniques.

[0043] The transmitting unit can encrypt the data to be transmitted to ensure data security. Data encryption techniques include, for example, AES encryption and RSA encryption. The transmitting unit, for example, encrypts the data to be transmitted to prevent unauthorized access. For example, the transmitting unit encrypts the data to be transmitted using AES encryption to prevent unauthorized access. The transmitting unit can also encrypt important data to enhance security. For example, the transmitting unit encrypts important data using RSA encryption to enhance security. The transmitting unit can also encrypt the data before transmission to transmit it securely. For example, the transmitting unit encrypts the data before transmission to transmit it securely. In this way, by encrypting the data to be transmitted, data security can be ensured.

[0044] The transmitting unit simultaneously transmits vehicle position information, and the analyzing unit can identify the position of the sign based on the position information. The vehicle position information includes, for example, GPS data, map data, etc. The transmitting unit, for example, transmits the vehicle position information and identifies the position of the sign. For example, the transmitting unit transmits the vehicle position information using GPS data, and the analyzing unit identifies the position of the sign based on the position information. The transmitting unit can also analyze the exact position of the sign based on the position information. For example, the transmitting unit identifies the position of the sign using map data. The transmitting unit can also combine the position information and the sign information and provide it to the driver. For example, the transmitting unit integrates the position information and the sign information to provide accurate information to the driver. In this way, accurate information can be provided to the driver by identifying the position of the sign based on the vehicle position information.

[0045] The transmitter can communicate with other vehicles and traffic infrastructure to share traffic information in real time. Sharing traffic information in real time includes, for example, a communication protocol, a data update frequency, and the like. The transmitter can communicate with other vehicles to share traffic information, for example. For example, the transmitter can share information with other vehicles using vehicle-to-vehicle (V2V) communication. The transmitter can also communicate with traffic infrastructure to update information in real time. For example, the transmitter can share information with traffic infrastructure using vehicle-to-infrastructure (V2I) communication. The transmitter can also share traffic information and provide it to a driver. For example, the transmitter can provide traffic information updated in real time to a driver. In this way, by communicating with other vehicles and traffic infrastructure, traffic information can be shared in real time and provided to a driver.

[0046] The transmitting unit can automatically back up the transmission data to protect the data in the event of a communication failure. The backup of the transmission data includes, for example, the frequency of the backup and the storage destination. The transmitting unit, for example, automatically backs up the transmission data to protect the data in the event of a communication failure. For example, the transmitting unit periodically backs up data and restores the data in the event of a communication failure. The transmitting unit can also back up important data and restore it in the event of a communication failure. For example, the transmitting unit backs up important data to the cloud and restores it in the event of a communication failure. The transmitting unit can also back up data before sending it and send it safely. For example, the transmitting unit backs up data before sending it and transmits it safely. In this way, by backing up the transmission data, the data can be protected in the event of a communication failure.

[0047] The analysis unit can use a multilingual algorithm to recognize signs of different countries or regions. The multilingual algorithm includes, for example, a translation algorithm, a language model, and the like. The analysis unit can, for example, use a multilingual algorithm to recognize signs of different countries or regions. For example, the analysis unit can recognize signs in different languages ​​using a translation algorithm. The analysis unit can also recognize international signs and provide the information to the driver. For example, the analysis unit can recognize international signs using a language model. The analysis unit can also recognize signs for each region and provide appropriate information to the driver. For example, the analysis unit can recognize signs using a language model for each region. In this way, by using a multilingual algorithm, signs for different countries or regions can be accurately recognized.

[0048] The analysis unit can correct deterioration and dirt on signs to enable accurate recognition. Deterioration and dirt on signs include, for example, fading, dirt, and breakage. The analysis unit, for example, corrects deteriorated signs to enable accurate recognition. For example, the analysis unit corrects deteriorated signs using an image correction algorithm. The analysis unit can also correct dirty signs to provide accurate information to drivers. For example, the analysis unit corrects dirty signs using filtering technology. The analysis unit can also correct deterioration and dirt to enable accurate recognition of the content of signs. For example, the analysis unit corrects deterioration and dirt using an image correction algorithm to enable accurate recognition of the content of signs. In this way, accurate recognition can be achieved by correcting deterioration and dirt on signs.

[0049] The analysis unit can perform analysis by taking into account not only the shape and color of the sign but also surrounding environmental information. The surrounding environmental information includes, for example, weather information and surrounding building information. The analysis unit, for example, analyzes and accurately recognizes the shape and color of the sign. For example, the analysis unit analyzes the shape of the sign using a shape recognition algorithm. The analysis unit also analyzes the color of the sign using a color recognition algorithm. The analysis unit can also accurately recognize the content of the sign by taking into account surrounding environmental information. For example, the analysis unit recognizes the content of the sign by taking into account weather information. The analysis unit can also recognize the content of the sign by taking into account surrounding building information. For example, the analysis unit identifies the position of the sign based on surrounding building information and recognizes the content. In this way, accurate recognition is possible by taking into account the shape and color of the sign and surrounding environmental information.

[0050] The analysis unit can share the sign recognition results with other vehicles and traffic infrastructure and update traffic information in real time. The real-time updating of traffic information includes, for example, a data update frequency and a communication protocol. The analysis unit can, for example, share the sign recognition results with other vehicles and update traffic information. For example, the analysis unit can share information with other vehicles using V2V (Vehicle-to-Vehicle) communication. The analysis unit can also share the sign recognition results with traffic infrastructure and provide traffic information in real time. For example, the analysis unit can share information with traffic infrastructure using V2I (Vehicle-to-Infrastructure) communication. The analysis unit can also store the sign recognition results in a cloud and share them with other vehicles. For example, the analysis unit can provide appropriate information to the driver based on the recognition results stored in the cloud. This allows traffic information to be updated in real time by sharing it with other vehicles and traffic infrastructure.

[0051] The analysis unit can store the sign recognition results in the cloud so that the analysis results can be referenced later. Storing in the cloud includes, for example, the type of cloud service and the storage period. The analysis unit can store the sign recognition results in the cloud so that the analysis results can be referenced later. For example, the analysis unit provides appropriate information to the driver based on the recognition results stored in the cloud. The analysis unit can also analyze the data stored in the cloud and analyze the driver's driving patterns. For example, the analysis unit identifies the driver's driving patterns based on the data stored in the cloud and provides appropriate advice. By storing the analysis results in the cloud, the analysis results can be referenced later.

[0052] The analysis unit can compare the sign recognition results with the driver's driving history and analyze the driving pattern. The driving pattern includes, for example, the type of driving history and an analysis algorithm. The analysis unit can compare the sign recognition results with the driving history and analyze the driving pattern. For example, the analysis unit provides appropriate advice to the driver based on the driving history. The analysis unit can also analyze the driving pattern and support the driver's safe driving. For example, the analysis unit can identify the driver's driving pattern based on the driving history and provide appropriate advice. In this way, by comparing the driving history, the driving pattern can be analyzed and appropriate advice can be provided to the driver.

[0053] The receiving unit can select an optimal receiving method depending on the communication environment. Communication environments include, for example, Wi-Fi, 4G, and 5G. For example, in a Wi-Fi environment, the receiving unit receives data at high speed. For example, the receiving unit quickly receives large amounts of data using Wi-Fi. In a 4G environment, the receiving unit can compress data to efficiently receive the data. For example, the receiving unit compresses data using 4G to improve the reception speed. In a 5G environment, the receiving unit can quickly receive large amounts of data. For example, the receiving unit receives high-resolution image data using 5G. As a result, data can be efficiently received by selecting an optimal receiving method depending on the communication environment.

[0054] The receiving unit can perform an error check on the received data to ensure the accuracy of the data. The error check on the received data includes, for example, a checksum, an error check algorithm, etc. The receiving unit can perform an error check on the received data to provide accurate data. For example, the receiving unit can use a checksum to perform an error check on the received data. The receiving unit can also perform an error check to eliminate inaccurate data. For example, the receiving unit can use an error check algorithm to eliminate inaccurate data. The receiving unit can also perform an error check before receiving the data to receive accurate data. For example, the receiving unit can perform an error check on the data before receiving it to receive accurate data. In this way, accurate data can be provided by performing an error check on the received data.

[0055] The receiving unit can cache a portion of the received data to reduce the need for retransmission. Cache storage includes, for example, the capacity of the cache and the storage period. The receiving unit can cache a portion of the received data to reduce the need for retransmission. For example, the receiving unit caches important data to use in the event of a communication failure. The receiving unit can also cache data before receiving it to efficiently receive data. For example, the receiving unit caches data before receiving it to efficiently receive data. By caching a portion of the received data, the need for retransmission can be reduced.

[0056] The receiving unit can also receive data from other vehicles and traffic infrastructure and provide comprehensive traffic information. The comprehensive traffic information includes, for example, data from other vehicles and data from traffic infrastructure. The receiving unit, for example, receives data from other vehicles and provides the comprehensive traffic information. For example, the receiving unit receives data from other vehicles using vehicle-to-vehicle (V2V) communication. The receiving unit can also receive data from traffic infrastructure and update the information in real time. For example, the receiving unit receives data from traffic infrastructure using vehicle-to-infrastructure (V2I) communication. The receiving unit can also combine data from other vehicles and traffic infrastructure and provide the combined data to the driver. For example, the receiving unit integrates data from other vehicles and traffic infrastructure and provides comprehensive traffic information to the driver. In this way, comprehensive traffic information can be provided by receiving data from other vehicles and traffic infrastructure.

[0057] The receiving unit can transmit the received data to the analyzing unit in real time, and analyze it immediately. Transmission to the analyzing unit in real time includes, for example, a communication protocol, a data update frequency, etc. The receiving unit can transmit the received data to the analyzing unit in real time, and analyze it immediately. For example, the receiving unit can transmit the received data in real time, and the analyzing unit can analyze it immediately. The receiving unit can also transmit important data to the analyzing unit in real time, and analyze it quickly. For example, the receiving unit can transmit important data in real time, and the analyzing unit can analyze it quickly. The receiving unit can also transmit the data to the analyzing unit immediately after receiving it, and provide it to the driver. For example, the receiving unit transmits the data to the analyzing unit immediately after receiving it, and provide the analysis results to the driver. By transmitting the received data to the analyzing unit in real time, analysis can be performed immediately.

[0058] The receiving unit can automatically back up received data to protect the data in the event of a communication failure. Backing up received data includes, for example, the frequency of backup and the storage destination. The receiving unit, for example, automatically backs up received data to protect the data in the event of a communication failure. For example, the receiving unit periodically backs up data and restores the data in the event of a communication failure. The receiving unit can also back up important data and restore it in the event of a communication failure. For example, the receiving unit backs up important data to the cloud and restores it in the event of a communication failure. The receiving unit can also back up data before receiving it and receive it safely. For example, the receiving unit backs up data before receiving it and receives it safely. In this way, by backing up received data, the data can be protected in the event of a communication failure.

[0059] The notification unit can refer to the driver's past driving history and select the optimal notification timing. The optimal notification timing includes, for example, the type of driving history and the urgency of the notification. The notification unit can refer to the driver's past driving history and select the optimal notification timing. For example, the notification unit notifies the driver at an appropriate timing based on the driving history. The notification unit can also analyze the driving history and notify the driver at the optimal timing. For example, the notification unit notifies the driver at the optimal timing based on the driving history. In this way, the optimal notification timing can be selected by referring to the driving history.

[0060] The notification unit may have a function to provide voice notifications in different languages ​​or dialects. Different languages ​​or dialects include, for example, Japanese, English, Kansai dialect, etc. The notification unit provides voice notifications in an appropriate language based on, for example, the driver's language setting. For example, when the notification unit is set to Japanese, the notification unit provides voice notifications in Japanese. The notification unit can also provide voice notifications corresponding to the dialect to provide a notification that is familiar to the driver. For example, when the notification unit is set to Kansai dialect, the notification unit provides voice notifications in Kansai dialect. The notification unit can also provide voice notifications in multiple languages ​​to accommodate international drivers. For example, when the notification unit is set to English, the notification unit provides voice notifications in English. In this way, by providing voice notifications in different languages ​​or dialects, a notification that is familiar to the driver can be provided.

[0061] The notification unit can randomly change the notification content to maintain the driver's attention. Randomly changing the notification content includes, for example, the type of notification and the frequency of change. The notification unit, for example, randomly changes the notification content to maintain the driver's attention. For example, the notification unit does not repeat notifications with the same content, but provides the driver with fresh information. The notification unit can also randomly change the notification content to attract the driver's attention. For example, the notification unit randomly changes the type and timing of notifications to attract the driver's attention. In this way, by randomly changing the notification content, the driver's attention can be maintained.

[0062] The notification unit can provide not only audio notification but also visual notification. Examples of visual notification include displaying on a display and flashing lights. The notification unit, for example, provides audio notification and displaying on a display at the same time to provide visual information to the driver. For example, the notification unit displays sign information on a display at the same time as the audio notification. The notification unit can also provide notification by flashing lights to alert the driver. For example, the notification unit notifies sign information by flashing lights. The notification unit can also combine audio notification and visual notification to provide effective information to the driver. For example, the notification unit combines audio notification and display to provide sign information. In this way, effective information can be provided to the driver by combining audio notification and visual notification.

[0063] The notification unit can monitor the driver's health condition and provide an appropriate notification. The driver's health condition includes, for example, heart rate, stress level, etc. The notification unit can, for example, monitor the driver's heart rate and provide a notification if an abnormality is detected. For example, the notification unit can monitor the driver's heart rate using a heart rate sensor and provide a notification if an abnormality is detected. The notification unit can also monitor the stress level and provide a notification at an appropriate time. For example, the notification unit can monitor the driver's stress level using a stress level sensor and provide a notification at an appropriate time. The notification unit can also monitor the health condition and provide appropriate advice to the driver. For example, the notification unit can provide a notification to the driver to take a break based on the health condition. In this way, by monitoring the driver's health condition, it is possible to provide a notification at an appropriate time.

[0064] The notification unit can receive feedback from the driver and continuously improve the notification content. Continuous improvement of the notification content includes, for example, a method of collecting feedback and a frequency of improvement. The notification unit, for example, receives feedback from the driver and improves the notification content. For example, the notification unit improves the notification content based on feedback from the driver. The notification unit can also provide an appropriate notification to the driver based on the feedback. For example, the notification unit optimizes the notification content based on the driver's feedback. The notification unit can also continuously collect feedback and optimize the notification content. For example, the notification unit periodically collects feedback and improves the notification content. In this way, by improving the notification content based on the driver's feedback, it is possible to provide a more appropriate notification.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The analysis unit not only recognizes the content of the sign, but also analyzes the surrounding environment of the sign's location and can provide appropriate driving advice to the driver. For example, the analysis unit can analyze the condition of buildings and roads around the sign and provide advice to the driver such as, "Be careful, the road continues to be narrow ahead." The analysis unit can also analyze the movement of pedestrians around the sign and notify the driver, "Be careful, there are many pedestrians ahead." Furthermore, the analysis unit can analyze the movement of other vehicles around the sign and provide advice to the driver such as, "Be careful, traffic congestion is expected ahead." This can support safe driving by providing driving advice that takes into account not only the content of the sign but also the surrounding environment.

[0067] When recognizing the contents of a sign, the analysis unit can also evaluate the deterioration state of the sign and provide that information to the driver. For example, the analysis unit can detect fading or dirt on a sign and notify the driver, "This sign is faded, so please be careful." The analysis unit can also detect the damage state of a sign and notify the driver, "This sign is damaged, so please be careful." Furthermore, if the visibility of a sign has decreased, the analysis unit can provide information such as, "This sign has decreased visibility, so please be careful." This makes it possible to support safe driving by evaluating the deterioration state of signs and providing appropriate information to the driver.

[0068] The camera unit can automatically adjust the image resolution according to the vehicle's speed and driving environment. For example, when driving on a highway, the camera unit captures signs at high resolution to obtain detailed information. When driving in an urban area, the camera unit can capture signs at normal resolution to obtain the necessary information. Furthermore, in bad weather, the camera unit can switch to high-sensitivity mode to improve the visibility of signs. This allows the camera unit to adjust the image resolution according to the vehicle's speed and driving environment, making it possible to accurately obtain sign information.

[0069] The analysis unit not only recognizes the content of the sign, but also analyzes the surrounding environment of the sign's location and can provide appropriate driving advice to the driver. For example, the analysis unit can analyze the condition of buildings and roads around the sign and provide advice to the driver such as, "Be careful, the road continues to be narrow ahead." The analysis unit can also analyze the movement of pedestrians around the sign and notify the driver, "Be careful, there are many pedestrians ahead." Furthermore, the analysis unit can analyze the movement of other vehicles around the sign and provide advice to the driver such as, "Be careful, traffic congestion is expected ahead." This can support safe driving by providing driving advice that takes into account not only the content of the sign but also the surrounding environment.

[0070] The analysis unit not only recognizes the content of the sign, but also analyzes the surrounding environment of the sign's location and can provide appropriate driving advice to the driver. For example, the analysis unit can analyze the condition of buildings and roads around the sign and provide advice to the driver such as, "Be careful, the road continues to be narrow ahead." The analysis unit can also analyze the movement of pedestrians around the sign and notify the driver, "Be careful, there are many pedestrians ahead." Furthermore, the analysis unit can analyze the movement of other vehicles around the sign and provide advice to the driver such as, "Be careful, traffic congestion is expected ahead." This can support safe driving by providing driving advice that takes into account not only the content of the sign but also the surrounding environment.

[0071] The camera unit can automatically adjust the image resolution according to the vehicle's speed and driving environment. For example, when driving on a highway, the camera unit captures signs at high resolution to obtain detailed information. When driving in an urban area, the camera unit can capture signs at normal resolution to obtain the necessary information. Furthermore, in bad weather, the camera unit can switch to high-sensitivity mode to improve the visibility of signs. This allows the camera unit to adjust the image resolution according to the vehicle's speed and driving environment, making it possible to accurately obtain sign information.

[0072] The analysis unit not only recognizes the content of the sign, but also analyzes the surrounding environment of the sign's location and can provide appropriate driving advice to the driver. For example, the analysis unit can analyze the condition of buildings and roads around the sign and provide advice to the driver such as, "Be careful, the road continues to be narrow ahead." The analysis unit can also analyze the movement of pedestrians around the sign and notify the driver, "Be careful, there are many pedestrians ahead." Furthermore, the analysis unit can analyze the movement of other vehicles around the sign and provide advice to the driver such as, "Be careful, traffic congestion is expected ahead." This can support safe driving by providing driving advice that takes into account not only the content of the sign but also the surrounding environment.

[0073] The processing flow of the first embodiment will be briefly explained below.

[0074] Step 1: The camera unit captures road signs with an in-vehicle camera. For example, the camera unit is installed in front of the vehicle and automatically captures road signs while driving. Step 2: The transmitting unit transmits the image captured by the capturing unit. For example, the transmitting unit can transmit the image using a communication means such as Wi-Fi or Bluetooth. Step 3: The analysis unit analyzes the image sent by the transmission unit. The analysis unit uses AI and image recognition technology to recognize the content of the sign. For example, the analysis unit can use deep learning to recognize speed limit signs and identify the speed limit. The analysis unit can also recognize stop signs and identify the stopping position indicated by the sign. Step 4: The receiving unit receives the analysis result obtained by the analyzing unit. For example, the receiving unit can receive the analysis result using a communication means such as Wi-Fi or Bluetooth. Step 5: The notification unit issues a voice alert based on the results received by the receiving unit. The notification unit uses AI and voice synthesis technology to issue the alert. For example, if the notification unit recognizes a speed limit sign, it will issue a voice alert saying, "The speed limit is 50 km / h." If it recognizes a stop sign, it will issue a voice alert saying, "Please stop."

[0075] (Example 2) A system according to an embodiment of the present invention analyzes images of road signs captured by an onboard camera and provides audio warnings to the driver. In this system, the onboard camera captures road signs, AI analyzes the images, recognizes the content of the signs, and provides audio warnings to the driver. For example, the onboard camera captures a speed limit sign, the AI ​​recognizes the speed limit, and provides an audio warning to the driver, saying, "The speed limit is 50 km / h." Furthermore, when a stop sign is recognized, the AI ​​provides an audio warning, saying, "Please stop." This complements areas that cannot be fully supported by car navigation systems and supports safe driving. This complements areas that cannot be fully supported by car navigation systems and supports safe driving. For example, the system can recognize road signs outside the range of navigation systems based on map data and provide audio warnings to the driver. This supports safe driving and contributes to preventing traffic accidents.

[0076] A road sign recognition system according to an embodiment includes a photographing unit, a transmission unit, an analysis unit, a receiving unit, and a notification unit. The photographing unit photographs road signs using an onboard camera. For example, the photographing unit is installed in front of a vehicle and automatically photographs signs while the vehicle is traveling. The transmission unit transmits the image photographed by the photographing unit. For example, the transmission unit can transmit the image using a communication method such as Wi-Fi or Bluetooth. The analysis unit analyzes the image transmitted by the transmission unit. The analysis unit recognizes the content of the sign using image recognition technology with AI. For example, the analysis unit can recognize speed limit signs and identify the speed limit using deep learning. The analysis unit can also recognize stop signs and identify the stopping position indicated by the sign. The receiving unit receives the results of the analysis performed by the analysis unit. For example, the receiving unit can receive the analysis results using a communication method such as Wi-Fi or Bluetooth. The notification unit issues a voice alert based on the results received by the receiving unit. The notification unit issues a voice alert using AI and voice synthesis technology. For example, when the notification unit recognizes a speed limit sign, it notifies the driver by voice, saying, "The speed limit is 50 km / h." When the notification unit recognizes a stop sign, it notifies the driver by voice, saying, "Please stop." In this way, the road sign recognition system according to the embodiment can analyze the image of the road sign read by the on-board camera and alert the driver by voice.

[0077] The analysis unit can recognize the content of the sign using image recognition technology. Image recognition technology includes, for example, deep learning and pattern recognition. The analysis unit can recognize the content of the sign using, for example, deep learning. For example, the analysis unit can recognize a speed limit sign and identify the speed limit. The analysis unit can also recognize a stop sign and identify the stopping position indicated by the sign. Furthermore, the analysis unit can recognize the content of the sign using pattern recognition. For example, the analysis unit recognizes the content based on the shape and color of the sign. As a result, the content of the sign can be accurately recognized using image recognition technology.

[0078] The notification unit can issue a warning using a voice synthesis technology. Examples of voice synthesis technology include text-to-speech synthesis and voice sample-based synthesis. The notification unit issues a warning using, for example, text-to-speech synthesis. For example, when the notification unit recognizes a speed limit sign, it issues a voice warning saying, "The speed limit is 50 km / h." Furthermore, when the notification unit recognizes a stop sign, it can also issue a voice warning saying, "Please stop." Furthermore, the notification unit can also issue a warning using voice sample-based synthesis. For example, the notification unit issues a warning using a pre-recorded voice sample. In this way, by using voice synthesis technology, it is possible to effectively warn the driver.

[0079] The camera unit is installed in front of the vehicle and can automatically photograph signs while the vehicle is traveling. The front of the vehicle includes, for example, the top of the windshield and near the bumper. The camera unit is installed, for example, in the top of the windshield and can automatically photograph signs while the vehicle is traveling. For example, the camera unit photographs signs that contain important driving information, such as speed limit signs and stop signs. The camera unit can also be installed near the bumper and can automatically photograph signs while the vehicle is traveling. This reduces the burden on the driver by allowing the camera unit installed in front of the vehicle to automatically photograph signs.

[0080] The analysis unit can recognize speed limit signs and identify the speed limit. Speed ​​limit signs include, for example, speed limits on general roads and speed limits on expressways. The analysis unit can recognize, for example, speed limit signs on general roads and identify the speed limit. For example, the analysis unit can identify that the speed limit is 50 km / h. The analysis unit can also recognize speed limit signs on expressways and identify the speed limit. For example, the analysis unit can identify that the speed limit is 100 km / h. In this way, by recognizing speed limit signs and identifying the speed limit, it is possible to provide the driver with appropriate speed information.

[0081] The analysis unit can recognize a stop sign and identify the stop position indicated by the sign. Stop signs include, for example, stop signs at intersections and stop signs at railroad crossings. The analysis unit can recognize, for example, a stop sign at an intersection and identify the stop position indicated by the sign. For example, the analysis unit can identify the position of the stop line at the intersection. The analysis unit can also recognize a stop sign at a railroad crossing and identify the stop position indicated by the sign. For example, the analysis unit can identify the position of the stop line at the railroad crossing. In this way, by recognizing a stop sign and identifying its stop position, it is possible to provide the driver with appropriate stopping information.

[0082] The camera unit can estimate the driver's emotions and adjust the timing of capturing images based on the estimated driver's emotions. Examples of driver emotions include tension, relaxation, and fatigue. For example, if the driver is tense, the camera unit can capture images frequently to ensure important signs are not overlooked. For example, the camera unit can capture the driver's facial expressions with a camera and detect the driver's level of tension using an emotion estimation algorithm. Furthermore, if the driver is relaxed, the camera unit can return the frequency of capturing images of signs to normal and capture only the necessary information. For example, the camera unit can record the driver's voice and detect the driver's level of relaxation using voice analysis technology. Furthermore, if the driver is tired, the camera unit can prioritize capturing important signs to reduce the driver's burden. For example, the camera unit can collect the driver's biometric data (heart rate and electrodermal activity) using a sensor and detect the driver's level of fatigue using an emotion estimation algorithm. This allows the camera unit to adjust the timing of capturing images according to the driver's emotions to ensure important signs are not overlooked. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0083] The image capturing unit can automatically adjust a filter to accommodate different weather conditions. Different weather conditions include, for example, rain, snow, and fog. For example, the image capturing unit automatically applies a waterproof filter when it is raining to maintain the clarity of the captured image. For example, the image capturing unit applies a filter that removes raindrops to improve the visibility of signs. The image capturing unit can also automatically apply a filter that suppresses snow reflections on snowy days to improve the visibility of signs. For example, the image capturing unit applies a filter that reduces snow reflections to make the content of signs clear. The image capturing unit can also automatically apply a filter that penetrates fog when it is foggy to clearly capture signs. For example, the image capturing unit applies a filter that penetrates fog to ensure the visibility of signs. In this way, the clarity of the captured image can be maintained by automatically adjusting a filter that corresponds to different weather conditions.

[0084] The image capturing unit can also use an infrared camera to accurately capture signs even in low-light environments such as at night or inside a tunnel. Low-light environments include, for example, at night or inside a tunnel. The image capturing unit, for example, automatically activates the infrared camera when driving at night to clearly capture signs. For example, the image capturing unit uses the infrared camera to capture signs at night to improve visibility. The image capturing unit can also use the infrared camera when driving inside a tunnel to ensure sign visibility. For example, the image capturing unit uses the infrared camera in the low-light environment of a tunnel to clearly capture signs. The image capturing unit can also use the infrared camera when driving in a low-light environment to accurately capture signs. For example, the image capturing unit uses the infrared camera to capture signs in a low-light environment to improve visibility. This allows accurate capture of signs even in low-light environments to provide appropriate information to the driver.

[0085] The camera unit adjusts the capture frequency according to the vehicle speed, capturing images more frequently when the vehicle is traveling at high speeds. Examples of vehicle speeds include 30 km / h and 100 km / h. The camera unit increases the capture frequency when the vehicle is traveling on a highway to ensure that important signs are not overlooked. For example, the camera unit detects the vehicle's speed using a sensor and increases the capture frequency as the speed increases. The camera unit can also return the capture frequency to normal when traveling in urban areas and capture only necessary information. For example, the camera unit reduces the capture frequency when the vehicle is traveling at a low speed. The camera unit can also reduce the capture frequency during traffic jams to reduce the burden on the driver. For example, the camera unit minimizes the capture frequency when the vehicle is traveling at a very low speed. This allows the camera unit to adjust the capture frequency according to the vehicle's speed to ensure that important signs are not overlooked.

[0086] The camera unit can estimate the driver's emotions and prioritize the signs to be photographed based on the estimated driver's emotions. Examples of driver emotions include tension, relaxation, and fatigue. For example, if the driver is tense, the camera unit prioritizes photographing important signs. For example, the camera unit captures the driver's facial expressions with a camera and detects the driver's state of tension using an emotion estimation algorithm. Furthermore, if the driver is relaxed, the camera unit can also photograph signs with a normal priority. For example, the camera unit records the driver's voice and detects the driver's state of relaxation using voice analysis technology. Furthermore, if the driver is tired, the camera unit can prioritize photographing signs at rest areas and service areas. For example, the camera unit collects the driver's biometric data (heart rate and electrodermal activity) using a sensor and detects the driver's state of fatigue using an emotion estimation algorithm. This allows the camera unit to prioritize the photographing of important signs by prioritizing the signs based on the driver's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0087] The image capturing unit can capture signs in all directions using multiple cameras that cover 360 degrees around the vehicle. Omnidirectional includes, for example, the front, sides, and rear. The image capturing unit can be installed with multiple cameras to capture signs not only in front but also on the sides and rear. For example, the image capturing unit can be installed with a front camera in front of the vehicle, side cameras on the sides, and a rear camera on the rear. The image capturing unit can also capture signs at intersections in all directions and provide necessary information to the driver. For example, the image capturing unit can be installed with cameras that cover intersections in all directions and capture signs. The image capturing unit can also capture signs in parking lots in all directions to provide parking support. For example, the image capturing unit can be installed with cameras that cover parking lots in all directions and capture signs. In this way, necessary information can be provided to the driver by capturing signs in all directions.

[0088] The camera unit can simultaneously capture the license plate of the vehicle and the movements of other vehicles to grasp the traffic situation. The traffic situation includes, for example, the movement of the vehicle and the recognition of the license plate. For example, the camera unit captures the license plate of another vehicle to grasp the traffic situation. For example, the camera unit recognizes the license plate of another vehicle using license plate recognition technology. The camera unit can also capture the movement of other vehicles and warn the driver. For example, the camera unit tracks the movement of other vehicles and warn the driver. The camera unit can also capture the traffic congestion situation and provide information to the driver in real time. For example, the camera unit captures the traffic congestion situation and provides information to the driver in real time. In this way, the traffic situation can be grasped and appropriate information can be provided to the driver.

[0089] The camera unit can track the driver's gaze and prioritize capturing images of signs that are in front of the driver's gaze. The driver's gaze includes, for example, an eye-tracking camera and an eye-gaze detection algorithm. The camera unit can, for example, track the driver's gaze and prioritize capturing images of signs that are in front of the driver's gaze. For example, the camera unit can detect the driver's gaze using an eye-tracking camera and capture images of signs that are in front of the driver's gaze. The camera unit can also use eye-tracking technology to capture and capture signs that are easy for the driver to miss. For example, the camera unit can analyze the driver's gaze movement using an eye-gaze detection algorithm to capture images of signs that are easy to miss. The camera unit can also analyze the driver's gaze movement and prioritize capturing images of important signs. For example, the camera unit can identify important signs based on the gaze movement and prioritize capturing images of them. In this way, by tracking the driver's gaze, it is possible to capture and capture images of signs that are easy to miss.

[0090] The transmitter can estimate the driver's emotions and adjust the priority of transmitted data based on the estimated driver's emotions. Examples of driver emotions include tension, relaxation, and fatigue. For example, if the driver is tense, the transmitter prioritizes the transmission of important data. For example, the transmitter can capture the driver's facial expression with a camera and detect the driver's state of tension using an emotion estimation algorithm. Furthermore, if the driver is relaxed, the transmitter can transmit data with normal priority. For example, the transmitter can record the driver's voice and detect the driver's state of relaxation using voice analysis technology. Furthermore, if the driver is tired, the transmitter can prioritize the transmission of data about rest areas and service areas. For example, the transmitter can collect the driver's biometric data (heart rate and electrodermal activity) using a sensor and detect the driver's state of fatigue using an emotion estimation algorithm. This allows the transmitter to prioritize the transmission of important data by adjusting the priority of transmitted data according to the driver's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0091] The transmitting unit can select an optimal transmission method depending on the communication environment. The communication environment includes, for example, Wi-Fi, 4G, 5G, etc. The transmitting unit transmits data at high speed in, for example, a Wi-Fi environment. For example, the transmitting unit transmits large amounts of data quickly using Wi-Fi. Furthermore, the transmitting unit can compress data and transmit it efficiently in a 4G environment. For example, the transmitting unit compresses data using 4G to improve the transmission speed. Furthermore, the transmitting unit can transmit large amounts of data quickly in a 5G environment. For example, the transmitting unit transmits high-resolution image data using 5G. In this way, data can be transmitted efficiently by selecting an optimal transmission method depending on the communication environment.

[0092] The transmitting unit can improve the transmission speed by using a data compression technique. Examples of data compression techniques include ZIP compression and JPEG compression. The transmitting unit, for example, compresses image data to improve the transmission speed. For example, the transmitting unit compresses image data using JPEG compression to improve the transmission speed. The transmitting unit can also compress video data to efficiently transmit the data. For example, the transmitting unit compresses video data using a video compression technique to improve the transmission speed. The transmitting unit can also compress text data to quickly transmit the data. For example, the transmitting unit compresses text data using ZIP compression to improve the transmission speed. In this way, the transmission speed can be improved by using data compression techniques.

[0093] The transmitting unit can encrypt the data to be transmitted to ensure data security. Data encryption techniques include, for example, AES encryption and RSA encryption. The transmitting unit, for example, encrypts the data to be transmitted to prevent unauthorized access. For example, the transmitting unit encrypts the data to be transmitted using AES encryption to prevent unauthorized access. The transmitting unit can also encrypt important data to enhance security. For example, the transmitting unit encrypts important data using RSA encryption to enhance security. The transmitting unit can also encrypt the data before transmission to transmit it securely. For example, the transmitting unit encrypts the data before transmission to transmit it securely. In this way, by encrypting the data to be transmitted, data security can be ensured.

[0094] The transmission unit can estimate the driver's emotions and adjust the content of the transmitted data based on the estimated driver's emotions. Examples of the driver's emotions include tension, relaxation, and fatigue. For example, the transmission unit prioritizes transmitting important information when the driver is tense. For example, the transmission unit captures the driver's facial expression with a camera and detects the driver's state of tension using an emotion estimation algorithm. Furthermore, the transmission unit can also transmit normal information when the driver is relaxed. For example, the transmission unit records the driver's voice and detects the driver's state of relaxation using voice analysis technology. Furthermore, the transmission unit can prioritize transmitting information about rest areas and service areas when the driver is tired. For example, the transmission unit collects the driver's biometric data (heart rate and electrodermal activity) using a sensor and detects the driver's state of fatigue using an emotion estimation algorithm. This allows the transmission of important information by adjusting the content of the transmitted data according to the driver's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The transmitting unit simultaneously transmits vehicle position information, and the analyzing unit can identify the position of the sign based on the position information. The vehicle position information includes, for example, GPS data, map data, etc. The transmitting unit, for example, transmits the vehicle position information and identifies the position of the sign. For example, the transmitting unit transmits the vehicle position information using GPS data, and the analyzing unit identifies the position of the sign based on the position information. The transmitting unit can also analyze the exact position of the sign based on the position information. For example, the transmitting unit identifies the position of the sign using map data. The transmitting unit can also combine the position information and the sign information and provide it to the driver. For example, the transmitting unit integrates the position information and the sign information to provide accurate information to the driver. In this way, accurate information can be provided to the driver by identifying the position of the sign based on the vehicle position information.

[0096] The transmitter can communicate with other vehicles and traffic infrastructure to share traffic information in real time. Sharing traffic information in real time includes, for example, a communication protocol, a data update frequency, and the like. The transmitter can communicate with other vehicles to share traffic information, for example. For example, the transmitter can share information with other vehicles using vehicle-to-vehicle (V2V) communication. The transmitter can also communicate with traffic infrastructure to update information in real time. For example, the transmitter can share information with traffic infrastructure using vehicle-to-infrastructure (V2I) communication. The transmitter can also share traffic information and provide it to a driver. For example, the transmitter can provide traffic information updated in real time to a driver. In this way, by communicating with other vehicles and traffic infrastructure, traffic information can be shared in real time and provided to a driver.

[0097] The transmitting unit can automatically back up the transmission data to protect the data in the event of a communication failure. The backup of the transmission data includes, for example, the frequency of the backup and the storage destination. The transmitting unit, for example, automatically backs up the transmission data to protect the data in the event of a communication failure. For example, the transmitting unit periodically backs up data and restores the data in the event of a communication failure. The transmitting unit can also back up important data and restore it in the event of a communication failure. For example, the transmitting unit backs up important data to the cloud and restores it in the event of a communication failure. The transmitting unit can also back up data before sending it and send it safely. For example, the transmitting unit backs up data before sending it and transmits it safely. In this way, by backing up the transmission data, the data can be protected in the event of a communication failure.

[0098] The analysis unit can estimate the driver's emotions and adjust the priority of analysis results based on the estimated driver's emotions. Examples of driver emotions include tension, relaxation, and fatigue. For example, if the driver is tense, the analysis unit can prioritize providing important analysis results. For example, the analysis unit can capture the driver's facial expressions with a camera and detect the tension state using an emotion estimation algorithm. Alternatively, if the driver is relaxed, the analysis unit can provide normal analysis results. For example, the analysis unit can record the driver's voice and detect the relaxation state using voice analysis technology. Alternatively, if the driver is tired, the analysis unit can prioritize providing analysis results for rest areas and service areas. For example, the analysis unit can collect the driver's biometric data (heart rate and electrodermal activity) with a sensor and detect the fatigue state using an emotion estimation algorithm. This allows the analysis unit to prioritize providing important analysis results by adjusting the priority of analysis results according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0099] The analysis unit can use a multilingual algorithm to recognize signs of different countries or regions. The multilingual algorithm includes, for example, a translation algorithm, a language model, and the like. The analysis unit can, for example, use a multilingual algorithm to recognize signs of different countries or regions. For example, the analysis unit can recognize signs in different languages ​​using a translation algorithm. The analysis unit can also recognize international signs and provide the information to the driver. For example, the analysis unit can recognize international signs using a language model. The analysis unit can also recognize signs for each region and provide appropriate information to the driver. For example, the analysis unit can recognize signs using a language model for each region. In this way, by using a multilingual algorithm, signs for different countries or regions can be accurately recognized.

[0100] The analysis unit can correct deterioration and dirt on signs to enable accurate recognition. Deterioration and dirt on signs include, for example, fading, dirt, and breakage. The analysis unit, for example, corrects deteriorated signs to enable accurate recognition. For example, the analysis unit corrects deteriorated signs using an image correction algorithm. The analysis unit can also correct dirty signs to provide accurate information to drivers. For example, the analysis unit corrects dirty signs using filtering technology. The analysis unit can also correct deterioration and dirt to enable accurate recognition of the content of signs. For example, the analysis unit corrects deterioration and dirt using an image correction algorithm to enable accurate recognition of the content of signs. In this way, accurate recognition can be achieved by correcting deterioration and dirt on signs.

[0101] The analysis unit can perform analysis by taking into account not only the shape and color of the sign but also surrounding environmental information. The surrounding environmental information includes, for example, weather information and surrounding building information. The analysis unit, for example, analyzes and accurately recognizes the shape and color of the sign. For example, the analysis unit analyzes the shape of the sign using a shape recognition algorithm. The analysis unit also analyzes the color of the sign using a color recognition algorithm. The analysis unit can also accurately recognize the content of the sign by taking into account surrounding environmental information. For example, the analysis unit recognizes the content of the sign by taking into account weather information. The analysis unit can also recognize the content of the sign by taking into account surrounding building information. For example, the analysis unit identifies the position of the sign based on surrounding building information and recognizes the content. In this way, accurate recognition is possible by taking into account the shape and color of the sign and surrounding environmental information.

[0102] The analysis unit can estimate the driver's emotions and adjust the display method of the analysis results based on the estimated driver's emotions. Examples of driver emotions include tension, relaxation, and fatigue. For example, if the driver is tense, the analysis unit provides a simple, highly visible display method. For example, the analysis unit captures the driver's facial expression with a camera, detects the tension state using an emotion estimation algorithm, and provides a simple display method. Furthermore, if the driver is relaxed, the analysis unit can provide a display method that includes detailed information. For example, the analysis unit records the driver's voice, detects the relaxation state using voice analysis technology, and provides a detailed display method. Furthermore, if the driver is in a hurry, the analysis unit can provide a display method that focuses on the key points. For example, the analysis unit collects the driver's biometric data (heart rate and electrodermal activity) using a sensor, detects the driver's hurry using an emotion estimation algorithm, and provides a display method that focuses on the key points. This allows the display method of the analysis results to be adjusted according to the driver's emotions, thereby providing a highly visible display method. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0103] The analysis unit can share the sign recognition results with other vehicles and traffic infrastructure and update traffic information in real time. The real-time updating of traffic information includes, for example, a data update frequency and a communication protocol. The analysis unit can, for example, share the sign recognition results with other vehicles and update traffic information. For example, the analysis unit can share information with other vehicles using V2V (Vehicle-to-Vehicle) communication. The analysis unit can also share the sign recognition results with traffic infrastructure and provide traffic information in real time. For example, the analysis unit can share information with traffic infrastructure using V2I (Vehicle-to-Infrastructure) communication. The analysis unit can also store the sign recognition results in a cloud and share them with other vehicles. For example, the analysis unit can provide appropriate information to the driver based on the recognition results stored in the cloud. This allows traffic information to be updated in real time by sharing it with other vehicles and traffic infrastructure.

[0104] The analysis unit can store the sign recognition results in the cloud so that the analysis results can be referenced later. Storing in the cloud includes, for example, the type of cloud service and the storage period. The analysis unit can store the sign recognition results in the cloud so that the analysis results can be referenced later. For example, the analysis unit provides appropriate information to the driver based on the recognition results stored in the cloud. The analysis unit can also analyze the data stored in the cloud and analyze the driver's driving patterns. For example, the analysis unit identifies the driver's driving patterns based on the data stored in the cloud and provides appropriate advice. By storing the analysis results in the cloud, the analysis results can be referenced later.

[0105] The analysis unit can compare the sign recognition results with the driver's driving history and analyze the driving pattern. The driving pattern includes, for example, the type of driving history and an analysis algorithm. The analysis unit can compare the sign recognition results with the driving history and analyze the driving pattern. For example, the analysis unit provides appropriate advice to the driver based on the driving history. The analysis unit can also analyze the driving pattern and support the driver's safe driving. For example, the analysis unit can identify the driver's driving pattern based on the driving history and provide appropriate advice. In this way, by comparing the driving history, the driving pattern can be analyzed and appropriate advice can be provided to the driver.

[0106] The receiving unit can estimate the driver's emotions and adjust the priority of received data based on the estimated driver's emotions. Examples of driver emotions include tension, relaxation, and fatigue. For example, the receiving unit prioritizes receiving important data when the driver is tense. For example, the receiving unit captures the driver's facial expressions with a camera and detects the driver's state of tension using an emotion estimation algorithm. Furthermore, the receiving unit can also receive data with normal priority when the driver is relaxed. For example, the receiving unit records the driver's voice and detects the driver's state of relaxation using voice analysis technology. Furthermore, the receiving unit can prioritize receiving data from rest areas and service areas when the driver is tired. For example, the receiving unit collects the driver's biometric data (heart rate and electrodermal activity) using a sensor and detects the driver's state of fatigue using an emotion estimation algorithm. This allows the priority of received data to be adjusted according to the driver's emotions, thereby prioritizing the reception of important data. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0107] The receiving unit can select an optimal receiving method depending on the communication environment. Communication environments include, for example, Wi-Fi, 4G, and 5G. For example, in a Wi-Fi environment, the receiving unit receives data at high speed. For example, the receiving unit quickly receives large amounts of data using Wi-Fi. In a 4G environment, the receiving unit can compress data to efficiently receive the data. For example, the receiving unit compresses data using 4G to improve the reception speed. In a 5G environment, the receiving unit can quickly receive large amounts of data. For example, the receiving unit receives high-resolution image data using 5G. As a result, data can be efficiently received by selecting an optimal receiving method depending on the communication environment.

[0108] The receiving unit can perform an error check on the received data to ensure the accuracy of the data. The error check on the received data includes, for example, a checksum, an error check algorithm, etc. The receiving unit can perform an error check on the received data to provide accurate data. For example, the receiving unit can use a checksum to perform an error check on the received data. The receiving unit can also perform an error check to eliminate inaccurate data. For example, the receiving unit can use an error check algorithm to eliminate inaccurate data. The receiving unit can also perform an error check before receiving the data to receive accurate data. For example, the receiving unit can perform an error check on the data before receiving it to receive accurate data. In this way, accurate data can be provided by performing an error check on the received data.

[0109] The receiving unit can cache a portion of the received data to reduce the need for retransmission. Cache storage includes, for example, the capacity of the cache and the storage period. The receiving unit can cache a portion of the received data to reduce the need for retransmission. For example, the receiving unit caches important data to use in the event of a communication failure. The receiving unit can also cache data before receiving it to efficiently receive data. For example, the receiving unit caches data before receiving it to efficiently receive data. By caching a portion of the received data, the need for retransmission can be reduced.

[0110] The receiving unit can estimate the driver's emotions and adjust the content of received data based on the estimated driver's emotions. Examples of driver emotions include tension, relaxation, and fatigue. For example, the receiving unit prioritizes receiving important information when the driver is tense. For example, the receiving unit captures the driver's facial expression with a camera and detects the tension state using an emotion estimation algorithm. Furthermore, the receiving unit can also receive normal information when the driver is relaxed. For example, the receiving unit records the driver's voice and detects the relaxation state using voice analysis technology. Furthermore, the receiving unit can prioritize receiving information about rest areas and service areas when the driver is tired. For example, the receiving unit collects the driver's biometric data (heart rate and electrodermal activity) with a sensor and detects the fatigue state using an emotion estimation algorithm. This allows the content of received data to be adjusted according to the driver's emotions, thereby prioritizing the reception of important information. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0111] The receiving unit can also receive data from other vehicles and traffic infrastructure and provide comprehensive traffic information. The comprehensive traffic information includes, for example, data from other vehicles and data from traffic infrastructure. The receiving unit, for example, receives data from other vehicles and provides the comprehensive traffic information. For example, the receiving unit receives data from other vehicles using vehicle-to-vehicle (V2V) communication. The receiving unit can also receive data from traffic infrastructure and update the information in real time. For example, the receiving unit receives data from traffic infrastructure using vehicle-to-infrastructure (V2I) communication. The receiving unit can also combine data from other vehicles and traffic infrastructure and provide the combined data to the driver. For example, the receiving unit integrates data from other vehicles and traffic infrastructure and provides comprehensive traffic information to the driver. In this way, comprehensive traffic information can be provided by receiving data from other vehicles and traffic infrastructure.

[0112] The receiving unit can transmit the received data to the analyzing unit in real time, and analyze it immediately. Transmission to the analyzing unit in real time includes, for example, a communication protocol, a data update frequency, etc. The receiving unit can transmit the received data to the analyzing unit in real time, and analyze it immediately. For example, the receiving unit can transmit the received data in real time, and the analyzing unit can analyze it immediately. The receiving unit can also transmit important data to the analyzing unit in real time, and analyze it quickly. For example, the receiving unit can transmit important data in real time, and the analyzing unit can analyze it quickly. The receiving unit can also transmit the data to the analyzing unit immediately after receiving it, and provide it to the driver. For example, the receiving unit transmits the data to the analyzing unit immediately after receiving it, and provide the analysis results to the driver. By transmitting the received data to the analyzing unit in real time, analysis can be performed immediately.

[0113] The receiving unit can automatically back up received data to protect the data in the event of a communication failure. Backing up received data includes, for example, the frequency of backup and the storage destination. The receiving unit, for example, automatically backs up received data to protect the data in the event of a communication failure. For example, the receiving unit periodically backs up data and restores the data in the event of a communication failure. The receiving unit can also back up important data and restore it in the event of a communication failure. For example, the receiving unit backs up important data to the cloud and restores it in the event of a communication failure. The receiving unit can also back up data before receiving it and receive it safely. For example, the receiving unit backs up data before receiving it and receives it safely. In this way, by backing up received data, the data can be protected in the event of a communication failure.

[0114] The notification unit can estimate the driver's emotions and adjust the notification content based on the estimated driver's emotions. Examples of driver emotions include tension, relaxation, and fatigue. For example, if the driver is tense, the notification unit can provide a notification in a calm voice. For example, the notification unit can capture the driver's facial expression with a camera, detect the driver's state of tension using an emotion estimation algorithm, and provide a notification in a calm voice. Furthermore, if the driver is relaxed, the notification unit can provide a notification in a cheerful voice. For example, the notification unit can record the driver's voice, detect the driver's state of relaxation using voice analysis technology, and provide a notification in a cheerful voice. Furthermore, the notification unit can provide a quick and concise notification if the driver is in a hurry. For example, the notification unit can collect the driver's biometric data (heart rate and electrodermal activity) using a sensor, detect the driver's state of hurry using an emotion estimation algorithm, and provide a quick and concise notification. This allows the notification content to be adjusted according to the driver's emotions, thereby providing an appropriate notification. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0115] The notification unit can refer to the driver's past driving history and select the optimal notification timing. The optimal notification timing includes, for example, the type of driving history and the urgency of the notification. The notification unit can refer to the driver's past driving history and select the optimal notification timing. For example, the notification unit notifies the driver at an appropriate timing based on the driving history. The notification unit can also analyze the driving history and notify the driver at the optimal timing. For example, the notification unit notifies the driver at the optimal timing based on the driving history. In this way, the optimal notification timing can be selected by referring to the driving history.

[0116] The notification unit may have a function to provide voice notifications in different languages ​​or dialects. Different languages ​​or dialects include, for example, Japanese, English, Kansai dialect, etc. The notification unit provides voice notifications in an appropriate language based on, for example, the driver's language setting. For example, when the notification unit is set to Japanese, the notification unit provides voice notifications in Japanese. The notification unit can also provide voice notifications corresponding to the dialect to provide a notification that is familiar to the driver. For example, when the notification unit is set to Kansai dialect, the notification unit provides voice notifications in Kansai dialect. The notification unit can also provide voice notifications in multiple languages ​​to accommodate international drivers. For example, when the notification unit is set to English, the notification unit provides voice notifications in English. In this way, by providing voice notifications in different languages ​​or dialects, a notification that is familiar to the driver can be provided.

[0117] The notification unit can randomly change the notification content to maintain the driver's attention. Randomly changing the notification content includes, for example, the type of notification and the frequency of change. The notification unit, for example, randomly changes the notification content to maintain the driver's attention. For example, the notification unit does not repeat notifications with the same content, but provides the driver with fresh information. The notification unit can also randomly change the notification content to attract the driver's attention. For example, the notification unit randomly changes the type and timing of notifications to attract the driver's attention. In this way, by randomly changing the notification content, the driver's attention can be maintained.

[0118] The notification unit can estimate the driver's emotions and determine the priority of notifications based on the estimated driver's emotions. Examples of driver emotions include tension, relaxation, and fatigue. For example, the notification unit prioritizes important notifications when the driver is tense. For example, the notification unit captures the driver's facial expression with a camera, detects the tension state using an emotion estimation algorithm, and prioritizes important notifications. The notification unit can also provide regular notifications when the driver is relaxed. For example, the notification unit records the driver's voice, detects the relaxation state using voice analysis technology, and provides regular notifications. The notification unit can also prioritize notifications of rest areas and service areas when the driver is tired. For example, the notification unit collects the driver's biometric data (heart rate and electrodermal activity) with a sensor, detects the fatigue state using an emotion estimation algorithm, and prioritizes notifications of rest areas and service areas. This allows the priority of notifications to be determined according to the driver's emotions, thereby prioritizing important notifications. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0119] The notification unit can provide not only audio notification but also visual notification. Examples of visual notification include displaying on a display and flashing lights. The notification unit, for example, provides audio notification and displaying on a display at the same time to provide visual information to the driver. For example, the notification unit displays sign information on a display at the same time as the audio notification. The notification unit can also provide notification by flashing lights to alert the driver. For example, the notification unit notifies sign information by flashing lights. The notification unit can also combine audio notification and visual notification to provide effective information to the driver. For example, the notification unit combines audio notification and display to provide sign information. In this way, effective information can be provided to the driver by combining audio notification and visual notification.

[0120] The notification unit can monitor the driver's health condition and provide an appropriate notification. The driver's health condition includes, for example, heart rate, stress level, etc. The notification unit can, for example, monitor the driver's heart rate and provide a notification if an abnormality is detected. For example, the notification unit can monitor the driver's heart rate using a heart rate sensor and provide a notification if an abnormality is detected. The notification unit can also monitor the stress level and provide a notification at an appropriate time. For example, the notification unit can monitor the driver's stress level using a stress level sensor and provide a notification at an appropriate time. The notification unit can also monitor the health condition and provide appropriate advice to the driver. For example, the notification unit can provide a notification to the driver to take a break based on the health condition. In this way, by monitoring the driver's health condition, it is possible to provide a notification at an appropriate time.

[0121] The notification unit can receive feedback from the driver and continuously improve the notification content. Continuous improvement of the notification content includes, for example, a method of collecting feedback and a frequency of improvement. The notification unit, for example, receives feedback from the driver and improves the notification content. For example, the notification unit improves the notification content based on feedback from the driver. The notification unit can also provide an appropriate notification to the driver based on the feedback. For example, the notification unit optimizes the notification content based on the driver's feedback. The notification unit can also continuously collect feedback and optimize the notification content. For example, the notification unit periodically collects feedback and improves the notification content. In this way, by improving the notification content based on the driver's feedback, it is possible to provide a more appropriate notification. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned photographing unit, transmitting unit, analyzing unit, receiving unit, and notifying unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the photographing unit can photograph a road sign using the camera 42 of the smart device 14. The transmitting unit can transmit the photographed image to the data processing device 12 using the communication I / F 44 of the smart device 14. The analyzing unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the transmitted image and recognizes the content of the sign. The receiving unit can receive the analysis result using the communication I / F 44 of the smart device 14. The notifying unit can issue an audio warning using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned photographing unit, transmitting unit, analyzing unit, receiving unit, and notifying unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the photographing unit can photograph a road sign using the camera 42 of the smart glasses 214. The transmitting unit can transmit the photographed image to the data processing device 12 using the communication I / F 44 of the smart glasses 214. The analyzing unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the transmitted image and recognizes the content of the sign. The receiving unit can receive the analysis result using the communication I / F 44 of the smart glasses 214. The notifying unit can issue an audio warning using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned photographing unit, transmitting unit, analyzing unit, receiving unit, and notifying unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the photographing unit can photograph a road sign using the camera 42 of the headset type terminal 314. The transmitting unit can transmit the photographed image to the data processing device 12 using the communication I / F 44 of the headset type terminal 314. The analyzing unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the transmitted image and recognizes the content of the sign. The receiving unit can receive the analysis result using the communication I / F 44 of the headset type terminal 314. The notifying unit can issue an audio warning using the speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned photographing unit, transmitting unit, analyzing unit, receiving unit, and notifying unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photographing unit can photograph a road sign using the camera 42 of the robot 414. The transmitting unit can transmit the photographed image to the data processing device 12 using the communication I / F 44 of the robot 414. The analyzing unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the transmitted image and recognizes the content of the sign. The receiving unit can receive the analysis result using the communication I / F 44 of the robot 414. The notifying unit can issue an audio warning using the speaker 240 of the robot 414.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The analysis unit not only recognizes the content of the sign, but also analyzes the surrounding environment of the sign's location and can provide appropriate driving advice to the driver. For example, the analysis unit can analyze the condition of buildings and roads around the sign and provide advice to the driver such as, "Be careful, the road continues to be narrow ahead." The analysis unit can also analyze the movement of pedestrians around the sign and notify the driver, "Be careful, there are many pedestrians ahead." Furthermore, the analysis unit can analyze the movement of other vehicles around the sign and provide advice to the driver such as, "Be careful, traffic congestion is expected ahead." This can support safe driving by providing driving advice that takes into account not only the content of the sign but also the surrounding environment.

[0124] When recognizing the contents of a sign, the analysis unit can also evaluate the deterioration state of the sign and provide that information to the driver. For example, the analysis unit can detect fading or dirt on a sign and notify the driver, "This sign is faded, so please be careful." The analysis unit can also detect the damage state of a sign and notify the driver, "This sign is damaged, so please be careful." Furthermore, if the visibility of a sign has decreased, the analysis unit can provide information such as, "This sign has decreased visibility, so please be careful." This makes it possible to support safe driving by evaluating the deterioration state of signs and providing appropriate information to the driver.

[0125] The notification unit can estimate the driver's emotions and adjust the content and timing of notifications based on the estimated driver's emotions. For example, if the driver is nervous, the notification unit can notify the driver in a calm voice, "Please drive relaxed." Also, if the driver is relaxed, the notification unit can notify the driver in a normal voice, "Pay attention to the next sign." Furthermore, if the driver is tired, the notification unit can notify the driver in a gentle voice, "We recommend that you take a break." In this way, the content and timing of notifications can be adjusted according to the driver's emotions, thereby supporting the driver's safe driving.

[0126] The camera unit can automatically adjust the image resolution according to the vehicle's speed and driving environment. For example, when driving on a highway, the camera unit captures signs at high resolution to obtain detailed information. When driving in an urban area, the camera unit can capture signs at normal resolution to obtain the necessary information. Furthermore, in bad weather, the camera unit can switch to high-sensitivity mode to improve the visibility of signs. This allows the camera unit to adjust the image resolution according to the vehicle's speed and driving environment, making it possible to accurately obtain sign information.

[0127] The analysis unit not only recognizes the content of the sign, but also analyzes the surrounding environment of the sign's location and can provide appropriate driving advice to the driver. For example, the analysis unit can analyze the condition of buildings and roads around the sign and provide advice to the driver such as, "Be careful, the road continues to be narrow ahead." The analysis unit can also analyze the movement of pedestrians around the sign and notify the driver, "Be careful, there are many pedestrians ahead." Furthermore, the analysis unit can analyze the movement of other vehicles around the sign and provide advice to the driver such as, "Be careful, traffic congestion is expected ahead." This can support safe driving by providing driving advice that takes into account not only the content of the sign but also the surrounding environment.

[0128] The camera unit can estimate the driver's emotions and adjust the timing of capturing images based on the estimated driver's emotions. For example, if the driver is nervous, the camera unit can capture images frequently to ensure that important signs are not overlooked. For example, the camera unit can capture the driver's facial expressions with a camera and detect the driver's nervous state using an emotion estimation algorithm. Furthermore, if the driver is relaxed, the camera unit can return the frequency of capturing images of signs to normal and capture only the necessary information. For example, the camera unit can record the driver's voice and detect the driver's relaxed state using voice analysis technology. Furthermore, if the driver is tired, the camera unit can prioritize capturing images of important signs to reduce the driver's burden. For example, the camera unit can collect the driver's biometric data (heart rate and electrodermal activity) using a sensor and detect the driver's fatigue state using an emotion estimation algorithm. This allows the camera unit to adjust the timing of capturing images according to the driver's emotions to ensure that important signs are not overlooked.

[0129] The analysis unit not only recognizes the content of the sign, but also analyzes the surrounding environment of the sign's location and can provide appropriate driving advice to the driver. For example, the analysis unit can analyze the condition of buildings and roads around the sign and provide advice to the driver such as, "Be careful, the road continues to be narrow ahead." The analysis unit can also analyze the movement of pedestrians around the sign and notify the driver, "Be careful, there are many pedestrians ahead." Furthermore, the analysis unit can analyze the movement of other vehicles around the sign and provide advice to the driver such as, "Be careful, traffic congestion is expected ahead." This can support safe driving by providing driving advice that takes into account not only the content of the sign but also the surrounding environment.

[0130] The notification unit can estimate the driver's emotions and adjust the content and timing of notifications based on the estimated driver's emotions. For example, if the driver is nervous, the notification unit can notify the driver in a calm voice, "Please drive relaxed." Also, if the driver is relaxed, the notification unit can notify the driver in a normal voice, "Pay attention to the next sign." Furthermore, if the driver is tired, the notification unit can notify the driver in a gentle voice, "We recommend that you take a break." In this way, the content and timing of notifications can be adjusted according to the driver's emotions, thereby supporting the driver's safe driving.

[0131] The camera unit can automatically adjust the image resolution according to the vehicle's speed and driving environment. For example, when driving on a highway, the camera unit captures signs at high resolution to obtain detailed information. When driving in an urban area, the camera unit can capture signs at normal resolution to obtain the necessary information. Furthermore, in bad weather, the camera unit can switch to high-sensitivity mode to improve the visibility of signs. This allows the camera unit to adjust the image resolution according to the vehicle's speed and driving environment, making it possible to accurately obtain sign information.

[0132] The analysis unit not only recognizes the content of the sign, but also analyzes the surrounding environment of the sign's location and can provide appropriate driving advice to the driver. For example, the analysis unit can analyze the condition of buildings and roads around the sign and provide advice to the driver such as, "Be careful, the road continues to be narrow ahead." The analysis unit can also analyze the movement of pedestrians around the sign and notify the driver, "Be careful, there are many pedestrians ahead." Furthermore, the analysis unit can analyze the movement of other vehicles around the sign and provide advice to the driver such as, "Be careful, traffic congestion is expected ahead." This can support safe driving by providing driving advice that takes into account not only the content of the sign but also the surrounding environment.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The camera unit captures road signs with an in-vehicle camera. For example, the camera unit is installed in front of the vehicle and automatically captures road signs while driving. Step 2: The transmitting unit transmits the image captured by the capturing unit. For example, the transmitting unit can transmit the image using a communication means such as Wi-Fi or Bluetooth. Step 3: The analysis unit analyzes the image sent by the transmission unit. The analysis unit uses AI and image recognition technology to recognize the content of the sign. For example, the analysis unit can use deep learning to recognize speed limit signs and identify the speed limit. The analysis unit can also recognize stop signs and identify the stopping position indicated by the sign. Step 4: The receiving unit receives the analysis result obtained by the analyzing unit. For example, the receiving unit can receive the analysis result using a communication means such as Wi-Fi or Bluetooth. Step 5: The notification unit issues a voice alert based on the results received by the receiving unit. The notification unit uses AI and voice synthesis technology to issue the alert. For example, if the notification unit recognizes a speed limit sign, it will issue a voice alert saying, "The speed limit is 50 km / h." If it recognizes a stop sign, it will issue a voice alert saying, "Please stop."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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 AI 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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 AI 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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 AI 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.

[0186] 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.

[0187] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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, in order to avoid confusion and to 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.

[0205] 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.

[0206] [Explanation of symbols]

[0207] 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 photography unit that takes pictures of road signs using an in-vehicle camera; a transmitting unit that transmits the image captured by the photographing unit; an analysis unit that analyzes the image transmitted by the transmission unit; a receiving unit that receives the results analyzed by the analyzing unit; a notification unit that issues a voice alert based on the result received by the receiving unit; Equipped with A system characterized by:

2. The analysis unit Recognizing the contents of signs using image recognition technology 2. The system of claim 1.

3. The notification unit Use voice synthesis technology to warn 2. The system of claim 1.

4. The imaging unit is It is installed at the front of the vehicle and automatically photographs signs while driving.

2. The system of claim 1.

5. The analysis unit Recognize speed limit signs and identify the speed limit 2. The system of claim 1.

6. The analysis unit Recognize stop signs and identify the stop location indicated by the sign 2. The system of claim 1.

7. The imaging unit is Estimate the driver's emotions and adjust the timing of shooting based on the estimated driver's emotions.

2. The system of claim 1.

8. The imaging unit is Automatically adjust filters to accommodate different weather conditions 2. The system of claim 1.

Images