System

Abstract

An object of a system according to an embodiment is to promote safe driving by allowing a driver to recognize a potential danger in advance during driving.SOLUTION: A system includes a video analysis unit, a danger detection unit, and a warning unit. The video analysis unit analyzes a camera video of a drive recorder or a smartphone. The danger detection unit detects a dangerous situation or a potential problem from the camera video analyzed by the video analysis unit. The warning unit warns the driver of the dangerous situation or the potential problem detected by the danger detection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology makes it difficult for drivers to recognize potential dangers in advance while driving, and there is room for improvement in terms of ensuring safe driving.

[0005] The system according to the embodiment aims to promote safe driving by enabling the driver to recognize potential dangers in advance while driving. [Means for solving the problem]

[0006] The system according to the embodiment includes a video analysis unit, a hazard detection unit, and a warning unit. The video analysis unit analyzes camera footage from a drive recorder or a smartphone. The hazard detection unit detects dangerous situations or potential problems from the camera footage analyzed by the video analysis unit. The warning unit warns the driver of the dangerous situation or potential problem detected by the hazard detection unit. [Effects of the Invention]

[0007] The system according to the embodiment enables the driver to recognize potential dangers in advance while driving, and promotes safe driving. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 driving assistance system according to an embodiment of the present invention is a system that enables a driver to recognize potential dangers in advance while driving. This system analyzes video footage from a dashcam or a smartphone camera and evaluates the driving situation in real time. This allows the driving assistance system to recognize potential dangers in advance while the driver is driving and encourage safe driving.

[0029] A driving assistance system according to an embodiment includes a video analysis unit, a hazard detection unit, and a warning unit. The video analysis unit analyzes camera footage from a dashcam or smartphone. For example, the video analysis unit receives video data as input information using a generation AI, and recognizes road conditions, surrounding vehicles, pedestrians, and the like. The video analysis unit also analyzes information about, for example, a vehicle braking suddenly ahead and issues a warning to the driver. The hazard detection unit detects dangerous situations and potential problems from the camera footage analyzed by the video analysis unit. For example, the hazard detection unit detects lane departures, rapidly approaching vehicles, pedestrians running out into the road, and the like. The hazard detection unit also integrates, for example, vehicle sensor data (such as speed and braking pressure) to more accurately evaluate the driving situation. The warning unit warns the driver of dangerous situations and potential problems detected by the hazard detection unit. For example, the warning unit issues audio or visual warnings to the driver. The warning unit also sends warnings to the driver's smartphone, for example, to encourage caution even when the driver is not driving. As a result, the driving assistance system according to the embodiment can recognize potential dangers in advance while the driver is driving and encourage safe driving. For example, the driver can recognize dangers such as a vehicle braking suddenly, lane departure, or pedestrians running out into the road in advance and take appropriate measures. In addition, by sending a warning to the driver's smartphone, the system can encourage caution even when the driver is not driving.

[0030] The danger detection unit can detect a vehicle in front that suddenly brakes and issue a warning to the driver. For example, when the generation AI analyzes video data, it detects a vehicle that suddenly brakes and issues an audio or visual warning to the driver. The danger detection unit can also analyze, for example, the position and speed of a vehicle that suddenly brakes and issue a warning to the driver. This makes it possible to quickly issue a warning to a vehicle that suddenly brakes.

[0031] The hazard detection unit can detect lane departure, rapidly approaching vehicles, and pedestrians running out into the lane, and issue a warning to the driver. The hazard detection unit, for example, detects lane departure and issues a warning to the driver. For example, when the generation AI analyzes video data, it detects lane departure and issues an audio or visual warning to the driver. In addition, the hazard detection unit, for example, detects rapidly approaching vehicles and issues a warning to the driver. For example, when the generation AI analyzes video data, it detects rapidly approaching vehicles and issues an audio or visual warning to the driver. In addition, the hazard detection unit, for example, detects pedestrians running out into the lane and issues a warning to the driver. For example, when the generation AI analyzes video data, it detects pedestrians running out into the lane, and issues an audio or visual warning to the driver. This makes it possible to quickly issue a warning to lane departure, rapidly approaching vehicles, and pedestrians running out into the lane.

[0032] The video analysis unit can dynamically adjust risk assessment based on specific driving risks according to weather and time of day. The video analysis unit, for example, acquires weather information in real time and dynamically adjusts risk assessment taking into account specific driving risks, such as on rainy or snowy days. For example, when the generation AI analyzes video data, it acquires weather information and evaluates the risk of slipping higher in rainy weather and issues a warning to the driver. The video analysis unit also dynamically adjusts risk assessment taking into account driving risks according to time of day, for example. For example, when the generation AI analyzes video data, it dynamically adjusts risk assessment taking into account driving risks during specific time periods, such as at night or during rush hour. This allows for dynamic adjustment of risk assessment according to weather and time of day.

[0033] The video analysis unit can perform risk assessment based on the driver's individual driving style, based on the driver's past driving history. The video analysis unit, for example, refers to the driver's past driving history and performs risk assessment based on the frequency of sudden braking and sudden acceleration. For example, when the generation AI analyzes video data, it refers to the driver's past driving history and issues an early warning to drivers who frequently brake suddenly. The video analysis unit also performs risk assessment based on the driver's driving style, for example. For example, when the generation AI analyzes video data, it analyzes the driver's driving style and performs individual risk assessment. This makes it possible to perform risk assessment based on the driver's past driving history.

[0034] The video analysis unit integrates vehicle sensor data to perform more accurate driving situation evaluations. The video analysis unit, for example, integrates vehicle speed sensor data to evaluate sudden braking and sudden acceleration situations in real time. For example, when the generation AI analyzes video data, it integrates vehicle speed sensor data and issues a warning to the driver if sudden braking occurs. The video analysis unit also integrates vehicle brake pressure sensor data to evaluate braking strength in real time. For example, when the generation AI analyzes video data, it integrates vehicle brake pressure sensor data and issues a warning to the driver if sudden braking occurs. The video analysis unit also integrates vehicle acceleration sensor data to evaluate sudden acceleration situations in real time. For example, when the generation AI analyzes video data, it integrates vehicle acceleration sensor data and issues a warning to the driver if sudden acceleration occurs. This allows for more accurate driving situation evaluations by integrating vehicle sensor data.

[0035] The video analysis unit uploads the results of the video analysis to the cloud and integrates them with data from other vehicles to evaluate the traffic conditions throughout the region in real time. For example, the video analysis unit uploads the results of the generation AI's analysis of video data to the cloud and integrates them with data from other vehicles to evaluate the traffic conditions throughout the region in real time. For example, the generation AI uploads the results of the video analysis to the cloud and integrates them with data from other vehicles to share congestion and accident information. In addition, the video analysis unit, for example, uploads the results of the generation AI's analysis of video data to the cloud and integrates them with data from other vehicles to build a system that evaluates the traffic conditions throughout the region in real time. For example, the generation AI uploads the results of the video analysis to the cloud and integrates them with data from other vehicles to evaluate the traffic conditions throughout the region in real time and provide appropriate driving advice to the driver. This enables the traffic conditions throughout the region to be evaluated in real time.

[0036] The hazard detection unit can issue an early warning based on the driver's reaction time. For example, when the generation AI detects a hazard, the hazard detection unit takes the driver's reaction time into consideration and issues an early warning. For example, the generation AI analyzes the driver's reaction time and issues an earlier warning than usual in situations where sudden braking is required. In addition, the hazard detection unit builds a system in which the generation AI takes the driver's reaction time into consideration and issues an earlier warning. For example, the generation AI analyzes the driver's reaction time and issues an earlier warning than usual in situations where sudden braking is required. In addition, the hazard detection unit develops an algorithm in which the generation AI takes the driver's reaction time into consideration and issues an earlier warning. For example, the generation AI analyzes the driver's reaction time and issues an earlier warning than usual in situations where sudden braking is required. In this way, it is possible to issue an earlier warning taking the driver's reaction time into consideration.

[0037] The hazard detection unit works in cooperation with an automatic braking system and can automatically apply the brakes as necessary. For example, when the generating AI detects a hazard, the hazard detection unit works in cooperation with an automatic braking system and automatically applies the brakes as necessary. For example, if the generating AI makes an unexpected stop in the vehicle ahead, the automatic braking will be activated. In addition, the hazard detection unit works in cooperation with an automatic braking system and builds a system that automatically applies the brakes as necessary when the generating AI detects a hazard. For example, if the generating AI makes an unexpected stop in the vehicle ahead, the automatic braking will be activated. In addition, the hazard detection unit works in cooperation with an automatic braking system and develops an algorithm that automatically applies the brakes as necessary when the generating AI detects a hazard. For example, if the generating AI makes an unexpected stop in the vehicle ahead, the automatic braking will be activated. This makes it possible to automatically apply the brakes as necessary.

[0038] The danger detection unit sends a warning to the driver's smartphone, urging caution even when not driving. For example, when the generation AI detects a danger, the danger detection unit sends a warning to the driver's smartphone, urging caution even when not driving. For example, the generation AI issues a warning if there is a risk of the vehicle being stolen while parked. In addition, the danger detection unit will build a system that sends a warning to the driver's smartphone when the generation AI detects a danger, urging caution even when not driving. For example, the generation AI issues a warning if there is a risk of the vehicle being stolen while parked. In addition, the danger detection unit will develop an algorithm that sends a warning to the driver's smartphone when the generation AI detects a danger, urging caution even when not driving. For example, the generation AI issues a warning if there is a risk of the vehicle being stolen while parked. This will enable caution even when not driving.

[0039] The video analysis unit can evaluate road congestion conditions in real time and propose routes to avoid congestion. For example, when the generation AI analyzes the driving situation, the video analysis unit evaluates road congestion conditions in real time and proposes routes to avoid congestion. For example, when the generation AI analyzes the driving situation, the video analysis unit evaluates road congestion conditions in real time and proposes routes that avoid roads where congestion occurs. Furthermore, the video analysis unit, for example, constructs a system that evaluates road congestion conditions in real time when the generation AI analyzes the driving situation and proposes routes to avoid congestion. For example, when the generation AI analyzes the driving situation, the video analysis unit evaluates road congestion conditions in real time and proposes routes that avoid roads where congestion occurs. Furthermore, the video analysis unit, for example, develops an algorithm that evaluates road congestion conditions in real time when the generation AI analyzes the driving situation and proposes routes to avoid congestion. For example, when the generation AI analyzes the driving situation, the video analysis unit evaluates road congestion conditions in real time and proposes routes that avoid roads where congestion occurs. This makes it possible to evaluate road congestion conditions in real time and propose routes to avoid congestion.

[0040] The video analysis unit can provide advice to promote eco-driving based on the vehicle's fuel efficiency data. For example, when the generating AI analyzes the driving situation, the video analysis unit takes the vehicle's fuel efficiency data into consideration and provides advice to promote eco-driving. For example, when the generating AI analyzes the driving situation, the generating AI takes the vehicle's fuel efficiency data into consideration and suggests avoiding sudden acceleration. Furthermore, the video analysis unit, for example, builds a system that takes the vehicle's fuel efficiency data into consideration and provides advice to promote eco-driving when the generating AI analyzes the driving situation. For example, when the generating AI analyzes the driving situation, the generating AI takes the vehicle's fuel efficiency data into consideration and suggests avoiding sudden acceleration. Furthermore, the video analysis unit, for example, develops an algorithm that takes the vehicle's fuel efficiency data into consideration and provides advice to promote eco-driving when the generating AI analyzes the driving situation. For example, when the generating AI analyzes the driving situation, the generating AI takes the vehicle's fuel efficiency data into consideration and suggests avoiding sudden acceleration. In this way, advice to promote eco-driving can be provided by taking the vehicle's fuel efficiency data into consideration.

[0041] The hazard detection unit can display the detected hazard information on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning. For example, the hazard detection unit can display the hazard information detected by the generation AI on the driver's smartwatch in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the smartwatch. The hazard detection unit can also display the hazard information detected by the generation AI on the driver's head-up display in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the head-up display. The hazard detection unit can also display the hazard information detected by the generation AI on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the smartwatch or head-up display. This allows the detected hazard information to be displayed on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning.

[0042] The hazard detection unit can share the detected hazard information with other vehicles, thereby improving traffic safety throughout the region. The hazard detection unit, for example, builds a system in which hazard information detected by the generation AI is uploaded to the cloud and shared with other vehicles. For example, accident information and congestion information detected by the generation AI is shared in real time, thereby improving traffic safety throughout the region. In addition, the hazard detection unit, for example, develops an algorithm to upload hazard information detected by the generation AI to the cloud and share it with other vehicles. For example, accident information and congestion information detected by the generation AI is shared in real time, thereby improving traffic safety throughout the region. In addition, the hazard detection unit, for example, builds a system in which hazard information detected by the generation AI is uploaded to the cloud and shared with other vehicles. For example, accident information and congestion information detected by the generation AI is shared in real time, thereby improving traffic safety throughout the region. In this way, the detected hazard information can be shared with other vehicles, thereby improving traffic safety throughout the region.

[0043] The hazard detection unit can display the detected hazard information on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning. For example, the hazard detection unit can display the hazard information detected by the generation AI on the driver's smartwatch in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the smartwatch. The hazard detection unit can also display the hazard information detected by the generation AI on the driver's head-up display in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the head-up display. The hazard detection unit can also display the hazard information detected by the generation AI on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the smartwatch or head-up display. This allows the detected hazard information to be displayed on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning.

[0044] The hazard detection unit can share the detected hazard information with other vehicles, thereby improving traffic safety throughout the region. The hazard detection unit, for example, builds a system in which hazard information detected by the generation AI is uploaded to the cloud and shared with other vehicles. For example, accident information and congestion information detected by the generation AI is shared in real time, thereby improving traffic safety throughout the region. In addition, the hazard detection unit, for example, develops an algorithm to upload hazard information detected by the generation AI to the cloud and share it with other vehicles. For example, accident information and congestion information detected by the generation AI is shared in real time, thereby improving traffic safety throughout the region. In addition, the hazard detection unit, for example, builds a system in which hazard information detected by the generation AI is uploaded to the cloud and shared with other vehicles. For example, accident information and congestion information detected by the generation AI is shared in real time, thereby improving traffic safety throughout the region. In this way, the detected hazard information can be shared with other vehicles, thereby improving traffic safety throughout the region.

[0045] The hazard detection unit can display the detected hazard information on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning. For example, the hazard detection unit can display the hazard information detected by the generation AI on the driver's smartwatch in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the smartwatch. The hazard detection unit can also display the hazard information detected by the generation AI on the driver's head-up display in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the head-up display. The hazard detection unit can also display the hazard information detected by the generation AI on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the smartwatch or head-up display. This allows the detected hazard information to be displayed on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning.

[0046] The hazard detection unit can display the detected hazard information on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning. For example, the hazard detection unit can display the hazard information detected by the generation AI on the driver's smartwatch in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the smartwatch. The hazard detection unit can also display the hazard information detected by the generation AI on the driver's head-up display in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the head-up display. The hazard detection unit can also display the hazard information detected by the generation AI on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the smartwatch or head-up display. This allows the detected hazard information to be displayed on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning.

[0047] The hazard detection unit can share the detected hazard information with other vehicles, thereby improving traffic safety throughout the region. The hazard detection unit, for example, builds a system in which hazard information detected by the generation AI is uploaded to the cloud and shared with other vehicles. For example, accident information and congestion information detected by the generation AI is shared in real time, thereby improving traffic safety throughout the region. In addition, the hazard detection unit, for example, develops an algorithm to upload hazard information detected by the generation AI to the cloud and share it with other vehicles. For example, accident information and congestion information detected by the generation AI is shared in real time, thereby improving traffic safety throughout the region. In addition, the hazard detection unit, for example, builds a system in which hazard information detected by the generation AI is uploaded to the cloud and shared with other vehicles. For example, accident information and congestion information detected by the generation AI is shared in real time, thereby improving traffic safety throughout the region. In this way, the detected hazard information can be shared with other vehicles, thereby improving traffic safety throughout the region.

[0048] The hazard detection unit can display the detected hazard information on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning. For example, the hazard detection unit can display the hazard information detected by the generation AI on the driver's smartwatch in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the smartwatch. The hazard detection unit can also display the hazard information detected by the generation AI on the driver's head-up display in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the head-up display. The hazard detection unit can also display the hazard information detected by the generation AI on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the smartwatch or head-up display. This allows the detected hazard information to be displayed on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The driving assistance system may further include a health management unit that monitors the driver's health condition. The health management unit, for example, measures the driver's heart rate and blood pressure in real time and issues a warning if an abnormality is detected. For example, if the heart rate rises suddenly, the health management unit issues a warning urging the driver to take a break. Furthermore, if the driver's blood pressure is high, for example, the health management unit issues a warning urging the driver to stop driving. In this way, the driver's health condition can be monitored, and if an abnormality is detected, the system can prompt the driver to take appropriate action.

[0051] The driving assistance system may further include a training unit for improving the driver's driving skills. The training unit may, for example, analyze the driver's driving data and point out areas for improvement in the driving skills. For example, if the frequency of sudden braking or sudden acceleration is high, the training unit may provide advice to reduce such occurrences. The training unit may also provide, for example, a simulation for improving the driver's driving skills. For example, the driver's driving skills may be improved through a driving simulation in a virtual environment. This may improve the driver's driving skills and promote safe driving.

[0052] The driving assistance system may further include a fuel efficiency management unit that provides advice to improve fuel efficiency based on the driver's driving style. The fuel efficiency management unit may, for example, analyze the driver's driving data and provide advice to improve fuel efficiency. For example, it may suggest avoiding sudden acceleration and braking. The fuel efficiency management unit may also build a system that provides advice to improve fuel efficiency based on the driver's driving style. For example, a generation AI may analyze the driving data and provide specific advice to improve fuel efficiency. This allows fuel efficiency to be improved based on the driver's driving style.

[0053] The driving assistance system can further include a posture management unit that monitors the driver's posture while driving. The posture management unit, for example, analyzes the driver's sitting position and posture in real time and provides advice to maintain proper posture. For example, if the driver's posture deteriorates after driving for a long time, it issues a warning to encourage the driver to return to proper posture. The posture management unit can also monitor the driver's posture and provide advice that helps prevent back pain and stiff shoulders. This allows the driver to maintain proper posture while driving and maintain good health.

[0054] The driving assistance system may further include an eye tracking unit that monitors the driver's gaze while driving. The eye tracking unit, for example, analyzes the driver's gaze movements in real time and issues a warning if the driver's attention is distracted. For example, if the driver's gaze is taken off the road for a long time, a warning is issued to urge the driver to be careful. The eye tracking unit may also analyze the driver's gaze movements and provide advice on how to maintain appropriate gaze movements. This allows the driver's gaze movements to be monitored and the driver to maintain attention.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The video analysis unit analyzes the video footage from the dashcam or smartphone camera. For example, the generation AI receives the video data as input and recognizes road conditions, surrounding vehicles, pedestrians, etc. If there is a vehicle ahead that suddenly brakes, it analyzes that information and issues a warning to the driver. Step 2: The danger detection unit detects dangerous situations and potential problems from the camera footage analyzed by the video analysis unit. For example, it detects lane departures, rapidly approaching vehicles, and pedestrians running out into the road. It also integrates vehicle sensor data (speed, braking pressure, etc.) to more accurately evaluate the driving situation. Step 3: The warning unit alerts the driver to dangerous situations or potential problems detected by the danger detection unit. For example, it issues audio or visual warnings to the driver. It also sends warnings to the driver's smartphone to encourage caution even when not driving.

[0057] (Example 2) A driving assistance system according to an embodiment of the present invention is a system that enables a driver to recognize potential dangers in advance while driving. This system analyzes video footage from a dashcam or a smartphone camera and evaluates the driving situation in real time. This allows the driving assistance system to recognize potential dangers in advance while the driver is driving and encourage safe driving.

[0058] A driving assistance system according to an embodiment includes a video analysis unit, a hazard detection unit, and a warning unit. The video analysis unit analyzes camera footage from a dashcam or smartphone. For example, the video analysis unit receives video data as input information using a generation AI, and recognizes road conditions, surrounding vehicles, pedestrians, and the like. The video analysis unit also analyzes information about, for example, a vehicle braking suddenly ahead and issues a warning to the driver. The hazard detection unit detects dangerous situations and potential problems from the camera footage analyzed by the video analysis unit. For example, the hazard detection unit detects lane departures, rapidly approaching vehicles, pedestrians running out into the road, and the like. The hazard detection unit also integrates, for example, vehicle sensor data (such as speed and braking pressure) to more accurately evaluate the driving situation. The warning unit warns the driver of dangerous situations and potential problems detected by the hazard detection unit. For example, the warning unit issues audio or visual warnings to the driver. The warning unit also sends warnings to the driver's smartphone, for example, to encourage caution even when the driver is not driving. As a result, the driving assistance system according to the embodiment can recognize potential dangers in advance while the driver is driving and encourage safe driving. For example, the driver can recognize dangers such as a vehicle braking suddenly, lane departure, or pedestrians running out into the road in advance and take appropriate measures. In addition, by sending a warning to the driver's smartphone, the system can encourage caution even when the driver is not driving.

[0059] The danger detection unit can detect a vehicle in front that suddenly brakes and issue a warning to the driver. For example, when the generation AI analyzes video data, it detects a vehicle that suddenly brakes and issues an audio or visual warning to the driver. The danger detection unit can also analyze, for example, the position and speed of a vehicle that suddenly brakes and issue a warning to the driver. This makes it possible to quickly issue a warning to a vehicle that suddenly brakes.

[0060] The hazard detection unit can detect lane departure, rapidly approaching vehicles, and pedestrians running out into the lane, and issue a warning to the driver. The hazard detection unit, for example, detects lane departure and issues a warning to the driver. For example, when the generation AI analyzes video data, it detects lane departure and issues an audio or visual warning to the driver. In addition, the hazard detection unit, for example, detects rapidly approaching vehicles and issues a warning to the driver. For example, when the generation AI analyzes video data, it detects rapidly approaching vehicles and issues an audio or visual warning to the driver. In addition, the hazard detection unit, for example, detects pedestrians running out into the lane and issues a warning to the driver. For example, when the generation AI analyzes video data, it detects pedestrians running out into the lane, and issues an audio or visual warning to the driver. This makes it possible to quickly issue a warning to lane departure, rapidly approaching vehicles, and pedestrians running out into the lane.

[0061] The video analysis unit can dynamically adjust risk assessment based on specific driving risks according to weather and time of day. The video analysis unit, for example, acquires weather information in real time and dynamically adjusts risk assessment taking into account specific driving risks, such as on rainy or snowy days. For example, when the generation AI analyzes video data, it acquires weather information and evaluates the risk of slipping higher in rainy weather and issues a warning to the driver. The video analysis unit also dynamically adjusts risk assessment taking into account driving risks according to time of day, for example. For example, when the generation AI analyzes video data, it dynamically adjusts risk assessment taking into account driving risks during specific time periods, such as at night or during rush hour. This allows for dynamic adjustment of risk assessment according to weather and time of day.

[0062] The video analysis unit can perform risk assessment based on the driver's individual driving style, based on the driver's past driving history. The video analysis unit, for example, refers to the driver's past driving history and performs risk assessment based on the frequency of sudden braking and sudden acceleration. For example, when the generation AI analyzes video data, it refers to the driver's past driving history and issues an early warning to drivers who frequently brake suddenly. The video analysis unit also performs risk assessment based on the driver's driving style, for example. For example, when the generation AI analyzes video data, it analyzes the driver's driving style and performs individual risk assessment. This makes it possible to perform risk assessment based on the driver's past driving history.

[0063] The video analysis unit uses its emotion estimation function to analyze the driver's emotional state and issue special warnings if stress or fatigue is increasing. The video analysis unit, for example, analyzes the driver's facial expressions to detect signs of stress or fatigue. For example, when the generation AI analyzes video data, it analyzes the driver's facial expressions, infers the driver's emotional state based on the frequency of eye opening and closing and changes in facial expression, and issues a warning to encourage the driver to take a break if necessary. The video analysis unit also analyzes the driver's voice, for example, to detect signs of stress or fatigue. For example, when the generation AI analyzes voice data, it analyzes the tone and speed of the driver's voice to infer the driver's emotional state and issues a warning to encourage the driver to take a break if necessary. The video analysis unit also analyzes the driver's biometric data (heart rate and electrodermal activity), for example, to detect signs of stress or fatigue. For example, when the generation AI analyzes biometric data, it infers the driver's emotional state based on fluctuations in heart rate and issues a warning to encourage the driver to take a break if necessary. This allows the system to issue special warnings based on the driver's emotional state.

[0064] The video analysis unit integrates vehicle sensor data to perform more accurate driving situation evaluations. The video analysis unit, for example, integrates vehicle speed sensor data to evaluate sudden braking and sudden acceleration situations in real time. For example, when the generation AI analyzes video data, it integrates vehicle speed sensor data and issues a warning to the driver if sudden braking occurs. The video analysis unit also integrates vehicle brake pressure sensor data to evaluate braking strength in real time. For example, when the generation AI analyzes video data, it integrates vehicle brake pressure sensor data and issues a warning to the driver if sudden braking occurs. The video analysis unit also integrates vehicle acceleration sensor data to evaluate sudden acceleration situations in real time. For example, when the generation AI analyzes video data, it integrates vehicle acceleration sensor data and issues a warning to the driver if sudden acceleration occurs. This allows for more accurate driving situation evaluations by integrating vehicle sensor data.

[0065] The video analysis unit uploads the results of the video analysis to the cloud and integrates them with data from other vehicles to evaluate the traffic conditions throughout the region in real time. For example, the video analysis unit uploads the results of the generation AI's analysis of video data to the cloud and integrates them with data from other vehicles to evaluate the traffic conditions throughout the region in real time. For example, the generation AI uploads the results of the video analysis to the cloud and integrates them with data from other vehicles to share congestion and accident information. In addition, the video analysis unit, for example, uploads the results of the generation AI's analysis of video data to the cloud and integrates them with data from other vehicles to build a system that evaluates the traffic conditions throughout the region in real time. For example, the generation AI uploads the results of the video analysis to the cloud and integrates them with data from other vehicles to evaluate the traffic conditions throughout the region in real time and provide appropriate driving advice to the driver. This enables the traffic conditions throughout the region to be evaluated in real time.

[0066] The video analysis unit can use the emotion estimation function to provide driving advice based on the driver's emotional state and promote a relaxed driving environment. For example, when the generation AI analyzes video data, the video analysis unit estimates the driver's emotional state and provides advice to promote a relaxed driving environment. For example, when the generation AI analyzes video data, the video analysis unit estimates the driver's emotional state and encourages the driver to take deep breaths if stress is high. In addition, the video analysis unit can estimate the driver's emotional state and play music to promote a relaxed driving environment. For example, when the generation AI analyzes video data, the video analysis unit estimates the driver's emotional state and plays music with a relaxing effect if stress is high. In addition, the video analysis unit can build a system that estimates the driver's emotional state and provides advice to promote a relaxed driving environment. For example, when the generation AI analyzes video data, the video analysis unit estimates the driver's emotional state and encourages the driver to take deep breaths if stress is high. This allows the system to provide driving advice based on the driver's emotional state and promote a relaxed driving environment.

[0067] The hazard detection unit can issue an early warning based on the driver's reaction time. For example, when the generation AI detects a hazard, the hazard detection unit takes the driver's reaction time into consideration and issues an early warning. For example, the generation AI analyzes the driver's reaction time and issues an earlier warning than usual in situations where sudden braking is required. In addition, the hazard detection unit builds a system in which the generation AI takes the driver's reaction time into consideration and issues an earlier warning. For example, the generation AI analyzes the driver's reaction time and issues an earlier warning than usual in situations where sudden braking is required. In addition, the hazard detection unit develops an algorithm in which the generation AI takes the driver's reaction time into consideration and issues an earlier warning. For example, the generation AI analyzes the driver's reaction time and issues an earlier warning than usual in situations where sudden braking is required. In this way, it is possible to issue an earlier warning taking the driver's reaction time into consideration.

[0068] The danger detection unit can use the emotion estimation function to adjust the intensity and format of the warning according to the driver's emotional state. For example, when the generation AI detects a danger, the danger detection unit analyzes the driver's emotional state and adjusts the intensity and format of the warning. For example, the generation AI analyzes the driver's emotional state and issues a gentler warning if stress is high. The danger detection unit can also build a system in which the generation AI analyzes the driver's emotional state and adjusts the intensity and format of the warning. For example, the generation AI analyzes the driver's emotional state and issues a gentler warning if stress is high. The danger detection unit can also develop an algorithm in which the generation AI analyzes the driver's emotional state and adjusts the intensity and format of the warning. For example, the generation AI analyzes the driver's emotional state and issues a gentler warning if stress is high. This makes it possible to adjust the intensity and format of the warning according to the driver's emotional state.

[0069] The hazard detection unit works in cooperation with an automatic braking system and can automatically apply the brakes as necessary. For example, when the generating AI detects a hazard, the hazard detection unit works in cooperation with an automatic braking system and automatically applies the brakes as necessary. For example, if the generating AI makes an unexpected stop in the vehicle ahead, the automatic braking will be activated. In addition, the hazard detection unit works in cooperation with an automatic braking system and builds a system that automatically applies the brakes as necessary when the generating AI detects a hazard. For example, if the generating AI makes an unexpected stop in the vehicle ahead, the automatic braking will be activated. In addition, the hazard detection unit works in cooperation with an automatic braking system and develops an algorithm that automatically applies the brakes as necessary when the generating AI detects a hazard. For example, if the generating AI makes an unexpected stop in the vehicle ahead, the automatic braking will be activated. This makes it possible to automatically apply the brakes as necessary.

[0070] The danger detection unit sends a warning to the driver's smartphone, urging caution even when not driving. For example, when the generation AI detects a danger, the danger detection unit sends a warning to the driver's smartphone, urging caution even when not driving. For example, the generation AI issues a warning if there is a risk of the vehicle being stolen while parked. In addition, the danger detection unit will build a system that sends a warning to the driver's smartphone when the generation AI detects a danger, urging caution even when not driving. For example, the generation AI issues a warning if there is a risk of the vehicle being stolen while parked. In addition, the danger detection unit will develop an algorithm that sends a warning to the driver's smartphone when the generation AI detects a danger, urging caution even when not driving. For example, the generation AI issues a warning if there is a risk of the vehicle being stolen while parked. This will enable caution even when not driving.

[0071] The danger detection unit uses the emotion estimation function to suggest relaxation methods (e.g., playing music) according to the driver's emotional state, thereby reducing stress. For example, when the generation AI detects a danger, the danger detection unit analyzes the driver's emotional state and suggests relaxation methods. For example, the generation AI analyzes the driver's emotional state and plays music with a relaxing effect if stress is high. In addition, the danger detection unit builds a system that analyzes the driver's emotional state when the generation AI detects a danger and suggests relaxation methods. For example, the generation AI analyzes the driver's emotional state and plays music with a relaxing effect if stress is high. In addition, the danger detection unit develops an algorithm that analyzes the driver's emotional state when the generation AI detects a danger and suggests relaxation methods. For example, the generation AI analyzes the driver's emotional state and plays music with a relaxing effect if stress is high. In this way, relaxation methods according to the driver's emotional state can be suggested, thereby reducing stress.

[0072] The video analysis unit can evaluate road congestion conditions in real time and propose routes to avoid congestion. For example, when the generation AI analyzes the driving situation, the video analysis unit evaluates road congestion conditions in real time and proposes routes to avoid congestion. For example, when the generation AI analyzes the driving situation, the video analysis unit evaluates road congestion conditions in real time and proposes routes that avoid roads where congestion occurs. Furthermore, the video analysis unit, for example, constructs a system that evaluates road congestion conditions in real time when the generation AI analyzes the driving situation and proposes routes to avoid congestion. For example, when the generation AI analyzes the driving situation, the video analysis unit evaluates road congestion conditions in real time and proposes routes that avoid roads where congestion occurs. Furthermore, the video analysis unit, for example, develops an algorithm that evaluates road congestion conditions in real time when the generation AI analyzes the driving situation and proposes routes to avoid congestion. For example, when the generation AI analyzes the driving situation, the video analysis unit evaluates road congestion conditions in real time and proposes routes that avoid roads where congestion occurs. This makes it possible to evaluate road congestion conditions in real time and propose routes to avoid congestion.

[0073] The video analysis unit can provide advice to promote eco-driving based on the vehicle's fuel efficiency data. For example, when the generating AI analyzes the driving situation, the video analysis unit takes the vehicle's fuel efficiency data into consideration and provides advice to promote eco-driving. For example, when the generating AI analyzes the driving situation, the generating AI takes the vehicle's fuel efficiency data into consideration and suggests avoiding sudden acceleration. Furthermore, the video analysis unit, for example, builds a system that takes the vehicle's fuel efficiency data into consideration and provides advice to promote eco-driving when the generating AI analyzes the driving situation. For example, when the generating AI analyzes the driving situation, the generating AI takes the vehicle's fuel efficiency data into consideration and suggests avoiding sudden acceleration. Furthermore, the video analysis unit, for example, develops an algorithm that takes the vehicle's fuel efficiency data into consideration and provides advice to promote eco-driving when the generating AI analyzes the driving situation. For example, when the generating AI analyzes the driving situation, the generating AI takes the vehicle's fuel efficiency data into consideration and suggests avoiding sudden acceleration. In this way, advice to promote eco-driving can be provided by taking the vehicle's fuel efficiency data into consideration.

[0074] The video analysis unit can use the emotion estimation function to provide driving advice based on the driver's emotional state and promote a relaxed driving environment. For example, when the generation AI analyzes video data, the video analysis unit estimates the driver's emotional state and provides advice to promote a relaxed driving environment. For example, when the generation AI analyzes video data, the video analysis unit estimates the driver's emotional state and encourages the driver to take deep breaths if stress is high. In addition, the video analysis unit can estimate the driver's emotional state and play music to promote a relaxed driving environment. For example, when the generation AI analyzes video data, the video analysis unit estimates the driver's emotional state and plays music with a relaxing effect if stress is high. In addition, the video analysis unit can build a system that estimates the driver's emotional state and provides advice to promote a relaxed driving environment. For example, when the generation AI analyzes video data, the video analysis unit estimates the driver's emotional state and encourages the driver to take deep breaths if stress is high. This allows the system to provide driving advice based on the driver's emotional state and promote a relaxed driving environment.

[0075] The hazard detection unit can display the detected hazard information on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning. For example, the hazard detection unit can display the hazard information detected by the generation AI on the driver's smartwatch in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the smartwatch. The hazard detection unit can also display the hazard information detected by the generation AI on the driver's head-up display in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the head-up display. The hazard detection unit can also display the hazard information detected by the generation AI on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the smartwatch or head-up display. This allows the detected hazard information to be displayed on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning.

[0076] The danger detection unit uses the emotion estimation function to suggest relaxation methods (e.g., playing music) according to the driver's emotional state, thereby reducing stress. For example, when the generation AI detects a danger, the danger detection unit analyzes the driver's emotional state and suggests relaxation methods. For example, the generation AI analyzes the driver's emotional state and plays music with a relaxing effect if stress is high. In addition, the danger detection unit builds a system that analyzes the driver's emotional state when the generation AI detects a danger and suggests relaxation methods. For example, the generation AI analyzes the driver's emotional state and plays music with a relaxing effect if stress is high. In addition, the danger detection unit develops an algorithm that analyzes the driver's emotional state when the generation AI detects a danger and suggests relaxation methods. For example, the generation AI analyzes the driver's emotional state and plays music with a relaxing effect if stress is high. In this way, relaxation methods according to the driver's emotional state can be suggested, thereby reducing stress.

[0077] The hazard detection unit can share the detected hazard information with other vehicles, thereby improving traffic safety throughout the region. The hazard detection unit, for example, builds a system in which hazard information detected by the generation AI is uploaded to the cloud and shared with other vehicles. For example, accident information and congestion information detected by the generation AI is shared in real time, thereby improving traffic safety throughout the region. In addition, the hazard detection unit, for example, develops an algorithm to upload hazard information detected by the generation AI to the cloud and share it with other vehicles. For example, accident information and congestion information detected by the generation AI is shared in real time, thereby improving traffic safety throughout the region. In addition, the hazard detection unit, for example, builds a system in which hazard information detected by the generation AI is uploaded to the cloud and shared with other vehicles. For example, accident information and congestion information detected by the generation AI is shared in real time, thereby improving traffic safety throughout the region. In this way, the detected hazard information can be shared with other vehicles, thereby improving traffic safety throughout the region.

[0078] The hazard detection unit can display the detected hazard information on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning. For example, the hazard detection unit can display the hazard information detected by the generation AI on the driver's smartwatch in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the smartwatch. The hazard detection unit can also display the hazard information detected by the generation AI on the driver's head-up display in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the head-up display. The hazard detection unit can also display the hazard information detected by the generation AI on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the smartwatch or head-up display. This allows the detected hazard information to be displayed on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning.

[0079] The danger detection unit uses the emotion estimation function to suggest relaxation methods (e.g., playing music) according to the driver's emotional state, thereby reducing stress. For example, when the generation AI detects a danger, the danger detection unit analyzes the driver's emotional state and suggests relaxation methods. For example, the generation AI analyzes the driver's emotional state and plays music with a relaxing effect if stress is high. In addition, the danger detection unit builds a system that analyzes the driver's emotional state when the generation AI detects a danger and suggests relaxation methods. For example, the generation AI analyzes the driver's emotional state and plays music with a relaxing effect if stress is high. In addition, the danger detection unit develops an algorithm that analyzes the driver's emotional state when the generation AI detects a danger and suggests relaxation methods. For example, the generation AI analyzes the driver's emotional state and plays music with a relaxing effect if stress is high. In this way, relaxation methods according to the driver's emotional state can be suggested, thereby reducing stress.

[0080] The hazard detection unit can share the detected hazard information with other vehicles, thereby improving traffic safety throughout the region. The hazard detection unit, for example, builds a system in which hazard information detected by the generation AI is uploaded to the cloud and shared with other vehicles. For example, accident information and congestion information detected by the generation AI is shared in real time, thereby improving traffic safety throughout the region. In addition, the hazard detection unit, for example, develops an algorithm to upload hazard information detected by the generation AI to the cloud and share it with other vehicles. For example, accident information and congestion information detected by the generation AI is shared in real time, thereby improving traffic safety throughout the region. In addition, the hazard detection unit, for example, builds a system in which hazard information detected by the generation AI is uploaded to the cloud and shared with other vehicles. For example, accident information and congestion information detected by the generation AI is shared in real time, thereby improving traffic safety throughout the region. In this way, the detected hazard information can be shared with other vehicles, thereby improving traffic safety throughout the region.

[0081] The hazard detection unit can display the detected hazard information on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning. For example, the hazard detection unit can display the hazard information detected by the generation AI on the driver's smartwatch in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the smartwatch. The hazard detection unit can also display the hazard information detected by the generation AI on the driver's head-up display in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the head-up display. The hazard detection unit can also display the hazard information detected by the generation AI on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the smartwatch or head-up display. This allows the detected hazard information to be displayed on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning.

[0082] The danger detection unit uses the emotion estimation function to suggest relaxation methods (e.g., playing music) according to the driver's emotional state, thereby reducing stress. For example, when the generation AI detects a danger, the danger detection unit analyzes the driver's emotional state and suggests relaxation methods. For example, the generation AI analyzes the driver's emotional state and plays music with a relaxing effect if stress is high. In addition, the danger detection unit builds a system that analyzes the driver's emotional state when the generation AI detects a danger and suggests relaxation methods. For example, the generation AI analyzes the driver's emotional state and plays music with a relaxing effect if stress is high. In addition, the danger detection unit develops an algorithm that analyzes the driver's emotional state when the generation AI detects a danger and suggests relaxation methods. For example, the generation AI analyzes the driver's emotional state and plays music with a relaxing effect if stress is high. In this way, relaxation methods according to the driver's emotional state can be suggested, thereby reducing stress.

[0083] The hazard detection unit can display the detected hazard information on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning. For example, the hazard detection unit can display the hazard information detected by the generation AI on the driver's smartwatch in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the smartwatch. The hazard detection unit can also display the hazard information detected by the generation AI on the driver's head-up display in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the head-up display. The hazard detection unit can also display the hazard information detected by the generation AI on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the smartwatch or head-up display. This allows the detected hazard information to be displayed on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning.

[0084] The danger detection unit uses the emotion estimation function to suggest relaxation methods (e.g., playing music) according to the driver's emotional state, thereby reducing stress. For example, when the generation AI detects a danger, the danger detection unit analyzes the driver's emotional state and suggests relaxation methods. For example, the generation AI analyzes the driver's emotional state and plays music with a relaxing effect if stress is high. In addition, the danger detection unit builds a system that analyzes the driver's emotional state when the generation AI detects a danger and suggests relaxation methods. For example, the generation AI analyzes the driver's emotional state and plays music with a relaxing effect if stress is high. In addition, the danger detection unit develops an algorithm that analyzes the driver's emotional state when the generation AI detects a danger and suggests relaxation methods. For example, the generation AI analyzes the driver's emotional state and plays music with a relaxing effect if stress is high. In this way, relaxation methods according to the driver's emotional state can be suggested, thereby reducing stress.

[0085] The hazard detection unit can share the detected hazard information with other vehicles, thereby improving traffic safety throughout the region. The hazard detection unit, for example, builds a system in which hazard information detected by the generation AI is uploaded to the cloud and shared with other vehicles. For example, accident information and congestion information detected by the generation AI is shared in real time, thereby improving traffic safety throughout the region. In addition, the hazard detection unit, for example, develops an algorithm to upload hazard information detected by the generation AI to the cloud and share it with other vehicles. For example, accident information and congestion information detected by the generation AI is shared in real time, thereby improving traffic safety throughout the region. In addition, the hazard detection unit, for example, builds a system in which hazard information detected by the generation AI is uploaded to the cloud and shared with other vehicles. For example, accident information and congestion information detected by the generation AI is shared in real time, thereby improving traffic safety throughout the region. In this way, the detected hazard information can be shared with other vehicles, thereby improving traffic safety throughout the region.

[0086] The hazard detection unit can display the detected hazard information on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning. For example, the hazard detection unit can display the hazard information detected by the generation AI on the driver's smartwatch in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the smartwatch. The hazard detection unit can also display the hazard information detected by the generation AI on the driver's head-up display in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the head-up display. The hazard detection unit can also display the hazard information detected by the generation AI on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning. For example, the generation AI displays information about sudden braking on the smartwatch or head-up display. This allows the detected hazard information to be displayed on the driver's smartwatch or head-up display in real time, thereby enhancing the visual warning.

[0087] The danger detection unit uses the emotion estimation function to suggest relaxation methods (e.g., playing music) according to the driver's emotional state, thereby reducing stress. For example, when the generation AI detects a danger, the danger detection unit analyzes the driver's emotional state and suggests relaxation methods. For example, the generation AI analyzes the driver's emotional state and plays music with a relaxing effect if stress is high. In addition, the danger detection unit builds a system that analyzes the driver's emotional state when the generation AI detects a danger and suggests relaxation methods. For example, the generation AI analyzes the driver's emotional state and plays music with a relaxing effect if stress is high. In addition, the danger detection unit develops an algorithm that analyzes the driver's emotional state when the generation AI detects a danger and suggests relaxation methods. For example, the generation AI analyzes the driver's emotional state and plays music with a relaxing effect if stress is high. In this way, relaxation methods according to the driver's emotional state can be suggested, thereby reducing stress.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The driving assistance system may further include a health management unit that monitors the driver's health condition. The health management unit, for example, measures the driver's heart rate and blood pressure in real time and issues a warning if an abnormality is detected. For example, if the heart rate rises suddenly, the health management unit issues a warning urging the driver to take a break. Furthermore, if the driver's blood pressure is high, for example, the health management unit issues a warning urging the driver to stop driving. In this way, the driver's health condition can be monitored, and if an abnormality is detected, the system can prompt the driver to take appropriate action.

[0090] The driving assistance system may further include a training unit for improving the driver's driving skills. The training unit may, for example, analyze the driver's driving data and point out areas for improvement in the driving skills. For example, if the frequency of sudden braking or sudden acceleration is high, the training unit may provide advice to reduce such occurrences. The training unit may also provide, for example, a simulation for improving the driver's driving skills. For example, the driver's driving skills may be improved through a driving simulation in a virtual environment. This may improve the driver's driving skills and promote safe driving.

[0091] The driving assistance system may further include a fuel efficiency management unit that provides advice to improve fuel efficiency based on the driver's driving style. The fuel efficiency management unit may, for example, analyze the driver's driving data and provide advice to improve fuel efficiency. For example, it may suggest avoiding sudden acceleration and braking. The fuel efficiency management unit may also build a system that provides advice to improve fuel efficiency based on the driver's driving style. For example, a generation AI may analyze the driving data and provide specific advice to improve fuel efficiency. This allows fuel efficiency to be improved based on the driver's driving style.

[0092] The driving assistance system can further include a posture management unit that monitors the driver's posture while driving. The posture management unit, for example, analyzes the driver's sitting position and posture in real time and provides advice to maintain proper posture. For example, if the driver's posture deteriorates after driving for a long time, it issues a warning to encourage the driver to return to proper posture. The posture management unit can also monitor the driver's posture and provide advice that helps prevent back pain and stiff shoulders. This allows the driver to maintain proper posture while driving and maintain good health.

[0093] The driving assistance system may further include an eye tracking unit that monitors the driver's gaze while driving. The eye tracking unit, for example, analyzes the driver's gaze movements in real time and issues a warning if the driver's attention is distracted. For example, if the driver's gaze is taken off the road for a long time, a warning is issued to urge the driver to be careful. The eye tracking unit may also analyze the driver's gaze movements and provide advice on how to maintain appropriate gaze movements. This allows the driver's gaze movements to be monitored and the driver to maintain attention.

[0094] The driving assistance system may further include a music selection unit that automatically selects music to play while driving based on the driver's emotional state. The music selection unit, for example, analyzes the driver's emotional state and plays music that has a relaxing effect. For example, if the driver is feeling stressed, music with a relaxing effect is automatically selected and played. The music selection unit may also be configured to build a system that automatically selects music to play while driving based on the driver's emotional state. For example, a generation AI analyzes the driver's emotional state and automatically selects and plays music with a relaxing effect. This provides music that matches the driver's emotional state, promoting a relaxing driving environment.

[0095] The driving assistance system may further include a lighting adjustment unit that adjusts lighting during driving based on the driver's emotional state. The lighting adjustment unit, for example, analyzes the driver's emotional state and provides lighting that has a relaxing effect. For example, if stress levels are high, soft light is provided to enhance the relaxation effect. The lighting adjustment unit also builds a system that adjusts lighting during driving based on the driver's emotional state. For example, a generative AI analyzes the driver's emotional state and provides lighting that has a relaxing effect. This provides lighting that corresponds to the driver's emotional state, promoting a relaxing driving environment.

[0096] The driving assistance system may further include an air conditioning adjustment unit that adjusts air conditioning settings while driving based on the driver's emotional state. The air conditioning adjustment unit, for example, analyzes the driver's emotional state and provides a comfortable temperature. For example, if the driver is feeling stressed, it adjusts the temperature to a level that has a relaxing effect. The air conditioning adjustment unit may also build a system that adjusts air conditioning settings while driving based on the driver's emotional state. For example, a generative AI analyzes the driver's emotional state and provides a comfortable temperature. This provides air conditioning settings according to the driver's emotional state, promoting a relaxed driving environment.

[0097] The driving assistance system may further include a seat massage unit that provides a seat massage function while driving based on the driver's emotional state. The seat massage unit, for example, analyzes the driver's emotional state and provides a massage with a relaxing effect. For example, if stress levels are high, a massage with a relaxing effect may be automatically initiated. The seat massage unit may also be configured to build a system that provides a seat massage function while driving based on the driver's emotional state. For example, a generation AI may analyze the driver's emotional state and provide a massage with a relaxing effect. This provides a seat massage function according to the driver's emotional state, promoting a relaxing driving environment.

[0098] The driving assistance system may further include a navigation voice adjustment unit that adjusts the navigation voice while driving based on the driver's emotional state. The navigation voice adjustment unit, for example, analyzes the driver's emotional state and provides voice that has a relaxing effect. For example, if the driver is feeling stressed, navigation may be performed in a calm voice. The navigation voice adjustment unit may also build a system that adjusts the navigation voice while driving based on the driver's emotional state. For example, a generation AI may analyze the driver's emotional state and provide voice that has a relaxing effect. This allows the navigation voice to be provided in accordance with the driver's emotional state, promoting a relaxing driving environment.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The video analysis unit analyzes the video footage from the dashcam or smartphone camera. For example, the generation AI receives the video data as input and recognizes road conditions, surrounding vehicles, pedestrians, etc. If there is a vehicle ahead that suddenly brakes, it analyzes that information and issues a warning to the driver. Step 2: The danger detection unit detects dangerous situations and potential problems from the camera footage analyzed by the video analysis unit. For example, it detects lane departures, rapidly approaching vehicles, and pedestrians running out into the road. It also integrates vehicle sensor data (speed, braking pressure, etc.) to more accurately evaluate the driving situation. Step 3: The warning unit alerts the driver to dangerous situations or potential problems detected by the danger detection unit. For example, it issues audio or visual warnings to the driver. It also sends warnings to the driver's smartphone to encourage caution even when not driving.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] 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.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] 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.

[0128] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, the 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0146] 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.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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. [Explanation of symbols]

[0168] 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 video analysis unit that analyzes video footage from dashcams and smartphone cameras, a danger detection unit that detects dangerous situations or potential problems from the camera images analyzed by the image analysis unit; a warning unit that warns the driver of a dangerous situation or potential problem detected by the danger detection unit. A system characterized by:

2. The video analysis unit Integrate vehicle sensor data to perform more accurate driving situation assessment 2. The system of claim 1.

3. The danger detection unit Referencing past accident data to perform highly accurate risk assessments for specific situations 2. The system of claim 1.

4. The danger detection unit The detected danger information is shared with other vehicles to improve traffic safety throughout the region.

2. The system of claim 1.

5. The video analysis unit Analyzing the driver's emotional state and issuing special warnings if stress or fatigue increases 2. The system of claim 1.

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