system

Abstract

The system according to this embodiment aims to analyze past accident data, predict accident risks in real time, and automatically correct driving behavior. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a prediction unit, a warning unit, and a correction unit. The collection unit collects past accident data. The analysis unit analyzes the data collected by the collection unit. The prediction unit predicts accident risk based on the analysis results obtained by the analysis unit. The warning unit issues a warning based on the accident risk predicted by the prediction unit. The correction unit automatically corrects driving behavior based on the warning issued by the warning unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is room for improvement because the past accident data has not been fully utilized to predict the accident risk in real time and automatically correct the driving behavior.

[0005] The system according to the embodiment aims to analyze past accident data, predict the accident risk in real time, and automatically correct the driving behavior.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a prediction unit, a warning unit, and a correction unit. The data collection unit collects past accident data. The analysis unit analyzes the data collected by the data collection unit. The prediction unit predicts accident risk based on the analysis results obtained by the analysis unit. The warning unit issues a warning based on the accident risk predicted by the prediction unit. The correction unit automatically corrects driving behavior based on the warning issued by the warning unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze past accident data, predict accident risks in real time, and automatically correct driving behavior. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The autonomous vehicle safety system according to an embodiment of the present invention is a system in which an AI agent analyzes past accident data and predicts and warns in real time areas and situations with a high risk of accidents while driving. This system prevents accidents by automatically correcting driving behavior when an accident is predicted. The autonomous vehicle safety system collects and analyzes past accident data. Next, it collects driving conditions and environmental data in real time and integrates this data to predict accident risk. If an accident is predicted, the AI ​​agent automatically corrects driving behavior to prevent an accident. This system is expected to improve the safety of autonomous vehicles and dramatically reduce the accident rate. For example, the AI ​​agent collects and analyzes past accident data. In this case, past accident data includes detailed information such as the location, time, and cause of the accident. For example, if accidents frequently occur at a particular intersection, that intersection is identified as an area with a high accident risk. Next, it collects driving conditions and environmental data in real time. This includes vehicle speed, location, surrounding traffic conditions, weather, and road surface conditions. For example, in rainy weather, the road surface becomes slippery, so an increased accident risk is predicted. By integrating this data, the AI ​​agent predicts accident risk. For example, if accidents frequently occur at a particular intersection, the AI ​​agent will issue a warning when approaching that intersection. It can also reduce accident risk by instructing the driver to slow down in rainy weather. If an accident is predicted, the AI ​​agent automatically corrects driving behavior. This includes actions such as sudden braking, lane changes, and speed adjustments. This can prevent accidents. This system is expected to improve the safety of autonomous vehicles and dramatically reduce accident rates. For example, integrating predictions based on past accident data with real-time data enables more accurate prediction of accident risk. In addition, the AI ​​agent's automatic correction of driving behavior reduces the driver's reaction time and prevents accidents. In this way, the safety system of autonomous vehicles can predict accident risk and prevent accidents by automatically correcting driving behavior.

[0029] The safety system for an autonomous vehicle according to this embodiment comprises a data collection unit, an analysis unit, a prediction unit, a warning unit, and a correction unit. The data collection unit collects past accident data. The data collection unit can collect data such as traffic accidents, industrial accidents, and natural disasters. For example, the data collection unit collects data on traffic accidents and obtains detailed information such as the location, time, and cause of the accident. The data collection unit can also collect data on industrial accidents and obtain detailed information such as the location, time, and cause of the accident. Furthermore, the data collection unit can also collect data on natural disasters and obtain detailed information such as the location, time, and cause of the accident. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the data using methods such as statistical analysis, machine learning algorithms, and data mining. For example, the analysis unit can analyze the data using statistical analysis to identify detailed information such as the location, time, and cause of the accident. Furthermore, the analysis unit can analyze the data using machine learning algorithms to identify detailed information such as the location, time, and cause of the accident. Furthermore, the analysis unit can analyze the data using data mining to identify detailed information such as the location, time, and cause of the accident. The prediction unit predicts accident risk based on the analysis results obtained by the analysis unit. The prediction unit can, for example, collect driving conditions and environmental data in real time and integrate this data to predict accident risk. The prediction unit can, for example, collect data such as vehicle speed, location, surrounding traffic conditions, weather, and road surface conditions and integrate this data to predict accident risk. The prediction unit can also predict accident risk based on data collected in real time. Furthermore, the prediction unit can also predict accident risk by integrating past accident data with real-time data. The warning unit issues a warning based on the accident risk predicted by the prediction unit. The warning unit can issue a warning using methods such as voice warnings, visual warnings, and vibration warnings. The warning unit can, for example, issue a warning to the driver using a voice warning. The warning unit can also issue a warning to the driver using a visual warning. Furthermore, the warning unit can also issue a warning to the driver using a vibration warning.The correction unit automatically corrects driving behavior based on warnings issued by the warning unit. The correction unit can correct driving behavior using methods such as speed adjustment, lane change, and braking. For example, the correction unit can adjust speed to reduce the risk of an accident. The correction unit can also change lanes to reduce the risk of an accident. Furthermore, the correction unit can also brake to reduce the risk of an accident. As a result, the safety system of the autonomous vehicle according to this embodiment can prevent accidents by predicting the risk of an accident and automatically correcting driving behavior.

[0030] The data collection unit collects historical accident data. For example, it can collect data on traffic accidents, industrial accidents, and natural disasters. Specifically, it collects traffic accident data, obtaining detailed information such as the location, time, cause, type of vehicle involved, driver's condition, weather conditions, and road conditions. This allows for the identification of traffic accident patterns and common factors. It can also collect industrial accident data, obtaining detailed information such as the location, time, cause, work performed, type of equipment or devices used, and working environment. This provides basic data for evaluating the risk of industrial accidents and implementing preventative measures. Furthermore, it can collect natural disaster data, obtaining detailed information such as the location, time, cause, type of disaster (earthquake, flood, typhoon, etc.), scale of damage, and evacuation status. This provides basic data for evaluating the risks associated with natural disasters and formulating appropriate countermeasures. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and prediction units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. The analysis department can analyze the data using methods such as statistical analysis, machine learning algorithms, and data mining. Specifically, statistical analysis is used to analyze data and identify detailed information such as the location, time, cause, and common factors of accidents. For example, if the accident rate is high at a particular intersection, there may be problems with the intersection's design or signal timing. Machine learning algorithms can also be used to analyze data and identify detailed information such as the location, time, and cause of accidents. Machine learning algorithms automatically extract patterns and trends from large amounts of data and build models to predict accident risk. Furthermore, data mining can be used to analyze data and identify detailed information such as the location, time, and cause of accidents. Data mining extracts useful information from the data and provides foundational data for evaluating accident risk. This allows the analysis department to quickly and accurately analyze the collected data and assess accident risk. Additionally, the analysis department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past accident data, they can predict risk fluctuations in specific areas and time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The prediction unit predicts accident risk based on the analysis results obtained by the analysis unit. For example, the prediction unit can collect driving conditions and environmental data in real time and integrate this data to predict accident risk. Specifically, it collects data such as vehicle speed, location, surrounding traffic conditions, weather, and road surface conditions, and integrates this data to predict accident risk. For example, if the vehicle speed is too high or the road surface is slippery, the risk of an accident is predicted to increase. The prediction unit can also predict accident risk based on data collected in real time. For example, if the vehicle's sensors detect an obstacle ahead, the unit can predict the risk of an accident based on that information and take appropriate measures. Furthermore, the prediction unit can also predict accident risk by integrating past accident data with real-time data. For example, if past data shows that the accident rate is high under certain conditions, the unit can rate the accident risk highly when those conditions occur in real time. The prediction unit uses AI to analyze this data and simulate multiple scenarios to identify the most likely risk. This allows the prediction unit to predict the risk of accidents with high accuracy and provide information for taking appropriate measures. Furthermore, the forecasting unit can continuously revise its forecast results based on real-time updated data, enabling it to respond to the latest situations. For example, it can instantly update forecast results in response to sudden changes in weather or fluctuations in traffic conditions, supporting appropriate responses. As a result, the forecasting unit can always provide highly accurate risk forecasts based on the latest information, supporting quick and appropriate responses.

[0033] The warning unit issues warnings based on the accident risk predicted by the prediction unit. The warning unit can issue warnings using methods such as voice warnings, visual warnings, and vibration warnings. Specifically, it can issue warnings to the driver using voice warnings. For example, it can alert the driver by playing a voice message such as "There is an obstacle ahead. Please slow down" from the vehicle's speakers. It can also alert the driver using visual warnings. For example, it can visually alert the driver by displaying warning messages or icons on the vehicle's dashboard or head-up display. Furthermore, it can alert the driver using vibration warnings. For example, it can provide the driver with a physical stimulus by generating vibrations in the driver's seat or steering wheel to draw their attention. In this way, the warning unit can quickly provide appropriate warnings to the driver and minimize the risk of accidents. In addition, the warning unit can adjust the type and intensity of warnings according to the situation. For example, it can effectively attract the driver's attention by issuing strong warnings when the risk is high and mild warnings when the risk is low. The warning unit can also use multiple warning methods in combination. For example, by issuing both audible and visual warnings simultaneously, a stronger alert can be given to the driver. This allows the warning unit to provide quick and effective warnings to the driver, minimizing the risk of accidents.

[0034] The correction unit automatically corrects driving behavior based on warnings issued by the warning unit. The correction unit can correct driving behavior using methods such as speed adjustment, lane changes, and braking. Specifically, it can adjust speed to reduce the risk of accidents. For example, if there is an obstacle ahead, the correction unit can automatically reduce the vehicle's speed to reduce the risk of collision. It can also change lanes to reduce the risk of accidents. For example, if the lane ahead is congested, the correction unit can automatically change lanes to maintain smooth driving. Furthermore, it can also brake to reduce the risk of accidents. For example, if an obstacle suddenly appears, the correction unit can automatically apply the brakes to avoid a collision. In this way, the correction unit can automatically perform appropriate driving actions to reduce the risk of accidents without requiring driver intervention. In addition, the correction unit can customize how it corrects driving behavior according to the driver's driving style and preferences. For example, if the driver prioritizes safe driving, the correction unit will select more conservative driving actions to minimize risk. If the driver prioritizes smooth driving, the correction unit will select more aggressive driving actions to maintain a comfortable ride. This allows the modification unit to flexibly modify driving behavior in accordance with the driver's needs, thereby improving the reliability and safety of the entire system.

[0035] The data collection unit can collect past accident data. For example, the data collection unit can collect data on traffic accidents, industrial accidents, and natural disasters. For instance, the data collection unit can collect traffic accident data and obtain detailed information such as the location, time, and cause of the accident. It can also collect industrial accident data and obtain detailed information such as the location, time, and cause of the accident. Furthermore, the data collection unit can collect natural disaster data and obtain detailed information such as the location, time, and cause of the accident. By collecting past accident data, it is possible to provide the data necessary for accident risk analysis. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past accident data into a generating AI and have the generating AI perform the data collection.

[0036] The analysis department can analyze the collected data and identify detailed information such as the location, time, and cause of an accident. The analysis department can analyze the data using methods such as statistical analysis, machine learning algorithms, and data mining. For example, the analysis department can use statistical analysis to analyze the data and identify detailed information such as the location, time, and cause of an accident. The analysis department can also use machine learning algorithms to analyze the data and identify detailed information such as the location, time, and cause of an accident. Furthermore, the analysis department can use data mining to analyze the data and identify detailed information such as the location, time, and cause of an accident. By identifying detailed information such as the location, time, and cause of an accident, it is possible to clearly identify areas and situations with a high risk of accidents. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0037] The prediction unit can collect driving conditions and environmental data in real time and integrate this data to predict accident risk. For example, the prediction unit collects data such as vehicle speed, location, surrounding traffic conditions, weather, and road surface conditions, and integrates this data to predict accident risk. The prediction unit can also predict accident risk based on data collected in real time. Furthermore, the prediction unit can also predict accident risk by integrating past accident data with real-time data. This makes it possible to predict accident risk more accurately by collecting and integrating driving conditions and environmental data in real time. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data collected in real time into a generating AI and have the generating AI perform accident risk prediction.

[0038] The warning unit can warn of high-risk areas and situations in real time. The warning unit issues warnings using methods such as voice warnings, visual warnings, and vibration warnings. For example, the warning unit can warn the driver using voice warnings. The warning unit can also warn the driver using visual warnings. Furthermore, the warning unit can also warn the driver using vibration warnings. This allows the driver to be alerted and accidents to be prevented by providing real-time warnings of high-risk areas and situations. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input high-risk areas and situations into a generating AI and have the generating AI issue warnings.

[0039] The correction unit can automatically correct driving behavior when an accident is predicted. The correction unit can correct driving behavior using methods such as speed adjustment, lane change, and braking. For example, the correction unit can adjust speed to reduce the risk of an accident. It can also change lanes to reduce the risk of an accident. Furthermore, it can apply the brakes to reduce the risk of an accident. In this way, accidents can be prevented by automatically correcting driving behavior when an accident is predicted. Some or all of the above processing in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input areas or situations with a high risk of accidents into a generating AI and have the generating AI perform the correction of driving behavior.

[0040] The data collection unit can collect not only past accident data but also nearby traffic violation data to perform a more detailed risk analysis. For example, the data collection unit can collect traffic violation data to identify areas with a high accident risk. The data collection unit can also analyze traffic violation data to assess the risk at specific times and locations. Furthermore, the data collection unit can integrate traffic violation data to improve the accuracy of accident risk predictions. Thus, by collecting traffic violation data, the accuracy of accident risk predictions can be improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input traffic violation data into a generating AI and have the generating AI perform the data collection.

[0041] The data collection unit can simultaneously collect environmental data such as weather and time of day when collecting accident data. For example, the data collection unit can collect weather data to evaluate accident risk during rainy weather. It can also collect time-of-day data to evaluate accident risk at night. Furthermore, the data collection unit can integrate environmental data to improve the accuracy of accident risk prediction. In this way, the accuracy of accident risk prediction can be improved by simultaneously collecting environmental data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather data and time-of-day data into a generating AI and have the generating AI perform the data collection.

[0042] The data collection unit can prioritize the collection of highly relevant data based on the vehicle's maintenance history when collecting accident data. For example, the data collection unit can refer to the maintenance history and collect accident data related to parts with a high risk of failure. The data collection unit can also analyze the maintenance history and collect accident data related to the replacement timing of specific parts. Furthermore, the data collection unit can integrate the maintenance history to improve the accuracy of accident risk prediction. This improves the accuracy of accident risk prediction by prioritizing the collection of highly relevant data based on the vehicle's maintenance history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input maintenance history data into a generating AI and have the generating AI perform the data collection.

[0043] The data collection unit can monitor surrounding traffic conditions in real time and collect relevant data when collecting accident data. For example, the data collection unit can monitor real-time traffic conditions and identify areas with a high accident risk. The data collection unit can also collect real-time traffic data to improve the accuracy of accident risk prediction. Furthermore, the data collection unit can analyze real-time traffic conditions and assess the risk at specific times and locations. This allows for improved accuracy in accident risk prediction by monitoring surrounding traffic conditions in real time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input real-time traffic data into a generating AI and have the generating AI perform data collection.

[0044] The analysis unit can improve the accuracy of its analysis by comparing past accident data with current traffic conditions when analyzing collected data. For example, the analysis unit can identify areas with a high accident risk by comparing past accident data with current traffic conditions. The analysis unit can also improve the accuracy of accident risk prediction by integrating past accident data with current traffic conditions. Furthermore, the analysis unit can analyze past accident data with current traffic conditions to assess the risk at specific times and locations. This allows for improved accuracy in predicting accident risk by comparing past accident data with current traffic conditions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past accident data and current traffic conditions into a generating AI and have the generating AI perform the data analysis.

[0045] The analysis unit can analyze not only the location, time, and cause of an accident, but also the driver's behavior patterns. For example, the analysis unit can analyze the location, time, and cause of an accident and identify the driver's behavior patterns. The analysis unit can also analyze the driver's behavior patterns and identify high-risk behaviors for accidents. Furthermore, the analysis unit can integrate the location, time, and cause of an accident with the driver's behavior patterns to improve the accuracy of accident risk prediction. This allows for the identification of high-risk behaviors and improved prediction accuracy by analyzing the driver's behavior patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input driver behavior pattern data into a generating AI and have the generating AI perform the data analysis.

[0046] The prediction unit can improve prediction accuracy by integrating past accident data with real-time driving conditions during prediction. For example, the prediction unit can improve the accuracy of accident risk prediction by integrating past accident data with real-time driving conditions. The prediction unit can also identify areas with a high accident risk by comparing past accident data with real-time driving conditions. Furthermore, the prediction unit can analyze past accident data and real-time driving conditions to evaluate the risk at specific time periods and locations. This improves the accuracy of accident risk prediction by integrating past accident data with real-time driving conditions. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past accident data and real-time driving conditions into a generating AI and have the generating AI perform data integration and prediction.

[0047] The prediction unit can apply different prediction algorithms based on specific driving patterns and environmental conditions during prediction. For example, the prediction unit can apply the optimal prediction algorithm based on a specific driving pattern. It can also apply the optimal prediction algorithm based on specific environmental conditions. Furthermore, the prediction unit can integrate driving patterns and environmental conditions to apply the optimal prediction algorithm. This allows for improved prediction accuracy by applying different prediction algorithms based on specific driving patterns and environmental conditions. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input specific driving pattern and environmental condition data into a generating AI and have the generating AI apply the optimal prediction algorithm.

[0048] The prediction unit can improve prediction accuracy based on the vehicle's maintenance status during prediction. For example, the prediction unit considers the vehicle's maintenance status and makes predictions related to parts with a high risk of failure. The prediction unit can also analyze the vehicle's maintenance status and make predictions related to the replacement timing of specific parts. Furthermore, the prediction unit can integrate the vehicle's maintenance status to improve prediction accuracy. This improves prediction accuracy based on the vehicle's maintenance status, enabling more accurate predictions. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input vehicle maintenance status data into a generating AI and have the generating AI perform data analysis and prediction.

[0049] The prediction unit can improve prediction accuracy by referencing real-time data from other autonomous vehicles during prediction. For example, the prediction unit can identify areas with a high accident risk by referencing real-time data from other autonomous vehicles. The prediction unit can also improve prediction accuracy by integrating real-time data from other autonomous vehicles. Furthermore, the prediction unit can analyze real-time data from other autonomous vehicles to assess risk at specific times and locations. This allows for improved accuracy in predicting accident risk by referencing real-time data from other autonomous vehicles. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input real-time data from other autonomous vehicles into a generating AI and have the generating AI perform data referencing and prediction.

[0050] The warning unit can use different warning sounds and displays depending on the area or situation where the risk of an accident is high. For example, the warning unit can emit a specific warning sound when approaching an intersection with a high risk of accidents. It can also provide a visual warning when approaching a slippery road surface in rainy weather. Furthermore, it can provide a bright warning display when approaching an area with low visibility at night. By using different warning sounds and displays depending on the area or situation where the risk of an accident is high, the system can alert the driver and prevent accidents. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input areas and situations with a high risk of accidents into a generating AI, and have the generating AI select the warning sound and display.

[0051] The warning unit can select the optimal warning method by referring to the driver's past response history when issuing a warning. For example, the warning unit may prioritize using a warning method that elicited a quick response in the past. The warning unit may also reuse warning sounds or displays that were effective in the past. Furthermore, the warning unit can analyze past response history to select the optimal warning method. This allows for the selection of the optimal warning method by referring to the driver's past response history, thereby improving warning accuracy. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input the driver's past response history data into a generating AI and have the generating AI select the optimal warning method.

[0052] The warning unit can adjust the timing of warnings based on the vehicle's speed and location information. For example, if the vehicle is moving too fast, the warning unit will issue a warning earlier. The warning unit can also issue a warning when the vehicle approaches a specific area based on its location information. Furthermore, the warning unit can integrate the vehicle's speed and location information to issue a warning at the optimal time. This allows the warning to be issued at the optimal time by adjusting the timing based on the vehicle's speed and location information. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input vehicle speed and location data into a generating AI and have the generating AI adjust the timing of the warning.

[0053] The warning unit can improve warning accuracy by referring to warning information from other autonomous vehicles when issuing a warning. For example, the warning unit can refer to warning information from other autonomous vehicles to identify areas with a high risk of accidents. The warning unit can also integrate warning information from other autonomous vehicles to improve warning accuracy. Furthermore, the warning unit can analyze warning information from other autonomous vehicles to assess risk at specific times and locations. This allows the warning unit to identify areas with a high risk of accidents and improve warning accuracy by referring to warning information from other autonomous vehicles. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input warning information from other autonomous vehicles into a generating AI and have the generating AI perform data referencing and warning issuance.

[0054] The correction unit can select the optimal correction method by referring to past driving behavior data during the correction process. For example, the correction unit may prioritize the use of correction methods that have been effective in the past. The correction unit can also analyze past driving behavior data to select the optimal correction method. Furthermore, the correction unit can integrate past driving behavior data to improve correction accuracy. This allows the optimal correction method to be selected and correction accuracy improved by referring to past driving behavior data. Some or all of the above processes in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input past driving behavior data into a generating AI and have the generating AI perform data analysis and select a correction method.

[0055] The correction unit can perform corrections based on the vehicle's current state and surrounding traffic conditions. For example, the correction unit can select the optimal correction method considering the vehicle's current state. It can also select the optimal correction method considering the surrounding traffic conditions. Furthermore, the correction unit can integrate the vehicle's current state and surrounding traffic conditions to improve correction accuracy. This improves correction accuracy by performing corrections based on the vehicle's current state and surrounding traffic conditions. Some or all of the above processing in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input data on the vehicle's current state and surrounding traffic conditions into a generating AI and have the generating AI perform data analysis and correction.

[0056] The correction unit can select a correction method based on the vehicle's maintenance status during the correction process. For example, the correction unit can consider the vehicle's maintenance status and perform corrections related to parts with a high risk of failure. The correction unit can also analyze the vehicle's maintenance status and perform corrections related to the replacement timing of specific parts. Furthermore, the correction unit can integrate the vehicle's maintenance status to improve correction accuracy. This allows for improved correction accuracy by selecting a correction method based on the vehicle's maintenance status. Some or all of the above-described processes in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input vehicle maintenance status data into a generating AI and have the generating AI perform data analysis and select a correction method.

[0057] The correction unit can improve the accuracy of corrections by referencing data from other autonomous vehicles during the correction process. For example, the correction unit can identify areas with a high accident risk by referencing data from other autonomous vehicles. The correction unit can also improve the accuracy of corrections by integrating data from other autonomous vehicles. Furthermore, the correction unit can analyze data from other autonomous vehicles to assess the risk at specific times and locations. This allows the correction unit to identify areas with a high accident risk and improve the accuracy of corrections by referencing data from other autonomous vehicles. Some or all of the above processes in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input data from other autonomous vehicles into a generating AI and have the generating AI perform data referencing and correction.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The safety systems in autonomous vehicles can further monitor the user's health and adjust driving behavior based on that health condition. For example, if the user is fatigued, the system will adjust driving behavior more cautiously and encourage them to take a break. If the user is in good health, the system can maintain normal driving behavior. Furthermore, if the user suddenly becomes ill, the system can perform an emergency stop and contact a medical institution. By adjusting driving behavior based on the user's health condition, the risk of accidents can be reduced and user safety can be ensured.

[0060] The safety systems of autonomous vehicles can further monitor the vehicle's fuel efficiency and adjust driving behavior based on that efficiency. For example, if fuel efficiency is low, the system can adjust speed to optimize fuel consumption. If fuel efficiency is high, it can maintain normal driving behavior. Furthermore, if fuel efficiency drops drastically, the system can guide the driver to the nearest gas station. In this way, by adjusting driving behavior based on fuel efficiency, fuel consumption can be optimized and economical driving can be achieved.

[0061] The safety systems in autonomous vehicles can further analyze the user's driving history and adjust their driving behavior based on that history. For example, if a user has had an accident in a specific area in the past, the system will issue a warning when approaching that area. Similarly, if a user has engaged in dangerous driving in a specific situation in the past, the system can adjust its driving behavior when approaching that situation. Furthermore, it can analyze the user's driving history and provide feedback to support the improvement of their driving skills. By adjusting driving behavior based on the user's driving history, it can reduce the risk of accidents and support safer driving.

[0062] The safety systems of autonomous vehicles can further monitor the movements of surrounding pedestrians and cyclists and adjust their driving behavior based on those movements. For example, if a pedestrian is about to cross a crosswalk, the system will slow down and yield to the pedestrian. Also, if a cyclist is traveling close to the vehicle, the system can change lanes and maintain a safe distance. Furthermore, if a pedestrian or cyclist suddenly appears, the system can apply emergency brakes. In this way, by adjusting driving behavior based on the movements of surrounding pedestrians and cyclists, the risk of accidents can be reduced and safe driving can be achieved.

[0063] The safety systems of autonomous vehicles can also collect road construction information in real time and adjust their driving behavior based on that information. For example, when approaching a road under construction, the system will slow down to maintain safe driving. If the road under construction is closed, the system can also guide the driver on an alternative route. Furthermore, it can predict traffic congestion on roads under construction and select the optimal route. By adjusting driving behavior based on road construction information, the risk of accidents can be reduced and driving can be made smoother.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit collects past accident data. The data collection unit can collect data such as traffic accidents, industrial accidents, and natural disasters. The data collection unit collects traffic accident data and obtains detailed information such as the location, time, and cause of the accident. It can also collect industrial accident data and natural disaster data and obtain similarly detailed information. Step 2: The analysis department analyzes the data collected by the data collection department. The analysis department analyzes the data using methods such as statistical analysis, machine learning algorithms, and data mining to identify detailed information such as the location, time, and cause of the accident. Step 3: The prediction unit predicts accident risk based on the analysis results obtained by the analysis unit. The prediction unit can collect driving conditions and environmental data in real time and integrate this data to predict accident risk. For example, it collects data such as vehicle speed, location, surrounding traffic conditions, weather, and road surface conditions, and integrates this data to predict accident risk. Step 4: The warning unit issues a warning based on the accident risk predicted by the prediction unit. The warning unit can warn the driver using methods such as voice warnings, visual warnings, and vibration warnings. Step 5: The correction unit automatically corrects driving behavior based on warnings issued by the warning unit. The correction unit corrects driving behavior using methods such as speed adjustment, lane changes, and braking, thereby reducing the risk of accidents.

[0066] (Example of form 2) The autonomous vehicle safety system according to an embodiment of the present invention is a system in which an AI agent analyzes past accident data and predicts and warns in real time areas and situations with a high risk of accidents while driving. This system prevents accidents by automatically correcting driving behavior when an accident is predicted. The autonomous vehicle safety system collects and analyzes past accident data. Next, it collects driving conditions and environmental data in real time and integrates this data to predict accident risk. If an accident is predicted, the AI ​​agent automatically corrects driving behavior to prevent an accident. This system is expected to improve the safety of autonomous vehicles and dramatically reduce the accident rate. For example, the AI ​​agent collects and analyzes past accident data. In this case, past accident data includes detailed information such as the location, time, and cause of the accident. For example, if accidents frequently occur at a particular intersection, that intersection is identified as an area with a high accident risk. Next, it collects driving conditions and environmental data in real time. This includes vehicle speed, location, surrounding traffic conditions, weather, and road surface conditions. For example, in rainy weather, the road surface becomes slippery, so an increased accident risk is predicted. By integrating this data, the AI ​​agent predicts accident risk. For example, if accidents frequently occur at a particular intersection, the AI ​​agent will issue a warning when approaching that intersection. It can also reduce accident risk by instructing the driver to slow down in rainy weather. If an accident is predicted, the AI ​​agent automatically corrects driving behavior. This includes actions such as sudden braking, lane changes, and speed adjustments. This can prevent accidents. This system is expected to improve the safety of autonomous vehicles and dramatically reduce accident rates. For example, integrating predictions based on past accident data with real-time data enables more accurate prediction of accident risk. In addition, the AI ​​agent's automatic correction of driving behavior reduces the driver's reaction time and prevents accidents. In this way, the safety system of autonomous vehicles can predict accident risk and prevent accidents by automatically correcting driving behavior.

[0067] The safety system for an autonomous vehicle according to this embodiment comprises a data collection unit, an analysis unit, a prediction unit, a warning unit, and a correction unit. The data collection unit collects past accident data. The data collection unit can collect data such as traffic accidents, industrial accidents, and natural disasters. For example, the data collection unit collects data on traffic accidents and obtains detailed information such as the location, time, and cause of the accident. The data collection unit can also collect data on industrial accidents and obtain detailed information such as the location, time, and cause of the accident. Furthermore, the data collection unit can also collect data on natural disasters and obtain detailed information such as the location, time, and cause of the accident. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the data using methods such as statistical analysis, machine learning algorithms, and data mining. For example, the analysis unit can analyze the data using statistical analysis to identify detailed information such as the location, time, and cause of the accident. Furthermore, the analysis unit can analyze the data using machine learning algorithms to identify detailed information such as the location, time, and cause of the accident. Furthermore, the analysis unit can analyze the data using data mining to identify detailed information such as the location, time, and cause of the accident. The prediction unit predicts accident risk based on the analysis results obtained by the analysis unit. The prediction unit can, for example, collect driving conditions and environmental data in real time and integrate this data to predict accident risk. The prediction unit can, for example, collect data such as vehicle speed, location, surrounding traffic conditions, weather, and road surface conditions and integrate this data to predict accident risk. The prediction unit can also predict accident risk based on data collected in real time. Furthermore, the prediction unit can also predict accident risk by integrating past accident data with real-time data. The warning unit issues a warning based on the accident risk predicted by the prediction unit. The warning unit can issue a warning using methods such as voice warnings, visual warnings, and vibration warnings. The warning unit can, for example, issue a warning to the driver using a voice warning. The warning unit can also issue a warning to the driver using a visual warning. Furthermore, the warning unit can also issue a warning to the driver using a vibration warning.The correction unit automatically corrects driving behavior based on warnings issued by the warning unit. The correction unit can correct driving behavior using methods such as speed adjustment, lane change, and braking. For example, the correction unit can adjust speed to reduce the risk of an accident. The correction unit can also change lanes to reduce the risk of an accident. Furthermore, the correction unit can also brake to reduce the risk of an accident. As a result, the safety system of the autonomous vehicle according to this embodiment can prevent accidents by predicting the risk of an accident and automatically correcting driving behavior.

[0068] The data collection unit collects historical accident data. For example, it can collect data on traffic accidents, industrial accidents, and natural disasters. Specifically, it collects traffic accident data, obtaining detailed information such as the location, time, cause, type of vehicle involved, driver's condition, weather conditions, and road conditions. This allows for the identification of traffic accident patterns and common factors. It can also collect industrial accident data, obtaining detailed information such as the location, time, cause, work performed, type of equipment or devices used, and working environment. This provides basic data for evaluating the risk of industrial accidents and implementing preventative measures. Furthermore, it can collect natural disaster data, obtaining detailed information such as the location, time, cause, type of disaster (earthquake, flood, typhoon, etc.), scale of damage, and evacuation status. This provides basic data for evaluating the risks associated with natural disasters and formulating appropriate countermeasures. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and prediction units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0069] The analysis department analyzes the data collected by the data collection department. The analysis department can analyze the data using methods such as statistical analysis, machine learning algorithms, and data mining. Specifically, statistical analysis is used to analyze data and identify detailed information such as the location, time, cause, and common factors of accidents. For example, if the accident rate is high at a particular intersection, there may be problems with the intersection's design or signal timing. Machine learning algorithms can also be used to analyze data and identify detailed information such as the location, time, and cause of accidents. Machine learning algorithms automatically extract patterns and trends from large amounts of data and build models to predict accident risk. Furthermore, data mining can be used to analyze data and identify detailed information such as the location, time, and cause of accidents. Data mining extracts useful information from the data and provides foundational data for evaluating accident risk. This allows the analysis department to quickly and accurately analyze the collected data and assess accident risk. Additionally, the analysis department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past accident data, they can predict risk fluctuations in specific areas and time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0070] The prediction unit predicts accident risk based on the analysis results obtained by the analysis unit. For example, the prediction unit can collect driving conditions and environmental data in real time and integrate this data to predict accident risk. Specifically, it collects data such as vehicle speed, location, surrounding traffic conditions, weather, and road surface conditions, and integrates this data to predict accident risk. For example, if the vehicle speed is too high or the road surface is slippery, the risk of an accident is predicted to increase. The prediction unit can also predict accident risk based on data collected in real time. For example, if the vehicle's sensors detect an obstacle ahead, the unit can predict the risk of an accident based on that information and take appropriate measures. Furthermore, the prediction unit can also predict accident risk by integrating past accident data with real-time data. For example, if past data shows that the accident rate is high under certain conditions, the unit can rate the accident risk highly when those conditions occur in real time. The prediction unit uses AI to analyze this data and simulate multiple scenarios to identify the most likely risk. This allows the prediction unit to predict the risk of accidents with high accuracy and provide information for taking appropriate measures. Furthermore, the forecasting unit can continuously revise its forecast results based on real-time updated data, enabling it to respond to the latest situations. For example, it can instantly update forecast results in response to sudden changes in weather or fluctuations in traffic conditions, supporting appropriate responses. As a result, the forecasting unit can always provide highly accurate risk forecasts based on the latest information, supporting quick and appropriate responses.

[0071] The warning unit issues warnings based on the accident risk predicted by the prediction unit. The warning unit can issue warnings using methods such as voice warnings, visual warnings, and vibration warnings. Specifically, it can issue warnings to the driver using voice warnings. For example, it can alert the driver by playing a voice message such as "There is an obstacle ahead. Please slow down" from the vehicle's speakers. It can also alert the driver using visual warnings. For example, it can visually alert the driver by displaying warning messages or icons on the vehicle's dashboard or head-up display. Furthermore, it can alert the driver using vibration warnings. For example, it can provide the driver with a physical stimulus by generating vibrations in the driver's seat or steering wheel to draw their attention. In this way, the warning unit can quickly provide appropriate warnings to the driver and minimize the risk of accidents. In addition, the warning unit can adjust the type and intensity of warnings according to the situation. For example, it can effectively attract the driver's attention by issuing strong warnings when the risk is high and mild warnings when the risk is low. The warning unit can also use multiple warning methods in combination. For example, by issuing both audible and visual warnings simultaneously, a stronger alert can be given to the driver. This allows the warning unit to provide quick and effective warnings to the driver, minimizing the risk of accidents.

[0072] The correction unit automatically corrects driving behavior based on warnings issued by the warning unit. The correction unit can correct driving behavior using methods such as speed adjustment, lane changes, and braking. Specifically, it can adjust speed to reduce the risk of accidents. For example, if there is an obstacle ahead, the correction unit can automatically reduce the vehicle's speed to reduce the risk of collision. It can also change lanes to reduce the risk of accidents. For example, if the lane ahead is congested, the correction unit can automatically change lanes to maintain smooth driving. Furthermore, it can also brake to reduce the risk of accidents. For example, if an obstacle suddenly appears, the correction unit can automatically apply the brakes to avoid a collision. In this way, the correction unit can automatically perform appropriate driving actions to reduce the risk of accidents without requiring driver intervention. In addition, the correction unit can customize how it corrects driving behavior according to the driver's driving style and preferences. For example, if the driver prioritizes safe driving, the correction unit will select more conservative driving actions to minimize risk. If the driver prioritizes smooth driving, the correction unit will select more aggressive driving actions to maintain a comfortable ride. This allows the modification unit to flexibly modify driving behavior in accordance with the driver's needs, thereby improving the reliability and safety of the entire system.

[0073] The data collection unit can collect past accident data. For example, the data collection unit can collect data on traffic accidents, industrial accidents, and natural disasters. For instance, the data collection unit can collect traffic accident data and obtain detailed information such as the location, time, and cause of the accident. It can also collect industrial accident data and obtain detailed information such as the location, time, and cause of the accident. Furthermore, the data collection unit can collect natural disaster data and obtain detailed information such as the location, time, and cause of the accident. By collecting past accident data, it is possible to provide the data necessary for accident risk analysis. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past accident data into a generating AI and have the generating AI perform the data collection.

[0074] The analysis department can analyze the collected data and identify detailed information such as the location, time, and cause of an accident. The analysis department can analyze the data using methods such as statistical analysis, machine learning algorithms, and data mining. For example, the analysis department can use statistical analysis to analyze the data and identify detailed information such as the location, time, and cause of an accident. The analysis department can also use machine learning algorithms to analyze the data and identify detailed information such as the location, time, and cause of an accident. Furthermore, the analysis department can use data mining to analyze the data and identify detailed information such as the location, time, and cause of an accident. By identifying detailed information such as the location, time, and cause of an accident, it is possible to clearly identify areas and situations with a high risk of accidents. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0075] The prediction unit can collect driving conditions and environmental data in real time and integrate this data to predict accident risk. For example, the prediction unit collects data such as vehicle speed, location, surrounding traffic conditions, weather, and road surface conditions, and integrates this data to predict accident risk. The prediction unit can also predict accident risk based on data collected in real time. Furthermore, the prediction unit can also predict accident risk by integrating past accident data with real-time data. This makes it possible to predict accident risk more accurately by collecting and integrating driving conditions and environmental data in real time. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data collected in real time into a generating AI and have the generating AI perform accident risk prediction.

[0076] The warning unit can warn of high-risk areas and situations in real time. The warning unit issues warnings using methods such as voice warnings, visual warnings, and vibration warnings. For example, the warning unit can warn the driver using voice warnings. The warning unit can also warn the driver using visual warnings. Furthermore, the warning unit can also warn the driver using vibration warnings. This allows the driver to be alerted and accidents to be prevented by providing real-time warnings of high-risk areas and situations. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input high-risk areas and situations into a generating AI and have the generating AI issue warnings.

[0077] The correction unit can automatically correct driving behavior when an accident is predicted. The correction unit can correct driving behavior using methods such as speed adjustment, lane change, and braking. For example, the correction unit can adjust speed to reduce the risk of an accident. It can also change lanes to reduce the risk of an accident. Furthermore, it can apply the brakes to reduce the risk of an accident. In this way, accidents can be prevented by automatically correcting driving behavior when an accident is predicted. Some or all of the above processing in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input areas or situations with a high risk of accidents into a generating AI and have the generating AI perform the correction of driving behavior.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of accident data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the user's burden. Conversely, if the user is relaxed, the data collection unit can advance the collection timing to obtain more detailed data. Furthermore, if the user is in a hurry, the data collection unit can optimize the collection timing to quickly obtain data. This reduces the user's burden and allows for the acquisition of more detailed data by adjusting the timing of accident data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The data collection unit can collect not only past accident data but also nearby traffic violation data to perform a more detailed risk analysis. For example, the data collection unit can collect traffic violation data to identify areas with a high accident risk. The data collection unit can also analyze traffic violation data to assess the risk at specific times and locations. Furthermore, the data collection unit can integrate traffic violation data to improve the accuracy of accident risk predictions. Thus, by collecting traffic violation data, the accuracy of accident risk predictions can be improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input traffic violation data into a generating AI and have the generating AI perform the data collection.

[0080] The data collection unit can simultaneously collect environmental data such as weather and time of day when collecting accident data. For example, the data collection unit can collect weather data to evaluate accident risk during rainy weather. It can also collect time-of-day data to evaluate accident risk at night. Furthermore, the data collection unit can integrate environmental data to improve the accuracy of accident risk prediction. In this way, the accuracy of accident risk prediction can be improved by simultaneously collecting environmental data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather data and time-of-day data into a generating AI and have the generating AI perform the data collection.

[0081] The data collection unit can estimate the user's emotions and determine the priority of accident data to collect based on the estimated user emotions. For example, if the user is tense, the data collection unit will prioritize collecting important accident data. It can also collect detailed accident data if the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting accident data that can be collected quickly. This allows for the priority collection of important data by prioritizing accident data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The data collection unit can prioritize the collection of highly relevant data based on the vehicle's maintenance history when collecting accident data. For example, the data collection unit can refer to the maintenance history and collect accident data related to parts with a high risk of failure. The data collection unit can also analyze the maintenance history and collect accident data related to the replacement timing of specific parts. Furthermore, the data collection unit can integrate the maintenance history to improve the accuracy of accident risk prediction. This improves the accuracy of accident risk prediction by prioritizing the collection of highly relevant data based on the vehicle's maintenance history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input maintenance history data into a generating AI and have the generating AI perform the data collection.

[0083] The data collection unit can monitor surrounding traffic conditions in real time and collect relevant data when collecting accident data. For example, the data collection unit can monitor real-time traffic conditions and identify areas with a high accident risk. The data collection unit can also collect real-time traffic data to improve the accuracy of accident risk prediction. Furthermore, the data collection unit can analyze real-time traffic conditions and assess the risk at specific times and locations. This allows for improved accuracy in accident risk prediction by monitoring surrounding traffic conditions in real time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input real-time traffic data into a generating AI and have the generating AI perform data collection.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis results based on the user's emotions, the analysis unit can provide results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The analysis unit can improve the accuracy of its analysis by comparing past accident data with current traffic conditions when analyzing collected data. For example, the analysis unit can identify areas with a high accident risk by comparing past accident data with current traffic conditions. The analysis unit can also improve the accuracy of accident risk prediction by integrating past accident data with current traffic conditions. Furthermore, the analysis unit can analyze past accident data with current traffic conditions to assess the risk at specific times and locations. This allows for improved accuracy in predicting accident risk by comparing past accident data with current traffic conditions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past accident data and current traffic conditions into a generating AI and have the generating AI perform the data analysis.

[0086] The analysis unit can analyze not only the location, time, and cause of an accident, but also the driver's behavior patterns. For example, the analysis unit can analyze the location, time, and cause of an accident and identify the driver's behavior patterns. The analysis unit can also analyze the driver's behavior patterns and identify high-risk behaviors for accidents. Furthermore, the analysis unit can integrate the location, time, and cause of an accident with the driver's behavior patterns to improve the accuracy of accident risk prediction. This allows for the identification of high-risk behaviors and improved prediction accuracy by analyzing the driver's behavior patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input driver behavior pattern data into a generating AI and have the generating AI perform the data analysis.

[0087] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, if the user is nervous, the prediction unit can provide a simple and highly visible display method. If the user is relaxed, the prediction unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the prediction unit can provide a concise display method. In this way, by adjusting the display method of the prediction results based on the user's emotions, prediction results that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The prediction unit can improve prediction accuracy by integrating past accident data with real-time driving conditions during prediction. For example, the prediction unit can improve the accuracy of accident risk prediction by integrating past accident data with real-time driving conditions. The prediction unit can also identify areas with a high accident risk by comparing past accident data with real-time driving conditions. Furthermore, the prediction unit can analyze past accident data and real-time driving conditions to evaluate the risk at specific time periods and locations. This improves the accuracy of accident risk prediction by integrating past accident data with real-time driving conditions. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past accident data and real-time driving conditions into a generating AI and have the generating AI perform data integration and prediction.

[0089] The prediction unit can apply different prediction algorithms based on specific driving patterns and environmental conditions during prediction. For example, the prediction unit can apply the optimal prediction algorithm based on a specific driving pattern. It can also apply the optimal prediction algorithm based on specific environmental conditions. Furthermore, the prediction unit can integrate driving patterns and environmental conditions to apply the optimal prediction algorithm. This allows for improved prediction accuracy by applying different prediction algorithms based on specific driving patterns and environmental conditions. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input specific driving pattern and environmental condition data into a generating AI and have the generating AI apply the optimal prediction algorithm.

[0090] The prediction unit can estimate the user's emotions and prioritize prediction results based on the estimated emotions. For example, if the user is stressed, the prediction unit will prioritize providing important prediction results. It can also provide detailed prediction results if the user is relaxed. Furthermore, if the user is in a hurry, the prediction unit can prioritize prediction results that can be provided quickly. This allows for the prioritization of important prediction results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The prediction unit can improve prediction accuracy based on the vehicle's maintenance status during prediction. For example, the prediction unit considers the vehicle's maintenance status and makes predictions related to parts with a high risk of failure. The prediction unit can also analyze the vehicle's maintenance status and make predictions related to the replacement timing of specific parts. Furthermore, the prediction unit can integrate the vehicle's maintenance status to improve prediction accuracy. This improves prediction accuracy based on the vehicle's maintenance status, enabling more accurate predictions. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input vehicle maintenance status data into a generating AI and have the generating AI perform data analysis and prediction.

[0092] The prediction unit can improve prediction accuracy by referencing real-time data from other autonomous vehicles during prediction. For example, the prediction unit can identify areas with a high accident risk by referencing real-time data from other autonomous vehicles. The prediction unit can also improve prediction accuracy by integrating real-time data from other autonomous vehicles. Furthermore, the prediction unit can analyze real-time data from other autonomous vehicles to assess risk at specific times and locations. This allows for improved accuracy in predicting accident risk by referencing real-time data from other autonomous vehicles. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input real-time data from other autonomous vehicles into a generating AI and have the generating AI perform data referencing and prediction.

[0093] The warning unit can estimate the user's emotions and adjust the way the warning is delivered based on the estimated emotions. For example, if the user is tense, the warning unit may issue a warning in a calm voice. If the user is relaxed, the warning unit may issue a warning in a cheerful voice. Furthermore, if the user is in a hurry, the warning unit may issue a quick and concise warning. By adjusting the way the warning is delivered based on the user's emotions, it is possible to provide warnings that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the warning unit may be performed using AI, for example, or not using AI. For example, the warning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The warning unit can use different warning sounds and displays depending on the area or situation where the risk of an accident is high. For example, the warning unit can emit a specific warning sound when approaching an intersection with a high risk of accidents. It can also provide a visual warning when approaching a slippery road surface in rainy weather. Furthermore, it can provide a bright warning display when approaching an area with low visibility at night. By using different warning sounds and displays depending on the area or situation where the risk of an accident is high, the system can alert the driver and prevent accidents. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input areas and situations with a high risk of accidents into a generating AI, and have the generating AI select the warning sound and display.

[0095] The warning unit can select the optimal warning method by referring to the driver's past response history when issuing a warning. For example, the warning unit may prioritize using a warning method that elicited a quick response in the past. The warning unit may also reuse warning sounds or displays that were effective in the past. Furthermore, the warning unit can analyze past response history to select the optimal warning method. This allows for the selection of the optimal warning method by referring to the driver's past response history, thereby improving warning accuracy. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input the driver's past response history data into a generating AI and have the generating AI select the optimal warning method.

[0096] The alert unit can estimate the user's emotions and determine the priority of alerts based on the estimated emotions. For example, if the user is tense, the alert unit will prioritize important alerts. It can also issue more detailed alerts if the user is relaxed. Furthermore, if the user is in a hurry, the alert unit can prioritize alerts that can be issued quickly. This allows for prioritizing important alerts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the alert unit may be performed using AI or not. For example, the alert unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The warning unit can adjust the timing of warnings based on the vehicle's speed and location information. For example, if the vehicle is moving too fast, the warning unit will issue a warning earlier. The warning unit can also issue a warning when the vehicle approaches a specific area based on its location information. Furthermore, the warning unit can integrate the vehicle's speed and location information to issue a warning at the optimal time. This allows the warning to be issued at the optimal time by adjusting the timing based on the vehicle's speed and location information. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input vehicle speed and location data into a generating AI and have the generating AI adjust the timing of the warning.

[0098] The warning unit can improve warning accuracy by referring to warning information from other autonomous vehicles when issuing a warning. For example, the warning unit can refer to warning information from other autonomous vehicles to identify areas with a high risk of accidents. The warning unit can also integrate warning information from other autonomous vehicles to improve warning accuracy. Furthermore, the warning unit can analyze warning information from other autonomous vehicles to assess risk at specific times and locations. This allows the warning unit to identify areas with a high risk of accidents and improve warning accuracy by referring to warning information from other autonomous vehicles. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input warning information from other autonomous vehicles into a generating AI and have the generating AI perform data referencing and warning issuance.

[0099] The modification unit can estimate the user's emotions and adjust the method of modifying driving behavior based on the estimated user emotions. For example, if the user is tense, the modification unit will prioritize calm driving behavior. It can also perform normal driving behavior if the user is relaxed. Furthermore, if the user is in a hurry, the modification unit can perform rapid driving behavior. This allows the system to provide the user with optimal driving behavior by adjusting the method of modifying driving behavior based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the modification unit may be performed using AI, or not. For example, the modification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The correction unit can select the optimal correction method by referring to past driving behavior data during the correction process. For example, the correction unit may prioritize the use of correction methods that have been effective in the past. The correction unit can also analyze past driving behavior data to select the optimal correction method. Furthermore, the correction unit can integrate past driving behavior data to improve correction accuracy. This allows the optimal correction method to be selected and correction accuracy improved by referring to past driving behavior data. Some or all of the above processes in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input past driving behavior data into a generating AI and have the generating AI perform data analysis and select a correction method.

[0101] The correction unit can perform corrections based on the vehicle's current state and surrounding traffic conditions. For example, the correction unit can select the optimal correction method considering the vehicle's current state. It can also select the optimal correction method considering the surrounding traffic conditions. Furthermore, the correction unit can integrate the vehicle's current state and surrounding traffic conditions to improve correction accuracy. This improves correction accuracy by performing corrections based on the vehicle's current state and surrounding traffic conditions. Some or all of the above processing in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input data on the vehicle's current state and surrounding traffic conditions into a generating AI and have the generating AI perform data analysis and correction.

[0102] The modification unit can estimate the user's emotions and determine the priority of modifications to driving behavior based on the estimated user emotions. For example, if the user is tense, the modification unit will prioritize important modifications. It can also perform more detailed modifications if the user is relaxed. Furthermore, if the user is in a hurry, the modification unit can prioritize quick modifications. This allows for prioritizing important modifications by determining the priority of modifications to driving behavior based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the modification unit may be performed using AI or not. For example, the modification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The correction unit can select a correction method based on the vehicle's maintenance status during the correction process. For example, the correction unit can consider the vehicle's maintenance status and perform corrections related to parts with a high risk of failure. The correction unit can also analyze the vehicle's maintenance status and perform corrections related to the replacement timing of specific parts. Furthermore, the correction unit can integrate the vehicle's maintenance status to improve correction accuracy. This allows for improved correction accuracy by selecting a correction method based on the vehicle's maintenance status. Some or all of the above-described processes in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input vehicle maintenance status data into a generating AI and have the generating AI perform data analysis and select a correction method.

[0104] The correction unit can improve the accuracy of corrections by referencing data from other autonomous vehicles during the correction process. For example, the correction unit can identify areas with a high accident risk by referencing data from other autonomous vehicles. The correction unit can also improve the accuracy of corrections by integrating data from other autonomous vehicles. Furthermore, the correction unit can analyze data from other autonomous vehicles to assess the risk at specific times and locations. This allows the correction unit to identify areas with a high accident risk and improve the accuracy of corrections by referencing data from other autonomous vehicles. Some or all of the above processes in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input data from other autonomous vehicles into a generating AI and have the generating AI perform data referencing and correction.

[0105] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0106] The safety systems in autonomous vehicles can further monitor the user's health and adjust driving behavior based on that health condition. For example, if the user is fatigued, the system will adjust driving behavior more cautiously and encourage them to take a break. If the user is in good health, the system can maintain normal driving behavior. Furthermore, if the user suddenly becomes ill, the system can perform an emergency stop and contact a medical institution. By adjusting driving behavior based on the user's health condition, the risk of accidents can be reduced and user safety can be ensured.

[0107] The safety systems of autonomous vehicles can further monitor the vehicle's fuel efficiency and adjust driving behavior based on that efficiency. For example, if fuel efficiency is low, the system can adjust speed to optimize fuel consumption. If fuel efficiency is high, it can maintain normal driving behavior. Furthermore, if fuel efficiency drops drastically, the system can guide the driver to the nearest gas station. In this way, by adjusting driving behavior based on fuel efficiency, fuel consumption can be optimized and economical driving can be achieved.

[0108] The safety systems in autonomous vehicles can further analyze the user's driving history and adjust their driving behavior based on that history. For example, if a user has had an accident in a specific area in the past, the system will issue a warning when approaching that area. Similarly, if a user has engaged in dangerous driving in a specific situation in the past, the system can adjust its driving behavior when approaching that situation. Furthermore, it can analyze the user's driving history and provide feedback to support the improvement of their driving skills. By adjusting driving behavior based on the user's driving history, it can reduce the risk of accidents and support safer driving.

[0109] The safety systems of autonomous vehicles can further monitor the movements of surrounding pedestrians and cyclists and adjust their driving behavior based on those movements. For example, if a pedestrian is about to cross a crosswalk, the system will slow down and yield to the pedestrian. Also, if a cyclist is traveling close to the vehicle, the system can change lanes and maintain a safe distance. Furthermore, if a pedestrian or cyclist suddenly appears, the system can apply emergency brakes. In this way, by adjusting driving behavior based on the movements of surrounding pedestrians and cyclists, the risk of accidents can be reduced and safe driving can be achieved.

[0110] The safety systems of autonomous vehicles can also collect road construction information in real time and adjust their driving behavior based on that information. For example, when approaching a road under construction, the system will slow down to maintain safe driving. If the road under construction is closed, the system can also guide the driver on an alternative route. Furthermore, it can predict traffic congestion on roads under construction and select the optimal route. By adjusting driving behavior based on road construction information, the risk of accidents can be reduced and driving can be made smoother.

[0111] The safety systems in autonomous vehicles can estimate the user's emotions and adjust their driving behavior based on those emotions. For example, if the user is stressed, the system will adjust its driving behavior more cautiously to help them relax. If the user is relaxed, the system can maintain normal driving behavior. Furthermore, if the user is in a hurry, the system can perform rapid driving actions. In this way, adjusting driving behavior based on the user's emotions can improve user comfort and safety.

[0112] The safety systems in autonomous vehicles can estimate the user's emotions and adjust the timing of warnings based on those emotions. For example, if the user is stressed, the system can issue a warning earlier to give the user time to think. If the user is relaxed, the system can issue a warning at the normal time. Furthermore, if the user is in a hurry, the system can issue a warning quickly. In this way, by adjusting the timing of warnings based on the user's emotions, warnings can be provided at the optimal time for the user.

[0113] The safety systems in autonomous vehicles can estimate the user's emotions and adjust the content of warnings based on those emotions. For example, if the user is stressed, the system will issue a simple and easy-to-understand warning. If the user is relaxed, it can issue a more detailed warning. Furthermore, if the user is in a hurry, it can issue a concise and to-the-point warning. In this way, by adjusting the content of warnings based on the user's emotions, it is possible to provide warnings that are easy for the user to understand.

[0114] The safety systems in autonomous vehicles can estimate the user's emotions and provide feedback on driving behavior based on those emotions. For example, if the user is tense, the system can provide positive feedback to boost their confidence. If the user is relaxed, it can provide detailed feedback. Furthermore, if the user is in a hurry, it can provide concise feedback. In this way, by providing feedback on driving behavior based on the user's emotions, it can support the improvement of the user's driving skills.

[0115] The safety systems in autonomous vehicles can estimate the user's emotions and prioritize driving actions based on those emotions. For example, if the user is tense, important driving actions will be prioritized. Conversely, if the user is relaxed, detailed driving actions can be performed. Furthermore, if the user is in a hurry, quick driving actions can be prioritized. In this way, by prioritizing driving actions based on the user's emotions, important driving actions can be prioritized.

[0116] The following briefly describes the processing flow for example form 2.

[0117] Step 1: The data collection unit collects past accident data. The data collection unit can collect data such as traffic accidents, industrial accidents, and natural disasters. The data collection unit collects traffic accident data and obtains detailed information such as the location, time, and cause of the accident. It can also collect industrial accident data and natural disaster data and obtain similarly detailed information. Step 2: The analysis department analyzes the data collected by the data collection department. The analysis department analyzes the data using methods such as statistical analysis, machine learning algorithms, and data mining to identify detailed information such as the location, time, and cause of the accident. Step 3: The prediction unit predicts accident risk based on the analysis results obtained by the analysis unit. The prediction unit can collect driving conditions and environmental data in real time and integrate this data to predict accident risk. For example, it collects data such as vehicle speed, location, surrounding traffic conditions, weather, and road surface conditions, and integrates this data to predict accident risk. Step 4: The warning unit issues a warning based on the accident risk predicted by the prediction unit. The warning unit can warn the driver using methods such as voice warnings, visual warnings, and vibration warnings. Step 5: The correction unit automatically corrects driving behavior based on warnings issued by the warning unit. The correction unit corrects driving behavior using methods such as speed adjustment, lane changes, and braking, thereby reducing the risk of accidents.

[0118] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0119] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0120] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0121] Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, warning unit, and correction unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects past accident data using the camera 42 and sensors of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and collects driving conditions and environmental data in real time to predict accident risk. The warning unit is implemented in the control unit 46A of the smart device 14 and issues voice and visual warnings. The correction unit is implemented in the specific processing unit 290 of the data processing unit 12 and automatically corrects driving behavior. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0122] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0123] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0128] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0129] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0130] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0131] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0132] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0134] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0136] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, warning unit, and correction unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects past accident data using the camera 42 and sensors of the smart glasses 214 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The prediction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and collects driving conditions and environmental data in real time to predict accident risk. The warning unit is implemented, for example, by the control unit 46A of the smart glasses 214 and issues voice and visual warnings. The correction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and automatically corrects driving behavior. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0138] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0139] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, warning unit, and correction unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects past accident data using the camera 42 and sensors of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and collects driving conditions and environmental data in real time to predict accident risk. The warning unit is implemented in the control unit 46A of the headset terminal 314 and issues voice and visual warnings. The correction unit is implemented in the specific processing unit 290 of the data processing unit 12 and automatically corrects driving behavior. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0154] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0155] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0158] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0160] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0161] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0162] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0163] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0164] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0165] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0166] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0167] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0168] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0169] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0170] Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, warning unit, and correction unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects past accident data using the camera 42 and sensors of the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and collects driving conditions and environmental data in real time to predict accident risk. The warning unit is implemented in the control unit 46A of the robot 414 and issues voice and visual warnings. The correction unit is implemented in the specific processing unit 290 of the data processing unit 12 and automatically corrects driving behavior. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0171] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0173] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0174] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0175] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0179] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0180] 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.

[0181] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0182] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0183] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0184] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0186] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0187] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0188] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0189] (Note 1) The data collection department collects past accident data, An analysis unit analyzes the data collected by the aforementioned collection unit, A prediction unit that predicts accident risk based on the analysis results obtained by the aforementioned analysis unit, A warning unit that issues a warning based on the accident risk predicted by the prediction unit, The system includes a correction unit that automatically corrects driving behavior based on warnings issued by the warning unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect past accident data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected data is analyzed to identify detailed information such as the location, time, and cause of the accident. The system described in Appendix 1, characterized by the features described herein. (Note 4) The prediction unit, The system collects real-time driving conditions and environmental data, and integrates this data to predict accident risks. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned warning unit is It provides real-time warnings about areas and situations with a high risk of accidents. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned modification section is, If an accident is predicted, the driving behavior will be automatically corrected. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates user emotions and adjusts the timing of accident data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is In addition to past accident data, we also collect nearby traffic violation data to conduct a more detailed risk analysis. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting accident data, environmental data such as weather and time of day will also be collected simultaneously. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates user emotions and prioritizes the accident data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting accident data, the system prioritizes collecting highly relevant data based on the vehicle's maintenance history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting accident data, the surrounding traffic conditions are monitored in real time, and relevant data is collected. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is When analyzing collected data, we improve the accuracy of the analysis by comparing past accident data with current traffic conditions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is In addition to the location, time, and cause of the accident, the driver's behavioral patterns will also be analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 16) The prediction unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The prediction unit, During prediction, historical accident data and real-time driving conditions are integrated to improve prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 18) The prediction unit, When making predictions, different prediction algorithms are applied based on specific driving patterns and environmental conditions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The prediction unit, It estimates the user's emotions and prioritizes the prediction results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, During prediction, improve prediction accuracy based on the vehicle's maintenance status. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, During prediction, the system references real-time data from other autonomous vehicles to improve prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned warning unit is The system estimates the user's emotions and adjusts the way warnings are presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned warning unit is When a warning is issued, different warning sounds and displays are used depending on the area or situation where the risk of an accident is high. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned warning unit is When a warning is issued, the system selects the most appropriate warning method by referring to the driver's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned warning unit is The system estimates the user's emotions and prioritizes warnings based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned warning unit is When a warning is issued, the timing of the warning is adjusted based on the vehicle's speed and location information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned warning unit is When a warning is issued, the system improves warning accuracy by referencing warning information from other autonomous vehicles. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned modification section is, It estimates the user's emotions and adjusts the method of modifying driving behavior based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned modification section is, When making corrections, the optimal correction method is selected by referring to past driving behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned modification section is, When making corrections, adjustments are made based on the vehicle's current condition and surrounding traffic conditions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned modification section is, The system estimates the user's emotions and determines the priority of modifying driving behavior based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned modification section is, When making repairs, the repair method will be selected based on the vehicle's maintenance status. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned modification section is, During corrections, data from other autonomous vehicles is referenced to improve the accuracy of the corrections. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects past accident data, An analysis unit analyzes the data collected by the aforementioned collection unit, A prediction unit that predicts accident risk based on the analysis results obtained by the aforementioned analysis unit, A warning unit that issues a warning based on the accident risk predicted by the prediction unit, The system includes a correction unit that automatically corrects driving behavior based on warnings issued by the warning unit. A system characterized by the following features.

2. The aforementioned analysis unit is The collected data is analyzed to identify detailed information such as the location, time, and cause of the accident. The system according to feature 1.

3. The prediction unit, The system collects real-time driving conditions and environmental data, and integrates this data to predict accident risks. The system according to feature 1.

4. The aforementioned warning unit is It provides real-time warnings about areas and situations with a high risk of accidents. The system according to feature 1.

5. The aforementioned modification section is, If an accident is predicted, the driving behavior will be automatically corrected. The system according to feature 1.

6. The aforementioned collection unit is The system estimates user emotions and adjusts the timing of accident data collection based on the estimated user emotions. The system according to feature 1.

7. The aforementioned collection unit is In addition to past accident data, we also collect nearby traffic violation data to conduct a more detailed risk analysis. The system according to feature 1.

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