System

Abstract

An object of the system according to the embodiment is to analyze moving image data of a drive recorder and feed back a dangerous driving point or a point at which there is a possibility of an accident to a driver.SOLUTION: A system includes a moving image data collection unit, an analysis unit, and a feedback unit. The moving image data collection unit collects moving image data of a drive recorder. The analysis unit analyzes the moving image data collected by the moving image data collection unit. The feedback unit feeds back the dangerous driving point or the point at which there is a possibility of an accident specified by the analysis unit to the driver.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 does not fully utilize video data from drive recorders to provide feedback to drivers about dangerous driving situations or areas where an accident could have occurred, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze video data from a drive recorder and provide feedback to the driver about dangerous driving points and points where an accident may have occurred. [Means for solving the problem]

[0006] The system according to the embodiment includes a video data collection unit, an analysis unit, and a feedback unit. The video data collection unit collects video data from a drive recorder. The analysis unit analyzes the video data collected by the video data collection unit. The feedback unit provides feedback to the driver about dangerous driving points and points where there is a possibility of an accident identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze video data from a drive recorder and provide feedback to the driver about dangerous driving points and points where an accident may have occurred. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The danger prediction system according to an embodiment of the present invention is a system that uses AI to analyze video data from a drive recorder and share dangerous driving points and points where there is a possibility of an accident. This makes it possible to reduce car accidents.

[0029] The risk prediction countermeasure system according to the embodiment includes a video data collection unit, an analysis unit, and a feedback unit. The video data collection unit collects video data from a drive recorder. For example, it records road conditions, surrounding vehicles, and pedestrian movements while driving. The video data collection unit can also collect video footage of waiting at traffic lights at intersections and changing lanes. The analysis unit allows a generation AI to analyze the video data collected by the video data collection unit. For example, the generation AI learns from past accident cases and identifies dangerous driving points and points where accidents could have occurred. The generation AI also analyzes the video data based on prompts containing instructions from the driver regarding what the generation AI wants the driver to do. For example, the driver may input a prompt such as, "Please identify dangerous driving points from this video data." The feedback unit then provides feedback to the driver regarding the dangerous driving points and points where accidents could have occurred. For example, the feedback may be in the form of, "There was a red light run at this intersection. Please be more careful next time." The feedback unit can also register the identified dangerous driving points and points where accidents could have occurred in a database and share them with other drivers. As a result, the danger prediction countermeasure system according to the embodiment is expected to reduce automobile accidents by providing drivers with feedback on dangerous driving points and points where an accident may have occurred.

[0030] The video data collection unit collects in-car audio data in addition to video data from a dashcam, and can detect signs of dangerous driving from the driver's voice and sounds inside the vehicle. For example, the video data collection unit builds a system that simultaneously collects in-car audio data along with video data from a dashcam. For example, the video data collection unit analyzes the driver's voice and sounds inside the vehicle to detect shouts and warning sounds during sudden braking. The video data collection unit also analyzes in-car audio data to detect signs of stress or impatience from the tone and content of the driver's voice. For example, if the driver suddenly raises their voice, the driving data for that moment can be identified. The video data collection unit also collects in-car audio data and analyzes the content of the driver's conversation and changes in voice. For example, the video data collection unit focuses on the moment the conversation stops while driving to detect signs of dangerous driving. This allows for a more detailed understanding of the driving situation by detecting signs of dangerous driving from the driver's voice and sounds inside the vehicle.

[0031] The video data collection unit can integrate GPS data with video data from a drive recorder to identify dangerous driving points under specific geographical conditions. The video data collection unit, for example, integrates video data from a drive recorder with GPS data to build a system that identifies dangerous driving points under specific geographical conditions. For example, it identifies dangerous driving at sharp curves and intersections. The video data collection unit also uses GPS data to analyze dangerous driving points in specific regions and roads. For example, it identifies intersections where accidents frequently occur and roads with steep slopes. The video data collection unit also combines video data from a drive recorder with GPS data to analyze driving behavior under specific geographical conditions. For example, it identifies dangerous driving points based on the locations of specific road signs and traffic lights. This allows for the identification of dangerous driving points under specific geographical conditions, making it possible to implement traffic safety measures for each region.

[0032] The video data collection unit collects vehicle sensor data (speed, brake usage, etc.) in addition to video data from the drive recorder, allowing for a more detailed understanding of driving conditions. For example, the video data collection unit integrates video data from the drive recorder with vehicle sensor data to build a system that analyzes speed and brake usage. For example, it identifies moments of sudden braking and sudden acceleration. The video data collection unit also uses vehicle sensor data to understand detailed driving conditions. For example, it analyzes changes in speed and frequency of brake usage to identify signs of dangerous driving. The video data collection unit also combines video data from the drive recorder with vehicle sensor data to develop a system that analyzes driving behavior in detail. For example, it identifies driving behavior within a specific speed range. In this way, by collecting vehicle sensor data, a more detailed understanding of driving conditions can be achieved.

[0033] The video data collection unit can also collect video data from other drivers and compare and analyze the data under different driving styles and conditions. The video data collection unit, for example, builds a system that collects video data from multiple drivers and compares and analyzes the data under different driving styles and conditions. For example, it compares driving behavior in urban and suburban areas. The video data collection unit also analyzes the video data collected from other drivers to identify common risky driving patterns. For example, it compares risky driving points at specific intersections. The video data collection unit also develops a system that uses video data from different drivers to analyze differences in driving styles and conditions. For example, it compares the driving behavior of young people and elderly people. This makes it possible to identify common risky driving patterns by collecting data from other drivers and comparing and analyzing the data under different driving styles and conditions.

[0034] The analysis unit can enable the generation AI to analyze video data by taking into account not only past accident cases but also recent changes in traffic rules and new traffic signs. For example, the analysis unit builds a system in which the generation AI analyzes video data by taking into account recent changes in traffic rules and new traffic signs, as well as past accident cases. For example, it identifies dangerous driving points based on new signs and rules. The analysis unit also analyzes video data using a generation AI that has learned about changes in traffic rules and new traffic signs. For example, it performs analysis by taking into account recently introduced stop signs. The analysis unit also develops a system in which the generation AI analyzes video data by taking into account recent changes in traffic rules and new traffic signs. For example, it identifies dangerous driving points based on new traffic rules. This makes it possible to more accurately identify dangerous driving points by taking into account recent changes in traffic rules and new traffic signs.

[0035] The analysis unit also takes into account environmental factors such as weather and time of day when the generation AI performs its analysis, enabling it to identify dangerous driving points under specific conditions. For example, the analysis unit builds a system in which the generation AI takes into account environmental factors such as weather and time of day when analyzing video data. For example, it analyzes driving data in the rain or at night and identifies dangerous driving points. The analysis unit also analyzes video data using the generation AI that has learned environmental factors such as weather and time of day. For example, it analyzes driving data in foggy conditions and identifies dangerous driving points. The analysis unit also develops a system in which the generation AI analyzes video data taking into account environmental factors such as weather and time of day. For example, it analyzes driving data on snowy roads and identifies dangerous driving points. This makes it possible to accurately identify dangerous driving points under specific conditions by taking into account environmental factors such as weather and time of day.

[0036] When the generating AI performs its analysis, the analysis unit learns the traffic rules and driving habits of different countries and regions, allowing it to identify dangerous driving points from a global perspective. For example, the analysis unit builds a system in which the generating AI learns the traffic rules and driving habits of different countries and regions and identifies dangerous driving points from a global perspective. For example, the analysis takes into account the difference between driving on the right and on the left. The analysis unit also analyzes video data using the generating AI that has learned the traffic rules and driving habits of different countries and regions. For example, it analyzes driving behavior in a specific country and identifies dangerous driving points. The analysis unit also develops a system in which the generating AI analyzes video data taking into account the traffic rules and driving habits of different countries and regions. For example, it performs analysis taking into account the differences in traffic signs and traffic lights between different countries. In this way, by learning the traffic rules and driving habits of different countries and regions, it is possible to identify dangerous driving points from a global perspective.

[0037] When the generation AI performs analysis, the analysis unit can compare it with data from other drivers and identify common risky driving patterns. For example, the analysis unit builds a system in which the generation AI compares it with data from other drivers and identifies common risky driving patterns. For example, if multiple drivers engage in dangerous driving at the same intersection, the analysis unit identifies that pattern. The analysis unit also identifies common risky driving patterns using the generation AI that has learned data from other drivers. For example, it analyzes the frequency of sudden braking on a specific road. The analysis unit also develops a system in which the generation AI compares it with data from other drivers and identifies common risky driving patterns. For example, it analyzes risky driving patterns during a specific time period. This makes it possible to identify common risky driving patterns by comparing it with data from other drivers.

[0038] The feedback unit can provide feedback on specific improvement measures and methods for improving driving skills for dangerous driving points identified by the generation AI. For example, the feedback unit could build a system that provides feedback on specific improvement measures for dangerous driving points identified by the generation AI. For example, it could suggest driving techniques to avoid sudden braking. The feedback unit could also provide feedback on methods for improving driving skills for dangerous driving points. For example, it could suggest specific methods for improving insufficient checking when changing lanes. The feedback unit could also develop a system that provides feedback on specific improvement measures to drivers for dangerous driving points identified by the generation AI. For example, it could suggest points to pay attention to to prevent ignoring traffic lights at intersections. In this way, by providing feedback on specific improvement measures and methods for improving driving skills, it is expected that drivers' skills will improve.

[0039] The feedback unit can compare the dangerous driving points identified by the generation AI with the driver's past driving data to evaluate the progress of their driving skills. For example, the feedback unit builds a system that compares the dangerous driving points identified by the generation AI with the driver's past driving data. For example, it evaluates the progress of their driving skills by comparing with the past data. The feedback unit also analyzes the driver's past driving data and compares it with the dangerous driving points identified by the generation AI. For example, it compares the past data with the current data to evaluate the improvement of their driving skills. The feedback unit also develops a system that compares the dangerous driving points identified by the generation AI with the driver's past driving data to evaluate the progress of their driving skills. For example, it compares the past dangerous driving points with the current data to evaluate the progress. This makes it possible to evaluate the progress of their driving skills by comparing with the driver's past driving data.

[0040] The feedback unit can share the dangerous driving points identified by the generation AI with other drivers to improve road safety awareness throughout the community. For example, the feedback unit builds a system to share the dangerous driving points identified by the generation AI with other drivers. For example, it shares the dangerous driving points through an online platform. The feedback unit also shares the dangerous driving points with other drivers to improve road safety awareness throughout the community. For example, it conducts road safety campaigns based on the shared data. The feedback unit also develops a system to share the dangerous driving points identified by the generation AI with other drivers to improve road safety awareness throughout the community. For example, it conducts road safety education based on the shared data. In this way, sharing the dangerous driving points with other drivers improves road safety awareness throughout the community.

[0041] The feedback unit recreates the dangerous driving points identified by the generation AI in a driving simulator, allowing the driver to learn through actual experience. The feedback unit, for example, builds a system that recreates the dangerous driving points identified by the generation AI in a driving simulator. For example, the driver learns by experiencing the dangerous driving points using the simulator. The feedback unit also uses a driving simulator to recreate the dangerous driving points identified by the generation AI, allowing the driver to learn through actual experience. For example, ignoring a traffic light at an intersection is recreated in the simulator. The feedback unit also develops a system that recreates the dangerous driving points identified by the generation AI in a driving simulator, allowing the driver to learn through actual experience. For example, the moment of sudden braking is recreated in the simulator. In this way, by recreating the dangerous driving points in the driving simulator, the driver can learn through actual experience.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The risk prediction and countermeasure system can further include 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 measure the driver's fatigue level and encourage them to take a break. For example, it can automatically suggest taking a break after driving for a long period of time. The health monitoring unit can also analyze the driver's health data and provide driving advice based on their health condition. In this way, by monitoring the driver's health condition, the risk of accidents can be further reduced.

[0044] The risk prediction system can also be equipped with a driving history recording unit that records the driver's driving history and supports the improvement of driving skills. For example, it can evaluate the progress of driving skills based on past driving data and suggest specific areas for improvement. The driving history recording unit can also analyze the driver's driving style and provide individual advice. For example, it can suggest specific methods for reducing the frequency of sudden braking and sudden acceleration. The driving history recording unit can also compare the driver's driving history with that of other drivers to find common areas for improvement. This can support the improvement of the driver's driving skills and reduce the risk of accidents.

[0045] The risk prediction and countermeasure system can further include a driving style learning unit that learns the driver's driving style and provides personalized driving advice. For example, it can analyze the driver's driving patterns and provide personalized advice. The driving style learning unit can also compare the driver's driving style with that of other drivers to find common areas for improvement. For example, it can suggest specific methods for reducing the frequency of sudden braking and sudden acceleration. The driving style learning unit can also monitor the driver's driving style in real time and provide appropriate advice while driving. In this way, the system can learn the driver's driving style and provide personalized advice to support the improvement of driving skills.

[0046] The risk prediction and countermeasure system can further include a driving behavior monitoring unit that monitors the driver's driving behavior in real time and issues a warning if an abnormality is detected. For example, a warning is issued if sudden braking or sudden acceleration is detected. The driving behavior monitoring unit can also analyze the driver's driving behavior and provide appropriate advice if an abnormality is detected. For example, it can suggest specific ways to avoid sudden braking. The driving behavior monitoring unit can also compare the driver's driving behavior with that of other drivers and find common areas for improvement. This allows the risk of accidents to be reduced by monitoring the driver's driving behavior in real time and issuing a warning if an abnormality is detected.

[0047] The risk prediction and countermeasure system can further include a driving behavior monitoring unit that monitors the driver's driving behavior in real time and issues a warning if an abnormality is detected. For example, a warning is issued if sudden braking or sudden acceleration is detected. The driving behavior monitoring unit can also analyze the driver's driving behavior and provide appropriate advice if an abnormality is detected. For example, it can suggest specific ways to avoid sudden braking. The driving behavior monitoring unit can also compare the driver's driving behavior with that of other drivers and find common areas for improvement. This allows the risk of accidents to be reduced by monitoring the driver's driving behavior in real time and issuing a warning if an abnormality is detected.

[0048] The risk prediction and countermeasure system can further include a driving behavior monitoring unit that monitors the driver's driving behavior in real time and issues a warning if an abnormality is detected. For example, a warning is issued if sudden braking or sudden acceleration is detected. The driving behavior monitoring unit can also analyze the driver's driving behavior and provide appropriate advice if an abnormality is detected. For example, it can suggest specific ways to avoid sudden braking. The driving behavior monitoring unit can also compare the driver's driving behavior with that of other drivers and find common areas for improvement. This allows the risk of accidents to be reduced by monitoring the driver's driving behavior in real time and issuing a warning if an abnormality is detected.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The video data collection unit collects video data from the drive recorder. For example, it records road conditions while driving, the movements of surrounding vehicles and pedestrians, etc. It can also collect footage of waiting at traffic lights at intersections and when changing lanes. Step 2: In the analysis unit, the generation AI analyzes the video data collected by the video data collection unit. The generation AI has learned from past accident cases and identifies dangerous driving points and points where an accident could have occurred. It also analyzes the video data based on prompts that include instructions on what the driver wants the generation AI to do. Step 3: The feedback unit provides the driver with feedback on dangerous driving points and points where there is a possibility of an accident identified by the analysis unit. For example, the feedback may be in the form of, "There was a red light run at this intersection. Please be more careful next time." The identified dangerous driving points and points where there is a possibility of an accident can also be registered in a database and shared with other drivers.

[0051] (Example 2) The danger prediction system according to an embodiment of the present invention is a system that uses AI to analyze video data from a drive recorder and share dangerous driving points and points where there is a possibility of an accident. This makes it possible to reduce car accidents.

[0052] The risk prediction countermeasure system according to the embodiment includes a video data collection unit, an analysis unit, and a feedback unit. The video data collection unit collects video data from a drive recorder. For example, it records road conditions, surrounding vehicles, and pedestrian movements while driving. The video data collection unit can also collect video footage of waiting at traffic lights at intersections and changing lanes. The analysis unit allows a generation AI to analyze the video data collected by the video data collection unit. For example, the generation AI learns from past accident cases and identifies dangerous driving points and points where accidents could have occurred. The generation AI also analyzes the video data based on prompts containing instructions from the driver regarding what the generation AI wants the driver to do. For example, the driver may input a prompt such as, "Please identify dangerous driving points from this video data." The feedback unit then provides feedback to the driver regarding the dangerous driving points and points where accidents could have occurred. For example, the feedback may be in the form of, "There was a red light run at this intersection. Please be more careful next time." The feedback unit can also register the identified dangerous driving points and points where accidents could have occurred in a database and share them with other drivers. As a result, the danger prediction countermeasure system according to the embodiment is expected to reduce automobile accidents by providing drivers with feedback on dangerous driving points and points where an accident may have occurred.

[0053] The video data collection unit collects in-car audio data in addition to video data from a dashcam, and can detect signs of dangerous driving from the driver's voice and sounds inside the vehicle. For example, the video data collection unit builds a system that simultaneously collects in-car audio data along with video data from a dashcam. For example, the video data collection unit analyzes the driver's voice and sounds inside the vehicle to detect shouts and warning sounds during sudden braking. The video data collection unit also analyzes in-car audio data to detect signs of stress or impatience from the tone and content of the driver's voice. For example, if the driver suddenly raises their voice, the driving data for that moment can be identified. The video data collection unit also collects in-car audio data and analyzes the content of the driver's conversation and changes in voice. For example, the video data collection unit focuses on the moment the conversation stops while driving to detect signs of dangerous driving. This allows for a more detailed understanding of the driving situation by detecting signs of dangerous driving from the driver's voice and sounds inside the vehicle.

[0054] The video data collection unit can integrate GPS data with video data from a drive recorder to identify dangerous driving points under specific geographical conditions. The video data collection unit, for example, integrates video data from a drive recorder with GPS data to build a system that identifies dangerous driving points under specific geographical conditions. For example, it identifies dangerous driving at sharp curves and intersections. The video data collection unit also uses GPS data to analyze dangerous driving points in specific regions and roads. For example, it identifies intersections where accidents frequently occur and roads with steep slopes. The video data collection unit also combines video data from a drive recorder with GPS data to analyze driving behavior under specific geographical conditions. For example, it identifies dangerous driving points based on the locations of specific road signs and traffic lights. This allows for the identification of dangerous driving points under specific geographical conditions, making it possible to implement traffic safety measures for each region.

[0055] The video data collection unit uses the emotion estimation function to infer emotions from the driver's facial expressions and tone of voice, and can collect driving data when stress or fatigue is high. For example, the video data collection unit combines video data from a dashcam with the emotion estimation function to build a system that infers emotions from the driver's facial expressions and tone of voice. For example, it analyzes the driver's facial expressions to detect signs of stress or fatigue. The video data collection unit also uses the emotion estimation function to infer emotions from the driver's tone and content of voice, and collects driving data when stress or fatigue is high. For example, if the tone of voice suddenly changes, driving data for that moment can be identified. The video data collection unit also develops a system that analyzes the driver's facial expressions and tone of voice in real time to detect changes in emotions. For example, it can pay attention to the moment the driver feels fatigued and identify signs of dangerous driving. This makes it possible to understand the driver's emotional state and collect driving data when stress or fatigue is high.

[0056] The video data collection unit collects vehicle sensor data (speed, brake usage, etc.) in addition to video data from the drive recorder, allowing for a more detailed understanding of driving conditions. For example, the video data collection unit integrates video data from the drive recorder with vehicle sensor data to build a system that analyzes speed and brake usage. For example, it identifies moments of sudden braking and sudden acceleration. The video data collection unit also uses vehicle sensor data to understand detailed driving conditions. For example, it analyzes changes in speed and frequency of brake usage to identify signs of dangerous driving. The video data collection unit also combines video data from the drive recorder with vehicle sensor data to develop a system that analyzes driving behavior in detail. For example, it identifies driving behavior within a specific speed range. In this way, by collecting vehicle sensor data, a more detailed understanding of driving conditions can be achieved.

[0057] The video data collection unit can also collect video data from other drivers and compare and analyze the data under different driving styles and conditions. The video data collection unit, for example, builds a system that collects video data from multiple drivers and compares and analyzes the data under different driving styles and conditions. For example, it compares driving behavior in urban and suburban areas. The video data collection unit also analyzes the video data collected from other drivers to identify common risky driving patterns. For example, it compares risky driving points at specific intersections. The video data collection unit also develops a system that uses video data from different drivers to analyze differences in driving styles and conditions. For example, it compares the driving behavior of young people and elderly people. This makes it possible to identify common risky driving patterns by collecting data from other drivers and comparing and analyzing the data under different driving styles and conditions.

[0058] The video data collection unit can use the emotion estimation function to collect the fear and anxiety felt by the driver in specific situations in real time and use the data for analysis. For example, the video data collection unit uses the emotion estimation function to build a system that collects the fear and anxiety felt by the driver in specific situations in real time. For example, it detects the sense of fear when braking suddenly. The video data collection unit also collects the driver's emotion data in real time and uses the data for analysis. For example, it analyzes the sense of anxiety at specific intersections and identifies dangerous driving points. The video data collection unit also uses the emotion estimation function to develop a system that monitors the fear and anxiety felt by the driver in real time and uses the data for analysis. For example, it detects changes in emotions while driving and identifies signs of dangerous driving. In this way, the fear and anxiety felt by the driver in specific situations can be collected in real time and used for analysis, making it possible to more accurately identify signs of dangerous driving.

[0059] The analysis unit can enable the generation AI to analyze video data by taking into account not only past accident cases but also recent changes in traffic rules and new traffic signs. For example, the analysis unit builds a system in which the generation AI analyzes video data by taking into account recent changes in traffic rules and new traffic signs, as well as past accident cases. For example, it identifies dangerous driving points based on new signs and rules. The analysis unit also analyzes video data using a generation AI that has learned about changes in traffic rules and new traffic signs. For example, it performs analysis by taking into account recently introduced stop signs. The analysis unit also develops a system in which the generation AI analyzes video data by taking into account recent changes in traffic rules and new traffic signs. For example, it identifies dangerous driving points based on new traffic rules. This makes it possible to more accurately identify dangerous driving points by taking into account recent changes in traffic rules and new traffic signs.

[0060] The analysis unit also takes into account environmental factors such as weather and time of day when the generation AI performs its analysis, enabling it to identify dangerous driving points under specific conditions. For example, the analysis unit builds a system in which the generation AI takes into account environmental factors such as weather and time of day when analyzing video data. For example, it analyzes driving data in the rain or at night and identifies dangerous driving points. The analysis unit also analyzes video data using the generation AI that has learned environmental factors such as weather and time of day. For example, it analyzes driving data in foggy conditions and identifies dangerous driving points. The analysis unit also develops a system in which the generation AI analyzes video data taking into account environmental factors such as weather and time of day. For example, it analyzes driving data on snowy roads and identifies dangerous driving points. This makes it possible to accurately identify dangerous driving points under specific conditions by taking into account environmental factors such as weather and time of day.

[0061] The analysis unit uses the emotion estimation function to incorporate the driver's emotional state into the analysis and identify driving data in an emotionally unstable state. The analysis unit, for example, uses the emotion estimation function to build a system that incorporates the driver's emotional state into the analysis. For example, it identifies driving data in a state where the driver is feeling stressed. The analysis unit also analyzes the driver's emotional state in real time and identifies driving data in an emotionally unstable state. For example, it analyzes driving data at a moment when the driver is feeling angry. The analysis unit also uses the emotion estimation function to develop a system that incorporates the driver's emotional state into the analysis and identifies driving data in an emotionally unstable state. For example, it identifies driving data in a state where the driver is feeling fatigued. In this way, by incorporating the driver's emotional state into the analysis, it is possible to identify driving data in an emotionally unstable state.

[0062] When the generating AI performs its analysis, the analysis unit learns the traffic rules and driving habits of different countries and regions, allowing it to identify dangerous driving points from a global perspective. For example, the analysis unit builds a system in which the generating AI learns the traffic rules and driving habits of different countries and regions and identifies dangerous driving points from a global perspective. For example, the analysis takes into account the difference between driving on the right and on the left. The analysis unit also analyzes video data using the generating AI that has learned the traffic rules and driving habits of different countries and regions. For example, it analyzes driving behavior in a specific country and identifies dangerous driving points. The analysis unit also develops a system in which the generating AI analyzes video data taking into account the traffic rules and driving habits of different countries and regions. For example, it performs analysis taking into account the differences in traffic signs and traffic lights between different countries. In this way, by learning the traffic rules and driving habits of different countries and regions, it is possible to identify dangerous driving points from a global perspective.

[0063] When the generation AI performs analysis, the analysis unit can compare it with data from other drivers and identify common risky driving patterns. For example, the analysis unit builds a system in which the generation AI compares it with data from other drivers and identifies common risky driving patterns. For example, if multiple drivers engage in dangerous driving at the same intersection, the analysis unit identifies that pattern. The analysis unit also identifies common risky driving patterns using the generation AI that has learned data from other drivers. For example, it analyzes the frequency of sudden braking on a specific road. The analysis unit also develops a system in which the generation AI compares it with data from other drivers and identifies common risky driving patterns. For example, it analyzes risky driving patterns during a specific time period. This makes it possible to identify common risky driving patterns by comparing it with data from other drivers.

[0064] The analysis unit uses the emotion estimation function to analyze the stress and impatience felt by the driver in specific situations and identify the impact of those emotions on driving. The analysis unit, for example, uses the emotion estimation function to build a system that analyzes the stress and impatience felt by the driver in specific situations. For example, the analysis unit analyzes the sense of stress felt during sudden braking and identifies the impact. The analysis unit also analyzes the driver's emotional data in real time and identifies the impact of stress and impatience on driving. For example, the analysis unit analyzes driving data at moments when the driver is feeling impatient. The analysis unit also uses the emotion estimation function to develop a system that analyzes the stress and impatience felt by the driver in specific situations and identifies the impact of those emotions on driving. For example, the analysis unit identifies driving data when the driver is feeling stressed. This allows for a deeper understanding of the causes of dangerous driving by analyzing the stress and impatience felt by the driver in specific situations and identifying the impact of those emotions on driving.

[0065] The feedback unit can provide feedback on specific improvement measures and methods for improving driving skills for dangerous driving points identified by the generation AI. For example, the feedback unit could build a system that provides feedback on specific improvement measures for dangerous driving points identified by the generation AI. For example, it could suggest driving techniques to avoid sudden braking. The feedback unit could also provide feedback on methods for improving driving skills for dangerous driving points. For example, it could suggest specific methods for improving insufficient checking when changing lanes. The feedback unit could also develop a system that provides feedback on specific improvement measures to drivers for dangerous driving points identified by the generation AI. For example, it could suggest points to pay attention to to prevent ignoring traffic lights at intersections. In this way, by providing feedback on specific improvement measures and methods for improving driving skills, it is expected that drivers' skills will improve.

[0066] The feedback unit can compare the dangerous driving points identified by the generation AI with the driver's past driving data to evaluate the progress of their driving skills. For example, the feedback unit builds a system that compares the dangerous driving points identified by the generation AI with the driver's past driving data. For example, it evaluates the progress of their driving skills by comparing with the past data. The feedback unit also analyzes the driver's past driving data and compares it with the dangerous driving points identified by the generation AI. For example, it compares the past data with the current data to evaluate the improvement of their driving skills. The feedback unit also develops a system that compares the dangerous driving points identified by the generation AI with the driver's past driving data to evaluate the progress of their driving skills. For example, it compares the past dangerous driving points with the current data to evaluate the progress. This makes it possible to evaluate the progress of their driving skills by comparing with the driver's past driving data.

[0067] The feedback unit can use the emotion estimation function to analyze the emotional response of the driver when receiving feedback and reinforce positive feedback. The feedback unit, for example, uses the emotion estimation function to build a system that analyzes the emotional response of the driver when receiving feedback. For example, feedback with a strong positive emotional response is reinforced. The feedback unit also analyzes the driver's emotional response in real time and evaluates the effectiveness of the feedback. For example, feedback with a high positive emotional response is preferentially provided. The feedback unit also uses the emotion estimation function to develop a system that analyzes the emotional response of the driver when receiving feedback and reinforces positive feedback. For example, feedback that makes the driver feel happy is reinforced. In this way, the driver's emotional response is analyzed and positive feedback is reinforced, thereby improving the driver's motivation.

[0068] The feedback unit can share the dangerous driving points identified by the generation AI with other drivers to improve road safety awareness throughout the community. For example, the feedback unit builds a system to share the dangerous driving points identified by the generation AI with other drivers. For example, it shares the dangerous driving points through an online platform. The feedback unit also shares the dangerous driving points with other drivers to improve road safety awareness throughout the community. For example, it conducts road safety campaigns based on the shared data. The feedback unit also develops a system to share the dangerous driving points identified by the generation AI with other drivers to improve road safety awareness throughout the community. For example, it conducts road safety education based on the shared data. In this way, sharing the dangerous driving points with other drivers improves road safety awareness throughout the community.

[0069] The feedback unit recreates the dangerous driving points identified by the generation AI in a driving simulator, allowing the driver to learn through actual experience. The feedback unit, for example, builds a system that recreates the dangerous driving points identified by the generation AI in a driving simulator. For example, the driver learns by experiencing the dangerous driving points using the simulator. The feedback unit also uses a driving simulator to recreate the dangerous driving points identified by the generation AI, allowing the driver to learn through actual experience. For example, ignoring a traffic light at an intersection is recreated in the simulator. The feedback unit also develops a system that recreates the dangerous driving points identified by the generation AI in a driving simulator, allowing the driver to learn through actual experience. For example, the moment of sudden braking is recreated in the simulator. In this way, by recreating the dangerous driving points in the driving simulator, the driver can learn through actual experience.

[0070] The feedback unit can use the emotion estimation function to monitor the emotional reactions of the driver when receiving feedback in real time and find the optimal feedback method. The feedback unit, for example, uses the emotion estimation function to build a system that monitors the emotional reactions of the driver when receiving feedback in real time. For example, the emotional reactions are analyzed according to the content of the feedback. The feedback unit also monitors the emotional reactions of the driver in real time and finds the optimal feedback method. For example, it identifies a feedback method that produces a high number of positive emotional reactions. The feedback unit also uses the emotion estimation function to develop a system that monitors the emotional reactions of the driver when receiving feedback in real time and finds the optimal feedback method. For example, it identifies a feedback method that makes the driver feel happy. This makes it possible to provide effective feedback by monitoring the driver's emotional reactions in real time and finding the optimal feedback method.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The risk prediction and countermeasure system can further include 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 measure the driver's fatigue level and encourage them to take a break. For example, it can automatically suggest taking a break after driving for a long period of time. The health monitoring unit can also analyze the driver's health data and provide driving advice based on their health condition. In this way, by monitoring the driver's health condition, the risk of accidents can be further reduced.

[0073] The risk prediction system can also be equipped with a driving history recording unit that records the driver's driving history and supports the improvement of driving skills. For example, it can evaluate the progress of driving skills based on past driving data and suggest specific areas for improvement. The driving history recording unit can also analyze the driver's driving style and provide individual advice. For example, it can suggest specific methods for reducing the frequency of sudden braking and sudden acceleration. The driving history recording unit can also compare the driver's driving history with that of other drivers to find common areas for improvement. This can support the improvement of the driver's driving skills and reduce the risk of accidents.

[0074] The risk prediction and countermeasure system can further include a driving style learning unit that learns the driver's driving style and provides personalized driving advice. For example, it can analyze the driver's driving patterns and provide personalized advice. The driving style learning unit can also compare the driver's driving style with that of other drivers to find common areas for improvement. For example, it can suggest specific methods for reducing the frequency of sudden braking and sudden acceleration. The driving style learning unit can also monitor the driver's driving style in real time and provide appropriate advice while driving. In this way, the system can learn the driver's driving style and provide personalized advice to support the improvement of driving skills.

[0075] The risk prediction system can further include an emotion analysis unit that analyzes the driver's emotional state and provides driving advice based on the emotion. For example, if the driver is feeling stressed, the emotion analysis unit can suggest ways to relax. The emotion analysis unit can also monitor the driver's emotional state in real time and provide advice based on emotional changes. For example, if the driver is feeling impatient, the emotion analysis unit can suggest ways to stay calm. The emotion analysis unit can also develop a system that analyzes the driver's emotional data and provides driving advice based on the emotion. This makes it possible to support the improvement of driving skills by analyzing the driver's emotional state and providing driving advice based on the emotion.

[0076] The risk prediction and countermeasure system can further include a driving behavior monitoring unit that monitors the driver's driving behavior in real time and issues a warning if an abnormality is detected. For example, a warning is issued if sudden braking or sudden acceleration is detected. The driving behavior monitoring unit can also analyze the driver's driving behavior and provide appropriate advice if an abnormality is detected. For example, it can suggest specific ways to avoid sudden braking. The driving behavior monitoring unit can also compare the driver's driving behavior with that of other drivers and find common areas for improvement. This allows the risk of accidents to be reduced by monitoring the driver's driving behavior in real time and issuing a warning if an abnormality is detected.

[0077] The risk prediction system can further include an emotion analysis unit that analyzes the driver's emotional state and provides driving advice based on the emotion. For example, if the driver is feeling stressed, the emotion analysis unit can suggest ways to relax. The emotion analysis unit can also monitor the driver's emotional state in real time and provide advice based on emotional changes. For example, if the driver is feeling impatient, the emotion analysis unit can suggest ways to stay calm. The emotion analysis unit can also develop a system that analyzes the driver's emotional data and provides driving advice based on the emotion. This makes it possible to support the improvement of driving skills by analyzing the driver's emotional state and providing driving advice based on the emotion.

[0078] The risk prediction and countermeasure system can further include a driving behavior monitoring unit that monitors the driver's driving behavior in real time and issues a warning if an abnormality is detected. For example, a warning is issued if sudden braking or sudden acceleration is detected. The driving behavior monitoring unit can also analyze the driver's driving behavior and provide appropriate advice if an abnormality is detected. For example, it can suggest specific ways to avoid sudden braking. The driving behavior monitoring unit can also compare the driver's driving behavior with that of other drivers and find common areas for improvement. This allows the risk of accidents to be reduced by monitoring the driver's driving behavior in real time and issuing a warning if an abnormality is detected.

[0079] The risk prediction system can further include an emotion analysis unit that analyzes the driver's emotional state and provides driving advice based on the emotion. For example, if the driver is feeling stressed, the emotion analysis unit can suggest ways to relax. The emotion analysis unit can also monitor the driver's emotional state in real time and provide advice based on emotional changes. For example, if the driver is feeling impatient, the emotion analysis unit can suggest ways to stay calm. The emotion analysis unit can also develop a system that analyzes the driver's emotional data and provides driving advice based on the emotion. This makes it possible to support the improvement of driving skills by analyzing the driver's emotional state and providing driving advice based on the emotion.

[0080] The risk prediction and countermeasure system can further include a driving behavior monitoring unit that monitors the driver's driving behavior in real time and issues a warning if an abnormality is detected. For example, a warning is issued if sudden braking or sudden acceleration is detected. The driving behavior monitoring unit can also analyze the driver's driving behavior and provide appropriate advice if an abnormality is detected. For example, it can suggest specific ways to avoid sudden braking. The driving behavior monitoring unit can also compare the driver's driving behavior with that of other drivers and find common areas for improvement. This allows the risk of accidents to be reduced by monitoring the driver's driving behavior in real time and issuing a warning if an abnormality is detected.

[0081] The risk prediction system can further include an emotion analysis unit that analyzes the driver's emotional state and provides driving advice based on the emotion. For example, if the driver is feeling stressed, the emotion analysis unit can suggest ways to relax. The emotion analysis unit can also monitor the driver's emotional state in real time and provide advice based on emotional changes. For example, if the driver is feeling impatient, the emotion analysis unit can suggest ways to stay calm. The emotion analysis unit can also develop a system that analyzes the driver's emotional data and provides driving advice based on the emotion. This makes it possible to support the improvement of driving skills by analyzing the driver's emotional state and providing driving advice based on the emotion.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The video data collection unit collects video data from the drive recorder. For example, it records road conditions while driving, the movements of surrounding vehicles and pedestrians, etc. It can also collect footage of waiting at traffic lights at intersections and when changing lanes. Step 2: In the analysis unit, the generation AI analyzes the video data collected by the video data collection unit. The generation AI has learned from past accident cases and identifies dangerous driving points and points where an accident could have occurred. It also analyzes the video data based on prompts that include instructions on what the driver wants the generation AI to do. Step 3: The feedback unit provides the driver with feedback on dangerous driving points and points where there is a possibility of an accident identified by the analysis unit. For example, the feedback may be in the form of, "There was a red light run at this intersection. Please be more careful next time." The identified dangerous driving points and points where there is a possibility of an accident can also be registered in a database and shared with other drivers.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] 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.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a video data collection unit that collects video data from a drive recorder; an analysis unit that analyzes the video data collected by the video data collection unit; a feedback unit that feeds back to the driver the dangerous driving points and points where there is a possibility of an accident identified by the analysis unit. A system characterized by:

2. The video data collection unit In addition to the video data from the drive recorder, audio data from inside the vehicle is also collected, and signs of dangerous driving are detected from the driver's voice and sounds inside the vehicle.

2. The system of claim 1.

3. The video data collection unit Integrating the GPS data with the video data from the drive recorder to identify the dangerous driving points under specific geographical conditions.

2. The system of claim 1.

4. The video data collection unit The emotions of the driver are estimated from the facial expressions and tone of voice of the driver, and driving data is collected when stress or fatigue increases.

2. The system of claim 1.

5. The video data collection unit In addition to the video data from the dashcam, vehicle sensor data (speed, brake usage, etc.) will be collected to understand driving conditions in more detail.

2. The system of claim 1.

6. The video data collection unit The video data is also collected from the other drivers, and the data is compared and analyzed under different driving styles and conditions.

2. The system of claim 1.

7. The video data collection unit The fear and anxiety felt by the driver in the specific situation is collected in real time, and the data is used for the analysis.

2. The system of claim 1.

8. The analysis unit When the AI ​​analyzes the video data, it takes into account not only past accident cases but also recent changes in traffic rules and the new traffic signs.

2. The system of claim 1.

Images