system

Abstract

The system according to this embodiment aims to prevent similar accidents from occurring in the future based on information about past accidents and near misses. [Solution] The system according to the embodiment comprises a collection unit, a storage unit, a monitoring unit, an analysis unit, a warning unit, and a braking unit. The collection unit collects information on locations where accidents or near misses have occurred in the past. The storage unit stores the information collected by the collection unit in a database. The monitoring unit monitors the current movement of the vehicle in real time. The analysis unit analyzes whether the current movement monitored by the monitoring unit is similar to the pattern of a previous accident. The warning unit issues a warning to the driver if the analysis unit determines that there is a similarity. The braking unit activates the automatic brake as needed after a warning is 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, the information of past accidents and near misses has not been fully utilized to prevent similar accidents, and there is room for improvement.

[0005] The system according to the embodiment aims to prevent similar accidents based on the information of past accidents and near misses.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a storage unit, a monitoring unit, an analysis unit, a warning unit, and a braking unit. The collection unit collects information on locations where accidents or near misses have occurred in the past. The storage unit stores the information collected by the collection unit in a database. The monitoring unit monitors the current movement of the vehicle in real time. The analysis unit analyzes whether the current movement monitored by the monitoring unit is similar to the pattern of a previous accident. The warning unit issues a warning to the driver if the analysis unit determines that there is a similarity. The braking unit activates the automatic brakes as necessary after a warning is issued by the warning unit. [Effects of the Invention]

[0007] The system according to this embodiment can prevent similar accidents from occurring based on information about past accidents and near misses. [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 multiple 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 reception 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 reception 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) An embodiment of the present invention provides a driver safety support system that, based on information about locations where accidents or near misses have occurred in the past, notifies the driver in advance if the current movement is similar to the pattern of a previous accident. The safety support system collects information about locations where accidents or near misses have occurred in the past and stores it in a database. Next, it monitors the current vehicle movement in real time, and the AI ​​analyzes whether it is similar to the pattern of a previous accident. If it is similar, it issues a warning to the driver. Furthermore, it can also activate automatic braking as needed. For example, the safety support system collects information about locations where accidents or near misses have occurred in the past and stores it in a database. This information includes the location, time, and circumstances of the accident. For example, if accidents frequently occur at a particular intersection, the information of that intersection is registered in the database. Next, the safety support system monitors the current vehicle movement in real time. The AI ​​acquires the vehicle's location information, speed, direction of travel, etc., using sensors and GPS, and analyzes it. For example, if the vehicle is approaching a particular intersection, it compares the current movement with past accident patterns at that intersection. The AI ​​analyzes whether the current movement is similar to past accident patterns. For example, if a particular intersection has a history of frequent accidents involving right turns, the system will issue a warning if the current vehicle attempts to turn right at the same intersection. This warning is communicated to the driver via voice and visual means. Furthermore, it can automatically apply the brakes if necessary. For instance, if the driver ignores the warning and continues making dangerous movements, the safety driving support system will automatically apply the brakes to prevent an accident. In this way, driver safety can be ensured. This system can prevent recurrence at locations where accidents or near misses have occurred in the past, thereby improving driver safety. In particular, it is expected to enable drivers to anticipate situations like experienced drivers, even in unfamiliar areas, significantly reducing the risk of accidents. Thus, safety driving support systems can improve driver safety.

[0029] The safe driving support system according to this embodiment comprises a collection unit, a storage unit, a monitoring unit, an analysis unit, a warning unit, and a braking unit. The collection unit collects information on locations where accidents or near misses have occurred in the past. For example, the collection unit collects information such as the location, time, and circumstances of an accident. For example, the collection unit identifies the location of an accident using sensors or cameras and records the time of occurrence using GPS data. The collection unit can also collect eyewitness testimonies and photographs of the scene in order to record the circumstances of the accident in detail. For example, the collection unit takes photographs of the accident scene and saves them in a database. The storage unit stores the information collected by the collection unit in a database. For example, the storage unit stores the collected information in a database and makes it searchable and updatable as needed. For example, the storage unit can select the type and format of the database and set the information retention period. The monitoring unit monitors the current movement of the vehicle in real time. For example, the monitoring unit acquires information such as the vehicle's location, speed, and direction of travel using sensors or GPS. The monitoring unit, for example, acquires vehicle location information as GPS data and monitors the vehicle's speed using speedometer data. The monitoring unit can also monitor the vehicle's direction of travel using a direction sensor. The analysis unit analyzes whether the current movement monitored by the monitoring unit is similar to patterns of previous accidents. The analysis unit, for example, compares past accident patterns with the current movement to determine if they are similar. The analysis unit, for example, acquires past accident patterns from a database and uses an algorithm to compare them with the current movement. The warning unit issues a warning to the driver if the analysis unit determines that the movements are similar. The warning unit issues a warning to the driver, for example, using voice warnings to draw the driver's attention. The warning unit can also issue a visual warning to the driver. After a warning is issued by the warning unit, the braking unit activates the automatic brakes as needed. The braking unit automatically activates the brakes, for example, if the driver ignores the warning and continues to make dangerous movements. The braking system can, for example, set the braking strength and activation timing to perform control measures that prevent accidents.As a result, the safety driving support system according to the embodiment can prevent recurrence at locations where accidents or near misses have previously occurred, thereby improving driver safety.

[0030] The data collection unit collects information from locations where accidents or near misses have occurred. For example, it collects information such as the location, time, and circumstances of an accident. Specifically, the unit uses sensors and cameras mounted on the vehicle to identify the location of the accident and records the time of the accident using GPS data. This allows for the precise location and time of the accident to be determined. The data collection unit can also collect eyewitness testimonies and photographs of the scene to record the accident circumstances in detail. For example, taking photographs of the accident scene and saving them to a database can be useful for later analysis. Furthermore, the data collection unit can also collect data such as the vehicle's speed, direction of travel, and brake usage. This allows for a detailed understanding of the vehicle's movements at the time of the accident. The data collection unit collects this data in real time and transmits it to a central database. As a result, the collected data is immediately stored in the storage unit and can be used for later analysis and monitoring. By accurately and quickly collecting information from locations where accidents or near misses have occurred, the data collection unit plays a crucial role in forming the foundation of the safe driving support system.

[0031] The storage unit stores the information collected by the collection unit in a database. For example, the storage unit saves the collected information in a database and makes it searchable and updatable as needed. Specifically, the storage unit selects the type of database and storage format to efficiently manage the collected information. For example, relational databases or NoSQL databases can be used to streamline the storage and retrieval of information. The storage unit can also manage the database capacity by setting the information retention period and appropriately archiving old data. Furthermore, the storage unit can classify and tag the collected information to facilitate subsequent searching and analysis. For example, data can be classified based on information such as the location, time, and circumstances of an accident, allowing for quick retrieval of data that matches specific conditions. The storage unit centrally manages the collected information and can collaborate with other systems and departments as needed. In this way, the storage unit plays a crucial role in supporting the information infrastructure of the safe driving support system.

[0032] The monitoring unit monitors the vehicle's current movements in real time. For example, it acquires vehicle location information, speed, and direction of travel using sensors and GPS. Specifically, the monitoring unit acquires vehicle location information using a GPS device mounted on the vehicle and monitors the vehicle's speed using speedometer data. It can also monitor the vehicle's direction of travel using a direction sensor. This allows the monitoring unit to accurately understand the vehicle's current movements. Furthermore, the monitoring unit can acquire data such as the vehicle's brake usage and accelerator operation. This allows for real-time monitoring of detailed information regarding the vehicle's movements. The monitoring unit transmits this data to a central database, making it accessible to the analysis and warning units. Thus, the monitoring unit plays a crucial role in ensuring driver safety by monitoring vehicle movements in real time and collaborating with other departments of the safety driving support system.

[0033] The analysis unit analyzes whether the current movements monitored by the monitoring unit resemble patterns from previous accidents. For example, the analysis unit compares past accident patterns with current movements to determine similarity. Specifically, the analysis unit retrieves past accident patterns from a database and uses algorithms to compare them with current movements. For example, machine learning algorithms can be used to extract features from past accident data and calculate similarity by matching them with current movements. This allows the analysis unit to determine with high accuracy whether current movements resemble past accident patterns. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past accident data, it can predict risk fluctuations in specific regions or time periods and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and 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, improving the reliability and safety of the entire system.

[0034] The warning unit issues a warning to the driver if the analysis unit determines that the situation is similar. The warning unit can issue warnings to the driver, for example, through voice or visual means. Specifically, the warning unit uses voice warnings to draw the driver's attention. For example, it may play a voice message such as "There is a hazard ahead. Please be careful" through the in-car speakers. The warning unit can also issue visual warnings to the driver. For example, it may display a warning message on the dashboard display to attract the driver's attention. Furthermore, the warning unit can issue tactile warnings to the driver using vibration warnings. For example, it may vibrate the steering wheel or seat to alert the driver to the hazard. In this way, the warning unit can issue warnings to the driver in a variety of ways and encourage actions to avoid danger. The warning unit plays an important role in attracting the driver's attention and preventing accidents.

[0035] The braking system automatically applies the brakes as needed after a warning is issued by the warning unit. For example, the braking system automatically applies the brakes if the driver ignores the warning and continues to make dangerous movements. Specifically, the braking system monitors the vehicle's speed, direction of travel, and surrounding conditions, and applies the brakes when it determines that danger is imminent. For example, by automatically applying the brakes when there is an obstacle ahead or when approaching a sharp curve, accidents can be prevented. The braking system can set the braking strength and timing to provide optimal control. For example, by starting with light braking and gradually increasing the strength, safety can be ensured without causing discomfort to the driver. In addition, the braking system can exert maximum braking force in emergencies to avoid collisions with other vehicles or pedestrians. Thus, the braking system plays a crucial role in ensuring driver safety and preventing accidents.

[0036] The data collection unit collects information such as the location, time, and circumstances of an accident. For example, the data collection unit records the location of the accident as GPS coordinates. The data collection unit can also record the time of the accident as a timestamp. Furthermore, the data collection unit can collect eyewitness testimonies and photographs of the scene in order to record the circumstances of the accident in detail. For example, the data collection unit takes photographs of the accident scene and saves them in a database. This allows for more detailed accident information to be obtained by collecting information such as the location, time, and circumstances of the accident. 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 photographs of the accident scene into AI, and the AI ​​can perform image analysis to automatically record the circumstances of the accident.

[0037] The monitoring unit acquires vehicle location information, speed, direction of travel, etc., using sensors and GPS. For example, the monitoring unit acquires vehicle location information as GPS data. The monitoring unit can also monitor vehicle speed using speedometer data, for example. The monitoring unit can also monitor the direction of travel of the vehicle using direction sensors. For example, the monitoring unit acquires vehicle location information in real time and stores it in a database. The monitoring unit can also monitor vehicle speed in real time and detect abnormal speed changes. The monitoring unit can also monitor the direction of travel of the vehicle in real time and detect abnormal changes in direction of travel. As a result, by acquiring vehicle location information, speed, direction of travel, etc., the current movement of the vehicle can be accurately monitored. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input vehicle location information into AI, and the AI ​​can analyze the location information to detect anomalies.

[0038] The analysis unit compares past accident patterns with current movements to determine if they are similar. For example, the analysis unit may obtain past accident patterns from a database and use an algorithm to compare them with current movements. The analysis unit may also analyze past accident patterns using a machine learning algorithm to determine if they are similar to current movements. Alternatively, the analysis unit may analyze past accident patterns using a rule-based algorithm to determine if they are similar to current movements. For example, the analysis unit may classify past accident patterns using a clustering algorithm to determine if they are similar to current movements. The analysis unit may also analyze past accident patterns using a deep learning algorithm to determine if they are similar to current movements. By comparing past accident patterns with current movements, it is possible to prevent accidents from recurring. 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 may input past accident patterns into an AI, which can perform pattern analysis to determine if they are similar to current movements.

[0039] The warning unit issues warnings to the driver via voice and visual means. For example, the warning unit can use voice warnings to draw the driver's attention. The warning unit can also issue visual warnings to the driver using visual warnings. Furthermore, the warning unit can combine voice and visual warnings to warn the driver. For example, the warning unit can play the message "Danger. Please be careful" as a voice warning. The warning unit can also display a warning icon on the dashboard as a visual warning. In addition, the warning unit can provide more effective warnings to the driver by issuing voice and visual warnings simultaneously. This reduces the risk of accidents by issuing warnings to the driver via voice and visual means. 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 state into the AI, which can then select an appropriate warning method and issue a warning.

[0040] The braking system automatically applies the brakes if the driver ignores warnings and continues to make dangerous movements. For example, the braking system can set the braking strength and timing to prevent accidents. The braking system can also gradually increase the braking strength if the driver ignores warnings and continues to make dangerous movements. For example, the braking system can initially apply a light brake and then apply a strong brake if the driver does not react. This prevents accidents by automatically applying the brakes if the driver ignores warnings and continues to make dangerous movements. Some or all of the above processing in the braking system may be performed using AI, or not. For example, the braking system can input the driver's state into the AI, which can then select the appropriate braking strength and timing to apply the brakes.

[0041] The data collection unit collects not only the location, time, and circumstances of an accident, but also environmental information such as weather and traffic volume. For example, the data collection unit collects weather information at the time of the accident (sunny, rainy, snowy, etc.) and stores it in a database. The data collection unit can also collect traffic volume information at the time of the accident (congestion, periods of low traffic, etc.) and store it in a database. Furthermore, the data collection unit can collect road conditions at the time of the accident (under construction, uneven road surface, etc.) and store it in a database. For example, the data collection unit collects weather information at the time of the accident using weather sensors and saves it in a database. The data collection unit can also collect traffic volume information at the time of the accident using traffic sensors and save it in a database. Furthermore, the data collection unit can collect road conditions at the time of the accident using cameras and save them in a database. By collecting environmental information such as weather and traffic volume, more detailed accident information can be obtained. 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 information into AI, and the AI ​​can analyze the weather data to collect accident information.

[0042] The data collection unit updates the collected information in real time, ensuring that the latest accident information is always maintained. For example, when accident information is updated, the data collection unit reflects this in the database in real time. The data collection unit can also update the database immediately when new accident information is collected, ensuring that the information is up-to-date. Furthermore, the data collection unit can periodically review the collected information and update the database as needed. For example, the data collection unit updates the accident information in real time and reflects this in the database. For example, when new accident information is collected, the data collection unit can update the database immediately, ensuring that the information is up-to-date. Furthermore, the data collection unit can periodically review the collected information and update the database as needed. This ensures that the latest accident information is always maintained by updating the collected information 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 the collected information into AI, which can then update the database in real time.

[0043] The data collection unit also collects information on surrounding traffic infrastructure (such as the status of traffic lights and road construction information) during the collection process. For example, the data collection unit can collect the status of traffic lights at the time of an accident (red light, green light, etc.) and store it in a database. The data collection unit can also collect information on road construction at the time of an accident (under construction, completed construction, etc.) and store it in a database. Furthermore, the data collection unit can collect information on traffic signs at the time of an accident (such as speed limits and no entry zones) and store it in a database. For example, the data collection unit can collect the status of traffic lights at the time of an accident using a traffic light sensor and save it in a database. The data collection unit can also collect information on road construction at the time of an accident using a construction sensor and save it in a database. Furthermore, the data collection unit can collect information on traffic signs at the time of an accident using a camera and save it in a database. This allows for the collection of more detailed accident information by collecting information on surrounding traffic infrastructure. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the status of traffic lights into an AI, which can then analyze the traffic light data to collect accident information.

[0044] The data collection unit integrates information from other vehicles during the collection process. For example, the data collection unit collects accident information from other vehicles and integrates it into a database. The data collection unit can also collect traffic condition information from other vehicles and integrate it into a database. Furthermore, the data collection unit can collect environmental information (weather, road conditions, etc.) from other vehicles and integrate it into a database. For example, the data collection unit collects accident information from other vehicles using vehicle-to-vehicle communication and stores it in a database. The data collection unit can also collect traffic condition information from other vehicles using vehicle-to-vehicle communication and store it in a database. Furthermore, the data collection unit can collect environmental information from other vehicles using vehicle-to-vehicle communication and store it in a database. By integrating and collecting information from other vehicles, more detailed accident information can be obtained. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information from other vehicles into AI, and the AI ​​can analyze the information to collect accident information.

[0045] The storage unit organizes the stored information by category, making it searchable efficiently. For example, the storage unit organizes accident information by category, such as location, time, and circumstances. The storage unit can also organize environmental information by category, such as weather, traffic volume, and road conditions. Furthermore, the storage unit can build a database to organize the collected information by category and make it searchable efficiently. For example, the storage unit organizes accident information by location and saves it to the database. For example, the storage unit can also organize accident information by time and save it to the database. Furthermore, the storage unit can organize accident information by circumstances and save it to the database. This allows for efficient information retrieval by organizing the stored information by category. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the collected information into AI, which can then organize the information by category and save it to the database.

[0046] The storage unit ensures data security by regularly backing up the stored information. For example, the storage unit ensures data security by regularly backing up the database. The storage unit can also perform immediate backups when important information is stored. Furthermore, the storage unit can ensure data security by saving backup data in multiple locations. For example, the storage unit can perform daily backups and save them to the database. The storage unit can also perform weekly backups and save them to cloud storage. Furthermore, the storage unit can perform immediate backups when important information is stored and save them in multiple locations. In this way, data security can be ensured by regularly backing up the stored information. Some or all of the above processes in the storage unit may be performed using AI, for example, or not using AI. For example, the storage unit can input backup data into AI, and the AI ​​can create a backup plan to ensure data security.

[0047] The storage unit collaborates with other systems to share data. For example, the storage unit can collaborate with other traffic management systems to share accident information. The storage unit can also collaborate with other vehicle management systems to share environmental information. Furthermore, the storage unit can collaborate with other database systems to share collected information. For example, the storage unit can collaborate with other traffic management systems via API to share accident information. For example, the storage unit can collaborate with other vehicle management systems using a data sharing protocol to share environmental information. Furthermore, the storage unit can collaborate with other database systems using a data sharing protocol to share collected information. This enables the effective use of information by collaborating with other systems and sharing data. Some or all of the above-described processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the collaboration with other systems into the AI, and the AI ​​can set up a data sharing protocol to share information.

[0048] The data storage unit visualizes the stored information so that drivers can easily understand it. For example, the data storage unit visualizes accident information on a map so that drivers can easily understand it. The data storage unit can also visualize environmental information in graphs or charts so that drivers can easily understand it. Furthermore, the data storage unit can visualize the collected information so that drivers can intuitively understand it. For example, the data storage unit visualizes accident information on a map so that drivers can easily understand it. For example, the data storage unit can visualize environmental information in graphs or charts so that drivers can easily understand it. Furthermore, the data storage unit can visualize the collected information so that drivers can intuitively understand it. In this way, by visualizing the stored information, drivers can easily understand the information. Some or all of the above processing in the data storage unit may be performed using AI, for example, or without AI. For example, the data storage unit can input the information to be visualized into the AI, and the AI ​​can visualize the information and provide it to the driver.

[0049] The monitoring unit monitors not only the movement of the vehicle but also the movement of surrounding pedestrians and other vehicles. For example, the monitoring unit monitors the movement of surrounding pedestrians in addition to the movement of the vehicle and stores it in a database. The monitoring unit can also monitor the movement of other vehicles in addition to the movement of the vehicle and store it in a database. Furthermore, the monitoring unit can monitor the movement of surrounding bicycles and motorcycles in addition to the movement of the vehicle and store it in a database. For example, the monitoring unit monitors the position of pedestrians in addition to the movement of the vehicle and stores it in a database. The monitoring unit can also monitor the speed of other vehicles in addition to the movement of the vehicle and store it in a database. Furthermore, the monitoring unit can monitor the direction of travel of bicycles and motorcycles in addition to the movement of the vehicle and store it in a database. This enables safer driving by monitoring the movement of surrounding pedestrians and other vehicles. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the movement of surrounding pedestrians and other vehicles into AI, and the AI ​​can analyze the movement and store it in a database.

[0050] The monitoring unit analyzes the monitoring data in real time and immediately detects anomalies. For example, the monitoring unit can analyze the monitoring data in real time and immediately detect abnormal movements. The monitoring unit can also analyze the monitoring data in real time and immediately detect abnormal speed changes. Furthermore, the monitoring unit can analyze the monitoring data in real time and immediately detect abnormal changes in direction of travel. For example, the monitoring unit analyzes the monitoring data in real time and immediately detects abnormal movements. For example, the monitoring unit can analyze the monitoring data in real time and immediately detect abnormal speed changes. Furthermore, the monitoring unit can analyze the monitoring data in real time and immediately detect abnormal changes in direction of travel. This allows for immediate detection of anomalies by analyzing the monitoring data in real time. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input monitoring data into AI, and the AI ​​can analyze the data in real time to detect anomalies.

[0051] The monitoring unit also monitors the vehicle's internal conditions (engine status, fuel level, etc.) during monitoring. For example, in addition to the vehicle's movement, the monitoring unit monitors the engine's condition (temperature, RPM, etc.) and stores it in a database. The monitoring unit can also monitor the fuel level in addition to the vehicle's movement and store it in a database. Furthermore, the monitoring unit can monitor the battery status in addition to the vehicle's movement and store it in a database. For example, the monitoring unit monitors the engine temperature in addition to the vehicle's movement and saves it in a database. The monitoring unit can also monitor the fuel level in addition to the vehicle's movement and save it in a database. Furthermore, the monitoring unit can monitor the battery status in addition to the vehicle's movement and save it in a database. This allows for obtaining more detailed information by monitoring the vehicle's internal conditions. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the vehicle's internal conditions into AI, which can analyze the data and save it in a database.

[0052] The analysis unit analyzes not only past accident patterns but also similar patterns. For example, the analysis unit analyzes patterns of similar situations in addition to past accident patterns. The analysis unit can also analyze similar accident patterns in different locations in addition to past accident patterns. Furthermore, the analysis unit can analyze similar accident patterns in different time periods in addition to past accident patterns. For example, the analysis unit analyzes patterns of similar situations in addition to past accident patterns. For example, the analysis unit can analyze similar accident patterns in different locations in addition to past accident patterns. Furthermore, the analysis unit can analyze similar accident patterns in different time periods in addition to past accident patterns. By including similar patterns in the analysis, a broader range of accident prevention becomes possible. 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 past accident patterns and similar patterns into AI, and the AI ​​can perform pattern analysis to provide information for accident prevention.

[0053] The analysis unit updates the analysis results in real time, always providing the latest information. For example, when the analysis results are updated, the analysis unit reflects them in the database in real time. The analysis unit can also, for example, immediately update the database to provide the latest information if new analysis results are obtained. The analysis unit can also periodically check the analysis results and update the database as needed. For example, the analysis unit updates the analysis results in real time and reflects them in the database. The analysis unit can also, for example, immediately update the database to provide the latest information if new analysis results are obtained. The analysis unit can also periodically check the analysis results and update the database as needed. This ensures that the latest information is always provided by updating the analysis results in real time. 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 the analysis results into AI, and the AI ​​can update the database in real time.

[0054] The analysis unit integrates data from other vehicles during analysis. For example, the analysis unit integrates accident information from other vehicles and includes it in its analysis. The analysis unit can also integrate traffic condition information from other vehicles and include it in its analysis. Furthermore, the analysis unit can integrate environmental information (weather, road conditions, etc.) from other vehicles and include it in its analysis. For example, the analysis unit collects accident information from other vehicles using vehicle-to-vehicle communication and stores it in a database. The analysis unit can also collect traffic condition information from other vehicles using vehicle-to-vehicle communication and store it in a database. Furthermore, the analysis unit can collect environmental information from other vehicles using vehicle-to-vehicle communication and store it in a database. This allows for more detailed analysis by integrating data from other vehicles. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from other vehicles into AI, and the AI ​​can analyze the data to provide information for accident prevention.

[0055] The analysis unit also considers environmental factors such as weather and traffic volume during analysis. For example, the analysis unit considers weather information (sunny, rainy, snowy, etc.) during analysis. The analysis unit can also consider traffic volume information (congestion, periods of low traffic, etc.) during analysis. Furthermore, the analysis unit can also consider road conditions (under construction, uneven road surface, etc.) during analysis. For example, the analysis unit collects weather information using weather sensors and stores it in a database during analysis. The analysis unit can also collect traffic volume information using traffic sensors and store it in a database during analysis. Furthermore, the analysis unit can collect road conditions using cameras and store them in a database during analysis. This allows for more accurate analysis by considering environmental factors such as weather and traffic volume. 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 weather information and traffic volume information into AI, and the AI ​​can analyze the data to provide information for accident prevention.

[0056] The warning unit provides feedback such as vibration in addition to audio and visuals when a warning is issued. For example, the warning unit can provide feedback through steering wheel vibration in addition to audio and visuals when a warning is issued. The warning unit can also provide feedback through seat vibration in addition to audio and visuals when a warning is issued. Furthermore, the warning unit can also provide feedback through pedal vibration in addition to audio and visuals when a warning is issued. For example, the warning unit can provide feedback through steering wheel vibration in addition to audio and visuals when a warning is issued. For example, the warning unit can provide feedback through seat vibration in addition to audio and visuals when a warning is issued. Furthermore, the warning unit can provide feedback through pedal vibration in addition to audio and visuals when a warning is issued. By providing feedback such as vibration in addition to audio and visuals, more effective warnings become possible. 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 feedback method for when a warning is issued into AI, and the AI ​​can select and provide an appropriate feedback method.

[0057] The warning unit customizes the content of warnings based on the driver's past response history. For example, the warning unit issues a customized warning based on the warnings the driver has responded to in the past. The warning unit can also issue a more emphasized warning based on the warnings the driver has ignored in the past. Furthermore, the warning unit can analyze the driver's past response history and customize the optimal warning content. For example, the warning unit retrieves the driver's past response history from a database and issues a customized warning. The warning unit can also issue a more emphasized warning based on the warnings the driver has ignored in the past. Furthermore, the warning unit can analyze the driver's past response history and customize the optimal warning content. This allows for more effective warnings by customizing the content of warnings based on the driver's past response history. 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 into AI, which can then customize and provide the optimal warning content.

[0058] The warning unit issues warnings to other vehicles and pedestrians when issuing a warning. For example, when the warning unit issues a warning to the driver, it also issues a warning to surrounding vehicles. The warning unit can also issue a warning to surrounding pedestrians when issuing a warning to the driver. Furthermore, the warning unit can also issue a warning to surrounding bicycles and motorcycles when issuing a warning to the driver. For example, when the warning unit issues a warning to the driver, it also issues a warning to surrounding vehicles. For example, when the warning unit issues a warning to the driver, it also issues a warning to surrounding pedestrians. Furthermore, the warning unit can also issue a warning to surrounding bicycles and motorcycles when issuing a warning to the driver. This ensures the safety of the surroundings by issuing warnings to other vehicles and pedestrians. 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 target of the warning into the AI, and the AI ​​can issue a warning to the appropriate target.

[0059] The warning unit considers the driver's current driving conditions (fatigue level, attention level, etc.) when issuing a warning. For example, if the driver is tired, the warning unit may increase the frequency of warnings to draw attention. For example, if the driver is not paying attention, the warning unit may also emphasize the content of the warning to draw attention. The warning unit can also consider the driver's driving conditions and issue a warning at the optimal time. For example, the warning unit may monitor the driver's fatigue level and adjust the frequency of warnings. For example, the warning unit may monitor the driver's attention level and emphasize the content of the warning. The warning unit can also monitor the driver's driving conditions and issue a warning at the optimal time. This allows the warning unit to issue warnings at a more appropriate time by considering the driver's current driving conditions. 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 may input the driver's driving conditions into the AI, which can then issue a warning at an appropriate time.

[0060] The braking system adjusts the optimal braking force when the brakes are applied, taking into account the vehicle speed and road conditions. For example, the braking system can adjust the optimal braking force when the brakes are applied, taking into account the vehicle speed. The braking system can also adjust the optimal braking force when the brakes are applied, taking into account the road conditions (dry, wet, icy, etc.). Furthermore, the braking system can also adjust the optimal braking force when the brakes are applied, taking into account the vehicle weight. For example, the braking system can adjust the optimal braking force when the brakes are applied, taking into account the vehicle speed. The braking system can also adjust the optimal braking force when the brakes are applied, taking into account the road conditions. Furthermore, the braking system can also adjust the optimal braking force when the brakes are applied, taking into account the vehicle weight. This allows for safer braking by adjusting the optimal braking force considering the vehicle speed and road conditions. Some or all of the above-described processes in the braking system may be performed using AI, for example, or without AI. For example, the braking system can input the vehicle speed and road conditions into the AI, which can then adjust the optimal braking force.

[0061] The braking system provides appropriate driving instructions to the driver after the brakes are applied. For example, the braking system may instruct the driver to change direction after the brakes are applied. The braking system may also instruct the driver to adjust speed after the brakes are applied. Furthermore, the braking system may also instruct the driver to indicate a safe stopping position after the brakes are applied. For example, the braking system may instruct the driver to change direction after the brakes are applied. For example, the braking system may also instruct the driver to adjust speed after the brakes are applied. Furthermore, the braking system may also instruct the driver to indicate a safe stopping position after the brakes are applied. This allows the driver to continue driving safely by providing appropriate driving instructions after the brakes are applied. Some or all of the above processing in the braking system may be performed using AI, for example, or without AI. For example, the braking system can input driving instructions after the brakes are applied to the AI, and the AI ​​can provide appropriate driving instructions.

[0062] The braking system adjusts the braking force while considering the movements of other vehicles and pedestrians when the brakes are applied. For example, the braking system adjusts the optimal braking force by considering the movements of surrounding vehicles when the brakes are applied. The braking system can also adjust the optimal braking force by considering the movements of surrounding pedestrians when the brakes are applied. Furthermore, the braking system can also adjust the optimal braking force by considering the movements of surrounding bicycles and motorcycles when the brakes are applied. For example, the braking system adjusts the optimal braking force by considering the movements of surrounding vehicles when the brakes are applied. For example, the braking system can adjust the optimal braking force by considering the movements of surrounding pedestrians when the brakes are applied. Furthermore, the braking system can also adjust the optimal braking force by considering the movements of surrounding bicycles and motorcycles when the brakes are applied. This makes safer braking possible by adjusting the braking force while considering the movements of other vehicles and pedestrians. Some or all of the above processing in the braking system may be performed using AI, for example, or without AI. For example, the braking system can input the movements of other vehicles and pedestrians into the AI, which can then adjust the optimal braking force.

[0063] The braking system also considers the vehicle's internal conditions (engine status, fuel level, etc.) when braking. For example, the braking system adjusts the optimal braking force by considering the engine status (temperature, rotational speed, etc.) when braking. The braking system can also adjust the optimal braking force by considering the fuel level when braking. Furthermore, the braking system can also adjust the optimal braking force by considering the battery status when braking. For example, the braking system adjusts the optimal braking force by considering the engine status when braking. For example, the braking system can adjust the optimal braking force by considering the fuel level when braking. Furthermore, the braking system can also adjust the optimal braking force by considering the battery status when braking. This allows for more appropriate braking operation by considering the vehicle's internal conditions. Some or all of the above processing in the braking system may be performed using AI, for example, or without AI. For example, the braking system can input the vehicle's internal conditions into the AI, which can then adjust the optimal braking force.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] Safety driving assistance systems can also learn the driver's driving style and provide individually optimized warnings. For example, if a driver frequently uses sudden braking, the system can learn this pattern and predict situations where sudden braking is necessary, issuing warnings in advance. Similarly, if a driver prefers driving on highways, the system can use this information to identify dangerous situations on highways and provide appropriate warnings. Furthermore, if a driver frequently drives at night, the system can take into account reduced visibility at night and issue warnings earlier. This allows for more effective safety driving assistance by providing personalized warnings tailored to the driver's driving style.

[0066] The data collection unit can also detect specific driving patterns based on the driver's driving history and assess the risk of accidents. For example, if a driver frequently brakes suddenly at a particular intersection, the system can determine that the risk of an accident at that intersection is high and issue a warning in advance. Furthermore, if a driver experiences a high number of accidents when driving during a specific time period, the system can issue a warning when driving during that time. Additionally, if a driver experiences a high number of accidents when driving under specific weather conditions, the system can issue a warning when driving under those conditions. This allows the system to assess the risk of accidents based on the driver's driving history and provide appropriate warnings, thereby reducing the risk of accidents.

[0067] The monitoring unit can learn the driver's driving style in real time and provide warnings tailored to that style. For example, if a driver frequently accelerates suddenly, the system can learn this pattern and predict situations where sudden acceleration is necessary, issuing a warning in advance. Similarly, if a driver frequently changes lanes, the system can use this information to identify dangerous situations during lane changes and provide appropriate warnings. Furthermore, if a driver drives for extended periods, the system can use this information to assess the risk of fatigue and issue a warning encouraging a break. By providing personalized warnings tailored to the driver's driving style, more effective safe driving support becomes possible.

[0068] The analysis unit can also detect specific driving patterns based on the driver's driving history and assess the risk of accidents. For example, if a driver frequently brakes suddenly at a particular intersection, the system can determine that the risk of an accident at that intersection is high and issue a warning in advance. Furthermore, if a driver experiences a high number of accidents when driving during a specific time period, the system can issue a warning when driving during that time. Additionally, if a driver experiences a high number of accidents when driving under specific weather conditions, the system can issue a warning when driving under those conditions. This allows the system to assess the risk of accidents based on the driver's driving history and provide appropriate warnings, thereby reducing the risk of accidents.

[0069] The warning system can also learn the driver's driving style and provide individually optimized warnings. For example, if a driver frequently uses sudden braking, the system can learn this pattern and predict situations where sudden braking is necessary, issuing a warning in advance. Similarly, if a driver prefers driving on highways, the system can use this information to identify dangerous situations on highways and provide appropriate warnings. Furthermore, if a driver frequently drives at night, the system can take into account reduced visibility at night and issue warnings earlier. This allows for more effective safe driving support by providing personalized warnings tailored to the driver's driving style.

[0070] The braking system can also learn the driver's driving style and provide individually optimized brake control. For example, if a driver frequently uses sudden braking, the system can learn this pattern and anticipate situations where sudden braking is necessary, activating the brakes in advance. Similarly, if a driver prefers driving on highways, the system can use this information to identify dangerous situations on highways and provide appropriate brake control. Furthermore, if a driver frequently drives at night, the system can take into account reduced visibility at night and activate the brakes earlier. This allows for more effective and safer driving assistance by providing individualized brake control tailored to the driver's driving style.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The data collection unit collects information about locations where accidents or near misses have occurred in the past. For example, it collects information such as the location, time, and circumstances of an accident, identifies the location using sensors and cameras, and records the time of the accident using GPS data. It can also collect eyewitness accounts and photographs of the scene. Step 2: The storage unit stores the information collected by the collection unit in a database. For example, it saves the collected information in a database so that it can be searched and updated as needed. You can also select the type of database and storage format, and set the information retention period. Step 3: The monitoring unit monitors the vehicle's movement in real time. For example, it acquires the vehicle's location, speed, and direction of travel using sensors and GPS. It obtains the vehicle's location as GPS data and monitors the vehicle's speed using speedometer data. It can also monitor the vehicle's direction of travel using a direction sensor. Step 4: The analysis unit analyzes whether the current movement monitored by the monitoring unit is similar to the pattern of a previous accident. For example, to compare past accident patterns with the current movement and determine if they are similar, the analysis unit retrieves past accident patterns from a database and uses an algorithm to compare them with the current movement. Step 5: The warning unit issues a warning to the driver if the analysis unit determines that the data is similar. For example, it may issue an audible or visual warning to the driver, using an audible warning to draw their attention. It can also issue a visual warning to the driver. Step 6: After a warning is issued by the warning unit, the braking unit will activate the automatic brakes as needed. For example, if the driver ignores the warning and continues to make dangerous movements, the brakes will be automatically activated, and the braking strength and activation timing can be set to prevent accidents.

[0073] (Example of form 2) An embodiment of the present invention provides a driver safety support system that, based on information about locations where accidents or near misses have occurred in the past, notifies the driver in advance if the current movement is similar to the pattern of a previous accident. The safety support system collects information about locations where accidents or near misses have occurred in the past and stores it in a database. Next, it monitors the current vehicle movement in real time, and the AI ​​analyzes whether it is similar to the pattern of a previous accident. If it is similar, it issues a warning to the driver. Furthermore, it can also activate automatic braking as needed. For example, the safety support system collects information about locations where accidents or near misses have occurred in the past and stores it in a database. This information includes the location, time, and circumstances of the accident. For example, if accidents frequently occur at a particular intersection, the information of that intersection is registered in the database. Next, the safety support system monitors the current vehicle movement in real time. The AI ​​acquires the vehicle's location information, speed, direction of travel, etc., using sensors and GPS, and analyzes it. For example, if the vehicle is approaching a particular intersection, it compares the current movement with past accident patterns at that intersection. The AI ​​analyzes whether the current movement is similar to past accident patterns. For example, if a particular intersection has a history of frequent accidents involving right turns, the system will issue a warning if the current vehicle attempts to turn right at the same intersection. This warning is communicated to the driver via voice and visual means. Furthermore, it can automatically apply the brakes if necessary. For instance, if the driver ignores the warning and continues making dangerous movements, the safety driving support system will automatically apply the brakes to prevent an accident. In this way, driver safety can be ensured. This system can prevent recurrence at locations where accidents or near misses have occurred in the past, thereby improving driver safety. In particular, it is expected to enable drivers to anticipate situations like experienced drivers, even in unfamiliar areas, significantly reducing the risk of accidents. Thus, safety driving support systems can improve driver safety.

[0074] The safe driving support system according to this embodiment comprises a collection unit, a storage unit, a monitoring unit, an analysis unit, a warning unit, and a braking unit. The collection unit collects information on locations where accidents or near misses have occurred in the past. For example, the collection unit collects information such as the location, time, and circumstances of an accident. For example, the collection unit identifies the location of an accident using sensors or cameras and records the time of occurrence using GPS data. The collection unit can also collect eyewitness testimonies and photographs of the scene in order to record the circumstances of the accident in detail. For example, the collection unit takes photographs of the accident scene and saves them in a database. The storage unit stores the information collected by the collection unit in a database. For example, the storage unit stores the collected information in a database and makes it searchable and updatable as needed. For example, the storage unit can select the type and format of the database and set the information retention period. The monitoring unit monitors the current movement of the vehicle in real time. For example, the monitoring unit acquires information such as the vehicle's location, speed, and direction of travel using sensors or GPS. The monitoring unit, for example, acquires vehicle location information as GPS data and monitors the vehicle's speed using speedometer data. The monitoring unit can also monitor the vehicle's direction of travel using a direction sensor. The analysis unit analyzes whether the current movement monitored by the monitoring unit is similar to patterns of previous accidents. The analysis unit, for example, compares past accident patterns with the current movement to determine if they are similar. The analysis unit, for example, acquires past accident patterns from a database and uses an algorithm to compare them with the current movement. The warning unit issues a warning to the driver if the analysis unit determines that the movements are similar. The warning unit issues a warning to the driver, for example, using voice warnings to draw the driver's attention. The warning unit can also issue a visual warning to the driver. After a warning is issued by the warning unit, the braking unit activates the automatic brakes as needed. The braking unit automatically activates the brakes, for example, if the driver ignores the warning and continues to make dangerous movements. The braking system can, for example, set the braking strength and activation timing to perform control measures that prevent accidents.As a result, the safety driving support system according to the embodiment can prevent recurrence at locations where accidents or near misses have previously occurred, thereby improving driver safety.

[0075] The data collection unit collects information from locations where accidents or near misses have occurred. For example, it collects information such as the location, time, and circumstances of an accident. Specifically, the unit uses sensors and cameras mounted on the vehicle to identify the location of the accident and records the time of the accident using GPS data. This allows for the precise location and time of the accident to be determined. The data collection unit can also collect eyewitness testimonies and photographs of the scene to record the accident circumstances in detail. For example, taking photographs of the accident scene and saving them to a database can be useful for later analysis. Furthermore, the data collection unit can also collect data such as the vehicle's speed, direction of travel, and brake usage. This allows for a detailed understanding of the vehicle's movements at the time of the accident. The data collection unit collects this data in real time and transmits it to a central database. As a result, the collected data is immediately stored in the storage unit and can be used for later analysis and monitoring. By accurately and quickly collecting information from locations where accidents or near misses have occurred, the data collection unit plays a crucial role in forming the foundation of the safe driving support system.

[0076] The storage unit stores the information collected by the collection unit in a database. For example, the storage unit saves the collected information in a database and makes it searchable and updatable as needed. Specifically, the storage unit selects the type of database and storage format to efficiently manage the collected information. For example, relational databases or NoSQL databases can be used to streamline the storage and retrieval of information. The storage unit can also manage the database capacity by setting the information retention period and appropriately archiving old data. Furthermore, the storage unit can classify and tag the collected information to facilitate subsequent searching and analysis. For example, data can be classified based on information such as the location, time, and circumstances of an accident, allowing for quick retrieval of data that matches specific conditions. The storage unit centrally manages the collected information and can collaborate with other systems and departments as needed. In this way, the storage unit plays a crucial role in supporting the information infrastructure of the safe driving support system.

[0077] The monitoring unit monitors the vehicle's current movements in real time. For example, it acquires vehicle location information, speed, and direction of travel using sensors and GPS. Specifically, the monitoring unit acquires vehicle location information using a GPS device mounted on the vehicle and monitors the vehicle's speed using speedometer data. It can also monitor the vehicle's direction of travel using a direction sensor. This allows the monitoring unit to accurately understand the vehicle's current movements. Furthermore, the monitoring unit can acquire data such as the vehicle's brake usage and accelerator operation. This allows for real-time monitoring of detailed information regarding the vehicle's movements. The monitoring unit transmits this data to a central database, making it accessible to the analysis and warning units. Thus, the monitoring unit plays a crucial role in ensuring driver safety by monitoring vehicle movements in real time and collaborating with other departments of the safety driving support system.

[0078] The analysis unit analyzes whether the current movements monitored by the monitoring unit resemble patterns from previous accidents. For example, the analysis unit compares past accident patterns with current movements to determine similarity. Specifically, the analysis unit retrieves past accident patterns from a database and uses algorithms to compare them with current movements. For example, machine learning algorithms can be used to extract features from past accident data and calculate similarity by matching them with current movements. This allows the analysis unit to determine with high accuracy whether current movements resemble past accident patterns. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past accident data, it can predict risk fluctuations in specific regions or time periods and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and 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, improving the reliability and safety of the entire system.

[0079] The warning unit issues a warning to the driver if the analysis unit determines that the situation is similar. The warning unit can issue warnings to the driver, for example, through voice or visual means. Specifically, the warning unit uses voice warnings to draw the driver's attention. For example, it may play a voice message such as "There is a hazard ahead. Please be careful" through the in-car speakers. The warning unit can also issue visual warnings to the driver. For example, it may display a warning message on the dashboard display to attract the driver's attention. Furthermore, the warning unit can issue tactile warnings to the driver using vibration warnings. For example, it may vibrate the steering wheel or seat to alert the driver to the hazard. In this way, the warning unit can issue warnings to the driver in a variety of ways and encourage actions to avoid danger. The warning unit plays an important role in attracting the driver's attention and preventing accidents.

[0080] The braking system automatically applies the brakes as needed after a warning is issued by the warning unit. For example, the braking system automatically applies the brakes if the driver ignores the warning and continues to make dangerous movements. Specifically, the braking system monitors the vehicle's speed, direction of travel, and surrounding conditions, and applies the brakes when it determines that danger is imminent. For example, by automatically applying the brakes when there is an obstacle ahead or when approaching a sharp curve, accidents can be prevented. The braking system can set the braking strength and timing to provide optimal control. For example, by starting with light braking and gradually increasing the strength, safety can be ensured without causing discomfort to the driver. In addition, the braking system can exert maximum braking force in emergencies to avoid collisions with other vehicles or pedestrians. Thus, the braking system plays a crucial role in ensuring driver safety and preventing accidents.

[0081] The data collection unit collects information such as the location, time, and circumstances of an accident. For example, the data collection unit records the location of the accident as GPS coordinates. The data collection unit can also record the time of the accident as a timestamp. Furthermore, the data collection unit can collect eyewitness testimonies and photographs of the scene in order to record the circumstances of the accident in detail. For example, the data collection unit takes photographs of the accident scene and saves them in a database. This allows for more detailed accident information to be obtained by collecting information such as the location, time, and circumstances of the accident. 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 photographs of the accident scene into AI, and the AI ​​can perform image analysis to automatically record the circumstances of the accident.

[0082] The monitoring unit acquires vehicle location information, speed, direction of travel, etc., using sensors and GPS. For example, the monitoring unit acquires vehicle location information as GPS data. The monitoring unit can also monitor vehicle speed using speedometer data, for example. The monitoring unit can also monitor the direction of travel of the vehicle using direction sensors. For example, the monitoring unit acquires vehicle location information in real time and stores it in a database. The monitoring unit can also monitor vehicle speed in real time and detect abnormal speed changes. The monitoring unit can also monitor the direction of travel of the vehicle in real time and detect abnormal changes in direction of travel. As a result, by acquiring vehicle location information, speed, direction of travel, etc., the current movement of the vehicle can be accurately monitored. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input vehicle location information into AI, and the AI ​​can analyze the location information to detect anomalies.

[0083] The analysis unit compares past accident patterns with current movements to determine if they are similar. For example, the analysis unit may obtain past accident patterns from a database and use an algorithm to compare them with current movements. The analysis unit may also analyze past accident patterns using a machine learning algorithm to determine if they are similar to current movements. Alternatively, the analysis unit may analyze past accident patterns using a rule-based algorithm to determine if they are similar to current movements. For example, the analysis unit may classify past accident patterns using a clustering algorithm to determine if they are similar to current movements. The analysis unit may also analyze past accident patterns using a deep learning algorithm to determine if they are similar to current movements. By comparing past accident patterns with current movements, it is possible to prevent accidents from recurring. 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 may input past accident patterns into an AI, which can perform pattern analysis to determine if they are similar to current movements.

[0084] The warning unit issues warnings to the driver via voice and visual means. For example, the warning unit can use voice warnings to draw the driver's attention. The warning unit can also issue visual warnings to the driver using visual warnings. Furthermore, the warning unit can combine voice and visual warnings to warn the driver. For example, the warning unit can play the message "Danger. Please be careful" as a voice warning. The warning unit can also display a warning icon on the dashboard as a visual warning. In addition, the warning unit can provide more effective warnings to the driver by issuing voice and visual warnings simultaneously. This reduces the risk of accidents by issuing warnings to the driver via voice and visual means. 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 state into the AI, which can then select an appropriate warning method and issue a warning.

[0085] The braking system automatically applies the brakes if the driver ignores warnings and continues to make dangerous movements. For example, the braking system can set the braking strength and timing to prevent accidents. The braking system can also gradually increase the braking strength if the driver ignores warnings and continues to make dangerous movements. For example, the braking system can initially apply a light brake and then apply a strong brake if the driver does not react. This prevents accidents by automatically applying the brakes if the driver ignores warnings and continues to make dangerous movements. Some or all of the above processing in the braking system may be performed using AI, or not. For example, the braking system can input the driver's state into the AI, which can then select the appropriate braking strength and timing to apply the brakes.

[0086] The data collection unit estimates the driver's emotions and adjusts the timing of accident information collection based on the estimated emotions. For example, if the driver is tense, the data collection unit increases the frequency of accident information collection and updates the information in real time. For example, if the driver is relaxed, the data collection unit can return the collection frequency to normal and update the information periodically. Furthermore, if the driver is tired, the data collection unit can appropriately adjust the collection frequency and prioritize the collection of only important information. For example, the data collection unit estimates the driver's emotions using facial recognition technology and adjusts the collection timing based on the estimated emotions. For example, the data collection unit can estimate the driver's emotions using voice analysis technology and adjust the collection timing based on the estimated emotions. Furthermore, the data collection unit can estimate the driver's emotions using heart rate monitoring technology and adjust the collection timing based on the estimated emotions. This allows for information to be collected at a more appropriate time by adjusting the timing of accident information collection based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generating AI may be, but is not limited to, text generating AI (e.g., LLM) or multimodal generating AI. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit can input a driver's facial image into an AI, which can estimate emotions and adjust the collection timing.

[0087] The data collection unit collects not only the location, time, and circumstances of an accident, but also environmental information such as weather and traffic volume. For example, the data collection unit collects weather information at the time of the accident (sunny, rainy, snowy, etc.) and stores it in a database. The data collection unit can also collect traffic volume information at the time of the accident (congestion, periods of low traffic, etc.) and store it in a database. Furthermore, the data collection unit can collect road conditions at the time of the accident (under construction, uneven road surface, etc.) and store it in a database. For example, the data collection unit collects weather information at the time of the accident using weather sensors and saves it in a database. The data collection unit can also collect traffic volume information at the time of the accident using traffic sensors and save it in a database. Furthermore, the data collection unit can collect road conditions at the time of the accident using cameras and save them in a database. By collecting environmental information such as weather and traffic volume, more detailed accident information can be obtained. 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 information into AI, and the AI ​​can analyze the weather data to collect accident information.

[0088] The data collection unit updates the collected information in real time, ensuring that the latest accident information is always maintained. For example, when accident information is updated, the data collection unit reflects this in the database in real time. The data collection unit can also update the database immediately when new accident information is collected, ensuring that the information is up-to-date. Furthermore, the data collection unit can periodically review the collected information and update the database as needed. For example, the data collection unit updates the accident information in real time and reflects this in the database. For example, when new accident information is collected, the data collection unit can update the database immediately, ensuring that the information is up-to-date. Furthermore, the data collection unit can periodically review the collected information and update the database as needed. This ensures that the latest accident information is always maintained by updating the collected information 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 the collected information into AI, which can then update the database in real time.

[0089] The data collection unit estimates the driver's emotions and determines the priority of information to collect based on the estimated emotions. For example, if the driver is tense, the data collection unit prioritizes the collection of accident information and postpones the collection of other information. For example, if the driver is relaxed, the data collection unit may prioritize the collection of environmental information and collect accident information periodically. Also, if the driver is tired, the data collection unit may prioritize the collection of only important accident information and postpone the collection of other information. For example, the data collection unit estimates the driver's emotions using facial recognition technology and determines the priority of information to collect based on the estimated emotions. The data collection unit may also estimate the driver's emotions using voice analysis technology and determine the priority of information to collect based on the estimated emotions. Furthermore, the data collection unit may also estimate the driver's emotions using heart rate monitoring technology and determine the priority of information to collect based on the estimated emotions. This allows for the priority collection of important information by determining the priority of information to collect based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generating AI may be, but is not limited to, text generating AI (e.g., LLM) or multimodal generating AI. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit can input a driver's facial image into an AI, which can then estimate emotions and determine the priority of information to collect.

[0090] The data collection unit also collects information on surrounding traffic infrastructure (such as the status of traffic lights and road construction information) during the collection process. For example, the data collection unit can collect the status of traffic lights at the time of an accident (red light, green light, etc.) and store it in a database. The data collection unit can also collect information on road construction at the time of an accident (under construction, completed construction, etc.) and store it in a database. Furthermore, the data collection unit can collect information on traffic signs at the time of an accident (such as speed limits and no entry zones) and store it in a database. For example, the data collection unit can collect the status of traffic lights at the time of an accident using a traffic light sensor and save it in a database. The data collection unit can also collect information on road construction at the time of an accident using a construction sensor and save it in a database. Furthermore, the data collection unit can collect information on traffic signs at the time of an accident using a camera and save it in a database. This allows for the collection of more detailed accident information by collecting information on surrounding traffic infrastructure. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the status of traffic lights into an AI, which can then analyze the traffic light data to collect accident information.

[0091] The data collection unit integrates information from other vehicles during the collection process. For example, the data collection unit collects accident information from other vehicles and integrates it into a database. The data collection unit can also collect traffic condition information from other vehicles and integrate it into a database. Furthermore, the data collection unit can collect environmental information (weather, road conditions, etc.) from other vehicles and integrate it into a database. For example, the data collection unit collects accident information from other vehicles using vehicle-to-vehicle communication and stores it in a database. The data collection unit can also collect traffic condition information from other vehicles using vehicle-to-vehicle communication and store it in a database. Furthermore, the data collection unit can collect environmental information from other vehicles using vehicle-to-vehicle communication and store it in a database. By integrating and collecting information from other vehicles, more detailed accident information can be obtained. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information from other vehicles into AI, and the AI ​​can analyze the information to collect accident information.

[0092] The data storage unit estimates the driver's emotions and adjusts the database update frequency based on the estimated emotions. For example, if the driver is stressed, the data storage unit increases the database update frequency and updates information in real time. For example, if the driver is relaxed, the data storage unit can return the update frequency to normal and update information periodically. Also, if the driver is tired, the data storage unit can adjust the update frequency appropriately and prioritize updating only important information. For example, the data storage unit estimates the driver's emotions using facial recognition technology and adjusts the database update frequency based on the estimated emotions. For example, the data storage unit can estimate the driver's emotions using voice analysis technology and adjust the database update frequency based on the estimated emotions. Also, the data storage unit can estimate the driver's emotions using heart rate monitoring technology and adjust the database update frequency based on the estimated emotions. This allows for information to be updated at a more appropriate time by adjusting the database update frequency based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generation AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the storage unit may be performed using AI, or not using AI. For example, the storage unit can input a driver's facial image into the AI, which can then estimate emotions and adjust the frequency of database updates.

[0093] The storage unit organizes the stored information by category, making it searchable efficiently. For example, the storage unit organizes accident information by category, such as location, time, and circumstances. The storage unit can also organize environmental information by category, such as weather, traffic volume, and road conditions. Furthermore, the storage unit can build a database to organize the collected information by category and make it searchable efficiently. For example, the storage unit organizes accident information by location and saves it to the database. For example, the storage unit can also organize accident information by time and save it to the database. Furthermore, the storage unit can organize accident information by circumstances and save it to the database. This allows for efficient information retrieval by organizing the stored information by category. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the collected information into AI, which can then organize the information by category and save it to the database.

[0094] The storage unit ensures data security by regularly backing up the stored information. For example, the storage unit ensures data security by regularly backing up the database. The storage unit can also perform immediate backups when important information is stored. Furthermore, the storage unit can ensure data security by saving backup data in multiple locations. For example, the storage unit can perform daily backups and save them to the database. The storage unit can also perform weekly backups and save them to cloud storage. Furthermore, the storage unit can perform immediate backups when important information is stored and save them in multiple locations. In this way, data security can be ensured by regularly backing up the stored information. Some or all of the above processes in the storage unit may be performed using AI, for example, or not using AI. For example, the storage unit can input backup data into AI, and the AI ​​can create a backup plan to ensure data security.

[0095] The data storage unit estimates the driver's emotions and prioritizes data based on the estimated emotions. For example, if the driver is tense, the storage unit will prioritize accident information and postpone other information. For example, if the driver is relaxed, the storage unit may prioritize environmental information and update accident information regularly. Also, if the driver is tired, the storage unit may prioritize updating only important accident information and postpone other information. For example, the storage unit can estimate the driver's emotions using facial recognition technology and prioritize data based on the estimated emotions. The storage unit can also estimate the driver's emotions using voice analysis technology and prioritize data based on the estimated emotions. Furthermore, the storage unit can estimate the driver's emotions using heart rate monitoring technology and prioritize data based on the estimated emotions. This allows important information to be updated preferentially by prioritizing data based on the driver's emotions. 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-described processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input a driver's facial image into the AI, which can then estimate emotions and determine the priority of the data.

[0096] The storage unit collaborates with other systems to share data. For example, the storage unit can collaborate with other traffic management systems to share accident information. The storage unit can also collaborate with other vehicle management systems to share environmental information. Furthermore, the storage unit can collaborate with other database systems to share collected information. For example, the storage unit can collaborate with other traffic management systems via API to share accident information. For example, the storage unit can collaborate with other vehicle management systems using a data sharing protocol to share environmental information. Furthermore, the storage unit can collaborate with other database systems using a data sharing protocol to share collected information. This enables the effective use of information by collaborating with other systems and sharing data. Some or all of the above-described processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the collaboration with other systems into the AI, and the AI ​​can set up a data sharing protocol to share information.

[0097] The data storage unit visualizes the stored information so that drivers can easily understand it. For example, the data storage unit visualizes accident information on a map so that drivers can easily understand it. The data storage unit can also visualize environmental information in graphs or charts so that drivers can easily understand it. Furthermore, the data storage unit can visualize the collected information so that drivers can intuitively understand it. For example, the data storage unit visualizes accident information on a map so that drivers can easily understand it. For example, the data storage unit can visualize environmental information in graphs or charts so that drivers can easily understand it. Furthermore, the data storage unit can visualize the collected information so that drivers can intuitively understand it. In this way, by visualizing the stored information, drivers can easily understand the information. Some or all of the above processing in the data storage unit may be performed using AI, for example, or without AI. For example, the data storage unit can input the information to be visualized into the AI, and the AI ​​can visualize the information and provide it to the driver.

[0098] The monitoring unit estimates the driver's emotions and adjusts the accuracy of monitoring based on the estimated emotions. For example, if the driver is tense, the monitoring unit increases the accuracy of monitoring and updates the information in real time. For example, if the driver is relaxed, the monitoring unit can return the accuracy of monitoring to normal and update the information periodically. Also, if the driver is tired, the monitoring unit can appropriately adjust the accuracy of monitoring and prioritize monitoring only important information. For example, the monitoring unit estimates the driver's emotions using facial recognition technology and adjusts the accuracy of monitoring based on the estimated emotions. For example, the monitoring unit can estimate the driver's emotions using voice analysis technology and adjust the accuracy of monitoring based on the estimated emotions. Also, the monitoring unit can estimate the driver's emotions using heart rate monitoring technology and adjust the accuracy of monitoring based on the estimated emotions. This allows for more appropriate monitoring by adjusting the accuracy of monitoring based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generating 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 monitoring unit may be performed using AI, or not using AI. For example, the monitoring unit can input a driver's facial image into the AI, which can then estimate emotions and adjust the accuracy of the monitoring.

[0099] The monitoring unit monitors not only the movement of the vehicle but also the movement of surrounding pedestrians and other vehicles. For example, the monitoring unit monitors the movement of surrounding pedestrians in addition to the movement of the vehicle and stores it in a database. The monitoring unit can also monitor the movement of other vehicles in addition to the movement of the vehicle and store it in a database. Furthermore, the monitoring unit can monitor the movement of surrounding bicycles and motorcycles in addition to the movement of the vehicle and store it in a database. For example, the monitoring unit monitors the position of pedestrians in addition to the movement of the vehicle and stores it in a database. The monitoring unit can also monitor the speed of other vehicles in addition to the movement of the vehicle and store it in a database. Furthermore, the monitoring unit can monitor the direction of travel of bicycles and motorcycles in addition to the movement of the vehicle and store it in a database. This enables safer driving by monitoring the movement of surrounding pedestrians and other vehicles. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the movement of surrounding pedestrians and other vehicles into AI, and the AI ​​can analyze the movement and store it in a database.

[0100] The monitoring unit analyzes the monitoring data in real time and immediately detects anomalies. For example, the monitoring unit can analyze the monitoring data in real time and immediately detect abnormal movements. The monitoring unit can also analyze the monitoring data in real time and immediately detect abnormal speed changes. Furthermore, the monitoring unit can analyze the monitoring data in real time and immediately detect abnormal changes in direction of travel. For example, the monitoring unit analyzes the monitoring data in real time and immediately detects abnormal movements. For example, the monitoring unit can analyze the monitoring data in real time and immediately detect abnormal speed changes. Furthermore, the monitoring unit can analyze the monitoring data in real time and immediately detect abnormal changes in direction of travel. This allows for immediate detection of anomalies by analyzing the monitoring data in real time. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input monitoring data into AI, and the AI ​​can analyze the data in real time to detect anomalies.

[0101] The monitoring unit estimates the driver's emotions and determines monitoring priorities based on the estimated emotions. For example, if the driver is tense, the monitoring unit prioritizes monitoring accident information and postpones monitoring other information. For example, if the driver is relaxed, the monitoring unit may prioritize monitoring environmental information and monitor accident information periodically. Also, if the driver is tired, the monitoring unit may prioritize monitoring only important accident information and postpone monitoring other information. For example, the monitoring unit can estimate the driver's emotions using facial recognition technology and determine monitoring priorities based on the estimated emotions. The monitoring unit can also estimate the driver's emotions using voice analysis technology and determine monitoring priorities based on the estimated emotions. Furthermore, the monitoring unit can estimate the driver's emotions using heart rate monitoring technology and determine monitoring priorities based on the estimated emotions. This allows for priority monitoring of important information by determining monitoring priorities based on the driver's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. The generating 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 monitoring unit may be performed using AI, or not using AI. For example, the monitoring unit can input a driver's facial image into an AI, which can then estimate emotions and determine monitoring priorities.

[0102] The monitoring unit also monitors the vehicle's internal conditions (engine status, fuel level, etc.) during monitoring. For example, in addition to the vehicle's movement, the monitoring unit monitors the engine's condition (temperature, RPM, etc.) and stores it in a database. The monitoring unit can also monitor the fuel level in addition to the vehicle's movement and store it in a database. Furthermore, the monitoring unit can monitor the battery status in addition to the vehicle's movement and store it in a database. For example, the monitoring unit monitors the engine temperature in addition to the vehicle's movement and saves it in a database. The monitoring unit can also monitor the fuel level in addition to the vehicle's movement and save it in a database. Furthermore, the monitoring unit can monitor the battery status in addition to the vehicle's movement and save it in a database. This allows for obtaining more detailed information by monitoring the vehicle's internal conditions. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the vehicle's internal conditions into AI, which can analyze the data and save it in a database.

[0103] The analysis unit estimates the driver's emotions and adjusts the analysis algorithm based on the estimated emotions. For example, if the driver is tense, the analysis unit increases the accuracy of the analysis and updates the information in real time. For example, if the driver is relaxed, the analysis unit can return the accuracy of the analysis to normal and update the information periodically. Also, if the driver is tired, the analysis unit can appropriately adjust the accuracy of the analysis and prioritize analyzing only important information. For example, the analysis unit estimates the driver's emotions using facial recognition technology and adjusts the analysis algorithm based on the estimated emotions. For example, the analysis unit can estimate the driver's emotions using voice analysis technology and adjust the analysis algorithm based on the estimated emotions. Also, the analysis unit can estimate the driver's emotions using heart rate monitoring technology and adjust the analysis algorithm based on the estimated emotions. This allows for more appropriate analysis by adjusting the analysis algorithm based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generating AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input a driver's facial image into the AI, which can then estimate emotions and adjust the analysis algorithm.

[0104] The analysis unit analyzes not only past accident patterns but also similar patterns. For example, the analysis unit analyzes patterns of similar situations in addition to past accident patterns. The analysis unit can also analyze similar accident patterns in different locations in addition to past accident patterns. Furthermore, the analysis unit can analyze similar accident patterns in different time periods in addition to past accident patterns. For example, the analysis unit analyzes patterns of similar situations in addition to past accident patterns. For example, the analysis unit can analyze similar accident patterns in different locations in addition to past accident patterns. Furthermore, the analysis unit can analyze similar accident patterns in different time periods in addition to past accident patterns. By including similar patterns in the analysis, a broader range of accident prevention becomes possible. 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 past accident patterns and similar patterns into AI, and the AI ​​can perform pattern analysis to provide information for accident prevention.

[0105] The analysis unit updates the analysis results in real time, always providing the latest information. For example, when the analysis results are updated, the analysis unit reflects them in the database in real time. The analysis unit can also, for example, immediately update the database to provide the latest information if new analysis results are obtained. The analysis unit can also periodically check the analysis results and update the database as needed. For example, the analysis unit updates the analysis results in real time and reflects them in the database. The analysis unit can also, for example, immediately update the database to provide the latest information if new analysis results are obtained. The analysis unit can also periodically check the analysis results and update the database as needed. This ensures that the latest information is always provided by updating the analysis results in real time. 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 the analysis results into AI, and the AI ​​can update the database in real time.

[0106] The analysis unit estimates the driver's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if the driver is tense, the analysis unit provides a simple and highly visible display method. For example, if the driver is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the driver is in a hurry, the analysis unit can provide a concise display method. For example, the analysis unit estimates the driver's emotions using facial recognition technology and adjusts the display method of the analysis results based on the estimated emotions. For example, the analysis unit can estimate the driver's emotions using voice analysis technology and adjust the display method of the analysis results based on the estimated emotions. Furthermore, the analysis unit can estimate the driver's emotions using heart rate monitoring technology and adjust the display method of the analysis results based on the estimated emotions. This allows for the provision of more appropriate information by adjusting the display method of the analysis results based on the driver's emotions. 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-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input a driver's facial image into the AI, which can then estimate emotions and adjust how the analysis results are displayed.

[0107] The analysis unit integrates data from other vehicles during analysis. For example, the analysis unit integrates accident information from other vehicles and includes it in its analysis. The analysis unit can also integrate traffic condition information from other vehicles and include it in its analysis. Furthermore, the analysis unit can integrate environmental information (weather, road conditions, etc.) from other vehicles and include it in its analysis. For example, the analysis unit collects accident information from other vehicles using vehicle-to-vehicle communication and stores it in a database. The analysis unit can also collect traffic condition information from other vehicles using vehicle-to-vehicle communication and store it in a database. Furthermore, the analysis unit can collect environmental information from other vehicles using vehicle-to-vehicle communication and store it in a database. This allows for more detailed analysis by integrating data from other vehicles. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from other vehicles into AI, and the AI ​​can analyze the data to provide information for accident prevention.

[0108] The analysis unit also considers environmental factors such as weather and traffic volume during analysis. For example, the analysis unit considers weather information (sunny, rainy, snowy, etc.) during analysis. The analysis unit can also consider traffic volume information (congestion, periods of low traffic, etc.) during analysis. Furthermore, the analysis unit can also consider road conditions (under construction, uneven road surface, etc.) during analysis. For example, the analysis unit collects weather information using weather sensors and stores it in a database during analysis. The analysis unit can also collect traffic volume information using traffic sensors and store it in a database during analysis. Furthermore, the analysis unit can collect road conditions using cameras and store them in a database during analysis. This allows for more accurate analysis by considering environmental factors such as weather and traffic volume. 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 weather information and traffic volume information into AI, and the AI ​​can analyze the data to provide information for accident prevention.

[0109] The warning unit estimates the driver's emotions and adjusts the way it delivers warnings based on those emotions. For example, if the driver is tense, the warning unit will issue a warning in a calm voice. If the driver is relaxed, the warning unit may also issue a warning in a cheerful voice. Furthermore, if the driver is in a hurry, the warning unit may also issue a warning in a quick and concise voice. For example, the warning unit can estimate the driver's emotions using facial recognition technology and adjust the way it delivers warnings based on those emotions. The warning unit can also estimate the driver's emotions using voice analysis technology and adjust the way it delivers warnings based on those emotions. Furthermore, the warning unit can estimate the driver's emotions using heart rate monitoring technology and adjust the way it delivers warnings based on those emotions. This allows for more effective warnings by adjusting the way the warning is delivered based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, 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 warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input a driver's facial image into the AI, which can then estimate the emotions and adjust the way the warning is expressed.

[0110] The warning unit provides feedback such as vibration in addition to audio and visuals when a warning is issued. For example, the warning unit can provide feedback through steering wheel vibration in addition to audio and visuals when a warning is issued. The warning unit can also provide feedback through seat vibration in addition to audio and visuals when a warning is issued. Furthermore, the warning unit can also provide feedback through pedal vibration in addition to audio and visuals when a warning is issued. For example, the warning unit can provide feedback through steering wheel vibration in addition to audio and visuals when a warning is issued. For example, the warning unit can provide feedback through seat vibration in addition to audio and visuals when a warning is issued. Furthermore, the warning unit can provide feedback through pedal vibration in addition to audio and visuals when a warning is issued. By providing feedback such as vibration in addition to audio and visuals, more effective warnings become possible. 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 feedback method for when a warning is issued into AI, and the AI ​​can select and provide an appropriate feedback method.

[0111] The warning unit customizes the content of warnings based on the driver's past response history. For example, the warning unit issues a customized warning based on the warnings the driver has responded to in the past. The warning unit can also issue a more emphasized warning based on the warnings the driver has ignored in the past. Furthermore, the warning unit can analyze the driver's past response history and customize the optimal warning content. For example, the warning unit retrieves the driver's past response history from a database and issues a customized warning. The warning unit can also issue a more emphasized warning based on the warnings the driver has ignored in the past. Furthermore, the warning unit can analyze the driver's past response history and customize the optimal warning content. This allows for more effective warnings by customizing the content of warnings based on the driver's past response history. 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 into AI, which can then customize and provide the optimal warning content.

[0112] The warning unit estimates the driver's emotions and determines the priority of warnings based on the estimated emotions. For example, if the driver is tense, the warning unit will prioritize important warnings and postpone other warnings. For example, if the driver is relaxed, the warning unit may prioritize normal warnings and issue important warnings periodically. Also, if the driver is tired, the warning unit may prioritize only important warnings and postpone other warnings. For example, the warning unit can estimate the driver's emotions using facial recognition technology and determine the priority of warnings based on the estimated emotions. For example, the warning unit can estimate the driver's emotions using voice analysis technology and determine the priority of warnings based on the estimated emotions. Also, the warning unit can estimate the driver's emotions using heart rate monitoring technology and determine the priority of warnings based on the estimated emotions. This allows important warnings to be issued preferentially by determining the priority of warnings based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generating AI may be, but is not limited to, text generating AI (e.g., LLM) or multimodal generating AI. Some or all of the processing described above in the warning unit may be performed using AI, or not using AI. For example, the warning unit may input a driver's facial image into the AI, which may estimate emotions and determine the priority of warnings.

[0113] The warning unit issues warnings to other vehicles and pedestrians when issuing a warning. For example, when the warning unit issues a warning to the driver, it also issues a warning to surrounding vehicles. The warning unit can also issue a warning to surrounding pedestrians when issuing a warning to the driver. Furthermore, the warning unit can also issue a warning to surrounding bicycles and motorcycles when issuing a warning to the driver. For example, when the warning unit issues a warning to the driver, it also issues a warning to surrounding vehicles. For example, when the warning unit issues a warning to the driver, it also issues a warning to surrounding pedestrians. Furthermore, the warning unit can also issue a warning to surrounding bicycles and motorcycles when issuing a warning to the driver. This ensures the safety of the surroundings by issuing warnings to other vehicles and pedestrians. 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 target of the warning into the AI, and the AI ​​can issue a warning to the appropriate target.

[0114] The warning unit considers the driver's current driving conditions (fatigue level, attention level, etc.) when issuing a warning. For example, if the driver is tired, the warning unit may increase the frequency of warnings to draw attention. For example, if the driver is not paying attention, the warning unit may also emphasize the content of the warning to draw attention. The warning unit can also consider the driver's driving conditions and issue a warning at the optimal time. For example, the warning unit may monitor the driver's fatigue level and adjust the frequency of warnings. For example, the warning unit may monitor the driver's attention level and emphasize the content of the warning. The warning unit can also monitor the driver's driving conditions and issue a warning at the optimal time. This allows the warning unit to issue warnings at a more appropriate time by considering the driver's current driving conditions. 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 may input the driver's driving conditions into the AI, which can then issue a warning at an appropriate time.

[0115] The braking system estimates the driver's emotions and adjusts the timing of brake activation based on those emotions. For example, if the driver is tense, the system may activate the brakes earlier to prevent accidents. If the driver is relaxed, the system can activate the brakes at the normal timing. Furthermore, if the driver is tired, the system can adjust the timing of brake activation appropriately to prevent accidents. For example, the system can estimate the driver's emotions using facial recognition technology and adjust the timing of brake activation based on those emotions. Alternatively, it can estimate the driver's emotions using voice analysis technology and adjust the timing of brake activation based on those emotions. It can also estimate the driver's emotions using heart rate monitoring technology and adjust the timing of brake activation based on those emotions. This allows for more appropriate brake activation by adjusting the timing of brake activation based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating 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 braking unit may be performed using AI, or not using AI. For example, the braking unit may input a driver's facial image into the AI, which may estimate emotions and adjust the timing of brake activation.

[0116] The braking system adjusts the optimal braking force when the brakes are applied, taking into account the vehicle speed and road conditions. For example, the braking system can adjust the optimal braking force when the brakes are applied, taking into account the vehicle speed. The braking system can also adjust the optimal braking force when the brakes are applied, taking into account the road conditions (dry, wet, icy, etc.). Furthermore, the braking system can also adjust the optimal braking force when the brakes are applied, taking into account the vehicle weight. For example, the braking system can adjust the optimal braking force when the brakes are applied, taking into account the vehicle speed. The braking system can also adjust the optimal braking force when the brakes are applied, taking into account the road conditions. Furthermore, the braking system can also adjust the optimal braking force when the brakes are applied, taking into account the vehicle weight. This allows for safer braking by adjusting the optimal braking force considering the vehicle speed and road conditions. Some or all of the above-described processes in the braking system may be performed using AI, for example, or without AI. For example, the braking system can input the vehicle speed and road conditions into the AI, which can then adjust the optimal braking force.

[0117] The braking system provides appropriate driving instructions to the driver after the brakes are applied. For example, the braking system may instruct the driver to change direction after the brakes are applied. The braking system may also instruct the driver to adjust speed after the brakes are applied. Furthermore, the braking system may also instruct the driver to indicate a safe stopping position after the brakes are applied. For example, the braking system may instruct the driver to change direction after the brakes are applied. For example, the braking system may also instruct the driver to adjust speed after the brakes are applied. Furthermore, the braking system may also instruct the driver to indicate a safe stopping position after the brakes are applied. This allows the driver to continue driving safely by providing appropriate driving instructions after the brakes are applied. Some or all of the above processing in the braking system may be performed using AI, for example, or without AI. For example, the braking system can input driving instructions after the brakes are applied to the AI, and the AI ​​can provide appropriate driving instructions.

[0118] The braking system estimates the driver's emotions and determines braking priorities based on those estimates. For example, if the driver is tense, the system will prioritize braking and postpone other operations. If the driver is relaxed, the system can also apply the brakes with normal priority. Furthermore, if the driver is fatigued, the system can adjust braking priorities appropriately to prevent accidents. For example, the system can estimate the driver's emotions using facial recognition technology and determine braking priorities based on those estimates. It can also estimate the driver's emotions using voice analysis technology and determine braking priorities based on those estimates. Additionally, it can estimate the driver's emotions using heart rate monitoring technology and determine braking priorities based on those estimates. This allows for prioritizing critical braking operations based on the driver's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the above-described processes in the braking unit may be performed using AI, or not using AI. For example, the braking unit can input a driver's facial image into an AI, which can then estimate emotions and determine the priority of braking.

[0119] The braking system adjusts the braking force while considering the movements of other vehicles and pedestrians when the brakes are applied. For example, the braking system adjusts the optimal braking force by considering the movements of surrounding vehicles when the brakes are applied. The braking system can also adjust the optimal braking force by considering the movements of surrounding pedestrians when the brakes are applied. Furthermore, the braking system can also adjust the optimal braking force by considering the movements of surrounding bicycles and motorcycles when the brakes are applied. For example, the braking system adjusts the optimal braking force by considering the movements of surrounding vehicles when the brakes are applied. For example, the braking system can adjust the optimal braking force by considering the movements of surrounding pedestrians when the brakes are applied. Furthermore, the braking system can also adjust the optimal braking force by considering the movements of surrounding bicycles and motorcycles when the brakes are applied. This makes safer braking possible by adjusting the braking force while considering the movements of other vehicles and pedestrians. Some or all of the above processing in the braking system may be performed using AI, for example, or without AI. For example, the braking system can input the movements of other vehicles and pedestrians into the AI, which can then adjust the optimal braking force.

[0120] The braking system also considers the vehicle's internal conditions (engine status, fuel level, etc.) when braking. For example, the braking system adjusts the optimal braking force by considering the engine status (temperature, rotational speed, etc.) when braking. The braking system can also adjust the optimal braking force by considering the fuel level when braking. Furthermore, the braking system can also adjust the optimal braking force by considering the battery status when braking. For example, the braking system adjusts the optimal braking force by considering the engine status when braking. For example, the braking system can adjust the optimal braking force by considering the fuel level when braking. Furthermore, the braking system can also adjust the optimal braking force by considering the battery status when braking. This allows for more appropriate braking operation by considering the vehicle's internal conditions. Some or all of the above processing in the braking system may be performed using AI, for example, or without AI. For example, the braking system can input the vehicle's internal conditions into the AI, which can then adjust the optimal braking force.

[0121] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0122] Safety driving assistance systems can also learn the driver's driving style and provide individually optimized warnings. For example, if a driver frequently uses sudden braking, the system can learn this pattern and predict situations where sudden braking is necessary, issuing warnings in advance. Similarly, if a driver prefers driving on highways, the system can use this information to identify dangerous situations on highways and provide appropriate warnings. Furthermore, if a driver frequently drives at night, the system can take into account reduced visibility at night and issue warnings earlier. This allows for more effective safety driving assistance by providing personalized warnings tailored to the driver's driving style.

[0123] The data collection unit can also detect specific driving patterns based on the driver's driving history and assess the risk of accidents. For example, if a driver frequently brakes suddenly at a particular intersection, the system can determine that the risk of an accident at that intersection is high and issue a warning in advance. Furthermore, if a driver experiences a high number of accidents when driving during a specific time period, the system can issue a warning when driving during that time. Additionally, if a driver experiences a high number of accidents when driving under specific weather conditions, the system can issue a warning when driving under those conditions. This allows the system to assess the risk of accidents based on the driver's driving history and provide appropriate warnings, thereby reducing the risk of accidents.

[0124] The monitoring unit can learn the driver's driving style in real time and provide warnings tailored to that style. For example, if a driver frequently accelerates suddenly, the system can learn this pattern and predict situations where sudden acceleration is necessary, issuing a warning in advance. Similarly, if a driver frequently changes lanes, the system can use this information to identify dangerous situations during lane changes and provide appropriate warnings. Furthermore, if a driver drives for extended periods, the system can use this information to assess the risk of fatigue and issue a warning encouraging a break. By providing personalized warnings tailored to the driver's driving style, more effective safe driving support becomes possible.

[0125] The analysis unit can also detect specific driving patterns based on the driver's driving history and assess the risk of accidents. For example, if a driver frequently brakes suddenly at a particular intersection, the system can determine that the risk of an accident at that intersection is high and issue a warning in advance. Furthermore, if a driver experiences a high number of accidents when driving during a specific time period, the system can issue a warning when driving during that time. Additionally, if a driver experiences a high number of accidents when driving under specific weather conditions, the system can issue a warning when driving under those conditions. This allows the system to assess the risk of accidents based on the driver's driving history and provide appropriate warnings, thereby reducing the risk of accidents.

[0126] The warning system can also learn the driver's driving style and provide individually optimized warnings. For example, if a driver frequently uses sudden braking, the system can learn this pattern and predict situations where sudden braking is necessary, issuing a warning in advance. Similarly, if a driver prefers driving on highways, the system can use this information to identify dangerous situations on highways and provide appropriate warnings. Furthermore, if a driver frequently drives at night, the system can take into account reduced visibility at night and issue warnings earlier. This allows for more effective safe driving support by providing personalized warnings tailored to the driver's driving style.

[0127] The braking system can also learn the driver's driving style and provide individually optimized brake control. For example, if a driver frequently uses sudden braking, the system can learn this pattern and anticipate situations where sudden braking is necessary, activating the brakes in advance. Similarly, if a driver prefers driving on highways, the system can use this information to identify dangerous situations on highways and provide appropriate brake control. Furthermore, if a driver frequently drives at night, the system can take into account reduced visibility at night and activate the brakes earlier. This allows for more effective and safer driving assistance by providing individualized brake control tailored to the driver's driving style.

[0128] The data collection unit estimates the driver's emotions and adjusts the timing of accident information collection based on the estimated emotions. For example, if the driver is tense, the data collection unit increases the frequency of accident information collection and updates the information in real time. For example, if the driver is relaxed, the data collection unit can return to the normal collection frequency and update the information periodically. Furthermore, if the driver is tired, the data collection unit can appropriately adjust the collection frequency and prioritize the collection of only important information. For example, the data collection unit estimates the driver's emotions using facial recognition technology and adjusts the collection timing based on the estimated emotions. For example, the data collection unit can estimate the driver's emotions using voice analysis technology and adjust the collection timing based on the estimated emotions. Furthermore, the data collection unit can estimate the driver's emotions using heart rate monitoring technology and adjust the collection timing based on the estimated emotions. By adjusting the timing of accident information collection based on the driver's emotions, information can be collected at a more appropriate time.

[0129] The data storage unit estimates the driver's emotions and adjusts the database update frequency based on the estimated emotions. For example, if the driver is stressed, the data storage unit increases the database update frequency and updates information in real time. For example, if the driver is relaxed, the data storage unit can return the update frequency to normal and update information periodically. Also, if the driver is tired, the data storage unit can adjust the update frequency appropriately and prioritize updating only important information. For example, the data storage unit estimates the driver's emotions using facial recognition technology and adjusts the database update frequency based on the estimated emotions. For example, the data storage unit can estimate the driver's emotions using voice analysis technology and adjust the database update frequency based on the estimated emotions. Also, the data storage unit can estimate the driver's emotions using heart rate monitoring technology and adjust the database update frequency based on the estimated emotions. By adjusting the database update frequency based on the driver's emotions, information can be updated at a more appropriate time.

[0130] The monitoring unit estimates the driver's emotions and adjusts the monitoring accuracy based on the estimated emotions. For example, if the driver is tense, the monitoring unit increases the monitoring accuracy and updates the information in real time. For example, if the driver is relaxed, the monitoring unit can return the monitoring accuracy to normal and update the information periodically. Also, if the driver is tired, the monitoring unit can appropriately adjust the monitoring accuracy and prioritize monitoring only important information. For example, the monitoring unit estimates the driver's emotions using facial recognition technology and adjusts the monitoring accuracy based on the estimated emotions. For example, the monitoring unit can estimate the driver's emotions using voice analysis technology and adjust the monitoring accuracy based on the estimated emotions. Also, the monitoring unit can estimate the driver's emotions using heart rate monitoring technology and adjust the monitoring accuracy based on the estimated emotions. This allows for more appropriate monitoring by adjusting the monitoring accuracy based on the driver's emotions.

[0131] The analysis unit estimates the driver's emotions and adjusts the analysis algorithm based on the estimated emotions. For example, if the driver is tense, the analysis unit increases the accuracy of the analysis and updates the information in real time. For example, if the driver is relaxed, the analysis unit can return the accuracy of the analysis to normal and update the information periodically. Furthermore, if the driver is tired, the analysis unit can appropriately adjust the accuracy of the analysis and prioritize analyzing only important information. For example, the analysis unit estimates the driver's emotions using facial recognition technology and adjusts the analysis algorithm based on the estimated emotions. For example, the analysis unit can estimate the driver's emotions using voice analysis technology and adjust the analysis algorithm based on the estimated emotions. Furthermore, the analysis unit can estimate the driver's emotions using heart rate monitoring technology and adjust the analysis algorithm based on the estimated emotions. This allows for more appropriate analysis by adjusting the analysis algorithm based on the driver's emotions.

[0132] The following briefly describes the processing flow for example form 2.

[0133] Step 1: The data collection unit collects information about locations where accidents or near misses have occurred in the past. For example, it collects information such as the location, time, and circumstances of an accident, identifies the location using sensors and cameras, and records the time of the accident using GPS data. It can also collect eyewitness accounts and photographs of the scene. Step 2: The storage unit stores the information collected by the collection unit in a database. For example, it saves the collected information in a database so that it can be searched and updated as needed. You can also select the type of database and storage format, and set the information retention period. Step 3: The monitoring unit monitors the vehicle's movement in real time. For example, it acquires the vehicle's location, speed, and direction of travel using sensors and GPS. It obtains the vehicle's location as GPS data and monitors the vehicle's speed using speedometer data. It can also monitor the vehicle's direction of travel using a direction sensor. Step 4: The analysis unit analyzes whether the current movement monitored by the monitoring unit is similar to the pattern of a previous accident. For example, to compare past accident patterns with the current movement and determine if they are similar, the analysis unit retrieves past accident patterns from a database and uses an algorithm to compare them with the current movement. Step 5: The warning unit issues a warning to the driver if the analysis unit determines that the data is similar. For example, it may issue an audible or visual warning to the driver, using an audible warning to draw their attention. It can also issue a visual warning to the driver. Step 6: After a warning is issued by the warning unit, the braking unit will activate the automatic brakes as needed. For example, if the driver ignores the warning and continues to make dangerous movements, the brakes will be automatically activated, and the braking strength and activation timing can be set to prevent accidents.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements described above, including the collection unit, storage unit, monitoring unit, analysis unit, warning unit, and braking unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects accident information using the camera 42 and sensors of the smart device 14, and the information is analyzed by the identification processing unit 290 of the data processing unit 12. The storage unit stores the collected information in the database 24, allowing it to be searched and updated as needed. The monitoring unit monitors the vehicle's movement in real time using the GPS and sensors of the smart device 14, and the information is analyzed by the identification processing unit 290 of the data processing unit 12. The analysis unit compares past accident patterns with the current movement and determines whether they are similar. The warning unit issues a warning to the driver using the speaker 40B and display 40A of the smart device 14. The braking unit activates the automatic brake as needed. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0138] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0139] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0153] Each of the multiple elements described above, including the collection unit, storage unit, monitoring unit, analysis unit, warning unit, and braking unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects accident information 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 storage unit stores the collected information in the database 24 so that it can be searched and updated as needed. The monitoring unit monitors the vehicle's movement in real time using the GPS 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 compares past accident patterns with the current movement and determines whether they are similar. The warning unit issues a warning to the driver using the speaker 240 and display of the smart glasses 214. The braking unit activates the automatic brake as needed. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0154] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0155] 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.

[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 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.

[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 (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).

[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] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the collection unit, storage unit, monitoring unit, analysis unit, warning unit, and braking unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects accident information using the camera 42 and sensors of the headset terminal 314 and analyzes it using the identification processing unit 290 of the data processing unit 12. The storage unit stores the collected information in the database 24 so that it can be searched and updated as needed. The monitoring unit monitors the vehicle's movement in real time using the GPS and sensors of the headset terminal 314 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit compares past accident patterns with the current movement and determines whether they are similar. The warning unit issues a warning to the driver using the speaker 240 and display 343 of the headset terminal 314. The braking unit activates the automatic brake as needed. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0170] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] Each of the multiple elements described above, including the collection unit, storage unit, monitoring unit, analysis unit, warning unit, and braking unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects accident information using the camera 42 and sensors of the robot 414, and the information is analyzed by the identification processing unit 290 of the data processing unit 12. The storage unit stores the collected information in the database 24, allowing it to be searched and updated as needed. The monitoring unit monitors the vehicle's movement in real time using the GPS and sensors of the robot 414, and the information is analyzed by the identification processing unit 290 of the data processing unit 12. The analysis unit compares past accident patterns with the current movement and determines whether they are similar. The warning unit issues a warning to the driver using the speaker 240 and display of the robot 414. The braking unit activates the automatic brakes as needed. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] (Note 1) The collection department collects information on locations where accidents or near misses have occurred in the past, A storage unit that stores the information collected by the collection unit in a database, A monitoring unit that monitors the current vehicle movements in real time, An analysis unit analyzes whether the current movements monitored by the aforementioned monitoring unit resemble patterns from previous accidents. A warning unit that issues a warning to the driver if the analysis unit determines that they are similar, The system includes a brake unit that activates the automatic brake as needed after a warning is issued by the aforementioned warning unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information such as the location, time, and circumstances of the accident. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned monitoring unit, Vehicle location information, speed, and direction of travel are acquired using sensors and GPS. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We compare past accident patterns with current movements to determine if they are similar. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned warning unit is It issues voice and visual warnings to the driver. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned brake unit is The system automatically applies the brakes if the driver ignores warnings and continues to make dangerous movements. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the driver's emotions and adjusts the timing of accident information collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is In addition to the location, time, and circumstances of the accident, environmental information such as weather and traffic volume will also be collected. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The collected information is updated in real time, ensuring that the latest accident information is always maintained. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the driver's emotions and prioritizes the information 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 During data collection, information on surrounding transportation infrastructure will also be collected. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, information from other vehicles is also integrated and collected. The system described in Appendix 1, characterized by the features described herein. (Note 13) The storage unit is The system estimates the driver's emotions and adjusts the database update frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The storage unit is The accumulated information is organized by category, making it easy to search efficiently. The system described in Appendix 1, characterized by the features described herein. (Note 15) The storage unit is Regularly back up accumulated information to ensure data security. The system described in Appendix 1, characterized by the features described herein. (Note 16) The storage unit is The system estimates the driver's emotions and prioritizes data based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The storage unit is The accumulated information is linked with other systems, and data is shared between them. The system described in Appendix 1, characterized by the features described herein. (Note 18) The storage unit is Visualizing accumulated information to make it easy for drivers to understand. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned monitoring unit, The system estimates the driver's emotions and adjusts the monitoring accuracy based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned monitoring unit, In addition to vehicle movement, the system also monitors the movements of surrounding pedestrians and other vehicles. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned monitoring unit, It analyzes monitoring data in real time and detects anomalies immediately. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned monitoring unit, The system estimates the driver's emotions and prioritizes monitoring based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned monitoring unit, During monitoring, the internal conditions of the vehicle are also monitored. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, The system estimates the driver's emotions and adjusts the analysis algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, In addition to past accident patterns, similar patterns will also be included in the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit, We update analysis results in real time, providing the latest information at all times. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit, The system estimates the driver's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit, During the analysis, data from other vehicles is also integrated and analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit, Environmental factors such as weather and traffic volume will also be considered during the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned warning unit is The system estimates the driver's emotions and adjusts the way warnings are expressed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned warning unit is In addition to audio and visual feedback, the system also provides feedback such as vibration during warnings. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned warning unit is Customize the content of the warning based on the driver's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned warning unit is The system estimates the driver's emotions and prioritizes warnings based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned warning unit is When a warning is issued, it also warns other vehicles and pedestrians. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned warning unit is When issuing a warning, the driver's current driving situation is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned brake unit is The system estimates the driver's emotions and adjusts the timing of brake application based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned brake unit is When braking, the system adjusts the optimal braking force considering the vehicle's speed and road conditions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned brake unit is After the brakes are applied, provide the driver with appropriate driving instructions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned brake unit is It estimates the driver's emotions and determines the braking priority based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned brake unit is When braking, the braking force is adjusted to take into account the movements of other vehicles and pedestrians. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned brake unit is When braking, the internal conditions of the vehicle should also be taken into consideration. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0206] 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 collection department collects information on locations where accidents or near misses have occurred in the past, A storage unit that stores the information collected by the collection unit in a database, A monitoring unit that monitors the current vehicle movements in real time, An analysis unit analyzes whether the current movements monitored by the aforementioned monitoring unit resemble patterns from previous accidents. A warning unit that issues a warning to the driver if the analysis unit determines that they are similar, The system includes a brake unit that activates the automatic brake as needed after a warning is issued by the aforementioned warning unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect information such as the location, time, and circumstances of the accident. The system according to feature 1.

3. The aforementioned monitoring unit, Vehicle location information, speed, and direction of travel are acquired using sensors and GPS. The system according to feature 1.

4. The aforementioned analysis unit, We compare past accident patterns with current movements to determine if they are similar. The system according to feature 1.

5. The aforementioned warning unit is It issues voice and visual warnings to the driver. The system according to feature 1.

6. The aforementioned brake unit is The system automatically applies the brakes if the driver ignores warnings and continues to make dangerous movements. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the driver's emotions and adjusts the timing of accident information collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is In addition to the location, time, and circumstances of the accident, environmental information such as weather and traffic volume will also be collected. The system according to feature 1.

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