system

Abstract

We provide the system. [Solution] A computer processing means for collecting user characteristic data and analyzing the user's personality characteristics, A generation engine means that generates specific driving situations based on the aforementioned personality characteristics and creates 3D simulation images, An interface device means that provides 3D simulations to the user and collects the user's driving information in real time, An evaluation unit means that analyzes collected driving information and generates specific driving improvement advice for the user, A training planning module means for customizing the next training situation based on the evaluation results, A system that includes this.

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 modern automotive society, the impact of a driver's personality characteristics on traffic accidents is often overlooked, and current driving training programs do not take appropriate measures according to the weaknesses of individual drivers. As a result, drivers cannot respond appropriately in various dangerous environments, and the risk of accidents is increasing. In particular, for those who do not drive regularly or the elderly, their driving ability in specific situations may be significantly reduced. In response to such problems, a driving training system that can be individually tailored is required.

Means for Solving the Problems

[0005] This invention provides a system that analyzes the personality traits of individual drivers in detail and generates driving scenarios based on that analysis. The system includes a generation engine that uses an AI model to generate specific driving situations based on collected personality data. Furthermore, it includes an interface device that performs real-time driving simulations using virtual reality goggles and a physical steering wheel, and collects user operation data. This allows for training that focuses on each driver's weaknesses while reproducing conditions close to actual driving environments, contributing to overall improvement of driving skills and a reduction in traffic accidents.

[0006] "User characteristic data" refers to information collected to show the individual characteristics of a driver, such as their age, gender, driving experience, and personality traits.

[0007] "Personality traits" refer to psychological and behavioral characteristics that influence driving, such as a driver's attention span, risk tendencies, and stress tolerance.

[0008] "Computer processing" refers to a series of operations that involve analyzing data and performing processing to obtain information using electronic or software-based means.

[0009] A "generative engine" refers to a system component that uses artificial intelligence and algorithms based on collected data to automatically construct specific driving scenarios.

[0010] "3D simulation video" refers to a video that uses digital technology to represent a virtual reality driving environment or situation in three dimensions.

[0011] An "interface device" is a device, such as virtual reality goggles or a steering wheel-type controller, used to exchange data and information between a user and a system.

[0012] An "evaluation unit" refers to a function or device used to analyze and evaluate collected user driving information, and plays a role in deriving specific advice.

[0013] The "training plan module" refers to a system component that customizes the next training scenario based on the user's evaluation results and creates a continuous training program for improving driving skills. [Brief explanation of the drawing]

[0014] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one 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), etc.

[0018] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the labeled storage is one or more nonvolatile storage devices that store various programs and various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0026] 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.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] The 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.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention provides a driving simulation system based on the driver's personality traits. This system includes multiple processes performed by a server, a terminal, and a user.

[0036] First, the server begins by collecting basic characteristic data from the user. This data includes the user's driving experience and the results of questionnaires regarding their personality traits. The collected data is analyzed by computer processing and used to evaluate the user's personality traits.

[0037] Subsequently, the server uses a generation engine to generate specific driving situations based on the user's personality traits. These situations are then formatted as 3D simulation images and sent to the terminal.

[0038] The terminal provides users with simulations using virtual reality goggles or physical steering wheel controllers. Users can use this interface device to experience real-world driving situations and improve their driving skills by performing actions appropriate to specific dangerous situations.

[0039] Meanwhile, the terminal collects user driving operation data in real time and transmits it to the server. This data includes information on steering and braking operations, reaction time, etc. The server uses this data to evaluate the user's performance, and the evaluation unit performs the analysis.

[0040] Based on the results, the server generates specific driving improvement advice and provides feedback to the user. This feedback serves as a guide for the user to try to improve their driving in the future.

[0041] Finally, the training planning module creates a new training scenario based on the user's evaluation results, preparing for the next simulation. Through this series of processes, drivers can improve not only their driving skills but also their awareness of safe driving. In this embodiment, the present invention enables customized safe driving training tailored to individual drivers.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] After a user logs into the system, the server collects basic characteristic data. This includes questionnaires about the user's driving experience and personality. The server saves this data in document format and prepares it for analysis.

[0045] Step 2:

[0046] The server uses the collected trait data to perform analysis with an AI model. This generates a user personality profile. The AI ​​model evaluates psychological traits such as attention and stress tolerance, and uses this to identify individual user traits.

[0047] Step 3:

[0048] The server uses a generation engine to create driving scenarios optimized for the user's personality profile. These driving scenarios are designed as simulations tailored to the user's characteristics, such as the appearance of unexpected obstacles or sudden lane changes.

[0049] Step 4:

[0050] The terminal provides the user with 3D simulation images received from the server, using VR goggles or a physical steering wheel. The user then experiences driving in a visually immersive and intuitive environment.

[0051] Step 5:

[0052] The user performs driving operations in a provided driving simulation environment via a terminal. During this time, the terminal records the user's operation data in real time, measuring things like steering movements and reaction times.

[0053] Step 6:

[0054] The terminal transmits collected driving operation data to the server. The server analyzes this data and evaluates the user's driving performance through an evaluation unit. The evaluation results include the user's reaction speed and operational accuracy.

[0055] Step 7:

[0056] The server generates specific improvement advice for the user based on the evaluation results. This advice is designed to improve driving behavior during the next training session and may include suggestions such as "try braking earlier."

[0057] Step 8:

[0058] The server uses a training planning module to customize the simulation for the user's next training session. This enables a step-by-step learning process that adapts to the user's progress.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] Conventional driving simulation systems have difficulty customizing them based on the individual driver's personality and driving style, and could only provide general driving training. As a result, they could not provide training content tailored to the driver's characteristics, making it difficult to achieve effective skill improvement and increased awareness of safe driving.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes an information processing device means for collecting user nature data and analyzing the user's personality characteristics; a generation device means for generating specific driving scenarios and creating three-dimensional visualizations based on the personality characteristics; and an operation device means for presenting the three-dimensional visualizations to the user and collecting the user's driving operation information in real time. This enables personalized driving training and allows for effective skill improvement and enhanced safe driving awareness based on the driver's characteristics.

[0064] "User characteristic data" refers to information including the driver's driving experience and personality traits, which is collected through questionnaires and personality tests.

[0065] "Personality traits" are numerical or indicator-based representations of a user's psychological characteristics and behavioral patterns, and are used to customize driving simulations.

[0066] An "information processing device" is a device that processes data, derives analysis results, and has the function of evaluating the user's personality traits.

[0067] A "generation device" is a device that has the function of designing a specific operating scenario based on the analysis results and generating a three-dimensional visualization.

[0068] "Three-dimensional visualization" refers to the representation of a virtual driving environment as a three-dimensional image, provided in a format that users can experience.

[0069] A "control device" is a device that presents a three-dimensional visualization to the user and collects user operation information in real time regarding the driving situation.

[0070] A "communication network" refers to a network such as the Internet, which is used as a means of collecting data from users in remote locations.

[0071] A "questionnaire" is a document or form used to collect user characteristic data, and includes questions to evaluate driving characteristics and personality.

[0072] A "personality test" is a test used to evaluate a user's psychological characteristics and to quantitatively analyze the driver's personality traits.

[0073] This invention provides a system that offers personalized driving simulation training based on the user's personality traits. This system performs multiple functions primarily involving a server, terminals, and users.

[0074] First, the user accesses an online questionnaire provided by the server via their terminal and inputs information about their driving experience and personality traits. This information is stored in the server's database as "user personality data." The server uses an information processing device to analyze the collected data and evaluates the user's "personality traits" based on a generating AI model. Here, for example, a prompt might be entered such as "Evaluate this user's cautiousness while driving."

[0075] Next, the server generates specific driving scenarios based on the evaluated personality traits. The generation device then uses three-dimensional visualization technology to design a driving simulation for the user. This provides a realistic driving experience tailored to the user's personality.

[0076] The terminal uses a virtual reality display and steering system to provide users with real-time three-dimensional visualizations. Users wear virtual reality goggles and operate a physical steering wheel to experience something similar to real driving.

[0077] The series of driving operations performed by the user within the simulation are accurately monitored in real time by a terminal and transmitted to a server. The operation data accumulated on the server is analyzed in detail by an evaluation device to assess the user's driving skills. Based on these results, specific driving improvement advice is generated for the user.

[0078] Finally, the server uses a training planning device to personalize and record the content of the next driving training session according to the analysis results. This allows the user to continuously improve their driving skills and deepen their awareness of safe driving. In this embodiment, the present invention makes it possible to provide users with customized driving training that is adapted to their individual driving characteristics.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] Users access online questionnaires provided by the server and enter information about their driving experience and personality traits.

[0082] Input: User-submitted data regarding driving experience and personality traits.

[0083] Data processing: The server formats the input data and stores it in the database.

[0084] Output: User profile data is saved to the server.

[0085] Specific actions: The user fills in the answers for each item on the questionnaire and clicks the submit button.

[0086] Step 2:

[0087] The server uses a generative AI model to analyze the user's personality traits based on the collected user behavior data.

[0088] Input: Saved user profile data.

[0089] Data calculation: The server inputs the prompt message "Evaluate this user's driving caution" into the generated AI model and analyzes its personality traits.

[0090] Output: Evaluation results of the user's personality traits.

[0091] Specific operation: The server runs the AI ​​model and generates an analytical report based on the data.

[0092] Step 3:

[0093] The server generates specific driving scenarios based on the user's personality traits and creates three-dimensional visualizations.

[0094] Input: Evaluation results of personality traits.

[0095] Data processing: The generation device generates three-dimensional graphics based on the scenario data.

[0096] Output: Three-dimensional visualization data of a specific driving scenario.

[0097] Specific operation: The server uses a graphics engine to render the scenario and saves it as a simulation file.

[0098] Step 4:

[0099] The terminal provides the user with three-dimensional visualization data received from the server.

[0100] Input: 3D visualization data from the server.

[0101] Data processing: The terminal formats the visual data for the virtual reality goggles and displays it on the screen.

[0102] Output: Visualization of the driving scenario presented to the user.

[0103] Specific operation: The device interacts with virtual reality goggles to project the scenario into the user's field of vision.

[0104] Step 5:

[0105] Users use a device to experience the simulation and perform driving operations.

[0106] Input: Visualization of driving scenarios.

[0107] Data generation: The user operates a steering wheel-type controller to simulate actual driving operations.

[0108] Output: Operation data during operation.

[0109] Specific actions: The user's steering and braking actions within the simulation are tracked.

[0110] Step 6:

[0111] The terminal sends user operation data to the server.

[0112] Input: Real-time operation data from the user.

[0113] Data transfer: The terminal organizes the operation data and transfers it to the server in a predictable format.

[0114] Output: Operation data in a format usable by the server.

[0115] Specific operation: The terminal uses a data transmission circuit to transmit information to the server.

[0116] Step 7:

[0117] The server analyzes the operation data and evaluates the user's driving skills.

[0118] Input: Operation data.

[0119] Data calculation: The evaluation device calculates quantitative indicators and generates an evaluation score.

[0120] Output: Evaluation results regarding the user's driving skills.

[0121] Specific operation: The server automates analysis based on operational data and generates a user's driving performance report.

[0122] Step 8:

[0123] The server generates driving improvement advice based on the evaluation results and provides feedback to the user.

[0124] Input: Evaluation results of driving skills.

[0125] Data generation: The server uses a generative AI model to document appropriate advice.

[0126] Output: Feedback message for improving driving performance.

[0127] Specific operation: The server provides advice to the user via email, screen display, etc.

[0128] Step 9:

[0129] The server uses the evaluation data to personalize the content of the next driving training session and updates the training plan.

[0130] Input: Evaluation data.

[0131] Data processing: The training planning device generates the next scenario and customizes it.

[0132] Output: Personalized next training plan.

[0133] Specific operation: The server designs a new simulation scenario and registers it in the database.

[0134] (Application Example 1)

[0135] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0136] One challenge in the use of autonomous vehicles is the lack of training methods to help drivers understand the compatibility between the autonomous driving system and their own driving characteristics, enabling them to use the system more safely and effectively. Therefore, there is a need to provide training environments that maximize the capabilities of autonomous driving systems while simultaneously improving drivers' safety awareness.

[0137] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0138] In this invention, the server includes data processing means for collecting user characteristic data and analyzing the user's personality traits; generation means for generating specific driving situations and creating three-dimensional simulated visual information based on the personality traits; and driving control means for providing a training program for the driver to experience the compatibility between the autonomous driving function and their own driving characteristics. This enables the driver to understand how the autonomous vehicle fits their driving style and to acquire better driving skills in subsequent drives.

[0139] "User characteristic data" refers to information about the user's driving experience and personality traits, and is data that the system uses to analyze the user's driving style.

[0140] A "data processing device" is a computer device used to collect user characteristic data, analyze it, and evaluate the user's personality traits.

[0141] A "generation device" is a device that creates specific driving conditions based on personality traits and provides those driving conditions as three-dimensional simulated visual information.

[0142] A "display device" is a device that presents a three-dimensional simulation to the user and collects information on the user's driving actions in real time, and includes virtual reality visual devices.

[0143] An "evaluation device" is a computer device that analyzes collected driving operation information and generates specific driving improvement guidelines for the user.

[0144] A "training plan creation device" is a device that customizes the next training session based on evaluation results and creates a training program that is suitable for each user.

[0145] A "driving control system" is a device that provides a training program for drivers to experience the compatibility between the autonomous driving function and their own driving characteristics, and controls the operation of the autonomous vehicle.

[0146] To realize this invention, a driving simulation system will be built through the collaboration of a server, terminal, and user. The server will collect characteristic data from the user's smartphone or computer and evaluate personality traits based on that information. Specifically, data collection and analysis will be performed using software such as Python or Django. Through this data processing, an evaluation will be conducted that is tailored to the user's driving experience and personality traits.

[0147] Next, the server uses Unity to generate three-dimensional simulation visual information. The simulation reproduces specific driving situations based on the user's personality traits. For example, if the user is determined to "lose composure when in a hurry," they can experience situations in the simulation where traffic jams and sudden stops occur. This allows the user to learn how to use the autonomous driving system in those situations.

[0148] On the terminal side, a head-mounted display (e.g., Oculus Quest) or smartphone is used as a means of representation to provide a three-dimensional simulation from the user's perspective. The terminal also collects the user's driving operation information in real time and sends it back to the server. This information includes steering and braking operations and reaction times.

[0149] The server analyzes this information again and evaluates the user's driving performance using machine learning tools such as Scikit-learn. Based on this evaluation, it generates and provides the user with driving improvement guidelines. Furthermore, it creates a plan to customize the next training scenario and prepares a more accurate training program.

[0150] Example of a prompt:

[0151] "Please generate driving scenarios that demonstrate how users can remain calm and efficiently utilize autonomous driving features during traffic jams."

[0152] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0153] Step 1:

[0154] The server acquires survey data about the user's driving experience and personality traits via their smartphone. Using this data as input, the server performs data analysis to evaluate personality traits. A Python data processing library is used for the analysis, and the user's personality tendencies are output.

[0155] Step 2:

[0156] The server generates specific driving conditions based on the outputted personality traits. It uses Unity to create three-dimensional simulation visual information and sends this simulation data to the terminal. At this stage, a driving scenario based on prompt text is constructed using a generated AI model.

[0157] Step 3:

[0158] The terminal presents the received 3D simulation to the user. It recreates the simulation from the user's perspective via a head-mounted display, providing a virtual driving experience. Based on user input, it collects driving operation data in real time and transmits it to the server.

[0159] Step 4:

[0160] The server receives and analyzes driving operation data sent from the terminal. Using machine learning libraries such as Scikit-learn, it analyzes how this data matches personality traits and evaluates the user's driving performance. The evaluation results output metrics such as operational accuracy and reaction time.

[0161] Step 5:

[0162] The server generates personalized driving improvement guidelines for the user based on the evaluation results and sends them to the terminal. Furthermore, it uses these results to create a plan for customizing the next training simulation. This process ensures that a training program tailored to the user's needs is prepared.

[0163] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0164] This invention relates to an advanced driving simulation system based on a user's driving characteristics and emotional state. The system includes multiple processes performed by a server, a terminal, and the user, and is particularly characterized by the incorporation of an emotion engine.

[0165] First, the server collects questionnaires from users via the internet regarding their driving experience and personality. This information is stored as trait data and analyzed using an AI model. This analysis evaluates the user's personality traits, and a profile is created based on these evaluations.

[0166] The server uses an emotion engine along with a profile to identify the user's emotional state. This emotional state is determined by analyzing data such as facial expressions, voice, and heart rate that the user displays during the driving simulation. This emotional data is collected and used to understand how it influences the user's driving behavior.

[0167] Next, the generation engine functions to create driving situations based on the collected personality traits and emotional states. These situations are generated as dynamically adjustable 3D simulation images and sent to the terminal.

[0168] The terminal provides the user with a simulation using virtual reality goggles and a physical steering wheel controller. The user uses this interface device to experience the simulation visually and intuitively, and to perform driving maneuvers in response to specific hazardous situations.

[0169] Simultaneously, the device monitors the user's emotional state and transmits the user's driving data to the server in real time. The server analyzes this data, and an evaluation unit analyzes the relationship between emotions and driving behavior. This allows for an objective evaluation of the user's performance.

[0170] Based on the evaluation results, the server provides the user with specific driving improvement advice. This advice takes emotional states into account and aims to minimize the impact on driving behavior. The server also uses a training planning module to design new training scenarios and optimize the environment the user will experience in the next simulation. Through this process, drivers can improve their overall driving ability and strengthen their emotional control.

[0171] The following describes the processing flow.

[0172] Step 1:

[0173] After a user logs into the system, the server provides personality-related questionnaires and psychological tests via the internet. Users provide their personality data by answering these questionnaires and submitting them to the server.

[0174] Step 2:

[0175] The server inputs the received characteristic data into an AI model to generate a user personality profile. This profile includes psychological characteristics such as attention span and risk tendencies, and analysis is performed based on these characteristics.

[0176] Step 3:

[0177] The terminal provides the user with virtual reality goggles and starts the driving simulation. The user prepares to operate the vehicle in a manner similar to actual driving using a steering wheel-type controller.

[0178] Step 4:

[0179] The server uses an emotion engine to analyze the user's facial expressions, voice, and heart rate transmitted in real time from the device, recognizing the user's emotional state. This information is used to customize the driving experience.

[0180] Step 5:

[0181] The generation engine automatically generates appropriate driving scenarios based on the user's personality profile and real-time emotional state, and sends them to the device. The scenarios are dynamically adjusted and optimized for the user's state.

[0182] Step 6:

[0183] The user performs driving operations in a generated simulation environment. The terminal continuously records driving information such as steering angle, reaction time, and braking operation, and sends it to the server.

[0184] Step 7:

[0185] The server analyzes the collected driving data and emotional information using an evaluation unit to assess the user's driving performance. This evaluation includes the relationship between emotional fluctuations and operational accuracy.

[0186] Step 8:

[0187] Based on the evaluation results, the server provides users with specific driving improvement advice. This advice takes into account the influence of emotions on driving and guides users to appropriately control their emotions while driving.

[0188] Step 9:

[0189] The training planning module designs the next training scenario based on previous evaluations and feedback, improving the user's next simulation environment. This creates a training program that enables gradual skill improvement.

[0190] (Example 2)

[0191] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0192] In driving simulations, it is necessary to comprehensively evaluate not only the user's driving skills but also the influence of their emotional state on driving behavior, and to provide personalized driving improvement measures for each user. In particular, it is important to understand the relationship between changes in emotional state and driving operations in real time and to reflect this in training.

[0193] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0194] In this invention, the server includes an information processing device means that collects user characteristic data and emotional data and analyzes the user's personality traits and emotional state; a generation means that generates specific driving situations and creates three-dimensional simulation images based on the personality traits and emotional state; and an interaction device means that provides the three-dimensional simulation to the user and collects the user's driving operation data and emotional state data in real time. This makes it possible to provide a training program that comprehensively improves driving skills and emotional control ability.

[0195] An "information processing device" is a device that collects user characteristic data and emotional data, and analyzes this data to understand the user's personality traits and emotional state.

[0196] "Generation means" refers to a method or apparatus that has the function of creating a specific driving situation based on the user's personality traits and emotional state, and generating a three-dimensional simulation image.

[0197] A "dialogue device" is a device that provides users with a three-dimensional simulation and has the function of collecting driving operation data and emotional state data in real time during that simulation.

[0198] "Evaluation means" refers to a method or apparatus that has the function of analyzing collected driving operation data and emotional state data and generating specific driving improvement advice for the user.

[0199] A "training plan formulation device" is a device that has the function of formulating a plan to customize the user's next training session based on evaluation results.

[0200] "Three-dimensional simulation video" refers to a three-dimensional image created by a generation method to provide users with a driving experience.

[0201] This invention relates to a driving simulation system in which a server, a terminal, and a user cooperate to perform the simulation. The server acquires characteristic data and emotional data from the user. The characteristic data includes information about driving experience and personality. This data is collected using an information processing device and analyzed using a generative AI model. The AI ​​model evaluates the user's personality traits and emotional state and defines them as a profile.

[0202] Next, the server uses a generation mechanism based on the profile information to design a specific operating situation. This operating situation is generated as a three-dimensional simulation image and transmitted to the terminal. The virtual reality display device and physical control device connected to the terminal provide this simulation image to the user.

[0203] These devices allow users to gain both visual and physical experiences. For example, by wearing virtual reality goggles and actually operating the steering wheel, they can experience a realistic driving simulation.

[0204] Simultaneously, the terminal transmits the user's driving operation data and emotional state data to the server in real time. The server analyzes this data using evaluation tools and assesses the relationship between driving behavior and emotional state. Based on these results, the server provides the user with specific advice for improving their driving. Furthermore, a training plan formulation device is used to plan for optimizing the conditions for the next driving training session.

[0205] For example, the server can improve performance in subsequent simulations by identifying scenarios that are likely to cause stress for the user and providing advice to help them relax. An example of a prompt would be, "Design a driving simulation in a congested road environment based on the user's driving experience and personality traits."

[0206] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0207] Step 1:

[0208] The server collects survey data from users regarding their driving experience and personality via the internet. It receives user survey responses as input and stores them as characteristic data in a database using an information processing device. This process includes data formatting and storage.

[0209] Step 2:

[0210] The server inputs stored trait data into a generating AI model to analyze the user's personality traits and emotional state. Using the trait data as input, the AI ​​model performs data calculations and outputs the user's profile. This profile includes tendencies and characteristics as a driver.

[0211] Step 3:

[0212] The server inputs profile information into the emotion engine to identify the user's emotional state. It receives profile and user emotion data as input and identifies the emotional state through analysis. The emotional state result is obtained as output, and the server prepares to adjust the driving scenario based on this result.

[0213] Step 4:

[0214] The server uses a generation mechanism to create driving simulation scenarios based on characteristic data and emotional states. The input is the user's personality traits and emotional state, which are used to generate dynamic and specific driving scenarios. The output is a three-dimensional simulation image, which is sent to the terminal.

[0215] Step 5:

[0216] The terminal provides the user with three-dimensional simulation images through a virtual reality display device and physical control devices. In this step, the user can participate in the simulation using VR goggles and a steering wheel controller, gaining a visual and operational experience. The output is the user's driving operation data.

[0217] Step 6:

[0218] The terminal transmits user driving operation data and emotional state data to the server in real time during the running simulation. It collects driving operation and emotional data as input and relays it to the server as output. This data is used for subsequent evaluation.

[0219] Step 7:

[0220] The server analyzes driving operation data and emotional state data using evaluation tools and generates driving improvement advice for the user. The input is collected data, which is analyzed and its correlations evaluated. The output is specific driving advice, which is then fed back to the user.

[0221] Step 8:

[0222] The server uses a training planning device to customize the next driving training scenario. Using the evaluation results as input, it designs a new training scenario and generates an improved scenario for the next simulation as output. This scenario is designed to improve the user's capabilities.

[0223] (Application Example 2)

[0224] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0225] In autonomous vehicles, there is a need to optimize the driving environment while taking into account the emotional state of the occupants. However, with conventional technology, it is difficult to grasp the emotional state of the occupants in real time and provide appropriate feedback based on that. In such a situation, there is a problem in that a safe and comfortable driving experience cannot be provided for the occupants.

[0226] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0227] In this invention, the server includes processing means by an electronic computer that collects user characteristic information and analyzes the user's personality traits based on this information; generation means that generates a specific driving environment and creates a three-dimensional simulation image based on the personality traits and emotional state; and learning means that monitors the occupant's emotional state in real time and provides a safe and comfortable driving environment. This makes it possible to dynamically optimize the driving environment according to the occupant's emotional state and provide a safer and more comfortable autonomous driving experience.

[0228] "User characteristic information" refers to information about the user's personal characteristics, such as their driving experience and personality.

[0229] "Personality traits" refer to the personality tendencies and characteristics of a person, analyzed based on their characteristic information.

[0230] "Processing by electronic computing devices" refers to processes that involve collecting and analyzing data using devices such as computers.

[0231] A "generation device" refers to a device that generates a specific driving environment based on personality traits and emotional states, and creates a three-dimensional simulation image.

[0232] A "three-dimensional simulation image" refers to a computer graphics image that reproduces the driving environment and conditions in three dimensions.

[0233] An "interface device" refers to a device that collects operational information while a user experiences a simulation. Examples include virtual reality goggles and steering wheel controllers.

[0234] An "evaluation device" refers to a device that analyzes collected operational information and provides users with advice on improving their driving.

[0235] A "training planning device" refers to a device that customizes the user's next training environment based on evaluation results.

[0236] A "learning device" refers to a device that monitors the emotional state of the occupants in real time and adjusts the driving environment based on that information.

[0237] This invention relates to a system for monitoring the emotional state of occupants in real time within an autonomous vehicle, thereby providing a safe and comfortable driving experience. The system consists of multiple computers and various sensors. Embodiments are described below.

[0238] The server first collects user characteristic information via the internet. This information is processed by a computer as data for analyzing personality traits. The analyzed personality traits are then used to create a profile based on the user's driving behavior.

[0239] The device works in conjunction with the vehicle's cameras, microphones, and heart rate sensors to collect and analyze data such as the occupant's facial expressions, voice, and heart rate using an emotion engine, and to identify their emotional state in real time. This process utilizes AI platforms such as Amazon SageMaker for data processing and analysis.

[0240] The generation device creates a driving environment corresponding to the obtained personality traits and emotional state, and creates a three-dimensional simulation image which is then transmitted to the terminal. This allows the user to experience visual feedback and operation via a virtual reality display device or physical control device.

[0241] The evaluation device analyzes driving information collected in real time and generates specific driving improvement advice for the user. Furthermore, the training planning device customizes the driving environment to provide an optimized scenario for the next training session.

[0242] For example, if the system detects that the occupants are fatigued during long drives, it can switch the car to a relaxation mode and automatically play soothing music. Another example of a prompt generated by the AI ​​model is, "Based on the occupants' current emotional state, please select the optimal driving mode to provide a safe and comfortable driving experience."

[0243] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0244] Step 1:

[0245] The server collects user characteristic information via the internet through questionnaires and psychological assessments. Inputs are web forms and questionnaire data, while outputs are characteristic information stored in a database for use in user personality analysis. This provides the necessary foundational data for subsequent processing.

[0246] Step 2:

[0247] The server analyzes personality traits using a generative AI model based on the collected user characteristic information. The input is the characteristic information obtained in step 1, and the output is the user's personality profile. This profile is used to predict the user's driving behavior and emotional state.

[0248] Step 3:

[0249] The device collects data in real time from the in-car camera, microphone, and heart rate sensor. The input is raw data from each sensor, and the output is integrated data necessary for the emotion engine to analyze it. This data is used to identify the emotional state of the occupants based on their facial expressions, voice, and heart rate.

[0250] Step 4:

[0251] The terminal uses an emotion engine to analyze integrated data and identify the occupant's emotional state in real time. The input is the integrated data obtained in step 3, and the output is the occupant's emotional state. This emotional state forms the basis for generating the next driving environment.

[0252] Step 5:

[0253] The generator creates an appropriate driving environment and produces a three-dimensional simulation image based on the personality profile and emotional state. The input is the personality profile from step 2 and the emotional state from step 4, and the output is the three-dimensional simulation image delivered to the virtual reality device. This allows the user to have an intuitive driving experience.

[0254] Step 6:

[0255] The terminal collects user operation information through three-dimensional simulation and transmits it to the server. The input is the operation information obtained during the three-dimensional simulation, and the output is data sent to the server for analysis on the evaluation device. This information forms the basis for operational improvement advice.

[0256] Step 7:

[0257] The server uses an evaluation device to analyze user operation information and generate specific driving improvement advice. The input is the operation information obtained in step 6, and the output is the improvement advice provided to the user. This advice is intended to improve the user's driving ability.

[0258] Step 8:

[0259] Based on the evaluation results, the server uses a training planning device to customize the next training environment. The input is the improvement advice and evaluation results from step 7, and the output is the customized next training scenario. This allows the user to have an optimized learning opportunity.

[0260] 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.

[0261] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0262] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0263] [Second Embodiment]

[0264] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0265] 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.

[0266] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0267] 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.

[0268] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0269] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0270] 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.

[0271] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0272] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0273] The 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.

[0274] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0275] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0276] This invention provides a driving simulation system based on the driver's personality traits. This system includes multiple processes performed by a server, a terminal, and a user.

[0277] First, the server begins by collecting basic characteristic data from the user. This data includes the user's driving experience and the results of questionnaires regarding their personality traits. The collected data is analyzed by computer processing and used to evaluate the user's personality traits.

[0278] After that, the server uses a generation engine to generate specific driving situations based on the user's personality characteristics. This situation is formalized as a 3D simulation video and sent to the terminal.

[0279] The terminal uses a virtual reality headset, a physical handle-type controller, etc. to provide the simulation to the user. The user experiences the actual driving situation using this interface device and improves their driving skills by performing operations according to specific dangerous situations.

[0280] On the other hand, the terminal collects the user's driving operation data in real time and sends it to the server. This data includes operation information such as steering and braking, reaction time, etc. The server uses this data to evaluate the user's performance and performs analysis in an evaluation unit.

[0281] Based on the results, the server generates specific driving improvement advice for the user and provides feedback. This feedback serves as a guideline for the user to attempt improvements in subsequent driving.

[0282] Finally, the training plan module creates a new training scenario based on the user's evaluation results in preparation for the next simulation. Through this series of processes, the driver can improve not only their driving skills but also their awareness of safe driving. With this embodiment, the present invention enables customized safe driving training tailored to individual drivers.

[0283] The following describes the processing flow

[0284] Step 1:

[0285] After the user logs in to the system, the server collects basic characteristic data. This includes questionnaires regarding the user's driving experience and personality input by the user. The server saves this data in document form and prepares for analysis.

[0286] Step 2:

[0287] The server analyzes the collected characteristic data using an AI model, thereby generating a user's personality profile. The AI model evaluates psychological characteristics such as attention and stress tolerance, and based on this, identifies the individual characteristics of the user.

[0288] Step 3:

[0289] The server uses a generation engine to create a driving situation optimized for the user's personality profile. The driving situation is designed as a simulation according to the user's characteristics, such as the appearance of unexpected obstacles or sudden lane changes.

[0290] Step 4:

[0291] The terminal provides the 3D simulation video received from the server to the user using VR goggles or a physical handle. The user experiences driving in a visual and tactile environment.

[0292] Step 5:

[0293] The user performs driving operations in the provided driving simulation environment through the terminal. The terminal records the user's operation data in real time during this period, and measures, for example, the movement of the steering wheel and reaction time.

[0294] Step 6:

[0295] The terminal transmits the collected driving operation data to the server. The server analyzes this data and evaluates the user's driving performance through an evaluation unit. The evaluation results include the user's reaction speed and operation accuracy.

[0296] Step 7:

[0297] The server generates specific improvement advice for the user based on the evaluation results. This advice is designed to improve driving behavior during the next training session and may include suggestions such as "try braking earlier."

[0298] Step 8:

[0299] The server uses a training planning module to customize the simulation for the user's next training session. This enables a step-by-step learning process that adapts to the user's progress.

[0300] (Example 1)

[0301] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0302] Conventional driving simulation systems have difficulty customizing them based on the individual driver's personality and driving style, and could only provide general driving training. As a result, they could not provide training content tailored to the driver's characteristics, making it difficult to achieve effective skill improvement and increased awareness of safe driving.

[0303] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0304] In this invention, the server includes an information processing device means for collecting user nature data and analyzing the user's personality characteristics; a generation device means for generating specific driving scenarios and creating three-dimensional visualizations based on the personality characteristics; and an operation device means for presenting the three-dimensional visualizations to the user and collecting the user's driving operation information in real time. This enables personalized driving training and allows for effective skill improvement and enhanced safe driving awareness based on the driver's characteristics.

[0305] "User characteristic data" refers to information including a driver's driving experience and personality traits, which is collected through questionnaires and personality tests.

[0306] "Personality traits" refer to the numerical or index expressions of a user's psychological characteristics and behavior patterns, which are used for customizing driving simulations.

[0307] "Information processing device" refers to a device that performs computational processing on data and derives analysis results, and has a function of evaluating a user's personality traits.

[0308] "Generation device" refers to a device that has a function of designing a specific driving scenario based on analysis results and generating a three-dimensional visualization.

[0309] "Three-dimensional visualization" refers to the representation of a virtual driving environment as a three-dimensional video, which is provided in a form that can be experienced by the user. <00,00975>

[0310] "Operating device" refers to a device that presents three-dimensional visualization to the user and collects the user's operation information in real time in a driving situation.

[0311] "Communication network" refers to a network such as the Internet, which is used as a means for collecting data from users located remotely.

[0312] "Questionnaire" refers to a document or form used to collect user characteristic data, which includes questions for evaluating driving characteristics and personality.

[0313] "Personality test" refers to a test for evaluating a user's psychological characteristics, which is used to quantitatively analyze a driver's personality traits.

[0314] The present invention is a system that provides individualized driving simulation training based on a user's personality traits. This system implements a plurality of functions mainly with a server, a terminal, and a user.

[0315] First, the user accesses an online questionnaire provided by the server via their terminal and inputs information about their driving experience and personality traits. This information is stored in the server's database as "user personality data." The server uses an information processing device to analyze the collected data and evaluates the user's "personality traits" based on a generating AI model. Here, for example, a prompt might be entered such as "Evaluate this user's cautiousness while driving."

[0316] Next, the server generates specific driving scenarios based on the evaluated personality traits. The generation device then uses three-dimensional visualization technology to design a driving simulation for the user. This provides a realistic driving experience tailored to the user's personality.

[0317] The terminal uses a virtual reality display and steering system to provide users with real-time three-dimensional visualizations. Users wear virtual reality goggles and operate a physical steering wheel to experience something similar to real driving.

[0318] The series of driving operations performed by the user within the simulation are accurately monitored in real time by a terminal and transmitted to a server. The operation data accumulated on the server is analyzed in detail by an evaluation device to assess the user's driving skills. Based on these results, specific driving improvement advice is generated for the user.

[0319] Finally, the server uses a training planning device to personalize and record the content of the next driving training session according to the analysis results. This allows the user to continuously improve their driving skills and deepen their awareness of safe driving. In this embodiment, the present invention makes it possible to provide users with customized driving training that is adapted to their individual driving characteristics.

[0320] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0321] Step 1:

[0322] Users access online questionnaires provided by the server and enter information about their driving experience and personality traits.

[0323] Input: User-submitted data regarding driving experience and personality traits.

[0324] Data processing: The server formats the input data and stores it in the database.

[0325] Output: User profile data is saved to the server.

[0326] Specific actions: The user fills in the answers for each item on the questionnaire and clicks the submit button.

[0327] Step 2:

[0328] The server uses a generative AI model to analyze the user's personality traits based on the collected user behavior data.

[0329] Input: Saved user profile data.

[0330] Data calculation: The server inputs the prompt message "Evaluate this user's driving caution" into the generated AI model and analyzes its personality traits.

[0331] Output: Evaluation results of the user's personality traits.

[0332] Specific operation: The server runs the AI ​​model and generates an analytical report based on the data.

[0333] Step 3:

[0334] The server generates specific driving scenarios based on the user's personality traits and creates three-dimensional visualizations.

[0335] Input: Evaluation results of personality traits.

[0336] Data processing: The generation device generates three-dimensional graphics based on the scenario data.

[0337] Output: Three-dimensional visualization data of a specific driving scenario.

[0338] Specific operation: The server uses a graphics engine to render the scenario and saves it as a simulation file.

[0339] Step 4:

[0340] The terminal provides the user with three-dimensional visualization data received from the server.

[0341] Input: 3D visualization data from the server.

[0342] Data processing: The terminal formats the visual data for the virtual reality goggles and displays it on the screen.

[0343] Output: Visualization of the driving scenario presented to the user.

[0344] Specific operation: The device interacts with virtual reality goggles to project the scenario into the user's field of vision.

[0345] Step 5:

[0346] Users use a device to experience the simulation and perform driving operations.

[0347] Input: Visualization of driving scenarios.

[0348] Data generation: The user operates a steering wheel-type controller to simulate actual driving operations.

[0349] Output: Operation data during operation.

[0350] Specific actions: The user's steering and braking actions within the simulation are tracked.

[0351] Step 6:

[0352] The terminal sends user operation data to the server.

[0353] Input: Real-time operation data from the user.

[0354] Data transfer: The terminal organizes the operation data and transfers it to the server in a predictable format.

[0355] Output: Operation data in a format usable by the server.

[0356] Specific operation: The terminal uses a data transmission circuit to transmit information to the server.

[0357] Step 7:

[0358] The server analyzes the operation data and evaluates the user's driving skills.

[0359] Input: Operation data.

[0360] Data calculation: The evaluation device calculates quantitative indicators and generates an evaluation score.

[0361] Output: Evaluation results regarding the user's driving skills.

[0362] Specific operation: The server automates analysis based on operational data and generates a user's driving performance report.

[0363] Step 8:

[0364] The server generates driving improvement advice based on the evaluation results and provides feedback to the user.

[0365] Input: Evaluation results of driving skills.

[0366] Data generation: The server uses a generative AI model to document appropriate advice.

[0367] Output: Feedback message for improving driving performance.

[0368] Specific operation: The server provides advice to the user via email, screen display, etc.

[0369] Step 9:

[0370] The server uses the evaluation data to personalize the content of the next driving training session and updates the training plan.

[0371] Input: Evaluation data.

[0372] Data processing: The training planning device generates the next scenario and customizes it.

[0373] Output: Personalized next training plan.

[0374] Specific operation: The server designs a new simulation scenario and registers it in the database.

[0375] (Application Example 1)

[0376] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0377] One challenge in the use of autonomous vehicles is the lack of training methods to help drivers understand the compatibility between the autonomous driving system and their own driving characteristics, enabling them to use the system more safely and effectively. Therefore, there is a need to provide training environments that maximize the capabilities of autonomous driving systems while simultaneously improving drivers' safety awareness.

[0378] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0379] In this invention, the server includes data processing means for collecting user characteristic data and analyzing the user's personality traits; generation means for generating specific driving situations and creating three-dimensional simulated visual information based on the personality traits; and driving control means for providing a training program for the driver to experience the compatibility between the autonomous driving function and their own driving characteristics. This enables the driver to understand how the autonomous vehicle fits their driving style and to acquire better driving skills in subsequent drives.

[0380] "User characteristic data" refers to information about the user's driving experience and personality traits, and is data that the system uses to analyze the user's driving style.

[0381] A "data processing device" is a computer device used to collect user characteristic data, analyze it, and evaluate the user's personality traits.

[0382] A "generation device" is a device that creates specific driving conditions based on personality traits and provides those driving conditions as three-dimensional simulated visual information.

[0383] A "display device" is a device that presents a three-dimensional simulation to the user and collects information on the user's driving actions in real time, and includes virtual reality visual devices.

[0384] An "evaluation device" is a computer device that analyzes collected driving operation information and generates specific driving improvement guidelines for the user.

[0385] A "training plan creation device" is a device that customizes the next training session based on evaluation results and creates a training program that is suitable for each user.

[0386] A "driving control system" is a device that provides a training program for drivers to experience the compatibility between the autonomous driving function and their own driving characteristics, and controls the operation of the autonomous vehicle.

[0387] To realize this invention, a driving simulation system will be built through the collaboration of a server, terminal, and user. The server will collect characteristic data from the user's smartphone or computer and evaluate personality traits based on that information. Specifically, data collection and analysis will be performed using software such as Python or Django. Through this data processing, an evaluation will be conducted that is tailored to the user's driving experience and personality traits.

[0388] Next, the server uses Unity to generate three-dimensional simulation visual information. The simulation reproduces specific driving situations based on the user's personality traits. For example, if the user is determined to "lose composure when in a hurry," they can experience situations in the simulation where traffic jams and sudden stops occur. This allows the user to learn how to use the autonomous driving system in those situations.

[0389] On the terminal side, a head-mounted display (e.g., Oculus Quest) or smartphone is used as a means of representation to provide a three-dimensional simulation from the user's perspective. The terminal also collects the user's driving operation information in real time and sends it back to the server. This information includes steering and braking operations and reaction times.

[0390] The server analyzes this information again and evaluates the user's driving performance using machine learning tools such as Scikit-learn. Based on this evaluation, it generates and provides the user with driving improvement guidelines. Furthermore, it creates a plan to customize the next training scenario and prepares a more accurate training program.

[0391] Example of a prompt:

[0392] "Please generate driving scenarios that demonstrate how users can remain calm and efficiently utilize autonomous driving features during traffic jams."

[0393] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0394] Step 1:

[0395] The server acquires survey data about the user's driving experience and personality traits via their smartphone. Using this data as input, the server performs data analysis to evaluate personality traits. A Python data processing library is used for the analysis, and the user's personality tendencies are output.

[0396] Step 2:

[0397] The server generates specific driving conditions based on the outputted personality traits. It uses Unity to create three-dimensional simulation visual information and sends this simulation data to the terminal. At this stage, a driving scenario based on prompt text is constructed using a generated AI model.

[0398] Step 3:

[0399] The terminal presents the received 3D simulation to the user. It recreates the simulation from the user's perspective via a head-mounted display, providing a virtual driving experience. Based on user input, it collects driving operation data in real time and transmits it to the server.

[0400] Step 4:

[0401] The server receives and analyzes driving operation data sent from the terminal. Using machine learning libraries such as Scikit-learn, it analyzes how this data matches personality traits and evaluates the user's driving performance. The evaluation results output metrics such as operational accuracy and reaction time.

[0402] Step 5:

[0403] The server generates personalized driving improvement guidelines for the user based on the evaluation results and sends them to the terminal. Furthermore, it uses these results to create a plan for customizing the next training simulation. This process ensures that a training program tailored to the user's needs is prepared.

[0404] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0405] This invention relates to an advanced driving simulation system based on a user's driving characteristics and emotional state. The system includes multiple processes performed by a server, a terminal, and the user, and is particularly characterized by the incorporation of an emotion engine.

[0406] First, the server collects questionnaires from users via the internet regarding their driving experience and personality. This information is stored as trait data and analyzed using an AI model. This analysis evaluates the user's personality traits, and a profile is created based on these evaluations.

[0407] The server uses an emotion engine along with a profile to identify the user's emotional state. This emotional state is determined by analyzing data such as facial expressions, voice, and heart rate that the user displays during the driving simulation. This emotional data is collected and used to understand how it influences the user's driving behavior.

[0408] Next, the generation engine functions to create driving situations based on the collected personality traits and emotional states. These situations are generated as dynamically adjustable 3D simulation images and sent to the terminal.

[0409] The terminal provides the user with a simulation using virtual reality goggles and a physical steering wheel controller. The user uses this interface device to experience the simulation visually and intuitively, and to perform driving maneuvers in response to specific hazardous situations.

[0410] Simultaneously, the device monitors the user's emotional state and transmits the user's driving data to the server in real time. The server analyzes this data, and an evaluation unit analyzes the relationship between emotions and driving behavior. This allows for an objective evaluation of the user's performance.

[0411] Based on the evaluation results, the server provides the user with specific driving improvement advice. This advice takes emotional states into account and aims to minimize the impact on driving behavior. The server also uses a training planning module to design new training scenarios and optimize the environment the user will experience in the next simulation. Through this process, drivers can improve their overall driving ability and strengthen their emotional control.

[0412] The following describes the processing flow.

[0413] Step 1:

[0414] After a user logs into the system, the server provides personality-related questionnaires and psychological tests via the internet. Users provide their personality data by answering these questionnaires and submitting them to the server.

[0415] Step 2:

[0416] The server inputs the received characteristic data into an AI model to generate a user personality profile. This profile includes psychological characteristics such as attention span and risk tendencies, and analysis is performed based on these characteristics.

[0417] Step 3:

[0418] The terminal provides the user with virtual reality goggles and starts the driving simulation. The user prepares to operate the vehicle in a manner similar to actual driving using a steering wheel-type controller.

[0419] Step 4:

[0420] The server uses an emotion engine to analyze the user's facial expressions, voice, and heart rate transmitted in real time from the device, recognizing the user's emotional state. This information is used to customize the driving experience.

[0421] Step 5:

[0422] The generation engine automatically generates appropriate driving scenarios based on the user's personality profile and real-time emotional state, and sends them to the device. The scenarios are dynamically adjusted and optimized for the user's state.

[0423] Step 6:

[0424] The user performs driving operations in a generated simulation environment. The terminal continuously records driving information such as steering angle, reaction time, and braking operation, and sends it to the server.

[0425] Step 7:

[0426] The server analyzes the collected driving data and emotional information using an evaluation unit to assess the user's driving performance. This evaluation includes the relationship between emotional fluctuations and operational accuracy.

[0427] Step 8:

[0428] Based on the evaluation results, the server provides users with specific driving improvement advice. This advice takes into account the influence of emotions on driving and guides users to appropriately control their emotions while driving.

[0429] Step 9:

[0430] The training planning module designs the next training scenario based on previous evaluations and feedback, improving the user's next simulation environment. This creates a training program that enables gradual skill improvement.

[0431] (Example 2)

[0432] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0433] In driving simulations, it is necessary to comprehensively evaluate not only the user's driving skills but also the influence of their emotional state on driving behavior, and to provide personalized driving improvement measures for each user. In particular, it is important to understand the relationship between changes in emotional state and driving operations in real time and to reflect this in training.

[0434] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0435] In this invention, the server includes an information processing device means that collects user characteristic data and emotional data and analyzes the user's personality traits and emotional state; a generation means that generates specific driving situations and creates three-dimensional simulation images based on the personality traits and emotional state; and an interaction device means that provides the three-dimensional simulation to the user and collects the user's driving operation data and emotional state data in real time. This makes it possible to provide a training program that comprehensively improves driving skills and emotional control ability.

[0436] An "information processing device" is a device that collects user characteristic data and emotional data, and analyzes this data to understand the user's personality traits and emotional state.

[0437] "Generation means" refers to a method or apparatus that has the function of creating a specific driving situation based on the user's personality traits and emotional state, and generating a three-dimensional simulation image.

[0438] A "dialogue device" is a device that provides users with a three-dimensional simulation and has the function of collecting driving operation data and emotional state data in real time during that simulation.

[0439] "Evaluation means" refers to a method or apparatus that has the function of analyzing collected driving operation data and emotional state data and generating specific driving improvement advice for the user.

[0440] A "training plan formulation device" is a device that has the function of formulating a plan to customize the user's next training session based on evaluation results.

[0441] "Three-dimensional simulation video" refers to a three-dimensional image created by a generation method to provide users with a driving experience.

[0442] This invention relates to a driving simulation system in which a server, a terminal, and a user cooperate to perform the simulation. The server acquires characteristic data and emotional data from the user. The characteristic data includes information about driving experience and personality. This data is collected using an information processing device and analyzed using a generative AI model. The AI ​​model evaluates the user's personality traits and emotional state and defines them as a profile.

[0443] Next, the server uses a generation mechanism based on the profile information to design a specific operating situation. This operating situation is generated as a three-dimensional simulation image and transmitted to the terminal. The virtual reality display device and physical control device connected to the terminal provide this simulation image to the user.

[0444] These devices allow users to gain both visual and physical experiences. For example, by wearing virtual reality goggles and actually operating the steering wheel, they can experience a realistic driving simulation.

[0445] Simultaneously, the terminal transmits the user's driving operation data and emotional state data to the server in real time. The server analyzes this data using evaluation tools and assesses the relationship between driving behavior and emotional state. Based on these results, the server provides the user with specific advice for improving their driving. Furthermore, a training plan formulation device is used to plan for optimizing the conditions for the next driving training session.

[0446] For example, the server can improve performance in subsequent simulations by identifying scenarios that are likely to cause stress for the user and providing advice to help them relax. An example of a prompt would be, "Design a driving simulation in a congested road environment based on the user's driving experience and personality traits."

[0447] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0448] Step 1:

[0449] The server collects survey data from users regarding their driving experience and personality via the internet. It receives user survey responses as input and stores them as characteristic data in a database using an information processing device. This process includes data formatting and storage.

[0450] Step 2:

[0451] The server inputs stored trait data into a generating AI model to analyze the user's personality traits and emotional state. Using the trait data as input, the AI ​​model performs data calculations and outputs the user's profile. This profile includes tendencies and characteristics as a driver.

[0452] Step 3:

[0453] The server inputs profile information into the emotion engine to identify the user's emotional state. It receives profile and user emotion data as input and identifies the emotional state through analysis. The emotional state result is obtained as output, and the server prepares to adjust the driving scenario based on this result.

[0454] Step 4:

[0455] The server uses a generation mechanism to create driving simulation scenarios based on characteristic data and emotional states. The input is the user's personality traits and emotional state, which are used to generate dynamic and specific driving scenarios. The output is a three-dimensional simulation image, which is sent to the terminal.

[0456] Step 5:

[0457] The terminal provides the user with three-dimensional simulation images through a virtual reality display device and physical control devices. In this step, the user can participate in the simulation using VR goggles and a steering wheel controller, gaining a visual and operational experience. The output is the user's driving operation data.

[0458] Step 6:

[0459] The terminal transmits user driving operation data and emotional state data to the server in real time during the running simulation. It collects driving operation and emotional data as input and relays it to the server as output. This data is used for subsequent evaluation.

[0460] Step 7:

[0461] The server analyzes driving operation data and emotional state data using evaluation tools and generates driving improvement advice for the user. The input is collected data, which is analyzed and its correlations evaluated. The output is specific driving advice, which is then fed back to the user.

[0462] Step 8:

[0463] The server uses a training planning device to customize the next driving training scenario. Using the evaluation results as input, it designs a new training scenario and generates an improved scenario for the next simulation as output. This scenario is designed to improve the user's capabilities.

[0464] (Application Example 2)

[0465] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0466] In autonomous vehicles, there is a need to optimize the driving environment while taking into account the emotional state of the occupants. However, with conventional technology, it is difficult to grasp the emotional state of the occupants in real time and provide appropriate feedback based on that. In such a situation, there is a problem in that a safe and comfortable driving experience cannot be provided for the occupants.

[0467] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0468] In this invention, the server includes processing means by an electronic computer that collects user characteristic information and analyzes the user's personality traits based on this information; generation means that generates a specific driving environment and creates a three-dimensional simulation image based on the personality traits and emotional state; and learning means that monitors the occupant's emotional state in real time and provides a safe and comfortable driving environment. This makes it possible to dynamically optimize the driving environment according to the occupant's emotional state and provide a safer and more comfortable autonomous driving experience.

[0469] "User characteristic information" refers to information about the user's personal characteristics, such as their driving experience and personality.

[0470] "Personality traits" refer to the personality tendencies and characteristics of a person, analyzed based on their characteristic information.

[0471] "Processing by electronic computing devices" refers to processes that involve collecting and analyzing data using devices such as computers.

[0472] A "generation device" refers to a device that generates a specific driving environment based on personality traits and emotional states, and creates a three-dimensional simulation image.

[0473] A "three-dimensional simulation image" refers to a computer graphics image that reproduces the driving environment and conditions in three dimensions.

[0474] An "interface device" refers to a device that collects operational information while a user experiences a simulation. Examples include virtual reality goggles and steering wheel controllers.

[0475] An "evaluation device" refers to a device that analyzes collected operational information and provides users with advice on improving their driving.

[0476] A "training planning device" refers to a device that customizes the user's next training environment based on evaluation results.

[0477] A "learning device" refers to a device that monitors the emotional state of the occupants in real time and adjusts the driving environment based on that information.

[0478] This invention relates to a system for monitoring the emotional state of occupants in real time within an autonomous vehicle, thereby providing a safe and comfortable driving experience. The system consists of multiple computers and various sensors. Embodiments are described below.

[0479] The server first collects user characteristic information via the internet. This information is processed by a computer as data for analyzing personality traits. The analyzed personality traits are then used to create a profile based on the user's driving behavior.

[0480] The device works in conjunction with the vehicle's cameras, microphones, and heart rate sensors to collect and analyze data such as the occupant's facial expressions, voice, and heart rate using an emotion engine, and to identify their emotional state in real time. This process utilizes AI platforms such as Amazon SageMaker for data processing and analysis.

[0481] The generation device creates a driving environment corresponding to the obtained personality traits and emotional state, and creates a three-dimensional simulation image which is then transmitted to the terminal. This allows the user to experience visual feedback and operation via a virtual reality display device or physical control device.

[0482] The evaluation device analyzes driving information collected in real time and generates specific driving improvement advice for the user. Furthermore, the training planning device customizes the driving environment to provide an optimized scenario for the next training session.

[0483] For example, if the system detects that the occupants are fatigued during long drives, it can switch the car to a relaxation mode and automatically play soothing music. Another example of a prompt generated by the AI ​​model is, "Based on the occupants' current emotional state, please select the optimal driving mode to provide a safe and comfortable driving experience."

[0484] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0485] Step 1:

[0486] The server collects user characteristic information via the internet through questionnaires and psychological assessments. Inputs are web forms and questionnaire data, while outputs are characteristic information stored in a database for use in user personality analysis. This provides the necessary foundational data for subsequent processing.

[0487] Step 2:

[0488] The server analyzes personality traits using a generative AI model based on the collected user characteristic information. The input is the characteristic information obtained in step 1, and the output is the user's personality profile. This profile is used to predict the user's driving behavior and emotional state.

[0489] Step 3:

[0490] The device collects data in real time from the in-car camera, microphone, and heart rate sensor. The input is raw data from each sensor, and the output is integrated data necessary for the emotion engine to analyze it. This data is used to identify the emotional state of the occupants based on their facial expressions, voice, and heart rate.

[0491] Step 4:

[0492] The terminal uses an emotion engine to analyze integrated data and identify the occupant's emotional state in real time. The input is the integrated data obtained in step 3, and the output is the occupant's emotional state. This emotional state forms the basis for generating the next driving environment.

[0493] Step 5:

[0494] The generator creates an appropriate driving environment and produces a three-dimensional simulation image based on the personality profile and emotional state. The input is the personality profile from step 2 and the emotional state from step 4, and the output is the three-dimensional simulation image delivered to the virtual reality device. This allows the user to have an intuitive driving experience.

[0495] Step 6:

[0496] The terminal collects user operation information through three-dimensional simulation and transmits it to the server. The input is the operation information obtained during the three-dimensional simulation, and the output is data sent to the server for analysis on the evaluation device. This information forms the basis for operational improvement advice.

[0497] Step 7:

[0498] The server uses an evaluation device to analyze user operation information and generate specific driving improvement advice. The input is the operation information obtained in step 6, and the output is the improvement advice provided to the user. This advice is intended to improve the user's driving ability.

[0499] Step 8:

[0500] Based on the evaluation results, the server uses a training planning device to customize the next training environment. The input is the improvement advice and evaluation results from step 7, and the output is the customized next training scenario. This allows the user to have an optimized learning opportunity.

[0501] 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.

[0502] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0503] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0504] [Third Embodiment]

[0505] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0506] 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.

[0507] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0508] 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.

[0509] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0510] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0511] 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.

[0512] 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.

[0513] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0514] The 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.

[0515] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0516] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0517] This invention provides a driving simulation system based on the driver's personality traits. This system includes multiple processes performed by a server, a terminal, and a user.

[0518] First, the server begins by collecting basic characteristic data from the user. This data includes the user's driving experience and the results of questionnaires regarding their personality traits. The collected data is analyzed by computer processing and used to evaluate the user's personality traits.

[0519] Subsequently, the server uses a generation engine to generate specific driving situations based on the user's personality traits. These situations are then formatted as 3D simulation images and sent to the terminal.

[0520] The terminal provides users with simulations using virtual reality goggles or physical steering wheel controllers. Users can use this interface device to experience real-world driving situations and improve their driving skills by performing actions appropriate to specific dangerous situations.

[0521] Meanwhile, the terminal collects user driving operation data in real time and transmits it to the server. This data includes information on steering and braking operations, reaction time, etc. The server uses this data to evaluate the user's performance, and the evaluation unit performs the analysis.

[0522] Based on the results, the server generates specific driving improvement advice and provides feedback to the user. This feedback serves as a guide for the user to try to improve their driving in the future.

[0523] Finally, the training planning module creates a new training scenario based on the user's evaluation results, preparing for the next simulation. Through this series of processes, drivers can improve not only their driving skills but also their awareness of safe driving. In this embodiment, the present invention enables customized safe driving training tailored to individual drivers.

[0524] The following describes the processing flow.

[0525] Step 1:

[0526] After a user logs into the system, the server collects basic characteristic data. This includes questionnaires about the user's driving experience and personality. The server saves this data in document format and prepares it for analysis.

[0527] Step 2:

[0528] The server uses the collected trait data to perform analysis with an AI model. This generates a user personality profile. The AI ​​model evaluates psychological traits such as attention and stress tolerance, and uses this to identify individual user traits.

[0529] Step 3:

[0530] The server uses a generation engine to create driving scenarios optimized for the user's personality profile. These driving scenarios are designed as simulations tailored to the user's characteristics, such as the appearance of unexpected obstacles or sudden lane changes.

[0531] Step 4:

[0532] The terminal provides the user with 3D simulation images received from the server, using VR goggles or a physical steering wheel. The user then experiences driving in a visually immersive and intuitive environment.

[0533] Step 5:

[0534] The user performs driving operations in a provided driving simulation environment via a terminal. During this time, the terminal records the user's operation data in real time, measuring things like steering movements and reaction times.

[0535] Step 6:

[0536] The terminal transmits collected driving operation data to the server. The server analyzes this data and evaluates the user's driving performance through an evaluation unit. The evaluation results include the user's reaction speed and operational accuracy.

[0537] Step 7:

[0538] The server generates specific improvement advice for the user based on the evaluation results. This advice is designed to improve driving behavior during the next training session and may include suggestions such as "try braking earlier."

[0539] Step 8:

[0540] The server uses a training planning module to customize the simulation for the user's next training session. This enables a step-by-step learning process that adapts to the user's progress.

[0541] (Example 1)

[0542] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0543] Conventional driving simulation systems have difficulty customizing them based on the individual driver's personality and driving style, and could only provide general driving training. As a result, they could not provide training content tailored to the driver's characteristics, making it difficult to achieve effective skill improvement and increased awareness of safe driving.

[0544] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0545] In this invention, the server includes an information processing device means for collecting user nature data and analyzing the user's personality characteristics; a generation device means for generating specific driving scenarios and creating three-dimensional visualizations based on the personality characteristics; and an operation device means for presenting the three-dimensional visualizations to the user and collecting the user's driving operation information in real time. This enables personalized driving training and allows for effective skill improvement and enhanced safe driving awareness based on the driver's characteristics.

[0546] "User characteristic data" refers to information including the driver's driving experience and personality traits, which is collected through questionnaires and personality tests.

[0547] "Personality traits" are numerical or indicator-based representations of a user's psychological characteristics and behavioral patterns, and are used to customize driving simulations.

[0548] An "information processing device" is a device that processes data, derives analysis results, and has the function of evaluating the user's personality traits.

[0549] A "generation device" is a device that has the function of designing a specific operating scenario based on the analysis results and generating a three-dimensional visualization.

[0550] "Three-dimensional visualization" refers to the representation of a virtual driving environment as a three-dimensional image, provided in a format that users can experience.

[0551] A "control device" is a device that presents a three-dimensional visualization to the user and collects user operation information in real time regarding the driving situation.

[0552] A "communication network" refers to a network such as the Internet, which is used as a means of collecting data from users in remote locations.

[0553] A "questionnaire" is a document or form used to collect user characteristic data, and includes questions to evaluate driving characteristics and personality.

[0554] A "personality test" is a test used to evaluate a user's psychological characteristics and to quantitatively analyze the driver's personality traits.

[0555] This invention provides a system that offers personalized driving simulation training based on the user's personality traits. This system performs multiple functions primarily involving a server, terminals, and users.

[0556] First, the user accesses an online questionnaire provided by the server via their terminal and inputs information about their driving experience and personality traits. This information is stored in the server's database as "user personality data." The server uses an information processing device to analyze the collected data and evaluates the user's "personality traits" based on a generating AI model. Here, for example, a prompt might be entered such as "Evaluate this user's cautiousness while driving."

[0557] Next, the server generates specific driving scenarios based on the evaluated personality traits. The generation device then uses three-dimensional visualization technology to design a driving simulation for the user. This provides a realistic driving experience tailored to the user's personality.

[0558] The terminal uses a virtual reality display and steering system to provide users with real-time three-dimensional visualizations. Users wear virtual reality goggles and operate a physical steering wheel to experience something similar to real driving.

[0559] The series of driving operations performed by the user within the simulation are accurately monitored in real time by a terminal and transmitted to a server. The operation data accumulated on the server is analyzed in detail by an evaluation device to assess the user's driving skills. Based on these results, specific driving improvement advice is generated for the user.

[0560] Finally, the server uses a training planning device to personalize and record the content of the next driving training session according to the analysis results. This allows the user to continuously improve their driving skills and deepen their awareness of safe driving. In this embodiment, the present invention makes it possible to provide users with customized driving training that is adapted to their individual driving characteristics.

[0561] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0562] Step 1:

[0563] Users access online questionnaires provided by the server and enter information about their driving experience and personality traits.

[0564] Input: User-submitted data regarding driving experience and personality traits.

[0565] Data processing: The server formats the input data and stores it in the database.

[0566] Output: User profile data is saved to the server.

[0567] Specific actions: The user fills in the answers for each item on the questionnaire and clicks the submit button.

[0568] Step 2:

[0569] The server uses a generative AI model to analyze the user's personality traits based on the collected user behavior data.

[0570] Input: Saved user profile data.

[0571] Data calculation: The server inputs the prompt message "Evaluate this user's driving caution" into the generated AI model and analyzes its personality traits.

[0572] Output: Evaluation results of the user's personality traits.

[0573] Specific operation: The server runs the AI ​​model and generates an analytical report based on the data.

[0574] Step 3:

[0575] The server generates specific driving scenarios based on the user's personality traits and creates three-dimensional visualizations.

[0576] Input: Evaluation results of personality traits.

[0577] Data processing: The generation device generates three-dimensional graphics based on the scenario data.

[0578] Output: Three-dimensional visualization data of a specific driving scenario.

[0579] Specific operation: The server uses a graphics engine to render the scenario and saves it as a simulation file.

[0580] Step 4:

[0581] The terminal provides the user with three-dimensional visualization data received from the server.

[0582] Input: 3D visualization data from the server.

[0583] Data processing: The terminal formats the visual data for the virtual reality goggles and displays it on the screen.

[0584] Output: Visualization of the driving scenario presented to the user.

[0585] Specific operation: The device interacts with virtual reality goggles to project the scenario into the user's field of vision.

[0586] Step 5:

[0587] Users use a device to experience the simulation and perform driving operations.

[0588] Input: Visualization of driving scenarios.

[0589] Data generation: The user operates a steering wheel-type controller to simulate actual driving operations.

[0590] Output: Operation data during operation.

[0591] Specific actions: The user's steering and braking actions within the simulation are tracked.

[0592] Step 6:

[0593] The terminal sends user operation data to the server.

[0594] Input: Real-time operation data from the user.

[0595] Data transfer: The terminal organizes the operation data and transfers it to the server in a predictable format.

[0596] Output: Operation data in a format usable by the server.

[0597] Specific operation: The terminal uses a data transmission circuit to transmit information to the server.

[0598] Step 7:

[0599] The server analyzes the operation data and evaluates the user's driving skills.

[0600] Input: Operation data.

[0601] Data calculation: The evaluation device calculates quantitative indicators and generates an evaluation score.

[0602] Output: Evaluation results regarding the user's driving skills.

[0603] Specific operation: The server automates analysis based on operational data and generates a user's driving performance report.

[0604] Step 8:

[0605] The server generates driving improvement advice based on the evaluation results and provides feedback to the user.

[0606] Input: Evaluation results of driving skills.

[0607] Data generation: The server uses a generative AI model to document appropriate advice.

[0608] Output: Feedback message for improving driving performance.

[0609] Specific operation: The server provides advice to the user via email, screen display, etc.

[0610] Step 9:

[0611] The server uses the evaluation data to personalize the content of the next driving training session and updates the training plan.

[0612] Input: Evaluation data.

[0613] Data processing: The training planning device generates the next scenario and customizes it.

[0614] Output: Personalized next training plan.

[0615] Specific operation: The server designs a new simulation scenario and registers it in the database.

[0616] (Application Example 1)

[0617] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0618] One challenge in the use of autonomous vehicles is the lack of training methods to help drivers understand the compatibility between the autonomous driving system and their own driving characteristics, enabling them to use the system more safely and effectively. Therefore, there is a need to provide training environments that maximize the capabilities of autonomous driving systems while simultaneously improving drivers' safety awareness.

[0619] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0620] In this invention, the server includes data processing means for collecting user characteristic data and analyzing the user's personality traits; generation means for generating specific driving situations and creating three-dimensional simulated visual information based on the personality traits; and driving control means for providing a training program for the driver to experience the compatibility between the autonomous driving function and their own driving characteristics. This enables the driver to understand how the autonomous vehicle fits their driving style and to acquire better driving skills in subsequent drives.

[0621] "User characteristic data" refers to information about the user's driving experience and personality traits, and is data that the system uses to analyze the user's driving style.

[0622] A "data processing device" is a computer device used to collect user characteristic data, analyze it, and evaluate the user's personality traits.

[0623] A "generation device" is a device that creates specific driving conditions based on personality traits and provides those driving conditions as three-dimensional simulated visual information.

[0624] A "display device" is a device that presents a three-dimensional simulation to the user and collects information on the user's driving actions in real time, and includes virtual reality visual devices.

[0625] An "evaluation device" is a computer device that analyzes collected driving operation information and generates specific driving improvement guidelines for the user.

[0626] A "training plan creation device" is a device that customizes the next training session based on evaluation results and creates a training program that is suitable for each user.

[0627] A "driving control system" is a device that provides a training program for drivers to experience the compatibility between the autonomous driving function and their own driving characteristics, and controls the operation of the autonomous vehicle.

[0628] To realize this invention, a driving simulation system will be built through the collaboration of a server, terminal, and user. The server will collect characteristic data from the user's smartphone or computer and evaluate personality traits based on that information. Specifically, data collection and analysis will be performed using software such as Python or Django. Through this data processing, an evaluation will be conducted that is tailored to the user's driving experience and personality traits.

[0629] Next, the server uses Unity to generate three-dimensional simulation visual information. The simulation reproduces specific driving situations based on the user's personality traits. For example, if the user is determined to "lose composure when in a hurry," they can experience situations in the simulation where traffic jams and sudden stops occur. This allows the user to learn how to use the autonomous driving system in those situations.

[0630] On the terminal side, a head-mounted display (e.g., Oculus Quest) or smartphone is used as a means of representation to provide a three-dimensional simulation from the user's perspective. The terminal also collects the user's driving operation information in real time and sends it back to the server. This information includes steering and braking operations and reaction times.

[0631] The server analyzes this information again and evaluates the user's driving performance using machine learning tools such as Scikit-learn. Based on this evaluation, it generates and provides the user with driving improvement guidelines. Furthermore, it creates a plan to customize the next training scenario and prepares a more accurate training program.

[0632] Example of a prompt:

[0633] "Please generate driving scenarios that demonstrate how users can remain calm and efficiently utilize autonomous driving features during traffic jams."

[0634] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0635] Step 1:

[0636] The server acquires survey data about the user's driving experience and personality traits via their smartphone. Using this data as input, the server performs data analysis to evaluate personality traits. A Python data processing library is used for the analysis, and the user's personality tendencies are output.

[0637] Step 2:

[0638] The server generates specific driving conditions based on the outputted personality traits. It uses Unity to create three-dimensional simulation visual information and sends this simulation data to the terminal. At this stage, a driving scenario based on prompt text is constructed using a generated AI model.

[0639] Step 3:

[0640] The terminal presents the received 3D simulation to the user. It recreates the simulation from the user's perspective via a head-mounted display, providing a virtual driving experience. Based on user input, it collects driving operation data in real time and transmits it to the server.

[0641] Step 4:

[0642] The server receives and analyzes driving operation data sent from the terminal. Using machine learning libraries such as Scikit-learn, it analyzes how this data matches personality traits and evaluates the user's driving performance. The evaluation results output metrics such as operational accuracy and reaction time.

[0643] Step 5:

[0644] The server generates personalized driving improvement guidelines for the user based on the evaluation results and sends them to the terminal. Furthermore, it uses these results to create a plan for customizing the next training simulation. This process ensures that a training program tailored to the user's needs is prepared.

[0645] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0646] This invention relates to an advanced driving simulation system based on a user's driving characteristics and emotional state. The system includes multiple processes performed by a server, a terminal, and the user, and is particularly characterized by the incorporation of an emotion engine.

[0647] First, the server collects questionnaires from users via the internet regarding their driving experience and personality. This information is stored as trait data and analyzed using an AI model. This analysis evaluates the user's personality traits, and a profile is created based on these evaluations.

[0648] The server uses an emotion engine along with a profile to identify the user's emotional state. This emotional state is determined by analyzing data such as facial expressions, voice, and heart rate that the user displays during the driving simulation. This emotional data is collected and used to understand how it influences the user's driving behavior.

[0649] Next, the generation engine functions to create driving situations based on the collected personality traits and emotional states. These situations are generated as dynamically adjustable 3D simulation images and sent to the terminal.

[0650] The terminal provides the user with a simulation using virtual reality goggles and a physical steering wheel controller. The user uses this interface device to experience the simulation visually and intuitively, and to perform driving maneuvers in response to specific hazardous situations.

[0651] Simultaneously, the device monitors the user's emotional state and transmits the user's driving data to the server in real time. The server analyzes this data, and an evaluation unit analyzes the relationship between emotions and driving behavior. This allows for an objective evaluation of the user's performance.

[0652] Based on the evaluation results, the server provides the user with specific driving improvement advice. This advice takes emotional states into account and aims to minimize the impact on driving behavior. The server also uses a training planning module to design new training scenarios and optimize the environment the user will experience in the next simulation. Through this process, drivers can improve their overall driving ability and strengthen their emotional control.

[0653] The following describes the processing flow.

[0654] Step 1:

[0655] After a user logs into the system, the server provides personality-related questionnaires and psychological tests via the internet. Users provide their personality data by answering these questionnaires and submitting them to the server.

[0656] Step 2:

[0657] The server inputs the received characteristic data into an AI model to generate a user personality profile. This profile includes psychological characteristics such as attention span and risk tendencies, and analysis is performed based on these characteristics.

[0658] Step 3:

[0659] The terminal provides the user with virtual reality goggles and starts the driving simulation. The user prepares to operate the vehicle in a manner similar to actual driving using a steering wheel-type controller.

[0660] Step 4:

[0661] The server uses an emotion engine to analyze the user's facial expressions, voice, and heart rate transmitted in real time from the device, recognizing the user's emotional state. This information is used to customize the driving experience.

[0662] Step 5:

[0663] The generation engine automatically generates appropriate driving scenarios based on the user's personality profile and real-time emotional state, and sends them to the device. The scenarios are dynamically adjusted and optimized for the user's state.

[0664] Step 6:

[0665] The user performs driving operations in a generated simulation environment. The terminal continuously records driving information such as steering angle, reaction time, and braking operation, and sends it to the server.

[0666] Step 7:

[0667] The server analyzes the collected driving data and emotional information using an evaluation unit to assess the user's driving performance. This evaluation includes the relationship between emotional fluctuations and operational accuracy.

[0668] Step 8:

[0669] Based on the evaluation results, the server provides users with specific driving improvement advice. This advice takes into account the influence of emotions on driving and guides users to appropriately control their emotions while driving.

[0670] Step 9:

[0671] The training planning module designs the next training scenario based on previous evaluations and feedback, improving the user's next simulation environment. This creates a training program that enables gradual skill improvement.

[0672] (Example 2)

[0673] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0674] In driving simulations, it is necessary to comprehensively evaluate not only the user's driving skills but also the influence of their emotional state on driving behavior, and to provide personalized driving improvement measures for each user. In particular, it is important to understand the relationship between changes in emotional state and driving operations in real time and to reflect this in training.

[0675] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0676] In this invention, the server includes an information processing device means that collects user characteristic data and emotional data and analyzes the user's personality traits and emotional state; a generation means that generates specific driving situations and creates three-dimensional simulation images based on the personality traits and emotional state; and an interaction device means that provides the three-dimensional simulation to the user and collects the user's driving operation data and emotional state data in real time. This makes it possible to provide a training program that comprehensively improves driving skills and emotional control ability.

[0677] An "information processing device" is a device that collects user characteristic data and emotional data, and analyzes this data to understand the user's personality traits and emotional state.

[0678] "Generation means" refers to a method or apparatus that has the function of creating a specific driving situation based on the user's personality traits and emotional state, and generating a three-dimensional simulation image.

[0679] A "dialogue device" is a device that provides users with a three-dimensional simulation and has the function of collecting driving operation data and emotional state data in real time during that simulation.

[0680] "Evaluation means" refers to a method or apparatus that has the function of analyzing collected driving operation data and emotional state data and generating specific driving improvement advice for the user.

[0681] A "training plan formulation device" is a device that has the function of formulating a plan to customize the user's next training session based on evaluation results.

[0682] "Three-dimensional simulation video" refers to a three-dimensional image created by a generation method to provide users with a driving experience.

[0683] This invention relates to a driving simulation system in which a server, a terminal, and a user cooperate to perform the simulation. The server acquires characteristic data and emotional data from the user. The characteristic data includes information about driving experience and personality. This data is collected using an information processing device and analyzed using a generative AI model. The AI ​​model evaluates the user's personality traits and emotional state and defines them as a profile.

[0684] Next, the server uses a generation mechanism based on the profile information to design a specific operating situation. This operating situation is generated as a three-dimensional simulation image and transmitted to the terminal. The virtual reality display device and physical control device connected to the terminal provide this simulation image to the user.

[0685] These devices allow users to gain both visual and physical experiences. For example, by wearing virtual reality goggles and actually operating the steering wheel, they can experience a realistic driving simulation.

[0686] Simultaneously, the terminal transmits the user's driving operation data and emotional state data to the server in real time. The server analyzes this data using evaluation tools and assesses the relationship between driving behavior and emotional state. Based on these results, the server provides the user with specific advice for improving their driving. Furthermore, a training plan formulation device is used to plan for optimizing the conditions for the next driving training session.

[0687] For example, the server can improve performance in subsequent simulations by identifying scenarios that are likely to cause stress for the user and providing advice to help them relax. An example of a prompt would be, "Design a driving simulation in a congested road environment based on the user's driving experience and personality traits."

[0688] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0689] Step 1:

[0690] The server collects survey data from users regarding their driving experience and personality via the internet. It receives user survey responses as input and stores them as characteristic data in a database using an information processing device. This process includes data formatting and storage.

[0691] Step 2:

[0692] The server inputs stored trait data into a generating AI model to analyze the user's personality traits and emotional state. Using the trait data as input, the AI ​​model performs data calculations and outputs the user's profile. This profile includes tendencies and characteristics as a driver.

[0693] Step 3:

[0694] The server inputs profile information into the emotion engine to identify the user's emotional state. It receives profile and user emotion data as input and identifies the emotional state through analysis. The emotional state result is obtained as output, and the server prepares to adjust the driving scenario based on this result.

[0695] Step 4:

[0696] The server uses a generation mechanism to create driving simulation scenarios based on characteristic data and emotional states. The input is the user's personality traits and emotional state, which are used to generate dynamic and specific driving scenarios. The output is a three-dimensional simulation image, which is sent to the terminal.

[0697] Step 5:

[0698] The terminal provides the user with three-dimensional simulation images through a virtual reality display device and physical control devices. In this step, the user can participate in the simulation using VR goggles and a steering wheel controller, gaining a visual and operational experience. The output is the user's driving operation data.

[0699] Step 6:

[0700] The terminal transmits user driving operation data and emotional state data to the server in real time during the running simulation. It collects driving operation and emotional data as input and relays it to the server as output. This data is used for subsequent evaluation.

[0701] Step 7:

[0702] The server analyzes driving operation data and emotional state data using evaluation tools and generates driving improvement advice for the user. The input is collected data, which is analyzed and its correlations evaluated. The output is specific driving advice, which is then fed back to the user.

[0703] Step 8:

[0704] The server uses a training planning device to customize the next driving training scenario. Using the evaluation results as input, it designs a new training scenario and generates an improved scenario for the next simulation as output. This scenario is designed to improve the user's capabilities.

[0705] (Application Example 2)

[0706] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0707] In autonomous vehicles, there is a need to optimize the driving environment while taking into account the emotional state of the occupants. However, with conventional technology, it is difficult to grasp the emotional state of the occupants in real time and provide appropriate feedback based on that. In such a situation, there is a problem in that a safe and comfortable driving experience cannot be provided for the occupants.

[0708] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0709] In this invention, the server includes processing means by an electronic computer that collects user characteristic information and analyzes the user's personality traits based on this information; generation means that generates a specific driving environment and creates a three-dimensional simulation image based on the personality traits and emotional state; and learning means that monitors the occupant's emotional state in real time and provides a safe and comfortable driving environment. This makes it possible to dynamically optimize the driving environment according to the occupant's emotional state and provide a safer and more comfortable autonomous driving experience.

[0710] "User characteristic information" refers to information about the user's personal characteristics, such as their driving experience and personality.

[0711] "Personality traits" refer to the personality tendencies and characteristics of a person, analyzed based on their characteristic information.

[0712] "Processing by electronic computing devices" refers to processes that involve collecting and analyzing data using devices such as computers.

[0713] A "generation device" refers to a device that generates a specific driving environment based on personality traits and emotional states, and creates a three-dimensional simulation image.

[0714] A "three-dimensional simulation image" refers to a computer graphics image that reproduces the driving environment and conditions in three dimensions.

[0715] An "interface device" refers to a device that collects operational information while a user experiences a simulation. Examples include virtual reality goggles and steering wheel controllers.

[0716] An "evaluation device" refers to a device that analyzes collected operational information and provides users with advice on improving their driving.

[0717] A "training planning device" refers to a device that customizes the user's next training environment based on evaluation results.

[0718] A "learning device" refers to a device that monitors the emotional state of the occupants in real time and adjusts the driving environment based on that information.

[0719] This invention relates to a system for monitoring the emotional state of occupants in real time within an autonomous vehicle, thereby providing a safe and comfortable driving experience. The system consists of multiple computers and various sensors. Embodiments are described below.

[0720] The server first collects user characteristic information via the internet. This information is processed by a computer as data for analyzing personality traits. The analyzed personality traits are then used to create a profile based on the user's driving behavior.

[0721] The device works in conjunction with the vehicle's cameras, microphones, and heart rate sensors to collect and analyze data such as the occupant's facial expressions, voice, and heart rate using an emotion engine, and to identify their emotional state in real time. This process utilizes AI platforms such as Amazon SageMaker for data processing and analysis.

[0722] The generation device creates a driving environment corresponding to the obtained personality traits and emotional state, and creates a three-dimensional simulation image which is then transmitted to the terminal. This allows the user to experience visual feedback and operation via a virtual reality display device or physical control device.

[0723] The evaluation device analyzes driving information collected in real time and generates specific driving improvement advice for the user. Furthermore, the training planning device customizes the driving environment to provide an optimized scenario for the next training session.

[0724] For example, if the system detects that the occupants are fatigued during long drives, it can switch the car to a relaxation mode and automatically play soothing music. Another example of a prompt generated by the AI ​​model is, "Based on the occupants' current emotional state, please select the optimal driving mode to provide a safe and comfortable driving experience."

[0725] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0726] Step 1:

[0727] The server collects user characteristic information via the internet through questionnaires and psychological assessments. Inputs are web forms and questionnaire data, while outputs are characteristic information stored in a database for use in user personality analysis. This provides the necessary foundational data for subsequent processing.

[0728] Step 2:

[0729] The server analyzes personality traits using a generative AI model based on the collected user characteristic information. The input is the characteristic information obtained in step 1, and the output is the user's personality profile. This profile is used to predict the user's driving behavior and emotional state.

[0730] Step 3:

[0731] The device collects data in real time from the in-car camera, microphone, and heart rate sensor. The input is raw data from each sensor, and the output is integrated data necessary for the emotion engine to analyze it. This data is used to identify the emotional state of the occupants based on their facial expressions, voice, and heart rate.

[0732] Step 4:

[0733] The terminal uses an emotion engine to analyze integrated data and identify the occupant's emotional state in real time. The input is the integrated data obtained in step 3, and the output is the occupant's emotional state. This emotional state forms the basis for generating the next driving environment.

[0734] Step 5:

[0735] The generator creates an appropriate driving environment and produces a three-dimensional simulation image based on the personality profile and emotional state. The input is the personality profile from step 2 and the emotional state from step 4, and the output is the three-dimensional simulation image delivered to the virtual reality device. This allows the user to have an intuitive driving experience.

[0736] Step 6:

[0737] The terminal collects user operation information through three-dimensional simulation and transmits it to the server. The input is the operation information obtained during the three-dimensional simulation, and the output is data sent to the server for analysis on the evaluation device. This information forms the basis for operational improvement advice.

[0738] Step 7:

[0739] The server uses an evaluation device to analyze user operation information and generate specific driving improvement advice. The input is the operation information obtained in step 6, and the output is the improvement advice provided to the user. This advice is intended to improve the user's driving ability.

[0740] Step 8:

[0741] Based on the evaluation results, the server uses a training planning device to customize the next training environment. The input is the improvement advice and evaluation results from step 7, and the output is the customized next training scenario. This allows the user to have an optimized learning opportunity.

[0742] 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.

[0743] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0744] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0745] [Fourth Embodiment]

[0746] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0747] 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.

[0748] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0749] 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.

[0750] The microphone 238 receives voice signals from the user 20 and accepts instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0751] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0752] 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.

[0753] 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 in the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0754] 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.

[0755] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0756] The 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.

[0757] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0758] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0759] This invention provides a driving simulation system based on the driver's personality traits. This system includes multiple processes performed by a server, a terminal, and a user.

[0760] First, the server begins by collecting basic characteristic data from the user. This data includes the user's driving experience and the results of questionnaires regarding their personality traits. The collected data is analyzed by computer processing and used to evaluate the user's personality traits.

[0761] Subsequently, the server uses a generation engine to generate specific driving situations based on the user's personality traits. These situations are then formatted as 3D simulation images and sent to the terminal.

[0762] The terminal provides users with simulations using virtual reality goggles or physical steering wheel controllers. Users can use this interface device to experience real-world driving situations and improve their driving skills by performing actions appropriate to specific dangerous situations.

[0763] Meanwhile, the terminal collects user driving operation data in real time and transmits it to the server. This data includes information on steering and braking operations, reaction time, etc. The server uses this data to evaluate the user's performance, and the evaluation unit performs the analysis.

[0764] Based on the results, the server generates specific driving improvement advice and provides feedback to the user. This feedback serves as a guide for the user to try to improve their driving in the future.

[0765] Finally, the training planning module creates a new training scenario based on the user's evaluation results, preparing for the next simulation. Through this series of processes, drivers can improve not only their driving skills but also their awareness of safe driving. In this embodiment, the present invention enables customized safe driving training tailored to individual drivers.

[0766] The following describes the processing flow.

[0767] Step 1:

[0768] After a user logs into the system, the server collects basic characteristic data. This includes questionnaires about the user's driving experience and personality. The server saves this data in document format and prepares it for analysis.

[0769] Step 2:

[0770] The server uses the collected trait data to perform analysis with an AI model. This generates a user personality profile. The AI ​​model evaluates psychological traits such as attention and stress tolerance, and uses this to identify individual user traits.

[0771] Step 3:

[0772] The server uses a generation engine to create driving scenarios optimized for the user's personality profile. These driving scenarios are designed as simulations tailored to the user's characteristics, such as the appearance of unexpected obstacles or sudden lane changes.

[0773] Step 4:

[0774] The terminal provides the user with 3D simulation images received from the server, using VR goggles or a physical steering wheel. The user then experiences driving in a visually immersive and intuitive environment.

[0775] Step 5:

[0776] The user performs driving operations in a provided driving simulation environment via a terminal. During this time, the terminal records the user's operation data in real time, measuring things like steering movements and reaction times.

[0777] Step 6:

[0778] The terminal transmits collected driving operation data to the server. The server analyzes this data and evaluates the user's driving performance through an evaluation unit. The evaluation results include the user's reaction speed and operational accuracy.

[0779] Step 7:

[0780] The server generates specific improvement advice for the user based on the evaluation results. This advice is designed to improve driving behavior during the next training session and may include suggestions such as "try braking earlier."

[0781] Step 8:

[0782] The server uses a training planning module to customize the simulation for the user's next training session. This enables a step-by-step learning process that adapts to the user's progress.

[0783] (Example 1)

[0784] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0785] Conventional driving simulation systems have difficulty customizing them based on the individual driver's personality and driving style, and could only provide general driving training. As a result, they could not provide training content tailored to the driver's characteristics, making it difficult to achieve effective skill improvement and increased awareness of safe driving.

[0786] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0787] In this invention, the server includes an information processing device means for collecting user nature data and analyzing the user's personality characteristics; a generation device means for generating specific driving scenarios and creating three-dimensional visualizations based on the personality characteristics; and an operation device means for presenting the three-dimensional visualizations to the user and collecting the user's driving operation information in real time. This enables personalized driving training and allows for effective skill improvement and enhanced safe driving awareness based on the driver's characteristics.

[0788] "User characteristic data" refers to information including the driver's driving experience and personality traits, which is collected through questionnaires and personality tests.

[0789] "Personality traits" are numerical or indicator-based representations of a user's psychological characteristics and behavioral patterns, and are used to customize driving simulations.

[0790] An "information processing device" is a device that processes data, derives analysis results, and has the function of evaluating the user's personality traits.

[0791] A "generation device" is a device that has the function of designing a specific operating scenario based on the analysis results and generating a three-dimensional visualization.

[0792] "Three-dimensional visualization" refers to the representation of a virtual driving environment as a three-dimensional image, provided in a format that users can experience.

[0793] A "control device" is a device that presents a three-dimensional visualization to the user and collects user operation information in real time regarding the driving situation.

[0794] A "communication network" refers to a network such as the Internet, which is used as a means of collecting data from users in remote locations.

[0795] A "questionnaire" is a document or form used to collect user characteristic data, and includes questions to evaluate driving characteristics and personality.

[0796] A "personality test" is a test used to evaluate a user's psychological characteristics and to quantitatively analyze the driver's personality traits.

[0797] This invention provides a system that offers personalized driving simulation training based on the user's personality traits. This system performs multiple functions primarily involving a server, terminals, and users.

[0798] First, the user accesses an online questionnaire provided by the server via their terminal and inputs information about their driving experience and personality traits. This information is stored in the server's database as "user personality data." The server uses an information processing device to analyze the collected data and evaluates the user's "personality traits" based on a generating AI model. Here, for example, a prompt might be entered such as "Evaluate this user's cautiousness while driving."

[0799] Next, the server generates specific driving scenarios based on the evaluated personality traits. The generation device then uses three-dimensional visualization technology to design a driving simulation for the user. This provides a realistic driving experience tailored to the user's personality.

[0800] The terminal uses a virtual reality display and steering system to provide users with real-time three-dimensional visualizations. Users wear virtual reality goggles and operate a physical steering wheel to experience something similar to real driving.

[0801] The series of driving operations performed by the user within the simulation are accurately monitored in real time by a terminal and transmitted to a server. The operation data accumulated on the server is analyzed in detail by an evaluation device to assess the user's driving skills. Based on these results, specific driving improvement advice is generated for the user.

[0802] Finally, the server uses a training planning device to personalize and record the content of the next driving training session according to the analysis results. This allows the user to continuously improve their driving skills and deepen their awareness of safe driving. In this embodiment, the present invention makes it possible to provide users with customized driving training that is adapted to their individual driving characteristics.

[0803] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0804] Step 1:

[0805] Users access online questionnaires provided by the server and enter information about their driving experience and personality traits.

[0806] Input: User-submitted data regarding driving experience and personality traits.

[0807] Data processing: The server formats the input data and stores it in the database.

[0808] Output: User profile data is saved to the server.

[0809] Specific actions: The user fills in the answers for each item on the questionnaire and clicks the submit button.

[0810] Step 2:

[0811] The server uses a generative AI model to analyze the user's personality traits based on the collected user behavior data.

[0812] Input: Saved user profile data.

[0813] Data calculation: The server inputs the prompt message "Evaluate this user's driving caution" into the generated AI model and analyzes its personality traits.

[0814] Output: Evaluation results of the user's personality traits.

[0815] Specific operation: The server runs the AI ​​model and generates an analytical report based on the data.

[0816] Step 3:

[0817] The server generates specific driving scenarios based on the user's personality traits and creates three-dimensional visualizations.

[0818] Input: Evaluation results of personality traits.

[0819] Data processing: The generation device generates three-dimensional graphics based on the scenario data.

[0820] Output: Three-dimensional visualization data of a specific driving scenario.

[0821] Specific operation: The server uses a graphics engine to render the scenario and saves it as a simulation file.

[0822] Step 4:

[0823] The terminal provides the user with three-dimensional visualization data received from the server.

[0824] Input: 3D visualization data from the server.

[0825] Data processing: The terminal formats the visual data for the virtual reality goggles and displays it on the screen.

[0826] Output: Visualization of the driving scenario presented to the user.

[0827] Specific operation: The device interacts with virtual reality goggles to project the scenario into the user's field of vision.

[0828] Step 5:

[0829] Users use a device to experience the simulation and perform driving operations.

[0830] Input: Visualization of driving scenarios.

[0831] Data generation: The user operates a steering wheel-type controller to simulate actual driving operations.

[0832] Output: Operation data during operation.

[0833] Specific actions: The user's steering and braking actions within the simulation are tracked.

[0834] Step 6:

[0835] The terminal sends user operation data to the server.

[0836] Input: Real-time operation data from the user.

[0837] Data transfer: The terminal organizes the operation data and transfers it to the server in a predictable format.

[0838] Output: Operation data in a format usable by the server.

[0839] Specific operation: The terminal uses a data transmission circuit to transmit information to the server.

[0840] Step 7:

[0841] The server analyzes the operation data and evaluates the user's driving skills.

[0842] Input: Operation data.

[0843] Data calculation: The evaluation device calculates quantitative indicators and generates an evaluation score.

[0844] Output: Evaluation results regarding the user's driving skills.

[0845] Specific operation: The server automates analysis based on operational data and generates a user's driving performance report.

[0846] Step 8:

[0847] The server generates driving improvement advice based on the evaluation results and provides feedback to the user.

[0848] Input: Evaluation results of driving skills.

[0849] Data generation: The server uses a generative AI model to document appropriate advice.

[0850] Output: Feedback message for improving driving performance.

[0851] Specific operation: The server provides advice to the user via email, screen display, etc.

[0852] Step 9:

[0853] The server uses the evaluation data to personalize the content of the next driving training session and updates the training plan.

[0854] Input: Evaluation data.

[0855] Data processing: The training planning device generates the next scenario and customizes it.

[0856] Output: Personalized next training plan.

[0857] Specific operation: The server designs a new simulation scenario and registers it in the database.

[0858] (Application Example 1)

[0859] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0860] One challenge in the use of autonomous vehicles is the lack of training methods to help drivers understand the compatibility between the autonomous driving system and their own driving characteristics, enabling them to use the system more safely and effectively. Therefore, there is a need to provide training environments that maximize the capabilities of autonomous driving systems while simultaneously improving drivers' safety awareness.

[0861] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0862] In this invention, the server includes data processing means for collecting user characteristic data and analyzing the user's personality traits; generation means for generating specific driving situations and creating three-dimensional simulated visual information based on the personality traits; and driving control means for providing a training program for the driver to experience the compatibility between the autonomous driving function and their own driving characteristics. This enables the driver to understand how the autonomous vehicle fits their driving style and to acquire better driving skills in subsequent drives.

[0863] "User characteristic data" refers to information about the user's driving experience and personality traits, and is data that the system uses to analyze the user's driving style.

[0864] A "data processing device" is a computer device used to collect user characteristic data, analyze it, and evaluate the user's personality traits.

[0865] A "generation device" is a device that creates specific driving conditions based on personality traits and provides those driving conditions as three-dimensional simulated visual information.

[0866] A "display device" is a device that presents a three-dimensional simulation to the user and collects information on the user's driving actions in real time, and includes virtual reality visual devices.

[0867] An "evaluation device" is a computer device that analyzes collected driving operation information and generates specific driving improvement guidelines for the user.

[0868] A "training plan creation device" is a device that customizes the next training session based on evaluation results and creates a training program that is suitable for each user.

[0869] A "driving control system" is a device that provides a training program for drivers to experience the compatibility between the autonomous driving function and their own driving characteristics, and controls the operation of the autonomous vehicle.

[0870] To realize this invention, a driving simulation system will be built through the collaboration of a server, terminal, and user. The server will collect characteristic data from the user's smartphone or computer and evaluate personality traits based on that information. Specifically, data collection and analysis will be performed using software such as Python or Django. Through this data processing, an evaluation will be conducted that is tailored to the user's driving experience and personality traits.

[0871] Next, the server uses Unity to generate three-dimensional simulation visual information. The simulation reproduces specific driving situations based on the user's personality traits. For example, if the user is determined to "lose composure when in a hurry," they can experience situations in the simulation where traffic jams and sudden stops occur. This allows the user to learn how to use the autonomous driving system in those situations.

[0872] On the terminal side, a head-mounted display (e.g., Oculus Quest) or smartphone is used as a means of representation to provide a three-dimensional simulation from the user's perspective. The terminal also collects the user's driving operation information in real time and sends it back to the server. This information includes steering and braking operations and reaction times.

[0873] The server analyzes this information again and evaluates the user's driving performance using machine learning tools such as Scikit-learn. Based on this evaluation, it generates and provides the user with driving improvement guidelines. Furthermore, it creates a plan to customize the next training scenario and prepares a more accurate training program.

[0874] Example of a prompt:

[0875] "Please generate driving scenarios that demonstrate how users can remain calm and efficiently utilize autonomous driving features during traffic jams."

[0876] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0877] Step 1:

[0878] The server acquires survey data about the user's driving experience and personality traits via their smartphone. Using this data as input, the server performs data analysis to evaluate personality traits. A Python data processing library is used for the analysis, and the user's personality tendencies are output.

[0879] Step 2:

[0880] The server generates specific driving conditions based on the outputted personality traits. It uses Unity to create three-dimensional simulation visual information and sends this simulation data to the terminal. At this stage, a driving scenario based on prompt text is constructed using a generated AI model.

[0881] Step 3:

[0882] The terminal presents the received 3D simulation to the user. It recreates the simulation from the user's perspective via a head-mounted display, providing a virtual driving experience. Based on user input, it collects driving operation data in real time and transmits it to the server.

[0883] Step 4:

[0884] The server receives and analyzes driving operation data sent from the terminal. Using machine learning libraries such as Scikit-learn, it analyzes how this data matches personality traits and evaluates the user's driving performance. The evaluation results output metrics such as operational accuracy and reaction time.

[0885] Step 5:

[0886] The server generates personalized driving improvement guidelines for the user based on the evaluation results and sends them to the terminal. Furthermore, it uses these results to create a plan for customizing the next training simulation. This process ensures that a training program tailored to the user's needs is prepared.

[0887] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0888] This invention relates to an advanced driving simulation system based on a user's driving characteristics and emotional state. The system includes multiple processes performed by a server, a terminal, and the user, and is particularly characterized by the incorporation of an emotion engine.

[0889] First, the server collects questionnaires from users via the internet regarding their driving experience and personality. This information is stored as trait data and analyzed using an AI model. This analysis evaluates the user's personality traits, and a profile is created based on these evaluations.

[0890] The server uses an emotion engine along with a profile to identify the user's emotional state. This emotional state is determined by analyzing data such as facial expressions, voice, and heart rate that the user displays during the driving simulation. This emotional data is collected and used to understand how it influences the user's driving behavior.

[0891] Next, the generation engine functions to create driving situations based on the collected personality traits and emotional states. These situations are generated as dynamically adjustable 3D simulation images and sent to the terminal.

[0892] The terminal provides the user with a simulation using virtual reality goggles and a physical steering wheel controller. The user uses this interface device to experience the simulation visually and intuitively, and to perform driving maneuvers in response to specific hazardous situations.

[0893] Simultaneously, the device monitors the user's emotional state and transmits the user's driving data to the server in real time. The server analyzes this data, and an evaluation unit analyzes the relationship between emotions and driving behavior. This allows for an objective evaluation of the user's performance.

[0894] Based on the evaluation results, the server provides the user with specific driving improvement advice. This advice takes emotional states into account and aims to minimize the impact on driving behavior. The server also uses a training planning module to design new training scenarios and optimize the environment the user will experience in the next simulation. Through this process, drivers can improve their overall driving ability and strengthen their emotional control.

[0895] The following describes the processing flow.

[0896] Step 1:

[0897] After a user logs into the system, the server provides personality-related questionnaires and psychological tests via the internet. Users provide their personality data by answering these questionnaires and submitting them to the server.

[0898] Step 2:

[0899] The server inputs the received characteristic data into an AI model to generate a user personality profile. This profile includes psychological characteristics such as attention span and risk tendencies, and analysis is performed based on these characteristics.

[0900] Step 3:

[0901] The terminal provides the user with virtual reality goggles and starts the driving simulation. The user prepares to operate the vehicle in a manner similar to actual driving using a steering wheel-type controller.

[0902] Step 4:

[0903] The server uses an emotion engine to analyze the user's facial expressions, voice, and heart rate transmitted in real time from the device, recognizing the user's emotional state. This information is used to customize the driving experience.

[0904] Step 5:

[0905] The generation engine automatically generates appropriate driving scenarios based on the user's personality profile and real-time emotional state, and sends them to the device. The scenarios are dynamically adjusted and optimized for the user's state.

[0906] Step 6:

[0907] The user performs driving operations in a generated simulation environment. The terminal continuously records driving information such as steering angle, reaction time, and braking operation, and sends it to the server.

[0908] Step 7:

[0909] The server analyzes the collected driving data and emotional information using an evaluation unit to assess the user's driving performance. This evaluation includes the relationship between emotional fluctuations and operational accuracy.

[0910] Step 8:

[0911] Based on the evaluation results, the server provides users with specific driving improvement advice. This advice takes into account the influence of emotions on driving and guides users to appropriately control their emotions while driving.

[0912] Step 9:

[0913] The training planning module designs the next training scenario based on previous evaluations and feedback, improving the user's next simulation environment. This creates a training program that enables gradual skill improvement.

[0914] (Example 2)

[0915] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0916] In driving simulations, it is necessary to comprehensively evaluate not only the user's driving skills but also the influence of their emotional state on driving behavior, and to provide personalized driving improvement measures for each user. In particular, it is important to understand the relationship between changes in emotional state and driving operations in real time and to reflect this in training.

[0917] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0918] In this invention, the server includes an information processing device means that collects user characteristic data and emotional data and analyzes the user's personality traits and emotional state; a generation means that generates specific driving situations and creates three-dimensional simulation images based on the personality traits and emotional state; and an interaction device means that provides the three-dimensional simulation to the user and collects the user's driving operation data and emotional state data in real time. This makes it possible to provide a training program that comprehensively improves driving skills and emotional control ability.

[0919] An "information processing device" is a device that collects user characteristic data and emotional data, and analyzes this data to understand the user's personality traits and emotional state.

[0920] "Generation means" refers to a method or apparatus that has the function of creating a specific driving situation based on the user's personality traits and emotional state, and generating a three-dimensional simulation image.

[0921] A "dialogue device" is a device that provides users with a three-dimensional simulation and has the function of collecting driving operation data and emotional state data in real time during that simulation.

[0922] "Evaluation means" refers to a method or apparatus that has the function of analyzing collected driving operation data and emotional state data and generating specific driving improvement advice for the user.

[0923] A "training plan formulation device" is a device that has the function of formulating a plan to customize the user's next training session based on evaluation results.

[0924] "Three-dimensional simulation video" refers to a three-dimensional image created by a generation method to provide users with a driving experience.

[0925] This invention relates to a driving simulation system in which a server, a terminal, and a user cooperate to perform the simulation. The server acquires characteristic data and emotional data from the user. The characteristic data includes information about driving experience and personality. This data is collected using an information processing device and analyzed using a generative AI model. The AI ​​model evaluates the user's personality traits and emotional state and defines them as a profile.

[0926] Next, the server uses a generation mechanism based on the profile information to design a specific operating situation. This operating situation is generated as a three-dimensional simulation image and transmitted to the terminal. The virtual reality display device and physical control device connected to the terminal provide this simulation image to the user.

[0927] These devices allow users to gain both visual and physical experiences. For example, by wearing virtual reality goggles and actually operating the steering wheel, they can experience a realistic driving simulation.

[0928] Simultaneously, the terminal transmits the user's driving operation data and emotional state data to the server in real time. The server analyzes this data using evaluation tools and assesses the relationship between driving behavior and emotional state. Based on these results, the server provides the user with specific advice for improving their driving. Furthermore, a training plan formulation device is used to plan for optimizing the conditions for the next driving training session.

[0929] For example, the server can improve performance in subsequent simulations by identifying scenarios that are likely to cause stress for the user and providing advice to help them relax. An example of a prompt would be, "Design a driving simulation in a congested road environment based on the user's driving experience and personality traits."

[0930] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0931] Step 1:

[0932] The server collects survey data from users regarding their driving experience and personality via the internet. It receives user survey responses as input and stores them as characteristic data in a database using an information processing device. This process includes data formatting and storage.

[0933] Step 2:

[0934] The server inputs stored trait data into a generating AI model to analyze the user's personality traits and emotional state. Using the trait data as input, the AI ​​model performs data calculations and outputs the user's profile. This profile includes tendencies and characteristics as a driver.

[0935] Step 3:

[0936] The server inputs profile information into the emotion engine to identify the user's emotional state. It receives profile and user emotion data as input and identifies the emotional state through analysis. The emotional state result is obtained as output, and the server prepares to adjust the driving scenario based on this result.

[0937] Step 4:

[0938] The server uses a generation mechanism to create driving simulation scenarios based on characteristic data and emotional states. The input is the user's personality traits and emotional state, which are used to generate dynamic and specific driving scenarios. The output is a three-dimensional simulation image, which is sent to the terminal.

[0939] Step 5:

[0940] The terminal provides the user with three-dimensional simulation images through a virtual reality display device and physical control devices. In this step, the user can participate in the simulation using VR goggles and a steering wheel controller, gaining a visual and operational experience. The output is the user's driving operation data.

[0941] Step 6:

[0942] The terminal transmits user driving operation data and emotional state data to the server in real time during the running simulation. It collects driving operation and emotional data as input and relays it to the server as output. This data is used for subsequent evaluation.

[0943] Step 7:

[0944] The server analyzes driving operation data and emotional state data using evaluation tools and generates driving improvement advice for the user. The input is collected data, which is analyzed and its correlations evaluated. The output is specific driving advice, which is then fed back to the user.

[0945] Step 8:

[0946] The server uses a training planning device to customize the next driving training scenario. Using the evaluation results as input, it designs a new training scenario and generates an improved scenario for the next simulation as output. This scenario is designed to improve the user's capabilities.

[0947] (Application Example 2)

[0948] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0949] In autonomous vehicles, there is a need to optimize the driving environment while taking into account the emotional state of the occupants. However, with conventional technology, it is difficult to grasp the emotional state of the occupants in real time and provide appropriate feedback based on that. In such a situation, there is a problem in that a safe and comfortable driving experience cannot be provided for the occupants.

[0950] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0951] In this invention, the server includes processing means by an electronic computer that collects user characteristic information and analyzes the user's personality traits based on this information; generation means that generates a specific driving environment and creates a three-dimensional simulation image based on the personality traits and emotional state; and learning means that monitors the occupant's emotional state in real time and provides a safe and comfortable driving environment. This makes it possible to dynamically optimize the driving environment according to the occupant's emotional state and provide a safer and more comfortable autonomous driving experience.

[0952] "User characteristic information" refers to information about the user's personal characteristics, such as their driving experience and personality.

[0953] "Personality traits" refer to the personality tendencies and characteristics of a person, analyzed based on their characteristic information.

[0954] "Processing by electronic computing devices" refers to processes that involve collecting and analyzing data using devices such as computers.

[0955] A "generation device" refers to a device that generates a specific driving environment based on personality traits and emotional states, and creates a three-dimensional simulation image.

[0956] A "three-dimensional simulation image" refers to a computer graphics image that reproduces the driving environment and conditions in three dimensions.

[0957] An "interface device" refers to a device that collects operational information while a user experiences a simulation. Examples include virtual reality goggles and steering wheel controllers.

[0958] An "evaluation device" refers to a device that analyzes collected operational information and provides users with advice on improving their driving.

[0959] A "training planning device" refers to a device that customizes the user's next training environment based on evaluation results.

[0960] A "learning device" refers to a device that monitors the emotional state of the occupants in real time and adjusts the driving environment based on that information.

[0961] This invention relates to a system for monitoring the emotional state of occupants in real time within an autonomous vehicle, thereby providing a safe and comfortable driving experience. The system consists of multiple computers and various sensors. Embodiments are described below.

[0962] The server first collects user characteristic information via the internet. This information is processed by a computer as data for analyzing personality traits. The analyzed personality traits are then used to create a profile based on the user's driving behavior.

[0963] The device works in conjunction with the vehicle's cameras, microphones, and heart rate sensors to collect and analyze data such as the occupant's facial expressions, voice, and heart rate using an emotion engine, and to identify their emotional state in real time. This process utilizes AI platforms such as Amazon SageMaker for data processing and analysis.

[0964] The generation device creates a driving environment corresponding to the obtained personality traits and emotional state, and creates a three-dimensional simulation image which is then transmitted to the terminal. This allows the user to experience visual feedback and operation via a virtual reality display device or physical control device.

[0965] The evaluation device analyzes driving information collected in real time and generates specific driving improvement advice for the user. Furthermore, the training planning device customizes the driving environment to provide an optimized scenario for the next training session.

[0966] For example, if the system detects that the occupants are fatigued during long drives, it can switch the car to a relaxation mode and automatically play soothing music. Another example of a prompt generated by the AI ​​model is, "Based on the occupants' current emotional state, please select the optimal driving mode to provide a safe and comfortable driving experience."

[0967] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0968] Step 1:

[0969] The server collects user characteristic information via the internet through questionnaires and psychological assessments. Inputs are web forms and questionnaire data, while outputs are characteristic information stored in a database for use in user personality analysis. This provides the necessary foundational data for subsequent processing.

[0970] Step 2:

[0971] The server analyzes personality traits using a generative AI model based on the collected user characteristic information. The input is the characteristic information obtained in step 1, and the output is the user's personality profile. This profile is used to predict the user's driving behavior and emotional state.

[0972] Step 3:

[0973] The device collects data in real time from the in-car camera, microphone, and heart rate sensor. The input is raw data from each sensor, and the output is integrated data necessary for the emotion engine to analyze it. This data is used to identify the emotional state of the occupants based on their facial expressions, voice, and heart rate.

[0974] Step 4:

[0975] The terminal uses an emotion engine to analyze integrated data and identify the occupant's emotional state in real time. The input is the integrated data obtained in step 3, and the output is the occupant's emotional state. This emotional state forms the basis for generating the next driving environment.

[0976] Step 5:

[0977] The generator creates an appropriate driving environment and produces a three-dimensional simulation image based on the personality profile and emotional state. The input is the personality profile from step 2 and the emotional state from step 4, and the output is the three-dimensional simulation image delivered to the virtual reality device. This allows the user to have an intuitive driving experience.

[0978] Step 6:

[0979] The terminal collects user operation information through three-dimensional simulation and transmits it to the server. The input is the operation information obtained during the three-dimensional simulation, and the output is data sent to the server for analysis on the evaluation device. This information forms the basis for operational improvement advice.

[0980] Step 7:

[0981] The server uses an evaluation device to analyze user operation information and generate specific driving improvement advice. The input is the operation information obtained in step 6, and the output is the improvement advice provided to the user. This advice is intended to improve the user's driving ability.

[0982] Step 8:

[0983] Based on the evaluation results, the server uses a training planning device to customize the next training environment. The input is the improvement advice and evaluation results from step 7, and the output is the customized next training scenario. This allows the user to have an optimized learning opportunity.

[0984] 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.

[0985] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0986] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0987] 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.

[0988] Figure 9 shows an 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.

[0989] 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.

[0990] 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.

[0991] 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, motorcycles, etc., 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, for example, based 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.

[0992] 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."

[0993] 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.

[0994] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0995] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0996] 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.

[0997] 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.

[0998] 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.

[0999] 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.

[1000] 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.

[1001] 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.

[1002] 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.

[1003] 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 the like 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.

[1004] 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.

[1005] The following is further disclosed regarding the embodiments described above.

[1006] (Claim 1)

[1007] A computer processing means for collecting user characteristic data and analyzing the user's personality characteristics,

[1008] A generation engine means that generates specific driving situations based on the aforementioned personality characteristics and creates 3D simulation images,

[1009] An interface device means that provides 3D simulations to the user and collects the user's driving information in real time,

[1010] An evaluation unit means that analyzes collected driving information and generates specific driving improvement advice for the user,

[1011] A training planning module means for customizing the next training situation based on the evaluation results,

[1012] A system that includes this.

[1013] (Claim 2)

[1014] The system according to claim 1, which provides a questionnaire or psychological test via the internet for collecting the aforementioned user characteristic data.

[1015] (Claim 3)

[1016] The system according to claim 1, wherein the interface device includes a virtual reality goggle and a physical handle controller.

[1017] "Example 1"

[1018] (Claim 1)

[1019] Information processing device means for collecting user characteristic data and analyzing the user's personality traits,

[1020] A generation apparatus means that generates a specific driving scenario based on the aforementioned personality characteristics and creates a three-dimensional visualization,

[1021] An operating device means that presents a three-dimensional visualization to the user and collects user driving operation information in real time,

[1022] An evaluation device means that analyzes collected driving operation information and generates specific driving technique improvement advice for the user,

[1023] A training planning device means for individualizing the next training scenario based on the evaluation results,

[1024] A system that includes this.

[1025] (Claim 2)

[1026] The system according to claim 1, which provides a questionnaire or personality test for collecting the aforementioned user characteristic data via a communication network.

[1027] (Claim 3)

[1028] The system according to claim 1, wherein the operating device includes a virtual reality display device and a physical steering device.

[1029] "Application Example 1"

[1030] (Claim 1)

[1031] A data processing device that collects user characteristic data and analyzes the user's personality characteristics,

[1032] A generation apparatus means that generates specific driving conditions based on the aforementioned personality characteristics and creates three-dimensional simulated visual information,

[1033] A display device that provides a three-dimensional simulation to the user and collects user driving information in real time,

[1034] An evaluation device means that analyzes collected driving operation information and generates specific driving improvement guidelines for the user,

[1035] A planning device means for customizing the next training session based on the evaluation results,

[1036] A driving control device that provides a training program for the driver to experience the compatibility between the autonomous driving function and their own driving characteristics,

[1037] A system that includes this.

[1038] (Claim 2)

[1039] The system according to claim 1, which provides a questionnaire or psychological test for collecting the aforementioned user characteristic data via an information and communication network.

[1040] (Claim 3)

[1041] The system according to claim 1, wherein the display device includes a virtual reality visual device and a physical operation controller.

[1042] "Example 2 of combining an emotion engine"

[1043] (Claim 1)

[1044] An information processing device that collects user characteristic data and emotional data, and analyzes the user's personality traits and emotional state,

[1045] A generation means that generates specific driving situations and creates three-dimensional simulation images based on the aforementioned personality traits and emotional states,

[1046] A dialogue device that provides users with three-dimensional simulations and collects user driving operation data and emotional state data in real time,

[1047] An evaluation means that analyzes collected driving operation data and emotional state data and generates specific driving improvement advice for the user,

[1048] A training plan formulation device means that customizes the conditions for the next driving training based on the evaluation results,

[1049] A system that includes this.

[1050] (Claim 2)

[1051] The system according to claim 1, which provides a survey or psychological evaluation via a communication network for collecting the aforementioned user characteristic data and emotional state data.

[1052] (Claim 3)

[1053] The system according to claim 1, wherein the dialogue device includes a virtual reality display device and a physical operating device.

[1054] "Application example 2 when combining with an emotional engine"

[1055] (Claim 1)

[1056] A processing means using an electronic computer that collects user characteristic information and analyzes the user's personality traits based on this information,

[1057] A generation apparatus means for generating a specific driving environment based on the aforementioned personality traits and emotional state, and for creating a three-dimensional simulation image,

[1058] An interface device means that provides a three-dimensional simulation to the user and collects the user's operation information and emotional state in real time,

[1059] An evaluation device means that analyzes the collected operation information and generates specific operation improvement advice for the user,

[1060] A training planning device means that customizes the next training environment based on the evaluation results,

[1061] A learning device means that monitors the emotional state of the occupants in real time and provides a safe and comfortable driving environment,

[1062] A system that includes this.

[1063] (Claim 2)

[1064] The system according to claim 1, which provides a questionnaire or psychological evaluation via a communication network for collecting the characteristic information of the user.

[1065] (Claim 3)

[1066] The system according to claim 1, wherein the interface device includes a virtual reality display device and a physical control device. [Explanation of Symbols]

[1067] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A computer processing means for collecting user characteristic data and analyzing the user's personality characteristics, A generation engine means that generates specific driving situations based on the aforementioned personality characteristics and creates 3D simulation images, An interface device means that provides 3D simulations to the user and collects the user's driving information in real time, An evaluation unit means that analyzes collected driving information and generates specific driving improvement advice for the user, A training planning module means for customizing the next training situation based on the evaluation results, A system that includes this.

2. The system according to claim 1, which provides a questionnaire or psychological test via the internet for collecting the characteristic data of the user.

3. The system according to claim 1, wherein the interface device includes a virtual reality goggle and a physical handle controller.

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