System

Abstract

An object of a system according to an embodiment is to analyze video data of a drive recorder and provide appropriate advice to a driver.SOLUTION: A system according to an embodiment includes an image analysis unit, a driving behavior detection unit, and an advice providing unit. The image analysis unit analyzes video data of the drive recorder. The driving behavior detection unit detects a dangerous driving behavior from the video data analyzed by the image analysis unit. The advice providing unit provides appropriate advice to the driver based on the driving behavior detected by the driving behavior 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 has had the problem of not being able to fully utilize video data from drive recorders to analyze driving behavior and provide appropriate advice to drivers.

[0005] The system according to the embodiment aims to analyze video data from a drive recorder and provide appropriate advice to the driver. [Means for solving the problem]

[0006] The system according to the embodiment includes an image analysis unit, a driving behavior detection unit, and an advice provision unit. The image analysis unit analyzes video data from a drive recorder. The driving behavior detection unit detects dangerous driving behaviors from the video data analyzed by the image analysis unit. The advice provision unit provides appropriate advice to the driver based on the driving behavior detected by the driving behavior detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze video data from a drive recorder and provide appropriate advice to the driver. [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 uses a generation AI to analyze footage from a dashcam, identify driving behaviors that constitute "aggressive driving" or "driving caused by tailgating," and provide the driver with advice on optimal driving behavior. This allows the driving assistance system to provide the driver with advice on appropriate driving behavior and reduce the risk of accidents.

[0029] A driving assistance system according to an embodiment includes an image analysis unit, a driving behavior detection unit, and an advice provision unit. The image analysis unit analyzes video data from a drive recorder. For example, the image analysis unit analyzes the video data using a generation AI to evaluate the following distance and the frequency of lane changes. The image analysis unit can also analyze the driver's facial expressions from the video data to evaluate their stress level. For example, the generation AI recognizes the driver's facial expressions in real time, extracts facial features such as wrinkles between the eyebrows and the degree of downward movement of the corners of the mouth, and quantifies their stress level. The driving behavior detection unit detects dangerous driving behaviors from the video data analyzed by the image analysis unit. For example, the driving behavior detection unit detects behaviors such as driving too close to a vehicle ahead or repeatedly honking the horn. The driving behavior detection unit can also detect risky behaviors such as sudden braking and sudden acceleration. For example, the generation AI analyzes video data from a drive recorder, detects the timing of sudden braking and sudden acceleration, and evaluates the risky behaviors. The advice providing unit provides appropriate advice to the driver based on the driving behavior detected by the driving behavior detection unit. For example, the advice providing unit provides specific advice to the driver such as "Keep a safe distance between vehicles" or "Avoid sudden lane changes." The advice providing unit can also provide advice based on the emotional state of the driver. For example, the emotional state of the driver can be analyzed using an emotion estimation function, and advice on how to relax when stress levels are high can be provided. In this way, the driving assistance system according to the embodiment can provide the driver with advice on appropriate driving behavior and reduce the risk of accidents.

[0030] The image analysis unit can detect when the distance between vehicles is extremely short or when sudden lane changes are frequent. For example, the image analysis unit uses a generation AI to analyze video data from a drive recorder and detect when the distance between vehicles is extremely short. For example, it issues a warning when the distance between vehicles is less than a certain number of meters. The image analysis unit also detects when sudden lane changes are frequent. For example, it issues a warning when multiple lane changes are made within a certain period of time. This allows for early detection of dangerous driving behavior.

[0031] The driving behavior detection unit can detect behaviors such as approaching a vehicle in front too closely or honking the horn repeatedly. For example, the generation AI analyzes video data from a dashcam to detect behaviors such as approaching a vehicle in front too closely. For example, it issues a warning if the distance to the vehicle in front is less than a certain number of meters. The driving behavior detection unit also detects behaviors such as honking the horn repeatedly. For example, it issues a warning if the horn is honked multiple times within a certain period of time. This allows for early detection of dangerous driving behaviors.

[0032] The advice providing unit can provide specific advice to the driver, such as "Keep a safe distance between vehicles" or "Avoid sudden lane changes." The advice providing unit, for example, provides advice to the driver, such as "Keep a safe distance between vehicles." For example, if the distance between vehicles is too short, the advice providing unit issues a warning to encourage the driver to maintain an appropriate distance between vehicles. The advice providing unit also provides advice to the driver, such as "Avoid sudden lane changes." For example, if sudden lane changes are made frequently, the advice providing unit issues a warning to encourage stable driving. In this way, safe driving behavior can be promoted by providing specific advice to the driver.

[0033] The advice providing unit can provide advice by voice through a speaker or display the advice as text on an in-vehicle display. The advice providing unit can provide advice by voice through a speaker, for example. For example, audio advice such as "Keep a safe distance between vehicles" can be provided while driving. The advice providing unit can also display advice as text on an in-vehicle display. For example, a message such as "Avoid sudden lane changes" can be displayed on the display. This allows the driver to take appropriate driving actions in real time.

[0034] The image analysis unit can analyze the driver's facial expressions and evaluate the driver's stress level. For example, the image analysis unit uses a generative AI to analyze video data from a dashcam and recognize the driver's facial expressions in real time. For example, it extracts facial features such as wrinkles between the eyebrows and the degree to which the corners of the mouth turn down, and quantifies the stress level. The image analysis unit also analyzes the driver's facial expressions and evaluates the stress level. For example, it evaluates the stress level based on changes in facial expressions and understands the driver's psychological state. In this way, it is possible to understand the driver's psychological state by evaluating the driver's stress level.

[0035] The image analysis unit can recognize road signs or traffic lights and evaluate the appropriateness of driving behavior. For example, the image analysis unit uses a generative AI to analyze video data from a drive recorder and recognize road signs and traffic lights in real time. For example, it detects speed limit signs and stop signs and evaluates the appropriateness of driving behavior. The image analysis unit also recognizes traffic lights and evaluates the appropriateness of driving behavior. For example, it analyzes the color and lighting status of traffic lights and detects violations such as ignoring traffic lights. This can promote safe driving by evaluating the appropriateness of driving behavior.

[0036] The image analysis unit can integrate and analyze vehicle sensor data in addition to video data from the drive recorder. The image analysis unit, for example, integrates video data from the drive recorder with vehicle sensor data to comprehensively analyze driving behavior. For example, it analyzes speed and brake usage in combination with video data to evaluate the appropriateness of driving behavior. The image analysis unit also analyzes driving behavior using sensor data to detect dangerous driving behavior. For example, it evaluates the timing of sudden braking and sudden acceleration based on sensor data and video data. In this way, by integrating and analyzing video data and sensor data, it is possible to comprehensively evaluate driving behavior.

[0037] The image analysis unit can upload video data to the cloud in real time and integrate and analyze multiple vehicle data. The image analysis unit, for example, uploads video data from a drive recorder to the cloud in real time and integrates and analyzes multiple vehicle data. For example, it analyzes multiple vehicle data to comprehensively grasp traffic conditions and detects dangerous driving behavior. The image analysis unit also analyzes data on the cloud and provides feedback to the driver in real time. For example, it provides appropriate advice to the driver based on the data analyzed on the cloud. In this way, by integrating and analyzing multiple vehicle data, it is possible to comprehensively grasp traffic conditions.

[0038] The driving behavior detection unit can analyze the driving behavior of the vehicle ahead and detect risky behavior such as sudden braking or sudden acceleration. For example, the driving behavior detection unit uses a generation AI to analyze video data from a dashcam and recognize the driving behavior of the vehicle ahead in real time. For example, it detects the timing of sudden braking or sudden acceleration and evaluates the risky behavior. The driving behavior detection unit also detects risky behavior based on data on sudden braking and sudden acceleration. For example, it analyzes the frequency and intensity of sudden braking and sudden acceleration and evaluates the possibility of risky behavior. In this way, by analyzing the driving behavior of the vehicle ahead and detecting risky behavior such as sudden braking or sudden acceleration, it is possible to prevent risky driving behavior before it occurs.

[0039] The driving behavior detection unit can issue a warning if the driver of a vehicle ahead is using a smartphone. For example, the generation AI in the driving behavior detection unit analyzes video data from a dashcam and issues a warning if the driver of a vehicle ahead is using a smartphone. For example, it analyzes the driver's hand movements and line of sight to detect smartphone use. The driving behavior detection unit also issues a warning based on smartphone usage status. For example, it issues a warning to drivers who are on a call or sending a message, urging them to be careful. This makes it possible to prevent dangerous driving behavior by issuing a warning if the driver of a vehicle ahead is using a smartphone.

[0040] The driving behavior detection unit can recognize the license plate of the vehicle behind and issue a warning by referring to the past history of tailgating. For example, the driving behavior detection unit uses a generation AI to analyze video data from a dashcam and recognize the license plate of the vehicle behind in real time. For example, it can identify the vehicle using license plate recognition technology and refer to the past history of tailgating. The driving behavior detection unit also issues a warning based on the past history of tailgating. For example, it can issue a warning to vehicles with a history of tailgating in the past, urging the driver to be careful. In this way, by issuing a warning by referring to the past history of tailgating, dangerous driving behavior can be prevented.

[0041] The driving behavior detection unit can analyze the driving behavior of the vehicle behind and detect risky behavior such as sudden braking or sudden acceleration. For example, the driving behavior detection unit uses a generation AI to analyze video data from a dashcam and recognize the driving behavior of the vehicle behind in real time. For example, it detects the timing of sudden braking or sudden acceleration and evaluates the risky behavior. The driving behavior detection unit also detects risky behavior based on data on sudden braking and sudden acceleration. For example, it analyzes the frequency and intensity of sudden braking and sudden acceleration and evaluates the possibility of risky behavior. This makes it possible to analyze the driving behavior of the vehicle behind and detect risky behavior such as sudden braking or sudden acceleration, thereby preventing risky driving behavior before it occurs.

[0042] The driving behavior detection unit can issue a warning if the driver of a vehicle behind is using a smartphone. For example, the generation AI in the driving behavior detection unit analyzes video data from a dashcam and issues a warning if the driver of a vehicle behind is using a smartphone. For example, it analyzes the driver's hand movements and line of sight to detect smartphone use. The driving behavior detection unit also issues a warning based on smartphone usage status. For example, it issues a warning to drivers who are on a call or sending a message, urging them to be careful. This makes it possible to prevent dangerous driving behavior by issuing a warning if the driver of a vehicle behind is using a smartphone.

[0043] The advice providing unit can analyze the driver's past driving data and provide individually optimized driving advice. For example, the generation AI in the advice providing unit analyzes the driver's past driving data and provides individually optimized driving advice. For example, appropriate advice is generated based on past driving behavior. The advice providing unit also analyzes the driver's driving data and provides advice to promote improvement of driving skills. For example, it suggests areas for improvement in driving technique based on the past driving data. In this way, by analyzing the driver's past driving data and providing individually optimized driving advice, it is possible to promote improvement of driving skills.

[0044] The advice providing unit can analyze the driver's driving style and provide eco-driving advice. For example, the generation AI analyzes the driver's driving style and provides eco-driving advice. For example, it provides advice to avoid sudden acceleration and sudden braking. The advice providing unit also analyzes the driver's driving style and provides advice to promote improved fuel efficiency. For example, it encourages the driver to maintain a constant speed. In this way, by analyzing the driver's driving style and providing eco-driving advice, it is possible to promote improved fuel efficiency and environmental protection.

[0045] The advice providing unit can evaluate the driving skills of the driver and suggest a training program to improve those skills. For example, the generation AI in the advice providing unit evaluates the driving skills of the driver and suggests a training program to improve those skills. For example, an individual training plan is created based on driving behavior data. The advice providing unit also evaluates the driving skills of the driver and suggests areas for improvement in driving techniques. For example, it suggests areas for improvement in driving techniques based on past driving data. In this way, by evaluating the driving skills of the driver and suggesting a training program to improve those skills, it is possible to promote the improvement of driving techniques.

[0046] The advice providing unit can customize the voice assistant according to the driver's preferences and provide advice. For example, the generation AI customizes the voice assistant according to the driver's preferences and provides advice. For example, the voice assistant settings are adjusted based on the driver's preferences and past feedback. The advice providing unit also customizes the voice assistant according to the driver's preferences and provides advice on how to improve driving behavior. For example, the voice assistant according to the driver's preferences is used to suggest how to improve driving behavior. In this way, the voice assistant can be customized according to the driver's preferences and advice can be provided, thereby improving driver satisfaction.

[0047] The advice providing unit can track the driver's gaze and provide advice according to the direction of the gaze. For example, the generation AI of the advice providing unit tracks the driver's gaze and provides advice according to the direction of the gaze. For example, if the gaze is averted from the road, advice is provided to urge caution. The advice providing unit also analyzes the driver's gaze and suggests improvements to driving behavior according to the direction of the gaze. For example, if the gaze is concentrated in a certain direction, the advice providing unit encourages the driver to disperse their gaze. In this way, safe driving can be promoted by tracking the driver's gaze and providing advice according to the direction of the gaze.

[0048] The advice providing unit can link with the driver's smartphone and provide advice through an app. For example, the generation AI of the advice providing unit links with the driver's smartphone and provides advice through an app. For example, advice is provided in real time through a smartphone app while driving. The advice providing unit also suggests improvements to driving behavior through the smartphone app. For example, it suggests improvements to driving technique through the app based on driving behavior data. In this way, by linking with the driver's smartphone and providing advice through the app, convenience for the driver can be improved.

[0049] The advice providing unit can cooperate with the driver's in-vehicle navigation system to provide advice along with route guidance. For example, the generation AI of the advice providing unit cooperates with the driver's in-vehicle navigation system to provide advice along with route guidance. For example, it suggests appropriate driving behavior during route guidance. The advice providing unit also cooperates with the in-vehicle navigation system to suggest areas for improving driving behavior. For example, it suggests areas for improving driving technique during route guidance. In this way, by cooperating with the driver's in-vehicle navigation system and providing advice along with route guidance, it is possible to improve convenience for the driver.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The driving assistance system can also be equipped with a health monitoring unit that monitors the driver's health condition. For example, it can measure the driver's heart rate and blood pressure in real time and issue a warning if an abnormality is detected. The health monitoring unit can also evaluate the driver's fatigue level and provide advice encouraging the driver to take a break. This allows the system to support safe driving by monitoring the driver's health condition and encouraging them to take a break at the appropriate time.

[0052] The driving assistance system may further include an eco-driving advice unit that analyzes the driver's driving style and provides eco-driving advice. For example, the eco-driving advice unit may provide advice to help improve fuel efficiency. For example, the eco-driving advice unit may encourage the driver to maintain a constant speed. In this way, by analyzing the driver's driving style and providing eco-driving advice, it is possible to improve fuel efficiency and protect the environment.

[0053] The driving assistance system may further include an eye tracking unit that tracks the driver's gaze and provides advice according to the direction of the gaze. For example, if the driver's gaze is averted from the road, the system may provide advice urging caution. The eye tracking unit may also suggest improvements to driving behavior according to the direction of the gaze. For example, if the driver's gaze is concentrated in a certain direction, the system may suggest the driver to disperse their gaze. In this way, safe driving can be promoted by tracking the driver's gaze and providing advice according to the direction of the gaze.

[0054] The driving assistance system can further include a smartphone linkage unit that links with the driver's smartphone and provides advice through an app. For example, advice can be provided in real time through a smartphone app while driving. The smartphone linkage unit can also suggest areas for improving driving behavior. For example, it can suggest areas for improving driving technique through an app based on driving behavior data. This allows the system to link with the driver's smartphone and provide advice through an app, thereby improving convenience for the driver.

[0055] The driving assistance system may further include a navigation linkage unit that links with the driver's in-vehicle navigation system and provides advice along with route guidance. For example, it may suggest appropriate driving behaviors during route guidance. The navigation linkage unit may also suggest areas for improving driving behavior. For example, it may suggest areas for improving driving skills during route guidance. This allows the system to link with the driver's in-vehicle navigation system and provide advice along with route guidance, thereby improving convenience for the driver.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The image analysis unit analyzes the video data from the dashcam. For example, the video data can be analyzed using a generative AI to evaluate the distance between vehicles and the frequency of lane changes. The video data can also be used to analyze the driver's facial expressions and evaluate their stress level. The generative AI recognizes the driver's facial expressions in real time, extracts facial features such as wrinkles between the eyebrows and the degree to which the corners of the mouth are turned down, and quantifies the stress level. Step 2: The driving behavior detection unit detects dangerous driving behavior from the video data analyzed by the image analysis unit. For example, it detects behaviors such as approaching the vehicle ahead too closely or honking the horn repeatedly. It can also detect dangerous behaviors such as sudden braking and sudden acceleration. The generation AI analyzes the video data from the dashcam, detects the timing of sudden braking and sudden acceleration, and evaluates the risky behavior. Step 3: The advice provider provides appropriate advice to the driver based on the driving behavior detected by the driving behavior detector. For example, it provides specific advice such as "Maintain a safe distance between vehicles" or "Avoid sudden lane changes." It can also provide advice based on the driver's emotional state. It uses the emotion estimation function to analyze the driver's emotional state and provides advice to relax if the driver is feeling stressed.

[0058] (Example 2) A driving assistance system according to an embodiment of the present invention uses a generation AI to analyze footage from a dashcam, identify driving behaviors that constitute "aggressive driving" or "driving caused by tailgating," and provide the driver with advice on optimal driving behavior. This allows the driving assistance system to provide the driver with advice on appropriate driving behavior and reduce the risk of accidents.

[0059] A driving assistance system according to an embodiment includes an image analysis unit, a driving behavior detection unit, and an advice provision unit. The image analysis unit analyzes video data from a drive recorder. For example, the image analysis unit analyzes the video data using a generation AI to evaluate the following distance and the frequency of lane changes. The image analysis unit can also analyze the driver's facial expressions from the video data to evaluate their stress level. For example, the generation AI recognizes the driver's facial expressions in real time, extracts facial features such as wrinkles between the eyebrows and the degree of downward movement of the corners of the mouth, and quantifies their stress level. The driving behavior detection unit detects dangerous driving behaviors from the video data analyzed by the image analysis unit. For example, the driving behavior detection unit detects behaviors such as driving too close to a vehicle ahead or repeatedly honking the horn. The driving behavior detection unit can also detect risky behaviors such as sudden braking and sudden acceleration. For example, the generation AI analyzes video data from a drive recorder, detects the timing of sudden braking and sudden acceleration, and evaluates the risky behaviors. The advice providing unit provides appropriate advice to the driver based on the driving behavior detected by the driving behavior detection unit. For example, the advice providing unit provides specific advice to the driver such as "Keep a safe distance between vehicles" or "Avoid sudden lane changes." The advice providing unit can also provide advice based on the emotional state of the driver. For example, the emotional state of the driver can be analyzed using an emotion estimation function, and advice on how to relax when stress levels are high can be provided. In this way, the driving assistance system according to the embodiment can provide the driver with advice on appropriate driving behavior and reduce the risk of accidents.

[0060] The image analysis unit can detect when the distance between vehicles is extremely short or when sudden lane changes are frequent. For example, the image analysis unit uses a generation AI to analyze video data from a drive recorder and detect when the distance between vehicles is extremely short. For example, it issues a warning when the distance between vehicles is less than a certain number of meters. The image analysis unit also detects when sudden lane changes are frequent. For example, it issues a warning when multiple lane changes are made within a certain period of time. This allows for early detection of dangerous driving behavior.

[0061] The driving behavior detection unit can detect behaviors such as approaching a vehicle in front too closely or honking the horn repeatedly. For example, the generation AI analyzes video data from a dashcam to detect behaviors such as approaching a vehicle in front too closely. For example, it issues a warning if the distance to the vehicle in front is less than a certain number of meters. The driving behavior detection unit also detects behaviors such as honking the horn repeatedly. For example, it issues a warning if the horn is honked multiple times within a certain period of time. This allows for early detection of dangerous driving behaviors.

[0062] The advice providing unit can provide specific advice to the driver, such as "Keep a safe distance between vehicles" or "Avoid sudden lane changes." The advice providing unit, for example, provides advice to the driver, such as "Keep a safe distance between vehicles." For example, if the distance between vehicles is too short, the advice providing unit issues a warning to encourage the driver to maintain an appropriate distance between vehicles. The advice providing unit also provides advice to the driver, such as "Avoid sudden lane changes." For example, if sudden lane changes are made frequently, the advice providing unit issues a warning to encourage stable driving. In this way, safe driving behavior can be promoted by providing specific advice to the driver.

[0063] The advice providing unit can provide advice by voice through a speaker or display the advice as text on an in-vehicle display. The advice providing unit can provide advice by voice through a speaker, for example. For example, audio advice such as "Keep a safe distance between vehicles" can be provided while driving. The advice providing unit can also display advice as text on an in-vehicle display. For example, a message such as "Avoid sudden lane changes" can be displayed on the display. This allows the driver to take appropriate driving actions in real time.

[0064] The image analysis unit can analyze the driver's facial expressions and evaluate the driver's stress level. For example, the image analysis unit uses a generative AI to analyze video data from a dashcam and recognize the driver's facial expressions in real time. For example, it extracts facial features such as wrinkles between the eyebrows and the degree to which the corners of the mouth turn down, and quantifies the stress level. The image analysis unit also analyzes the driver's facial expressions and evaluates the stress level. For example, it evaluates the stress level based on changes in facial expressions and understands the driver's psychological state. In this way, it is possible to understand the driver's psychological state by evaluating the driver's stress level.

[0065] The image analysis unit can recognize road signs or traffic lights and evaluate the appropriateness of driving behavior. For example, the image analysis unit uses a generative AI to analyze video data from a drive recorder and recognize road signs and traffic lights in real time. For example, it detects speed limit signs and stop signs and evaluates the appropriateness of driving behavior. The image analysis unit also recognizes traffic lights and evaluates the appropriateness of driving behavior. For example, it analyzes the color and lighting status of traffic lights and detects violations such as ignoring traffic lights. This can promote safe driving by evaluating the appropriateness of driving behavior.

[0066] The image analysis unit can use the emotion estimation function to analyze the driver's emotional state and issue a warning if stress or impatience is increasing. For example, the image analysis unit uses a generative AI to analyze video data from a dashcam and estimate the driver's emotional state in real time. For example, it analyzes facial expressions and voice tone to evaluate the level of stress or impatience. The image analysis unit also uses the emotion estimation function to analyze the driver's emotional state and issue a warning if stress or impatience is increasing. For example, it can issue a warning if the stress level exceeds a certain threshold and encourage the driver to relax. This allows safe driving to be promoted by analyzing the driver's emotional state and issuing a warning if stress or impatience is increasing.

[0067] The image analysis unit can integrate and analyze vehicle sensor data in addition to video data from the drive recorder. The image analysis unit, for example, integrates video data from the drive recorder with vehicle sensor data to comprehensively analyze driving behavior. For example, it analyzes speed and brake usage in combination with video data to evaluate the appropriateness of driving behavior. The image analysis unit also analyzes driving behavior using sensor data to detect dangerous driving behavior. For example, it evaluates the timing of sudden braking and sudden acceleration based on sensor data and video data. In this way, by integrating and analyzing video data and sensor data, it is possible to comprehensively evaluate driving behavior.

[0068] The image analysis unit can upload video data to the cloud in real time and integrate and analyze multiple vehicle data. The image analysis unit, for example, uploads video data from a drive recorder to the cloud in real time and integrates and analyzes multiple vehicle data. For example, it analyzes multiple vehicle data to comprehensively grasp traffic conditions and detects dangerous driving behavior. The image analysis unit also analyzes data on the cloud and provides feedback to the driver in real time. For example, it provides appropriate advice to the driver based on the data analyzed on the cloud. In this way, by integrating and analyzing multiple vehicle data, it is possible to comprehensively grasp traffic conditions.

[0069] The image analysis unit can cooperate with a system that uses the emotion estimation function to provide relaxing music or aromas according to the emotional state of the driver. The image analysis unit, for example, uses the emotion estimation function to analyze the emotional state of the driver and cooperates with a system that provides relaxing music or aromas. For example, when stress is high, relaxing music is played to reduce the driver's stress. The image analysis unit also uses the emotion estimation function to analyze the emotional state of the driver and cooperates with a system that provides aromas. For example, when stress is high, an aroma with a relaxing effect is provided. In this way, the driver's stress can be reduced by providing relaxing music or aromas according to the driver's emotional state.

[0070] The driving behavior detection unit can analyze the facial expressions of the driver of the vehicle ahead and predict aggressive behavior. For example, the driving behavior detection unit uses a generation AI to analyze video data from a dashcam and recognize the facial expressions of the driver of the vehicle ahead in real time. For example, it detects expressions of anger or impatience and predicts aggressive behavior. The driving behavior detection unit also predicts aggressive behavior based on changes in facial expression. For example, it analyzes changes in facial expression and evaluates the possibility of aggressive behavior. In this way, by analyzing the facial expressions of the driver of the vehicle ahead and predicting aggressive behavior, it is possible to prevent dangerous driving behavior before it occurs.

[0071] The driving behavior detection unit can use an emotion estimation function to estimate the emotional state of the driver of the vehicle ahead and issue a warning if aggressive emotions are increasing. For example, the driving behavior detection unit uses a generation AI to analyze video data from a dashcam and estimate the emotional state of the driver of the vehicle ahead in real time. For example, it analyzes facial expressions and voice tone to evaluate aggressive emotions. The driving behavior detection unit also uses the emotion estimation function to estimate the emotional state of the driver of the vehicle ahead and issue a warning if aggressive emotions are increasing. For example, it issues a warning if the score of aggressive emotions exceeds a certain threshold, alerting the driver to be careful. This makes it possible to prevent dangerous driving behavior by estimating the emotional state of the driver of the vehicle ahead and issuing a warning if aggressive emotions are increasing.

[0072] The driving behavior detection unit can analyze the driving behavior of the vehicle ahead and detect risky behavior such as sudden braking or sudden acceleration. For example, the driving behavior detection unit uses a generation AI to analyze video data from a dashcam and recognize the driving behavior of the vehicle ahead in real time. For example, it detects the timing of sudden braking or sudden acceleration and evaluates the risky behavior. The driving behavior detection unit also detects risky behavior based on data on sudden braking and sudden acceleration. For example, it analyzes the frequency and intensity of sudden braking and sudden acceleration and evaluates the possibility of risky behavior. In this way, by analyzing the driving behavior of the vehicle ahead and detecting risky behavior such as sudden braking or sudden acceleration, it is possible to prevent risky driving behavior before it occurs.

[0073] The driving behavior detection unit can issue a warning if the driver of a vehicle ahead is using a smartphone. For example, the generation AI in the driving behavior detection unit analyzes video data from a dashcam and issues a warning if the driver of a vehicle ahead is using a smartphone. For example, it analyzes the driver's hand movements and line of sight to detect smartphone use. The driving behavior detection unit also issues a warning based on smartphone usage status. For example, it issues a warning to drivers who are on a call or sending a message, urging them to be careful. This makes it possible to prevent dangerous driving behavior by issuing a warning if the driver of a vehicle ahead is using a smartphone.

[0074] The driving behavior detection unit can use the emotion estimation function to provide driving advice according to the emotional state of the driver of the vehicle ahead. The driving behavior detection unit, for example, uses the emotion estimation function to analyze the emotional state of the driver of the vehicle ahead and provide appropriate driving advice. For example, it can suggest relaxation methods according to the driver's emotional state. The driving behavior detection unit also uses the emotion estimation function to analyze the driver's emotional state and provide advice on how to improve driving behavior. For example, it can provide advice on how to relax when stress is building up. In this way, by providing driving advice according to the emotional state of the driver of the vehicle ahead, it is possible to prevent dangerous driving behavior.

[0075] The driving behavior detection unit can recognize the license plate of the vehicle behind and issue a warning by referring to the past history of tailgating. For example, the driving behavior detection unit uses a generation AI to analyze video data from a dashcam and recognize the license plate of the vehicle behind in real time. For example, it can identify the vehicle using license plate recognition technology and refer to the past history of tailgating. The driving behavior detection unit also issues a warning based on the past history of tailgating. For example, it can issue a warning to vehicles with a history of tailgating in the past, urging the driver to be careful. In this way, by issuing a warning by referring to the past history of tailgating, dangerous driving behavior can be prevented.

[0076] The driving behavior detection unit can analyze the facial expressions of the driver of the vehicle behind and predict aggressive behavior. For example, the driving behavior detection unit uses a generation AI to analyze video data from a dashcam and recognize the facial expressions of the driver of the vehicle behind in real time. For example, it detects expressions of anger or impatience and predicts aggressive behavior. The driving behavior detection unit also predicts aggressive behavior based on changes in facial expression. For example, it analyzes changes in facial expression and evaluates the possibility of aggressive behavior. In this way, by analyzing the facial expressions of the driver of the vehicle behind and predicting aggressive behavior, it is possible to prevent dangerous driving behavior before it occurs.

[0077] The driving behavior detection unit can use an emotion estimation function to estimate the emotional state of the driver of the rear vehicle and issue a warning if aggressive emotions are increasing. For example, the driving behavior detection unit uses a generation AI to analyze video data from a dashcam and estimate the emotional state of the driver of the rear vehicle in real time. For example, it analyzes facial expressions and voice tone to evaluate aggressive emotions. The driving behavior detection unit also uses the emotion estimation function to estimate the emotional state of the driver of the rear vehicle and issue a warning if aggressive emotions are increasing. For example, it issues a warning if the aggressive emotion score exceeds a certain threshold, alerting the driver. This makes it possible to prevent dangerous driving behavior by estimating the emotional state of the driver of the rear vehicle and issuing a warning if aggressive emotions are increasing.

[0078] The driving behavior detection unit can analyze the driving behavior of the vehicle behind and detect risky behavior such as sudden braking or sudden acceleration. For example, the driving behavior detection unit uses a generation AI to analyze video data from a dashcam and recognize the driving behavior of the vehicle behind in real time. For example, it detects the timing of sudden braking or sudden acceleration and evaluates the risky behavior. The driving behavior detection unit also detects risky behavior based on data on sudden braking and sudden acceleration. For example, it analyzes the frequency and intensity of sudden braking and sudden acceleration and evaluates the possibility of risky behavior. This makes it possible to analyze the driving behavior of the vehicle behind and detect risky behavior such as sudden braking or sudden acceleration, thereby preventing risky driving behavior before it occurs.

[0079] The driving behavior detection unit can issue a warning if the driver of a vehicle behind is using a smartphone. For example, the generation AI in the driving behavior detection unit analyzes video data from a dashcam and issues a warning if the driver of a vehicle behind is using a smartphone. For example, it analyzes the driver's hand movements and line of sight to detect smartphone use. The driving behavior detection unit also issues a warning based on smartphone usage status. For example, it issues a warning to drivers who are on a call or sending a message, urging them to be careful. This makes it possible to prevent dangerous driving behavior by issuing a warning if the driver of a vehicle behind is using a smartphone.

[0080] The driving behavior detection unit can use the emotion estimation function to provide driving advice according to the emotional state of the driver of the rear vehicle. The driving behavior detection unit, for example, uses the emotion estimation function to analyze the emotional state of the driver of the rear vehicle and provide appropriate driving advice. For example, it suggests relaxation methods according to the driver's emotional state. The driving behavior detection unit also uses the emotion estimation function to analyze the driver's emotional state and provide advice on how to improve driving behavior. For example, it provides advice on how to relax when stress is high. In this way, by providing driving advice according to the emotional state of the driver of the rear vehicle, dangerous driving behavior can be prevented in advance.

[0081] The advice providing unit can analyze the driver's past driving data and provide individually optimized driving advice. For example, the generation AI in the advice providing unit analyzes the driver's past driving data and provides individually optimized driving advice. For example, appropriate advice is generated based on past driving behavior. The advice providing unit also analyzes the driver's driving data and provides advice to promote improvement of driving skills. For example, it suggests areas for improvement in driving technique based on the past driving data. In this way, by analyzing the driver's past driving data and providing individually optimized driving advice, it is possible to promote improvement of driving skills.

[0082] The advice providing unit can use the emotion estimation function to suggest a relaxation method according to the emotional state of the driver. The advice providing unit, for example, uses the emotion estimation function to analyze the emotional state of the driver and suggest a relaxation method. For example, if stress is high, relaxing music is played. The advice providing unit also analyzes the emotional state of the driver and provides an aroma with a relaxing effect. For example, if stress is high, an aroma with a relaxing effect is provided. In this way, by suggesting a relaxation method according to the emotional state of the driver, it is possible to reduce the driver's stress and promote safe driving.

[0083] The advice providing unit can analyze the driver's driving style and provide eco-driving advice. For example, the generation AI analyzes the driver's driving style and provides eco-driving advice. For example, it provides advice to avoid sudden acceleration and sudden braking. The advice providing unit also analyzes the driver's driving style and provides advice to promote improved fuel efficiency. For example, it encourages the driver to maintain a constant speed. In this way, by analyzing the driver's driving style and providing eco-driving advice, it is possible to promote improved fuel efficiency and environmental protection.

[0084] The advice providing unit can evaluate the driving skills of the driver and suggest a training program to improve those skills. For example, the generation AI in the advice providing unit evaluates the driving skills of the driver and suggests a training program to improve those skills. For example, an individual training plan is created based on driving behavior data. The advice providing unit also evaluates the driving skills of the driver and suggests areas for improvement in driving techniques. For example, it suggests areas for improvement in driving techniques based on past driving data. In this way, by evaluating the driving skills of the driver and suggesting a training program to improve those skills, it is possible to promote the improvement of driving techniques.

[0085] The advice providing unit can provide driving advice according to the emotional state of the driver using the emotion estimation function. The advice providing unit, for example, uses the emotion estimation function to analyze the emotional state of the driver and provide appropriate driving advice. For example, it provides advice to relax when stress is high. The advice providing unit also analyzes the emotional state of the driver and provides advice on how to improve driving behavior. For example, it suggests how to improve driving behavior according to the emotional state. In this way, safe driving can be promoted by providing driving advice according to the emotional state of the driver.

[0086] The advice providing unit can customize the voice assistant according to the driver's preferences and provide advice. For example, the generation AI customizes the voice assistant according to the driver's preferences and provides advice. For example, the voice assistant settings are adjusted based on the driver's preferences and past feedback. The advice providing unit also customizes the voice assistant according to the driver's preferences and provides advice on how to improve driving behavior. For example, the voice assistant according to the driver's preferences is used to suggest how to improve driving behavior. In this way, the voice assistant can be customized according to the driver's preferences and advice can be provided, thereby improving driver satisfaction.

[0087] The advice providing unit can track the driver's gaze and provide advice according to the direction of the gaze. For example, the generation AI of the advice providing unit tracks the driver's gaze and provides advice according to the direction of the gaze. For example, if the gaze is averted from the road, advice is provided to urge caution. The advice providing unit also analyzes the driver's gaze and suggests improvements to driving behavior according to the direction of the gaze. For example, if the gaze is concentrated in a certain direction, the advice providing unit encourages the driver to disperse their gaze. In this way, safe driving can be promoted by tracking the driver's gaze and providing advice according to the direction of the gaze.

[0088] The advice providing unit can adjust the tone and content of advice according to the emotional state of the driver using the emotion estimation function. The advice providing unit, for example, uses the emotion estimation function to analyze the emotional state of the driver and adjust the tone and content of advice. For example, when stress is high, the advice providing unit provides advice in a gentle tone. The advice providing unit also analyzes the emotional state of the driver and adjusts the content of the advice. For example, the content of the advice is changed according to the emotional state. In this way, by adjusting the tone and content of advice according to the emotional state of the driver, it is possible to reduce the driver's stress and promote safe driving.

[0089] The advice providing unit can link with the driver's smartphone and provide advice through an app. For example, the generation AI of the advice providing unit links with the driver's smartphone and provides advice through an app. For example, advice is provided in real time through a smartphone app while driving. The advice providing unit also suggests improvements to driving behavior through the smartphone app. For example, it suggests improvements to driving technique through the app based on driving behavior data. In this way, by linking with the driver's smartphone and providing advice through the app, convenience for the driver can be improved.

[0090] The advice providing unit can cooperate with the driver's in-vehicle navigation system to provide advice along with route guidance. For example, the generation AI of the advice providing unit cooperates with the driver's in-vehicle navigation system to provide advice along with route guidance. For example, it suggests appropriate driving behavior during route guidance. The advice providing unit also cooperates with the in-vehicle navigation system to suggest areas for improving driving behavior. For example, it suggests areas for improving driving technique during route guidance. In this way, by cooperating with the driver's in-vehicle navigation system and providing advice along with route guidance, it is possible to improve convenience for the driver.

[0091] The advice providing unit can provide advice according to the emotional state of the driver using the emotion estimation function. The advice providing unit, for example, uses the emotion estimation function to analyze the emotional state of the driver and provide appropriate advice. For example, it provides advice to relax when stress is high. The advice providing unit also analyzes the emotional state of the driver and provides advice on how to improve driving behavior. For example, it suggests how to improve driving behavior depending on the emotional state. In this way, by providing advice according to the emotional state of the driver, it is possible to reduce the driver's stress and promote safe driving.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The driving assistance system can also be equipped with a health monitoring unit that monitors the driver's health condition. For example, it can measure the driver's heart rate and blood pressure in real time and issue a warning if an abnormality is detected. The health monitoring unit can also evaluate the driver's fatigue level and provide advice encouraging the driver to take a break. This allows the system to support safe driving by monitoring the driver's health condition and encouraging them to take a break at the appropriate time.

[0094] The driving assistance system may further include an eco-driving advice unit that analyzes the driver's driving style and provides eco-driving advice. For example, the eco-driving advice unit may provide advice to help improve fuel efficiency. For example, the eco-driving advice unit may encourage the driver to maintain a constant speed. In this way, by analyzing the driver's driving style and providing eco-driving advice, it is possible to improve fuel efficiency and protect the environment.

[0095] The driving assistance system may further include an eye tracking unit that tracks the driver's gaze and provides advice according to the direction of the gaze. For example, if the driver's gaze is averted from the road, the system may provide advice urging caution. The eye tracking unit may also suggest improvements to driving behavior according to the direction of the gaze. For example, if the driver's gaze is concentrated in a certain direction, the system may suggest the driver to disperse their gaze. In this way, safe driving can be promoted by tracking the driver's gaze and providing advice according to the direction of the gaze.

[0096] The driving assistance system can further include a smartphone linkage unit that links with the driver's smartphone and provides advice through an app. For example, advice can be provided in real time through a smartphone app while driving. The smartphone linkage unit can also suggest areas for improving driving behavior. For example, it can suggest areas for improving driving technique through an app based on driving behavior data. This allows the system to link with the driver's smartphone and provide advice through an app, thereby improving convenience for the driver.

[0097] The driving assistance system may further include a navigation linkage unit that links with the driver's in-vehicle navigation system and provides advice along with route guidance. For example, it may suggest appropriate driving behaviors during route guidance. The navigation linkage unit may also suggest areas for improving driving behavior. For example, it may suggest areas for improving driving skills during route guidance. This allows the system to link with the driver's in-vehicle navigation system and provide advice along with route guidance, thereby improving convenience for the driver.

[0098] The driving assistance system may further include a relaxation suggestion unit that suggests relaxation methods according to the driver's emotional state. For example, the emotional state of the driver may be analyzed using an emotion estimation function, and relaxing music may be played if stress levels are high. The relaxation suggestion unit may also provide aromas with a relaxing effect. For example, if stress levels are high, an aroma with a relaxing effect may be provided. By suggesting relaxation methods according to the driver's emotional state, stress in the driver may be reduced and safe driving may be promoted.

[0099] The driving assistance system may further include an advice tone adjustment unit that adjusts the tone and content of advice according to the emotional state of the driver. For example, the emotional state of the driver may be analyzed using an emotion estimation function, and advice may be provided in a gentler tone if the driver is under stress. The advice tone adjustment unit may also change the content of advice according to the emotional state. For example, the advice content may be adjusted according to the emotional state. In this way, by adjusting the tone and content of advice according to the emotional state of the driver, it is possible to reduce the driver's stress and promote safe driving.

[0100] The driving assistance system may further include an emotion advice unit that provides driving advice according to the driver's emotional state. For example, the emotion estimation function may be used to analyze the driver's emotional state, and advice on how to relax when stress levels are high may be provided. The emotion advice unit may also suggest improvements to driving behavior according to the driver's emotional state. For example, the emotion advice unit may suggest improvements to driving behavior according to the driver's emotional state. This allows safe driving to be promoted by providing driving advice according to the driver's emotional state.

[0101] The driving assistance system may further include a relaxation providing unit that provides relaxing music or aromas according to the emotional state of the driver. For example, the emotional state of the driver may be analyzed using an emotion estimation function, and relaxing music may be played if stress levels are high. The relaxation providing unit may also provide aromas with a relaxing effect. For example, an aroma with a relaxing effect may be provided if stress levels are high. In this way, providing relaxing music or aromas according to the emotional state of the driver can reduce the driver's stress and promote safe driving.

[0102] The driving assistance system may further include an emotion advice unit that provides driving advice according to the driver's emotional state. For example, the emotion estimation function may be used to analyze the driver's emotional state, and advice on how to relax when stress levels are high may be provided. The emotion advice unit may also suggest improvements to driving behavior according to the driver's emotional state. For example, the emotion advice unit may suggest improvements to driving behavior according to the driver's emotional state. This allows safe driving to be promoted by providing driving advice according to the driver's emotional state.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The image analysis unit analyzes the video data from the dashcam. For example, the video data can be analyzed using a generative AI to evaluate the distance between vehicles and the frequency of lane changes. The video data can also be used to analyze the driver's facial expressions and evaluate their stress level. The generative AI recognizes the driver's facial expressions in real time, extracts facial features such as wrinkles between the eyebrows and the degree to which the corners of the mouth are turned down, and quantifies the stress level. Step 2: The driving behavior detection unit detects dangerous driving behavior from the video data analyzed by the image analysis unit. For example, it detects behaviors such as approaching the vehicle ahead too closely or honking the horn repeatedly. It can also detect dangerous behaviors such as sudden braking and sudden acceleration. The generation AI analyzes the video data from the dashcam, detects the timing of sudden braking and sudden acceleration, and evaluates the risky behavior. Step 3: The advice provider provides appropriate advice to the driver based on the driving behavior detected by the driving behavior detector. For example, it provides specific advice such as "Maintain a safe distance between vehicles" or "Avoid sudden lane changes." It can also provide advice based on the driver's emotional state. It uses the emotion estimation function to analyze the driver's emotional state and provides advice to relax if the driver is feeling stressed.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0149] 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.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] 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]

[0172] 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. An image analysis unit that analyzes the video data from the drive recorder, a driving behavior detection unit that detects dangerous driving behavior from the video data analyzed by the image analysis unit; an advice providing unit that provides appropriate advice to the driver based on the driving behavior detected by the driving behavior detection unit; A system characterized by:

2. The image analysis unit In addition to the video data from the drive recorder, vehicle sensor data is integrated and analyzed.

2. The system of claim 1.

3. The driving behavior detection unit The system recognizes the license plate of the vehicle ahead and issues a warning based on the history of past aggressive driving.

2. The system of claim 1.

4. The driving behavior detection unit The system recognizes the license plate of the vehicle behind and issues a warning based on the past record of tailgating.

2. The system of claim 1.

5. The advice providing unit Analyzing the driver's past driving data and providing individually optimized driving advice 2. The system of claim 1.

6. The advice providing unit Customizing a voice assistant according to the driver's preferences and providing the advice 2. The system of claim 1.

7. The image analysis unit Analyzing the driver's emotional state and issuing a warning if stress or impatience is increasing 2. The system of claim 1.

8. The driving behavior detection unit Analyzing the facial expressions of the driver of the vehicle ahead and predicting aggressive behavior 2. The system of claim 1.

Images