system

Abstract

We provide the system. [Solution] Means for acquiring driving data from vehicles, A means of utilizing a machine learning model to analyze the acquired data, A means for providing real-time feedback to the driver based on the aforementioned analysis 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the development of autonomous driving technology, drivers need to understand and safely utilize new driving assistance technologies, but there is a problem that it is difficult for conventional technologies to support this understanding and education in real time. In addition, due to the lack of real-time risk information provision and specific driving support, it is required to improve the safety of drivers.

Means for Solving the Problems

[0005] The present invention provides a system that includes means for acquiring driving data from a vehicle, means for utilizing a machine learning model to analyze the acquired data, and means for providing real-time feedback to the driver based on the analysis results. Furthermore, by including means for predicting risks during driving and assisting the driver in making situational judgments through voice guidance, and means for generating training data aimed at improving the driver's skills by conducting driving simulations, the system enables drivers to safely understand and utilize autonomous driving technology and to immediately recognize potential traffic risks.

[0006] "Driving data" is a general term for various types of information generated and acquired during driving, such as vehicle speed, acceleration, location information, and the conditions around the vehicle.

[0007] A "machine learning model" is a mathematical and algorithmic framework for automatically performing pattern recognition and prediction based on data.

[0008] "Analysis results" refer to the analysis of driving data obtained by machine learning models, and the conclusions and recognized patterns based on that analysis.

[0009] "Feedback" refers to information provided to the driver, such as audio and visual instructions, warnings, and advice, designed to support decision-making while driving.

[0010] "Risk prediction" involves evaluating potential hazards based on driving conditions and predicting potential accidents and problems.

[0011] "Driving simulation" is a method for drivers to safely learn skills by creating a virtual driving environment and reproducing driving situations within it.

[0012] "Training data" refers to datasets used for the purpose of improving driving skills, and includes information to reinforce or evaluate the driver's behavior and reactions. [Brief explanation of the drawing]

[0013] [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] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is implemented as a system in which a terminal installed in a vehicle collects driving data, a server analyzes that data, and provides real-time feedback to the user.

[0035] The terminal is installed in the vehicle and acquires data from various sensors such as speed sensors, cameras, and GPS. This raw data includes information about various driving conditions, such as speed, distance between vehicles, vehicle movement, and the positions of surrounding vehicles. The terminal converts this acquired data into a predetermined format and sends it to the server.

[0036] The server receives data from the terminal and performs analysis using a pre-trained machine learning model. This analysis includes risk assessment and understanding of traffic patterns while driving, and utilizes generative AI to make more accurate predictions. For example, if a sudden lane change or braking occurs on a highway, the server immediately assesses the risk and determines appropriate action guidance.

[0037] The user receives driving assistance through feedback from the device. Based on analysis results sent from the server, the device provides information to the driver through voice guidance and display messages. For example, if there is a vehicle that has suddenly stopped ahead, the device will give the user specific instructions such as, "There is an obstacle ahead. Prepare to brake."

[0038] As a concrete example, suppose a user is driving in an urban area and may encounter a vehicle running a red light at an intersection. The terminal monitors the behavior of surrounding vehicles in real time, and if it detects a vehicle entering the intersection at an abnormal speed, it immediately sends that data to the server. The server evaluates this information based on traffic patterns and real-time analysis, and if it determines that the risk is high, the terminal provides the user with a voice command saying, "Slow down."

[0039] Thus, the system of the present invention can improve the driver's skills by utilizing data generated through prior simulation training. Throughout the entire system, users can improve their driving skills and enhance their ability to drive safely.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device uses various sensors installed in the vehicle, such as speed sensors, cameras, and GPS, to acquire real-time data while driving. This includes vehicle speed, acceleration, location information, and distance to the vehicle in front.

[0043] Step 2:

[0044] The terminal preprocesses the acquired raw data, performing necessary noise reduction and data format conversion. This process prepares the data for easy analysis.

[0045] Step 3:

[0046] The terminal transmits pre-processed data to the server via wireless communication. This transmission is optimized to minimize latency.

[0047] Step 4:

[0048] The server receives data sent from the terminal and performs data analysis using a pre-trained machine learning model. This involves recognizing the driving environment and predicting potential hazards.

[0049] Step 5:

[0050] The server generates feedback for the driver based on the analysis results of the machine learning model. This includes warnings and driving assistance information tailored to the risk assessment.

[0051] Step 6:

[0052] The server sends the generated feedback to the terminal. The feedback is output through a visual display or voice assistant.

[0053] Step 7:

[0054] The terminal provides the user with feedback sent from the server. This allows the user to understand points to watch out for and necessary actions while driving in real time.

[0055] Step 8:

[0056] The user adjusts their driving actions based on the feedback provided. For example, they might take measures such as applying the brakes if a hazard is anticipated.

[0057] Step 9:

[0058] The terminal collects data on user actions again and records it as a log. This data will be used for future analysis and system improvements.

[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 driver assistance systems sometimes fail to provide drivers with the necessary feedback in real time, making them insufficient for ensuring safety and improving driver skills. Furthermore, the accuracy of driving data analysis is limited, making proper risk assessment difficult.

[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 means for acquiring driving data from a vehicle and collecting data using different types of sensory sensors; means for converting the acquired data into a predetermined format and transmitting it to an information and communication device; and means for receiving the transmitted data, analyzing the data using a generated AI model, and evaluating the risk. This enables the driver to receive real-time, highly accurate feedback, improve safety, and allow for the continuous improvement of driving skills.

[0064] "Driving data" refers to various types of information related to the operation of a vehicle, including speed, position, acceleration, and information about the surrounding environment.

[0065] A "perceptual sensor" is a device used to acquire information from the physical environment, and includes devices such as cameras, speedometers, and GPS.

[0066] An "information and communication device" is a device used to send and receive data, and is a device that establishes network connections via a specific protocol.

[0067] A "generative AI model" is a computational model trained using artificial intelligence technology to perform a specific task, and is used for data analysis and prediction.

[0068] "Risk assessment" is the process of analyzing potential hazards that may occur while driving and determining their impact and frequency.

[0069] "Feedback" refers to instructions and information provided to the driver based on the analysis results, and is communicated through voice or visual means.

[0070] This invention relates to a driver assistance system comprising a terminal mounted in a vehicle and a server located in the cloud or a dedicated data center. The terminal collects driving data using the vehicle's sensory sensors, such as speed sensors, cameras, and GPS. These sensors have the function of acquiring various operational information of the vehicle, such as speed, distance between vehicles, position, and surrounding environment data.

[0071] The terminal converts the collected raw data into a predetermined format in real time and transmits it to the server via the network through an information and communication device. This conversion includes compressing the collected data into CSV format and image data into JPEG format. The terminal also supports the communication protocols necessary for data transmission.

[0072] The server receives data transferred from the terminal and performs analysis using a pre-trained generative AI model. The generative AI model is used for risk assessment and traffic pattern analysis during driving, and is designed to improve the accuracy of the feedback provided to the driver. An example of a prompt message is, "Analyze the driving situation when entering the intersection and generate necessary driving instructions."

[0073] Users receive feedback from the terminal through voice guidance and display information, and perform driving actions based on this feedback. For example, if the server determines from its analysis that the distance between vehicles is too short, the terminal will provide the user with voice instructions such as "Please slow down," supporting appropriate driving behavior. This system allows users to receive real-time driving assistance, improving their driving skills and ensuring safety.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The device collects driving data in real time using a speed sensor, camera, and GPS. Inputs include vehicle speed, location information, and image data. This data is temporarily stored as raw data within the device.

[0077] Step 2:

[0078] The terminal converts the collected raw data into a predetermined format. This conversion process converts speed data to CSV format and compresses camera video to JPEG format. The output is formatted data, which is then prepared for network transmission.

[0079] Step 3:

[0080] The terminal sends formatted data to the server according to a predetermined communication protocol. The input is the converted data, and the output is status information indicating successful transmission. Specifically, the data is transmitted to the server via Wi-Fi or LTE.

[0081] Step 4:

[0082] The server receives data transmitted from the terminal. The input data includes all information related to the vehicle's operation. Based on this data, the server uses a generative AI model to perform data analysis. The output of the process is the result of driving instructions and risk assessment.

[0083] Step 5:

[0084] The server sends the analysis results back to the terminal. This is feedback data used for voice guidance and display information. Specifically, it includes instructions such as, "The distance to the vehicle in front is short, please slow down."

[0085] Step 6:

[0086] The terminal provides feedback to the user based on the analysis results sent from the server. The input is the server's analysis results, and the output is voice instructions and warning messages on the display. The system immediately provides voice guidance to the user and displays instructions on the screen if necessary.

[0087] Step 7:

[0088] The user performs driving operations based on feedback from the device. For example, if instructed to decelerate, the user takes action such as applying the brakes. This enables safe driving that responds immediately to the feedback received.

[0089] (Application Example 1)

[0090] 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."

[0091] In systems that support safe driving by analyzing driving data in real time and providing immediate feedback to drivers, there is a need to effectively utilize the acquired analysis results and provide drivers with more appropriate instructions. Furthermore, a challenge is to develop a mechanism that uses mobile devices to provide driving information visually and audibly, thereby promoting driver understanding and response.

[0092] 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.

[0093] In this invention, the server includes means for acquiring driving data from a vehicle, means for utilizing a machine learning model to analyze the acquired data, means for providing real-time feedback to the driver based on the analysis results, means for displaying the driving data on a personal information terminal, and means for providing the obtained analysis results as an audio alert on the personal information terminal. This enables the driver to receive real-time feedback through both sight and hearing.

[0094] "Driving data" is a general term for information that indicates the state and movement of a vehicle while it is in operation, such as the vehicle's speed, position, and surrounding conditions.

[0095] A "portable information terminal" is an information device that a driver can carry and use, and includes smartphones, tablets, and other similar devices.

[0096] A "machine learning model" refers to an algorithm or structure that learns patterns and rules based on large amounts of data to perform predictions and classifications.

[0097] "Feedback" refers to the information and instructions provided to the driver based on analysis results during vehicle operation.

[0098] A "voice alert" is an audio message that conveys attention or warnings to the driver in a perceptible manner, depending on the analysis results and circumstances.

[0099] In this invention, an application installed on the driver's mobile device (smartphone or tablet) communicates with the vehicle's terminal to acquire driving data in real time and provide analysis results. The server receives the data acquired from the vehicle and uses a pre-trained machine learning model to analyze it, assess the risks during driving, and provide instructions for appropriate driving actions. The analysis uses a generative AI model to predict, for example, sudden changes in the distance between vehicles or instability in speed.

[0100] The terminal uses speakers and display functions to provide feedback to the driver as voice alerts, conveying the analysis results to the user visually and audibly. These voice alerts may include instructions such as, "There is a vehicle ahead that has suddenly decelerated. Prepare to brake."

[0101] This system requires vehicle terminals equipped with sensors and communication modules, a computer server for analyzing driving data, and a mobile information terminal held by the driver to work together in coordination to perform necessary data processing and provide feedback to the driver in real time.

[0102] For example, consider a situation where a driver is driving on a highway and the vehicle in front suddenly changes lanes. In this case, the terminal immediately transmits this situation to the server, which analyzes the risk based on the acquired information and feeds the results back to the mobile device. As a result, the application informs the driver via voice guidance, "Please slow down." In this way, it supports safe driving.

[0103] An example of a prompt message for the generating AI model might be, "Based on this driving data pattern, perform the most effective analysis to identify high-risk scenarios. Also, what appropriate feedback should be provided to the driver?" Using this prompt message, the server analyzes the driving data.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The terminal collects driving data in real time from sensors mounted on the vehicle (speed sensors, cameras, GPS, etc.). The input is raw sensor data, which is then converted into a predetermined format for output. Specifically, the data is standardized, and necessary parameters are extracted and structured.

[0107] Step 2:

[0108] The terminal sends the converted driving data to the server. The server receives this formatted data as input. After receiving the data, the server feeds it into a machine learning model to analyze driving patterns and assess risks. The output is a risk assessment and behavioral guidance.

[0109] Step 3:

[0110] The server utilizes a generative AI model to determine appropriate feedback to provide to the driver based on the analysis results. Prompt messages are used to request the generative AI model to predict scenarios and generate feedback. The output consists of specific instructions and alert messages.

[0111] Step 4:

[0112] The server returns the analysis results and instruction messages to the terminal. The terminal receives this data as input and prepares to notify the driver. Specifically, it prepares to process the notification content as audio or text.

[0113] Step 5:

[0114] The device provides feedback to the driver in the form of voice alerts and on-screen messages. This allows the user (driver) to receive information and gain assistance in taking appropriate driving actions. The output is specific feedback that appeals to the user's sight and hearing.

[0115] 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.

[0116] This invention is implemented as an autonomous driving assistance system that combines an emotion engine that recognizes the user's emotions. In addition to standard data collection functions such as speed sensors, cameras, and GPS, the terminal is equipped with an emotion engine. This emotion engine analyzes the user's facial expressions and tone of voice in real time through cameras and voice analysis, etc., and identifies their emotional state.

[0117] The device sends this emotion recognition result along with other driving data to the server. The server uses a machine learning model to analyze the data and generate customized feedback tailored to the user's emotions. For example, if the emotion engine detects that the user is stressed, the server can offer relaxation guidance to help the user calm down.

[0118] Users can receive real-time feedback from their device and benefit from driving assistance. This feedback includes not only regular driving guidance but also assistance based on emotional state. For example, if a user is feeling stressed in traffic, the device can offer encouraging words such as, "Take a deep breath. The roads will clear in a few minutes."

[0119] Furthermore, the emotion engine plays a role in training mode by adjusting the driving simulation to match the user's emotional state. This creates an environment where the user can practice with confidence, supporting the effective improvement of driving skills. For example, users who tend to get easily irritated in certain situations can be provided with scenarios to help them deal with those situations calmly and learn emotional control techniques.

[0120] In this way, the present invention improves user safety and driving skills by providing driving assistance and education that takes into account the user's emotional state.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] The terminal uses sensors and cameras installed in the vehicle to collect driving data and emotional data such as the user's facial expressions and voice. This data is acquired in real time and forms the basis for a comprehensive understanding of the user's driving situation and emotional state.

[0124] Step 2:

[0125] The terminal preprocesses the collected driving and emotional data, removing noise from each data set and converting them into a format that is easy to analyze. The processed data is then ready to be sent to the server.

[0126] Step 3:

[0127] The terminal sends pre-processed data to the server. This data is important for analyzing driving safety and the user's emotional state, and it is transferred with minimal delay.

[0128] Step 4:

[0129] The server receives driving and emotional data transmitted from the terminal and analyzes it using machine learning models. The server assesses the risks during driving, identifies the user's emotional state, and identifies elements that generate necessary feedback.

[0130] Step 5:

[0131] Based on the analyzed results, the server generates feedback tailored to the driving situation and perceived emotions. If the user is experiencing stress, this includes voice guidance and visual instructions with advice to alleviate those emotions.

[0132] Step 6:

[0133] The server sends the generated feedback to the terminal. This feedback is used as real-time guidance for the driver.

[0134] Step 7:

[0135] The terminal provides the user with feedback received from the server. The user receives this feedback through a visual display and voice assistance, and can adjust their driving operations based on it.

[0136] Step 8:

[0137] Users improve their safety awareness and skills through driving that incorporates feedback. If a user receives a suggestion to relax, they can take measures such as taking deep breaths or maintaining a steady gaze.

[0138] Step 9:

[0139] The device continuously monitors the user's driving behavior and emotional changes, recording this data as logs. The recorded data will be used for future analysis and system improvements.

[0140] (Example 2)

[0141] 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".

[0142] Conventional driver assistance systems only provided feedback based on simple driving data, without considering the driver's emotional state. This resulted in a problem where drivers, even when experiencing stress or anxiety, did not receive appropriate support that reflected their emotional state. Furthermore, training programs designed to improve driving skills also lacked an environment where drivers could practice effectively and confidently, as they did not consider the individual driver's emotional state.

[0143] 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.

[0144] In this invention, the server includes means for acquiring driving-related data from the vehicle, means for analyzing the acquired data using an artificial intelligence model, and means for having an emotion analysis function that recognizes the user's emotions. This makes it possible to provide highly customized feedback in real time based on the acquired data and the user's emotional state. Furthermore, by performing driving simulations tailored to the emotional state, it provides training for skill improvement that is appropriate for each individual driver, thereby realizing optimal support for the driver.

[0145] "Driving-related data" refers to a collection of operational parameters related to the vehicle, such as speed, location information, and driving environment.

[0146] An "artificial intelligence model" is a computational model that automatically learns patterns and features from large amounts of data to perform predictions and classifications.

[0147] "Customized feedback" refers to information that provides instructions and advice optimized for individual users based on specific conditions and circumstances.

[0148] "Emotion analysis function" is a technology that uses cameras, microphones, etc., to analyze the user's facial expressions and voice and identify their psychological state.

[0149] "Driving simulation" is a process that recreates driving situations in a virtual environment, allowing drivers to practice scenarios they might actually face.

[0150] This invention relates to an automated driving assistance system equipped with an emotion analysis function that recognizes the user's emotions. The terminal is installed in the vehicle and acquires driving-related data using hardware such as a speed sensor, camera, and GPS. In particular, the emotion analysis function using the camera and microphone analyzes the user's facial expressions and tone of voice to identify their emotional state in real time.

[0151] The device sends collected driving-related data and emotion analysis results to a server. The server receives this data and performs analysis using an artificial intelligence model. This AI model learns from past driving and emotion data and generates personalized feedback optimized for each user. This feedback includes specific advice and relaxation guides based on the user's psychological state while driving.

[0152] For example, if a user encounters traffic congestion while driving and feels stressed, the device will provide feedback such as, "Take a deep breath. The road will clear in a few minutes," to calm the user. The server also adjusts the driving simulation based on the user's emotional state, supporting improvement of driving skills through specific scenarios.

[0153] Examples of prompts for a generating AI model include: "When the user's emotional state is one of fatigue, suggest ways to refresh them," or "Create a scenario in training mode simulation to help the user reduce anxiety." Using such prompts allows the server to generate more effective feedback and simulation content.

[0154] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0155] Step 1:

[0156] The device collects driving-related data from a speed sensor, camera, and GPS. Inputs include vehicle speed, location information, and the user's face and voice. The device acquires this data in real time and uses emotion analysis capabilities to analyze the user's facial expressions and voice tone. Specifically, the camera captures the user's face, and an emotion estimation algorithm is applied. Outputs include the user's emotional state and driving data.

[0157] Step 2:

[0158] The terminal transmits the emotional state and driving data obtained in Step 1 to the server. The input consists of the emotional state and driving-related data obtained through emotion analysis. The terminal transfers these to the server using a secure communication protocol. Specifically, this is done by encrypting the data and transmitting it over the network. The output is the data packets received by the server.

[0159] Step 3:

[0160] The server analyzes the received data using an artificial intelligence model. The input consists of emotional state and driving data sent from the terminal. The server uses machine learning algorithms to analyze the data and generate feedback tailored to the user's current situation. In this process, it also refers to past data to select the most appropriate advice. Specifically, the model classifies and evaluates the data to determine the content of the feedback. The output is customized feedback.

[0161] Step 4:

[0162] The server sends the generated feedback to the terminal. The input is the feedback content generated through analysis. The server structures this information, converts it into a format that the terminal can display and output as sound, and then transmits it using a communication protocol. Specifically, it converts the feedback message into the appropriate data format and sends it. The output is the feedback provided to the user.

[0163] Step 5:

[0164] The user utilizes the feedback received from the device while driving. The input is feedback messages from the device. The user listens to advice through the voice assistant or checks the information on the display. By following the advice as appropriate, the user aims to achieve psychological stability while driving. Specifically, the user adjusts their driving style in response to the feedback. The output is a safer and more comfortable driving experience.

[0165] Step 6:

[0166] The server adjusts the training mode and provides simulations based on the user's emotional state. Inputs are the user's emotional state and training data to support skill improvement. The server uses this to construct simulation scenarios and adjust the simulations provided to the user. Specifically, it generates scenarios and incorporates feedback. The output is customized driving training that the user can perform.

[0167] (Application Example 2)

[0168] 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".

[0169] In autonomous vehicles, the driver's emotional state may not be adequately managed, leading to stress and anxiety, which can compromise driving safety and comfort. It is necessary to address this issue and enable drivers to use vehicles more safely and comfortably.

[0170] 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.

[0171] In this invention, the server includes means for acquiring driving data from a vehicle, means for utilizing a machine learning model to analyze the acquired data, means for providing real-time feedback to the driver based on the analysis results, means for identifying the driver's emotional state using an emotion recognition system, and means for providing visual or auditory feedback corresponding to the emotional state. This enables feedback that takes into account the driver's emotional state.

[0172] "Driving data" refers to information about the vehicle's operating status and condition, including information such as speed, position, and acceleration.

[0173] A "machine learning model" refers to algorithms and statistical methods used to analyze driving data, and is designed to generate patterns and predictions from input data.

[0174] "Real-time feedback" refers to advice and information provided to the driver immediately based on the analysis results, with the aim of improving driving conditions.

[0175] An "emotion recognition system" analyzes the driver's facial expressions and tone of voice to identify their emotional state, utilizing various sensors and analytical algorithms.

[0176] "Visual or auditory feedback" refers to information provided to the driver in a visible or audible form, including appropriate instructions and suggestions that are relevant to their emotional state.

[0177] The system for implementing this invention consists of both a server and a terminal. The server is responsible for collecting and analyzing driving data and data related to emotion recognition. The hardware used consists of various sensors and cameras mounted on the vehicle, and is equipped with a high-performance processor for speech recognition and image recognition. As for the software, a machine learning model is used to analyze the driving data. Specifically, the data is processed using libraries such as TENSORFLOW® and PyTorch.

[0178] The server analyzes driving data in real time and generates visual and auditory feedback based on the driver's emotional state. The terminal provides this feedback to the driver via smart glasses or a smartphone. The information displayed on the terminal is designed to be visually easy to understand using a GUI framework.

[0179] Through feedback received from the device, users can reduce stress while driving and improve driving efficiency and safety. For example, if a driver is feeling fatigued from long hours of driving on a highway, a visual prompt suggesting "take a break" will be displayed. In addition, auditory announcements such as "There will be a service area in XX minutes" will be played.

[0180] An example of a prompt is, "If the emotion engine detects that the user is tired while driving, please explain what visual guidance the smart glasses should provide." By submitting this prompt to the generating AI model, specific feedback suggestions can be obtained.

[0181] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0182] Step 1:

[0183] The server acquires driving data from sensors and cameras installed in the vehicle. Inputs include speed sensor data, GPS data, and camera footage, which are collected in real time. Raw data is generated as output. This raw data includes the vehicle's movement and location information during operation, as well as the driver's facial expressions and voice input.

[0184] Step 2:

[0185] The server inputs the acquired driving data into a machine learning model for analysis. The data processing used here includes signal processing and data preprocessing. The machine learning model is built using TensorFlow or PyTorch and extracts driving patterns and emotional states. The output is an analysis result showing the driver's current driving situation and emotional state.

[0186] Step 3:

[0187] The server identifies the driver's emotional state through an emotion recognition system. This step utilizes camera footage and audio data as input. Image and speech recognition algorithms are used for data processing, inferring the driver's emotional state from their facial features and tone of voice. A tag indicating the driver's emotional state is generated as output.

[0188] Step 4:

[0189] The terminal generates visual or auditory feedback based on analysis results and emotional states. Analysis data and emotional tags received from the server are used as input. Based on this data, a GUI framework is used to design visual messages, and audio guidance is prepared using TTS (Text-to-Speech) technology. The output is feedback in the form of instructions and suggestions presented to the driver.

[0190] Step 5:

[0191] Users receive feedback through their devices and incorporate it into their driving. Once the user receives feedback, it is used to improve their next driving actions and situational judgments. Furthermore, responses to the received feedback are sent back to the server, which is then used for subsequent analysis. This cycle ensures continuous improvement of driver assistance.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] [Second Embodiment]

[0196] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0197] 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.

[0198] 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).

[0199] 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.

[0200] 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.

[0201] 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).

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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".

[0208] This invention is implemented as a system in which a terminal installed in a vehicle collects driving data, a server analyzes that data, and provides real-time feedback to the user.

[0209] The terminal is installed in the vehicle and acquires data from various sensors such as speed sensors, cameras, and GPS. This raw data includes information about various driving conditions, such as speed, distance between vehicles, vehicle movement, and the positions of surrounding vehicles. The terminal converts this acquired data into a predetermined format and sends it to the server.

[0210] The server receives data from the terminal and performs analysis using a pre-trained machine learning model. This analysis includes risk assessment and understanding of traffic patterns while driving, and utilizes generative AI to make more accurate predictions. For example, if a sudden lane change or braking occurs on a highway, the server immediately assesses the risk and determines appropriate action guidance.

[0211] The user receives driving assistance through feedback from the device. Based on analysis results sent from the server, the device provides information to the driver through voice guidance and display messages. For example, if there is a vehicle that has suddenly stopped ahead, the device will give the user specific instructions such as, "There is an obstacle ahead. Prepare to brake."

[0212] As a concrete example, suppose a user is driving in an urban area and may encounter a vehicle running a red light at an intersection. The terminal monitors the behavior of surrounding vehicles in real time, and if it detects a vehicle entering the intersection at an abnormal speed, it immediately sends that data to the server. The server evaluates this information based on traffic patterns and real-time analysis, and if it determines that the risk is high, the terminal provides the user with a voice command saying, "Slow down."

[0213] Thus, the system of the present invention can improve the driver's skills by utilizing data generated through prior simulation training. Throughout the entire system, users can improve their driving skills and enhance their ability to drive safely.

[0214] The following describes the processing flow.

[0215] Step 1:

[0216] The device uses various sensors installed in the vehicle, such as speed sensors, cameras, and GPS, to acquire real-time data while driving. This includes vehicle speed, acceleration, location information, and distance to the vehicle in front.

[0217] Step 2:

[0218] The terminal preprocesses the acquired raw data, performing necessary noise reduction and data format conversion. This process prepares the data for easy analysis.

[0219] Step 3:

[0220] The terminal transmits pre-processed data to the server via wireless communication. This transmission is optimized to minimize latency.

[0221] Step 4:

[0222] The server receives data sent from the terminal and performs data analysis using a pre-trained machine learning model. This involves recognizing the driving environment and predicting potential hazards.

[0223] Step 5:

[0224] The server generates feedback for the driver based on the analysis results of the machine learning model. This includes warnings and driving assistance information tailored to the risk assessment.

[0225] Step 6:

[0226] The server sends the generated feedback to the terminal. The feedback is output through a visual display or voice assistant.

[0227] Step 7:

[0228] The terminal provides the user with feedback sent from the server. This allows the user to understand points to watch out for and necessary actions while driving in real time.

[0229] Step 8:

[0230] The user adjusts their driving actions based on the feedback provided. For example, they might take measures such as applying the brakes if a hazard is anticipated.

[0231] Step 9:

[0232] The terminal collects data on user actions again and records it as a log. This data will be used for future analysis and system improvements.

[0233] (Example 1)

[0234] 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".

[0235] Conventional driver assistance systems sometimes fail to provide drivers with the necessary feedback in real time, making them insufficient for ensuring safety and improving driver skills. Furthermore, the accuracy of driving data analysis is limited, making proper risk assessment difficult.

[0236] 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.

[0237] In this invention, the server includes means for acquiring driving data from a vehicle and collecting data using different types of sensory sensors; means for converting the acquired data into a predetermined format and transmitting it to an information and communication device; and means for receiving the transmitted data, analyzing the data using a generated AI model, and evaluating the risk. This enables the driver to receive real-time, highly accurate feedback, improve safety, and allow for the continuous improvement of driving skills.

[0238] "Driving data" refers to various types of information related to the operation of a vehicle, including speed, position, acceleration, and information about the surrounding environment.

[0239] A "perceptual sensor" is a device used to acquire information from the physical environment, and includes devices such as cameras, speedometers, and GPS.

[0240] An "information and communication device" is a device used to send and receive data, and is a device that establishes network connections via a specific protocol.

[0241] A "generative AI model" is a computational model trained using artificial intelligence technology to perform a specific task, and is used for data analysis and prediction.

[0242] "Risk assessment" is the process of analyzing potential hazards that may occur while driving and determining their impact and frequency.

[0243] "Feedback" refers to instructions and information provided to the driver based on the analysis results, and is communicated through voice or visual means.

[0244] This invention relates to a driver assistance system comprising a terminal mounted in a vehicle and a server located in the cloud or a dedicated data center. The terminal collects driving data using the vehicle's sensory sensors, such as speed sensors, cameras, and GPS. These sensors have the function of acquiring various operational information of the vehicle, such as speed, distance between vehicles, position, and surrounding environment data.

[0245] The terminal converts the collected raw data into a predetermined format in real time and transmits it to the server via the network through an information and communication device. This conversion includes compressing the collected data into CSV format and image data into JPEG format. The terminal also supports the communication protocols necessary for data transmission.

[0246] The server receives data transferred from the terminal and performs analysis using a pre-trained generative AI model. The generative AI model is used for risk assessment and traffic pattern analysis during driving, and is designed to improve the accuracy of the feedback provided to the driver. An example of a prompt message is, "Analyze the driving situation when entering the intersection and generate necessary driving instructions."

[0247] Users receive feedback from the terminal through voice guidance and display information, and perform driving actions based on this feedback. For example, if the server determines from its analysis that the distance between vehicles is too short, the terminal will provide the user with voice instructions such as "Please slow down," supporting appropriate driving behavior. This system allows users to receive real-time driving assistance, improving their driving skills and ensuring safety.

[0248] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0249] Step 1:

[0250] The device collects driving data in real time using a speed sensor, camera, and GPS. Inputs include vehicle speed, location information, and image data. This data is temporarily stored as raw data within the device.

[0251] Step 2:

[0252] The terminal converts the collected raw data into a predetermined format. This conversion process converts speed data to CSV format and compresses camera video to JPEG format. The output is formatted data, which is then prepared for network transmission.

[0253] Step 3:

[0254] The terminal sends formatted data to the server according to a predetermined communication protocol. The input is the converted data, and the output is status information indicating successful transmission. Specifically, the data is transmitted to the server via Wi-Fi or LTE.

[0255] Step 4:

[0256] The server receives data transmitted from the terminal. The input data includes all information related to the vehicle's operation. Based on this data, the server uses a generative AI model to perform data analysis. The output of the process is the result of driving instructions and risk assessment.

[0257] Step 5:

[0258] The server sends the analysis results back to the terminal. This is feedback data used for voice guidance and display information. Specifically, it includes instructions such as, "The distance to the vehicle in front is short, please slow down."

[0259] Step 6:

[0260] The terminal provides feedback to the user based on the analysis results sent from the server. The input is the server's analysis results, and the output is voice instructions and warning messages on the display. The system immediately provides voice guidance to the user and displays instructions on the screen if necessary.

[0261] Step 7:

[0262] The user performs driving operations based on feedback from the device. For example, if instructed to decelerate, the user takes action such as applying the brakes. This enables safe driving that responds immediately to the feedback received.

[0263] (Application Example 1)

[0264] 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."

[0265] In systems that support safe driving by analyzing driving data in real time and providing immediate feedback to drivers, there is a need to effectively utilize the acquired analysis results and provide drivers with more appropriate instructions. Furthermore, a challenge is to develop a mechanism that uses mobile devices to provide driving information visually and audibly, thereby promoting driver understanding and response.

[0266] 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.

[0267] In this invention, the server includes means for acquiring driving data from a vehicle, means for utilizing a machine learning model to analyze the acquired data, means for providing real-time feedback to the driver based on the analysis results, means for displaying the driving data on a personal information terminal, and means for providing the obtained analysis results as an audio alert on the personal information terminal. This enables the driver to receive real-time feedback through both sight and hearing.

[0268] "Driving data" is a general term for information that indicates the state and movement of a vehicle while it is in operation, such as the vehicle's speed, position, and surrounding conditions.

[0269] A "portable information terminal" is an information device that a driver can carry and use, and includes smartphones, tablets, and other similar devices.

[0270] A "machine learning model" refers to an algorithm or structure that learns patterns and rules based on large amounts of data to perform predictions and classifications.

[0271] "Feedback" refers to the information and instructions provided to the driver based on analysis results during vehicle operation.

[0272] A "voice alert" is an audio message that conveys attention or warnings to the driver in a perceptible manner, depending on the analysis results and circumstances.

[0273] In this invention, an application installed on the driver's mobile device (smartphone or tablet) communicates with the vehicle's terminal to acquire driving data in real time and provide analysis results. The server receives the data acquired from the vehicle and uses a pre-trained machine learning model to analyze it, assess the risks during driving, and provide instructions for appropriate driving actions. The analysis uses a generative AI model to predict, for example, sudden changes in the distance between vehicles or instability in speed.

[0274] The terminal uses speakers and display functions to provide feedback to the driver as voice alerts, conveying the analysis results to the user visually and audibly. These voice alerts may include instructions such as, "There is a vehicle ahead that has suddenly decelerated. Prepare to brake."

[0275] This system requires vehicle terminals equipped with sensors and communication modules, a computer server for analyzing driving data, and a mobile information terminal held by the driver to work together in coordination to perform necessary data processing and provide feedback to the driver in real time.

[0276] For example, consider a situation where a driver is driving on a highway and the vehicle in front suddenly changes lanes. In this case, the terminal immediately transmits this situation to the server, which analyzes the risk based on the acquired information and feeds the results back to the mobile device. As a result, the application informs the driver via voice guidance, "Please slow down." In this way, it supports safe driving.

[0277] As an example of a prompt sentence for a generative AI model, there is one such as "Based on this driving data pattern, please perform the most effective analysis to identify high-risk scenarios. Also, what would be the appropriate feedback to provide to the driver?" Using this prompt sentence, the server analyzes the driving data.

[0278] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0279] Step 1:

[0280] The terminal collects driving data in real time from sensors (such as speed sensors, cameras, GPS, etc.) mounted on the vehicle. The input is raw sensor data, which is converted into a predetermined format and output. Specifically, the data is normalized, and necessary parameters are extracted and structured.

[0281] Step 2:

[0282] The terminal transmits the converted driving data to the server. The server receives this data in the formatted state as input. After receiving the data, the server inputs it into a machine learning model to perform analysis of driving patterns and risk assessment. The output is risk assessment and action guidance.

[0283] Step 3:

[0284] The server determines the appropriate feedback to provide to the driver from the analysis results by utilizing the generative AI model. Using the prompt sentence, the server requests the generative AI model to predict scenarios and generate feedback. The output is specific instructions or alert messages.

[0285] Step 4:

[0286] The server returns the analysis result and the instruction message to the terminal. The terminal receives this data as input and prepares to notify the driver. Specifically, preparations are made to process the notification content as voice or text.

[0287] Step 5:

[0288] The terminal provides feedback to the driver as a voice alert or a message on the display. This enables the user (driver) to receive information and get assistance for taking appropriate driving actions. The output is specific feedback that appeals to the user's vision and hearing.

[0289] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0290] The present invention is implemented as an automatic driving support system combined with an emotion engine that recognizes the user's emotion. In addition to data collection functions such as a standard speed sensor, camera, and GPS, the terminal is equipped with an emotion engine. This emotion engine analyzes the user's facial expressions and tone of voice in real time through a camera or voice analysis, etc., to identify the emotional state.

[0291] The terminal transmits this emotion recognition result and other driving data to the server together. The server analyzes the data using a machine learning model and generates customized feedback according to the user's emotion. For example, when the emotion engine recognizes that the user is nervous, the server can present a relaxation guide to calm the user down.

[0292] Users can receive real-time feedback from their device and benefit from driving assistance. This feedback includes not only regular driving guidance but also assistance based on emotional state. For example, if a user is feeling stressed in traffic, the device can offer encouraging words such as, "Take a deep breath. The roads will clear in a few minutes."

[0293] Furthermore, the emotion engine plays a role in training mode by adjusting the driving simulation to match the user's emotional state. This creates an environment where the user can practice with confidence, supporting the effective improvement of driving skills. For example, users who tend to get easily irritated in certain situations can be provided with scenarios to help them deal with those situations calmly and learn emotional control techniques.

[0294] In this way, the present invention improves user safety and driving skills by providing driving assistance and education that takes into account the user's emotional state.

[0295] The following describes the processing flow.

[0296] Step 1:

[0297] The terminal uses sensors and cameras installed in the vehicle to collect driving data and emotional data such as the user's facial expressions and voice. This data is acquired in real time and forms the basis for a comprehensive understanding of the user's driving situation and emotional state.

[0298] Step 2:

[0299] The terminal preprocesses the collected driving and emotional data, removing noise from each data set and converting them into a format that is easy to analyze. The processed data is then ready to be sent to the server.

[0300] Step 3:

[0301] The terminal sends the preprocessed data to the server. The data is important for analyzing driving safety and the user's emotional state, and it is transferred with minimal delay.

[0302] Step 4:

[0303] The server receives the driving data and emotional data sent from the terminal and performs analysis by utilizing a machine learning model. The server evaluates the risks during driving, identifies the user's emotional state, and determines the elements for generating the necessary feedback.

[0304] Step 5:

[0305] Based on the analyzed results, the server generates feedback according to the driving situation and the recognized emotions. If the user is feeling stressed, it includes voice guides and visual instructions containing advice to relieve that emotion.

[0306] Step 6:

[0307] The server sends the generated feedback to the terminal. The feedback is used as real-time guidance for the driver.

[0308] Step 7:

[0309] The terminal provides the feedback received from the server to the user. The user can receive the feedback through a visual display and voice assist, and adjust their driving operations based on it.

[0310] Step 8:

[0311] The user drives while reflecting the feedback, and through this experience, improves their awareness and skills regarding safety. If the user receives a proposal to relax, they can take measures such as taking a deep breath and keeping their line of sight steady.

[0312] Step 9:

[0313] The device continuously monitors the user's driving behavior and emotional changes, recording this data as logs. The recorded data will be used for future analysis and system improvements.

[0314] (Example 2)

[0315] 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".

[0316] Conventional driver assistance systems only provided feedback based on simple driving data, without considering the driver's emotional state. This resulted in a problem where drivers, even when experiencing stress or anxiety, did not receive appropriate support that reflected their emotional state. Furthermore, training programs designed to improve driving skills also lacked an environment where drivers could practice effectively and confidently, as they did not consider the individual driver's emotional state.

[0317] 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.

[0318] In this invention, the server includes means for acquiring driving-related data from the vehicle, means for analyzing the acquired data using an artificial intelligence model, and means for having an emotion analysis function that recognizes the user's emotions. This makes it possible to provide highly customized feedback in real time based on the acquired data and the user's emotional state. Furthermore, by performing driving simulations tailored to the emotional state, it provides training for skill improvement that is appropriate for each individual driver, thereby realizing optimal support for the driver.

[0319] "Driving-related data" refers to a collection of operational parameters related to the vehicle, such as speed, location information, and driving environment.

[0320] An "artificial intelligence model" is a computational model that automatically learns patterns and features from large amounts of data to perform predictions and classifications.

[0321] "Customized feedback" refers to information that provides instructions and advice optimized for individual users based on specific conditions and circumstances.

[0322] "Emotion analysis function" is a technology that uses cameras, microphones, etc., to analyze the user's facial expressions and voice and identify their psychological state.

[0323] "Driving simulation" is a process that recreates driving situations in a virtual environment, allowing drivers to practice scenarios they might actually face.

[0324] This invention relates to an automated driving assistance system equipped with an emotion analysis function that recognizes the user's emotions. The terminal is installed in the vehicle and acquires driving-related data using hardware such as a speed sensor, camera, and GPS. In particular, the emotion analysis function using the camera and microphone analyzes the user's facial expressions and tone of voice to identify their emotional state in real time.

[0325] The device sends collected driving-related data and emotion analysis results to a server. The server receives this data and performs analysis using an artificial intelligence model. This AI model learns from past driving and emotion data and generates personalized feedback optimized for each user. This feedback includes specific advice and relaxation guides based on the user's psychological state while driving.

[0326] For example, if a user encounters traffic congestion while driving and feels stressed, the device will provide feedback such as, "Take a deep breath. The road will clear in a few minutes," to calm the user. The server also adjusts the driving simulation based on the user's emotional state, supporting improvement of driving skills through specific scenarios.

[0327] Examples of prompts for a generating AI model include: "When the user's emotional state is one of fatigue, suggest ways to refresh them," or "Create a scenario in training mode simulation to help the user reduce anxiety." Using such prompts allows the server to generate more effective feedback and simulation content.

[0328] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0329] Step 1:

[0330] The device collects driving-related data from a speed sensor, camera, and GPS. Inputs include vehicle speed, location information, and the user's face and voice. The device acquires this data in real time and uses emotion analysis capabilities to analyze the user's facial expressions and voice tone. Specifically, the camera captures the user's face, and an emotion estimation algorithm is applied. Outputs include the user's emotional state and driving data.

[0331] Step 2:

[0332] The terminal transmits the emotional state and driving data obtained in Step 1 to the server. The input consists of the emotional state and driving-related data obtained through emotion analysis. The terminal transfers these to the server using a secure communication protocol. Specifically, this is done by encrypting the data and transmitting it over the network. The output is the data packets received by the server.

[0333] Step 3:

[0334] The server analyzes the received data using an artificial intelligence model. The input consists of emotional state and driving data sent from the terminal. The server uses machine learning algorithms to analyze the data and generate feedback tailored to the user's current situation. In this process, it also refers to past data to select the most appropriate advice. Specifically, the model classifies and evaluates the data to determine the content of the feedback. The output is customized feedback.

[0335] Step 4:

[0336] The server sends the generated feedback to the terminal. The input is the feedback content generated through analysis. The server structures this information, converts it into a format that the terminal can display and output as sound, and then transmits it using a communication protocol. Specifically, it converts the feedback message into the appropriate data format and sends it. The output is the feedback provided to the user.

[0337] Step 5:

[0338] The user utilizes the feedback received from the device while driving. The input is feedback messages from the device. The user listens to advice through the voice assistant or checks the information on the display. By following the advice as appropriate, the user aims to achieve psychological stability while driving. Specifically, the user adjusts their driving style in response to the feedback. The output is a safer and more comfortable driving experience.

[0339] Step 6:

[0340] The server adjusts the training mode and provides simulations based on the user's emotional state. Inputs are the user's emotional state and training data to support skill improvement. The server uses this to construct simulation scenarios and adjust the simulations provided to the user. Specifically, it generates scenarios and incorporates feedback. The output is customized driving training that the user can perform.

[0341] (Application Example 2)

[0342] 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."

[0343] In autonomous vehicles, the driver's emotional state may not be adequately managed, leading to stress and anxiety, which can compromise driving safety and comfort. It is necessary to address this issue and enable drivers to use vehicles more safely and comfortably.

[0344] 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.

[0345] In this invention, the server includes means for acquiring driving data from a vehicle, means for utilizing a machine learning model to analyze the acquired data, means for providing real-time feedback to the driver based on the analysis results, means for identifying the driver's emotional state using an emotion recognition system, and means for providing visual or auditory feedback corresponding to the emotional state. This enables feedback that takes into account the driver's emotional state.

[0346] "Driving data" refers to information about the vehicle's operating status and condition, including information such as speed, position, and acceleration.

[0347] A "machine learning model" refers to algorithms and statistical methods used to analyze driving data, and is designed to generate patterns and predictions from input data.

[0348] "Real-time feedback" refers to advice and information provided to the driver immediately based on the analysis results, with the aim of improving driving conditions.

[0349] An "emotion recognition system" analyzes the driver's facial expressions and tone of voice to identify their emotional state, utilizing various sensors and analytical algorithms.

[0350] "Visual or auditory feedback" refers to information provided to the driver in a visible or audible form, including appropriate instructions and suggestions that are relevant to their emotional state.

[0351] The system for implementing this invention consists of both a server and a terminal. The server is responsible for collecting and analyzing driving data and data related to emotion recognition. The hardware used consists of various sensors and cameras mounted on the vehicle, and is equipped with a high-performance processor for speech recognition and image recognition. As for the software, a machine learning model is used to analyze the driving data. Specifically, libraries such as TensorFlow and PyTorch are used to process the data.

[0352] The server analyzes driving data in real time and generates visual and auditory feedback based on the driver's emotional state. The terminal provides this feedback to the driver via smart glasses or a smartphone. The information displayed on the terminal is designed to be visually easy to understand using a GUI framework.

[0353] Through feedback received from the device, users can reduce stress while driving and improve driving efficiency and safety. For example, if a driver is feeling fatigued from long hours of driving on a highway, a visual prompt suggesting "take a break" will be displayed. In addition, auditory announcements such as "There will be a service area in XX minutes" will be played.

[0354] An example of a prompt is, "If the emotion engine detects that the user is tired while driving, please explain what visual guidance the smart glasses should provide." By submitting this prompt to the generating AI model, specific feedback suggestions can be obtained.

[0355] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0356] Step 1:

[0357] The server acquires driving data from sensors and cameras installed in the vehicle. Inputs include speed sensor data, GPS data, and camera footage, which are collected in real time. Raw data is generated as output. This raw data includes the vehicle's movement and location information during operation, as well as the driver's facial expressions and voice input.

[0358] Step 2:

[0359] The server inputs the acquired driving data into a machine learning model for analysis. The data processing used here includes signal processing and data preprocessing. The machine learning model is built using TensorFlow or PyTorch and extracts driving patterns and emotional states. The output is an analysis result showing the driver's current driving situation and emotional state.

[0360] Step 3:

[0361] The server identifies the driver's emotional state through an emotion recognition system. This step utilizes camera footage and audio data as input. Image and speech recognition algorithms are used for data processing, inferring the driver's emotional state from their facial features and tone of voice. A tag indicating the driver's emotional state is generated as output.

[0362] Step 4:

[0363] The terminal generates visual or auditory feedback based on analysis results and emotional states. Analysis data and emotional tags received from the server are used as input. Based on this data, a GUI framework is used to design visual messages, and audio guidance is prepared using TTS (Text-to-Speech) technology. The output is feedback in the form of instructions and suggestions presented to the driver.

[0364] Step 5:

[0365] Users receive feedback through their devices and incorporate it into their driving. Once the user receives feedback, it is used to improve their next driving actions and situational judgments. Furthermore, responses to the received feedback are sent back to the server, which is then used for subsequent analysis. This cycle ensures continuous improvement of driver assistance.

[0366] 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.

[0367] 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.

[0368] 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.

[0369] [Third Embodiment]

[0370] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0371] 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.

[0372] 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).

[0373] 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.

[0374] 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.

[0375] 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).

[0376] 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.

[0377] 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.

[0378] 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.

[0379] 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.

[0380] 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.

[0381] 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".

[0382] This invention is implemented as a system in which a terminal installed in a vehicle collects driving data, a server analyzes that data, and provides real-time feedback to the user.

[0383] The terminal is installed in the vehicle and acquires data from various sensors such as speed sensors, cameras, and GPS. This raw data includes information about various driving conditions, such as speed, distance between vehicles, vehicle movement, and the positions of surrounding vehicles. The terminal converts this acquired data into a predetermined format and sends it to the server.

[0384] The server receives data from the terminal and performs analysis using a pre-trained machine learning model. This analysis includes risk assessment and understanding of traffic patterns while driving, and utilizes generative AI to make more accurate predictions. For example, if a sudden lane change or braking occurs on a highway, the server immediately assesses the risk and determines appropriate action guidance.

[0385] The user receives driving assistance through feedback from the device. Based on analysis results sent from the server, the device provides information to the driver through voice guidance and display messages. For example, if there is a vehicle that has suddenly stopped ahead, the device will give the user specific instructions such as, "There is an obstacle ahead. Prepare to brake."

[0386] As a concrete example, suppose a user is driving in an urban area and may encounter a vehicle running a red light at an intersection. The terminal monitors the behavior of surrounding vehicles in real time, and if it detects a vehicle entering the intersection at an abnormal speed, it immediately sends that data to the server. The server evaluates this information based on traffic patterns and real-time analysis, and if it determines that the risk is high, the terminal provides the user with a voice command saying, "Slow down."

[0387] Thus, the system of the present invention can improve the driver's skills by utilizing data generated through prior simulation training. Throughout the entire system, users can improve their driving skills and enhance their ability to drive safely.

[0388] The following describes the processing flow.

[0389] Step 1:

[0390] The device uses various sensors installed in the vehicle, such as speed sensors, cameras, and GPS, to acquire real-time data while driving. This includes vehicle speed, acceleration, location information, and distance to the vehicle in front.

[0391] Step 2:

[0392] The terminal preprocesses the acquired raw data, performing necessary noise reduction and data format conversion. This process prepares the data for easy analysis.

[0393] Step 3:

[0394] The terminal transmits pre-processed data to the server via wireless communication. This transmission is optimized to minimize latency.

[0395] Step 4:

[0396] The server receives data sent from the terminal and performs data analysis using a pre-trained machine learning model. This involves recognizing the driving environment and predicting potential hazards.

[0397] Step 5:

[0398] The server generates feedback for the driver based on the analysis results of the machine learning model. This includes warnings and driving assistance information tailored to the risk assessment.

[0399] Step 6:

[0400] The server sends the generated feedback to the terminal. The feedback is output through a visual display or voice assistant.

[0401] Step 7:

[0402] The terminal provides the user with feedback sent from the server. This allows the user to understand points to watch out for and necessary actions while driving in real time.

[0403] Step 8:

[0404] The user adjusts their driving actions based on the feedback provided. For example, they might take measures such as applying the brakes if a hazard is anticipated.

[0405] Step 9:

[0406] The terminal collects data on user actions again and records it as a log. This data will be used for future analysis and system improvements.

[0407] (Example 1)

[0408] 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."

[0409] Conventional driver assistance systems sometimes fail to provide drivers with the necessary feedback in real time, making them insufficient for ensuring safety and improving driver skills. Furthermore, the accuracy of driving data analysis is limited, making proper risk assessment difficult.

[0410] 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.

[0411] In this invention, the server includes means for acquiring driving data from a vehicle and collecting data using different types of sensory sensors; means for converting the acquired data into a predetermined format and transmitting it to an information and communication device; and means for receiving the transmitted data, analyzing the data using a generated AI model, and evaluating the risk. This enables the driver to receive real-time, highly accurate feedback, improve safety, and allow for the continuous improvement of driving skills.

[0412] "Driving data" refers to various types of information related to the operation of a vehicle, including speed, position, acceleration, and information about the surrounding environment.

[0413] A "perceptual sensor" is a device used to acquire information from the physical environment, and includes devices such as cameras, speedometers, and GPS.

[0414] An "information and communication device" is a device used to send and receive data, and is a device that establishes network connections via a specific protocol.

[0415] A "generative AI model" is a computational model trained using artificial intelligence technology to perform a specific task, and is used for data analysis and prediction.

[0416] "Risk assessment" is the process of analyzing potential hazards that may occur while driving and determining their impact and frequency.

[0417] "Feedback" refers to instructions and information provided to the driver based on the analysis results, and is communicated through voice or visual means.

[0418] This invention relates to a driver assistance system comprising a terminal mounted in a vehicle and a server located in the cloud or a dedicated data center. The terminal collects driving data using the vehicle's sensory sensors, such as speed sensors, cameras, and GPS. These sensors have the function of acquiring various operational information of the vehicle, such as speed, distance between vehicles, position, and surrounding environment data.

[0419] The terminal converts the collected raw data into a predetermined format in real time and transmits it to the server via the network through an information and communication device. This conversion includes compressing the collected data into CSV format and image data into JPEG format. The terminal also supports the communication protocols necessary for data transmission.

[0420] The server receives data transferred from the terminal and performs analysis using a pre-trained generative AI model. The generative AI model is used for risk assessment and traffic pattern analysis during driving, and is designed to improve the accuracy of the feedback provided to the driver. An example of a prompt message is, "Analyze the driving situation when entering the intersection and generate necessary driving instructions."

[0421] Users receive feedback from the terminal through voice guidance and display information, and perform driving actions based on this feedback. For example, if the server determines from its analysis that the distance between vehicles is too short, the terminal will provide the user with voice instructions such as "Please slow down," supporting appropriate driving behavior. This system allows users to receive real-time driving assistance, improving their driving skills and ensuring safety.

[0422] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0423] Step 1:

[0424] The device collects driving data in real time using a speed sensor, camera, and GPS. Inputs include vehicle speed, location information, and image data. This data is temporarily stored as raw data within the device.

[0425] Step 2:

[0426] The terminal converts the collected raw data into a predetermined format. This conversion process converts speed data to CSV format and compresses camera video to JPEG format. The output is formatted data, which is then prepared for network transmission.

[0427] Step 3:

[0428] The terminal sends formatted data to the server according to a predetermined communication protocol. The input is the converted data, and the output is status information indicating successful transmission. Specifically, the data is transmitted to the server via Wi-Fi or LTE.

[0429] Step 4:

[0430] The server receives data transmitted from the terminal. The input data includes all information related to the vehicle's operation. Based on this data, the server uses a generative AI model to perform data analysis. The output of the process is the result of driving instructions and risk assessment.

[0431] Step 5:

[0432] The server sends the analysis results back to the terminal. This is feedback data used for voice guidance and display information. Specifically, it includes instructions such as, "The distance to the vehicle in front is short, please slow down."

[0433] Step 6:

[0434] The terminal provides feedback to the user based on the analysis results sent from the server. The input is the server's analysis results, and the output is voice instructions and warning messages on the display. The system immediately provides voice guidance to the user and displays instructions on the screen if necessary.

[0435] Step 7:

[0436] The user performs driving operations based on feedback from the device. For example, if instructed to decelerate, the user takes action such as applying the brakes. This enables safe driving that responds immediately to the feedback received.

[0437] (Application Example 1)

[0438] 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."

[0439] In systems that support safe driving by analyzing driving data in real time and providing immediate feedback to drivers, there is a need to effectively utilize the acquired analysis results and provide drivers with more appropriate instructions. Furthermore, a challenge is to develop a mechanism that uses mobile devices to provide driving information visually and audibly, thereby promoting driver understanding and response.

[0440] 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.

[0441] In this invention, the server includes means for acquiring driving data from a vehicle, means for utilizing a machine learning model to analyze the acquired data, means for providing real-time feedback to the driver based on the analysis results, means for displaying the driving data on a personal information terminal, and means for providing the obtained analysis results as an audio alert on the personal information terminal. This enables the driver to receive real-time feedback through both sight and hearing.

[0442] "Driving data" is a general term for information that indicates the state and movement of a vehicle while it is in operation, such as the vehicle's speed, position, and surrounding conditions.

[0443] A "portable information terminal" is an information device that a driver can carry and use, and includes smartphones, tablets, and other similar devices.

[0444] A "machine learning model" refers to an algorithm or structure that learns patterns and rules based on large amounts of data to perform predictions and classifications.

[0445] "Feedback" refers to the information and instructions provided to the driver based on analysis results during vehicle operation.

[0446] A "voice alert" is an audio message that conveys attention or warnings to the driver in a perceptible manner, depending on the analysis results and circumstances.

[0447] In this invention, an application installed on the driver's mobile device (smartphone or tablet) communicates with the vehicle's terminal to acquire driving data in real time and provide analysis results. The server receives the data acquired from the vehicle and uses a pre-trained machine learning model to analyze it, assess the risks during driving, and provide instructions for appropriate driving actions. The analysis uses a generative AI model to predict, for example, sudden changes in the distance between vehicles or instability in speed.

[0448] The terminal uses speakers and display functions to provide feedback to the driver as voice alerts, conveying the analysis results to the user visually and audibly. These voice alerts may include instructions such as, "There is a vehicle ahead that has suddenly decelerated. Prepare to brake."

[0449] This system requires vehicle terminals equipped with sensors and communication modules, a computer server for analyzing driving data, and a mobile information terminal held by the driver to work together in coordination to perform necessary data processing and provide feedback to the driver in real time.

[0450] For example, consider a situation where a driver is driving on a highway and the vehicle in front suddenly changes lanes. In this case, the terminal immediately transmits this situation to the server, which analyzes the risk based on the acquired information and feeds the results back to the mobile device. As a result, the application informs the driver via voice guidance, "Please slow down." In this way, it supports safe driving.

[0451] An example of a prompt message for the generating AI model might be, "Based on this driving data pattern, perform the most effective analysis to identify high-risk scenarios. Also, what appropriate feedback should be provided to the driver?" Using this prompt message, the server analyzes the driving data.

[0452] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0453] Step 1:

[0454] The terminal collects driving data in real time from sensors mounted on the vehicle (speed sensors, cameras, GPS, etc.). The input is raw sensor data, which is then converted into a predetermined format for output. Specifically, the data is standardized, and necessary parameters are extracted and structured.

[0455] Step 2:

[0456] The terminal sends the converted driving data to the server. The server receives this formatted data as input. After receiving the data, the server feeds it into a machine learning model to analyze driving patterns and assess risks. The output is a risk assessment and behavioral guidance.

[0457] Step 3:

[0458] The server utilizes a generative AI model to determine appropriate feedback to provide to the driver based on the analysis results. Prompt messages are used to request the generative AI model to predict scenarios and generate feedback. The output consists of specific instructions and alert messages.

[0459] Step 4:

[0460] The server returns the analysis results and instruction messages to the terminal. The terminal receives this data as input and prepares to notify the driver. Specifically, it prepares to process the notification content as audio or text.

[0461] Step 5:

[0462] The device provides feedback to the driver in the form of voice alerts and on-screen messages. This allows the user (driver) to receive information and gain assistance in taking appropriate driving actions. The output is specific feedback that appeals to the user's sight and hearing.

[0463] 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.

[0464] This invention is implemented as an autonomous driving assistance system that combines an emotion engine that recognizes the user's emotions. In addition to standard data collection functions such as speed sensors, cameras, and GPS, the terminal is equipped with an emotion engine. This emotion engine analyzes the user's facial expressions and tone of voice in real time through cameras and voice analysis, etc., and identifies their emotional state.

[0465] The device sends this emotion recognition result along with other driving data to the server. The server uses a machine learning model to analyze the data and generate customized feedback tailored to the user's emotions. For example, if the emotion engine detects that the user is stressed, the server can offer relaxation guidance to help the user calm down.

[0466] Users can receive real-time feedback from their device and benefit from driving assistance. This feedback includes not only regular driving guidance but also assistance based on emotional state. For example, if a user is feeling stressed in traffic, the device can offer encouraging words such as, "Take a deep breath. The roads will clear in a few minutes."

[0467] Furthermore, the emotion engine plays a role in training mode by adjusting the driving simulation to match the user's emotional state. This creates an environment where the user can practice with confidence, supporting the effective improvement of driving skills. For example, users who tend to get easily irritated in certain situations can be provided with scenarios to help them deal with those situations calmly and learn emotional control techniques.

[0468] In this way, the present invention improves user safety and driving skills by providing driving assistance and education that takes into account the user's emotional state.

[0469] The following describes the processing flow.

[0470] Step 1:

[0471] The terminal uses sensors and cameras installed in the vehicle to collect driving data and emotional data such as the user's facial expressions and voice. This data is acquired in real time and forms the basis for a comprehensive understanding of the user's driving situation and emotional state.

[0472] Step 2:

[0473] The terminal preprocesses the collected driving and emotional data, removing noise from each data set and converting them into a format that is easy to analyze. The processed data is then ready to be sent to the server.

[0474] Step 3:

[0475] The terminal sends pre-processed data to the server. This data is important for analyzing driving safety and the user's emotional state, and it is transferred with minimal delay.

[0476] Step 4:

[0477] The server receives driving and emotional data transmitted from the terminal and analyzes it using machine learning models. The server assesses the risks during driving, identifies the user's emotional state, and identifies elements that generate necessary feedback.

[0478] Step 5:

[0479] Based on the analyzed results, the server generates feedback tailored to the driving situation and perceived emotions. If the user is experiencing stress, this includes voice guidance and visual instructions with advice to alleviate those emotions.

[0480] Step 6:

[0481] The server sends the generated feedback to the terminal. This feedback is used as real-time guidance for the driver.

[0482] Step 7:

[0483] The terminal provides the user with feedback received from the server. The user receives this feedback through a visual display and voice assistance, and can adjust their driving operations based on it.

[0484] Step 8:

[0485] Users improve their safety awareness and skills through driving that incorporates feedback. If a user receives a suggestion to relax, they can take measures such as taking deep breaths or maintaining a steady gaze.

[0486] Step 9:

[0487] The device continuously monitors the user's driving behavior and emotional changes, recording this data as logs. The recorded data will be used for future analysis and system improvements.

[0488] (Example 2)

[0489] 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."

[0490] Conventional driver assistance systems only provided feedback based on simple driving data, without considering the driver's emotional state. This resulted in a problem where drivers, even when experiencing stress or anxiety, did not receive appropriate support that reflected their emotional state. Furthermore, training programs designed to improve driving skills also lacked an environment where drivers could practice effectively and confidently, as they did not consider the individual driver's emotional state.

[0491] 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.

[0492] In this invention, the server includes means for acquiring driving-related data from the vehicle, means for analyzing the acquired data using an artificial intelligence model, and means for having an emotion analysis function that recognizes the user's emotions. This makes it possible to provide highly customized feedback in real time based on the acquired data and the user's emotional state. Furthermore, by performing driving simulations tailored to the emotional state, it provides training for skill improvement that is appropriate for each individual driver, thereby realizing optimal support for the driver.

[0493] "Driving-related data" refers to a collection of operational parameters related to the vehicle, such as speed, location information, and driving environment.

[0494] An "artificial intelligence model" is a computational model that automatically learns patterns and features from large amounts of data to perform predictions and classifications.

[0495] "Customized feedback" refers to information that provides instructions and advice optimized for individual users based on specific conditions and circumstances.

[0496] "Emotion analysis function" is a technology that uses cameras, microphones, etc., to analyze the user's facial expressions and voice and identify their psychological state.

[0497] "Driving simulation" is a process that recreates driving situations in a virtual environment, allowing drivers to practice scenarios they might actually face.

[0498] This invention relates to an automated driving assistance system equipped with an emotion analysis function that recognizes the user's emotions. The terminal is installed in the vehicle and acquires driving-related data using hardware such as a speed sensor, camera, and GPS. In particular, the emotion analysis function using the camera and microphone analyzes the user's facial expressions and tone of voice to identify their emotional state in real time.

[0499] The device sends collected driving-related data and emotion analysis results to a server. The server receives this data and performs analysis using an artificial intelligence model. This AI model learns from past driving and emotion data and generates personalized feedback optimized for each user. This feedback includes specific advice and relaxation guides based on the user's psychological state while driving.

[0500] For example, if a user encounters traffic congestion while driving and feels stressed, the device will provide feedback such as, "Take a deep breath. The road will clear in a few minutes," to calm the user. The server also adjusts the driving simulation based on the user's emotional state, supporting improvement of driving skills through specific scenarios.

[0501] Examples of prompts for a generating AI model include: "When the user's emotional state is one of fatigue, suggest ways to refresh them," or "Create a scenario in training mode simulation to help the user reduce anxiety." Using such prompts allows the server to generate more effective feedback and simulation content.

[0502] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0503] Step 1:

[0504] The device collects driving-related data from a speed sensor, camera, and GPS. Inputs include vehicle speed, location information, and the user's face and voice. The device acquires this data in real time and uses emotion analysis capabilities to analyze the user's facial expressions and voice tone. Specifically, the camera captures the user's face, and an emotion estimation algorithm is applied. Outputs include the user's emotional state and driving data.

[0505] Step 2:

[0506] The terminal transmits the emotional state and driving data obtained in Step 1 to the server. The input consists of the emotional state and driving-related data obtained through emotion analysis. The terminal transfers these to the server using a secure communication protocol. Specifically, this is done by encrypting the data and transmitting it over the network. The output is the data packets received by the server.

[0507] Step 3:

[0508] The server analyzes the received data using an artificial intelligence model. The input consists of emotional state and driving data sent from the terminal. The server uses machine learning algorithms to analyze the data and generate feedback tailored to the user's current situation. In this process, it also refers to past data to select the most appropriate advice. Specifically, the model classifies and evaluates the data to determine the content of the feedback. The output is customized feedback.

[0509] Step 4:

[0510] The server sends the generated feedback to the terminal. The input is the feedback content generated through analysis. The server structures this information, converts it into a format that the terminal can display and output as sound, and then transmits it using a communication protocol. Specifically, it converts the feedback message into the appropriate data format and sends it. The output is the feedback provided to the user.

[0511] Step 5:

[0512] The user utilizes the feedback received from the device while driving. The input is feedback messages from the device. The user listens to advice through the voice assistant or checks the information on the display. By following the advice as appropriate, the user aims to achieve psychological stability while driving. Specifically, the user adjusts their driving style in response to the feedback. The output is a safer and more comfortable driving experience.

[0513] Step 6:

[0514] The server adjusts the training mode and provides simulations based on the user's emotional state. Inputs are the user's emotional state and training data to support skill improvement. The server uses this to construct simulation scenarios and adjust the simulations provided to the user. Specifically, it generates scenarios and incorporates feedback. The output is customized driving training that the user can perform.

[0515] (Application Example 2)

[0516] 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."

[0517] In autonomous vehicles, the driver's emotional state may not be adequately managed, leading to stress and anxiety, which can compromise driving safety and comfort. It is necessary to address this issue and enable drivers to use vehicles more safely and comfortably.

[0518] 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.

[0519] In this invention, the server includes means for acquiring driving data from a vehicle, means for utilizing a machine learning model to analyze the acquired data, means for providing real-time feedback to the driver based on the analysis results, means for identifying the driver's emotional state using an emotion recognition system, and means for providing visual or auditory feedback corresponding to the emotional state. This enables feedback that takes into account the driver's emotional state.

[0520] "Driving data" refers to information about the vehicle's operating status and condition, including information such as speed, position, and acceleration.

[0521] A "machine learning model" refers to algorithms and statistical methods used to analyze driving data, and is designed to generate patterns and predictions from input data.

[0522] "Real-time feedback" refers to advice and information provided to the driver immediately based on the analysis results, with the aim of improving driving conditions.

[0523] An "emotion recognition system" analyzes the driver's facial expressions and tone of voice to identify their emotional state, utilizing various sensors and analytical algorithms.

[0524] "Visual or auditory feedback" refers to information provided to the driver in a visible or audible form, including appropriate instructions and suggestions that are relevant to their emotional state.

[0525] The system for implementing this invention consists of both a server and a terminal. The server is responsible for collecting and analyzing driving data and data related to emotion recognition. The hardware used consists of various sensors and cameras mounted on the vehicle, and is equipped with a high-performance processor for speech recognition and image recognition. As for the software, a machine learning model is used to analyze the driving data. Specifically, libraries such as TensorFlow and PyTorch are used to process the data.

[0526] The server analyzes driving data in real time and generates visual and auditory feedback based on the driver's emotional state. The terminal provides this feedback to the driver via smart glasses or a smartphone. The information displayed on the terminal is designed to be visually easy to understand using a GUI framework.

[0527] Through feedback received from the device, users can reduce stress while driving and improve driving efficiency and safety. For example, if a driver is feeling fatigued from long hours of driving on a highway, a visual prompt suggesting "take a break" will be displayed. In addition, auditory announcements such as "There will be a service area in XX minutes" will be played.

[0528] An example of a prompt is, "If the emotion engine detects that the user is tired while driving, please explain what visual guidance the smart glasses should provide." By submitting this prompt to the generating AI model, specific feedback suggestions can be obtained.

[0529] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0530] Step 1:

[0531] The server acquires driving data from sensors and cameras installed in the vehicle. Inputs include speed sensor data, GPS data, and camera footage, which are collected in real time. Raw data is generated as output. This raw data includes the vehicle's movement and location information during operation, as well as the driver's facial expressions and voice input.

[0532] Step 2:

[0533] The server inputs the acquired driving data into a machine learning model for analysis. The data processing used here includes signal processing and data preprocessing. The machine learning model is built using TensorFlow or PyTorch and extracts driving patterns and emotional states. The output is an analysis result showing the driver's current driving situation and emotional state.

[0534] Step 3:

[0535] The server identifies the driver's emotional state through an emotion recognition system. This step utilizes camera footage and audio data as input. Image and speech recognition algorithms are used for data processing, inferring the driver's emotional state from their facial features and tone of voice. A tag indicating the driver's emotional state is generated as output.

[0536] Step 4:

[0537] The terminal generates visual or auditory feedback based on analysis results and emotional states. Analysis data and emotional tags received from the server are used as input. Based on this data, a GUI framework is used to design visual messages, and audio guidance is prepared using TTS (Text-to-Speech) technology. The output is feedback in the form of instructions and suggestions presented to the driver.

[0538] Step 5:

[0539] Users receive feedback through their devices and incorporate it into their driving. Once the user receives feedback, it is used to improve their next driving actions and situational judgments. Furthermore, responses to the received feedback are sent back to the server, which is then used for subsequent analysis. This cycle ensures continuous improvement of driver assistance.

[0540] 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.

[0541] 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.

[0542] 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.

[0543] [Fourth Embodiment]

[0544] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0545] 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.

[0546] 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).

[0547] 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.

[0548] 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.

[0549] 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).

[0550] 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.

[0551] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0552] 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.

[0553] 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.

[0554] 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.

[0555] 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.

[0556] 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".

[0557] This invention is implemented as a system in which a terminal installed in a vehicle collects driving data, a server analyzes that data, and provides real-time feedback to the user.

[0558] The terminal is installed in the vehicle and acquires data from various sensors such as speed sensors, cameras, and GPS. This raw data includes information about various driving conditions, such as speed, distance between vehicles, vehicle movement, and the positions of surrounding vehicles. The terminal converts this acquired data into a predetermined format and sends it to the server.

[0559] The server receives data from the terminal and performs analysis using a pre-trained machine learning model. This analysis includes risk assessment and understanding of traffic patterns while driving, and utilizes generative AI to make more accurate predictions. For example, if a sudden lane change or braking occurs on a highway, the server immediately assesses the risk and determines appropriate action guidance.

[0560] The user receives driving assistance through feedback from the device. Based on analysis results sent from the server, the device provides information to the driver through voice guidance and display messages. For example, if there is a vehicle that has suddenly stopped ahead, the device will give the user specific instructions such as, "There is an obstacle ahead. Prepare to brake."

[0561] As a concrete example, suppose a user is driving in an urban area and may encounter a vehicle running a red light at an intersection. The terminal monitors the behavior of surrounding vehicles in real time, and if it detects a vehicle entering the intersection at an abnormal speed, it immediately sends that data to the server. The server evaluates this information based on traffic patterns and real-time analysis, and if it determines that the risk is high, the terminal provides the user with a voice command saying, "Slow down."

[0562] Thus, the system of the present invention can improve the driver's skills by utilizing data generated through prior simulation training. Throughout the entire system, users can improve their driving skills and enhance their ability to drive safely.

[0563] The following describes the processing flow.

[0564] Step 1:

[0565] The device uses various sensors installed in the vehicle, such as speed sensors, cameras, and GPS, to acquire real-time data while driving. This includes vehicle speed, acceleration, location information, and distance to the vehicle in front.

[0566] Step 2:

[0567] The terminal preprocesses the acquired raw data, performing necessary noise reduction and data format conversion. This process prepares the data for easy analysis.

[0568] Step 3:

[0569] The terminal transmits pre-processed data to the server via wireless communication. This transmission is optimized to minimize latency.

[0570] Step 4:

[0571] The server receives data sent from the terminal and performs data analysis using a pre-trained machine learning model. This involves recognizing the driving environment and predicting potential hazards.

[0572] Step 5:

[0573] The server generates feedback for the driver based on the analysis results of the machine learning model. This includes warnings and driving assistance information tailored to the risk assessment.

[0574] Step 6:

[0575] The server sends the generated feedback to the terminal. The feedback is output through a visual display or voice assistant.

[0576] Step 7:

[0577] The terminal provides the user with feedback sent from the server. This allows the user to understand points to watch out for and necessary actions while driving in real time.

[0578] Step 8:

[0579] The user adjusts their driving actions based on the feedback provided. For example, they might take measures such as applying the brakes if a hazard is anticipated.

[0580] Step 9:

[0581] The terminal collects data on user actions again and records it as a log. This data will be used for future analysis and system improvements.

[0582] (Example 1)

[0583] 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".

[0584] Conventional driver assistance systems sometimes fail to provide drivers with the necessary feedback in real time, making them insufficient for ensuring safety and improving driver skills. Furthermore, the accuracy of driving data analysis is limited, making proper risk assessment difficult.

[0585] 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.

[0586] In this invention, the server includes means for acquiring driving data from a vehicle and collecting data using different types of sensory sensors; means for converting the acquired data into a predetermined format and transmitting it to an information and communication device; and means for receiving the transmitted data, analyzing the data using a generated AI model, and evaluating the risk. This enables the driver to receive real-time, highly accurate feedback, improve safety, and allow for the continuous improvement of driving skills.

[0587] "Driving data" refers to various types of information related to the operation of a vehicle, including speed, position, acceleration, and information about the surrounding environment.

[0588] A "perceptual sensor" is a device used to acquire information from the physical environment, and includes devices such as cameras, speedometers, and GPS.

[0589] An "information and communication device" is a device used to send and receive data, and is a device that establishes network connections via a specific protocol.

[0590] A "generative AI model" is a computational model trained using artificial intelligence technology to perform a specific task, and is used for data analysis and prediction.

[0591] "Risk assessment" is the process of analyzing potential hazards that may occur while driving and determining their impact and frequency.

[0592] "Feedback" refers to instructions and information provided to the driver based on the analysis results, and is communicated through voice or visual means.

[0593] This invention relates to a driver assistance system comprising a terminal mounted in a vehicle and a server located in the cloud or a dedicated data center. The terminal collects driving data using the vehicle's sensory sensors, such as speed sensors, cameras, and GPS. These sensors have the function of acquiring various operational information of the vehicle, such as speed, distance between vehicles, position, and surrounding environment data.

[0594] The terminal converts the collected raw data into a predetermined format in real time and transmits it to the server via the network through an information and communication device. This conversion includes compressing the collected data into CSV format and image data into JPEG format. The terminal also supports the communication protocols necessary for data transmission.

[0595] The server receives data transferred from the terminal and performs analysis using a pre-trained generative AI model. The generative AI model is used for risk assessment and traffic pattern analysis during driving, and is designed to improve the accuracy of the feedback provided to the driver. An example of a prompt message is, "Analyze the driving situation when entering the intersection and generate necessary driving instructions."

[0596] Users receive feedback from the terminal through voice guidance and display information, and perform driving actions based on this feedback. For example, if the server determines from its analysis that the distance between vehicles is too short, the terminal will provide the user with voice instructions such as "Please slow down," supporting appropriate driving behavior. This system allows users to receive real-time driving assistance, improving their driving skills and ensuring safety.

[0597] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0598] Step 1:

[0599] The device collects driving data in real time using a speed sensor, camera, and GPS. Inputs include vehicle speed, location information, and image data. This data is temporarily stored as raw data within the device.

[0600] Step 2:

[0601] The terminal converts the collected raw data into a predetermined format. This conversion process converts speed data to CSV format and compresses camera video to JPEG format. The output is formatted data, which is then prepared for network transmission.

[0602] Step 3:

[0603] The terminal sends formatted data to the server according to a predetermined communication protocol. The input is the converted data, and the output is status information indicating successful transmission. Specifically, the data is transmitted to the server via Wi-Fi or LTE.

[0604] Step 4:

[0605] The server receives data transmitted from the terminal. The input data includes all information related to the vehicle's operation. Based on this data, the server uses a generative AI model to perform data analysis. The output of the process is the result of driving instructions and risk assessment.

[0606] Step 5:

[0607] The server sends the analysis results back to the terminal. This is feedback data used for voice guidance and display information. Specifically, it includes instructions such as, "The distance to the vehicle in front is short, please slow down."

[0608] Step 6:

[0609] The terminal provides feedback to the user based on the analysis results sent from the server. The input is the server's analysis results, and the output is voice instructions and warning messages on the display. The system immediately provides voice guidance to the user and displays instructions on the screen if necessary.

[0610] Step 7:

[0611] The user performs driving operations based on feedback from the device. For example, if instructed to decelerate, the user takes action such as applying the brakes. This enables safe driving that responds immediately to the feedback received.

[0612] (Application Example 1)

[0613] 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".

[0614] In systems that support safe driving by analyzing driving data in real time and providing immediate feedback to drivers, there is a need to effectively utilize the acquired analysis results and provide drivers with more appropriate instructions. Furthermore, a challenge is to develop a mechanism that uses mobile devices to provide driving information visually and audibly, thereby promoting driver understanding and response.

[0615] 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.

[0616] In this invention, the server includes means for acquiring driving data from a vehicle, means for utilizing a machine learning model to analyze the acquired data, means for providing real-time feedback to the driver based on the analysis results, means for displaying the driving data on a personal information terminal, and means for providing the obtained analysis results as an audio alert on the personal information terminal. This enables the driver to receive real-time feedback through both sight and hearing.

[0617] "Driving data" is a general term for information that indicates the state and movement of a vehicle while it is in operation, such as the vehicle's speed, position, and surrounding conditions.

[0618] A "portable information terminal" is an information device that a driver can carry and use, and includes smartphones, tablets, and other similar devices.

[0619] A "machine learning model" refers to an algorithm or structure that learns patterns and rules based on large amounts of data to perform predictions and classifications.

[0620] "Feedback" refers to the information and instructions provided to the driver based on analysis results during vehicle operation.

[0621] A "voice alert" is an audio message that conveys attention or warnings to the driver in a perceptible manner, depending on the analysis results and circumstances.

[0622] In this invention, an application installed on the driver's mobile device (smartphone or tablet) communicates with the vehicle's terminal to acquire driving data in real time and provide analysis results. The server receives the data acquired from the vehicle and uses a pre-trained machine learning model to analyze it, assess the risks during driving, and provide instructions for appropriate driving actions. The analysis uses a generative AI model to predict, for example, sudden changes in the distance between vehicles or instability in speed.

[0623] The terminal uses speakers and display functions to provide feedback to the driver as voice alerts, conveying the analysis results to the user visually and audibly. These voice alerts may include instructions such as, "There is a vehicle ahead that has suddenly decelerated. Prepare to brake."

[0624] This system requires vehicle terminals equipped with sensors and communication modules, a computer server for analyzing driving data, and a mobile information terminal held by the driver to work together in coordination to perform necessary data processing and provide feedback to the driver in real time.

[0625] For example, consider a situation where a driver is driving on a highway and the vehicle in front suddenly changes lanes. In this case, the terminal immediately transmits this situation to the server, which analyzes the risk based on the acquired information and feeds the results back to the mobile device. As a result, the application informs the driver via voice guidance, "Please slow down." In this way, it supports safe driving.

[0626] An example of a prompt message for the generating AI model might be, "Based on this driving data pattern, perform the most effective analysis to identify high-risk scenarios. Also, what appropriate feedback should be provided to the driver?" Using this prompt message, the server analyzes the driving data.

[0627] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0628] Step 1:

[0629] The terminal collects driving data in real time from sensors mounted on the vehicle (speed sensors, cameras, GPS, etc.). The input is raw sensor data, which is then converted into a predetermined format for output. Specifically, the data is standardized, and necessary parameters are extracted and structured.

[0630] Step 2:

[0631] The terminal sends the converted driving data to the server. The server receives this formatted data as input. After receiving the data, the server feeds it into a machine learning model to analyze driving patterns and assess risks. The output is a risk assessment and behavioral guidance.

[0632] Step 3:

[0633] The server utilizes a generative AI model to determine appropriate feedback to provide to the driver based on the analysis results. Prompt messages are used to request the generative AI model to predict scenarios and generate feedback. The output consists of specific instructions and alert messages.

[0634] Step 4:

[0635] The server returns the analysis results and instruction messages to the terminal. The terminal receives this data as input and prepares to notify the driver. Specifically, it prepares to process the notification content as audio or text.

[0636] Step 5:

[0637] The device provides feedback to the driver in the form of voice alerts and on-screen messages. This allows the user (driver) to receive information and gain assistance in taking appropriate driving actions. The output is specific feedback that appeals to the user's sight and hearing.

[0638] 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.

[0639] This invention is implemented as an autonomous driving assistance system that combines an emotion engine that recognizes the user's emotions. In addition to standard data collection functions such as speed sensors, cameras, and GPS, the terminal is equipped with an emotion engine. This emotion engine analyzes the user's facial expressions and tone of voice in real time through cameras and voice analysis, etc., and identifies their emotional state.

[0640] The device sends this emotion recognition result along with other driving data to the server. The server uses a machine learning model to analyze the data and generate customized feedback tailored to the user's emotions. For example, if the emotion engine detects that the user is stressed, the server can offer relaxation guidance to help the user calm down.

[0641] Users can receive real-time feedback from their device and benefit from driving assistance. This feedback includes not only regular driving guidance but also assistance based on emotional state. For example, if a user is feeling stressed in traffic, the device can offer encouraging words such as, "Take a deep breath. The roads will clear in a few minutes."

[0642] Furthermore, the emotion engine plays a role in training mode by adjusting the driving simulation to match the user's emotional state. This creates an environment where the user can practice with confidence, supporting the effective improvement of driving skills. For example, users who tend to get easily irritated in certain situations can be provided with scenarios to help them deal with those situations calmly and learn emotional control techniques.

[0643] In this way, the present invention improves user safety and driving skills by providing driving assistance and education that takes into account the user's emotional state.

[0644] The following describes the processing flow.

[0645] Step 1:

[0646] The terminal uses sensors and cameras installed in the vehicle to collect driving data and emotional data such as the user's facial expressions and voice. This data is acquired in real time and forms the basis for a comprehensive understanding of the user's driving situation and emotional state.

[0647] Step 2:

[0648] The terminal preprocesses the collected driving and emotional data, removing noise from each data set and converting them into a format that is easy to analyze. The processed data is then ready to be sent to the server.

[0649] Step 3:

[0650] The terminal sends pre-processed data to the server. This data is important for analyzing driving safety and the user's emotional state, and it is transferred with minimal delay.

[0651] Step 4:

[0652] The server receives driving and emotional data transmitted from the terminal and analyzes it using machine learning models. The server assesses the risks during driving, identifies the user's emotional state, and identifies elements that generate necessary feedback.

[0653] Step 5:

[0654] Based on the analyzed results, the server generates feedback tailored to the driving situation and perceived emotions. If the user is experiencing stress, this includes voice guidance and visual instructions with advice to alleviate those emotions.

[0655] Step 6:

[0656] The server sends the generated feedback to the terminal. This feedback is used as real-time guidance for the driver.

[0657] Step 7:

[0658] The terminal provides the user with feedback received from the server. The user receives this feedback through a visual display and voice assistance, and can adjust their driving operations based on it.

[0659] Step 8:

[0660] Users improve their safety awareness and skills through driving that incorporates feedback. If a user receives a suggestion to relax, they can take measures such as taking deep breaths or maintaining a steady gaze.

[0661] Step 9:

[0662] The device continuously monitors the user's driving behavior and emotional changes, recording this data as logs. The recorded data will be used for future analysis and system improvements.

[0663] (Example 2)

[0664] 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".

[0665] Conventional driver assistance systems only provided feedback based on simple driving data, without considering the driver's emotional state. This resulted in a problem where drivers, even when experiencing stress or anxiety, did not receive appropriate support that reflected their emotional state. Furthermore, training programs designed to improve driving skills also lacked an environment where drivers could practice effectively and confidently, as they did not consider the individual driver's emotional state.

[0666] 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.

[0667] In this invention, the server includes means for acquiring driving-related data from the vehicle, means for analyzing the acquired data using an artificial intelligence model, and means for having an emotion analysis function that recognizes the user's emotions. This makes it possible to provide highly customized feedback in real time based on the acquired data and the user's emotional state. Furthermore, by performing driving simulations tailored to the emotional state, it provides training for skill improvement that is appropriate for each individual driver, thereby realizing optimal support for the driver.

[0668] "Driving-related data" refers to a collection of operational parameters related to the vehicle, such as speed, location information, and driving environment.

[0669] An "artificial intelligence model" is a computational model that automatically learns patterns and features from large amounts of data to perform predictions and classifications.

[0670] "Customized feedback" refers to information that provides instructions and advice optimized for individual users based on specific conditions and circumstances.

[0671] "Emotion analysis function" is a technology that uses cameras, microphones, etc., to analyze the user's facial expressions and voice and identify their psychological state.

[0672] "Driving simulation" is a process that recreates driving situations in a virtual environment, allowing drivers to practice scenarios they might actually face.

[0673] This invention relates to an automated driving assistance system equipped with an emotion analysis function that recognizes the user's emotions. The terminal is installed in the vehicle and acquires driving-related data using hardware such as a speed sensor, camera, and GPS. In particular, the emotion analysis function using the camera and microphone analyzes the user's facial expressions and tone of voice to identify their emotional state in real time.

[0674] The device sends collected driving-related data and emotion analysis results to a server. The server receives this data and performs analysis using an artificial intelligence model. This AI model learns from past driving and emotion data and generates personalized feedback optimized for each user. This feedback includes specific advice and relaxation guides based on the user's psychological state while driving.

[0675] For example, if a user encounters traffic congestion while driving and feels stressed, the device will provide feedback such as, "Take a deep breath. The road will clear in a few minutes," to calm the user. The server also adjusts the driving simulation based on the user's emotional state, supporting improvement of driving skills through specific scenarios.

[0676] Examples of prompts for a generating AI model include: "When the user's emotional state is one of fatigue, suggest ways to refresh them," or "Create a scenario in training mode simulation to help the user reduce anxiety." Using such prompts allows the server to generate more effective feedback and simulation content.

[0677] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0678] Step 1:

[0679] The device collects driving-related data from a speed sensor, camera, and GPS. Inputs include vehicle speed, location information, and the user's face and voice. The device acquires this data in real time and uses emotion analysis capabilities to analyze the user's facial expressions and voice tone. Specifically, the camera captures the user's face, and an emotion estimation algorithm is applied. Outputs include the user's emotional state and driving data.

[0680] Step 2:

[0681] The terminal transmits the emotional state and driving data obtained in Step 1 to the server. The input consists of the emotional state and driving-related data obtained through emotion analysis. The terminal transfers these to the server using a secure communication protocol. Specifically, this is done by encrypting the data and transmitting it over the network. The output is the data packets received by the server.

[0682] Step 3:

[0683] The server analyzes the received data using an artificial intelligence model. The input consists of emotional state and driving data sent from the terminal. The server uses machine learning algorithms to analyze the data and generate feedback tailored to the user's current situation. In this process, it also refers to past data to select the most appropriate advice. Specifically, the model classifies and evaluates the data to determine the content of the feedback. The output is customized feedback.

[0684] Step 4:

[0685] The server sends the generated feedback to the terminal. The input is the feedback content generated through analysis. The server structures this information, converts it into a format that the terminal can display and output as sound, and then transmits it using a communication protocol. Specifically, it converts the feedback message into the appropriate data format and sends it. The output is the feedback provided to the user.

[0686] Step 5:

[0687] The user utilizes the feedback received from the device while driving. The input is feedback messages from the device. The user listens to advice through the voice assistant or checks the information on the display. By following the advice as appropriate, the user aims to achieve psychological stability while driving. Specifically, the user adjusts their driving style in response to the feedback. The output is a safer and more comfortable driving experience.

[0688] Step 6:

[0689] The server adjusts the training mode and provides simulations based on the user's emotional state. Inputs are the user's emotional state and training data to support skill improvement. The server uses this to construct simulation scenarios and adjust the simulations provided to the user. Specifically, it generates scenarios and incorporates feedback. The output is customized driving training that the user can perform.

[0690] (Application Example 2)

[0691] 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".

[0692] In autonomous vehicles, the driver's emotional state may not be adequately managed, leading to stress and anxiety, which can compromise driving safety and comfort. It is necessary to address this issue and enable drivers to use vehicles more safely and comfortably.

[0693] 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.

[0694] In this invention, the server includes means for acquiring driving data from a vehicle, means for utilizing a machine learning model to analyze the acquired data, means for providing real-time feedback to the driver based on the analysis results, means for identifying the driver's emotional state using an emotion recognition system, and means for providing visual or auditory feedback corresponding to the emotional state. This enables feedback that takes into account the driver's emotional state.

[0695] "Driving data" refers to information about the vehicle's operating status and condition, including information such as speed, position, and acceleration.

[0696] A "machine learning model" refers to algorithms and statistical methods used to analyze driving data, and is designed to generate patterns and predictions from input data.

[0697] "Real-time feedback" refers to advice and information provided to the driver immediately based on the analysis results, with the aim of improving driving conditions.

[0698] An "emotion recognition system" analyzes the driver's facial expressions and tone of voice to identify their emotional state, utilizing various sensors and analytical algorithms.

[0699] "Visual or auditory feedback" refers to information provided to the driver in a visible or audible form, including appropriate instructions and suggestions that are relevant to their emotional state.

[0700] The system for implementing this invention consists of both a server and a terminal. The server is responsible for collecting and analyzing driving data and data related to emotion recognition. The hardware used consists of various sensors and cameras mounted on the vehicle, and is equipped with a high-performance processor for speech recognition and image recognition. As for the software, a machine learning model is used to analyze the driving data. Specifically, libraries such as TensorFlow and PyTorch are used to process the data.

[0701] The server analyzes driving data in real time and generates visual and auditory feedback based on the driver's emotional state. The terminal provides this feedback to the driver via smart glasses or a smartphone. The information displayed on the terminal is designed to be visually easy to understand using a GUI framework.

[0702] Through feedback received from the device, users can reduce stress while driving and improve driving efficiency and safety. For example, if a driver is feeling fatigued from long hours of driving on a highway, a visual prompt suggesting "take a break" will be displayed. In addition, auditory announcements such as "There will be a service area in XX minutes" will be played.

[0703] An example of a prompt is, "If the emotion engine detects that the user is tired while driving, please explain what visual guidance the smart glasses should provide." By submitting this prompt to the generating AI model, specific feedback suggestions can be obtained.

[0704] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0705] Step 1:

[0706] The server acquires driving data from sensors and cameras installed in the vehicle. Inputs include speed sensor data, GPS data, and camera footage, which are collected in real time. Raw data is generated as output. This raw data includes the vehicle's movement and location information during operation, as well as the driver's facial expressions and voice input.

[0707] Step 2:

[0708] The server inputs the acquired driving data into a machine learning model for analysis. The data processing used here includes signal processing and data preprocessing. The machine learning model is built using TensorFlow or PyTorch and extracts driving patterns and emotional states. The output is an analysis result showing the driver's current driving situation and emotional state.

[0709] Step 3:

[0710] The server identifies the driver's emotional state through an emotion recognition system. This step utilizes camera footage and audio data as input. Image and speech recognition algorithms are used for data processing, inferring the driver's emotional state from their facial features and tone of voice. A tag indicating the driver's emotional state is generated as output.

[0711] Step 4:

[0712] The terminal generates visual or auditory feedback based on analysis results and emotional states. Analysis data and emotional tags received from the server are used as input. Based on this data, a GUI framework is used to design visual messages, and audio guidance is prepared using TTS (Text-to-Speech) technology. The output is feedback in the form of instructions and suggestions presented to the driver.

[0713] Step 5:

[0714] Users receive feedback through their devices and incorporate it into their driving. Once the user receives feedback, it is used to improve their next driving actions and situational judgments. Furthermore, responses to the received feedback are sent back to the server, which is then used for subsequent analysis. This cycle ensures continuous improvement of driver assistance.

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] 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.

[0723] 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."

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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 as being incorporated by reference.

[0736] The following is further disclosed regarding the embodiments described above.

[0737] (Claim 1)

[0738] Means for acquiring driving data from vehicles,

[0739] A means of utilizing a machine learning model to analyze the acquired data,

[0740] A means for providing real-time feedback to the driver based on the aforementioned analysis results,

[0741] A system that includes this.

[0742] (Claim 2)

[0743] The system according to claim 1, further comprising means for predicting risks while driving and providing voice guidance to the driver as part of the feedback.

[0744] (Claim 3)

[0745] The system according to claim 1, further comprising means for conducting driving simulations and generating training data for the purpose of improving the driver's skills.

[0746] "Example 1"

[0747] (Claim 1)

[0748] A means of acquiring driving data from a vehicle and collecting data using different types of sensory sensors,

[0749] Means for converting the acquired data into a predetermined format and transmitting it to an information communication device,

[0750] A means for receiving the transmitted data, analyzing the data using a generated AI model, and evaluating the risk,

[0751] A means for providing real-time feedback to the driver based on the aforementioned analysis results,

[0752] A system that includes this.

[0753] (Claim 2)

[0754] The system according to claim 1, further comprising means for evaluating anticipated hazards while driving and providing the driver with voice and visual instructions as part of the feedback.

[0755] (Claim 3)

[0756] The system according to claim 1, further comprising means for generating training data intended to improve the driver's skills by utilizing driving simulations.

[0757] "Application Example 1"

[0758] (Claim 1)

[0759] Means for acquiring driving data from vehicles,

[0760] A means of utilizing a machine learning model to analyze the acquired data,

[0761] A means for providing real-time feedback to the driver based on the aforementioned analysis results,

[0762] A means of displaying driving data on a mobile device,

[0763] A means of providing the obtained analysis results as an audio alert on a mobile device,

[0764] A system that includes this.

[0765] (Claim 2)

[0766] The system according to claim 1, further comprising means for predicting risks while driving and providing voice guidance to the driver as part of the feedback.

[0767] (Claim 3)

[0768] The system according to claim 1, further comprising means for conducting driving simulations and generating training data for the purpose of improving the driver's skills.

[0769] "Example 2 of combining an emotion engine"

[0770] (Claim 1)

[0771] Means for acquiring driving-related data from vehicles,

[0772] A means of utilizing an artificial intelligence model to analyze the acquired data,

[0773] A means for providing the driver with customized feedback in real time based on the analysis results and the user's emotional state,

[0774] A means equipped with an emotion analysis function that recognizes the user's emotions,

[0775] A means for integrating and transmitting the results obtained by the aforementioned emotion recognition with driving data,

[0776] A system that includes this.

[0777] (Claim 2)

[0778] The system according to claim 1, further comprising means for providing the driver with voice guidance as part of feedback based on the driver's psychological state while driving, and for presenting a customized relaxation guide according to the user's emotional state.

[0779] (Claim 3)

[0780] The system according to claim 1, further comprising means for conducting driving simulations, adjusting driving scenarios according to the user's emotional state, and generating skill improvement training data that also takes into account the driver's emotional control.

[0781] "Application example 2 when combining with an emotional engine"

[0782] (Claim 1)

[0783] Means for acquiring driving data from vehicles,

[0784] A means of utilizing a machine learning model to analyze the acquired data,

[0785] A means for providing real-time feedback to the driver based on the aforementioned analysis results,

[0786] A means of identifying the driver's emotional state using an emotion recognition system,

[0787] Means for providing visual or auditory feedback corresponding to the aforementioned emotional state,

[0788] A system that includes this.

[0789] (Claim 2)

[0790] The system according to claim 1, further comprising means for predicting risks while driving and providing voice guidance to the driver according to their emotional state.

[0791] (Claim 3)

[0792] The system according to claim 1, further comprising means for conducting driving simulations and generating training data aimed at improving the driver's skills while taking into account their emotional state. [Explanation of Symbols]

[0793] 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. Means for acquiring driving data from vehicles, A means of utilizing a machine learning model to analyze the acquired data, A means for providing real-time feedback to the driver based on the aforementioned analysis results, A system that includes this.

2. The system according to claim 1, further comprising means for predicting risks while driving and providing voice guidance to the driver as part of the feedback.

3. The system according to claim 1, further comprising means for conducting driving simulations and generating training data for the purpose of improving the driver's skills.

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