system

Abstract

Provide a system. [Solution] A means of monitoring seniors' driving behavior in real time and collecting location and speed data; A means of analyzing the collected data and evaluating the safety of driving; means for detecting abnormal behavior and providing warnings based on a driving safety assessment; a means for notifying the senior of the generated alert; A system including:

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In a super-aging society, the decline in driving ability of elderly drivers has become a social problem. Elderly people are confident in their driving ability and often do not voluntarily surrender their licenses, which increases the risk of car accidents. Local governments encourage voluntary license surrender, but the psychological barriers facing elderly drivers make it difficult for them to decline. Therefore, there is a need for a system that can evaluate the driving ability of elderly drivers in detail and suggest that they surrender their licenses at the appropriate time when they have reached the limit of safe driving. [Means for solving the problem]

[0005] The present invention is a system that includes a means for monitoring elderly driving behavior in real time and collecting location and speed data, a means for analyzing the collected data and evaluating driving safety, a means for detecting abnormal behavior and providing warnings based on the driving safety evaluation, and a means for notifying the elderly of the generated warnings. The system also includes a means for monitoring lane keeping in driving behavior and detecting lane departure, a means for analyzing and evaluating the frequency of lane departure and the occurrence of sudden braking, and a means for providing feedback to the elderly in real time on the generated evaluation results. The system also includes a means for evaluating changes in driving route memory ability and judgment, a means for detecting these changes and comprehensively evaluating driving ability, and a means for providing psychological support to the elderly using generative AI. This makes it easier for elderly people to understand the limitations of their driving ability and encourages them to accept license surrender in a positive manner. To support their lives after license surrender, the system also works with local governments to provide benefits and alternative transportation options.

[0006] "Driving behavior" refers to the series of operations and reactions a driver takes when operating a vehicle.

[0007] "Real-time" refers to data processing and information provision occurring immediately, without delay.

[0008] "Location information" refers to data that indicates a specific location or coordinates using technology such as GPS.

[0009] "Speed ​​data" refers to information about the speed at which a vehicle is traveling.

[0010] "Analysis" refers to the process of analyzing collected data and deriving meaningful information or results.

[0011] "Driving safety" refers to the driving skills and behaviors that enable drivers to comply with traffic laws and regulations and prevent accidents.

[0012] "Abnormal behavior" refers to dangerous driving behavior such as sudden braking or lane departure that differs from normal driving.

[0013] "Warning" refers to a message or alert that alerts the driver to their driving behavior.

[0014] The term "older adults" generally refers to people who are older and experiencing physical and cognitive changes.

[0015] "Generative AI" refers to artificial intelligence that uses machine learning technology to analyze data and automatically generate appropriate messages and feedback.

[0016] "Lane keeping" refers to the driving behavior of keeping a vehicle precisely within a predefined lane as it travels.

[0017] "Lane departure" refers to a driving behavior in which a vehicle unintentionally leaves its lane.

[0018] "Sudden braking" refers to a braking operation that suddenly reduces the vehicle's speed.

[0019] "Driving route" refers to the path taken by a vehicle when traveling.

[0020] "Memory ability" refers to the ability to remember past information.

[0021] "Judgment" refers to the ability to understand a situation and choose appropriate actions.

[0022] "Psychological support" refers to providing advice and messages to enhance psychological security and self-esteem.

[0023] "Municipality" refers to a local administrative unit, including cities, towns, villages, prefectures, etc. [Brief explanation of the drawings]

[0024] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2]1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0025] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0026] First, the terms used in the following description will be explained.

[0027] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0028] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0029] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0030] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0031] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0032] [First embodiment]

[0033] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0034] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0035] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0036] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0037] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0038] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0039] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0040] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0041] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0042] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0043] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0044] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0045] This invention relates to a system that evaluates the driving ability of elderly people in detail and suggests that they surrender their license when they reach the limit of safe driving. The system mainly consists of a terminal installed in the vehicle and a server that analyzes the data.

[0046] System Configuration

[0047] 1. Terminal: A device installed inside the vehicle that collects GPS and speed data in real time. The terminal also has the function of closely monitoring the driving behavior of elderly drivers using multiple sensors, including cameras, speedometers, and accelerometers.

[0048] 2. Server: A central processing unit that receives collected data and analyzes it using a dedicated algorithm. Based on the analysis results, the server detects abnormal behavior and generates a warning if it determines that safe operation cannot be continued.

[0049] 3. Generative AI: This is installed on the server and generates psychological support messages for the elderly based on the analysis results. The Generative AI reduces the user's psychological burden and encourages them to surrender their driver's license in a positive manner without damaging their self-esteem.

[0050] Program processing

[0051] Collecting and analyzing driving behavior

[0052] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. This data is sent to a server at regular intervals. For example, the device acquires location information every second and processes camera images for lane keeping. This allows for detailed recording of elderly driving behavior.

[0053] Examples:

[0054] Once the user starts driving, the device will continuously acquire location information, collect speed data, and record any lane departures or sudden braking.

[0055] Data reception and analysis

[0056] The server analyzes the received data using a dedicated algorithm. The server analyzes driving behavior patterns and counts the number of times the driver habitually brakes suddenly or deviates from their lane, etc. This allows the overall driving safety to be evaluated.

[0057] Examples:

[0058] The server analyzes the data for a given day and obtains the results of "3 instances of sudden braking," "2 instances of speeding," and "1 instance of lane departure." Based on these results, an overall safe driving score is calculated and evaluated.

[0059] Detecting abnormal behavior and generating alerts

[0060] The server detects abnormal behavior based on the analysis results. If the standard for safe driving is exceeded, a warning message is generated. For example, if the frequency of sudden braking is too high, the server generates a warning saying, "Safe driving is difficult due to frequent sudden braking."

[0061] Examples:

[0062] The server analyzes the user's driving data and, if it determines that the user is frequently drifting out of their lane, it generates a warning message and sends it to the device.

[0063] Psychological support for the elderly

[0064] Based on the analysis results, the AI ​​generates a psychological support message for the elderly. This helps them understand their declining driving ability and encourages them to positively consider returning their license. The generated message is displayed to the user via the device.

[0065] Examples:

[0066] When a user receives a driving evaluation message, the device will display, "Based on your recent driving data, we would like to provide you with some advice to help you continue driving safely," and provide specific suggestions for improvement.

[0067] conclusion

[0068] The system of this invention ensures traffic safety throughout society by providing a detailed assessment of the driving ability of elderly people and identifying the limits of safe driving. It also uses generative AI to provide psychological support, helping elderly people to think positively about returning their driver's licenses while preserving their self-esteem. By collaborating with local governments, the system also provides support for daily life after license surrender, contributing to the resolution of social issues.

[0069] The processing flow will be explained below.

[0070] Step 1:

[0071] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. The device obtains location information every second and also records the vehicle's speed and acceleration.

[0072] Step 2:

[0073] The device sends the collected data to a server at regular intervals (e.g., every minute). The data sent includes location information, speed information, lane identification information, and records of sudden braking.

[0074] Step 3:

[0075] The server inputs the received data into an analytical algorithm, which analyzes driving patterns based on this data and detects abnormal behavior.

[0076] Step 4:

[0077] The server analyzes the results of abnormal behavior detection and evaluates the safe driving, calculating a safe driving score based on a comprehensive evaluation of factors such as the number of sudden braking attempts, frequency of lane departures, and whether or not the driver exceeded the speed limit.

[0078] Step 5:

[0079] The server generates a warning message based on the analysis results. For example, if the frequency of sudden braking is high, it creates a warning message such as "Sudden braking is occurring frequently. Please drive safely."

[0080] Step 6:

[0081] The server generates a warning message and sends it to the terminal, which then receives it and notifies the user via an in-car display or voice message.

[0082] Step 7:

[0083] The AI ​​generates a psychological support message based on the analysis results and warning message, creating a message that encourages the user to surrender their license while maintaining their self-esteem.

[0084] Step 8:

[0085] The AI ​​generates and sends psychological support messages to the device, which displays the messages to provide the user with a sense of psychological security and encourage them to improve their driving behavior.

[0086] Step 9:

[0087] The device provides users with a comprehensive driving evaluation and feedback, displays detailed analysis results of their driving behavior, and suggests specific improvements and changes to their driving habits.

[0088] Step 10:

[0089] Users will receive feedback and strive to improve their driving skills. If necessary, we will work with local governments to provide support for life after surrendering their license.

[0090] Example 1

[0091] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0092] The decline in driving ability among the elderly increases the risk of traffic accidents, and this problem needs to be addressed. However, conventional methods make it difficult for elderly people to recognize their own decline in driving ability and voluntarily return their licenses, which could have a significant impact on traffic safety throughout society. Another issue is the psychological burden that the process of returning a license places on elderly people.

[0093] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0094] In this invention, the server includes a device installed in the vehicle that monitors the driving behavior of the elderly in real time and collects location information, speed data, lane identification data, and sudden braking data, a means for buffering the collected data and periodically sending it to a central processing unit, a means for analyzing the received data with a dedicated algorithm and evaluating driving safety, a means for detecting abnormal behavior based on the evaluation of driving safety and generating a warning message, and a means for notifying the elderly of the generated warning message via the device in the vehicle. This allows a detailed evaluation of the driving ability of the elderly to determine the limits of safe driving and enables the elderly to proactively consider surrendering their driver's licenses.

[0095] The "in-vehicle device" refers to a device that monitors the driving behavior of seniors in real time and collects location information, speed data, lane identification data, and sudden braking data. This device may include sensors, cameras, GPS receivers, etc.

[0096] The "Central Processing Unit" is a computer system that analyzes received driving data and performs safety evaluations. This system includes a server, database, and analysis algorithms.

[0097] "Real-time monitoring" means that data is collected continuously while the vehicle is moving, creating an instantaneous record of driving behavior.

[0098] "Location information" refers to the vehicle's current geographic coordinate data obtained from a GPS sensor.

[0099] "Speed ​​data" refers to data on the instantaneous travel speed obtained from the vehicle's speedometer.

[0100] "Lane identification data" refers to data about the lane in which a vehicle is traveling, obtained using a camera sensor mounted on the vehicle and image processing algorithms.

[0101] "Sudden braking data" is data detected using an acceleration sensor when the vehicle suddenly brakes.

[0102] "Data buffering" is the process of temporarily storing collected data and preparing it for transmission to other systems as needed.

[0103] A "proprietary algorithm" is a specific computational method or procedure designed to analyze collected driving data.

[0104] "Driving safety assessment" is the process of measuring and evaluating the safety of driving behavior based on analyzed data.

[0105] "Abnormal behavior" refers to potentially dangerous driving behavior that deviates from normal driving patterns, such as frequent sudden braking or lane departure.

[0106] A "warning message" is a message issued to alert or warn the driver when abnormal behavior is detected.

[0107] "Real-time feedback to the elderly" is the process of instantly conveying analysis results and warning messages to the elderly.

[0108] "Generative AI" is a system that uses artificial intelligence technology to automatically generate psychological support messages based on analysis results.

[0109] "Psychological support messages" are messages designed to help elderly people understand their declining driving ability and encourage them to think positively about returning their license.

[0110] This invention is a system that evaluates the driving ability of elderly people in detail and suggests that they return their license when they reach the limit of safe driving. The system's main components are a terminal installed in the vehicle and a server that analyzes the data.

[0111] Equipment configuration

[0112] Terminal

[0113] The device is installed in the vehicle and is used to monitor the driving behavior of elderly drivers in real time. The device includes the following sensors and devices:

[0114] GPS receiver: Obtains location information.

[0115] Speedometer: Captures speed data.

[0116] Camera sensor: A device for acquiring lane identification data.

[0117] Acceleration sensor: A device for detecting sudden braking.

[0118] server

[0119] The server analyzes the collected data and acts as the central processing unit.

[0120] Database: Stores the received data.

[0121] Analysis algorithm: Analyzes collected data and evaluates driving safety.

[0122] Generative AI: Generates psychological support messages based on driving evaluation.

[0123] Program processing

[0124] Data collection

[0125] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. The data is buffered internally and sent to the server at regular intervals. For example, the device obtains location information once per second and processes camera images in real time to keep the vehicle in its lane.

[0126] Data analysis

[0127] The server analyzes the data received from the device and uses specialized algorithms to evaluate driving behavior patterns and safety. The analysis includes the following elements:

[0128] Frequency of sudden braking and speeding: Count the number of times and use it to evaluate safe driving.

[0129] Lane departure detection: Analyzes the image processing results from the camera sensor and evaluates the frequency of lane departures.

[0130] Detecting Abnormal Behavior

[0131] The server detects abnormal behavior based on the analysis results. For example, it identifies abnormal driving patterns based on results such as "three instances of sudden braking," "two instances of speeding," and "one instance of lane departure." When an abnormality is detected, a warning message is automatically generated.

[0132] Sending a warning message

[0133] If the server detects abnormal behavior, it generates a warning message and sends it to the terminal. The terminal uses a display device to notify the driver of this warning message. For example, a message such as "Sudden braking is occurring frequently. Please drive safely" is displayed.

[0134] Psychological support

[0135] The AI ​​then generates a psychological support message based on the analysis results. The support message includes content to encourage elderly people to think positively about returning their driver's license. The generated message is then displayed to the user via their device.

[0136] Specific examples

[0137] When the user starts driving, the device continuously acquires location information and collects speed data. It also records any instances when the driver nearly leaves their lane or when the driver suddenly brakes. The server analyzes the data for a given day and obtains results such as "three instances of sudden braking," "two instances of speeding," and "one instance of lane departure." Based on these results, an overall safe driving score is calculated and evaluated. Based on the analysis results, the server generates a warning message that is notified to the user. In addition, the AI ​​generates psychological support messages, offering advice to the user on improving their driving ability or suggesting that they consider surrendering their license in a positive manner.

[0138] Prompt Sentence Examples

[0139] "Data has been collected showing that users frequently brake suddenly while driving. Analyze this data and generate a message to advise users on safe driving and suggest that they surrender their license."

[0140] Thus, the invention provides a holistic system for properly assessing the driving ability of elderly people and improving road safety.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Step 1:

[0143] The terminal collects the vehicle's GPS data, speed data, lane identification data and sudden braking data in real time.

[0144] Inputs: Data from GPS sensor, speedometer, camera sensor, and accelerometer.

[0145] Data processing: Each data is converted into a specified format and a timestamp is added.

[0146] Specific behavior:

[0147] The device obtains its current location information from the GPS module every second.

[0148] Speed ​​information is also obtained from the speedometer every second and the current speed is recorded.

[0149] The camera sensor captures images at regular intervals and the image processing algorithm analyzes the possibility of lane departure.

[0150] If the acceleration sensor detects sudden braking, it records the data.

[0151] Output: Collected data (location, speed, lane identification, and hard braking).

[0152] Step 2:

[0153] The terminal buffers the collected data and periodically transmits it to the server.

[0154] Input: Data collected in step 1.

[0155] Data processing: Buffer data at regular intervals (e.g., every minute).

[0156] Specific behavior:

[0157] Data is temporarily stored in the device's internal memory.

[0158] The buffered data is collected at set intervals and sent to the server via the communication module when it is ready.

[0159] Output: The data sent to the server.

[0160] Step 3:

[0161] The server analyzes the data received from the terminal.

[0162] Input: Data sent from the device (location, speed, lane identification, and emergency braking).

[0163] Data processing: Received data is synchronized on the same timeline and converted into an analyzable format.

[0164] Specific behavior:

[0165] The server stores the data in a database and applies an analysis algorithm.

[0166] Data from different sensors will be integrated on the same time axis to create a dataset for analysis.

[0167] Output: A dataset for analysis.

[0168] Step 4:

[0169] The server uses an analytical algorithm to evaluate driving behavior patterns.

[0170] Input: The analysis dataset created in step 3.

[0171] Data processing: Analysis of driving behavior patterns and safety evaluation.

[0172] Specific behavior:

[0173] The analysis algorithm counts the frequency of sudden braking and speeding and assesses the possibility of lane departure.

[0174] The evaluation results of each driving behavior are combined to calculate an overall safe driving score.

[0175] Output: Evaluation results of driving behavior patterns.

[0176] Step 5:

[0177] The server detects abnormal behavior based on the evaluation results of driving behavior patterns and generates a warning message.

[0178] Input: Evaluation results of driving behavior patterns in step 4.

[0179] Data processing: Identify abnormal behavior from the evaluation results and generate warning messages.

[0180] Specific behavior:

[0181] If abnormal driving patterns (e.g., frequent sudden braking or frequent lane departures) are detected, a warning message will be generated based on this.

[0182] Sends a warning message to the terminal.

[0183] Output: The warning message generated.

[0184] Step 6:

[0185] The terminal notifies the user of the warning message received from the server.

[0186] Input: The warning message sent by the server.

[0187] Data processing: Converting messages into a format that is easy for users to understand.

[0188] Specific behavior:

[0189] A warning message is notified to the user in real time using the display device of the terminal.

[0190] For example, a message such as "Sudden braking is occurring frequently. Please drive safely" may be displayed.

[0191] Output: The warning message posted.

[0192] Step 7:

[0193] The generation AI generates a psychological support message based on the evaluation results of driving behavior and sends it to the device.

[0194] Input: Evaluation results of driving behavior patterns.

[0195] Data processing: Generate psychological support messages based on the analysis results.

[0196] Specific behavior:

[0197] Generative AI automatically generates positive messages to reduce the psychological burden on the elderly.

[0198] The message is sent to the terminal, which displays it to the user.

[0199] For example, create a message such as, "Based on your recent driving data, we would like to provide you with advice on how to continue driving safely."

[0200] Output: The generated psychological support message.

[0201] In this way, each step works in conjunction with the others to carry out a series of processes from data collection to analysis, warnings, and psychological support.

[0202] (Application example 1)

[0203] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0204] The driving ability of elderly people can decline with age, increasing the risk of traffic accidents. However, it is difficult for elderly people to recognize this decline in their driving ability, and they often do not surrender their licenses at the appropriate time. For this reason, there is a need for a system that can evaluate elderly people's driving behavior in detail and support safe driving. Furthermore, if abnormal behavior occurs while driving and it becomes difficult to continue driving safely, safety measures such as switching to autonomous driving mode are necessary. There is also a need for support to help elderly people think positively about surrendering their licenses without feeling any psychological burden.

[0205] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0206] In this invention, the server includes a means for analyzing collected driving data and evaluating driving safety, a means for detecting abnormal driving behavior, and a means for switching to autonomous driving mode when abnormal behavior is detected. This allows elderly drivers to drive safely by immediately switching to autonomous driving mode when abnormal behavior is detected. Furthermore, by using generative AI to provide psychological support to elderly drivers and generate prompts that positively encourage them to switch to safe driving mode, it is possible to protect the elderly's self-esteem, encourage them to be aware of their declining driving ability, and support them in surrendering their licenses at the appropriate time.

[0207] The term "elderly" refers to people over a certain age, generally including those aged 65 and over.

[0208] "Driving behavior" refers to a series of actions and behaviors when operating a vehicle, and specifically includes acceleration, deceleration, steering, lane keeping, etc.

[0209] "Real-time" means that data and information processing and feedback occurs immediately.

[0210] "Monitoring" refers to the continuous observation of a specific object or situation to detect abnormalities or changes.

[0211] "Location Information" means data indicating the current geographic coordinates of a vehicle or person.

[0212] "Speed ​​information" is data indicating the moving speed of an object, and in the case of a vehicle, indicates the vehicle speed.

[0213] "Collection" refers to the act of obtaining the target data or information using various sensors and devices.

[0214] "Analysis" means examining the data and information obtained in detail and deriving its meaning and trends.

[0215] "Safety assessment" refers to the analysis and evaluation of a vehicle to determine whether it is safe to operate the vehicle based on driving behavior.

[0216] "Abnormal behavior" refers to actions or behavior that are different from or deviate from normal, and in driving it refers to sudden braking or lane departure.

[0217] "Warning" means a message or signal that calls attention to a potential danger or abnormality.

[0218] "Informing" refers to the act of conveying specific information or data to a target person.

[0219] "Autonomous driving mode" refers to systems and functions that allow a vehicle to operate automatically without human intervention.

[0220] "Lane maintenance" refers to the act of a vehicle continuing to travel steadily within the lane in which it is currently traveling.

[0221] "Lane departure" refers to the phenomenon in which a vehicle unintentionally leaves the lane in which it is currently traveling.

[0222] "Sudden braking" refers to the act of suddenly braking the vehicle, deviating from normal driving conditions.

[0223] "Feedback" refers to returning information to an original subject based on the results of an action or process in order to improve or adjust it.

[0224] "Memory ability" refers to the ability to properly retain past information and retrieve it when needed.

[0225] "Judgment" refers to the ability to take appropriate actions and decisions based on the situation and information.

[0226] "Generative AI" refers to a system that uses artificial intelligence technology to automatically generate text or messages.

[0227] "Psychological support" means assistance to provide a sense of psychological security and stability.

[0228] A "prompt" is a message that the generation AI displays to the user to encourage voluntary action.

[0229] A specific system configuration for implementing the present invention will be described. The overall operation of the system that supports safe driving for elderly people and switches to automatic driving mode when an abnormality is detected will be described.

[0230] Overall system configuration

[0231] The system consists of the following main components:

[0232] 1. Terminal: A device installed inside the vehicle that incorporates various sensors such as a GPS module, speed sensor, and brake sensor. This terminal monitors the driving behavior of elderly people in real time and transmits the collected location and speed information to a server.

[0233] 2. Server: This is the central processing unit that receives data sent from the terminals. The server analyzes the driving data and executes dedicated algorithms to evaluate safety. It is also responsible for detecting abnormal behavior and switching to autonomous driving mode.

[0234] 3. Generative AI model: This is built into the server and generates psychological support messages based on the analysis of driving data, providing prompts that reduce the elderly's resistance to switching to safe driving mode at the appropriate time.

[0235] Data collection

[0236] The terminal is installed inside the vehicle and collects the following data in real time:

[0237] Location information: The current location of the vehicle is obtained using the GPS module.

[0238] Speed ​​information: Measure the vehicle's moving speed through a speed sensor.

[0239] Driving behavior: Equipped with sensors that monitor driving behavior such as sudden braking and lane departure.

[0240] Data analysis

[0241] The server receives the collected driving data and evaluates the driving safety using a dedicated analysis algorithm. This analysis includes the following steps:

[0242] Sudden braking detection: Analyzes data from the brake sensor to determine whether sudden braking occurs frequently.

[0243] Lane departure detection: Using cameras and sensors, counts the number of times the vehicle deviates from its lane.

[0244] Overall evaluation: Based on the above data, a driving safety score is calculated and abnormal behavior is detected.

[0245] Switching to autonomous driving mode

[0246] If abnormal behavior is detected, the server sends an instruction to the device to switch to autonomous driving mode. This function allows the device to immediately switch to autonomous driving mode when safe driving becomes difficult.

[0247] Psychological support

[0248] The generative AI model generates prompts to provide psychological support to the elderly when abnormal behavior is detected. For example, the following message is displayed:

[0249] Generator prompt:

[0250] Your recent driving data has revealed that you have experienced frequent sudden braking. For your safety, we recommend switching to Autopilot mode. This feature will allow you to continue your journey with peace of mind.

[0251] In this way, a system is realized that monitors elderly driving in real time and provides a series of functions to ensure safety.

[0252] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0253] Step 1:

[0254] The device collects data in real time from GPS modules, speed sensors, brake sensors, etc.

[0255] Input: Vehicle location, speed, and emergency braking data.

[0256] Output: The raw data collected.

[0257] Specific operation: The GPS module acquires location information every second, the speed sensor measures the vehicle's speed, and the brake sensor detects sudden braking. This data is stored in the terminal.

[0258] Step 2:

[0259] The terminal transmits the collected driving data to the server at regular intervals.

[0260] Input: Raw data collected.

[0261] Output: Driving data sent to the server.

[0262] Specific operation: The terminal compiles the collected data into packets at regular intervals and sends them to the server via the network.

[0263] Step 3:

[0264] The server receives the driving data and runs an analysis algorithm to evaluate the safety of the driving.

[0265] Input: Driving data sent from the terminal.

[0266] Output: Driving safety rating score.

[0267] Specific operation: The server stores the received data in a database, analyzes the number of sudden braking and lane departures, and then calculates an overall safe driving score based on the analysis results.

[0268] Step 4:

[0269] The server detects abnormal behavior and decides whether to switch to autonomous driving mode.

[0270] Input: Driving safety rating score.

[0271] Output: Instruction to switch to automatic driving mode.

[0272] Specific operation: If the safe driving score falls below a certain standard, the server detects abnormal behavior and sends an instruction to the device to switch to autonomous driving mode.

[0273] Step 5:

[0274] The device receives instructions from the server and switches to autonomous driving mode.

[0275] Input: Instruction to switch to autonomous driving mode.

[0276] Output: Start of Autopilot mode.

[0277] Specific operation: The terminal activates the autonomous driving system and switches operation to autonomous driving mode, allowing the vehicle to continue driving without human intervention.

[0278] Step 6:

[0279] The server uses the generative AI model to generate psychological support messages for the elderly.

[0280] Input: Analysis results of abnormal behavior.

[0281] Output: The generated prompt statement.

[0282] Specific operation: Based on the results of the abnormal behavior analysis, the server uses the generative AI model to generate positive instructions and support messages for the elderly. For example, it generates a message such as, "Based on your recent driving data, we have noticed that you have been braking frequently. For your safety, we recommend switching to autonomous driving mode. This function will allow you to continue traveling safely, so please rest assured."

[0283] Step 7:

[0284] The generated psychological support message is displayed to the user through the terminal.

[0285] Input: The generated prompt statement.

[0286] Output: The message that is displayed to the user.

[0287] Specific operation: The terminal displays the generated message obtained from the server on the display, allowing the user to check the message.

[0288] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0289] This invention relates to a system that closely monitors the driving behavior of elderly people, detects any decline in driving ability early, and provides appropriate feedback. In particular, by combining it with an emotion engine, it is possible to support safe driving while taking into consideration the driver's emotional state.

[0290] System Configuration

[0291] 1. Terminal: A device installed in a vehicle that collects GPS data, speed data, lane identification data, and sudden braking data in real time. The terminal also incorporates sensors such as a camera and microphone, and collects emotional data by analyzing the driver's facial expressions and voice.

[0292] 2. Server: A central processing unit that receives collected driving data and emotional data and analyzes them using dedicated algorithms. Based on the analysis results, the server detects abnormal behavior and emotional states and generates appropriate feedback.

[0293] 3. Generative AI: This will be installed on the server and generate psychological support messages for the elderly based on the analysis results. This will reduce the user's psychological burden, encourage them to surrender their driver's license in a positive manner, and prevent damage to their self-esteem.

[0294] 4. Emotion Engine: The emotion engine recognizes and analyzes the user's emotional state in real time and makes a comprehensive evaluation in conjunction with driving data.

[0295] Program processing

[0296] Collecting and analyzing driving behavior and emotion data

[0297] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. It also uses a camera and microphone to collect the driver's facial expressions and voice data, which it then sends to the emotion engine.

[0298] Examples:

[0299] When the user starts driving, the device collects real-time information on location, speed, and lane, while also using a facial recognition camera and microphone to analyze the user's facial expressions and tone of voice to collect emotional data.

[0300] Data reception and analysis

[0301] The server inputs the received data into an analytical algorithm, which analyzes driving behavior patterns and detects abnormal behavior. The emotion engine also analyzes the transmitted emotional data and evaluates the user's emotional state, such as whether they are stressed or relaxed.

[0302] Examples:

[0303] The server analyzes the driving data for the day and obtains the results of "3 instances of sudden braking," "2 instances of speeding," and "1 instance of lane departure." At the same time, the emotion engine evaluates that "the user's stress level is high while driving."

[0304] Detecting and alerting abnormal behavior and emotional states

[0305] The server then performs a comprehensive evaluation of the abnormal behavior and the user's emotional state based on the analysis results. For example, if the emotion engine detects a high frequency of sudden braking and the user is under stress, it generates a warning message saying, "You are braking frequently and appear to be stressed. We recommend that you drive safely and relax."

[0306] Examples:

[0307] If the server determines based on the analysis results that "the vehicle is frequently drifting out of its lane and the user is feeling high stress," it generates a corresponding warning message and sends it to the device.

[0308] Comprehensive feedback to the elderly

[0309] The AI ​​generates psychological support messages for elderly people based on driving behavior data and emotional data. This helps users understand their own driving ability and emotional state, encouraging them to accept surrendering their license in a positive manner. The generated messages are displayed to the user via their device.

[0310] Examples:

[0311] The device displays a feedback message saying, "Based on your recent driving data and emotional state, we'll provide you with advice to help you continue driving safely. Take short breaks to reduce stress."

[0312] conclusion

[0313] The system of this invention ensures traffic safety throughout society by providing a detailed assessment of the driving ability and emotional state of elderly people and identifying the limits of safe driving. By utilizing generative AI and an emotion engine, it encourages elderly people to surrender their licenses at the appropriate time while protecting their self-esteem. Furthermore, by collaborating with local governments, the system contributes to solving social issues by providing support for daily life after license surrender.

[0314] The processing flow will be explained below.

[0315] Step 1:

[0316] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. The device acquires location information every second and also records the vehicle's speed and acceleration. The device also has a built-in camera and microphone, which are used to collect the driver's facial expressions and voice data.

[0317] Step 2:

[0318] The data collected by the device is sent to a server at regular intervals (e.g., every minute). The data sent includes location information, speed information, lane identification information, records of sudden braking, facial expression data, and voice data.

[0319] Step 3:

[0320] The server inputs the received data into an analytical algorithm, which then analyzes driving behavior patterns based on this data to detect abnormal behavior, analyzes the emotional data sent by the emotion engine, and evaluates the user's emotional state.

[0321] Step 4:

[0322] The server evaluates the driver's safe driving based on the analysis of driving behavior data and emotional data, and calculates a safe driving score by comprehensively evaluating factors such as the number of sudden braking attempts, speeding, lane departure frequency, and emotional states recognized from facial expressions and voice.

[0323] Step 5:

[0324] The server generates a warning message based on the analysis results. For example, if the emotion engine detects a high frequency of sudden braking and the user is under stress, it will create a warning message such as, "You are braking frequently. You seem to be under stress. We recommend that you drive carefully and relax."

[0325] Step 6:

[0326] The server generates a warning message and sends it to the terminal, which then receives it and notifies the user via an in-car display or voice message.

[0327] Step 7:

[0328] The AI ​​then generates a psychological support message based on the analysis results and warning message. It creates a message that encourages the user to surrender their license while maintaining their self-esteem. For example, it makes a positive suggestion such as, "Why not get a professional diagnosis to ensure safe driving?"

[0329] Step 8:

[0330] The AI ​​generates and sends psychological support messages to the device, which displays the messages to provide the driver with a sense of psychological security and encourage them to improve their driving behavior.

[0331] Step 9:

[0332] The device provides the user with a comprehensive driving evaluation and feedback. It displays detailed analysis results of driving behavior and emotional data, and suggests specific improvements and changes to driving habits. For example, it provides specific advice such as, "There are many cases of sudden braking at intersections, so try to slow down when entering intersections."

[0333] Step 10:

[0334] Users will receive feedback and work to improve their driving skills and emotional state. If necessary, we will work with local governments to provide support for life after surrendering their license.

[0335] Example 2

[0336] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0337] The decline in driving ability among the elderly increases the risk of traffic accidents, but currently it is difficult to assess and respond to this decline. Furthermore, unilateral warnings and guidance without considering the driver's emotional state and psychological burden can damage the elderly's self-esteem. This can lead to problems such as the risk of continuing to drive and resistance to surrendering their licenses. Therefore, future solutions that support safe driving and reduce psychological burden are needed.

[0338] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: a means for monitoring the driving behavior of the elderly in real time and collecting location information, speed information, lane identification information, and sudden braking information; a means for analyzing the collected data and evaluating the driving safety; a means for acquiring the driving behavior data as well as the driver's facial expression and voice data to analyze the emotional state; a means for detecting abnormal behavior and providing a warning based on the results of the driving safety evaluation and the emotional state analysis; and a means for notifying the elderly of the generated warning. This not only enables support for safe driving but also provides appropriate feedback taking into account the driver's emotional state, making it possible to reduce the psychological burden on the elderly and encourage them to continue driving safely and surrender their licenses.

[0339] "Elderly driving behavior" refers to the vehicle operation methods and driving patterns of elderly drivers, including specific behaviors such as speed, lane changes, and braking.

[0340] "Real-time" refers to the near-instant processing and reflection of data and events as they occur, with almost no delay.

[0341] "Location Information" refers to the current geographic location of a vehicle or driver, collected through technologies such as GPS.

[0342] "Speed ​​information" is data relating to the vehicle's moving speed, and is generally obtained from the vehicle's speedometer or sensors.

[0343] "Lane identification information" is data relating to the lane of the road on which the vehicle is traveling, and includes information for identifying driving behavior such as lane departure.

[0344] "Sudden braking information" refers to data on when quick and strong braking is applied while driving, and is used to evaluate safe driving.

[0345] "Facial expression data" refers to the collection of data by using sensors such as cameras to recognize the driver's facial expressions.

[0346] "Voice data" refers to collecting the driver's speech and tones using a voice input device such as a microphone and analyzing that data.

[0347] "Emotional state analysis" refers to the process of assessing the driver's emotional state (e.g., stress or relaxation) based on collected facial expression and voice data.

[0348] "Abnormal behavior" refers to actions or patterns that are not considered safe driving, such as sudden braking, speeding, and lane departure.

[0349] "Providing a warning" means alerting a driver to a safety risk or abnormal behavior.

[0350] "Feedback" refers to providing information and advice to drivers based on the analysis results, encouraging them to improve their driving behavior.

[0351] This invention relates to a system that closely monitors the driving behavior of elderly people, detects any decline in driving ability early, and provides appropriate feedback. In particular, by combining it with an emotion engine, it is possible to support safe driving while taking into consideration the driver's emotional state.

[0352] System Configuration

[0353] Terminal

[0354] The terminal is installed in the vehicle and collects GPS data, speed data, lane identification data, and sudden braking data in real time. The terminal is equipped with sensors such as a camera and microphone, which can analyze the driver's facial expressions and voice and collect emotional data. Specific hardware includes a GPS sensor, speed sensor, facial recognition camera, microphone, lane identification camera, and acceleration sensor.

[0355] server

[0356] The server is a central processing unit that receives collected data sent from the terminal and analyzes it using analytical algorithms. The server analyzes driving data and emotional data, evaluates abnormal behavior and the driver's emotional state, and generates appropriate feedback. Specific software used includes data analysis algorithms and an emotion engine.

[0357] Generation AI

[0358] The AI ​​generator is installed on a server and generates psychological support messages for the elderly based on the analysis results. The AI ​​generator can create messages that reduce the psychological burden on the elderly and encourage them to surrender their driver's licenses in a positive manner without damaging their self-esteem.

[0359] Emotion Engine

[0360] The emotion engine recognizes and analyzes the user's emotional state in real time, and performs a comprehensive evaluation in conjunction with driving data. The emotion engine analyzes facial expression data and voice data, and can evaluate stress, relaxation state, etc.

[0361] Program processing explanation

[0362] Data collection

[0363] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. At the same time, it uses the device's camera and microphone to collect the driver's facial expression and voice data, which are then sent to the emotion engine. For example, when a user starts driving, the device collects location, speed, and lane information in real time, and uses the facial recognition camera and microphone to analyze the user's facial expression and tone of voice to collect emotion data.

[0364] Data analysis

[0365] The server receives the driving data and emotional data sent from the device and begins processing it using an analytical algorithm. The server analyzes driving behavior patterns and detects whether there are any abnormal behaviors. It also analyzes the emotional data sent by the emotion engine to evaluate whether the user is stressed or relaxed. As a specific example, if a day's driving data is analyzed and the results show "three instances of sudden braking," "two instances of speeding," and "one instance of lane departure," a comprehensive evaluation is made based on these results.

[0366] Generate feedback

[0367] The AI ​​generates psychological support messages for users based on driving behavior data and emotional data. The generated messages are presented to the user via the device. For example, the AI ​​could generate a message such as, "Based on your recent driving data and emotional state, we would like to provide you with advice on how to continue driving safely. Please take a short break to reduce stress," and the message would be displayed on the device.

[0368] Examples of concrete examples and prompts

[0369] For example, if the emotion engine detects high stress levels due to frequent sudden braking while driving, it will generate a warning message stating, "You are braking frequently and appear to be stressed. We recommend that you drive safely and relax," and send it to the device. An example of a prompt is, "Please generate advice for safe driving based on driving data of elderly people."

[0370] This system not only supports safe driving, but also provides appropriate feedback that takes into account the driver's emotional state, reducing the psychological burden on elderly people and encouraging them to continue driving safely and surrender their licenses.

[0371] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0372] Step 1: Collecting driving and emotion data

[0373] The device is installed in the vehicle and collects real-time GPS data, speed data, lane identification data, and sudden braking data, and also uses a camera and microphone to collect the driver's facial expressions and voice data.

[0374] Input: User's driving behavior, facial expressions, and voice

[0375] Output: Driving data and emotion data (position information, speed information, lane identification information, sudden braking information, facial expression data, voice data)

[0376] Specific behavior:

[0377] When the user starts driving, the device instantly acquires location information using the GPS sensor, measures speed using the speed sensor, monitors lane departure using the lane identification camera, collects sudden braking data using the acceleration sensor, analyzes the driver's facial expressions using the facial recognition camera, and collects voice tones using the microphone.

[0378] Step 2: Sending data

[0379] The device sends the collected driving data and emotion data to a server.

[0380] Input: Collected driving data and emotion data

[0381] Output: Sending data from the device to the server

[0382] Specific behavior:

[0383] The device transmits location, speed, lane departure, sudden braking, facial expressions, and voice data to the server at regular intervals, for example, every minute.

[0384] Step 3: Receiving and analyzing data

[0385] The server receives the transmitted driving data and emotion data and analyzes the data using an analytical algorithm.

[0386] Input: Driving data and emotion data sent from the device

[0387] Output: Driving behavior patterns, presence or absence of abnormal behavior, and evaluation results of emotional state

[0388] Specific behavior:

[0389] The server distributes the received data to the analysis module, and the driving data is analyzed using a driving pattern recognition algorithm to detect the frequency of sudden braking, speeding, and lane departure.The emotion engine evaluates the user's stress and relaxation state based on facial expression and voice data.

[0390] Step 4: Detecting abnormal behavior and emotional states and generating alerts

[0391] The server comprehensively evaluates abnormal behavior and emotional state based on the analysis results and generates appropriate warnings.

[0392] Input: Analyzed driving and emotion data

[0393] Output: Appropriate warning message

[0394] Specific behavior:

[0395] If the server determines that "sudden braking is occurring frequently and the emotion engine has detected high stress," it will generate a warning message stating, "You are braking frequently and appear to be under stress. We recommend that you drive safely and relax," and send it to the device.

[0396] Step 5: Generate a psychological support message

[0397] The generative AI generates psychological support messages for the elderly based on driving behavior data and emotional data.

[0398] Input: driving behavior data, emotion data, analysis results

[0399] Output: Psychological support message

[0400] Specific behavior:

[0401] The AI ​​generates a message saying, "Based on your recent driving data and emotional state, we'd like to provide you with advice on how to continue driving safely. Take a short break to reduce stress," and the device displays it to the user.

[0402] In this way, the present invention comprehensively evaluates the driving behavior and emotional state of elderly people and supports safe and secure driving.

[0403] (Application example 2)

[0404] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0405] The driving behavior of elderly people is often a safety concern due to the decline in physical and cognitive abilities that comes with aging. While abnormal behavior, such as sudden braking and lane departure, is particularly common, drivers themselves may have difficulty noticing these changes. Furthermore, because emotional stress also affects driving stability, understanding the psychological state of drivers while driving and providing appropriate feedback is essential. Current driving assistance systems lack a mechanism for comprehensively evaluating these complex factors and providing feedback in real time. Therefore, a system is needed that simultaneously monitors driving behavior and emotional state and provides comprehensive support to enable elderly people to continue driving with peace of mind.

[0406] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0407] In this invention, the server includes a means for collecting and analyzing driving behavior data and emotional data in real time, a means for evaluating the psychological state of the driver and generating appropriate feedback, and a means for comprehensively evaluating the collected data and detecting abnormal behavior, thereby enabling simultaneous monitoring of the driving behavior and emotional state of elderly people and providing comprehensive support to ensure safety and psychological stability.

[0408] "Driving behavior data" refers to location information, speed data, lane identification data, and sudden braking data obtained from a vehicle while an elderly person is driving.

[0409] "Emotional data" refers to data obtained by analyzing the psychological state of the driver, as determined from their facial expressions, voice, etc.

[0410] "Psychological state" refers to the mental state, including emotions such as stress and relaxation, that a driver feels while driving.

[0411] "Feedback" refers to information such as warnings and advice provided to the driver based on the analysis results.

[0412] "Abnormal behavior" refers to actions that deviate from normal safe driving, such as sudden braking or lane departure.

[0413] "Real-time" refers to data collection, analysis, and notification occurring almost simultaneously within a very short period of time.

[0414] "Analysis means" refers to methods and devices for evaluating driving behavior and emotional state based on collected data and determining driving safety and psychological stability.

[0415] "Means for analyzing the driver's facial expressions and voice" refers to methods and devices that use cameras and microphones to collect the driver's facial expressions and tone of voice and identify their emotional state.

[0416] A system for monitoring elderly driving behavior and emotional state

[0417] This invention is a system that provides safe driving and psychological support by closely monitoring the driving behavior of elderly people and analyzing their emotional state. The system consists of a terminal installed in the vehicle, a server that analyzes the data, a generative AI, and an emotion engine.

[0418] System Components

[0419] 1. Device:

[0420] The device is installed in the vehicle and collects GPS data, speed data, lane identification data, and sudden braking data in real time. It also has a built-in camera and microphone to detect the driver's facial expressions and voice, allowing it to obtain driving behavior data and emotional data.

[0421] 2. Server:

[0422] The server receives and analyzes the collected driving behavior data and emotion data. It uses a dedicated algorithm to analyze driving behavior patterns and detect abnormal behavior. It also analyzes the emotion data sent by the emotion engine to evaluate the driver's emotional state. Specifically, it uses Python®-based analysis algorithms (e.g., Scikit-learn, TENSORFLOW®) and emotion engines (e.g., IBM Watson®, Microsoft® Azure® Cognitive Services).

[0423] 3. Generation AI:

[0424] The AI ​​generates psychological support messages for elderly people based on driving behavior data and emotion data on a server. These messages are used to reduce the psychological burden on drivers and promote safe driving.

[0425] 4. Emotion Engine:

[0426] The emotion engine recognizes and analyzes the driver's emotional state in real time, and this data is later combined with driving behavior data for a comprehensive evaluation.

[0427] Data collection and analysis

[0428] Once the driver starts driving, the device collects real-time GPS data, speed data, lane identification data, and sudden braking data, and also uses a facial recognition camera and microphone to collect the driver's facial expressions and voice to generate emotion data, thereby monitoring both driving behavior and psychological state.

[0429] The server receives the data sent from the device and inputs it into an analytical algorithm. It analyzes driving behavior patterns and emotional states to detect abnormal behavior and high stress levels. This allows for an evaluation of driving safety and psychological stability.

[0430] Feedback generation and notification

[0431] The AI ​​then generates a feedback message for the elderly based on the analysis results received from the server. This message provides appropriate advice to the driver and promotes safe driving. For example, a message might be generated that reads, "You are braking suddenly frequently and feeling stressed. Take a short break to relax."

[0432] The device then generates and sends feedback messages to the driver in real time, helping the driver understand not only their driving behavior but also their emotional state.

[0433] Examples and prompts

[0434] Examples:

[0435] When an elderly person begins driving, the vehicle's various sensors, cameras, and microphones collect data. The camera and microphone detect frequent sudden braking and an increase in the driver's stress level. The data is analyzed on the server, and the AI ​​generates a feedback message saying, "Drive safely and take short breaks to relax." This message is displayed in real time on the driver's smartphone.

[0436] Example prompt sentence:

[0437] Develop a system that analyzes elderly driving behavior and emotion data, detects sudden braking and increased stress levels, and provides real-time feedback. Use IBM Watson as the emotion engine and Scikit-learn for data analysis.

[0438] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0439] Step 1:

[0440] Data collection

[0441] The device collects real-time GPS data, speed data, lane identification data, and sudden braking data when the driver starts the vehicle. It also uses a camera and microphone to collect the driver's facial expressions and voice data, thereby obtaining driving behavior data and emotional data as input.

[0442] Step 2:

[0443] Data transmission

[0444] The device transmits the collected driving behavior data and emotion data to the server via Bluetooth or Wi-Fi. The transmitted data is processed in real time, so it is desirable to have no delay. Once the data transmission is complete, the transmitted data becomes input to the server.

[0445] Step 3:

[0446] Data analysis

[0447] The server analyzes the received driving behavior data and emotion data. Specifically, it uses Python-based analysis algorithms (e.g., Scikit-learn, TensorFlow) to analyze driving behavior patterns and determine whether or not there are any abnormal behaviors. It also evaluates the driver's stress level and relaxation state based on the data sent by the emotion engine. The analysis results are saved as intermediate output.

[0448] Step 4:

[0449] Detecting Abnormal Behavior

[0450] The server detects abnormal behavior based on the analysis results. For example, if the driver brakes frequently or leaves the lane frequently, this is judged to be abnormal behavior. In parallel, if the emotion engine detects a high stress level in the driver, this is also recognized as an abnormality. When abnormal behavior is detected, the corresponding evaluation result is output.

[0451] Step 5:

[0452] Feedback Message Generation

[0453] Based on the analysis results, the server uses the generation AI to generate a feedback message for the elderly. Here, the driving behavior and emotional state are comprehensively evaluated, and a message is generated that provides appropriate advice to the driver. For example, a message such as "You are braking suddenly frequently and are feeling stressed. Take a short break to relax" may be generated. This is the final output.

[0454] Step 6:

[0455] Feedback Notification

[0456] The device receives the feedback messages generated by the generation AI and notifies the driver in real time via a smartphone or in-car display. Once the notification is complete, the driver can understand their own driving behavior and emotional state and take appropriate action. This provides feedback to the driver as the final output.

[0457] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0458] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0459] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0460] [Second embodiment]

[0461] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0462] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0463] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0464] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0465] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0466] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0467] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0468] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0469] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0470] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0471] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0472] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0473] This invention relates to a system that evaluates the driving ability of elderly people in detail and suggests that they surrender their license when they reach the limit of safe driving. The system mainly consists of a terminal installed in the vehicle and a server that analyzes the data.

[0474] System Configuration

[0475] 1. Terminal: A device installed inside the vehicle that collects GPS and speed data in real time. The terminal also has the function of closely monitoring the driving behavior of elderly drivers using multiple sensors, including cameras, speedometers, and accelerometers.

[0476] 2. Server: A central processing unit that receives collected data and analyzes it using a dedicated algorithm. Based on the analysis results, the server detects abnormal behavior and generates a warning if it determines that safe operation cannot be continued.

[0477] 3. Generative AI: This is installed on the server and generates psychological support messages for the elderly based on the analysis results. The Generative AI reduces the user's psychological burden and encourages them to surrender their driver's license in a positive manner without damaging their self-esteem.

[0478] Program processing

[0479] Collecting and analyzing driving behavior

[0480] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. This data is sent to a server at regular intervals. For example, the device acquires location information every second and processes camera images for lane keeping. This allows for detailed recording of elderly driving behavior.

[0481] Examples:

[0482] Once the user starts driving, the device will continuously acquire location information, collect speed data, and record any lane departures or sudden braking.

[0483] Data reception and analysis

[0484] The server analyzes the received data using a dedicated algorithm. The server analyzes driving behavior patterns and counts the number of times the driver habitually brakes suddenly or deviates from their lane, etc. This allows the overall driving safety to be evaluated.

[0485] Examples:

[0486] The server analyzes the data for a given day and obtains the results of "3 instances of sudden braking," "2 instances of speeding," and "1 instance of lane departure." Based on these results, an overall safe driving score is calculated and evaluated.

[0487] Detecting abnormal behavior and generating alerts

[0488] The server detects abnormal behavior based on the analysis results. If the standard for safe driving is exceeded, a warning message is generated. For example, if the frequency of sudden braking is too high, the server generates a warning saying, "Safe driving is difficult due to frequent sudden braking."

[0489] Examples:

[0490] The server analyzes the user's driving data and, if it determines that the user is frequently drifting out of their lane, it generates a warning message and sends it to the device.

[0491] Psychological support for the elderly

[0492] Based on the analysis results, the AI ​​generates a psychological support message for the elderly. This helps them understand their declining driving ability and encourages them to positively consider returning their license. The generated message is displayed to the user via the device.

[0493] Examples:

[0494] When a user receives a driving evaluation message, the device will display, "Based on your recent driving data, we would like to provide you with some advice to help you continue driving safely," and provide specific suggestions for improvement.

[0495] conclusion

[0496] The system of this invention ensures traffic safety throughout society by providing a detailed assessment of the driving ability of elderly people and identifying the limits of safe driving. It also uses generative AI to provide psychological support, helping elderly people to think positively about returning their driver's licenses while preserving their self-esteem. By collaborating with local governments, the system also provides support for daily life after license surrender, contributing to the resolution of social issues.

[0497] The processing flow will be explained below.

[0498] Step 1:

[0499] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. The device obtains location information every second and also records the vehicle's speed and acceleration.

[0500] Step 2:

[0501] The device sends the collected data to a server at regular intervals (e.g., every minute). The data sent includes location information, speed information, lane identification information, and records of sudden braking.

[0502] Step 3:

[0503] The server inputs the received data into an analytical algorithm, which analyzes driving patterns based on this data and detects abnormal behavior.

[0504] Step 4:

[0505] The server analyzes the results of abnormal behavior detection and evaluates the safe driving, calculating a safe driving score based on a comprehensive evaluation of factors such as the number of sudden braking attempts, frequency of lane departures, and whether or not the driver exceeded the speed limit.

[0506] Step 5:

[0507] The server generates a warning message based on the analysis results. For example, if the frequency of sudden braking is high, it creates a warning message such as "Sudden braking is occurring frequently. Please drive safely."

[0508] Step 6:

[0509] The server generates a warning message and sends it to the terminal, which then receives it and notifies the user via an in-car display or voice message.

[0510] Step 7:

[0511] The AI ​​generates a psychological support message based on the analysis results and warning message, creating a message that encourages the user to surrender their license while maintaining their self-esteem.

[0512] Step 8:

[0513] The AI ​​generates and sends psychological support messages to the device, which displays the messages to provide the user with a sense of psychological security and encourage them to improve their driving behavior.

[0514] Step 9:

[0515] The device provides users with a comprehensive driving evaluation and feedback, displays detailed analysis results of their driving behavior, and suggests specific improvements and changes to their driving habits.

[0516] Step 10:

[0517] Users will receive feedback and strive to improve their driving skills. If necessary, we will work with local governments to provide support for life after surrendering their license.

[0518] Example 1

[0519] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0520] The decline in driving ability among the elderly increases the risk of traffic accidents, and this problem needs to be addressed. However, conventional methods make it difficult for elderly people to recognize their own decline in driving ability and voluntarily return their licenses, which could have a significant impact on traffic safety throughout society. Another issue is the psychological burden that the process of returning a license places on elderly people.

[0521] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0522] In this invention, the server includes a device installed in the vehicle that monitors the driving behavior of the elderly in real time and collects location information, speed data, lane identification data, and sudden braking data, a means for buffering the collected data and periodically sending it to a central processing unit, a means for analyzing the received data with a dedicated algorithm and evaluating driving safety, a means for detecting abnormal behavior based on the evaluation of driving safety and generating a warning message, and a means for notifying the elderly of the generated warning message via the device in the vehicle. This allows a detailed evaluation of the driving ability of the elderly to determine the limits of safe driving and enables the elderly to proactively consider surrendering their driver's licenses.

[0523] The "in-vehicle device" refers to a device that monitors the driving behavior of seniors in real time and collects location information, speed data, lane identification data, and sudden braking data. This device may include sensors, cameras, GPS receivers, etc.

[0524] The "Central Processing Unit" is a computer system that analyzes received driving data and performs safety evaluations. This system includes a server, database, and analysis algorithms.

[0525] "Real-time monitoring" means that data is collected continuously while the vehicle is moving, creating an instantaneous record of driving behavior.

[0526] "Location information" refers to the vehicle's current geographic coordinate data obtained from a GPS sensor.

[0527] "Speed ​​data" refers to data on the instantaneous travel speed obtained from the vehicle's speedometer.

[0528] "Lane identification data" refers to data about the lane in which a vehicle is traveling, obtained using a camera sensor mounted on the vehicle and image processing algorithms.

[0529] "Sudden braking data" is data detected using an acceleration sensor when the vehicle suddenly brakes.

[0530] "Data buffering" is the process of temporarily storing collected data and preparing it for transmission to other systems as needed.

[0531] A "proprietary algorithm" is a specific computational method or procedure designed to analyze collected driving data.

[0532] "Driving safety assessment" is the process of measuring and evaluating the safety of driving behavior based on analyzed data.

[0533] "Abnormal behavior" refers to potentially dangerous driving behavior that deviates from normal driving patterns, such as frequent sudden braking or lane departure.

[0534] A "warning message" is a message issued to alert or warn the driver when abnormal behavior is detected.

[0535] "Real-time feedback to the elderly" is the process of instantly conveying analysis results and warning messages to the elderly.

[0536] "Generative AI" is a system that uses artificial intelligence technology to automatically generate psychological support messages based on analysis results.

[0537] "Psychological support messages" are messages designed to help elderly people understand their declining driving ability and encourage them to think positively about returning their license.

[0538] This invention is a system that evaluates the driving ability of elderly people in detail and suggests that they return their license when they reach the limit of safe driving. The system's main components are a terminal installed in the vehicle and a server that analyzes the data.

[0539] Equipment configuration

[0540] Terminal

[0541] The device is installed in the vehicle and is used to monitor the driving behavior of elderly drivers in real time. The device includes the following sensors and devices:

[0542] GPS receiver: Obtains location information.

[0543] Speedometer: Captures speed data.

[0544] Camera sensor: A device for acquiring lane identification data.

[0545] Acceleration sensor: A device for detecting sudden braking.

[0546] server

[0547] The server analyzes the collected data and acts as the central processing unit.

[0548] Database: Stores the received data.

[0549] Analysis algorithm: Analyzes collected data and evaluates driving safety.

[0550] Generative AI: Generates psychological support messages based on driving evaluation.

[0551] Program processing

[0552] Data collection

[0553] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. The data is buffered internally and sent to the server at regular intervals. For example, the device obtains location information once per second and processes camera images in real time to keep the vehicle in its lane.

[0554] Data analysis

[0555] The server analyzes the data received from the device and uses specialized algorithms to evaluate driving behavior patterns and safety. The analysis includes the following elements:

[0556] Frequency of sudden braking and speeding: Count the number of times and use it to evaluate safe driving.

[0557] Lane departure detection: Analyzes the image processing results from the camera sensor and evaluates the frequency of lane departures.

[0558] Detecting Abnormal Behavior

[0559] The server detects abnormal behavior based on the analysis results. For example, it identifies abnormal driving patterns based on results such as "three instances of sudden braking," "two instances of speeding," and "one instance of lane departure." When an abnormality is detected, a warning message is automatically generated.

[0560] Sending a warning message

[0561] If the server detects abnormal behavior, it generates a warning message and sends it to the terminal. The terminal uses a display device to notify the driver of this warning message. For example, a message such as "Sudden braking is occurring frequently. Please drive safely" is displayed.

[0562] Psychological support

[0563] The AI ​​then generates a psychological support message based on the analysis results. The support message includes content to encourage elderly people to think positively about returning their driver's license. The generated message is then displayed to the user via their device.

[0564] Specific examples

[0565] When the user starts driving, the device continuously acquires location information and collects speed data. It also records any instances when the driver nearly leaves their lane or when the driver suddenly brakes. The server analyzes the data for a given day and obtains results such as "three instances of sudden braking," "two instances of speeding," and "one instance of lane departure." Based on these results, an overall safe driving score is calculated and evaluated. Based on the analysis results, the server generates a warning message that is notified to the user. In addition, the AI ​​generates psychological support messages, offering advice to the user on improving their driving ability or suggesting that they consider surrendering their license in a positive manner.

[0566] Prompt Sentence Examples

[0567] "Data has been collected showing that users frequently brake suddenly while driving. Analyze this data and generate a message to advise users on safe driving and suggest that they surrender their license."

[0568] Thus, the invention provides a holistic system for properly assessing the driving ability of elderly people and improving road safety.

[0569] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0570] Step 1:

[0571] The terminal collects the vehicle's GPS data, speed data, lane identification data and sudden braking data in real time.

[0572] Inputs: Data from GPS sensor, speedometer, camera sensor, and accelerometer.

[0573] Data processing: Each data is converted into a specified format and a timestamp is added.

[0574] Specific behavior:

[0575] The device obtains its current location information from the GPS module every second.

[0576] Speed ​​information is also obtained from the speedometer every second and the current speed is recorded.

[0577] The camera sensor captures images at regular intervals and the image processing algorithm analyzes the possibility of lane departure.

[0578] If the acceleration sensor detects sudden braking, it records the data.

[0579] Output: Collected data (location, speed, lane identification, and hard braking).

[0580] Step 2:

[0581] The terminal buffers the collected data and periodically transmits it to the server.

[0582] Input: Data collected in step 1.

[0583] Data processing: Buffer data at regular intervals (e.g., every minute).

[0584] Specific behavior:

[0585] Data is temporarily stored in the device's internal memory.

[0586] The buffered data is collected at set intervals and sent to the server via the communication module when it is ready.

[0587] Output: The data sent to the server.

[0588] Step 3:

[0589] The server analyzes the data received from the terminal.

[0590] Input: Data sent from the device (location, speed, lane identification, and emergency braking).

[0591] Data processing: Received data is synchronized on the same timeline and converted into an analyzable format.

[0592] Specific behavior:

[0593] The server stores the data in a database and applies an analysis algorithm.

[0594] Data from different sensors will be integrated on the same time axis to create a dataset for analysis.

[0595] Output: A dataset for analysis.

[0596] Step 4:

[0597] The server uses an analytical algorithm to evaluate driving behavior patterns.

[0598] Input: The analysis dataset created in step 3.

[0599] Data processing: Analysis of driving behavior patterns and safety evaluation.

[0600] Specific behavior:

[0601] The analysis algorithm counts the frequency of sudden braking and speeding and assesses the possibility of lane departure.

[0602] The evaluation results of each driving behavior are combined to calculate an overall safe driving score.

[0603] Output: Evaluation results of driving behavior patterns.

[0604] Step 5:

[0605] The server detects abnormal behavior based on the evaluation results of driving behavior patterns and generates a warning message.

[0606] Input: Evaluation results of driving behavior patterns in step 4.

[0607] Data processing: Identify abnormal behavior from the evaluation results and generate warning messages.

[0608] Specific behavior:

[0609] If abnormal driving patterns (e.g., frequent sudden braking or frequent lane departures) are detected, a warning message will be generated based on this.

[0610] Sends a warning message to the terminal.

[0611] Output: The warning message generated.

[0612] Step 6:

[0613] The terminal notifies the user of the warning message received from the server.

[0614] Input: The warning message sent by the server.

[0615] Data processing: Converting messages into a format that is easy for users to understand.

[0616] Specific behavior:

[0617] A warning message is notified to the user in real time using the display device of the terminal.

[0618] For example, a message such as "Sudden braking is occurring frequently. Please drive safely" may be displayed.

[0619] Output: The warning message posted.

[0620] Step 7:

[0621] The generation AI generates a psychological support message based on the evaluation results of driving behavior and sends it to the device.

[0622] Input: Evaluation results of driving behavior patterns.

[0623] Data processing: Generate psychological support messages based on the analysis results.

[0624] Specific behavior:

[0625] Generative AI automatically generates positive messages to reduce the psychological burden on the elderly.

[0626] The message is sent to the terminal, which displays it to the user.

[0627] For example, create a message such as, "Based on your recent driving data, we would like to provide you with advice on how to continue driving safely."

[0628] Output: The generated psychological support message.

[0629] In this way, each step works in conjunction with the others to carry out a series of processes from data collection to analysis, warnings, and psychological support.

[0630] (Application example 1)

[0631] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0632] The driving ability of elderly people can decline with age, increasing the risk of traffic accidents. However, it is difficult for elderly people to recognize this decline in their driving ability, and they often do not surrender their licenses at the appropriate time. For this reason, there is a need for a system that can evaluate elderly people's driving behavior in detail and support safe driving. Furthermore, if abnormal behavior occurs while driving and it becomes difficult to continue driving safely, safety measures such as switching to autonomous driving mode are necessary. There is also a need for support to help elderly people think positively about surrendering their licenses without feeling any psychological burden.

[0633] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0634] In this invention, the server includes a means for analyzing collected driving data and evaluating driving safety, a means for detecting abnormal driving behavior, and a means for switching to autonomous driving mode when abnormal behavior is detected. This allows elderly drivers to drive safely by immediately switching to autonomous driving mode when abnormal behavior is detected. Furthermore, by using generative AI to provide psychological support to elderly drivers and generate prompts that positively encourage them to switch to safe driving mode, it is possible to protect the elderly's self-esteem, encourage them to be aware of their declining driving ability, and support them in surrendering their licenses at the appropriate time.

[0635] The term "elderly" refers to people over a certain age, generally including those aged 65 and over.

[0636] "Driving behavior" refers to a series of actions and behaviors when operating a vehicle, and specifically includes acceleration, deceleration, steering, lane keeping, etc.

[0637] "Real-time" means that data and information processing and feedback occurs immediately.

[0638] "Monitoring" refers to the continuous observation of a specific object or situation to detect abnormalities or changes.

[0639] "Location Information" means data indicating the current geographic coordinates of a vehicle or person.

[0640] "Speed ​​information" is data indicating the moving speed of an object, and in the case of a vehicle, indicates the vehicle speed.

[0641] "Collection" refers to the act of obtaining the target data or information using various sensors and devices.

[0642] "Analysis" means examining the data and information obtained in detail and deriving its meaning and trends.

[0643] "Safety assessment" refers to the analysis and evaluation of a vehicle to determine whether it is safe to operate the vehicle based on driving behavior.

[0644] "Abnormal behavior" refers to actions or behavior that are different from or deviate from normal, and in driving it refers to sudden braking or lane departure.

[0645] "Warning" means a message or signal that calls attention to a potential danger or abnormality.

[0646] "Informing" refers to the act of conveying specific information or data to a target person.

[0647] "Autonomous driving mode" refers to systems and functions that allow a vehicle to operate automatically without human intervention.

[0648] "Lane maintenance" refers to the act of a vehicle continuing to travel steadily within the lane in which it is currently traveling.

[0649] "Lane departure" refers to the phenomenon in which a vehicle unintentionally leaves the lane in which it is currently traveling.

[0650] "Sudden braking" refers to the act of suddenly braking the vehicle, deviating from normal driving conditions.

[0651] "Feedback" refers to returning information to an original subject based on the results of an action or process in order to improve or adjust it.

[0652] "Memory ability" refers to the ability to properly retain past information and retrieve it when needed.

[0653] "Judgment" refers to the ability to take appropriate actions and decisions based on the situation and information.

[0654] "Generative AI" refers to a system that uses artificial intelligence technology to automatically generate text or messages.

[0655] "Psychological support" means assistance to provide a sense of psychological security and stability.

[0656] A "prompt" is a message that the generation AI displays to the user to encourage voluntary action.

[0657] A specific system configuration for implementing the present invention will be described. The overall operation of the system that supports safe driving for elderly people and switches to automatic driving mode when an abnormality is detected will be described.

[0658] Overall system configuration

[0659] The system consists of the following main components:

[0660] 1. Terminal: A device installed inside the vehicle that incorporates various sensors such as a GPS module, speed sensor, and brake sensor. This terminal monitors the driving behavior of elderly people in real time and transmits the collected location and speed information to a server.

[0661] 2. Server: This is the central processing unit that receives data sent from the terminals. The server analyzes the driving data and executes dedicated algorithms to evaluate safety. It is also responsible for detecting abnormal behavior and switching to autonomous driving mode.

[0662] 3. Generative AI model: This is built into the server and generates psychological support messages based on the analysis of driving data, providing prompts that reduce the elderly's resistance to switching to safe driving mode at the appropriate time.

[0663] Data collection

[0664] The terminal is installed inside the vehicle and collects the following data in real time:

[0665] Location information: The current location of the vehicle is obtained using the GPS module.

[0666] Speed ​​information: Measure the vehicle's moving speed through a speed sensor.

[0667] Driving behavior: Equipped with sensors that monitor driving behavior such as sudden braking and lane departure.

[0668] Data analysis

[0669] The server receives the collected driving data and evaluates the driving safety using a dedicated analysis algorithm. This analysis includes the following steps:

[0670] Sudden braking detection: Analyzes data from the brake sensor to determine whether sudden braking occurs frequently.

[0671] Lane departure detection: Using cameras and sensors, counts the number of times the vehicle deviates from its lane.

[0672] Overall evaluation: Based on the above data, a driving safety score is calculated and abnormal behavior is detected.

[0673] Switching to autonomous driving mode

[0674] If abnormal behavior is detected, the server sends an instruction to the device to switch to autonomous driving mode. This function allows the device to immediately switch to autonomous driving mode when safe driving becomes difficult.

[0675] Psychological support

[0676] The generative AI model generates prompts to provide psychological support to the elderly when abnormal behavior is detected. For example, the following message is displayed:

[0677] Generator prompt:

[0678] Your recent driving data has revealed that you have experienced frequent sudden braking. For your safety, we recommend switching to Autopilot mode. This feature will allow you to continue your journey with peace of mind.

[0679] In this way, a system is realized that monitors elderly driving in real time and provides a series of functions to ensure safety.

[0680] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0681] Step 1:

[0682] The device collects data in real time from GPS modules, speed sensors, brake sensors, etc.

[0683] Input: Vehicle location, speed, and emergency braking data.

[0684] Output: The raw data collected.

[0685] Specific operation: The GPS module acquires location information every second, the speed sensor measures the vehicle's speed, and the brake sensor detects sudden braking. This data is stored in the terminal.

[0686] Step 2:

[0687] The terminal transmits the collected driving data to the server at regular intervals.

[0688] Input: Raw data collected.

[0689] Output: Driving data sent to the server.

[0690] Specific operation: The terminal compiles the collected data into packets at regular intervals and sends them to the server via the network.

[0691] Step 3:

[0692] The server receives the driving data and runs an analysis algorithm to evaluate the safety of the driving.

[0693] Input: Driving data sent from the terminal.

[0694] Output: Driving safety rating score.

[0695] Specific operation: The server stores the received data in a database, analyzes the number of sudden braking and lane departures, and then calculates an overall safe driving score based on the analysis results.

[0696] Step 4:

[0697] The server detects abnormal behavior and decides whether to switch to autonomous driving mode.

[0698] Input: Driving safety rating score.

[0699] Output: Instruction to switch to automatic driving mode.

[0700] Specific operation: If the safe driving score falls below a certain standard, the server detects abnormal behavior and sends an instruction to the device to switch to autonomous driving mode.

[0701] Step 5:

[0702] The device receives instructions from the server and switches to autonomous driving mode.

[0703] Input: Instruction to switch to autonomous driving mode.

[0704] Output: Start of Autopilot mode.

[0705] Specific operation: The terminal activates the autonomous driving system and switches operation to autonomous driving mode, allowing the vehicle to continue driving without human intervention.

[0706] Step 6:

[0707] The server uses the generative AI model to generate psychological support messages for the elderly.

[0708] Input: Analysis results of abnormal behavior.

[0709] Output: The generated prompt statement.

[0710] Specific operation: Based on the results of the abnormal behavior analysis, the server uses the generative AI model to generate positive instructions and support messages for the elderly. For example, it generates a message such as, "Based on your recent driving data, we have noticed that you have been braking frequently. For your safety, we recommend switching to autonomous driving mode. This function will allow you to continue traveling safely, so please rest assured."

[0711] Step 7:

[0712] The generated psychological support message is displayed to the user through the terminal.

[0713] Input: The generated prompt statement.

[0714] Output: The message that is displayed to the user.

[0715] Specific operation: The terminal displays the generated message obtained from the server on the display, allowing the user to check the message.

[0716] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0717] This invention relates to a system that closely monitors the driving behavior of elderly people, detects any decline in driving ability early, and provides appropriate feedback. In particular, by combining it with an emotion engine, it is possible to support safe driving while taking into consideration the driver's emotional state.

[0718] System Configuration

[0719] 1. Terminal: A device installed in a vehicle that collects GPS data, speed data, lane identification data, and sudden braking data in real time. The terminal also incorporates sensors such as a camera and microphone, and collects emotional data by analyzing the driver's facial expressions and voice.

[0720] 2. Server: A central processing unit that receives collected driving data and emotional data and analyzes them using dedicated algorithms. Based on the analysis results, the server detects abnormal behavior and emotional states and generates appropriate feedback.

[0721] 3. Generative AI: This will be installed on the server and generate psychological support messages for the elderly based on the analysis results. This will reduce the user's psychological burden, encourage them to surrender their driver's license in a positive manner, and prevent damage to their self-esteem.

[0722] 4. Emotion Engine: The emotion engine recognizes and analyzes the user's emotional state in real time and makes a comprehensive evaluation in conjunction with driving data.

[0723] Program processing

[0724] Collecting and analyzing driving behavior and emotion data

[0725] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. It also uses a camera and microphone to collect the driver's facial expressions and voice data, which it then sends to the emotion engine.

[0726] Examples:

[0727] When the user starts driving, the device collects real-time information on location, speed, and lane, while also using a facial recognition camera and microphone to analyze the user's facial expressions and tone of voice to collect emotional data.

[0728] Data reception and analysis

[0729] The server inputs the received data into an analytical algorithm, which analyzes driving behavior patterns and detects abnormal behavior. The emotion engine also analyzes the transmitted emotional data and evaluates the user's emotional state, such as whether they are stressed or relaxed.

[0730] Examples:

[0731] The server analyzes the driving data for the day and obtains the results of "3 instances of sudden braking," "2 instances of speeding," and "1 instance of lane departure." At the same time, the emotion engine evaluates that "the user's stress level is high while driving."

[0732] Detecting and alerting abnormal behavior and emotional states

[0733] The server then performs a comprehensive evaluation of the abnormal behavior and the user's emotional state based on the analysis results. For example, if the emotion engine detects a high frequency of sudden braking and the user is under stress, it generates a warning message saying, "You are braking frequently and appear to be stressed. We recommend that you drive safely and relax."

[0734] Examples:

[0735] If the server determines based on the analysis results that "the vehicle is frequently drifting out of its lane and the user is feeling high stress," it generates a corresponding warning message and sends it to the device.

[0736] Comprehensive feedback to the elderly

[0737] The AI ​​generates psychological support messages for elderly people based on driving behavior data and emotional data. This helps users understand their own driving ability and emotional state, encouraging them to accept surrendering their license in a positive manner. The generated messages are displayed to the user via their device.

[0738] Examples:

[0739] The device displays a feedback message saying, "Based on your recent driving data and emotional state, we'll provide you with advice to help you continue driving safely. Take short breaks to reduce stress."

[0740] conclusion

[0741] The system of this invention ensures traffic safety throughout society by providing a detailed assessment of the driving ability and emotional state of elderly people and identifying the limits of safe driving. By utilizing generative AI and an emotion engine, it encourages elderly people to surrender their licenses at the appropriate time while protecting their self-esteem. Furthermore, by collaborating with local governments, the system contributes to solving social issues by providing support for daily life after license surrender.

[0742] The processing flow will be explained below.

[0743] Step 1:

[0744] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. The device acquires location information every second and also records the vehicle's speed and acceleration. The device also has a built-in camera and microphone, which are used to collect the driver's facial expressions and voice data.

[0745] Step 2:

[0746] The data collected by the device is sent to a server at regular intervals (e.g., every minute). The data sent includes location information, speed information, lane identification information, records of sudden braking, facial expression data, and voice data.

[0747] Step 3:

[0748] The server inputs the received data into an analytical algorithm, which then analyzes driving behavior patterns based on this data to detect abnormal behavior, analyzes the emotional data sent by the emotion engine, and evaluates the user's emotional state.

[0749] Step 4:

[0750] The server evaluates the driver's safe driving based on the analysis of driving behavior data and emotional data, and calculates a safe driving score by comprehensively evaluating factors such as the number of sudden braking attempts, speeding, lane departure frequency, and emotional states recognized from facial expressions and voice.

[0751] Step 5:

[0752] The server generates a warning message based on the analysis results. For example, if the emotion engine detects a high frequency of sudden braking and the user is under stress, it will create a warning message such as, "You are braking frequently. You seem to be under stress. We recommend that you drive carefully and relax."

[0753] Step 6:

[0754] The server generates a warning message and sends it to the terminal, which then receives it and notifies the user via an in-car display or voice message.

[0755] Step 7:

[0756] The AI ​​then generates a psychological support message based on the analysis results and warning message. It creates a message that encourages the user to surrender their license while maintaining their self-esteem. For example, it makes a positive suggestion such as, "Why not get a professional diagnosis to ensure safe driving?"

[0757] Step 8:

[0758] The AI ​​generates and sends psychological support messages to the device, which displays the messages to provide the driver with a sense of psychological security and encourage them to improve their driving behavior.

[0759] Step 9:

[0760] The device provides the user with a comprehensive driving evaluation and feedback. It displays detailed analysis results of driving behavior and emotional data, and suggests specific improvements and changes to driving habits. For example, it provides specific advice such as, "There are many cases of sudden braking at intersections, so try to slow down when entering intersections."

[0761] Step 10:

[0762] Users will receive feedback and work to improve their driving skills and emotional state. If necessary, we will work with local governments to provide support for life after surrendering their license.

[0763] Example 2

[0764] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0765] The decline in driving ability among the elderly increases the risk of traffic accidents, but currently it is difficult to assess and respond to this decline. Furthermore, unilateral warnings and guidance without considering the driver's emotional state and psychological burden can damage the elderly's self-esteem. This can lead to problems such as the risk of continuing to drive and resistance to surrendering their licenses. Therefore, future solutions that support safe driving and reduce psychological burden are needed.

[0766] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: a means for monitoring the driving behavior of the elderly in real time and collecting location information, speed information, lane identification information, and sudden braking information; a means for analyzing the collected data and evaluating the driving safety; a means for acquiring the driving behavior data as well as the driver's facial expression and voice data to analyze the emotional state; a means for detecting abnormal behavior and providing a warning based on the results of the driving safety evaluation and the emotional state analysis; and a means for notifying the elderly of the generated warning. This not only enables support for safe driving but also provides appropriate feedback taking into account the driver's emotional state, making it possible to reduce the psychological burden on the elderly and encourage them to continue driving safely and surrender their licenses.

[0767] "Elderly driving behavior" refers to the vehicle operation methods and driving patterns of elderly drivers, including specific behaviors such as speed, lane changes, and braking.

[0768] "Real-time" refers to the near-instant processing and reflection of data and events as they occur, with almost no delay.

[0769] "Location Information" refers to the current geographic location of a vehicle or driver, collected through technologies such as GPS.

[0770] "Speed ​​information" is data relating to the vehicle's moving speed, and is generally obtained from the vehicle's speedometer or sensors.

[0771] "Lane identification information" is data relating to the lane of the road on which the vehicle is traveling, and includes information for identifying driving behavior such as lane departure.

[0772] "Sudden braking information" refers to data on when quick and strong braking is applied while driving, and is used to evaluate safe driving.

[0773] "Facial expression data" refers to the collection of data by using sensors such as cameras to recognize the driver's facial expressions.

[0774] "Voice data" refers to collecting the driver's speech and tones using a voice input device such as a microphone and analyzing that data.

[0775] "Emotional state analysis" refers to the process of assessing the driver's emotional state (e.g., stress or relaxation) based on collected facial expression and voice data.

[0776] "Abnormal behavior" refers to actions or patterns that are not considered safe driving, such as sudden braking, speeding, and lane departure.

[0777] "Providing a warning" means alerting a driver to a safety risk or abnormal behavior.

[0778] "Feedback" refers to providing information and advice to drivers based on the analysis results, encouraging them to improve their driving behavior.

[0779] This invention relates to a system that closely monitors the driving behavior of elderly people, detects any decline in driving ability early, and provides appropriate feedback. In particular, by combining it with an emotion engine, it is possible to support safe driving while taking into consideration the driver's emotional state.

[0780] System Configuration

[0781] Terminal

[0782] The terminal is installed in the vehicle and collects GPS data, speed data, lane identification data, and sudden braking data in real time. The terminal is equipped with sensors such as a camera and microphone, which can analyze the driver's facial expressions and voice and collect emotional data. Specific hardware includes a GPS sensor, speed sensor, facial recognition camera, microphone, lane identification camera, and acceleration sensor.

[0783] server

[0784] The server is a central processing unit that receives collected data sent from the terminal and analyzes it using analytical algorithms. The server analyzes driving data and emotional data, evaluates abnormal behavior and the driver's emotional state, and generates appropriate feedback. Specific software used includes data analysis algorithms and an emotion engine.

[0785] Generation AI

[0786] The AI ​​generator is installed on a server and generates psychological support messages for the elderly based on the analysis results. The AI ​​generator can create messages that reduce the psychological burden on the elderly and encourage them to surrender their driver's licenses in a positive manner without damaging their self-esteem.

[0787] Emotion Engine

[0788] The emotion engine recognizes and analyzes the user's emotional state in real time, and performs a comprehensive evaluation in conjunction with driving data. The emotion engine analyzes facial expression data and voice data, and can evaluate stress, relaxation state, etc.

[0789] Program processing explanation

[0790] Data collection

[0791] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. At the same time, it uses the device's camera and microphone to collect the driver's facial expression and voice data, which are then sent to the emotion engine. For example, when a user starts driving, the device collects location, speed, and lane information in real time, and uses the facial recognition camera and microphone to analyze the user's facial expression and tone of voice to collect emotion data.

[0792] Data analysis

[0793] The server receives the driving data and emotional data sent from the device and begins processing it using an analytical algorithm. The server analyzes driving behavior patterns and detects whether there are any abnormal behaviors. It also analyzes the emotional data sent by the emotion engine to evaluate whether the user is stressed or relaxed. As a specific example, if a day's driving data is analyzed and the results show "three instances of sudden braking," "two instances of speeding," and "one instance of lane departure," a comprehensive evaluation is made based on these results.

[0794] Generate feedback

[0795] The AI ​​generates psychological support messages for users based on driving behavior data and emotional data. The generated messages are presented to the user via the device. For example, the AI ​​could generate a message such as, "Based on your recent driving data and emotional state, we would like to provide you with advice on how to continue driving safely. Please take a short break to reduce stress," and the message would be displayed on the device.

[0796] Examples of concrete examples and prompts

[0797] For example, if the emotion engine detects high stress levels due to frequent sudden braking while driving, it will generate a warning message stating, "You are braking frequently and appear to be stressed. We recommend that you drive safely and relax," and send it to the device. An example of a prompt is, "Please generate advice for safe driving based on driving data of elderly people."

[0798] This system not only supports safe driving, but also provides appropriate feedback that takes into account the driver's emotional state, reducing the psychological burden on elderly people and encouraging them to continue driving safely and surrender their licenses.

[0799] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0800] Step 1: Collecting driving and emotion data

[0801] The device is installed in the vehicle and collects real-time GPS data, speed data, lane identification data, and sudden braking data, and also uses a camera and microphone to collect the driver's facial expressions and voice data.

[0802] Input: User's driving behavior, facial expressions, and voice

[0803] Output: Driving data and emotion data (position information, speed information, lane identification information, sudden braking information, facial expression data, voice data)

[0804] Specific behavior:

[0805] When the user starts driving, the device instantly acquires location information using the GPS sensor, measures speed using the speed sensor, monitors lane departure using the lane identification camera, collects sudden braking data using the acceleration sensor, analyzes the driver's facial expressions using the facial recognition camera, and collects voice tones using the microphone.

[0806] Step 2: Sending data

[0807] The device sends the collected driving data and emotion data to a server.

[0808] Input: Collected driving data and emotion data

[0809] Output: Sending data from the device to the server

[0810] Specific behavior:

[0811] The device transmits location, speed, lane departure, sudden braking, facial expressions, and voice data to the server at regular intervals, for example, every minute.

[0812] Step 3: Receiving and analyzing data

[0813] The server receives the transmitted driving data and emotion data and analyzes the data using an analytical algorithm.

[0814] Input: Driving data and emotion data sent from the device

[0815] Output: Driving behavior patterns, presence or absence of abnormal behavior, and evaluation results of emotional state

[0816] Specific behavior:

[0817] The server distributes the received data to the analysis module, and the driving data is analyzed using a driving pattern recognition algorithm to detect the frequency of sudden braking, speeding, and lane departure.The emotion engine evaluates the user's stress and relaxation state based on facial expression and voice data.

[0818] Step 4: Detecting abnormal behavior and emotional states and generating alerts

[0819] The server comprehensively evaluates abnormal behavior and emotional state based on the analysis results and generates appropriate warnings.

[0820] Input: Analyzed driving and emotion data

[0821] Output: Appropriate warning message

[0822] Specific behavior:

[0823] If the server determines that "sudden braking is occurring frequently and the emotion engine has detected high stress," it will generate a warning message stating, "You are braking frequently and appear to be under stress. We recommend that you drive safely and relax," and send it to the device.

[0824] Step 5: Generate a psychological support message

[0825] The generative AI generates psychological support messages for the elderly based on driving behavior data and emotional data.

[0826] Input: driving behavior data, emotion data, analysis results

[0827] Output: Psychological support message

[0828] Specific behavior:

[0829] The AI ​​generates a message saying, "Based on your recent driving data and emotional state, we'd like to provide you with advice on how to continue driving safely. Take a short break to reduce stress," and the device displays it to the user.

[0830] In this way, the present invention comprehensively evaluates the driving behavior and emotional state of elderly people and supports safe and secure driving.

[0831] (Application example 2)

[0832] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0833] The driving behavior of elderly people is often a safety concern due to the decline in physical and cognitive abilities that comes with aging. While abnormal behavior, such as sudden braking and lane departure, is particularly common, drivers themselves may have difficulty noticing these changes. Furthermore, because emotional stress also affects driving stability, understanding the psychological state of drivers while driving and providing appropriate feedback is essential. Current driving assistance systems lack a mechanism for comprehensively evaluating these complex factors and providing feedback in real time. Therefore, a system is needed that simultaneously monitors driving behavior and emotional state and provides comprehensive support to enable elderly people to continue driving with peace of mind.

[0834] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0835] In this invention, the server includes a means for collecting and analyzing driving behavior data and emotional data in real time, a means for evaluating the psychological state of the driver and generating appropriate feedback, and a means for comprehensively evaluating the collected data and detecting abnormal behavior, thereby enabling simultaneous monitoring of the driving behavior and emotional state of elderly people and providing comprehensive support to ensure safety and psychological stability.

[0836] "Driving behavior data" refers to location information, speed data, lane identification data, and sudden braking data obtained from a vehicle while an elderly person is driving.

[0837] "Emotional data" refers to data obtained by analyzing the psychological state of the driver, as determined from their facial expressions, voice, etc.

[0838] "Psychological state" refers to the mental state, including emotions such as stress and relaxation, that a driver feels while driving.

[0839] "Feedback" refers to information such as warnings and advice provided to the driver based on the analysis results.

[0840] "Abnormal behavior" refers to actions that deviate from normal safe driving, such as sudden braking or lane departure.

[0841] "Real-time" refers to data collection, analysis, and notification occurring almost simultaneously within a very short period of time.

[0842] "Analysis means" refers to methods and devices for evaluating driving behavior and emotional state based on collected data and determining driving safety and psychological stability.

[0843] "Means for analyzing the driver's facial expressions and voice" refers to methods and devices that use cameras and microphones to collect the driver's facial expressions and tone of voice and identify their emotional state.

[0844] A system for monitoring elderly driving behavior and emotional state

[0845] This invention is a system that provides safe driving and psychological support by closely monitoring the driving behavior of elderly people and analyzing their emotional state. The system consists of a terminal installed in the vehicle, a server that analyzes the data, a generative AI, and an emotion engine.

[0846] System Components

[0847] 1. Device:

[0848] The device is installed in the vehicle and collects GPS data, speed data, lane identification data, and sudden braking data in real time. It also has a built-in camera and microphone to detect the driver's facial expressions and voice, allowing it to obtain driving behavior data and emotional data.

[0849] 2. Server:

[0850] The server receives and analyzes the collected driving behavior data and emotion data. It uses a dedicated algorithm to analyze driving behavior patterns and detect abnormal behavior. It also analyzes the emotion data sent by the emotion engine to evaluate the driver's emotional state. Specifically, it uses Python-based analysis algorithms (e.g., Scikit-learn, TensorFlow) and emotion engines (e.g., IBM Watson, Microsoft Azure Cognitive Services).

[0851] 3. Generation AI:

[0852] The AI ​​generates psychological support messages for elderly people based on driving behavior data and emotion data on a server. These messages are used to reduce the psychological burden on drivers and promote safe driving.

[0853] 4. Emotion Engine:

[0854] The emotion engine recognizes and analyzes the driver's emotional state in real time, and this data is later combined with driving behavior data for a comprehensive evaluation.

[0855] Data collection and analysis

[0856] Once the driver starts driving, the device collects real-time GPS data, speed data, lane identification data, and sudden braking data, and also uses a facial recognition camera and microphone to collect the driver's facial expressions and voice to generate emotion data, thereby monitoring both driving behavior and psychological state.

[0857] The server receives the data sent from the device and inputs it into an analytical algorithm. It analyzes driving behavior patterns and emotional states to detect abnormal behavior and high stress levels. This allows for an evaluation of driving safety and psychological stability.

[0858] Feedback generation and notification

[0859] The AI ​​then generates a feedback message for the elderly based on the analysis results received from the server. This message provides appropriate advice to the driver and promotes safe driving. For example, a message might be generated that reads, "You are braking suddenly frequently and feeling stressed. Take a short break to relax."

[0860] The device then generates and sends feedback messages to the driver in real time, helping the driver understand not only their driving behavior but also their emotional state.

[0861] Examples and prompts

[0862] Examples:

[0863] When an elderly person begins driving, the vehicle's various sensors, cameras, and microphones collect data. The camera and microphone detect frequent sudden braking and an increase in the driver's stress level. The data is analyzed on the server, and the AI ​​generates a feedback message saying, "Drive safely and take short breaks to relax." This message is displayed in real time on the driver's smartphone.

[0864] Example prompt sentence:

[0865] Develop a system that analyzes elderly driving behavior and emotion data, detects sudden braking and increased stress levels, and provides real-time feedback. Use IBM Watson as the emotion engine and Scikit-learn for data analysis.

[0866] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0867] Step 1:

[0868] Data collection

[0869] The device collects real-time GPS data, speed data, lane identification data, and sudden braking data when the driver starts the vehicle. It also uses a camera and microphone to collect the driver's facial expressions and voice data, thereby obtaining driving behavior data and emotional data as input.

[0870] Step 2:

[0871] Data transmission

[0872] The device transmits the collected driving behavior data and emotion data to the server via Bluetooth or Wi-Fi. The transmitted data is processed in real time, so it is desirable to have no delay. Once the data transmission is complete, the transmitted data becomes input to the server.

[0873] Step 3:

[0874] Data analysis

[0875] The server analyzes the received driving behavior data and emotion data. Specifically, it uses Python-based analysis algorithms (e.g., Scikit-learn, TensorFlow) to analyze driving behavior patterns and determine whether or not there are any abnormal behaviors. It also evaluates the driver's stress level and relaxation state based on the data sent by the emotion engine. The analysis results are saved as intermediate output.

[0876] Step 4:

[0877] Detecting Abnormal Behavior

[0878] The server detects abnormal behavior based on the analysis results. For example, if the driver brakes frequently or leaves the lane frequently, this is judged to be abnormal behavior. In parallel, if the emotion engine detects a high stress level in the driver, this is also recognized as an abnormality. When abnormal behavior is detected, the corresponding evaluation result is output.

[0879] Step 5:

[0880] Feedback Message Generation

[0881] Based on the analysis results, the server uses the generation AI to generate a feedback message for the elderly. Here, the driving behavior and emotional state are comprehensively evaluated, and a message is generated that provides appropriate advice to the driver. For example, a message such as "You are braking suddenly frequently and are feeling stressed. Take a short break to relax" may be generated. This is the final output.

[0882] Step 6:

[0883] Feedback Notification

[0884] The device receives the feedback messages generated by the generation AI and notifies the driver in real time via a smartphone or in-car display. Once the notification is complete, the driver can understand their own driving behavior and emotional state and take appropriate action. This provides feedback to the driver as the final output.

[0885] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0886] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0887] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0888] [Third embodiment]

[0889] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0890] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0891] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0892] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0893] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0894] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0895] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0896] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0897] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0898] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0899] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0900] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0901] This invention relates to a system that evaluates the driving ability of elderly people in detail and suggests that they surrender their license when they reach the limit of safe driving. The system mainly consists of a terminal installed in the vehicle and a server that analyzes the data.

[0902] System Configuration

[0903] 1. Terminal: A device installed inside the vehicle that collects GPS and speed data in real time. The terminal also has the function of closely monitoring the driving behavior of elderly drivers using multiple sensors, including cameras, speedometers, and accelerometers.

[0904] 2. Server: A central processing unit that receives collected data and analyzes it using a dedicated algorithm. Based on the analysis results, the server detects abnormal behavior and generates a warning if it determines that safe operation cannot be continued.

[0905] 3. Generative AI: This is installed on the server and generates psychological support messages for the elderly based on the analysis results. The Generative AI reduces the user's psychological burden and encourages them to surrender their driver's license in a positive manner without damaging their self-esteem.

[0906] Program processing

[0907] Collecting and analyzing driving behavior

[0908] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. This data is sent to a server at regular intervals. For example, the device acquires location information every second and processes camera images for lane keeping. This allows for detailed recording of elderly driving behavior.

[0909] Examples:

[0910] Once the user starts driving, the device will continuously acquire location information, collect speed data, and record any lane departures or sudden braking.

[0911] Data reception and analysis

[0912] The server analyzes the received data using a dedicated algorithm. The server analyzes driving behavior patterns and counts the number of times the driver habitually brakes suddenly or deviates from their lane, etc. This allows the overall driving safety to be evaluated.

[0913] Examples:

[0914] The server analyzes the data for a given day and obtains the results of "3 instances of sudden braking," "2 instances of speeding," and "1 instance of lane departure." Based on these results, an overall safe driving score is calculated and evaluated.

[0915] Detecting abnormal behavior and generating alerts

[0916] The server detects abnormal behavior based on the analysis results. If the standard for safe driving is exceeded, a warning message is generated. For example, if the frequency of sudden braking is too high, the server generates a warning saying, "Safe driving is difficult due to frequent sudden braking."

[0917] Examples:

[0918] The server analyzes the user's driving data and, if it determines that the user is frequently drifting out of their lane, it generates a warning message and sends it to the device.

[0919] Psychological support for the elderly

[0920] Based on the analysis results, the AI ​​generates a psychological support message for the elderly. This helps them understand their declining driving ability and encourages them to positively consider returning their license. The generated message is displayed to the user via the device.

[0921] Examples:

[0922] When a user receives a driving evaluation message, the device will display, "Based on your recent driving data, we would like to provide you with some advice to help you continue driving safely," and provide specific suggestions for improvement.

[0923] conclusion

[0924] The system of this invention ensures traffic safety throughout society by providing a detailed assessment of the driving ability of elderly people and identifying the limits of safe driving. It also uses generative AI to provide psychological support, helping elderly people to think positively about returning their driver's licenses while preserving their self-esteem. By collaborating with local governments, the system also provides support for daily life after license surrender, contributing to the resolution of social issues.

[0925] The processing flow will be explained below.

[0926] Step 1:

[0927] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. The device obtains location information every second and also records the vehicle's speed and acceleration.

[0928] Step 2:

[0929] The device sends the collected data to a server at regular intervals (e.g., every minute). The data sent includes location information, speed information, lane identification information, and records of sudden braking.

[0930] Step 3:

[0931] The server inputs the received data into an analytical algorithm, which analyzes driving patterns based on this data and detects abnormal behavior.

[0932] Step 4:

[0933] The server analyzes the results of abnormal behavior detection and evaluates the safe driving, calculating a safe driving score based on a comprehensive evaluation of factors such as the number of sudden braking attempts, frequency of lane departures, and whether or not the driver exceeded the speed limit.

[0934] Step 5:

[0935] The server generates a warning message based on the analysis results. For example, if the frequency of sudden braking is high, it creates a warning message such as "Sudden braking is occurring frequently. Please drive safely."

[0936] Step 6:

[0937] The server generates a warning message and sends it to the terminal, which then receives it and notifies the user via an in-car display or voice message.

[0938] Step 7:

[0939] The AI ​​generates a psychological support message based on the analysis results and warning message, creating a message that encourages the user to surrender their license while maintaining their self-esteem.

[0940] Step 8:

[0941] The AI ​​generates and sends psychological support messages to the device, which displays the messages to provide the user with a sense of psychological security and encourage them to improve their driving behavior.

[0942] Step 9:

[0943] The device provides users with a comprehensive driving evaluation and feedback, displays detailed analysis results of their driving behavior, and suggests specific improvements and changes to their driving habits.

[0944] Step 10:

[0945] Users will receive feedback and strive to improve their driving skills. If necessary, we will work with local governments to provide support for life after surrendering their license.

[0946] Example 1

[0947] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0948] The decline in driving ability among the elderly increases the risk of traffic accidents, and this problem needs to be addressed. However, conventional methods make it difficult for elderly people to recognize their own decline in driving ability and voluntarily return their licenses, which could have a significant impact on traffic safety throughout society. Another issue is the psychological burden that the process of returning a license places on elderly people.

[0949] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0950] In this invention, the server includes a device installed in the vehicle that monitors the driving behavior of the elderly in real time and collects location information, speed data, lane identification data, and sudden braking data, a means for buffering the collected data and periodically sending it to a central processing unit, a means for analyzing the received data with a dedicated algorithm and evaluating driving safety, a means for detecting abnormal behavior based on the evaluation of driving safety and generating a warning message, and a means for notifying the elderly of the generated warning message via the device in the vehicle. This allows a detailed evaluation of the driving ability of the elderly to determine the limits of safe driving and enables the elderly to proactively consider surrendering their driver's licenses.

[0951] The "in-vehicle device" refers to a device that monitors the driving behavior of seniors in real time and collects location information, speed data, lane identification data, and sudden braking data. This device may include sensors, cameras, GPS receivers, etc.

[0952] The "Central Processing Unit" is a computer system that analyzes received driving data and performs safety evaluations. This system includes a server, database, and analysis algorithms.

[0953] "Real-time monitoring" means that data is collected continuously while the vehicle is moving, creating an instantaneous record of driving behavior.

[0954] "Location information" refers to the vehicle's current geographic coordinate data obtained from a GPS sensor.

[0955] "Speed ​​data" refers to data on the instantaneous travel speed obtained from the vehicle's speedometer.

[0956] "Lane identification data" refers to data about the lane in which a vehicle is traveling, obtained using a camera sensor mounted on the vehicle and image processing algorithms.

[0957] "Sudden braking data" is data detected using an acceleration sensor when the vehicle suddenly brakes.

[0958] "Data buffering" is the process of temporarily storing collected data and preparing it for transmission to other systems as needed.

[0959] A "proprietary algorithm" is a specific computational method or procedure designed to analyze collected driving data.

[0960] "Driving safety assessment" is the process of measuring and evaluating the safety of driving behavior based on analyzed data.

[0961] "Abnormal behavior" refers to potentially dangerous driving behavior that deviates from normal driving patterns, such as frequent sudden braking or lane departure.

[0962] A "warning message" is a message issued to alert or warn the driver when abnormal behavior is detected.

[0963] "Real-time feedback to the elderly" is the process of instantly conveying analysis results and warning messages to the elderly.

[0964] "Generative AI" is a system that uses artificial intelligence technology to automatically generate psychological support messages based on analysis results.

[0965] "Psychological support messages" are messages designed to help elderly people understand their declining driving ability and encourage them to think positively about returning their license.

[0966] This invention is a system that evaluates the driving ability of elderly people in detail and suggests that they return their license when they reach the limit of safe driving. The system's main components are a terminal installed in the vehicle and a server that analyzes the data.

[0967] Equipment configuration

[0968] Terminal

[0969] The device is installed in the vehicle and is used to monitor the driving behavior of elderly drivers in real time. The device includes the following sensors and devices:

[0970] GPS receiver: Obtains location information.

[0971] Speedometer: Captures speed data.

[0972] Camera sensor: A device for acquiring lane identification data.

[0973] Acceleration sensor: A device for detecting sudden braking.

[0974] server

[0975] The server analyzes the collected data and acts as the central processing unit.

[0976] Database: Stores the received data.

[0977] Analysis algorithm: Analyzes collected data and evaluates driving safety.

[0978] Generative AI: Generates psychological support messages based on driving evaluation.

[0979] Program processing

[0980] Data collection

[0981] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. The data is buffered internally and sent to the server at regular intervals. For example, the device obtains location information once per second and processes camera images in real time to keep the vehicle in its lane.

[0982] Data analysis

[0983] The server analyzes the data received from the device and uses specialized algorithms to evaluate driving behavior patterns and safety. The analysis includes the following elements:

[0984] Frequency of sudden braking and speeding: Count the number of times and use it to evaluate safe driving.

[0985] Lane departure detection: Analyzes the image processing results from the camera sensor and evaluates the frequency of lane departures.

[0986] Detecting Abnormal Behavior

[0987] The server detects abnormal behavior based on the analysis results. For example, it identifies abnormal driving patterns based on results such as "three instances of sudden braking," "two instances of speeding," and "one instance of lane departure." When an abnormality is detected, a warning message is automatically generated.

[0988] Sending a warning message

[0989] If the server detects abnormal behavior, it generates a warning message and sends it to the terminal. The terminal uses a display device to notify the driver of this warning message. For example, a message such as "Sudden braking is occurring frequently. Please drive safely" is displayed.

[0990] Psychological support

[0991] The AI ​​then generates a psychological support message based on the analysis results. The support message includes content to encourage elderly people to think positively about returning their driver's license. The generated message is then displayed to the user via their device.

[0992] Specific examples

[0993] When the user starts driving, the device continuously acquires location information and collects speed data. It also records any instances when the driver nearly leaves their lane or when the driver suddenly brakes. The server analyzes the data for a given day and obtains results such as "three instances of sudden braking," "two instances of speeding," and "one instance of lane departure." Based on these results, an overall safe driving score is calculated and evaluated. Based on the analysis results, the server generates a warning message that is notified to the user. In addition, the AI ​​generates psychological support messages, offering advice to the user on improving their driving ability or suggesting that they consider surrendering their license in a positive manner.

[0994] Prompt Sentence Examples

[0995] "Data has been collected showing that users frequently brake suddenly while driving. Analyze this data and generate a message to advise users on safe driving and suggest that they surrender their license."

[0996] Thus, the invention provides a holistic system for properly assessing the driving ability of elderly people and improving road safety.

[0997] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0998] Step 1:

[0999] The terminal collects the vehicle's GPS data, speed data, lane identification data and sudden braking data in real time.

[1000] Inputs: Data from GPS sensor, speedometer, camera sensor, and accelerometer.

[1001] Data processing: Each data is converted into a specified format and a timestamp is added.

[1002] Specific behavior:

[1003] The device obtains its current location information from the GPS module every second.

[1004] Speed ​​information is also obtained from the speedometer every second and the current speed is recorded.

[1005] The camera sensor captures images at regular intervals and the image processing algorithm analyzes the possibility of lane departure.

[1006] If the acceleration sensor detects sudden braking, it records the data.

[1007] Output: Collected data (location, speed, lane identification, and hard braking).

[1008] Step 2:

[1009] The terminal buffers the collected data and periodically transmits it to the server.

[1010] Input: Data collected in step 1.

[1011] Data processing: Buffer data at regular intervals (e.g., every minute).

[1012] Specific behavior:

[1013] Data is temporarily stored in the device's internal memory.

[1014] The buffered data is collected at set intervals and sent to the server via the communication module when it is ready.

[1015] Output: The data sent to the server.

[1016] Step 3:

[1017] The server analyzes the data received from the terminal.

[1018] Input: Data sent from the device (location, speed, lane identification, and emergency braking).

[1019] Data processing: Received data is synchronized on the same timeline and converted into an analyzable format.

[1020] Specific behavior:

[1021] The server stores the data in a database and applies an analysis algorithm.

[1022] Data from different sensors will be integrated on the same time axis to create a dataset for analysis.

[1023] Output: A dataset for analysis.

[1024] Step 4:

[1025] The server uses an analytical algorithm to evaluate driving behavior patterns.

[1026] Input: The analysis dataset created in step 3.

[1027] Data processing: Analysis of driving behavior patterns and safety evaluation.

[1028] Specific behavior:

[1029] The analysis algorithm counts the frequency of sudden braking and speeding and assesses the possibility of lane departure.

[1030] The evaluation results of each driving behavior are combined to calculate an overall safe driving score.

[1031] Output: Evaluation results of driving behavior patterns.

[1032] Step 5:

[1033] The server detects abnormal behavior based on the evaluation results of driving behavior patterns and generates a warning message.

[1034] Input: Evaluation results of driving behavior patterns in step 4.

[1035] Data processing: Identify abnormal behavior from the evaluation results and generate warning messages.

[1036] Specific behavior:

[1037] If abnormal driving patterns (e.g., frequent sudden braking or frequent lane departures) are detected, a warning message will be generated based on this.

[1038] Sends a warning message to the terminal.

[1039] Output: The warning message generated.

[1040] Step 6:

[1041] The terminal notifies the user of the warning message received from the server.

[1042] Input: The warning message sent by the server.

[1043] Data processing: Converting messages into a format that is easy for users to understand.

[1044] Specific behavior:

[1045] A warning message is notified to the user in real time using the display device of the terminal.

[1046] For example, a message such as "Sudden braking is occurring frequently. Please drive safely" may be displayed.

[1047] Output: The warning message posted.

[1048] Step 7:

[1049] The generation AI generates a psychological support message based on the evaluation results of driving behavior and sends it to the device.

[1050] Input: Evaluation results of driving behavior patterns.

[1051] Data processing: Generate psychological support messages based on the analysis results.

[1052] Specific behavior:

[1053] Generative AI automatically generates positive messages to reduce the psychological burden on the elderly.

[1054] The message is sent to the terminal, which displays it to the user.

[1055] For example, create a message such as, "Based on your recent driving data, we would like to provide you with advice on how to continue driving safely."

[1056] Output: The generated psychological support message.

[1057] In this way, each step works in conjunction with the others to carry out a series of processes from data collection to analysis, warnings, and psychological support.

[1058] (Application example 1)

[1059] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1060] The driving ability of elderly people can decline with age, increasing the risk of traffic accidents. However, it is difficult for elderly people to recognize this decline in their driving ability, and they often do not surrender their licenses at the appropriate time. For this reason, there is a need for a system that can evaluate elderly people's driving behavior in detail and support safe driving. Furthermore, if abnormal behavior occurs while driving and it becomes difficult to continue driving safely, safety measures such as switching to autonomous driving mode are necessary. There is also a need for support to help elderly people think positively about surrendering their licenses without feeling any psychological burden.

[1061] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1062] In this invention, the server includes a means for analyzing collected driving data and evaluating driving safety, a means for detecting abnormal driving behavior, and a means for switching to autonomous driving mode when abnormal behavior is detected. This allows elderly drivers to drive safely by immediately switching to autonomous driving mode when abnormal behavior is detected. Furthermore, by using generative AI to provide psychological support to elderly drivers and generate prompts that positively encourage them to switch to safe driving mode, it is possible to protect the elderly's self-esteem, encourage them to be aware of their declining driving ability, and support them in surrendering their licenses at the appropriate time.

[1063] The term "elderly" refers to people over a certain age, generally including those aged 65 and over.

[1064] "Driving behavior" refers to a series of actions and behaviors when operating a vehicle, and specifically includes acceleration, deceleration, steering, lane keeping, etc.

[1065] "Real-time" means that data and information processing and feedback occurs immediately.

[1066] "Monitoring" refers to the continuous observation of a specific object or situation to detect abnormalities or changes.

[1067] "Location Information" means data indicating the current geographic coordinates of a vehicle or person.

[1068] "Speed ​​information" is data indicating the moving speed of an object, and in the case of a vehicle, indicates the vehicle speed.

[1069] "Collection" refers to the act of obtaining the target data or information using various sensors and devices.

[1070] "Analysis" means examining the data and information obtained in detail and deriving its meaning and trends.

[1071] "Safety assessment" refers to the analysis and evaluation of a vehicle to determine whether it is safe to operate the vehicle based on driving behavior.

[1072] "Abnormal behavior" refers to actions or behavior that are different from or deviate from normal, and in driving it refers to sudden braking or lane departure.

[1073] "Warning" means a message or signal that calls attention to a potential danger or abnormality.

[1074] "Informing" refers to the act of conveying specific information or data to a target person.

[1075] "Autonomous driving mode" refers to systems and functions that allow a vehicle to operate automatically without human intervention.

[1076] "Lane maintenance" refers to the act of a vehicle continuing to travel steadily within the lane in which it is currently traveling.

[1077] "Lane departure" refers to the phenomenon in which a vehicle unintentionally leaves the lane in which it is currently traveling.

[1078] "Sudden braking" refers to the act of suddenly braking the vehicle, deviating from normal driving conditions.

[1079] "Feedback" refers to returning information to an original subject based on the results of an action or process in order to improve or adjust it.

[1080] "Memory ability" refers to the ability to properly retain past information and retrieve it when needed.

[1081] "Judgment" refers to the ability to take appropriate actions and decisions based on the situation and information.

[1082] "Generative AI" refers to a system that uses artificial intelligence technology to automatically generate text or messages.

[1083] "Psychological support" means assistance to provide a sense of psychological security and stability.

[1084] A "prompt" is a message that the generation AI displays to the user to encourage voluntary action.

[1085] A specific system configuration for implementing the present invention will be described. The overall operation of the system that supports safe driving for elderly people and switches to automatic driving mode when an abnormality is detected will be described.

[1086] Overall system configuration

[1087] The system consists of the following main components:

[1088] 1. Terminal: A device installed inside the vehicle that incorporates various sensors such as a GPS module, speed sensor, and brake sensor. This terminal monitors the driving behavior of elderly people in real time and transmits the collected location and speed information to a server.

[1089] 2. Server: This is the central processing unit that receives data sent from the terminals. The server analyzes the driving data and executes dedicated algorithms to evaluate safety. It is also responsible for detecting abnormal behavior and switching to autonomous driving mode.

[1090] 3. Generative AI model: This is built into the server and generates psychological support messages based on the analysis of driving data, providing prompts that reduce the elderly's resistance to switching to safe driving mode at the appropriate time.

[1091] Data collection

[1092] The terminal is installed inside the vehicle and collects the following data in real time:

[1093] Location information: The current location of the vehicle is obtained using the GPS module.

[1094] Speed ​​information: Measure the vehicle's moving speed through a speed sensor.

[1095] Driving behavior: Equipped with sensors that monitor driving behavior such as sudden braking and lane departure.

[1096] Data analysis

[1097] The server receives the collected driving data and evaluates the driving safety using a dedicated analysis algorithm. This analysis includes the following steps:

[1098] Sudden braking detection: Analyzes data from the brake sensor to determine whether sudden braking occurs frequently.

[1099] Lane departure detection: Using cameras and sensors, counts the number of times the vehicle deviates from its lane.

[1100] Overall evaluation: Based on the above data, a driving safety score is calculated and abnormal behavior is detected.

[1101] Switching to autonomous driving mode

[1102] If abnormal behavior is detected, the server sends an instruction to the device to switch to autonomous driving mode. This function allows the device to immediately switch to autonomous driving mode when safe driving becomes difficult.

[1103] Psychological support

[1104] The generative AI model generates prompts to provide psychological support to the elderly when abnormal behavior is detected. For example, the following message is displayed:

[1105] Generator prompt:

[1106] Your recent driving data has revealed that you have experienced frequent sudden braking. For your safety, we recommend switching to Autopilot mode. This feature will allow you to continue your journey with peace of mind.

[1107] In this way, a system is realized that monitors elderly driving in real time and provides a series of functions to ensure safety.

[1108] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1109] Step 1:

[1110] The device collects data in real time from GPS modules, speed sensors, brake sensors, etc.

[1111] Input: Vehicle location, speed, and emergency braking data.

[1112] Output: The raw data collected.

[1113] Specific operation: The GPS module acquires location information every second, the speed sensor measures the vehicle's speed, and the brake sensor detects sudden braking. This data is stored in the terminal.

[1114] Step 2:

[1115] The terminal transmits the collected driving data to the server at regular intervals.

[1116] Input: Raw data collected.

[1117] Output: Driving data sent to the server.

[1118] Specific operation: The terminal compiles the collected data into packets at regular intervals and sends them to the server via the network.

[1119] Step 3:

[1120] The server receives the driving data and runs an analysis algorithm to evaluate the safety of the driving.

[1121] Input: Driving data sent from the terminal.

[1122] Output: Driving safety rating score.

[1123] Specific operation: The server stores the received data in a database, analyzes the number of sudden braking and lane departures, and then calculates an overall safe driving score based on the analysis results.

[1124] Step 4:

[1125] The server detects abnormal behavior and decides whether to switch to autonomous driving mode.

[1126] Input: Driving safety rating score.

[1127] Output: Instruction to switch to automatic driving mode.

[1128] Specific operation: If the safe driving score falls below a certain standard, the server detects abnormal behavior and sends an instruction to the device to switch to autonomous driving mode.

[1129] Step 5:

[1130] The device receives instructions from the server and switches to autonomous driving mode.

[1131] Input: Instruction to switch to autonomous driving mode.

[1132] Output: Start of Autopilot mode.

[1133] Specific operation: The terminal activates the autonomous driving system and switches operation to autonomous driving mode, allowing the vehicle to continue driving without human intervention.

[1134] Step 6:

[1135] The server uses the generative AI model to generate psychological support messages for the elderly.

[1136] Input: Analysis results of abnormal behavior.

[1137] Output: The generated prompt statement.

[1138] Specific operation: Based on the results of the abnormal behavior analysis, the server uses the generative AI model to generate positive instructions and support messages for the elderly. For example, it generates a message such as, "Based on your recent driving data, we have noticed that you have been braking frequently. For your safety, we recommend switching to autonomous driving mode. This function will allow you to continue traveling safely, so please rest assured."

[1139] Step 7:

[1140] The generated psychological support message is displayed to the user through the terminal.

[1141] Input: The generated prompt statement.

[1142] Output: The message that is displayed to the user.

[1143] Specific operation: The terminal displays the generated message obtained from the server on the display, allowing the user to check the message.

[1144] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1145] This invention relates to a system that closely monitors the driving behavior of elderly people, detects any decline in driving ability early, and provides appropriate feedback. In particular, by combining it with an emotion engine, it is possible to support safe driving while taking into consideration the driver's emotional state.

[1146] System Configuration

[1147] 1. Terminal: A device installed in a vehicle that collects GPS data, speed data, lane identification data, and sudden braking data in real time. The terminal also incorporates sensors such as a camera and microphone, and collects emotional data by analyzing the driver's facial expressions and voice.

[1148] 2. Server: A central processing unit that receives collected driving data and emotional data and analyzes them using dedicated algorithms. Based on the analysis results, the server detects abnormal behavior and emotional states and generates appropriate feedback.

[1149] 3. Generative AI: This will be installed on the server and generate psychological support messages for the elderly based on the analysis results. This will reduce the user's psychological burden, encourage them to surrender their driver's license in a positive manner, and prevent damage to their self-esteem.

[1150] 4. Emotion Engine: The emotion engine recognizes and analyzes the user's emotional state in real time and makes a comprehensive evaluation in conjunction with driving data.

[1151] Program processing

[1152] Collecting and analyzing driving behavior and emotion data

[1153] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. It also uses a camera and microphone to collect the driver's facial expressions and voice data, which it then sends to the emotion engine.

[1154] Examples:

[1155] When the user starts driving, the device collects real-time information on location, speed, and lane, while also using a facial recognition camera and microphone to analyze the user's facial expressions and tone of voice to collect emotional data.

[1156] Data reception and analysis

[1157] The server inputs the received data into an analytical algorithm, which analyzes driving behavior patterns and detects abnormal behavior. The emotion engine also analyzes the transmitted emotional data and evaluates the user's emotional state, such as whether they are stressed or relaxed.

[1158] Examples:

[1159] The server analyzes the driving data for the day and obtains the results of "3 instances of sudden braking," "2 instances of speeding," and "1 instance of lane departure." At the same time, the emotion engine evaluates that "the user's stress level is high while driving."

[1160] Detecting and alerting abnormal behavior and emotional states

[1161] The server then performs a comprehensive evaluation of the abnormal behavior and the user's emotional state based on the analysis results. For example, if the emotion engine detects a high frequency of sudden braking and the user is under stress, it generates a warning message saying, "You are braking frequently and appear to be stressed. We recommend that you drive safely and relax."

[1162] Examples:

[1163] If the server determines based on the analysis results that "the vehicle is frequently drifting out of its lane and the user is feeling high stress," it generates a corresponding warning message and sends it to the device.

[1164] Comprehensive feedback to the elderly

[1165] The AI ​​generates psychological support messages for elderly people based on driving behavior data and emotional data. This helps users understand their own driving ability and emotional state, encouraging them to accept surrendering their license in a positive manner. The generated messages are displayed to the user via their device.

[1166] Examples:

[1167] The device displays a feedback message saying, "Based on your recent driving data and emotional state, we'll provide you with advice to help you continue driving safely. Take short breaks to reduce stress."

[1168] conclusion

[1169] The system of this invention ensures traffic safety throughout society by providing a detailed assessment of the driving ability and emotional state of elderly people and identifying the limits of safe driving. By utilizing generative AI and an emotion engine, it encourages elderly people to surrender their licenses at the appropriate time while protecting their self-esteem. Furthermore, by collaborating with local governments, the system contributes to solving social issues by providing support for daily life after license surrender.

[1170] The processing flow will be explained below.

[1171] Step 1:

[1172] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. The device acquires location information every second and also records the vehicle's speed and acceleration. The device also has a built-in camera and microphone, which are used to collect the driver's facial expressions and voice data.

[1173] Step 2:

[1174] The data collected by the device is sent to a server at regular intervals (e.g., every minute). The data sent includes location information, speed information, lane identification information, records of sudden braking, facial expression data, and voice data.

[1175] Step 3:

[1176] The server inputs the received data into an analytical algorithm, which then analyzes driving behavior patterns based on this data to detect abnormal behavior, analyzes the emotional data sent by the emotion engine, and evaluates the user's emotional state.

[1177] Step 4:

[1178] The server evaluates the driver's safe driving based on the analysis of driving behavior data and emotional data, and calculates a safe driving score by comprehensively evaluating factors such as the number of sudden braking attempts, speeding, lane departure frequency, and emotional states recognized from facial expressions and voice.

[1179] Step 5:

[1180] The server generates a warning message based on the analysis results. For example, if the emotion engine detects a high frequency of sudden braking and the user is under stress, it will create a warning message such as, "You are braking frequently. You seem to be under stress. We recommend that you drive carefully and relax."

[1181] Step 6:

[1182] The server generates a warning message and sends it to the terminal, which then receives it and notifies the user via an in-car display or voice message.

[1183] Step 7:

[1184] The AI ​​then generates a psychological support message based on the analysis results and warning message. It creates a message that encourages the user to surrender their license while maintaining their self-esteem. For example, it makes a positive suggestion such as, "Why not get a professional diagnosis to ensure safe driving?"

[1185] Step 8:

[1186] The AI ​​generates and sends psychological support messages to the device, which displays the messages to provide the driver with a sense of psychological security and encourage them to improve their driving behavior.

[1187] Step 9:

[1188] The device provides the user with a comprehensive driving evaluation and feedback. It displays detailed analysis results of driving behavior and emotional data, and suggests specific improvements and changes to driving habits. For example, it provides specific advice such as, "There are many cases of sudden braking at intersections, so try to slow down when entering intersections."

[1189] Step 10:

[1190] Users will receive feedback and work to improve their driving skills and emotional state. If necessary, we will work with local governments to provide support for life after surrendering their license.

[1191] Example 2

[1192] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1193] The decline in driving ability among the elderly increases the risk of traffic accidents, but currently it is difficult to assess and respond to this decline. Furthermore, unilateral warnings and guidance without considering the driver's emotional state and psychological burden can damage the elderly's self-esteem. This can lead to problems such as the risk of continuing to drive and resistance to surrendering their licenses. Therefore, future solutions that support safe driving and reduce psychological burden are needed.

[1194] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: a means for monitoring the driving behavior of the elderly in real time and collecting location information, speed information, lane identification information, and sudden braking information; a means for analyzing the collected data and evaluating the driving safety; a means for acquiring the driving behavior data as well as the driver's facial expression and voice data to analyze the emotional state; a means for detecting abnormal behavior and providing a warning based on the results of the driving safety evaluation and the emotional state analysis; and a means for notifying the elderly of the generated warning. This not only enables support for safe driving but also provides appropriate feedback taking into account the driver's emotional state, making it possible to reduce the psychological burden on the elderly and encourage them to continue driving safely and surrender their licenses.

[1195] "Elderly driving behavior" refers to the vehicle operation methods and driving patterns of elderly drivers, including specific behaviors such as speed, lane changes, and braking.

[1196] "Real-time" refers to the near-instant processing and reflection of data and events as they occur, with almost no delay.

[1197] "Location Information" refers to the current geographic location of a vehicle or driver, collected through technologies such as GPS.

[1198] "Speed ​​information" is data relating to the vehicle's moving speed, and is generally obtained from the vehicle's speedometer or sensors.

[1199] "Lane identification information" is data relating to the lane of the road on which the vehicle is traveling, and includes information for identifying driving behavior such as lane departure.

[1200] "Sudden braking information" refers to data on when quick and strong braking is applied while driving, and is used to evaluate safe driving.

[1201] "Facial expression data" refers to the collection of data by using sensors such as cameras to recognize the driver's facial expressions.

[1202] "Voice data" refers to collecting the driver's speech and tones using a voice input device such as a microphone and analyzing that data.

[1203] "Emotional state analysis" refers to the process of assessing the driver's emotional state (e.g., stress or relaxation) based on collected facial expression and voice data.

[1204] "Abnormal behavior" refers to actions or patterns that are not considered safe driving, such as sudden braking, speeding, and lane departure.

[1205] "Providing a warning" means alerting a driver to a safety risk or abnormal behavior.

[1206] "Feedback" refers to providing information and advice to drivers based on the analysis results, encouraging them to improve their driving behavior.

[1207] This invention relates to a system that closely monitors the driving behavior of elderly people, detects any decline in driving ability early, and provides appropriate feedback. In particular, by combining it with an emotion engine, it is possible to support safe driving while taking into consideration the driver's emotional state.

[1208] System Configuration

[1209] Terminal

[1210] The terminal is installed in the vehicle and collects GPS data, speed data, lane identification data, and sudden braking data in real time. The terminal is equipped with sensors such as a camera and microphone, which can analyze the driver's facial expressions and voice and collect emotional data. Specific hardware includes a GPS sensor, speed sensor, facial recognition camera, microphone, lane identification camera, and acceleration sensor.

[1211] server

[1212] The server is a central processing unit that receives collected data sent from the terminal and analyzes it using analytical algorithms. The server analyzes driving data and emotional data, evaluates abnormal behavior and the driver's emotional state, and generates appropriate feedback. Specific software used includes data analysis algorithms and an emotion engine.

[1213] Generation AI

[1214] The AI ​​generator is installed on a server and generates psychological support messages for the elderly based on the analysis results. The AI ​​generator can create messages that reduce the psychological burden on the elderly and encourage them to surrender their driver's licenses in a positive manner without damaging their self-esteem.

[1215] Emotion Engine

[1216] The emotion engine recognizes and analyzes the user's emotional state in real time, and performs a comprehensive evaluation in conjunction with driving data. The emotion engine analyzes facial expression data and voice data, and can evaluate stress, relaxation state, etc.

[1217] Program processing explanation

[1218] Data collection

[1219] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. At the same time, it uses the device's camera and microphone to collect the driver's facial expression and voice data, which are then sent to the emotion engine. For example, when a user starts driving, the device collects location, speed, and lane information in real time, and uses the facial recognition camera and microphone to analyze the user's facial expression and tone of voice to collect emotion data.

[1220] Data analysis

[1221] The server receives the driving data and emotional data sent from the device and begins processing it using an analytical algorithm. The server analyzes driving behavior patterns and detects whether there are any abnormal behaviors. It also analyzes the emotional data sent by the emotion engine to evaluate whether the user is stressed or relaxed. As a specific example, if a day's driving data is analyzed and the results show "three instances of sudden braking," "two instances of speeding," and "one instance of lane departure," a comprehensive evaluation is made based on these results.

[1222] Generate feedback

[1223] The AI ​​generates psychological support messages for users based on driving behavior data and emotional data. The generated messages are presented to the user via the device. For example, the AI ​​could generate a message such as, "Based on your recent driving data and emotional state, we would like to provide you with advice on how to continue driving safely. Please take a short break to reduce stress," and the message would be displayed on the device.

[1224] Examples of concrete examples and prompts

[1225] For example, if the emotion engine detects high stress levels due to frequent sudden braking while driving, it will generate a warning message stating, "You are braking frequently and appear to be stressed. We recommend that you drive safely and relax," and send it to the device. An example of a prompt is, "Please generate advice for safe driving based on driving data of elderly people."

[1226] This system not only supports safe driving, but also provides appropriate feedback that takes into account the driver's emotional state, reducing the psychological burden on elderly people and encouraging them to continue driving safely and surrender their licenses.

[1227] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1228] Step 1: Collecting driving and emotion data

[1229] The device is installed in the vehicle and collects real-time GPS data, speed data, lane identification data, and sudden braking data, and also uses a camera and microphone to collect the driver's facial expressions and voice data.

[1230] Input: User's driving behavior, facial expressions, and voice

[1231] Output: Driving data and emotion data (position information, speed information, lane identification information, sudden braking information, facial expression data, voice data)

[1232] Specific behavior:

[1233] When the user starts driving, the device instantly acquires location information using the GPS sensor, measures speed using the speed sensor, monitors lane departure using the lane identification camera, collects sudden braking data using the acceleration sensor, analyzes the driver's facial expressions using the facial recognition camera, and collects voice tones using the microphone.

[1234] Step 2: Sending data

[1235] The device sends the collected driving data and emotion data to a server.

[1236] Input: Collected driving data and emotion data

[1237] Output: Sending data from the device to the server

[1238] Specific behavior:

[1239] The device transmits location, speed, lane departure, sudden braking, facial expressions, and voice data to the server at regular intervals, for example, every minute.

[1240] Step 3: Receiving and analyzing data

[1241] The server receives the transmitted driving data and emotion data and analyzes the data using an analytical algorithm.

[1242] Input: Driving data and emotion data sent from the device

[1243] Output: Driving behavior patterns, presence or absence of abnormal behavior, and evaluation results of emotional state

[1244] Specific behavior:

[1245] The server distributes the received data to the analysis module, and the driving data is analyzed using a driving pattern recognition algorithm to detect the frequency of sudden braking, speeding, and lane departure.The emotion engine evaluates the user's stress and relaxation state based on facial expression and voice data.

[1246] Step 4: Detecting abnormal behavior and emotional states and generating alerts

[1247] The server comprehensively evaluates abnormal behavior and emotional state based on the analysis results and generates appropriate warnings.

[1248] Input: Analyzed driving and emotion data

[1249] Output: Appropriate warning message

[1250] Specific behavior:

[1251] If the server determines that "sudden braking is occurring frequently and the emotion engine has detected high stress," it will generate a warning message stating, "You are braking frequently and appear to be under stress. We recommend that you drive safely and relax," and send it to the device.

[1252] Step 5: Generate a psychological support message

[1253] The generative AI generates psychological support messages for the elderly based on driving behavior data and emotional data.

[1254] Input: driving behavior data, emotion data, analysis results

[1255] Output: Psychological support message

[1256] Specific behavior:

[1257] The AI ​​generates a message saying, "Based on your recent driving data and emotional state, we'd like to provide you with advice on how to continue driving safely. Take a short break to reduce stress," and the device displays it to the user.

[1258] In this way, the present invention comprehensively evaluates the driving behavior and emotional state of elderly people and supports safe and secure driving.

[1259] (Application example 2)

[1260] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1261] The driving behavior of elderly people is often a safety concern due to the decline in physical and cognitive abilities that comes with aging. While abnormal behavior, such as sudden braking and lane departure, is particularly common, drivers themselves may have difficulty noticing these changes. Furthermore, because emotional stress also affects driving stability, understanding the psychological state of drivers while driving and providing appropriate feedback is essential. Current driving assistance systems lack a mechanism for comprehensively evaluating these complex factors and providing feedback in real time. Therefore, a system is needed that simultaneously monitors driving behavior and emotional state and provides comprehensive support to enable elderly people to continue driving with peace of mind.

[1262] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1263] In this invention, the server includes a means for collecting and analyzing driving behavior data and emotional data in real time, a means for evaluating the psychological state of the driver and generating appropriate feedback, and a means for comprehensively evaluating the collected data and detecting abnormal behavior, thereby enabling simultaneous monitoring of the driving behavior and emotional state of elderly people and providing comprehensive support to ensure safety and psychological stability.

[1264] "Driving behavior data" refers to location information, speed data, lane identification data, and sudden braking data obtained from a vehicle while an elderly person is driving.

[1265] "Emotional data" refers to data obtained by analyzing the psychological state of the driver, as determined from their facial expressions, voice, etc.

[1266] "Psychological state" refers to the mental state, including emotions such as stress and relaxation, that a driver feels while driving.

[1267] "Feedback" refers to information such as warnings and advice provided to the driver based on the analysis results.

[1268] "Abnormal behavior" refers to actions that deviate from normal safe driving, such as sudden braking or lane departure.

[1269] "Real-time" refers to data collection, analysis, and notification occurring almost simultaneously within a very short period of time.

[1270] "Analysis means" refers to methods and devices for evaluating driving behavior and emotional state based on collected data and determining driving safety and psychological stability.

[1271] "Means for analyzing the driver's facial expressions and voice" refers to methods and devices that use cameras and microphones to collect the driver's facial expressions and tone of voice and identify their emotional state.

[1272] A system for monitoring elderly driving behavior and emotional state

[1273] This invention is a system that provides safe driving and psychological support by closely monitoring the driving behavior of elderly people and analyzing their emotional state. The system consists of a terminal installed in the vehicle, a server that analyzes the data, a generative AI, and an emotion engine.

[1274] System Components

[1275] 1. Device:

[1276] The device is installed in the vehicle and collects GPS data, speed data, lane identification data, and sudden braking data in real time. It also has a built-in camera and microphone to detect the driver's facial expressions and voice, allowing it to obtain driving behavior data and emotional data.

[1277] 2. Server:

[1278] The server receives and analyzes the collected driving behavior data and emotion data. It uses a dedicated algorithm to analyze driving behavior patterns and detect abnormal behavior. It also analyzes the emotion data sent by the emotion engine to evaluate the driver's emotional state. Specifically, it uses Python-based analysis algorithms (e.g., Scikit-learn, TensorFlow) and emotion engines (e.g., IBM Watson, Microsoft Azure Cognitive Services).

[1279] 3. Generation AI:

[1280] The AI ​​generates psychological support messages for elderly people based on driving behavior data and emotion data on a server. These messages are used to reduce the psychological burden on drivers and promote safe driving.

[1281] 4. Emotion Engine:

[1282] The emotion engine recognizes and analyzes the driver's emotional state in real time, and this data is later combined with driving behavior data for a comprehensive evaluation.

[1283] Data collection and analysis

[1284] Once the driver starts driving, the device collects real-time GPS data, speed data, lane identification data, and sudden braking data, and also uses a facial recognition camera and microphone to collect the driver's facial expressions and voice to generate emotion data, thereby monitoring both driving behavior and psychological state.

[1285] The server receives the data sent from the device and inputs it into an analytical algorithm. It analyzes driving behavior patterns and emotional states to detect abnormal behavior and high stress levels. This allows for an evaluation of driving safety and psychological stability.

[1286] Feedback generation and notification

[1287] The AI ​​then generates a feedback message for the elderly based on the analysis results received from the server. This message provides appropriate advice to the driver and promotes safe driving. For example, a message might be generated that reads, "You are braking suddenly frequently and feeling stressed. Take a short break to relax."

[1288] The device then generates and sends feedback messages to the driver in real time, helping the driver understand not only their driving behavior but also their emotional state.

[1289] Examples and prompts

[1290] Examples:

[1291] When an elderly person begins driving, the vehicle's various sensors, cameras, and microphones collect data. The camera and microphone detect frequent sudden braking and an increase in the driver's stress level. The data is analyzed on the server, and the AI ​​generates a feedback message saying, "Drive safely and take short breaks to relax." This message is displayed in real time on the driver's smartphone.

[1292] Example prompt sentence:

[1293] Develop a system that analyzes elderly driving behavior and emotion data, detects sudden braking and increased stress levels, and provides real-time feedback. Use IBM Watson as the emotion engine and Scikit-learn for data analysis.

[1294] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1295] Step 1:

[1296] Data collection

[1297] The device collects real-time GPS data, speed data, lane identification data, and sudden braking data when the driver starts the vehicle. It also uses a camera and microphone to collect the driver's facial expressions and voice data, thereby obtaining driving behavior data and emotional data as input.

[1298] Step 2:

[1299] Data transmission

[1300] The device transmits the collected driving behavior data and emotion data to the server via Bluetooth or Wi-Fi. The transmitted data is processed in real time, so it is desirable to have no delay. Once the data transmission is complete, the transmitted data becomes input to the server.

[1301] Step 3:

[1302] Data analysis

[1303] The server analyzes the received driving behavior data and emotion data. Specifically, it uses Python-based analysis algorithms (e.g., Scikit-learn, TensorFlow) to analyze driving behavior patterns and determine whether or not there are any abnormal behaviors. It also evaluates the driver's stress level and relaxation state based on the data sent by the emotion engine. The analysis results are saved as intermediate output.

[1304] Step 4:

[1305] Detecting Abnormal Behavior

[1306] The server detects abnormal behavior based on the analysis results. For example, if the driver brakes frequently or leaves the lane frequently, this is judged to be abnormal behavior. In parallel, if the emotion engine detects a high stress level in the driver, this is also recognized as an abnormality. When abnormal behavior is detected, the corresponding evaluation result is output.

[1307] Step 5:

[1308] Feedback Message Generation

[1309] Based on the analysis results, the server uses the generation AI to generate a feedback message for the elderly. Here, the driving behavior and emotional state are comprehensively evaluated, and a message is generated that provides appropriate advice to the driver. For example, a message such as "You are braking suddenly frequently and are feeling stressed. Take a short break to relax" may be generated. This is the final output.

[1310] Step 6:

[1311] Feedback Notification

[1312] The device receives the feedback messages generated by the generation AI and notifies the driver in real time via a smartphone or in-car display. Once the notification is complete, the driver can understand their own driving behavior and emotional state and take appropriate action. This provides feedback to the driver as the final output.

[1313] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1314] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1315] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1316] [Fourth embodiment]

[1317] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1318] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1319] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1320] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1321] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1322] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1323] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1324] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1325] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1326] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1327] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1328] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1329] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1330] This invention relates to a system that evaluates the driving ability of elderly people in detail and suggests that they surrender their license when they reach the limit of safe driving. The system mainly consists of a terminal installed in the vehicle and a server that analyzes the data.

[1331] System Configuration

[1332] 1. Terminal: A device installed inside the vehicle that collects GPS and speed data in real time. The terminal also has the function of closely monitoring the driving behavior of elderly drivers using multiple sensors, including cameras, speedometers, and accelerometers.

[1333] 2. Server: A central processing unit that receives collected data and analyzes it using a dedicated algorithm. Based on the analysis results, the server detects abnormal behavior and generates a warning if it determines that safe operation cannot be continued.

[1334] 3. Generative AI: This is installed on the server and generates psychological support messages for the elderly based on the analysis results. The Generative AI reduces the user's psychological burden and encourages them to surrender their driver's license in a positive manner without damaging their self-esteem.

[1335] Program processing

[1336] Collecting and analyzing driving behavior

[1337] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. This data is sent to a server at regular intervals. For example, the device acquires location information every second and processes camera images for lane keeping. This allows for detailed recording of elderly driving behavior.

[1338] Examples:

[1339] Once the user starts driving, the device will continuously acquire location information, collect speed data, and record any lane departures or sudden braking.

[1340] Data reception and analysis

[1341] The server analyzes the received data using a dedicated algorithm. The server analyzes driving behavior patterns and counts the number of times the driver habitually brakes suddenly or deviates from their lane, etc. This allows the overall driving safety to be evaluated.

[1342] Examples:

[1343] The server analyzes the data for a given day and obtains the results of "3 instances of sudden braking," "2 instances of speeding," and "1 instance of lane departure." Based on these results, an overall safe driving score is calculated and evaluated.

[1344] Detecting abnormal behavior and generating alerts

[1345] The server detects abnormal behavior based on the analysis results. If the standard for safe driving is exceeded, a warning message is generated. For example, if the frequency of sudden braking is too high, the server generates a warning saying, "Safe driving is difficult due to frequent sudden braking."

[1346] Examples:

[1347] The server analyzes the user's driving data and, if it determines that the user is frequently drifting out of their lane, it generates a warning message and sends it to the device.

[1348] Psychological support for the elderly

[1349] Based on the analysis results, the AI ​​generates a psychological support message for the elderly. This helps them understand their declining driving ability and encourages them to positively consider returning their license. The generated message is displayed to the user via the device.

[1350] Examples:

[1351] When a user receives a driving evaluation message, the device will display, "Based on your recent driving data, we would like to provide you with some advice to help you continue driving safely," and provide specific suggestions for improvement.

[1352] conclusion

[1353] The system of this invention ensures traffic safety throughout society by providing a detailed assessment of the driving ability of elderly people and identifying the limits of safe driving. It also uses generative AI to provide psychological support, helping elderly people to think positively about returning their driver's licenses while preserving their self-esteem. By collaborating with local governments, the system also provides support for daily life after license surrender, contributing to the resolution of social issues.

[1354] The processing flow will be explained below.

[1355] Step 1:

[1356] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. The device obtains location information every second and also records the vehicle's speed and acceleration.

[1357] Step 2:

[1358] The device sends the collected data to a server at regular intervals (e.g., every minute). The data sent includes location information, speed information, lane identification information, and records of sudden braking.

[1359] Step 3:

[1360] The server inputs the received data into an analytical algorithm, which analyzes driving patterns based on this data and detects abnormal behavior.

[1361] Step 4:

[1362] The server analyzes the results of abnormal behavior detection and evaluates the safe driving, calculating a safe driving score based on a comprehensive evaluation of factors such as the number of sudden braking attempts, frequency of lane departures, and whether or not the driver exceeded the speed limit.

[1363] Step 5:

[1364] The server generates a warning message based on the analysis results. For example, if the frequency of sudden braking is high, it creates a warning message such as "Sudden braking is occurring frequently. Please drive safely."

[1365] Step 6:

[1366] The server generates a warning message and sends it to the terminal, which then receives it and notifies the user via an in-car display or voice message.

[1367] Step 7:

[1368] The AI ​​generates a psychological support message based on the analysis results and warning message, creating a message that encourages the user to surrender their license while maintaining their self-esteem.

[1369] Step 8:

[1370] The AI ​​generates and sends psychological support messages to the device, which displays the messages to provide the user with a sense of psychological security and encourage them to improve their driving behavior.

[1371] Step 9:

[1372] The device provides users with a comprehensive driving evaluation and feedback, displays detailed analysis results of their driving behavior, and suggests specific improvements and changes to their driving habits.

[1373] Step 10:

[1374] Users will receive feedback and strive to improve their driving skills. If necessary, we will work with local governments to provide support for life after surrendering their license.

[1375] Example 1

[1376] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1377] The decline in driving ability among the elderly increases the risk of traffic accidents, and this problem needs to be addressed. However, conventional methods make it difficult for elderly people to recognize their own decline in driving ability and voluntarily return their licenses, which could have a significant impact on traffic safety throughout society. Another issue is the psychological burden that the process of returning a license places on elderly people.

[1378] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1379] In this invention, the server includes a device installed in the vehicle that monitors the driving behavior of the elderly in real time and collects location information, speed data, lane identification data, and sudden braking data, a means for buffering the collected data and periodically sending it to a central processing unit, a means for analyzing the received data with a dedicated algorithm and evaluating driving safety, a means for detecting abnormal behavior based on the evaluation of driving safety and generating a warning message, and a means for notifying the elderly of the generated warning message via the device in the vehicle. This allows a detailed evaluation of the driving ability of the elderly to determine the limits of safe driving and enables the elderly to proactively consider surrendering their driver's licenses.

[1380] The "in-vehicle device" refers to a device that monitors the driving behavior of seniors in real time and collects location information, speed data, lane identification data, and sudden braking data. This device may include sensors, cameras, GPS receivers, etc.

[1381] The "Central Processing Unit" is a computer system that analyzes received driving data and performs safety evaluations. This system includes a server, database, and analysis algorithms.

[1382] "Real-time monitoring" means that data is collected continuously while the vehicle is moving, creating an instantaneous record of driving behavior.

[1383] "Location information" refers to the vehicle's current geographic coordinate data obtained from a GPS sensor.

[1384] "Speed ​​data" refers to data on the instantaneous travel speed obtained from the vehicle's speedometer.

[1385] "Lane identification data" refers to data about the lane in which a vehicle is traveling, obtained using a camera sensor mounted on the vehicle and image processing algorithms.

[1386] "Sudden braking data" is data detected using an acceleration sensor when the vehicle suddenly brakes.

[1387] "Data buffering" is the process of temporarily storing collected data and preparing it for transmission to other systems as needed.

[1388] A "proprietary algorithm" is a specific computational method or procedure designed to analyze collected driving data.

[1389] "Driving safety assessment" is the process of measuring and evaluating the safety of driving behavior based on analyzed data.

[1390] "Abnormal behavior" refers to potentially dangerous driving behavior that deviates from normal driving patterns, such as frequent sudden braking or lane departure.

[1391] A "warning message" is a message issued to alert or warn the driver when abnormal behavior is detected.

[1392] "Real-time feedback to the elderly" is the process of instantly conveying analysis results and warning messages to the elderly.

[1393] "Generative AI" is a system that uses artificial intelligence technology to automatically generate psychological support messages based on analysis results.

[1394] "Psychological support messages" are messages designed to help elderly people understand their declining driving ability and encourage them to think positively about returning their license.

[1395] This invention is a system that evaluates the driving ability of elderly people in detail and suggests that they return their license when they reach the limit of safe driving. The system's main components are a terminal installed in the vehicle and a server that analyzes the data.

[1396] Equipment configuration

[1397] Terminal

[1398] The device is installed in the vehicle and is used to monitor the driving behavior of elderly drivers in real time. The device includes the following sensors and devices:

[1399] GPS receiver: Obtains location information.

[1400] Speedometer: Captures speed data.

[1401] Camera sensor: A device for acquiring lane identification data.

[1402] Acceleration sensor: A device for detecting sudden braking.

[1403] server

[1404] The server analyzes the collected data and acts as the central processing unit.

[1405] Database: Stores the received data.

[1406] Analysis algorithm: Analyzes collected data and evaluates driving safety.

[1407] Generative AI: Generates psychological support messages based on driving evaluation.

[1408] Program processing

[1409] Data collection

[1410] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. The data is buffered internally and sent to the server at regular intervals. For example, the device obtains location information once per second and processes camera images in real time to keep the vehicle in its lane.

[1411] Data analysis

[1412] The server analyzes the data received from the device and uses specialized algorithms to evaluate driving behavior patterns and safety. The analysis includes the following elements:

[1413] Frequency of sudden braking and speeding: Count the number of times and use it to evaluate safe driving.

[1414] Lane departure detection: Analyzes the image processing results from the camera sensor and evaluates the frequency of lane departures.

[1415] Detecting Abnormal Behavior

[1416] The server detects abnormal behavior based on the analysis results. For example, it identifies abnormal driving patterns based on results such as "three instances of sudden braking," "two instances of speeding," and "one instance of lane departure." When an abnormality is detected, a warning message is automatically generated.

[1417] Sending a warning message

[1418] If the server detects abnormal behavior, it generates a warning message and sends it to the terminal. The terminal uses a display device to notify the driver of this warning message. For example, a message such as "Sudden braking is occurring frequently. Please drive safely" is displayed.

[1419] Psychological support

[1420] The AI ​​then generates a psychological support message based on the analysis results. The support message includes content to encourage elderly people to think positively about returning their driver's license. The generated message is then displayed to the user via their device.

[1421] Specific examples

[1422] When the user starts driving, the device continuously acquires location information and collects speed data. It also records any instances when the driver nearly leaves their lane or when the driver suddenly brakes. The server analyzes the data for a given day and obtains results such as "three instances of sudden braking," "two instances of speeding," and "one instance of lane departure." Based on these results, an overall safe driving score is calculated and evaluated. Based on the analysis results, the server generates a warning message that is notified to the user. In addition, the AI ​​generates psychological support messages, offering advice to the user on improving their driving ability or suggesting that they consider surrendering their license in a positive manner.

[1423] Prompt Sentence Examples

[1424] "Data has been collected showing that users frequently brake suddenly while driving. Analyze this data and generate a message to advise users on safe driving and suggest that they surrender their license."

[1425] Thus, the invention provides a holistic system for properly assessing the driving ability of elderly people and improving road safety.

[1426] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1427] Step 1:

[1428] The terminal collects the vehicle's GPS data, speed data, lane identification data and sudden braking data in real time.

[1429] Inputs: Data from GPS sensor, speedometer, camera sensor, and accelerometer.

[1430] Data processing: Each data is converted into a specified format and a timestamp is added.

[1431] Specific behavior:

[1432] The device obtains its current location information from the GPS module every second.

[1433] Speed ​​information is also obtained from the speedometer every second and the current speed is recorded.

[1434] The camera sensor captures images at regular intervals and the image processing algorithm analyzes the possibility of lane departure.

[1435] If the acceleration sensor detects sudden braking, it records the data.

[1436] Output: Collected data (location, speed, lane identification, and hard braking).

[1437] Step 2:

[1438] The terminal buffers the collected data and periodically transmits it to the server.

[1439] Input: Data collected in step 1.

[1440] Data processing: Buffer data at regular intervals (e.g., every minute).

[1441] Specific behavior:

[1442] Data is temporarily stored in the device's internal memory.

[1443] The buffered data is collected at set intervals and sent to the server via the communication module when it is ready.

[1444] Output: The data sent to the server.

[1445] Step 3:

[1446] The server analyzes the data received from the terminal.

[1447] Input: Data sent from the device (location, speed, lane identification, and emergency braking).

[1448] Data processing: Received data is synchronized on the same timeline and converted into an analyzable format.

[1449] Specific behavior:

[1450] The server stores the data in a database and applies an analysis algorithm.

[1451] Data from different sensors will be integrated on the same time axis to create a dataset for analysis.

[1452] Output: A dataset for analysis.

[1453] Step 4:

[1454] The server uses an analytical algorithm to evaluate driving behavior patterns.

[1455] Input: The analysis dataset created in step 3.

[1456] Data processing: Analysis of driving behavior patterns and safety evaluation.

[1457] Specific behavior:

[1458] The analysis algorithm counts the frequency of sudden braking and speeding and assesses the possibility of lane departure.

[1459] The evaluation results of each driving behavior are combined to calculate an overall safe driving score.

[1460] Output: Evaluation results of driving behavior patterns.

[1461] Step 5:

[1462] The server detects abnormal behavior based on the evaluation results of driving behavior patterns and generates a warning message.

[1463] Input: Evaluation results of driving behavior patterns in step 4.

[1464] Data processing: Identify abnormal behavior from the evaluation results and generate warning messages.

[1465] Specific behavior:

[1466] If abnormal driving patterns (e.g., frequent sudden braking or frequent lane departures) are detected, a warning message will be generated based on this.

[1467] Sends a warning message to the terminal.

[1468] Output: The warning message generated.

[1469] Step 6:

[1470] The terminal notifies the user of the warning message received from the server.

[1471] Input: The warning message sent by the server.

[1472] Data processing: Converting messages into a format that is easy for users to understand.

[1473] Specific behavior:

[1474] A warning message is notified to the user in real time using the display device of the terminal.

[1475] For example, a message such as "Sudden braking is occurring frequently. Please drive safely" may be displayed.

[1476] Output: The warning message posted.

[1477] Step 7:

[1478] The generation AI generates a psychological support message based on the evaluation results of driving behavior and sends it to the device.

[1479] Input: Evaluation results of driving behavior patterns.

[1480] Data processing: Generate psychological support messages based on the analysis results.

[1481] Specific behavior:

[1482] Generative AI automatically generates positive messages to reduce the psychological burden on the elderly.

[1483] The message is sent to the terminal, which displays it to the user.

[1484] For example, create a message such as, "Based on your recent driving data, we would like to provide you with advice on how to continue driving safely."

[1485] Output: The generated psychological support message.

[1486] In this way, each step works in conjunction with the others to carry out a series of processes from data collection to analysis, warnings, and psychological support.

[1487] (Application example 1)

[1488] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1489] The driving ability of elderly people can decline with age, increasing the risk of traffic accidents. However, it is difficult for elderly people to recognize this decline in their driving ability, and they often do not surrender their licenses at the appropriate time. For this reason, there is a need for a system that can evaluate elderly people's driving behavior in detail and support safe driving. Furthermore, if abnormal behavior occurs while driving and it becomes difficult to continue driving safely, safety measures such as switching to autonomous driving mode are necessary. There is also a need for support to help elderly people think positively about surrendering their licenses without feeling any psychological burden.

[1490] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1491] In this invention, the server includes a means for analyzing collected driving data and evaluating driving safety, a means for detecting abnormal driving behavior, and a means for switching to autonomous driving mode when abnormal behavior is detected. This allows elderly drivers to drive safely by immediately switching to autonomous driving mode when abnormal behavior is detected. Furthermore, by using generative AI to provide psychological support to elderly drivers and generate prompts that positively encourage them to switch to safe driving mode, it is possible to protect the elderly's self-esteem, encourage them to be aware of their declining driving ability, and support them in surrendering their licenses at the appropriate time.

[1492] The term "elderly" refers to people over a certain age, generally including those aged 65 and over.

[1493] "Driving behavior" refers to a series of actions and behaviors when operating a vehicle, and specifically includes acceleration, deceleration, steering, lane keeping, etc.

[1494] "Real-time" means that data and information processing and feedback occurs immediately.

[1495] "Monitoring" refers to the continuous observation of a specific object or situation to detect abnormalities or changes.

[1496] "Location Information" means data indicating the current geographic coordinates of a vehicle or person.

[1497] "Speed ​​information" is data indicating the moving speed of an object, and in the case of a vehicle, indicates the vehicle speed.

[1498] "Collection" refers to the act of obtaining the target data or information using various sensors and devices.

[1499] "Analysis" means examining the data and information obtained in detail and deriving its meaning and trends.

[1500] "Safety assessment" refers to the analysis and evaluation of a vehicle to determine whether it is safe to operate the vehicle based on driving behavior.

[1501] "Abnormal behavior" refers to actions or behavior that are different from or deviate from normal, and in driving it refers to sudden braking or lane departure.

[1502] "Warning" means a message or signal that calls attention to a potential danger or abnormality.

[1503] "Informing" refers to the act of conveying specific information or data to a target person.

[1504] "Autonomous driving mode" refers to systems and functions that allow a vehicle to operate automatically without human intervention.

[1505] "Lane maintenance" refers to the act of a vehicle continuing to travel steadily within the lane in which it is currently traveling.

[1506] "Lane departure" refers to the phenomenon in which a vehicle unintentionally leaves the lane in which it is currently traveling.

[1507] "Sudden braking" refers to the act of suddenly braking the vehicle, deviating from normal driving conditions.

[1508] "Feedback" refers to returning information to an original subject based on the results of an action or process in order to improve or adjust it.

[1509] "Memory ability" refers to the ability to properly retain past information and retrieve it when needed.

[1510] "Judgment" refers to the ability to take appropriate actions and decisions based on the situation and information.

[1511] "Generative AI" refers to a system that uses artificial intelligence technology to automatically generate text or messages.

[1512] "Psychological support" means assistance to provide a sense of psychological security and stability.

[1513] A "prompt" is a message that the generation AI displays to the user to encourage voluntary action.

[1514] A specific system configuration for implementing the present invention will be described. The overall operation of the system that supports safe driving for elderly people and switches to automatic driving mode when an abnormality is detected will be described.

[1515] Overall system configuration

[1516] The system consists of the following main components:

[1517] 1. Terminal: A device installed inside the vehicle that incorporates various sensors such as a GPS module, speed sensor, and brake sensor. This terminal monitors the driving behavior of elderly people in real time and transmits the collected location and speed information to a server.

[1518] 2. Server: This is the central processing unit that receives data sent from the terminals. The server analyzes the driving data and executes dedicated algorithms to evaluate safety. It is also responsible for detecting abnormal behavior and switching to autonomous driving mode.

[1519] 3. Generative AI model: This is built into the server and generates psychological support messages based on the analysis of driving data, providing prompts that reduce the elderly's resistance to switching to safe driving mode at the appropriate time.

[1520] Data collection

[1521] The terminal is installed inside the vehicle and collects the following data in real time:

[1522] Location information: The current location of the vehicle is obtained using the GPS module.

[1523] Speed ​​information: Measure the vehicle's moving speed through a speed sensor.

[1524] Driving behavior: Equipped with sensors that monitor driving behavior such as sudden braking and lane departure.

[1525] Data analysis

[1526] The server receives the collected driving data and evaluates the driving safety using a dedicated analysis algorithm. This analysis includes the following steps:

[1527] Sudden braking detection: Analyzes data from the brake sensor to determine whether sudden braking occurs frequently.

[1528] Lane departure detection: Using cameras and sensors, counts the number of times the vehicle deviates from its lane.

[1529] Overall evaluation: Based on the above data, a driving safety score is calculated and abnormal behavior is detected.

[1530] Switching to autonomous driving mode

[1531] If abnormal behavior is detected, the server sends an instruction to the device to switch to autonomous driving mode. This function allows the device to immediately switch to autonomous driving mode when safe driving becomes difficult.

[1532] Psychological support

[1533] The generative AI model generates prompts to provide psychological support to the elderly when abnormal behavior is detected. For example, the following message is displayed:

[1534] Generator prompt:

[1535] Your recent driving data has revealed that you have experienced frequent sudden braking. For your safety, we recommend switching to Autopilot mode. This feature will allow you to continue your journey with peace of mind.

[1536] In this way, a system is realized that monitors elderly driving in real time and provides a series of functions to ensure safety.

[1537] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1538] Step 1:

[1539] The device collects data in real time from GPS modules, speed sensors, brake sensors, etc.

[1540] Input: Vehicle location, speed, and emergency braking data.

[1541] Output: The raw data collected.

[1542] Specific operation: The GPS module acquires location information every second, the speed sensor measures the vehicle's speed, and the brake sensor detects sudden braking. This data is stored in the terminal.

[1543] Step 2:

[1544] The terminal transmits the collected driving data to the server at regular intervals.

[1545] Input: Raw data collected.

[1546] Output: Driving data sent to the server.

[1547] Specific operation: The terminal compiles the collected data into packets at regular intervals and sends them to the server via the network.

[1548] Step 3:

[1549] The server receives the driving data and runs an analysis algorithm to evaluate the safety of the driving.

[1550] Input: Driving data sent from the terminal.

[1551] Output: Driving safety rating score.

[1552] Specific operation: The server stores the received data in a database, analyzes the number of sudden braking and lane departures, and then calculates an overall safe driving score based on the analysis results.

[1553] Step 4:

[1554] The server detects abnormal behavior and decides whether to switch to autonomous driving mode.

[1555] Input: Driving safety rating score.

[1556] Output: Instruction to switch to automatic driving mode.

[1557] Specific operation: If the safe driving score falls below a certain standard, the server detects abnormal behavior and sends an instruction to the device to switch to autonomous driving mode.

[1558] Step 5:

[1559] The device receives instructions from the server and switches to autonomous driving mode.

[1560] Input: Instruction to switch to autonomous driving mode.

[1561] Output: Start of Autopilot mode.

[1562] Specific operation: The terminal activates the autonomous driving system and switches operation to autonomous driving mode, allowing the vehicle to continue driving without human intervention.

[1563] Step 6:

[1564] The server uses the generative AI model to generate psychological support messages for the elderly.

[1565] Input: Analysis results of abnormal behavior.

[1566] Output: The generated prompt statement.

[1567] Specific operation: Based on the results of the abnormal behavior analysis, the server uses the generative AI model to generate positive instructions and support messages for the elderly. For example, it generates a message such as, "Based on your recent driving data, we have noticed that you have been braking frequently. For your safety, we recommend switching to autonomous driving mode. This function will allow you to continue traveling safely, so please rest assured."

[1568] Step 7:

[1569] The generated psychological support message is displayed to the user through the terminal.

[1570] Input: The generated prompt statement.

[1571] Output: The message that is displayed to the user.

[1572] Specific operation: The terminal displays the generated message obtained from the server on the display, allowing the user to check the message.

[1573] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1574] This invention relates to a system that closely monitors the driving behavior of elderly people, detects any decline in driving ability early, and provides appropriate feedback. In particular, by combining it with an emotion engine, it is possible to support safe driving while taking into consideration the driver's emotional state.

[1575] System Configuration

[1576] 1. Terminal: A device installed in a vehicle that collects GPS data, speed data, lane identification data, and sudden braking data in real time. The terminal also incorporates sensors such as a camera and microphone, and collects emotional data by analyzing the driver's facial expressions and voice.

[1577] 2. Server: A central processing unit that receives collected driving data and emotional data and analyzes them using dedicated algorithms. Based on the analysis results, the server detects abnormal behavior and emotional states and generates appropriate feedback.

[1578] 3. Generative AI: This will be installed on the server and generate psychological support messages for the elderly based on the analysis results. This will reduce the user's psychological burden, encourage them to surrender their driver's license in a positive manner, and prevent damage to their self-esteem.

[1579] 4. Emotion Engine: The emotion engine recognizes and analyzes the user's emotional state in real time and makes a comprehensive evaluation in conjunction with driving data.

[1580] Program processing

[1581] Collecting and analyzing driving behavior and emotion data

[1582] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. It also uses a camera and microphone to collect the driver's facial expressions and voice data, which it then sends to the emotion engine.

[1583] Examples:

[1584] When the user starts driving, the device collects real-time information on location, speed, and lane, while also using a facial recognition camera and microphone to analyze the user's facial expressions and tone of voice to collect emotional data.

[1585] Data reception and analysis

[1586] The server inputs the received data into an analytical algorithm, which analyzes driving behavior patterns and detects abnormal behavior. The emotion engine also analyzes the transmitted emotional data and evaluates the user's emotional state, such as whether they are stressed or relaxed.

[1587] Examples:

[1588] The server analyzes the driving data for the day and obtains the results of "3 instances of sudden braking," "2 instances of speeding," and "1 instance of lane departure." At the same time, the emotion engine evaluates that "the user's stress level is high while driving."

[1589] Detecting and alerting abnormal behavior and emotional states

[1590] The server then performs a comprehensive evaluation of the abnormal behavior and the user's emotional state based on the analysis results. For example, if the emotion engine detects a high frequency of sudden braking and the user is under stress, it generates a warning message saying, "You are braking frequently and appear to be stressed. We recommend that you drive safely and relax."

[1591] Examples:

[1592] If the server determines based on the analysis results that "the vehicle is frequently drifting out of its lane and the user is feeling high stress," it generates a corresponding warning message and sends it to the device.

[1593] Comprehensive feedback to the elderly

[1594] The AI ​​generates psychological support messages for elderly people based on driving behavior data and emotional data. This helps users understand their own driving ability and emotional state, encouraging them to accept surrendering their license in a positive manner. The generated messages are displayed to the user via their device.

[1595] Examples:

[1596] The device displays a feedback message saying, "Based on your recent driving data and emotional state, we'll provide you with advice to help you continue driving safely. Take short breaks to reduce stress."

[1597] conclusion

[1598] The system of this invention ensures traffic safety throughout society by providing a detailed assessment of the driving ability and emotional state of elderly people and identifying the limits of safe driving. By utilizing generative AI and an emotion engine, it encourages elderly people to surrender their licenses at the appropriate time while protecting their self-esteem. Furthermore, by collaborating with local governments, the system contributes to solving social issues by providing support for daily life after license surrender.

[1599] The processing flow will be explained below.

[1600] Step 1:

[1601] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. The device acquires location information every second and also records the vehicle's speed and acceleration. The device also has a built-in camera and microphone, which are used to collect the driver's facial expressions and voice data.

[1602] Step 2:

[1603] The data collected by the device is sent to a server at regular intervals (e.g., every minute). The data sent includes location information, speed information, lane identification information, records of sudden braking, facial expression data, and voice data.

[1604] Step 3:

[1605] The server inputs the received data into an analytical algorithm, which then analyzes driving behavior patterns based on this data to detect abnormal behavior, analyzes the emotional data sent by the emotion engine, and evaluates the user's emotional state.

[1606] Step 4:

[1607] The server evaluates the driver's safe driving based on the analysis of driving behavior data and emotional data, and calculates a safe driving score by comprehensively evaluating factors such as the number of sudden braking attempts, speeding, lane departure frequency, and emotional states recognized from facial expressions and voice.

[1608] Step 5:

[1609] The server generates a warning message based on the analysis results. For example, if the emotion engine detects a high frequency of sudden braking and the user is under stress, it will create a warning message such as, "You are braking frequently. You seem to be under stress. We recommend that you drive carefully and relax."

[1610] Step 6:

[1611] The server generates a warning message and sends it to the terminal, which then receives it and notifies the user via an in-car display or voice message.

[1612] Step 7:

[1613] The AI ​​then generates a psychological support message based on the analysis results and warning message. It creates a message that encourages the user to surrender their license while maintaining their self-esteem. For example, it makes a positive suggestion such as, "Why not get a professional diagnosis to ensure safe driving?"

[1614] Step 8:

[1615] The AI ​​generates and sends psychological support messages to the device, which displays the messages to provide the driver with a sense of psychological security and encourage them to improve their driving behavior.

[1616] Step 9:

[1617] The device provides the user with a comprehensive driving evaluation and feedback. It displays detailed analysis results of driving behavior and emotional data, and suggests specific improvements and changes to driving habits. For example, it provides specific advice such as, "There are many cases of sudden braking at intersections, so try to slow down when entering intersections."

[1618] Step 10:

[1619] Users will receive feedback and work to improve their driving skills and emotional state. If necessary, we will work with local governments to provide support for life after surrendering their license.

[1620] Example 2

[1621] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1622] The decline in driving ability among the elderly increases the risk of traffic accidents, but currently it is difficult to assess and respond to this decline. Furthermore, unilateral warnings and guidance without considering the driver's emotional state and psychological burden can damage the elderly's self-esteem. This can lead to problems such as the risk of continuing to drive and resistance to surrendering their licenses. Therefore, future solutions that support safe driving and reduce psychological burden are needed.

[1623] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: a means for monitoring the driving behavior of the elderly in real time and collecting location information, speed information, lane identification information, and sudden braking information; a means for analyzing the collected data and evaluating the driving safety; a means for acquiring the driving behavior data as well as the driver's facial expression and voice data to analyze the emotional state; a means for detecting abnormal behavior and providing a warning based on the results of the driving safety evaluation and the emotional state analysis; and a means for notifying the elderly of the generated warning. This not only enables support for safe driving but also provides appropriate feedback taking into account the driver's emotional state, making it possible to reduce the psychological burden on the elderly and encourage them to continue driving safely and surrender their licenses.

[1624] "Elderly driving behavior" refers to the vehicle operation methods and driving patterns of elderly drivers, including specific behaviors such as speed, lane changes, and braking.

[1625] "Real-time" refers to the near-instant processing and reflection of data and events as they occur, with almost no delay.

[1626] "Location Information" refers to the current geographic location of a vehicle or driver, collected through technologies such as GPS.

[1627] "Speed ​​information" is data relating to the vehicle's moving speed, and is generally obtained from the vehicle's speedometer or sensors.

[1628] "Lane identification information" is data relating to the lane of the road on which the vehicle is traveling, and includes information for identifying driving behavior such as lane departure.

[1629] "Sudden braking information" refers to data on when quick and strong braking is applied while driving, and is used to evaluate safe driving.

[1630] "Facial expression data" refers to the collection of data by using sensors such as cameras to recognize the driver's facial expressions.

[1631] "Voice data" refers to collecting the driver's speech and tones using a voice input device such as a microphone and analyzing that data.

[1632] "Emotional state analysis" refers to the process of assessing the driver's emotional state (e.g., stress or relaxation) based on collected facial expression and voice data.

[1633] "Abnormal behavior" refers to actions or patterns that are not considered safe driving, such as sudden braking, speeding, and lane departure.

[1634] "Providing a warning" means alerting a driver to a safety risk or abnormal behavior.

[1635] "Feedback" refers to providing information and advice to drivers based on the analysis results, encouraging them to improve their driving behavior.

[1636] This invention relates to a system that closely monitors the driving behavior of elderly people, detects any decline in driving ability early, and provides appropriate feedback. In particular, by combining it with an emotion engine, it is possible to support safe driving while taking into consideration the driver's emotional state.

[1637] System Configuration

[1638] Terminal

[1639] The terminal is installed in the vehicle and collects GPS data, speed data, lane identification data, and sudden braking data in real time. The terminal is equipped with sensors such as a camera and microphone, which can analyze the driver's facial expressions and voice and collect emotional data. Specific hardware includes a GPS sensor, speed sensor, facial recognition camera, microphone, lane identification camera, and acceleration sensor.

[1640] server

[1641] The server is a central processing unit that receives collected data sent from the terminal and analyzes it using analytical algorithms. The server analyzes driving data and emotional data, evaluates abnormal behavior and the driver's emotional state, and generates appropriate feedback. Specific software used includes data analysis algorithms and an emotion engine.

[1642] Generation AI

[1643] The AI ​​generator is installed on a server and generates psychological support messages for the elderly based on the analysis results. The AI ​​generator can create messages that reduce the psychological burden on the elderly and encourage them to surrender their driver's licenses in a positive manner without damaging their self-esteem.

[1644] Emotion Engine

[1645] The emotion engine recognizes and analyzes the user's emotional state in real time, and performs a comprehensive evaluation in conjunction with driving data. The emotion engine analyzes facial expression data and voice data, and can evaluate stress, relaxation state, etc.

[1646] Program processing explanation

[1647] Data collection

[1648] The device collects the vehicle's GPS data, speed data, lane identification data, and sudden braking data in real time. At the same time, it uses the device's camera and microphone to collect the driver's facial expression and voice data, which are then sent to the emotion engine. For example, when a user starts driving, the device collects location, speed, and lane information in real time, and uses the facial recognition camera and microphone to analyze the user's facial expression and tone of voice to collect emotion data.

[1649] Data analysis

[1650] The server receives the driving data and emotional data sent from the device and begins processing it using an analytical algorithm. The server analyzes driving behavior patterns and detects whether there are any abnormal behaviors. It also analyzes the emotional data sent by the emotion engine to evaluate whether the user is stressed or relaxed. As a specific example, if a day's driving data is analyzed and the results show "three instances of sudden braking," "two instances of speeding," and "one instance of lane departure," a comprehensive evaluation is made based on these results.

[1651] Generate feedback

[1652] The AI ​​generates psychological support messages for users based on driving behavior data and emotional data. The generated messages are presented to the user via the device. For example, the AI ​​could generate a message such as, "Based on your recent driving data and emotional state, we would like to provide you with advice on how to continue driving safely. Please take a short break to reduce stress," and the message would be displayed on the device.

[1653] Examples of concrete examples and prompts

[1654] For example, if the emotion engine detects high stress levels due to frequent sudden braking while driving, it will generate a warning message stating, "You are braking frequently and appear to be stressed. We recommend that you drive safely and relax," and send it to the device. An example of a prompt is, "Please generate advice for safe driving based on driving data of elderly people."

[1655] This system not only supports safe driving, but also provides appropriate feedback that takes into account the driver's emotional state, reducing the psychological burden on elderly people and encouraging them to continue driving safely and surrender their licenses.

[1656] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1657] Step 1: Collecting driving and emotion data

[1658] The device is installed in the vehicle and collects real-time GPS data, speed data, lane identification data, and sudden braking data, and also uses a camera and microphone to collect the driver's facial expressions and voice data.

[1659] Input: User's driving behavior, facial expressions, and voice

[1660] Output: Driving data and emotion data (position information, speed information, lane identification information, sudden braking information, facial expression data, voice data)

[1661] Specific behavior:

[1662] When the user starts driving, the device instantly acquires location information using the GPS sensor, measures speed using the speed sensor, monitors lane departure using the lane identification camera, collects sudden braking data using the acceleration sensor, analyzes the driver's facial expressions using the facial recognition camera, and collects voice tones using the microphone.

[1663] Step 2: Sending data

[1664] The device sends the collected driving data and emotion data to a server.

[1665] Input: Collected driving data and emotion data

[1666] Output: Sending data from the device to the server

[1667] Specific behavior:

[1668] The device transmits location, speed, lane departure, sudden braking, facial expressions, and voice data to the server at regular intervals, for example, every minute.

[1669] Step 3: Receiving and analyzing data

[1670] The server receives the transmitted driving data and emotion data and analyzes the data using an analytical algorithm.

[1671] Input: Driving data and emotion data sent from the device

[1672] Output: Driving behavior patterns, presence or absence of abnormal behavior, and evaluation results of emotional state

[1673] Specific behavior:

[1674] The server distributes the received data to the analysis module, and the driving data is analyzed using a driving pattern recognition algorithm to detect the frequency of sudden braking, speeding, and lane departure.The emotion engine evaluates the user's stress and relaxation state based on facial expression and voice data.

[1675] Step 4: Detecting abnormal behavior and emotional states and generating alerts

[1676] The server comprehensively evaluates abnormal behavior and emotional state based on the analysis results and generates appropriate warnings.

[1677] Input: Analyzed driving and emotion data

[1678] Output: Appropriate warning message

[1679] Specific behavior:

[1680] If the server determines that "sudden braking is occurring frequently and the emotion engine has detected high stress," it will generate a warning message stating, "You are braking frequently and appear to be under stress. We recommend that you drive safely and relax," and send it to the device.

[1681] Step 5: Generate a psychological support message

[1682] The generative AI generates psychological support messages for the elderly based on driving behavior data and emotional data.

[1683] Input: driving behavior data, emotion data, analysis results

[1684] Output: Psychological support message

[1685] Specific behavior:

[1686] The AI ​​generates a message saying, "Based on your recent driving data and emotional state, we'd like to provide you with advice on how to continue driving safely. Take a short break to reduce stress," and the device displays it to the user.

[1687] In this way, the present invention comprehensively evaluates the driving behavior and emotional state of elderly people and supports safe and secure driving.

[1688] (Application example 2)

[1689] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1690] The driving behavior of elderly people is often a safety concern due to the decline in physical and cognitive abilities that comes with aging. While abnormal behavior, such as sudden braking and lane departure, is particularly common, drivers themselves may have difficulty noticing these changes. Furthermore, because emotional stress also affects driving stability, understanding the psychological state of drivers while driving and providing appropriate feedback is essential. Current driving assistance systems lack a mechanism for comprehensively evaluating these complex factors and providing feedback in real time. Therefore, a system is needed that simultaneously monitors driving behavior and emotional state and provides comprehensive support to enable elderly people to continue driving with peace of mind.

[1691] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1692] In this invention, the server includes a means for collecting and analyzing driving behavior data and emotional data in real time, a means for evaluating the psychological state of the driver and generating appropriate feedback, and a means for comprehensively evaluating the collected data and detecting abnormal behavior, thereby enabling simultaneous monitoring of the driving behavior and emotional state of elderly people and providing comprehensive support to ensure safety and psychological stability.

[1693] "Driving behavior data" refers to location information, speed data, lane identification data, and sudden braking data obtained from a vehicle while an elderly person is driving.

[1694] "Emotional data" refers to data obtained by analyzing the psychological state of the driver, as determined from their facial expressions, voice, etc.

[1695] "Psychological state" refers to the mental state, including emotions such as stress and relaxation, that a driver feels while driving.

[1696] "Feedback" refers to information such as warnings and advice provided to the driver based on the analysis results.

[1697] "Abnormal behavior" refers to actions that deviate from normal safe driving, such as sudden braking or lane departure.

[1698] "Real-time" refers to data collection, analysis, and notification occurring almost simultaneously within a very short period of time.

[1699] "Analysis means" refers to methods and devices for evaluating driving behavior and emotional state based on collected data and determining driving safety and psychological stability.

[1700] "Means for analyzing the driver's facial expressions and voice" refers to methods and devices that use cameras and microphones to collect the driver's facial expressions and tone of voice and identify their emotional state.

[1701] A system for monitoring elderly driving behavior and emotional state

[1702] This invention is a system that provides safe driving and psychological support by closely monitoring the driving behavior of elderly people and analyzing their emotional state. The system consists of a terminal installed in the vehicle, a server that analyzes the data, a generative AI, and an emotion engine.

[1703] System Components

[1704] 1. Device:

[1705] The device is installed in the vehicle and collects GPS data, speed data, lane identification data, and sudden braking data in real time. It also has a built-in camera and microphone to detect the driver's facial expressions and voice, allowing it to obtain driving behavior data and emotional data.

[1706] 2. Server:

[1707] The server receives and analyzes the collected driving behavior data and emotion data. It uses a dedicated algorithm to analyze driving behavior patterns and detect abnormal behavior. It also analyzes the emotion data sent by the emotion engine to evaluate the driver's emotional state. Specifically, it uses Python-based analysis algorithms (e.g., Scikit-learn, TensorFlow) and emotion engines (e.g., IBM Watson, Microsoft Azure Cognitive Services).

[1708] 3. Generation AI:

[1709] The AI ​​generates psychological support messages for elderly people based on driving behavior data and emotion data on a server. These messages are used to reduce the psychological burden on drivers and promote safe driving.

[1710] 4. Emotion Engine:

[1711] The emotion engine recognizes and analyzes the driver's emotional state in real time, and this data is later combined with driving behavior data for a comprehensive evaluation.

[1712] Data collection and analysis

[1713] Once the driver starts driving, the device collects real-time GPS data, speed data, lane identification data, and sudden braking data, and also uses a facial recognition camera and microphone to collect the driver's facial expressions and voice to generate emotion data, thereby monitoring both driving behavior and psychological state.

[1714] The server receives the data sent from the device and inputs it into an analytical algorithm. It analyzes driving behavior patterns and emotional states to detect abnormal behavior and high stress levels. This allows for an evaluation of driving safety and psychological stability.

[1715] Feedback generation and notification

[1716] The AI ​​then generates a feedback message for the elderly based on the analysis results received from the server. This message provides appropriate advice to the driver and promotes safe driving. For example, a message might be generated that reads, "You are braking suddenly frequently and feeling stressed. Take a short break to relax."

[1717] The device then generates and sends feedback messages to the driver in real time, helping the driver understand not only their driving behavior but also their emotional state.

[1718] Examples and prompts

[1719] Examples:

[1720] When an elderly person begins driving, the vehicle's various sensors, cameras, and microphones collect data. The camera and microphone detect frequent sudden braking and an increase in the driver's stress level. The data is analyzed on the server, and the AI ​​generates a feedback message saying, "Drive safely and take short breaks to relax." This message is displayed in real time on the driver's smartphone.

[1721] Example prompt sentence:

[1722] Develop a system that analyzes elderly driving behavior and emotion data, detects sudden braking and increased stress levels, and provides real-time feedback. Use IBM Watson as the emotion engine and Scikit-learn for data analysis.

[1723] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1724] Step 1:

[1725] Data collection

[1726] The device collects real-time GPS data, speed data, lane identification data, and sudden braking data when the driver starts the vehicle. It also uses a camera and microphone to collect the driver's facial expressions and voice data, thereby obtaining driving behavior data and emotional data as input.

[1727] Step 2:

[1728] Data transmission

[1729] The device transmits the collected driving behavior data and emotion data to the server via Bluetooth or Wi-Fi. The transmitted data is processed in real time, so it is desirable to have no delay. Once the data transmission is complete, the transmitted data becomes input to the server.

[1730] Step 3:

[1731] Data analysis

[1732] The server analyzes the received driving behavior data and emotion data. Specifically, it uses Python-based analysis algorithms (e.g., Scikit-learn, TensorFlow) to analyze driving behavior patterns and determine whether or not there are any abnormal behaviors. It also evaluates the driver's stress level and relaxation state based on the data sent by the emotion engine. The analysis results are saved as intermediate output.

[1733] Step 4:

[1734] Detecting Abnormal Behavior

[1735] The server detects abnormal behavior based on the analysis results. For example, if the driver brakes frequently or leaves the lane frequently, this is judged to be abnormal behavior. In parallel, if the emotion engine detects a high stress level in the driver, this is also recognized as an abnormality. When abnormal behavior is detected, the corresponding evaluation result is output.

[1736] Step 5:

[1737] Feedback Message Generation

[1738] Based on the analysis results, the server uses the generation AI to generate a feedback message for the elderly. Here, the driving behavior and emotional state are comprehensively evaluated, and a message is generated that provides appropriate advice to the driver. For example, a message such as "You are braking suddenly frequently and are feeling stressed. Take a short break to relax" may be generated. This is the final output.

[1739] Step 6:

[1740] Feedback Notification

[1741] The device receives the feedback messages generated by the generation AI and notifies the driver in real time via a smartphone or in-car display. Once the notification is complete, the driver can understand their own driving behavior and emotional state and take appropriate action. This provides feedback to the driver as the final output.

[1742] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1743] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1744] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1745] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1746] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1747] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1748] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1749] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1750] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1751] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1752] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1753] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1754] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1755] 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.

[1756] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1757] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1758] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1759] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1760] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1761] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1762] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1763] The following is further disclosed regarding the above embodiment.

[1764] (Claim 1)

[1765] A means of monitoring seniors' driving behavior in real time and collecting location and speed data;

[1766] A means of analyzing the collected data and evaluating the safety of driving;

[1767] means for detecting abnormal behavior and providing warnings based on a driving safety assessment;

[1768] a means for notifying the senior of the generated alert;

[1769] A system including:

[1770] (Claim 2)

[1771] means for monitoring lane keeping in driving behavior and detecting lane departure;

[1772] A means for analyzing and evaluating the frequency of lane departures and the occurrence of sudden braking;

[1773] and further including means for providing feedback of the generated assessment results to the elderly person in real time.

[1774] 10. The system of claim 1.

[1775] (Claim 3)

[1776] a means of assessing changes in driving route memory ability and judgment;

[1777] A means of detecting these changes and comprehensively assessing driving ability;

[1778] Further including a means for providing psychological support to older adults using generative AI;

[1779] 10. The system of claim 1.

[1780] "Example 1"

[1781] (Claim 1)

[1782] A means for a device installed in a vehicle to monitor the driving behavior of the senior citizen in real time and collect location information, speed data, lane identification data, and sudden braking data;

[1783] means for buffering the collected data and periodically transmitting it to a central processing unit;

[1784] A means of analyzing the received data using a dedicated algorithm and evaluating the safety of driving;

[1785] means for detecting abnormal behavior and generating a warning message based on a driving safety assessment;

[1786] a means for notifying the elderly person of the generated warning message through a device in the vehicle;

[1787] A system including:

[1788] (Claim 2)

[1789] means for monitoring lane keeping and detecting lane departure in driving behavior;

[1790] A means for analyzing and evaluating the frequency of lane departures and the occurrence of sudden braking;

[1791] Further, the method includes a means for generating psychological support messages based on the analysis results using a generative AI and providing feedback to the elderly in real time.

[1792] 10. The system of claim 1.

[1793] (Claim 3)

[1794] a means of assessing changes in driving route memory ability and judgment;

[1795] A means of detecting these changes and comprehensively assessing driving ability;

[1796] Further comprising a means for providing the elderly person with a psychological support message generated based on the analysis result through the terminal.

[1797] 10. The system of claim 1.

[1798] "Application Example 1"

[1799] (Claim 1)

[1800] A means for monitoring the driving behavior of elderly people in real time and collecting location information and speed information;

[1801] A means of analyzing the collected information and evaluating the safety of driving;

[1802] means for detecting abnormal behavior and providing warnings based on a driving safety assessment;

[1803] a means for notifying the senior of the generated alert;

[1804] a means for switching to an autonomous driving mode when abnormal behavior is detected;

[1805] A system including:

[1806] (Claim 2)

[1807] means for monitoring lane keeping in driving behavior and detecting lane departure;

[1808] A means for analyzing and evaluating the frequency of lane departures and the occurrence of sudden braking;

[1809] A means for providing feedback to the elderly on the generated evaluation results in real time;

[1810] and means for recommending an autonomous driving mode if abnormal behavior is detected.

[1811] 10. The system of claim 1.

[1812] (Claim 3)

[1813] a means for assessing changes in driving route memory ability and judgment;

[1814] A means of detecting these changes and comprehensively assessing driving ability;

[1815] A means of providing psychological support to the elderly using generative AI;

[1816] The method further includes generating a prompt sentence that positively encourages the elderly person to switch to a safe driving mode when abnormal behavior is detected.

[1817] 10. The system of claim 1.

[1818] "Example 2: Combining Emotion Engines"

[1819] (Claim 1)

[1820] A means for monitoring the driving behavior of elderly people in real time and collecting location information, speed information, lane identification information, and sudden braking information;

[1821] A means of analyzing the collected data and evaluating the safety of driving;

[1822] A means for acquiring facial expression and voice data of the driver in addition to driving behavior data to analyze the emotional state of the driver;

[1823] A means for detecting abnormal behavior and providing a warning based on the results of driving safety assessment and emotional state analysis;

[1824] a means for notifying the senior of the generated alert;

[1825] A system including:

[1826] (Claim 2)

[1827] means for monitoring lane keeping in driving behavior and detecting lane departure;

[1828] A means for analyzing and evaluating the frequency of lane departures and the occurrence of sudden braking;

[1829] and further including means for feeding back the evaluation results, including the emotion data, to the elderly person in real time.

[1830] 10. The system of claim 1.

[1831] (Claim 3)

[1832] a means of assessing changes in driving route memory ability and judgment;

[1833] A means of detecting these changes and comprehensively assessing driving ability;

[1834] Further including a means for...

Claims

1. A means of monitoring the driving behavior of seniors in real time and collecting location and speed data; A means of analyzing the collected data and evaluating the safety of driving; means for detecting abnormal behavior and providing warnings based on a driving safety assessment; a means for notifying the senior of the generated alert; A system including:

2. means for monitoring lane keeping in driving behavior and detecting lane departure; A means for analyzing and evaluating the frequency of lane departures and the occurrence of sudden braking; and further including means for providing feedback of the generated assessment results to the elderly person in real time. The system of claim 1 .

3. a means of assessing changes in driving route memory ability and judgment; A means of detecting these changes and comprehensively assessing driving ability; Further including a means for providing psychological support to older adults using generative AI; The system of claim 1 .

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