System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting driving data; means for acquiring physiological data and in-vehicle environment data of a driver; means for evaluating a driving skill and a fatigue state from the driving data and the physiological data; and means for proposing a transition to an automatic driving mode based on an evaluation result. To evaluate a driving skill and a fatigue state of a driver, to smoothly shift to an automatic driving mode, and to perform appropriate communication according to a surrounding situation.SELECTED DRAWING: Figure 1

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] Although automated driving technology is rapidly developing, many challenges remain in its operation. In particular, it is difficult to adapt to different driver skills and fatigue levels. Furthermore, there is a need to increase social acceptance by appropriately communicating with surrounding traffic conditions and people. Current automated driving systems are insufficient in effectively handling these complex factors and supporting the driver while ensuring safety. This invention aims to improve safety and social acceptance by assessing the driver's driving skill and fatigue level, smoothly transitioning to automated driving mode, and providing appropriate communication according to the surrounding situation. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for collecting driving data, a means for acquiring physiological data and in-vehicle environmental data of the driver, a means for evaluating driving skills and fatigue state based on the driving data and physiological data, and a means for proposing transition to autonomous driving mode based on the evaluation results. Furthermore, by adding a means for transmitting the driving data and physiological data to a natural language processing model and generating advice regarding safe driving, support for the driver can be enhanced. Furthermore, by including a means for detecting the surrounding situation, determining the need for communication, and appropriately communicating with surrounding people as necessary, the social acceptability of autonomous driving systems can be improved. This can increase the safety of the driver and surrounding people, and improve the efficiency and reliability of the entire transportation system.

[0006] Understood. Below are definitions for important terms included in the claims.

[0007] "Driving data" refers to data collected when a driver operates a vehicle, and specifically includes information such as speed, braking operation, accelerator operation, and steering angle.

[0008] "Physiological data" refers to data that indicates the physical condition of the driver, and specifically refers to information that includes biological signals such as heart rate, blood pressure, body temperature, and stress level.

[0009] "In-vehicle environment data" refers to data that indicates the state of the interior of the vehicle, and specifically includes environmental information such as the temperature, humidity, CO2 concentration, and sound inside the vehicle.

[0010] "Driving skill" is a measure for evaluating a driver's driving skills and techniques, and specifically includes the accuracy of driving operations and the degree of adherence to safe driving.

[0011] "Fatigue state" refers to a state that indicates the sense of fatigue or tiredness felt by the driver, and specifically includes indicators such as drowsiness, delayed reaction time, and impaired judgment.

[0012] "Autonomous driving mode" refers to a mode in which the driver entrusts control of the vehicle to the system, and specifically refers to a state in which vehicle control is performed automatically.

[0013] A "natural language processing model" is a form of artificial intelligence that analyzes sensor data and generates advice and instructions in natural language.

[0014] "Advice" refers to instructions or advice provided to the driver, and specifically includes warnings and recommended actions regarding safe driving.

[0015] "Communication" refers to the exchange of information between the system and people around it, specifically through voice messages and visual displays. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This system collects driving data and the driver's physiological data, analyzes them to evaluate driving skills and fatigue levels, and suggests switching to autonomous driving mode at the appropriate time. It also aims to increase social acceptance by providing safe driving advice to drivers using natural language processing models and communicating appropriately according to the surrounding situation.

[0038] As a specific embodiment for implementing this system, the following processing is performed.

[0039] Driving and physiological data collection

[0040] Subject: Device

[0041] The device uses various sensors installed in the vehicle to collect driving data and physiological data of the driver in real time. Specifically, it acquires driving data such as speed, braking operation, accelerator operation, steering angle, etc. It also collects physiological data such as heart rate, body temperature, interior temperature, and voice, as well as in-vehicle environmental data.

[0042] Evaluation of driving skills and fatigue

[0043] Subject: Server

[0044] The server analyzes the collected driving and physiological data to evaluate driving skills and fatigue levels. Driving skills are evaluated and scored based on data such as the accuracy of driving operations, speed, and frequency of braking. Fatigue levels are measured based on heart rate and body temperature fluctuations, and the fatigue level is calculated.

[0045] Proposal for transition to autonomous driving mode

[0046] Subject: Server

[0047] Based on the evaluation results, the server will suggest the appropriate timing to switch to autonomous driving mode. For example, if the driver's fatigue level is high, the server will generate a message saying, "Due to high fatigue level, we suggest switching to autonomous driving mode," and notify the driver. A similar suggestion will also be made if the driver's driving skills are low.

[0048] Providing advice using natural language processing models

[0049] Subject: Server

[0050] The server sends the collected data to a natural language processing (NLP) model, which then generates safe driving advice for the driver based on the analysis results, such as "Please be careful not to drive too fast."

[0051] Context-sensitive communication

[0052] Subject: Autonomous Driving System

[0053] The autonomous driving system detects the surrounding situation from sensor data and communicates as necessary. When the conditions are met (for example, when there is a pedestrian at a crosswalk), the system generates a voice message to notify those around it, such as "There is a pedestrian at the crosswalk, please be careful."

[0054] Specific examples

[0055] Example 1: High fatigue

[0056] The device monitors the driver's heart rate and body temperature, and if these exceed the standard values, the server notifies the driver, "You are highly fatigued, so we suggest switching to autonomous driving mode."

[0057] Example 2: Safe driving advice

[0058] While driving, the device detects frequent use of the brakes. The server analyzes the situation and provides advice such as, "Because you are braking suddenly a lot, please be careful to maintain a safe distance from other vehicles."

[0059] Example 3: When there is a pedestrian on the crosswalk

[0060] The autonomous driving system uses sensors to detect pedestrians approaching a crosswalk, and generates a voice message to alert those around it, saying, "There is a pedestrian on the crosswalk. Please be careful."

[0061] As described above, the present invention is a system that supports safe and comfortable driving for drivers and can also increase social acceptance of automated driving technology.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] Subject: Device

[0065] The device collects driving data and physiological data. Specifically, it obtains driving data such as speed, braking, and steering angle from various sensors built into the vehicle. It also simultaneously collects physiological data such as the driver's heart rate and interior temperature using heart rate and temperature sensors.

[0066] Step 2:

[0067] Subject: Device

[0068] The collected driving and physiological data is preprocessed, for example, by removing noise from the data and converting it into an analyzable format. The preprocessed data is then sent to the server, where it is packaged appropriately according to the data format and protocol.

[0069] Step 3:

[0070] Subject: Server

[0071] The server analyzes the received data, calculates a driving skill score from the driving data, and measures fatigue level from physiological data. For example, driving skill is calculated based on the accuracy and smoothness of steering, while fatigue level is evaluated based on heart rate fluctuations and body temperature rise.

[0072] Step 4:

[0073] Subject: Server

[0074] Based on the evaluation results, the server will suggest switching to autonomous driving mode. If the driving skill score is low or the fatigue level is high, the server will generate a message such as "Due to high fatigue level, we suggest switching to autonomous driving mode" and send it to the device.

[0075] Step 5:

[0076] Subject: Device

[0077] The device notifies the driver of the proposal message received from the server. The notification method may be a screen display or an audio alert. The driver (user) who receives this notification decides whether to approve the transition to autonomous driving mode.

[0078] Step 6:

[0079] Subject: Server

[0080] The server inputs driving data and physiological data into a natural language processing model to generate advice on safe driving. For example, if frequent sudden braking is detected, the server generates advice such as, "There are many sudden braking incidents, so please be careful to maintain a safe distance between vehicles."

[0081] Step 7:

[0082] Subject: Device

[0083] The device receives safe driving advice from the server and provides it to the driver, also via screen display and audio alerts, to encourage the driver to be more careful.

[0084] Step 8:

[0085] Subject: Autonomous Driving System

[0086] The autonomous driving system uses sensors to monitor the surroundings and communicates with people around it as needed. For example, if a pedestrian is detected at a crosswalk, the system will generate a voice message saying, "There is a pedestrian at the crosswalk. Please be careful."

[0087] Step 9:

[0088] Subject: User

[0089] The user (driver) decides whether to accept the autonomous driving mode suggestion. If accepted, the system switches to autonomous driving mode. If not accepted, the system continues to collect data and provide safe driving advice.

[0090] Through these steps, the system of the present invention enhances driver safety and establishes proper communication with surrounding people, thereby improving the reliability and acceptability of the overall transportation system.

[0091] Example 1

[0092] 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."

[0093] Conventional driver assistance systems have had difficulty accurately assessing the driver's level of fatigue and driving skill in real time and suggesting transition to autonomous driving mode at the appropriate time. They also lacked the ability to provide detailed advice on safe driving and communicate in accordance with the surrounding situation. As a result, they were unable to adequately support the driver in safe and comfortable driving, and social acceptance of autonomous driving technology was low.

[0094] 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.

[0095] In this invention, the server includes means for collecting driving data and physiological data of the driver, means for performing noise removal and outlier correction as preprocessing of the data, means for evaluating driving skill and fatigue state from the driving data and the physiological data, means for proposing transition to autonomous driving mode based on the evaluation results, means for transmitting the driving data and the physiological data to a natural language processing model, means for generating advice on safe driving from the natural language processing model, means for detecting surrounding conditions and determining the need for communication, and means for detecting surrounding conditions in real time with sensors and generating and notifying voice messages. This makes it possible to accurately evaluate the driver's condition and driving skill in real time, provide advice on safe driving, and further communicate with those around the driver as needed.

[0096] "Driving data" refers to data related to driving behavior such as vehicle speed, braking operation, accelerator operation, and steering angle.

[0097] "Physiological data" refers to data that indicates the physiological state of the driver, such as heart rate and body temperature.

[0098] "In-vehicle environment data" is data indicating environmental conditions such as temperature, humidity, and sound inside the vehicle.

[0099] "Driving skill" is an indicator that shows the accuracy and safety of a driver's driving behavior.

[0100] "Fatigue state" is an index that indicates the degree of mental and physical fatigue of the driver.

[0101] "Autonomous driving mode" is a mode in which the system automatically performs driving operations of the vehicle.

[0102] The "evaluation results" are the results of the driving skills and fatigue state analyzed from the collected data.

[0103] A "natural language processing model" is an algorithm that analyzes collected data and generates advice in natural language.

[0104] "Safe driving advice" is a specific suggestion to encourage drivers to drive safely.

[0105] "Surrounding conditions" is data that indicates the surrounding environment and conditions of the vehicle.

[0106] "Communication" refers to the act of conveying information to the driver and people around them by voice or message.

[0107] A "sensor" is a device used to collect data from a vehicle or driver.

[0108] "Noise reduction" is the process of removing unwanted noise from collected data.

[0109] "Abnormal value correction" is a process for correcting abnormal values ​​contained in collected data.

[0110] This system collects driving data and the driver's physiological data, analyzes them to evaluate driving skills and fatigue levels, and suggests switching to autonomous driving mode at the appropriate time. It also aims to provide safe driving advice to the driver using a natural language processing model and communicate appropriately according to the surrounding situation.

[0111] Hardware and Software Configuration

[0112] The system uses the following hardware and software:

[0113] Device: A group of sensors installed in the vehicle (speed sensor, brake sensor, accelerator sensor, steering angle sensor, heart rate sensor, body temperature sensor, etc.)

[0114] Server: A computer that analyzes collected data, evaluates driving skills and fatigue, and proposes transitioning to autonomous driving mode.

[0115] Natural language processing model (NLP model): an algorithm for generating safe driving advice from collected data

[0116] Sensors: Cameras, LIDAR, and radar for real-time detection of surrounding conditions

[0117] Data processing and calculation

[0118] The device uses sensors to collect driving data and the driver's physiological data in real time. This data is recorded at regular intervals and sent to a server, where it is first preprocessed by noise removal and outlier correction. The driving data and physiological data are then input into a machine learning algorithm to evaluate driving skill and fatigue state.

[0119] Driving skill assessment: Driving skills are scored based on the accuracy of driving operations, speed, frequency of braking, etc.

[0120] Fatigue assessment: Calculate fatigue level from fluctuations in heart rate and body temperature.

[0121] Based on the analysis results, the server will suggest the appropriate timing to switch to autonomous driving mode. For example, if the driver's fatigue level is high, the server will generate a message to notify the driver, such as "Due to high fatigue level, we suggest switching to autonomous driving mode." The autonomous driving system also detects the surrounding situation using data from sensors and generates voice messages to notify those around it as necessary.

[0122] Specific examples

[0123] Example 1: High fatigue

[0124] The device monitors the driver's heart rate and body temperature, and if these exceed the standard values, the server notifies the driver, "You are highly fatigued, so we suggest switching to autonomous driving mode."

[0125] Example 2: Safe driving advice

[0126] While driving, the device detects frequent use of the brakes. The server analyzes the situation and provides advice such as, "Because you are braking suddenly a lot, please be careful to maintain a safe distance from other vehicles."

[0127] Example 3: When there is a pedestrian on the crosswalk

[0128] The autonomous driving system uses sensors to detect pedestrians approaching a crosswalk, and generates a voice message to alert those around it, saying, "There is a pedestrian on the crosswalk. Please be careful."

[0129] Prompt Sentence Examples

[0130] "Please explain the system that uses driving and physiological data to assess the driver's fatigue level and suggest transitioning to autonomous driving mode. Please also provide details on the specific steps and the sensors and algorithms used."

[0131] This allows the present invention to accurately evaluate the driver's condition and driving skills in real time, provide advice on safe driving, and communicate with those around the vehicle as needed.

[0132] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0133] Step 1:

[0134] Data collection

[0135] Input: Data from various sensors installed in the vehicle

[0136] Operation: The device activates speed sensors, brake sensors, accelerator sensors, steering angle sensors, heart rate sensors, body temperature sensors, etc. to collect driving data and driver physiological data in real time.

[0137] Data processing: The collected data is recorded at regular time intervals.

[0138] Output: The collected driving and physiological data are stored on the device and sent to the server.

[0139] Step 2:

[0140] Data Preprocessing

[0141] Input: Driving data and physiological data sent from the device

[0142] How it works: The server performs noise removal and outlier correction on the received data before analyzing it.

[0143] Data calculations: Data cleaning algorithms are used to remove noise and correct outliers.

[0144] Output: A preprocessed, clean dataset

[0145] Step 3:

[0146] Evaluation of driving skills and fatigue

[0147] Input: Preprocessed driving and physiological data

[0148] How it works: The server inputs data into a machine learning algorithm to assess driving skill and fatigue state.

[0149] Data calculation: For driving skills, the system calculates a skill score by analyzing the accuracy of driving operations, speed, frequency of braking, etc. For fatigue, the system calculates the fatigue level by analyzing fluctuations in heart rate and body temperature.

[0150] Output: Driving skill score and fatigue level

[0151] Step 4:

[0152] Proposal for transition to autonomous driving mode

[0153] Input: Driving skill score and fatigue level

[0154] Operation: Based on the evaluation results, the server determines whether to suggest to the driver to switch to autonomous driving mode.

[0155] Data calculation: For example, if the fatigue level is high, a message such as "Due to high fatigue level, we suggest switching to autonomous driving mode" is generated.

[0156] Output: A message is generated and notified to the driver via the terminal.

[0157] Step 5:

[0158] Providing safe driving advice

[0159] Input: Preprocessed driving and physiological data

[0160] How it works: The server sends these data to a natural language processing (NLP) model.

[0161] Data Computation: The NLP model analyzes the data and generates safe driving advice for the driver.

[0162] Output: The generated advice is notified to the driver via the terminal.

[0163] Step 6:

[0164] Context-sensitive communication

[0165] Input: Surroundings data from sensors installed in the vehicle

[0166] How it works: The autonomous driving system uses sensors (cameras, LIDAR, radar) to detect the surroundings in real time.

[0167] Data processing: When certain conditions are met (for example, when there is a pedestrian in a crosswalk), a voice message is generated to notify the driver.

[0168] Output: The generated message is transmitted to the surroundings through the vehicle's speakers.

[0169] (Application example 1)

[0170] 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."

[0171] Conventional driver assistance systems are limited to simply monitoring driving data and the driver's physiological data, and are limited in their ability to evaluate driving skills and fatigue levels in real time and provide appropriate advice. Furthermore, they lack the ability to appropriately transition to autonomous driving mode depending on the surrounding conditions or provide specific safe driving advice to the driver, making it impossible to fully guarantee the driver's safety and comfort. Furthermore, there are also challenges such as difficulty in smoothly communicating with the driver and providing appropriate advice.

[0172] 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.

[0173] In this invention, the server includes a means for collecting driving data, a means for acquiring physiological data and in-vehicle environmental data of the driver, a means for evaluating the driving skill and fatigue state from the driving data and the physiological data, a means for suggesting switching to an autonomous driving mode based on the evaluation results, a means for generating safe driving advice for the driver using a natural language processing model, and a means for notifying the driver of the generated advice by voice or text. This allows the driver to receive a real-time evaluation of their driving situation and receive a suggestion to switch to an autonomous driving mode at an appropriate time, enabling safe and efficient driving. Furthermore, by utilizing the natural language processing model, specific and effective safe driving advice is provided to the driver, improving the driver's safety and comfort. Furthermore, by appropriately communicating with people around the driver according to the surrounding situation, a good harmony between the driver and the surrounding environment can be achieved.

[0174] "Driving data" refers to information related to the driver's driving behavior, such as vehicle speed, braking operation, accelerator operation, and steering angle.

[0175] "Physiological data" is information that indicates the physical condition of the driver, such as the driver's heart rate and body temperature.

[0176] "In-vehicle environment data" is information indicating the environmental conditions inside the vehicle, such as the temperature, humidity, and sound level inside the vehicle.

[0177] "Driving skill" is an evaluation of the driver's accuracy of driving operations, judgment, reaction speed, etc.

[0178] The "fatigue state" indicates the degree of fatigue of the driver and is calculated based on physiological data.

[0179] "Autonomous driving mode" is a mode in which the vehicle performs driving operations autonomously.

[0180] A "natural language processing model" is an algorithm or system for analyzing and understanding human language.

[0181] "Safe driving advice" is information that encourages drivers to drive properly and take precautions.

[0182] A "sensor" is a device that senses physical or environmental information and converts it into digital data.

[0183] "Notification" refers to the act and means of conveying information to the driver.

[0184] "Real-time" means that processing and communication occurs almost instantly, with little delay.

[0185] "Surrounding conditions" refers to information about pedestrians, other vehicles, traffic signals, and the like around the vehicle.

[0186] "Communication" is the act and means of transmitting information to each other.

[0187] This system collects and analyzes driving data and driver physiological data to evaluate driving skills and fatigue levels, and provides specific advice for safe driving. This system is designed to improve driver safety and comfort.

[0188] Driving and physiological data collection

[0189] The device collects driving data and physiological data of the driver in real time using multiple sensors installed in the vehicle. Specifically, driving data is acquired from the speedometer, brake operation sensor, accelerator operation sensor, steering angle sensor, etc. In addition, physiological data of the driver and in-vehicle environmental data are collected from the heart rate sensor, body temperature sensor, in-vehicle temperature sensor, microphone, etc.

[0190] Data analysis and evaluation

[0191] The server analyzes the collected driving and physiological data to evaluate driving skills and fatigue levels. Driving skills are scored based on data such as the accuracy of driving operations, speed, and frequency of braking. Fatigue levels are evaluated and quantified based on fluctuations in heart rate and body temperature.

[0192] Proposal for transition to autonomous driving mode

[0193] Based on the evaluation results, the server will suggest the appropriate timing to switch to autonomous driving mode. For example, if the driver's fatigue level is high, the server will generate a message such as "Due to high fatigue level, we suggest switching to autonomous driving mode" and notify the driver via the device.

[0194] Providing advice using natural language processing models

[0195] The server sends the collected data to a natural language processing (NLP) model and generates safe driving advice for the driver based on the analysis results. For example, if the frequency of sudden braking is high, a message such as "There are many sudden braking incidents, so please be careful to maintain a safe distance between vehicles" will be generated.

[0196] Context-sensitive communication

[0197] The autonomous driving system uses sensors to detect the surrounding situation and communicates by voice or text as necessary. For example, if there is a pedestrian in a crosswalk, the system will generate a voice message such as "There is a pedestrian in the crosswalk, please be careful" to notify the driver and people around.

[0198] Specific example explanation

[0199] Example 1: High fatigue

[0200] The driver's heart rate and body temperature are monitored by the terminal, and if these exceed the standard values, the server notifies the driver, "Due to high fatigue level, we suggest switching to autonomous driving mode."

[0201] Example 2: Safe driving advice

[0202] If the device detects that the driver is using the brakes frequently while driving, the server will analyze the situation and provide advice such as, "There are many sudden braking attempts, so please be careful to maintain a safe distance from other vehicles."

[0203] Example 3: When there is a pedestrian on the crosswalk

[0204] The autonomous driving system uses sensors to detect pedestrians on the crosswalk and generates a voice message saying, "There is a pedestrian on the crosswalk. Please be careful," to notify those around.

[0205] Prompt Sentence Examples

[0206] For example, the following prompt sentences can be input into a generative AI model and used:

[0207] User input: "I've been feeling tired a lot lately while driving. What should I do?"

[0208] Generative AI: "If you feel tired, it's important to take regular breaks while driving. Also, try to avoid long periods of driving and consider driving in autonomous mode."

[0209] In this way, the present invention can provide comprehensive assistance to the driver and provide a safer and more comfortable driving environment.

[0210] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0211] Step 1:

[0212] The device collects driving data and the driver's physiological data in real time through sensors installed in the vehicle (speedometer, brake operation sensor, accelerator operation sensor, steering angle sensor, heart rate sensor, body temperature sensor, interior temperature sensor, microphone). This collected data indicates the vehicle's driving situation and the driver's physical condition, and the device stores this as digital data.

[0213] Input: Data from various sensors installed in the vehicle

[0214] Output: Driving data and driver physiological data

[0215] Step 2:

[0216] The device transmits the collected driving and physiological data to a server, where the data is transmitted along with location and time information while maintaining real-time performance, and is converted into a format that can be analyzed by the server.

[0217] Input: Driving data and driver physiological data

[0218] Output: Driving and physiological data sent to the server

[0219] Step 3:

[0220] The server analyzes the received driving data and physiological data to evaluate driving skills and fatigue levels. Specifically, it scores driving skills based on the accuracy of driving operations, speed, frequency of braking, etc., and quantifies fatigue levels from fluctuations in heart rate and body temperature. This generates an evaluation result based on each data.

[0221] Input: Driving data and driver physiological data sent to the server

[0222] Output: Evaluation results of driving skills and fatigue state

[0223] Step 4:

[0224] Based on the evaluation results, the server will suggest the appropriate timing to switch to autonomous driving mode. For example, if the driver's fatigue level is high, the server will generate a message saying, "Due to high fatigue level, we suggest switching to autonomous driving mode," and notify the driver of this message via the device.

[0225] Input: Evaluation results of driving skills and fatigue state

[0226] Output: Message proposing to switch to autonomous driving mode

[0227] Step 5:

[0228] The server sends the collected data to a natural language processing (NLP) model and generates safe driving advice for the driver based on the analysis results. Specifically, if the frequency of sudden braking is high, a message such as "There are many sudden braking incidents, so please be careful to maintain a safe distance between vehicles" is generated and provided to the driver via the terminal.

[0229] Input: Driving and physiological data

[0230] Output: Safe driving advice

[0231] Step 6:

[0232] The device receives input from the driver and sends it to the server. The server uses a natural language processing model to generate a response and provides it to the driver via the device. For example, in response to an input such as, "I often feel tired while driving recently. What should I do?", the server responds, "If you feel tired, it is important to take appropriate breaks while driving. Also, try to avoid driving for long periods of time and consider driving in autonomous driving mode."

[0233] Input: Input from the driver

[0234] Output: Response from the generative AI model

[0235] Step 7:

[0236] The autonomous driving system uses sensors installed around the vehicle to detect the surrounding situation. For example, if there is a pedestrian in a crosswalk, the system will detect this and generate a voice message such as "There is a pedestrian in the crosswalk, please be careful" to notify the driver and people around.

[0237] Input: Vehicle surroundings data

[0238] Output: Messages depending on the surroundings

[0239] Step 8:

[0240] The device provides all generated messages and notifications to the driver, allowing the driver to receive safe driving advice in real time and suggestions for switching to autonomous driving mode at the same time, improving driver safety and comfort.

[0241] Input: Generated messages and notifications

[0242] Output: Notification to the driver

[0243] 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.

[0244] This invention combines a system that collects driving data and physiological data, analyzes them to evaluate driving skills and fatigue levels, and suggests switching to autonomous driving mode at the appropriate time with an emotion engine that recognizes the user's emotions. It also aims to increase social acceptance by providing safe driving advice to drivers using a natural language processing model and communicating appropriately according to the surrounding situation.

[0245] As a specific embodiment for implementing this system, the following processing is performed.

[0246] Driving and physiological data collection

[0247] Subject: Device

[0248] The device uses various sensors installed in the vehicle to collect driving data and physiological data of the driver in real time. Specifically, it acquires driving data such as speed, braking operation, accelerator operation, steering angle, etc. It also collects physiological data such as heart rate, body temperature, interior temperature, and voice, as well as in-vehicle environmental data.

[0249] Emotional state assessment by emotion engine

[0250] Subject: Emotion Engine

[0251] The emotion engine analyzes the driver's facial expression, tone of voice, heart rate variability patterns, etc. to determine the driver's emotional state (e.g., stress, impatience, relaxation, etc.) and transmits this emotion data to the server.

[0252] Evaluation of driving skills and fatigue

[0253] Subject: Server

[0254] The server analyzes the collected driving and physiological data to evaluate driving skills and fatigue levels. Driving skills are evaluated and scored based on data such as the accuracy of driving operations, speed, and frequency of braking. Fatigue levels are measured based on heart rate and body temperature fluctuations, and the fatigue level is calculated.

[0255] Proposal for transition to autonomous driving mode taking into account emotional state

[0256] Subject: Server

[0257] Based on the evaluation results, the server will suggest the appropriate timing to switch to autonomous driving mode. If the driving skill score is low, the fatigue level is high, or the emotional state is unstable, the server will generate a message such as "We suggest switching to autonomous driving mode because your fatigue level is high" or "We suggest switching to autonomous driving mode because your stress level is increasing" and send it to the device.

[0258] Providing advice using natural language processing models

[0259] Subject: Server

[0260] The server sends the collected data to a natural language processing (NLP) model, which then generates safe driving advice for the driver based on the analysis results, such as "Please be careful not to drive too fast."

[0261] Context-sensitive communication

[0262] Subject: Autonomous Driving System

[0263] The autonomous driving system detects the surrounding situation from sensor data and communicates as necessary. When the conditions are met (for example, when there is a pedestrian at a crosswalk), the system generates a voice message such as "There is a pedestrian at the crosswalk, please be careful" and notifies those around.

[0264] Specific examples

[0265] Example 1: High fatigue and unstable emotional state

[0266] The device monitors the driver's heart rate and body temperature, and if these exceed the standard values ​​and the emotion engine detects a high stress level, the server notifies the driver, "Due to high levels of fatigue and stress, we suggest switching to autonomous driving mode."

[0267] Example 2: Safe driving advice

[0268] While driving, the device detects frequent use of the brakes. The server analyzes the situation and provides advice such as, "Because you are braking suddenly a lot, please be careful to maintain a safe distance from other vehicles."

[0269] Example 3: When there is a pedestrian on the crosswalk

[0270] The autonomous driving system uses sensors to detect pedestrians approaching a crosswalk, and generates a voice message to alert those around it, saying, "There is a pedestrian on the crosswalk. Please be careful."

[0271] Through these steps, the system of the present invention enhances driver safety and establishes proper communication with surrounding people, thereby improving the reliability and acceptability of the overall transportation system.

[0272] The processing flow will be explained below.

[0273] Step 1:

[0274] Subject: Device

[0275] The device collects driving data and physiological data from various sensors installed in the vehicle. For example, it obtains the vehicle's speed from a speed sensor and the frequency of brake use from a brake pedal sensor. Furthermore, it obtains the driver's physiological data (heart rate, body temperature) using a heart rate sensor and a body temperature sensor.

[0276] Step 2:

[0277] Subject: Emotion Engine

[0278] The emotion engine analyzes the driver's facial expressions, voice, and heart rate fluctuation patterns to recognize the driver's emotional state. For example, facial expression analysis technology can determine whether the driver is stressed, and voice tone can determine whether the driver is irritated. This emotional data is sent to the server.

[0279] Step 3:

[0280] Subject: Device

[0281] The device preprocesses the driving and physiological data collected, removes noise from the data, and converts it into an analyzable format. The preprocessed data is then sent to the server.

[0282] Step 4:

[0283] Subject: Server

[0284] The server analyzes the pre-processed driving data and physiological data to calculate a driving skill score and fatigue level. Driving skill is evaluated based on data such as steering and speed fluctuations, while fatigue level is measured based on fluctuations in heart rate and body temperature.

[0285] Step 5:

[0286] Subject: Server

[0287] The server analyzes the emotion data sent from the emotion engine and combines it with the driving skill and fatigue data to make an overall evaluation. For example, if the driving skill is poor, the fatigue level is high, and the emotional state is stressed, the overall score will be low.

[0288] Step 6:

[0289] Subject: Server

[0290] Based on the evaluation results, the server generates a message such as "Your driving skills are low, and your fatigue and stress levels are high. We suggest you switch to autonomous driving mode," and sends it to the device.

[0291] Step 7:

[0292] Subject: Device

[0293] The device notifies the driver (user) of the proposal message received from the server, using a screen display and audio alert to inform the driver that "Due to high levels of fatigue and stress, we propose switching to autonomous driving mode."

[0294] Step 8:

[0295] Subject: Server

[0296] The server inputs driving and physiological data into a natural language processing model to generate specific advice on safe driving, such as "Please be careful to maintain a safe distance as there are many instances of sudden braking."

[0297] Step 9:

[0298] Subject: Device

[0299] The device receives safe driving advice from the server and provides it to the driver, allowing the driver to receive specific advice via screen displays and audio alerts.

[0300] Step 10:

[0301] Subject: Autonomous Driving System

[0302] The autonomous driving system monitors the surrounding situation and communicates with people around it as needed. For example, if a pedestrian is detected at a crosswalk, it will generate a voice message saying, "There is a pedestrian at the crosswalk. Please be careful."

[0303] Step 11:

[0304] Subject: User

[0305] The user (driver) decides whether to accept the proposal to switch to autonomous driving mode. If the proposal is accepted, the system switches to autonomous driving mode. If not accepted, the system continues to collect data and provide safe driving advice.

[0306] Example 2

[0307] 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."

[0308] Conventional driver assistance systems were able to collect driving and physiological data to assess driving skills and fatigue levels, but they lacked the functionality to consider the driver's emotional state and suggest transitioning to autonomous driving mode at the appropriate time. As a result, the system was unable to provide appropriate driving assistance due to a lack of consideration for the driver's emotional state, such as stress or impatience. Furthermore, the system lacked the functionality to provide specific advice on safe driving based on the collected data. Furthermore, the system lacked the functionality to communicate in response to the surrounding situation, making it difficult to improve the reliability and acceptability of the overall transportation system.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0310] In this invention, the server includes a means for collecting driving data, a means for acquiring physiological data and in-vehicle environmental data of the driver, a means for analyzing the driver's emotional state, a means for evaluating the driver's driving skill and fatigue state from the driving data and the physiological data, and a means for proposing transition to autonomous driving mode based on the evaluation results and taking the driver's emotional state into consideration. This makes it possible to comprehensively evaluate the driver's driving skill, fatigue state, and emotional state and to propose transition to autonomous driving mode at an appropriate time. Furthermore, it is possible to provide specific advice on safe driving using a natural language processing model and communicate according to the surrounding situation, thereby improving the reliability and acceptability of the overall transportation system.

[0311] "Driving data" refers to data relating to driving operations such as vehicle speed, braking operation, accelerator operation, and steering angle, as well as vehicle behavior.

[0312] "Physiological data" is data relating to the driver's heart rate, body temperature, breathing patterns and other physiological conditions.

[0313] "In-vehicle environment data" refers to data relating to the environment inside the vehicle, such as the temperature, humidity, and sound environment inside the vehicle.

[0314] "Means of evaluation" refers to the process of analyzing collected driving data and physiological data and quantifying and evaluating driving skills and fatigue levels.

[0315] "Means for suggesting transition to automated driving mode" refers to a process for generating a message to recommend to the driver that they switch to automated driving mode based on the results of an evaluation of their driving skills, fatigue state, and emotional state.

[0316] A "natural language processing model" is an algorithm or computer program that analyzes collected data and generates specific advice for drivers in natural language.

[0317] "Means for analyzing emotional state" refers to a process for analyzing the driver's facial expression, tone of voice, heart rate fluctuation patterns, etc. to determine the driver's emotional state (stress, impatience, relaxation, etc.).

[0318] "Means for detecting surrounding conditions" refers to the process by which an automated driving system uses external sensors to monitor the surrounding environment and detect the location and movement of pedestrians and other vehicles.

[0319] "Means of communication" refers to the process of notifying the driver and those around them of necessary information and warnings as audio or visual messages depending on the detected surrounding conditions.

[0320] This invention combines a system that collects driving data and physiological data, analyzes them to evaluate driving skills and fatigue levels, and suggests switching to autonomous driving mode at the appropriate time with an emotion engine that recognizes the user's emotions. It also aims to increase social acceptance by providing safe driving advice to drivers using a natural language processing model and communicating appropriately according to the surrounding situation.

[0321] Driving and physiological data collection

[0322] Subject: Device

[0323] The device collects driving data and the driver's physiological data in real time using various sensors installed in the vehicle, such as speed sensors, brake sensors, accelerator sensors, and steering angle sensors. It also collects physiological data such as the driver's heart rate, body temperature, interior temperature, and voice using a heart rate monitor, thermometer, interior temperature sensor, and microphone, as well as in-vehicle environmental data.

[0324] Specific examples

[0325] Speed ​​sensor: Get the current speed of the vehicle

[0326] Heart rate monitor: Monitors the driver's heart rate fluctuations

[0327] Emotional state analysis using emotion engine

[0328] Subject: Emotion Engine

[0329] The emotion engine analyzes facial expression data, voice data, and data from a heart rate monitor acquired from a camera and microphone to determine the driver's emotional state, which can include stress, impatience, relaxation, etc. This emotional data is then sent to a server.

[0330] Specific examples

[0331] Analyzing the driver's stress level from facial expressions captured on camera

[0332] Determine your relaxation state using heart rate fluctuation patterns

[0333] Sending data to the server

[0334] Subject: Device

[0335] The device sends the collected and analyzed data to a server, including driving data, physiological data, and emotional data.

[0336] Evaluation of driving skills and fatigue

[0337] Subject: Server

[0338] The server analyzes all the data sent and evaluates the driver's driving skills and fatigue level, including scoring the driver's driving skills based on the accuracy of driving maneuvers, speed, frequency of sudden braking, etc., and calculating the driver's fatigue level based on fluctuations in heart rate and body temperature.

[0339] Specific examples

[0340] Low driving skill score based on frequent hard braking and sudden acceleration

[0341] Increased heart rate and body temperature indicate high levels of fatigue.

[0342] Proposal for transition to autonomous driving mode

[0343] Subject: Server

[0344] Based on the evaluation results, the server will suggest the appropriate timing to switch to autonomous driving mode. If the driving skill score is low, the fatigue level is high, or the emotional state is unstable, the server will generate a message such as "Due to high fatigue level, we suggest switching to autonomous driving mode" or "Due to increasing stress, we suggest switching to autonomous driving mode" and send it to the device.

[0345] Providing advice using natural language processing models

[0346] Subject: Server

[0347] The server uses natural language processing (NLP) models to analyze the collected data and generate specific safe driving advice for the driver, such as "Please be careful not to drive too fast."

[0348] Specific examples

[0349] The advice is "Please be careful to maintain a safe distance from other vehicles due to frequent sudden braking."

[0350] Context-sensitive communication

[0351] Subject: Autonomous Driving System

[0352] The autonomous driving system detects the surrounding situation using various sensors and communicates by generating audio and visual messages as needed. For example, if there is a pedestrian at a crosswalk, it will generate a warning message such as "There is a pedestrian at the crosswalk, please be careful."

[0353] Specific examples

[0354] If there is a pedestrian on the crosswalk, a warning message will be generated saying "There is a pedestrian on the crosswalk, please be careful."

[0355] This invention makes it possible to comprehensively evaluate a driver's driving skill, fatigue level, and emotional state, and provide appropriate safe driving support. In addition, by communicating in accordance with the surrounding situation, the reliability and acceptability of the entire transportation system can be improved.

[0356] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0357] Step 1: Collect driving and physiological data

[0358] Subject: Device

[0359] Inputs: Speed ​​sensor, brake sensor, accelerator sensor, steering angle sensor, heart rate monitor, thermometer, interior temperature sensor, microphone

[0360] Specific operation: The terminal uses various sensors installed in the vehicle to collect driving data and physiological data of the driver. The speed sensor measures the vehicle's speed in real time, the brake sensor detects brake pedal operation status, the heart rate monitor obtains the driver's heart rate, and the thermometer records the driver's body temperature. This data is integrated to understand the driver's condition while driving.

[0361] Output: A set of driving and physiological data

[0362] Step 2: Analyze emotional state

[0363] Subject: Emotion Engine

[0364] Input: facial expression data, voice data, heart rate data

[0365] Specific operation: The emotion engine analyzes facial expression data, voice data, and heart rate data acquired from the camera and microphone. It analyzes the driver's facial expressions captured by the camera to determine their emotional state, such as stress or impatience. It also analyzes the voice data to infer emotions from the tone and speed of the driver's voice. This determines the driver's emotional state.

[0366] Output: Emotional state data

[0367] Step 3: Sending data to the server

[0368] Subject: Device

[0369] Input: driving data, physiological data, emotional state data

[0370] How it works: The device sends collected and analyzed data to a server, including real-time collected driving data, physiological data, and emotional state data. The data is transmitted using a secure protocol.

[0371] Output: Notify the server that data has been sent

[0372] Step 4: Evaluate driving skills and fatigue

[0373] Subject: Server

[0374] Input: driving data, physiological data, emotional state data

[0375] Specific operation: The server analyzes the received data and evaluates the driver's driving skills and fatigue level. Driving skills are quantified based on the accuracy of driving operations, speed, frequency of sudden braking, etc. Fatigue level is calculated based on fluctuations in heart rate and body temperature. If the evaluation result exceeds a certain standard, the system proceeds to the next step.

[0376] Output: Driving skill evaluation score, fatigue state evaluation score

[0377] Step 5: Proposal to transition to autonomous driving mode

[0378] Subject: Server

[0379] Input: Driving skill evaluation score, fatigue state evaluation score, emotional state data

[0380] Specific operation: Based on the evaluation results, the server determines the appropriate timing to switch to autonomous driving mode. If the driving skill score is low, the fatigue level is high, or the emotional state is unstable, a message suggesting switching to autonomous driving mode will be generated and sent to the device.

[0381] Output: Message proposing to switch to autonomous driving mode

[0382] Step 6: Providing safe driving advice

[0383] Subject: Server

[0384] Input: driving data, physiological data, natural language processing model

[0385] Specific operation: The server uses a natural language processing (NLP) model to analyze the collected data and generate specific safe driving advice for the driver. For example, if the speed is too high or there are frequent sudden braking, specific instructions and advice will be generated in natural language and sent to the device.

[0386] Output: Safe driving advice

[0387] Step 7: Communicate in context

[0388] Subject: Autonomous Driving System

[0389] Input: Sensor data (e.g., environmental monitoring results)

[0390] Specific operation: The autonomous driving system uses various sensors to monitor the surrounding situation and generates audio and visual messages to notify the driver and those around the vehicle as needed. For example, if there is a pedestrian at a crosswalk, the system will generate a message such as "There is a pedestrian at the crosswalk, please be careful" and notify the driver through a speaker.

[0391] Output: Warning message to the surroundings

[0392] (Application example 2)

[0393] 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."

[0394] Conventional automated driving systems provide means to evaluate driving skills and fatigue levels, but they have issues in taking the driver's emotional state into account. It is also necessary to closely monitor the driver's stress and fatigue and suggest transitioning to automated driving mode at the appropriate time. Furthermore, advice on safe driving is provided without taking the driver's emotional state into consideration, which has led to issues with the acceptability and effectiveness of the advice. There is a need to design a system that solves these issues and significantly improves driver safety and comfort.

[0395] 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.

[0396] In this invention, the server includes means for collecting driving data, means for acquiring physiological data and in-vehicle environmental data of the driver, means for evaluating the emotional state of the driver, means for evaluating the driving skill and fatigue state from the driving data and the physiological data, means for proposing a transition to an autonomous driving mode based on the evaluation results, and means for appropriate communication based on the emotional state. This makes it possible to comprehensively evaluate the driver's driving skill, fatigue state, and emotional state, thereby significantly improving the safety and comfort of the driver.

[0397] "Driving data" refers to information such as speed, braking operation, accelerator operation, and steering angle that occurs when a driver operates a vehicle.

[0398] "Physiological data" refers to information that indicates the driver's physical condition and physiological responses, such as the driver's heart rate, body temperature, and tone of voice.

[0399] "In-vehicle environment data" refers to information indicating the environmental conditions inside the vehicle, such as the temperature, sound, and lighting inside the vehicle.

[0400] "Emotional state" refers to the driver's emotional state, such as stress, impatience, or relaxation, which can be obtained by analyzing the driver's facial expression, tone of voice, heart rate variability pattern, etc.

[0401] "Autonomous driving mode" refers to a mode in which the vehicle drives automatically, without requiring driver operation.

[0402] A "natural language processing model" refers to an artificial intelligence technology for analyzing and understanding human language, and is used to generate advice on safe driving.

[0403] "Communication" refers to the act of sharing information with the driver and people around, and includes notifications via audio and display.

[0404] "Evaluation results" refer to the results of driving skills and fatigue state calculated based on an analysis of driving data, physiological data, and emotional state.

[0405] This invention is a system that collects and analyzes driving data, physiological data, and emotional state, evaluates the driver's driving skill and fatigue level, and suggests transitioning to autonomous driving mode at the appropriate time. Furthermore, it can provide advice on safe driving using a natural language processing model and communicate according to the surrounding situation. The specific configuration and operation of this system are described below.

[0406] System configuration

[0407] The system includes the following hardware and software:

[0408] Terminal: A device used to collect and display data, such as a smartphone.

[0409] Vehicle sensors: Various sensors for collecting driving data such as speed, braking, accelerating, and steering angle, as well as physiological data such as heart rate, body temperature, interior temperature, and voice.

[0410] Server: A cloud server that analyzes and evaluates data and provides appropriate advice and suggestions to the driver.

[0411] Natural Language Processing Library: An NLP library for generating safe driving advice.

[0412] Emotion engine: An engine for analyzing the driver's emotional state.

[0413] Data collection and analysis

[0414] The device uses various sensors installed in the vehicle to collect real-time driving data and physiological data of the driver, including speed, braking operation, accelerator operation, steering angle, heart rate, body temperature, interior temperature, and voice. In addition, the emotion engine analyzes the driver's facial expressions, tone of voice, heart rate variability patterns, etc. to determine the driver's emotional state.

[0415] Data evaluation

[0416] The server analyzes the collected driving and physiological data to evaluate driving skills and fatigue levels. Driving skills are evaluated and scored based on data such as the accuracy of driving operations, speed, and frequency of braking. Fatigue levels are measured based on heart rate and body temperature fluctuations, and the fatigue level is calculated. These evaluation results, along with emotional states, are stored on the server.

[0417] Proposal for transition to autonomous driving mode

[0418] Based on the evaluation results, the server will suggest switching to autonomous driving mode at an appropriate time. If the driving skill score is low, the fatigue level is high, or the emotional state is unstable, the server will generate a message suggesting switching to autonomous driving mode and send it to the device.

[0419] Providing advice and communicating

[0420] The server sends the collected data to a natural language processing model and generates safe driving advice for the driver based on the analysis results. For example, a message such as "Please be careful not to drive too fast" is provided to the driver. The autonomous driving system also detects the surrounding situation from sensor data and generates voice messages such as "There is a pedestrian on the crosswalk, please be careful" to notify those around it as necessary.

[0421] Specific examples

[0422] Example 1: High fatigue and unstable emotional state

[0423] The device monitors the driver's heart rate and body temperature, and if these exceed the standard values ​​or the emotion engine detects a high stress level, the server notifies the driver, "Due to high levels of fatigue and stress, we suggest switching to autonomous driving mode."

[0424] Example 2: Safe driving advice

[0425] If the device detects that the driver is using the brakes frequently while driving, the server will analyze the situation and provide advice such as, "There are many sudden braking attempts, so please be careful to maintain a safe distance from other vehicles."

[0426] Example prompt sentence:

[0427] The data collected during driving is analyzed based on the following prompts, and suggestions and advice are provided to the driver.

[0428] "Driving data: {driving_data}, Physiological data: {physiological_data}, Emotional state: {emotional_state}"

[0429] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0430] Step 1:

[0431] Data collection

[0432] Subject: Device

[0433] The terminal collects driving data and the driver's physiological data in real time through various sensors installed in the vehicle. Specifically, it acquires driving data such as speed, braking operation, accelerator operation, and steering angle, as well as physiological data such as heart rate, body temperature, interior temperature, and voice. The input is real-time data from the various sensors, and the output is the collected driving data and physiological data.

[0434] Step 2:

[0435] Emotional state assessment

[0436] Subject: Emotion Engine

[0437] The physiological data collected by the device is sent to the emotion engine to evaluate the driver's emotional state. Specifically, facial expressions, tone of voice, heart rate variability patterns, etc. are analyzed to determine the driver's emotional state, such as stress, impatience, or relaxation. The input is physiological data, and the output is the evaluation result of the emotional state.

[0438] Step 3:

[0439] Evaluation of driving skills and fatigue

[0440] Subject: Server

[0441] The server analyzes the collected driving data and physiological data to evaluate driving skills and fatigue levels. Specifically, it scores driving skills based on data such as the accuracy of driving operations, speed, and braking frequency, and calculates fatigue levels based on heart rate and body temperature fluctuations. The inputs are driving data and physiological data, and the outputs are driving skill scores and fatigue levels.

[0442] Step 4:

[0443] Proposal for transition to autonomous driving mode

[0444] Subject: Server

[0445] The server determines the appropriate timing to switch to autonomous driving mode based on the evaluation results. If the driving skill score is low, the fatigue level is high, or the emotional state is unstable, the server generates a message suggesting that "switching to autonomous driving mode is appropriate" and sends it to the terminal. The inputs are the driving skill score, fatigue level, and emotional state, and the output is a message suggesting switching to autonomous driving mode.

[0446] Step 5:

[0447] Providing safe driving advice

[0448] Subject: Server

[0449] The server sends the collected data to a natural language processing model and generates safe driving advice for the driver based on the analysis results. Specifically, it generates messages such as "Please be careful not to drive too fast" and provides them to the driver via their device. The input is driving data and physiological data, and the output is a safe driving advice message.

[0450] Step 6:

[0451] Context-sensitive communication

[0452] Subject: Autonomous Driving System

[0453] The autonomous driving system analyzes surrounding sensor data and communicates as needed. For example, if there is a pedestrian at a crosswalk, it generates a voice message such as "Please be careful, there is a pedestrian at the crosswalk" to notify people around. The input is the surrounding sensor data, and the output is the appropriate communication message.

[0454] 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.

[0455] 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.

[0456] 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.

[0457] [Second embodiment]

[0458] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0459] 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.

[0460] 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).

[0461] 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.

[0462] 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.

[0463] 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).

[0464] 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.

[0465] 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.

[0466] 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.

[0467] 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.

[0468] 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.

[0469] 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."

[0470] This system collects driving data and the driver's physiological data, analyzes them to evaluate driving skills and fatigue levels, and suggests switching to autonomous driving mode at the appropriate time. It also aims to increase social acceptance by providing safe driving advice to drivers using natural language processing models and communicating appropriately according to the surrounding situation.

[0471] As a specific embodiment for implementing this system, the following processing is performed.

[0472] Driving and physiological data collection

[0473] Subject: Device

[0474] The device uses various sensors installed in the vehicle to collect driving data and physiological data of the driver in real time. Specifically, it acquires driving data such as speed, braking operation, accelerator operation, steering angle, etc. It also collects physiological data such as heart rate, body temperature, interior temperature, and voice, as well as in-vehicle environmental data.

[0475] Evaluation of driving skills and fatigue

[0476] Subject: Server

[0477] The server analyzes the collected driving and physiological data to evaluate driving skills and fatigue levels. Driving skills are evaluated and scored based on data such as the accuracy of driving operations, speed, and frequency of braking. Fatigue levels are measured based on heart rate and body temperature fluctuations, and the fatigue level is calculated.

[0478] Proposal for transition to autonomous driving mode

[0479] Subject: Server

[0480] Based on the evaluation results, the server will suggest the appropriate timing to switch to autonomous driving mode. For example, if the driver's fatigue level is high, the server will generate a message saying, "Due to high fatigue level, we suggest switching to autonomous driving mode," and notify the driver. A similar suggestion will also be made if the driver's driving skills are low.

[0481] Providing advice using natural language processing models

[0482] Subject: Server

[0483] The server sends the collected data to a natural language processing (NLP) model, which then generates safe driving advice for the driver based on the analysis results, such as "Please be careful not to drive too fast."

[0484] Context-sensitive communication

[0485] Subject: Autonomous Driving System

[0486] The autonomous driving system detects the surrounding situation from sensor data and communicates as necessary. When the conditions are met (for example, when there is a pedestrian at a crosswalk), the system generates a voice message to notify those around it, such as "There is a pedestrian at the crosswalk, please be careful."

[0487] Specific examples

[0488] Example 1: High fatigue

[0489] The device monitors the driver's heart rate and body temperature, and if these exceed the standard values, the server notifies the driver, "You are highly fatigued, so we suggest switching to autonomous driving mode."

[0490] Example 2: Safe driving advice

[0491] While driving, the device detects frequent use of the brakes. The server analyzes the situation and provides advice such as, "Because you are braking suddenly a lot, please be careful to maintain a safe distance from other vehicles."

[0492] Example 3: When there is a pedestrian on the crosswalk

[0493] The autonomous driving system uses sensors to detect pedestrians approaching a crosswalk, and generates a voice message to alert those around it, saying, "There is a pedestrian on the crosswalk. Please be careful."

[0494] As described above, the present invention is a system that supports safe and comfortable driving for drivers and can also increase social acceptance of automated driving technology.

[0495] The processing flow will be explained below.

[0496] Step 1:

[0497] Subject: Device

[0498] The device collects driving data and physiological data. Specifically, it obtains driving data such as speed, braking, and steering angle from various sensors built into the vehicle. It also simultaneously collects physiological data such as the driver's heart rate and interior temperature using heart rate and temperature sensors.

[0499] Step 2:

[0500] Subject: Device

[0501] The collected driving and physiological data is preprocessed, for example, by removing noise from the data and converting it into an analyzable format. The preprocessed data is then sent to the server, where it is packaged appropriately according to the data format and protocol.

[0502] Step 3:

[0503] Subject: Server

[0504] The server analyzes the received data, calculates a driving skill score from the driving data, and measures fatigue level from physiological data. For example, driving skill is calculated based on the accuracy and smoothness of steering, while fatigue level is evaluated based on heart rate fluctuations and body temperature rise.

[0505] Step 4:

[0506] Subject: Server

[0507] Based on the evaluation results, the server will suggest switching to autonomous driving mode. If the driving skill score is low or the fatigue level is high, the server will generate a message such as "Due to high fatigue level, we suggest switching to autonomous driving mode" and send it to the device.

[0508] Step 5:

[0509] Subject: Device

[0510] The device notifies the driver of the proposal message received from the server. The notification method may be a screen display or an audio alert. The driver (user) who receives this notification decides whether to approve the transition to autonomous driving mode.

[0511] Step 6:

[0512] Subject: Server

[0513] The server inputs driving data and physiological data into a natural language processing model to generate advice on safe driving. For example, if frequent sudden braking is detected, the server generates advice such as, "There are many sudden braking incidents, so please be careful to maintain a safe distance between vehicles."

[0514] Step 7:

[0515] Subject: Device

[0516] The device receives safe driving advice from the server and provides it to the driver, also via screen display and audio alerts, to encourage the driver to be more careful.

[0517] Step 8:

[0518] Subject: Autonomous Driving System

[0519] The autonomous driving system uses sensors to monitor the surroundings and communicates with people around it as needed. For example, if a pedestrian is detected at a crosswalk, the system will generate a voice message saying, "There is a pedestrian at the crosswalk. Please be careful."

[0520] Step 9:

[0521] Subject: User

[0522] The user (driver) decides whether to accept the autonomous driving mode suggestion. If accepted, the system switches to autonomous driving mode. If not accepted, the system continues to collect data and provide safe driving advice.

[0523] Through these steps, the system of the present invention enhances driver safety and establishes proper communication with surrounding people, thereby improving the reliability and acceptability of the overall transportation system.

[0524] Example 1

[0525] 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."

[0526] Conventional driver assistance systems have had difficulty accurately assessing the driver's level of fatigue and driving skill in real time and suggesting transition to autonomous driving mode at the appropriate time. They also lacked the ability to provide detailed advice on safe driving and communicate in accordance with the surrounding situation. As a result, they were unable to adequately support the driver in safe and comfortable driving, and social acceptance of autonomous driving technology was low.

[0527] 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.

[0528] In this invention, the server includes means for collecting driving data and physiological data of the driver, means for performing noise removal and outlier correction as preprocessing of the data, means for evaluating driving skill and fatigue state from the driving data and the physiological data, means for proposing transition to autonomous driving mode based on the evaluation results, means for transmitting the driving data and the physiological data to a natural language processing model, means for generating advice on safe driving from the natural language processing model, means for detecting surrounding conditions and determining the need for communication, and means for detecting surrounding conditions in real time with sensors and generating and notifying voice messages. This makes it possible to accurately evaluate the driver's condition and driving skill in real time, provide advice on safe driving, and further communicate with those around the driver as needed.

[0529] "Driving data" refers to data related to driving behavior such as vehicle speed, braking operation, accelerator operation, and steering angle.

[0530] "Physiological data" refers to data that indicates the physiological state of the driver, such as heart rate and body temperature.

[0531] "In-vehicle environment data" is data indicating environmental conditions such as temperature, humidity, and sound inside the vehicle.

[0532] "Driving skill" is an indicator that shows the accuracy and safety of a driver's driving behavior.

[0533] "Fatigue state" is an index that indicates the degree of mental and physical fatigue of the driver.

[0534] "Autonomous driving mode" is a mode in which the system automatically performs driving operations of the vehicle.

[0535] The "evaluation results" are the results of the driving skills and fatigue state analyzed from the collected data.

[0536] A "natural language processing model" is an algorithm that analyzes collected data and generates advice in natural language.

[0537] "Safe driving advice" is a specific suggestion to encourage drivers to drive safely.

[0538] "Surrounding conditions" is data that indicates the surrounding environment and conditions of the vehicle.

[0539] "Communication" refers to the act of conveying information to the driver and people around them by voice or message.

[0540] A "sensor" is a device used to collect data from a vehicle or driver.

[0541] "Noise reduction" is the process of removing unwanted noise from collected data.

[0542] "Abnormal value correction" is a process for correcting abnormal values ​​contained in collected data.

[0543] This system collects driving data and the driver's physiological data, analyzes them to evaluate driving skills and fatigue levels, and suggests switching to autonomous driving mode at the appropriate time. It also aims to provide safe driving advice to the driver using a natural language processing model and communicate appropriately according to the surrounding situation.

[0544] Hardware and Software Configuration

[0545] The system uses the following hardware and software:

[0546] Device: A group of sensors installed in the vehicle (speed sensor, brake sensor, accelerator sensor, steering angle sensor, heart rate sensor, body temperature sensor, etc.)

[0547] Server: A computer that analyzes collected data, evaluates driving skills and fatigue, and proposes transitioning to autonomous driving mode.

[0548] Natural language processing model (NLP model): an algorithm for generating safe driving advice from collected data

[0549] Sensors: Cameras, LIDAR, and radar for real-time detection of surrounding conditions

[0550] Data processing and calculation

[0551] The device uses sensors to collect driving data and the driver's physiological data in real time. This data is recorded at regular intervals and sent to a server, where it is first preprocessed by noise removal and outlier correction. The driving data and physiological data are then input into a machine learning algorithm to evaluate driving skill and fatigue state.

[0552] Driving skill assessment: Driving skills are scored based on the accuracy of driving operations, speed, frequency of braking, etc.

[0553] Fatigue assessment: Calculate fatigue level from fluctuations in heart rate and body temperature.

[0554] Based on the analysis results, the server will suggest the appropriate timing to switch to autonomous driving mode. For example, if the driver's fatigue level is high, the server will generate a message to notify the driver, such as "Due to high fatigue level, we suggest switching to autonomous driving mode." The autonomous driving system also detects the surrounding situation using data from sensors and generates voice messages to notify those around it as necessary.

[0555] Specific examples

[0556] Example 1: High fatigue

[0557] The device monitors the driver's heart rate and body temperature, and if these exceed the standard values, the server notifies the driver, "You are highly fatigued, so we suggest switching to autonomous driving mode."

[0558] Example 2: Safe driving advice

[0559] While driving, the device detects frequent use of the brakes. The server analyzes the situation and provides advice such as, "Because you are braking suddenly a lot, please be careful to maintain a safe distance from other vehicles."

[0560] Example 3: When there is a pedestrian on the crosswalk

[0561] The autonomous driving system uses sensors to detect pedestrians approaching a crosswalk, and generates a voice message to alert those around it, saying, "There is a pedestrian on the crosswalk. Please be careful."

[0562] Prompt Sentence Examples

[0563] "Please explain the system that uses driving and physiological data to assess the driver's fatigue level and suggest transitioning to autonomous driving mode. Please also provide details on the specific steps and the sensors and algorithms used."

[0564] This allows the present invention to accurately evaluate the driver's condition and driving skills in real time, provide advice on safe driving, and communicate with those around the vehicle as needed.

[0565] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0566] Step 1:

[0567] Data collection

[0568] Input: Data from various sensors installed in the vehicle

[0569] Operation: The device activates speed sensors, brake sensors, accelerator sensors, steering angle sensors, heart rate sensors, body temperature sensors, etc. to collect driving data and driver physiological data in real time.

[0570] Data processing: The collected data is recorded at regular time intervals.

[0571] Output: The collected driving and physiological data are stored on the device and sent to the server.

[0572] Step 2:

[0573] Data Preprocessing

[0574] Input: Driving data and physiological data sent from the device

[0575] How it works: The server performs noise removal and outlier correction on the received data before analyzing it.

[0576] Data calculations: Data cleaning algorithms are used to remove noise and correct outliers.

[0577] Output: A preprocessed, clean dataset

[0578] Step 3:

[0579] Evaluation of driving skills and fatigue

[0580] Input: Preprocessed driving and physiological data

[0581] How it works: The server inputs data into a machine learning algorithm to assess driving skill and fatigue state.

[0582] Data calculation: For driving skills, the system calculates a skill score by analyzing the accuracy of driving operations, speed, frequency of braking, etc. For fatigue, the system calculates the fatigue level by analyzing fluctuations in heart rate and body temperature.

[0583] Output: Driving skill score and fatigue level

[0584] Step 4:

[0585] Proposal for transition to autonomous driving mode

[0586] Input: Driving skill score and fatigue level

[0587] Operation: Based on the evaluation results, the server determines whether to suggest to the driver to switch to autonomous driving mode.

[0588] Data calculation: For example, if the fatigue level is high, a message such as "Due to high fatigue level, we suggest switching to autonomous driving mode" is generated.

[0589] Output: A message is generated and notified to the driver via the terminal.

[0590] Step 5:

[0591] Providing safe driving advice

[0592] Input: Preprocessed driving and physiological data

[0593] How it works: The server sends these data to a natural language processing (NLP) model.

[0594] Data Computation: The NLP model analyzes the data and generates safe driving advice for the driver.

[0595] Output: The generated advice is notified to the driver via the terminal.

[0596] Step 6:

[0597] Context-sensitive communication

[0598] Input: Surroundings data from sensors installed in the vehicle

[0599] How it works: The autonomous driving system uses sensors (cameras, LIDAR, radar) to detect the surroundings in real time.

[0600] Data processing: When certain conditions are met (for example, when there is a pedestrian in a crosswalk), a voice message is generated to notify the driver.

[0601] Output: The generated message is transmitted to the surroundings through the vehicle's speakers.

[0602] (Application example 1)

[0603] 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."

[0604] Conventional driver assistance systems are limited to simply monitoring driving data and the driver's physiological data, and are limited in their ability to evaluate driving skills and fatigue levels in real time and provide appropriate advice. Furthermore, they lack the ability to appropriately transition to autonomous driving mode depending on the surrounding conditions or provide specific safe driving advice to the driver, making it impossible to fully guarantee the driver's safety and comfort. Furthermore, there are also challenges such as difficulty in smoothly communicating with the driver and providing appropriate advice.

[0605] 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.

[0606] In this invention, the server includes a means for collecting driving data, a means for acquiring physiological data and in-vehicle environmental data of the driver, a means for evaluating the driving skill and fatigue state from the driving data and the physiological data, a means for suggesting switching to an autonomous driving mode based on the evaluation results, a means for generating safe driving advice for the driver using a natural language processing model, and a means for notifying the driver of the generated advice by voice or text. This allows the driver to receive a real-time evaluation of their driving situation and receive a suggestion to switch to an autonomous driving mode at an appropriate time, enabling safe and efficient driving. Furthermore, by utilizing the natural language processing model, specific and effective safe driving advice is provided to the driver, improving the driver's safety and comfort. Furthermore, by appropriately communicating with people around the driver according to the surrounding situation, a good harmony between the driver and the surrounding environment can be achieved.

[0607] "Driving data" refers to information related to the driver's driving behavior, such as vehicle speed, braking operation, accelerator operation, and steering angle.

[0608] "Physiological data" is information that indicates the physical condition of the driver, such as the driver's heart rate and body temperature.

[0609] "In-vehicle environment data" is information indicating the environmental conditions inside the vehicle, such as the temperature, humidity, and sound level inside the vehicle.

[0610] "Driving skill" is an evaluation of the driver's accuracy of driving operations, judgment, reaction speed, etc.

[0611] The "fatigue state" indicates the degree of fatigue of the driver and is calculated based on physiological data.

[0612] "Autonomous driving mode" is a mode in which the vehicle performs driving operations autonomously.

[0613] A "natural language processing model" is an algorithm or system for analyzing and understanding human language.

[0614] "Safe driving advice" is information that encourages drivers to drive properly and take precautions.

[0615] A "sensor" is a device that senses physical or environmental information and converts it into digital data.

[0616] "Notification" refers to the act and means of conveying information to the driver.

[0617] "Real-time" means that processing and communication occurs almost instantly, with little delay.

[0618] "Surrounding conditions" refers to information about pedestrians, other vehicles, traffic signals, and the like around the vehicle.

[0619] "Communication" is the act and means of transmitting information to each other.

[0620] This system collects and analyzes driving data and driver physiological data to evaluate driving skills and fatigue levels, and provides specific advice for safe driving. This system is designed to improve driver safety and comfort.

[0621] Driving and physiological data collection

[0622] The device collects driving data and physiological data of the driver in real time using multiple sensors installed in the vehicle. Specifically, driving data is acquired from the speedometer, brake operation sensor, accelerator operation sensor, steering angle sensor, etc. In addition, physiological data of the driver and in-vehicle environmental data are collected from the heart rate sensor, body temperature sensor, in-vehicle temperature sensor, microphone, etc.

[0623] Data analysis and evaluation

[0624] The server analyzes the collected driving and physiological data to evaluate driving skills and fatigue levels. Driving skills are scored based on data such as the accuracy of driving operations, speed, and frequency of braking. Fatigue levels are evaluated and quantified based on fluctuations in heart rate and body temperature.

[0625] Proposal for transition to autonomous driving mode

[0626] Based on the evaluation results, the server will suggest the appropriate timing to switch to autonomous driving mode. For example, if the driver's fatigue level is high, the server will generate a message such as "Due to high fatigue level, we suggest switching to autonomous driving mode" and notify the driver via the device.

[0627] Providing advice using natural language processing models

[0628] The server sends the collected data to a natural language processing (NLP) model and generates safe driving advice for the driver based on the analysis results. For example, if the frequency of sudden braking is high, a message such as "There are many sudden braking incidents, so please be careful to maintain a safe distance between vehicles" will be generated.

[0629] Context-sensitive communication

[0630] The autonomous driving system uses sensors to detect the surrounding situation and communicates by voice or text as necessary. For example, if there is a pedestrian in a crosswalk, the system will generate a voice message such as "There is a pedestrian in the crosswalk, please be careful" to notify the driver and people around.

[0631] Specific example explanation

[0632] Example 1: High fatigue

[0633] The driver's heart rate and body temperature are monitored by the terminal, and if these exceed the standard values, the server notifies the driver, "Due to high fatigue level, we suggest switching to autonomous driving mode."

[0634] Example 2: Safe driving advice

[0635] If the device detects that the driver is using the brakes frequently while driving, the server will analyze the situation and provide advice such as, "There are many sudden braking attempts, so please be careful to maintain a safe distance from other vehicles."

[0636] Example 3: When there is a pedestrian on the crosswalk

[0637] The autonomous driving system uses sensors to detect pedestrians on the crosswalk and generates a voice message saying, "There is a pedestrian on the crosswalk. Please be careful," to notify those around.

[0638] Prompt Sentence Examples

[0639] For example, the following prompt sentences can be input into a generative AI model and used:

[0640] User input: "I've been feeling tired a lot lately while driving. What should I do?"

[0641] Generative AI: "If you feel tired, it's important to take regular breaks while driving. Also, try to avoid long periods of driving and consider driving in autonomous mode."

[0642] In this way, the present invention can provide comprehensive assistance to the driver and provide a safer and more comfortable driving environment.

[0643] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0644] Step 1:

[0645] The device collects driving data and the driver's physiological data in real time through sensors installed in the vehicle (speedometer, brake operation sensor, accelerator operation sensor, steering angle sensor, heart rate sensor, body temperature sensor, interior temperature sensor, microphone). This collected data indicates the vehicle's driving situation and the driver's physical condition, and the device stores this as digital data.

[0646] Input: Data from various sensors installed in the vehicle

[0647] Output: Driving data and driver physiological data

[0648] Step 2:

[0649] The device transmits the collected driving and physiological data to a server, where the data is transmitted along with location and time information while maintaining real-time performance, and is converted into a format that can be analyzed by the server.

[0650] Input: Driving data and driver physiological data

[0651] Output: Driving and physiological data sent to the server

[0652] Step 3:

[0653] The server analyzes the received driving data and physiological data to evaluate driving skills and fatigue levels. Specifically, it scores driving skills based on the accuracy of driving operations, speed, frequency of braking, etc., and quantifies fatigue levels from fluctuations in heart rate and body temperature. This generates an evaluation result based on each data.

[0654] Input: Driving data and driver physiological data sent to the server

[0655] Output: Evaluation results of driving skills and fatigue state

[0656] Step 4:

[0657] Based on the evaluation results, the server will suggest the appropriate timing to switch to autonomous driving mode. For example, if the driver's fatigue level is high, the server will generate a message saying, "Due to high fatigue level, we suggest switching to autonomous driving mode," and notify the driver of this message via the device.

[0658] Input: Evaluation results of driving skills and fatigue state

[0659] Output: Message proposing to switch to autonomous driving mode

[0660] Step 5:

[0661] The server sends the collected data to a natural language processing (NLP) model and generates safe driving advice for the driver based on the analysis results. Specifically, if the frequency of sudden braking is high, a message such as "There are many sudden braking incidents, so please be careful to maintain a safe distance between vehicles" is generated and provided to the driver via the terminal.

[0662] Input: Driving and physiological data

[0663] Output: Safe driving advice

[0664] Step 6:

[0665] The device receives input from the driver and sends it to the server. The server uses a natural language processing model to generate a response and provides it to the driver via the device. For example, in response to an input such as, "I often feel tired while driving recently. What should I do?", the server responds, "If you feel tired, it is important to take appropriate breaks while driving. Also, try to avoid driving for long periods of time and consider driving in autonomous driving mode."

[0666] Input: Input from the driver

[0667] Output: Response from the generative AI model

[0668] Step 7:

[0669] The autonomous driving system uses sensors installed around the vehicle to detect the surrounding situation. For example, if there is a pedestrian in a crosswalk, the system will detect this and generate a voice message such as "There is a pedestrian in the crosswalk, please be careful" to notify the driver and people around.

[0670] Input: Vehicle surroundings data

[0671] Output: Messages depending on the surroundings

[0672] Step 8:

[0673] The device provides all generated messages and notifications to the driver, allowing the driver to receive safe driving advice in real time and suggestions for switching to autonomous driving mode at the same time, improving driver safety and comfort.

[0674] Input: Generated messages and notifications

[0675] Output: Notification to the driver

[0676] 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.

[0677] This invention combines a system that collects driving data and physiological data, analyzes them to evaluate driving skills and fatigue levels, and suggests switching to autonomous driving mode at the appropriate time with an emotion engine that recognizes the user's emotions. It also aims to increase social acceptance by providing safe driving advice to drivers using a natural language processing model and communicating appropriately according to the surrounding situation.

[0678] As a specific embodiment for implementing this system, the following processing is performed.

[0679] Driving and physiological data collection

[0680] Subject: Device

[0681] The device uses various sensors installed in the vehicle to collect driving data and physiological data of the driver in real time. Specifically, it acquires driving data such as speed, braking operation, accelerator operation, steering angle, etc. It also collects physiological data such as heart rate, body temperature, interior temperature, and voice, as well as in-vehicle environmental data.

[0682] Emotional state assessment by emotion engine

[0683] Subject: Emotion Engine

[0684] The emotion engine analyzes the driver's facial expression, tone of voice, heart rate variability patterns, etc. to determine the driver's emotional state (e.g., stress, impatience, relaxation, etc.) and transmits this emotion data to the server.

[0685] Evaluation of driving skills and fatigue

[0686] Subject: Server

[0687] The server analyzes the collected driving and physiological data to evaluate driving skills and fatigue levels. Driving skills are evaluated and scored based on data such as the accuracy of driving operations, speed, and frequency of braking. Fatigue levels are measured based on heart rate and body temperature fluctuations, and the fatigue level is calculated.

[0688] Proposal for transition to autonomous driving mode taking into account emotional state

[0689] Subject: Server

[0690] Based on the evaluation results, the server will suggest the appropriate timing to switch to autonomous driving mode. If the driving skill score is low, the fatigue level is high, or the emotional state is unstable, the server will generate a message such as "We suggest switching to autonomous driving mode because your fatigue level is high" or "We suggest switching to autonomous driving mode because your stress level is increasing" and send it to the device.

[0691] Providing advice using natural language processing models

[0692] Subject: Server

[0693] The server sends the collected data to a natural language processing (NLP) model, which then generates safe driving advice for the driver based on the analysis results, such as "Please be careful not to drive too fast."

[0694] Context-sensitive communication

[0695] Subject: Autonomous Driving System

[0696] The autonomous driving system detects the surrounding situation from sensor data and communicates as necessary. When the conditions are met (for example, when there is a pedestrian at a crosswalk), the system generates a voice message such as "There is a pedestrian at the crosswalk, please be careful" and notifies those around.

[0697] Specific examples

[0698] Example 1: High fatigue and unstable emotional state

[0699] The device monitors the driver's heart rate and body temperature, and if these exceed the standard values ​​and the emotion engine detects a high stress level, the server notifies the driver, "Due to high levels of fatigue and stress, we suggest switching to autonomous driving mode."

[0700] Example 2: Safe driving advice

[0701] While driving, the device detects frequent use of the brakes. The server analyzes the situation and provides advice such as, "Because you are braking suddenly a lot, please be careful to maintain a safe distance from other vehicles."

[0702] Example 3: When there is a pedestrian on the crosswalk

[0703] The autonomous driving system uses sensors to detect pedestrians approaching a crosswalk, and generates a voice message to alert those around it, saying, "There is a pedestrian on the crosswalk. Please be careful."

[0704] Through these steps, the system of the present invention enhances driver safety and establishes proper communication with surrounding people, thereby improving the reliability and acceptability of the overall transportation system.

[0705] The processing flow will be explained below.

[0706] Step 1:

[0707] Subject: Device

[0708] The device collects driving data and physiological data from various sensors installed in the vehicle. For example, it obtains the vehicle's speed from a speed sensor and the frequency of brake use from a brake pedal sensor. Furthermore, it obtains the driver's physiological data (heart rate, body temperature) using a heart rate sensor and a body temperature sensor.

[0709] Step 2:

[0710] Subject: Emotion Engine

[0711] The emotion engine analyzes the driver's facial expressions, voice, and heart rate fluctuation patterns to recognize the driver's emotional state. For example, facial expression analysis technology can determine whether the driver is stressed, and voice tone can determine whether the driver is irritated. This emotional data is sent to the server.

[0712] Step 3:

[0713] Subject: Device

[0714] The device preprocesses the driving and physiological data collected, removes noise from the data, and converts it into an analyzable format. The preprocessed data is then sent to the server.

[0715] Step 4:

[0716] Subject: Server

[0717] The server analyzes the pre-processed driving data and physiological data to calculate a driving skill score and fatigue level. Driving skill is evaluated based on data such as steering and speed fluctuations, while fatigue level is measured based on fluctuations in heart rate and body temperature.

[0718] Step 5:

[0719] Subject: Server

[0720] The server analyzes the emotion data sent from the emotion engine and combines it with the driving skill and fatigue data to make an overall evaluation. For example, if the driving skill is poor, the fatigue level is high, and the emotional state is stressed, the overall score will be low.

[0721] Step 6:

[0722] Subject: Server

[0723] Based on the evaluation results, the server generates a message such as "Your driving skills are low, and your fatigue and stress levels are high. We suggest you switch to autonomous driving mode," and sends it to the device.

[0724] Step 7:

[0725] Subject: Device

[0726] The device notifies the driver (user) of the proposal message received from the server, using a screen display and audio alert to inform the driver that "Due to high levels of fatigue and stress, we propose switching to autonomous driving mode."

[0727] Step 8:

[0728] Subject: Server

[0729] The server inputs driving and physiological data into a natural language processing model to generate specific advice on safe driving, such as "Please be careful to maintain a safe distance as there are many instances of sudden braking."

[0730] Step 9:

[0731] Subject: Device

[0732] The device receives safe driving advice from the server and provides it to the driver, allowing the driver to receive specific advice via screen displays and audio alerts.

[0733] Step 10:

[0734] Subject: Autonomous Driving System

[0735] The autonomous driving system monitors the surrounding situation and communicates with people around it as needed. For example, if a pedestrian is detected at a crosswalk, it will generate a voice message saying, "There is a pedestrian at the crosswalk. Please be careful."

[0736] Step 11:

[0737] Subject: User

[0738] The user (driver) decides whether to accept the proposal to switch to autonomous driving mode. If the proposal is accepted, the system switches to autonomous driving mode. If not accepted, the system continues to collect data and provide safe driving advice.

[0739] Example 2

[0740] 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."

[0741] Conventional driver assistance systems were able to collect driving and physiological data to assess driving skills and fatigue levels, but they lacked the functionality to consider the driver's emotional state and suggest transitioning to autonomous driving mode at the appropriate time. As a result, the system was unable to provide appropriate driving assistance due to a lack of consideration for the driver's emotional state, such as stress or impatience. Furthermore, the system lacked the functionality to provide specific advice on safe driving based on the collected data. Furthermore, the system lacked the functionality to communicate in response to the surrounding situation, making it difficult to improve the reliability and acceptability of the overall transportation system.

[0742] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0743] In this invention, the server includes a means for collecting driving data, a means for acquiring physiological data and in-vehicle environmental data of the driver, a means for analyzing the driver's emotional state, a means for evaluating the driver's driving skill and fatigue state from the driving data and the physiological data, and a means for proposing transition to autonomous driving mode based on the evaluation results and taking the driver's emotional state into consideration. This makes it possible to comprehensively evaluate the driver's driving skill, fatigue state, and emotional state and to propose transition to autonomous driving mode at an appropriate time. Furthermore, it is possible to provide specific advice on safe driving using a natural language processing model and communicate according to the surrounding situation, thereby improving the reliability and acceptability of the overall transportation system.

[0744] "Driving data" refers to data relating to driving operations such as vehicle speed, braking operation, accelerator operation, and steering angle, as well as vehicle behavior.

[0745] "Physiological data" is data relating to the driver's heart rate, body temperature, breathing patterns and other physiological conditions.

[0746] "In-vehicle environment data" refers to data relating to the environment inside the vehicle, such as the temperature, humidity, and sound environment inside the vehicle.

[0747] "Means of evaluation" refers to the process of analyzing collected driving data and physiological data and quantifying and evaluating driving skills and fatigue levels.

[0748] "Means for suggesting transition to automated driving mode" refers to a process for generating a message to recommend to the driver that they switch to automated driving mode based on the results of an evaluation of their driving skills, fatigue state, and emotional state.

[0749] A "natural language processing model" is an algorithm or computer program that analyzes collected data and generates specific advice for drivers in natural language.

[0750] "Means for analyzing emotional state" refers to a process for analyzing the driver's facial expression, tone of voice, heart rate fluctuation patterns, etc. to determine the driver's emotional state (stress, impatience, relaxation, etc.).

[0751] "Means for detecting surrounding conditions" refers to the process by which an automated driving system uses external sensors to monitor the surrounding environment and detect the location and movement of pedestrians and other vehicles.

[0752] "Means of communication" refers to the process of notifying the driver and those around them of necessary information and warnings as audio or visual messages depending on the detected surrounding conditions.

[0753] This invention combines a system that collects driving data and physiological data, analyzes them to evaluate driving skills and fatigue levels, and suggests switching to autonomous driving mode at the appropriate time with an emotion engine that recognizes the user's emotions. It also aims to increase social acceptance by providing safe driving advice to drivers using a natural language processing model and communicating appropriately according to the surrounding situation.

[0754] Driving and physiological data collection

[0755] Subject: Device

[0756] The device collects driving data and the driver's physiological data in real time using various sensors installed in the vehicle, such as speed sensors, brake sensors, accelerator sensors, and steering angle sensors. It also collects physiological data such as the driver's heart rate, body temperature, interior temperature, and voice using a heart rate monitor, thermometer, interior temperature sensor, and microphone, as well as in-vehicle environmental data.

[0757] Specific examples

[0758] Speed ​​sensor: Get the current speed of the vehicle

[0759] Heart rate monitor: Monitors the driver's heart rate fluctuations

[0760] Emotional state analysis using emotion engine

[0761] Subject: Emotion Engine

[0762] The emotion engine analyzes facial expression data, voice data, and data from a heart rate monitor acquired from a camera and microphone to determine the driver's emotional state, which can include stress, impatience, relaxation, etc. This emotional data is then sent to a server.

[0763] Specific examples

[0764] Analyzing the driver's stress level from facial expressions captured on camera

[0765] Determine your relaxation state using heart rate fluctuation patterns

[0766] Sending data to the server

[0767] Subject: Device

[0768] The device sends the collected and analyzed data to a server, including driving data, physiological data, and emotional data.

[0769] Evaluation of driving skills and fatigue

[0770] Subject: Server

[0771] The server analyzes all the data sent and evaluates the driver's driving skills and fatigue level, including scoring the driver's driving skills based on the accuracy of driving maneuvers, speed, frequency of sudden braking, etc., and calculating the driver's fatigue level based on fluctuations in heart rate and body temperature.

[0772] Specific examples

[0773] Low driving skill score based on frequent hard braking and sudden acceleration

[0774] Increased heart rate and body temperature indicate high levels of fatigue.

[0775] Proposal for transition to autonomous driving mode

[0776] Subject: Server

[0777] Based on the evaluation results, the server will suggest the appropriate timing to switch to autonomous driving mode. If the driving skill score is low, the fatigue level is high, or the emotional state is unstable, the server will generate a message such as "Due to high fatigue level, we suggest switching to autonomous driving mode" or "Due to increasing stress, we suggest switching to autonomous driving mode" and send it to the device.

[0778] Providing advice using natural language processing models

[0779] Subject: Server

[0780] The server uses natural language processing (NLP) models to analyze the collected data and generate specific safe driving advice for the driver, such as "Please be careful not to drive too fast."

[0781] Specific examples

[0782] The advice is "Please be careful to maintain a safe distance from other vehicles due to frequent sudden braking."

[0783] Context-sensitive communication

[0784] Subject: Autonomous Driving System

[0785] The autonomous driving system detects the surrounding situation using various sensors and communicates by generating audio and visual messages as needed. For example, if there is a pedestrian at a crosswalk, it will generate a warning message such as "There is a pedestrian at the crosswalk, please be careful."

[0786] Specific examples

[0787] If there is a pedestrian on the crosswalk, a warning message will be generated saying "There is a pedestrian on the crosswalk, please be careful."

[0788] This invention makes it possible to comprehensively evaluate a driver's driving skill, fatigue level, and emotional state, and provide appropriate safe driving support. In addition, by communicating in accordance with the surrounding situation, the reliability and acceptability of the entire transportation system can be improved.

[0789] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0790] Step 1: Collect driving and physiological data

[0791] Subject: Device

[0792] Inputs: Speed ​​sensor, brake sensor, accelerator sensor, steering angle sensor, heart rate monitor, thermometer, interior temperature sensor, microphone

[0793] Specific operation: The terminal uses various sensors installed in the vehicle to collect driving data and physiological data of the driver. The speed sensor measures the vehicle's speed in real time, the brake sensor detects brake pedal operation status, the heart rate monitor obtains the driver's heart rate, and the thermometer records the driver's body temperature. This data is integrated to understand the driver's condition while driving.

[0794] Output: A set of driving and physiological data

[0795] Step 2: Analyze emotional state

[0796] Subject: Emotion Engine

[0797] Input: facial expression data, voice data, heart rate data

[0798] Specific operation: The emotion engine analyzes facial expression data, voice data, and heart rate data acquired from the camera and microphone. It analyzes the driver's facial expressions captured by the camera to determine their emotional state, such as stress or impatience. It also analyzes the voice data to infer emotions from the tone and speed of the driver's voice. This determines the driver's emotional state.

[0799] Output: Emotional state data

[0800] Step 3: Sending data to the server

[0801] Subject: Device

[0802] Input: driving data, physiological data, emotional state data

[0803] How it works: The device sends collected and analyzed data to a server, including real-time collected driving data, physiological data, and emotional state data. The data is transmitted using a secure protocol.

[0804] Output: Notify the server that data has been sent

[0805] Step 4: Evaluate driving skills and fatigue

[0806] Subject: Server

[0807] Input: driving data, physiological data, emotional state data

[0808] Specific operation: The server analyzes the received data and evaluates the driver's driving skills and fatigue level. Driving skills are quantified based on the accuracy of driving operations, speed, frequency of sudden braking, etc. Fatigue level is calculated based on fluctuations in heart rate and body temperature. If the evaluation result exceeds a certain standard, the system proceeds to the next step.

[0809] Output: Driving skill evaluation score, fatigue state evaluation score

[0810] Step 5: Proposal to transition to autonomous driving mode

[0811] Subject: Server

[0812] Input: Driving skill evaluation score, fatigue state evaluation score, emotional state data

[0813] Specific operation: Based on the evaluation results, the server determines the appropriate timing to switch to autonomous driving mode. If the driving skill score is low, the fatigue level is high, or the emotional state is unstable, a message suggesting switching to autonomous driving mode will be generated and sent to the device.

[0814] Output: Message proposing to switch to autonomous driving mode

[0815] Step 6: Providing safe driving advice

[0816] Subject: Server

[0817] Input: driving data, physiological data, natural language processing model

[0818] Specific operation: The server uses a natural language processing (NLP) model to analyze the collected data and generate specific safe driving advice for the driver. For example, if the speed is too high or there are frequent sudden braking, specific instructions and advice will be generated in natural language and sent to the device.

[0819] Output: Safe driving advice

[0820] Step 7: Communicate in context

[0821] Subject: Autonomous Driving System

[0822] Input: Sensor data (e.g., environmental monitoring results)

[0823] Specific operation: The autonomous driving system uses various sensors to monitor the surrounding situation and generates audio and visual messages to notify the driver and those around the vehicle as needed. For example, if there is a pedestrian at a crosswalk, the system will generate a message such as "There is a pedestrian at the crosswalk, please be careful" and notify the driver through a speaker.

[0824] Output: Warning message to the surroundings

[0825] (Application example 2)

[0826] 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."

[0827] Conventional automated driving systems provide means to evaluate driving skills and fatigue levels, but they have issues in taking the driver's emotional state into account. It is also necessary to closely monitor the driver's stress and fatigue and suggest transitioning to automated driving mode at the appropriate time. Furthermore, advice on safe driving is provided without taking the driver's emotional state into consideration, which has led to issues with the acceptability and effectiveness of the advice. There is a need to design a system that solves these issues and significantly improves driver safety and comfort.

[0828] 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.

[0829] In this invention, the server includes means for collecting driving data, means for acquiring physiological data and in-vehicle environmental data of the driver, means for evaluating the emotional state of the driver, means for evaluating the driving skill and fatigue state from the driving data and the physiological data, means for proposing a transition to an autonomous driving mode based on the evaluation results, and means for appropriate communication based on the emotional state. This makes it possible to comprehensively evaluate the driver's driving skill, fatigue state, and emotional state, thereby significantly improving the safety and comfort of the driver.

[0830] "Driving data" refers to information such as speed, braking operation, accelerator operation, and steering angle that occurs when a driver operates a vehicle.

[0831] "Physiological data" refers to information that indicates the driver's physical condition and physiological responses, such as the driver's heart rate, body temperature, and tone of voice.

[0832] "In-vehicle environment data" refers to information indicating the environmental conditions inside the vehicle, such as the temperature, sound, and lighting inside the vehicle.

[0833] "Emotional state" refers to the driver's emotional state, such as stress, impatience, or relaxation, which can be obtained by analyzing the driver's facial expression, tone of voice, heart rate variability pattern, etc.

[0834] "Autonomous driving mode" refers to a mode in which the vehicle drives automatically, without requiring driver operation.

[0835] A "natural language processing model" refers to an artificial intelligence technology for analyzing and understanding human language, and is used to generate advice on safe driving.

[0836] "Communication" refers to the act of sharing information with the driver and people around, and includes notifications via audio and display.

[0837] "Evaluation results" refer to the results of driving skills and fatigue state calculated based on an analysis of driving data, physiological data, and emotional state.

[0838] This invention is a system that collects and analyzes driving data, physiological data, and emotional state, evaluates the driver's driving skill and fatigue level, and suggests transitioning to autonomous driving mode at the appropriate time. Furthermore, it can provide advice on safe driving using a natural language processing model and communicate according to the surrounding situation. The specific configuration and operation of this system are described below.

[0839] System configuration

[0840] The system includes the following hardware and software:

[0841] Terminal: A device used to collect and display data, such as a smartphone.

[0842] Vehicle sensors: Various sensors for collecting driving data such as speed, braking, accelerating, and steering angle, as well as physiological data such as heart rate, body temperature, interior temperature, and voice.

[0843] Server: A cloud server that analyzes and evaluates data and provides appropriate advice and suggestions to the driver.

[0844] Natural Language Processing Library: An NLP library for generating safe driving advice.

[0845] Emotion engine: An engine for analyzing the driver's emotional state.

[0846] Data collection and analysis

[0847] The device uses various sensors installed in the vehicle to collect real-time driving data and physiological data of the driver, including speed, braking operation, accelerator operation, steering angle, heart rate, body temperature, interior temperature, and voice. In addition, the emotion engine analyzes the driver's facial expressions, tone of voice, heart rate variability patterns, etc. to determine the driver's emotional state.

[0848] Data evaluation

[0849] The server analyzes the collected driving and physiological data to evaluate driving skills and fatigue levels. Driving skills are evaluated and scored based on data such as the accuracy of driving operations, speed, and frequency of braking. Fatigue levels are measured based on heart rate and body temperature fluctuations, and the fatigue level is calculated. These evaluation results, along with emotional states, are stored on the server.

[0850] Proposal for transition to autonomous driving mode

[0851] Based on the evaluation results, the server will suggest switching to autonomous driving mode at an appropriate time. If the driving skill score is low, the fatigue level is high, or the emotional state is unstable, the server will generate a message suggesting switching to autonomous driving mode and send it to the device.

[0852] Providing advice and communicating

[0853] The server sends the collected data to a natural language processing model and generates safe driving advice for the driver based on the analysis results. For example, a message such as "Please be careful not to drive too fast" is provided to the driver. The autonomous driving system also detects the surrounding situation from sensor data and generates voice messages such as "There is a pedestrian on the crosswalk, please be careful" to notify those around it as necessary.

[0854] Specific examples

[0855] Example 1: High fatigue and unstable emotional state

[0856] The device monitors the driver's heart rate and body temperature, and if these exceed the standard values ​​or the emotion engine detects a high stress level, the server notifies the driver, "Due to high levels of fatigue and stress, we suggest switching to autonomous driving mode."

[0857] Example 2: Safe driving advice

[0858] If the device detects that the driver is using the brakes frequently while driving, the server will analyze the situation and provide advice such as, "There are many sudden braking attempts, so please be careful to maintain a safe distance from other vehicles."

[0859] Example prompt sentence:

[0860] The data collected during driving is analyzed based on the following prompts, and suggestions and advice are provided to the driver.

[0861] "Driving data: {driving_data}, Physiological data: {physiological_data}, Emotional state: {emotional_state}"

[0862] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0863] Step 1:

[0864] Data collection

[0865] Subject: Device

[0866] The terminal collects driving data and the driver's physiological data in real time through various sensors installed in the vehicle. Specifically, it acquires driving data such as speed, braking operation, accelerator operation, and steering angle, as well as physiological data such as heart rate, body temperature, interior temperature, and voice. The input is real-time data from the various sensors, and the output is the collected driving data and physiological data.

[0867] Step 2:

[0868] Emotional state assessment

[0869] Subject: Emotion Engine

[0870] The physiological data collected by the device is sent to the emotion engine to evaluate the driver's emotional state. Specifically, facial expressions, tone of voice, heart rate variability patterns, etc. are analyzed to determine the driver's emotional state, such as stress, impatience, or relaxation. The input is physiological data, and the output is the evaluation result of the emotional state.

[0871] Step 3:

[0872] Evaluation of driving skills and fatigue

[0873] Subject: Server

[0874] The server analyzes the collected driving data and physiological data to evaluate driving skills and fatigue levels. Specifically, it scores driving skills based on data such as the accuracy of driving operations, speed, and braking frequency, and calculates fatigue levels based on heart rate and body temperature fluctuations. The inputs are driving data and physiological data, and the outputs are driving skill scores and fatigue levels.

[0875] Step 4:

[0876] Proposal for transition to autonomous driving mode

[0877] Subject: Server

[0878] The server determines the appropriate timing to switch to autonomous driving mode based on the evaluation results. If the driving skill score is low, the fatigue level is high, or the emotional state is unstable, the server generates a message suggesting that "switching to autonomous driving mode is appropriate" and sends it to the terminal. The inputs are the driving skill score, fatigue level, and emotional state, and the output is a message suggesting switching to autonomous driving mode.

[0879] Step 5:

[0880] Providing safe driving advice

[0881] Subject: Server

[0882] The server sends the collected data to a natural language processing model and generates safe driving advice for the driver based on the analysis results. Specifically, it generates messages such as "Please be careful not to drive too fast" and provides them to the driver via their device. The input is driving data and physiological data, and the output is a safe driving advice message.

[0883] Step 6:

[0884] Context-sensitive communication

[0885] Subject: Autonomous Driving System

[0886] The autonomous driving system analyzes surrounding sensor data and communicates as needed. For example, if there is a pedestrian at a crosswalk, it generates a voice message such as "Please be careful, there is a pedestrian at the crosswalk" to notify people around. The input is the surrounding sensor data, and the output is the appropriate communication message.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] [Third embodiment]

[0891] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0892] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0893] 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).

[0894] 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.

[0895] 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.

[0896] 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).

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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."

[0903] This system collects driving data and the driver's physiological data, analyzes them to evaluate driving skills and fatigue levels, and suggests switching to autonomous driving mode at the appropriate time. It also aims to increase social acceptance by providing safe driving advice to drivers using natural language processing models and communicating appropriately according to the surrounding situation.

[0904] As a specific embodiment for implementing this system, the following processing is performed.

[0905] Driving and physiological data collection

[0906] Subject: Device

[0907] The device uses various sensors installed in the vehicle to collect driving data and physiological data of the driver in real time. Specifically, it acquires driving data such as speed, braking operation, accelerator operation, steering angle, etc. It also collects physiological data such as heart rate, body temperature, interior temperature, and voice, as well as in-vehicle environmental data.

[0908] Evaluation of driving skills and fatigue

[0909] Subject: Server

[0910] The server analyzes the collected driving and physiological data to evaluate driving skills and fatigue levels. Driving skills are evaluated and scored based on data such as the accuracy of driving operations, speed, and frequency of braking. Fatigue levels are measured based on heart rate and body temperature fluctuations, and the fatigue level is calculated.

[0911] Proposal for transition to autonomous driving mode

[0912] Subject: Server

[0913] Based on the evaluation results, the server will suggest the appropriate timing to switch to autonomous driving mode. For example, if the driver's fatigue level is high, the server will generate a message saying, "Due to high fatigue level, we suggest switching to autonomous driving mode," and notify the driver. A similar suggestion will also be made if the driver's driving skills are low.

[0914] Providing advice using natural language processing models

[0915] Subject: Server

[0916] The server sends the collected data to a natural language processing (NLP) model, which then generates safe driving advice for the driver based on the analysis results, such as "Please be careful not to drive too fast."

[0917] Context-sensitive communication

[0918] Subject: Autonomous Driving System

[0919] The autonomous driving system detects the surrounding situation from sensor data and communicates as necessary. When the conditions are met (for example, when there is a pedestrian at a crosswalk), the system generates a voice message to notify those around it, such as "There is a pedestrian at the crosswalk, please be careful."

[0920] Specific examples

[0921] Example 1: High fatigue

[0922] The device monitors the driver's heart rate and body temperature, and if these exceed the standard values, the server notifies the driver, "You are highly fatigued, so we suggest switching to autonomous driving mode."

[0923] Example 2: Safe driving advice

[0924] While driving, the device detects frequent use of the brakes. The server analyzes the situation and provides advice such as, "Because you are braking suddenly a lot, please be careful to maintain a safe distance from other vehicles."

[0925] Example 3: When there is a pedestrian on the crosswalk

[0926] The autonomous driving system uses sensors to detect pedestrians approaching a crosswalk, and generates a voice message to alert those around it, saying, "There is a pedestrian on the crosswalk. Please be careful."

[0927] As described above, the present invention is a system that supports safe and comfortable driving for drivers and can also increase social acceptance of automated driving technology.

[0928] The processing flow will be explained below.

[0929] Step 1:

[0930] Subject: Device

[0931] The device collects driving data and physiological data. Specifically, it obtains driving data such as speed, braking, and steering angle from various sensors built into the vehicle. It also simultaneously collects physiological data such as the driver's heart rate and interior temperature using heart rate and temperature sensors.

[0932] Step 2:

[0933] Subject: Device

[0934] The collected driving and physiological data is preprocessed, for example, by removing noise from the data and converting it into an analyzable format. The preprocessed data is then sent to the server, where it is packaged appropriately according to the data format and protocol.

[0935] Step 3:

[0936] Subject: Server

[0937] The server analyzes the received data, calculates a driving skill score from the driving data, and measures fatigue level from physiological data. For example, driving skill is calculated based on the accuracy and smoothness of steering, while fatigue level is evaluated based on heart rate fluctuations and body temperature rise.

[0938] Step 4:

[0939] Subject: Server

[0940] Based on the evaluation results, the server will suggest switching to autonomous driving mode. If the driving skill score is low or the fatigue level is high, the server will generate a message such as "Due to high fatigue level, we suggest switching to autonomous driving mode" and send it to the device.

[0941] Step 5:

[0942] Subject: Device

[0943] The device notifies the driver of the proposal message received from the server. The notification method may be a screen display or an audio alert. The driver (user) who receives this notification decides whether to approve the transition to autonomous driving mode.

[0944] Step 6:

[0945] Subject: Server

[0946] The server inputs driving data and physiological data into a natural language processing model to generate advice on safe driving. For example, if frequent sudden braking is detected, the server generates advice such as, "There are many sudden braking incidents, so please be careful to maintain a safe distance between vehicles."

[0947] Step 7:

[0948] Subject: Device

[0949] The device receives safe driving advice from the server and provides it to the driver, also via screen display and audio alerts, to encourage the driver to be more careful.

[0950] Step 8:

[0951] Subject: Autonomous Driving System

[0952] The autonomous driving system uses sensors to monitor the surroundings and communicates with people around it as needed. For example, if a pedestrian is detected at a crosswalk, the system will generate a voice message saying, "There is a pedestrian at the crosswalk. Please be careful."

[0953] Step 9:

[0954] Subject: User

[0955] The user (driver) decides whether to accept the autonomous driving mode suggestion. If accepted, the system switches to autonomous driving mode. If not accepted, the system continues to collect data and provide safe driving advice.

[0956] Through these steps, the system of the present invention enhances driver safety and establishes proper communication with surrounding people, thereby improving the reliability and acceptability of the overall transportation system.

[0957] Example 1

[0958] 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."

[0959] Conventional driver assistance systems have had difficulty accurately assessing the driver's level of fatigue and driving skill in real time and suggesting transition to autonomous driving mode at the appropriate time. They also lacked the ability to provide detailed advice on safe driving and communicate in accordance with the surrounding situation. As a result, they were unable to adequately support the driver in safe and comfortable driving, and social acceptance of autonomous driving technology was low.

[0960] 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.

[0961] In this invention, the server includes means for collecting driving data and physiological data of the driver, means for performing noise removal and outlier correction as preprocessing of the data, means for evaluating driving skill and fatigue state from the driving data and the physiological data, means for proposing transition to autonomous driving mode based on the evaluation results, means for transmitting the driving data and the physiological data to a natural language processing model, means for generating advice on safe driving from the natural language processing model, means for detecting surrounding conditions and determining the need for communication, and means for detecting surrounding conditions in real time with sensors and generating and notifying voice messages. This makes it possible to accurately evaluate the driver's condition and driving skill in real time, provide advice on safe driving, and further communicate with those around the driver as needed.

[0962] "Driving data" refers to data related to driving behavior such as vehicle speed, braking operation, accelerator operation, and steering angle.

[0963] "Physiological data" refers to data that indicates the physiological state of the driver, such as heart rate and body temperature.

[0964] "In-vehicle environment data" is data indicating environmental conditions such as temperature, humidity, and sound inside the vehicle.

[0965] "Driving skill" is an indicator that shows the accuracy and safety of a driver's driving behavior.

[0966] "Fatigue state" is an index that indicates the degree of mental and physical fatigue of the driver.

[0967] "Autonomous driving mode" is a mode in which the system automatically performs driving operations of the vehicle.

[0968] The "evaluation results" are the results of the driving skills and fatigue state analyzed from the collected data.

[0969] A "natural language processing model" is an algorithm that analyzes collected data and generates advice in natural language.

[0970] "Safe driving advice" is a specific suggestion to encourage drivers to drive safely.

[0971] "Surrounding conditions" is data that indicates the surrounding environment and conditions of the vehicle.

[0972] "Communication" refers to the act of conveying information to the driver and people around them by voice or message.

[0973] A "sensor" is a device used to collect data from a vehicle or driver.

[0974] "Noise reduction" is the process of removing unwanted noise from collected data.

[0975] "Abnormal value correction" is a process for correcting abnormal values ​​contained in collected data.

[0976] This system collects driving data and the driver's physiological data, analyzes them to evaluate driving skills and fatigue levels, and suggests switching to autonomous driving mode at the appropriate time. It also aims to provide safe driving advice to the driver using a natural language processing model and communicate appropriately according to the surrounding situation.

[0977] Hardware and Software Configuration

[0978] The system uses the following hardware and software:

[0979] Device: A group of sensors installed in the vehicle (speed sensor, brake sensor, accelerator sensor, steering angle sensor, heart rate sensor, body temperature sensor, etc.)

[0980] Server: A computer that analyzes collected data, evaluates driving skills and fatigue, and proposes transitioning to autonomous driving mode.

[0981] Natural language processing model (NLP model): an algorithm for generating safe driving advice from collected data

[0982] Sensors: Cameras, LIDAR, and radar for real-time detection of surrounding conditions

[0983] Data processing and calculation

[0984] The device uses sensors to collect driving data and the driver's physiological data in real time. This data is recorded at regular intervals and sent to a server, where it is first preprocessed by noise removal and outlier correction. The driving data and physiological data are then input into a machine learning algorithm to evaluate driving skill and fatigue state.

[0985] Driving skill assessment: Driving skills are scored based on the accuracy of driving operations, speed, frequency of braking, etc.

[0986] Fatigue assessment: Calculate fatigue level from fluctuations in heart rate and body temperature.

[0987] Based on the analysis results, the server will suggest the appropriate timing to switch to autonomous driving mode. For example, if the driver's fatigue level is high, the server will generate a message to notify the driver, such as "Due to high fatigue level, we suggest switching to autonomous driving mode." The autonomous driving system also detects the surrounding situation using data from sensors and generates voice messages to notify those around it as necessary.

[0988] Specific examples

[0989] Example 1: High fatigue

[0990] The device monitors the driver's heart rate and body temperature, and if these exceed the standard values, the server notifies the driver, "You are highly fatigued, so we suggest switching to autonomous driving mode."

[0991] Example 2: Safe driving advice

[0992] While driving, the device detects frequent use of the brakes. The server analyzes the situation and provides advice such as, "Because you are braking suddenly a lot, please be careful to maintain a safe distance from other vehicles."

[0993] Example 3: When there is a pedestrian on the crosswalk

[0994] The autonomous driving system uses sensors to detect pedestrians approaching a crosswalk, and generates a voice message to alert those around it, saying, "There is a pedestrian on the crosswalk. Please be careful."

[0995] Prompt Sentence Examples

[0996] "Please explain the system that uses driving and physiological data to assess the driver's fatigue level and suggest transitioning to autonomous driving mode. Please also provide details on the specific steps and the sensors and algorithms used."

[0997] This allows the present invention to accurately evaluate the driver's condition and driving skills in real time, provide advice on safe driving, and communicate with those around the vehicle as needed.

[0998] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0999] Step 1:

[1000] Data collection

[1001] Input: Data from various sensors installed in the vehicle

[1002] Operation: The device activates speed sensors, brake sensors, accelerator sensors, steering angle sensors, heart rate sensors, body temperature sensors, etc. to collect driving data and driver physiological data in real time.

[1003] Data processing: The collected data is recorded at regular time intervals.

[1004] Output: The collected driving and physiological data are stored on the device and sent to the server.

[1005] Step 2:

[1006] Data Preprocessing

[1007] Input: Driving data and physiological data sent from the device

[1008] How it works: The server performs noise removal and outlier correction on the received data before analyzing it.

[1009] Data calculations: Data cleaning algorithms are used to remove noise and correct outliers.

[1010] Output: A preprocessed, clean dataset

[1011] Step 3:

[1012] Evaluation of driving skills and fatigue

[1013] Input: Preprocessed driving and physiological data

[1014] How it works: The server inputs data into a machine learning algorithm to assess driving skill and fatigue state.

[1015] Data calculation: For driving skills, the system calculates a skill score by analyzing the accuracy of driving operations, speed, frequency of braking, etc. For fatigue, the system calculates the fatigue level by analyzing fluctuations in heart rate and body temperature.

[1016] Output: Driving skill score and fatigue level

[1017] Step 4:

[1018] Proposal for transition to autonomous driving mode

[1019] Input: Driving skill score and fatigue level

[1020] Operation: Based on the evaluation results, the server determines whether to suggest to the driver to switch to autonomous driving mode.

[1021] Data calculation: For example, if the fatigue level is high, a message such as "Due to high fatigue level, we suggest switching to autonomous driving mode" is generated.

[1022] Output: A message is generated and notified to the driver via the terminal.

[1023] Step 5:

[1024] Providing safe driving advice

[1025] Input: Preprocessed driving and physiological data

[1026] How it works: The server sends these data to a natural language processing (NLP) model.

[1027] Data Computation: The NLP model analyzes the data and generates safe driving advice for the driver.

[1028] Output: The generated advice is notified to the driver via the terminal.

[1029] Step 6:

[1030] Context-sensitive communication

[1031] Input: Surroundings data from sensors installed in the vehicle

[1032] How it works: The autonomous driving system uses sensors (cameras, LIDAR, radar) to detect the surroundings in real time.

[1033] Data processing: When certain conditions are met (for example, when there is a pedestrian in a crosswalk), a voice message is generated to notify the driver.

[1034] Output: The generated message is transmitted to the surroundings through the vehicle's speakers.

[1035] (Application example 1)

[1036] 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."

[1037] Conventional driver assistance systems are limited to simply monitoring driving data and the driver's physiological data, and are limited in their ability to evaluate driving skills and fatigue levels in real time and provide appropriate advice. Furthermore, they lack the ability to appropriately transition to autonomous driving mode depending on the surrounding conditions or provide specific safe driving advice to the driver, making it impossible to fully guarantee the driver's safety and comfort. Furthermore, there are also challenges such as difficulty in smoothly communicating with the driver and providing appropriate advice.

[1038] 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.

[1039] In this invention, the server includes a means for collecting driving data, a means for acquiring physiological data and in-vehicle environmental data of the driver, a means for evaluating the driving skill and fatigue state from the driving data and the physiological data, a means for suggesting switching to an autonomous driving mode based on the evaluation results, a means for generating safe driving advice for the driver using a natural language processing model, and a means for notifying the driver of the generated advice by voice or text. This allows the driver to receive a real-time evaluation of their driving situation and receive a suggestion to switch to an autonomous driving mode at an appropriate time, enabling safe and efficient driving. Furthermore, by utilizing the natural language processing model, specific and effective safe driving advice is provided to the driver, improving the driver's safety and comfort. Furthermore, by appropriately communicating with people around the driver according to the surrounding situation, a good harmony between the driver and the surrounding environment can be achieved.

[1040] "Driving data" refers to information related to the driver's driving behavior, such as vehicle speed, braking operation, accelerator operation, and steering angle.

[1041] "Physiological data" is information that indicates the physical condition of the driver, such as the driver's heart rate and body temperature.

[1042] "In-vehicle environment data" is information indicating the environmental conditions inside the vehicle, such as the temperature, humidity, and sound level inside the vehicle.

[1043] "Driving skill" is an evaluation of the driver's accuracy of driving operations, judgment, reaction speed, etc.

[1044] The "fatigue state" indicates the degree of fatigue of the driver and is calculated based on physiological data.

[1045] "Autonomous driving mode" is a mode in which the vehicle performs driving operations autonomously.

[1046] A "natural language processing model" is an algorithm or system for analyzing and understanding human language.

[1047] "Safe driving advice" is information that encourages drivers to drive properly and take precautions.

[1048] A "sensor" is a device that senses physical or environmental information and converts it into digital data.

[1049] "Notification" refers to the act and means of conveying information to the driver.

[1050] "Real-time" means that processing and communication occurs almost instantly, with little delay.

[1051] "Surrounding conditions" refers to information about pedestrians, other vehicles, traffic signals, and the like around the vehicle.

[1052] "Communication" is the act and means of transmitting information to each other.

[1053] This system collects and analyzes driving data and driver physiological data to evaluate driving skills and fatigue levels, and provides specific advice for safe driving. This system is designed to improve driver safety and comfort.

[1054] Driving and physiological data collection

[1055] The device collects driving data and physiological data of the driver in real time using multiple sensors installed in the vehicle. Specifically, driving data is acquired from the speedometer, brake operation sensor, accelerator operation sensor, steering angle sensor, etc. In addition, physiological data of the driver and in-vehicle environmental data are collected from the heart rate sensor, body temperature sensor, in-vehicle temperature sensor, microphone, etc.

[1056] Data analysis and evaluation

[1057] The server analyzes the collected driving and physiological data to evaluate driving skills and fatigue levels. Driving skills are scored based on data such as the accuracy of driving operations, speed, and frequency of braking. Fatigue levels are evaluated and quantified based on fluctuations in heart rate and body temperature.

[1058] Proposal for transition to autonomous driving mode

[1059] Based on the evaluation results, the server will suggest the appropriate timing to switch to autonomous driving mode. For example, if the driver's fatigue level is high, the server will generate a message such as "Due to high fatigue level, we suggest switching to autonomous driving mode" and notify the driver via the device.

[1060] Providing advice using natural language processing models

[1061] The server sends the collected data to a natural language processing (NLP) model and generates safe driving advice for the driver based on the analysis results. For example, if the frequency of sudden braking is high, a message such as "There are many sudden braking incidents, so please be careful to maintain a safe distance between vehicles" will be generated.

[1062] Context-sensitive communication

[1063] The autonomous driving system uses sensors to detect the surrounding situation and communicates by voice or text as necessary. For example, if there is a pedestrian in a crosswalk, the system will generate a voice message such as "There is a pedestrian in the crosswalk, please be careful" to notify the driver and people around.

[1064] Specific example explanation

[1065] Example 1: High fatigue

[1066] The driver's heart rate and body temperature are monitored by the terminal, and if these exceed the standard values, the server notifies the driver, "Due to high fatigue level, we suggest switching to autonomous driving mode."

[1067] Example 2: Safe driving advice

[1068] If the device detects that the driver is using the brakes frequently while driving, the server will analyze the situation and provide advice such as, "There are many sudden braking attempts, so please be careful to maintain a safe distance from other vehicles."

[1069] Example 3: When there is a pedestrian on the crosswalk

[1070] The autonomous driving system uses sensors to detect pedestrians on the crosswalk and generates a voice message saying, "There is a pedestrian on the crosswalk. Please be careful," to notify those around.

[1071] Prompt Sentence Examples

[1072] For example, the following prompt sentences can be input into a generative AI model and used:

[1073] User input: "I've been feeling tired a lot lately while driving. What should I do?"

[1074] Generative AI: "If you feel tired, it's important to take regular breaks while driving. Also, try to avoid long periods of driving and consider driving in autonomous mode."

[1075] In this way, the present invention can provide comprehensive assistance to the driver and provide a safer and more comfortable driving environment.

[1076] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1077] Step 1:

[1078] The device collects driving data and the driver's physiological data in real time through sensors installed in the vehicle (speedometer, brake operation sensor, accelerator operation sensor, steering angle sensor, heart rate sensor, body temperature sensor, interior temperature sensor, microphone). This collected data indicates the vehicle's driving situation and the driver's physical condition, and the device stores this as digital data.

[1079] Input: Data from various sensors installed in the vehicle

[1080] Output: Driving data and driver physiological data

[1081] Step 2:

[1082] The device transmits the collected driving and physiological data to a server, where the data is transmitted along with location and time information while maintaining real-time performance, and is converted into a format that can be analyzed by the server.

[1083] Input: Driving data and driver physiological data

[1084] Output: Driving and physiological data sent to the server

[1085] Step 3:

[1086] The server analyzes the received driving data and physiological data to evaluate driving skills and fatigue levels. Specifically, it scores driving skills based on the accuracy of driving operations, speed, frequency of braking, etc., and quantifies fatigue levels from fluctuations in heart rate and body temperature. This generates an evaluation result based on each data.

[1087] Input: Driving data and driver physiological data sent to the server

[1088] Output: Evaluation results of driving skills and fatigue state

[1089] Step 4:

[1090] Based on the evaluation results, the server will suggest the appropriate timing to switch to autonomous driving mode. For example, if the driver's fatigue level is high, the server will generate a message saying, "Due to high fatigue level, we suggest switching to autonomous driving mode," and notify the driver of this message via the device.

[1091] Input: Evaluation results of driving skills and fatigue state

[1092] Output: Message proposing to switch to autonomous driving mode

[1093] Step 5:

[1094] The server sends the collected data to a natural language processing (NLP) model and generates safe driving advice for the driver based on the analysis results. Specifically, if the frequency of sudden braking is high, a message such as "There are many sudden braking incidents, so please be careful to maintain a safe distance between vehicles" is generated and provided to the driver via the terminal.

[1095] Input: Driving and physiological data

[1096] Output: Safe driving advice

[1097] Step 6:

[1098] The device receives input from the driver and sends it to the server. The server uses a natural language processing model to generate a response and provides it to the driver via the device. For example, in response to an input such as, "I often feel tired while driving recently. What should I do?", the server responds, "If you feel tired, it is important to take appropriate breaks while driving. Also, try to avoid driving for long periods of time and consider driving in autonomous driving mode."

[1099] Input: Input from the driver

[1100] Output: Response from the generative AI model

[1101] Step 7:

[1102] The autonomous driving system uses sensors installed around the vehicle to detect the surrounding situation. For example, if there is a pedestrian in a crosswalk, the system will detect this and generate a voice message such as "There is a pedestrian in the crosswalk, please be careful" to notify the driver and people around.

[1103] Input: Vehicle surroundings data

[1104] Output: Messages depending on the surroundings

[1105] Step 8:

[1106] The device provides all generated messages and notifications to the driver, allowing the driver to receive safe driving advice in real time and suggestions for switching to autonomous driving mode at the same time, improving driver safety and comfort.

[1107] Input: Generated messages and notifications

[1108] Output: Notification to the driver

[1109] 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.

[1110] This invention combines a system that collects driving data and physiological data, analyzes them to evaluate driving skills and fatigue levels, and suggests switching to autonomous driving mode at the appropriate time with an emotion engine that recognizes the user's emotions. It also aims to increase social acceptance by providing safe driving advice to drivers using a natural language processing model and communicating appropriately according to the surrounding situation.

[1111] As a specific embodiment for implementing this system, the following processing is performed.

[1112] Driving and physiological data collection

[1113] Subject: Device

[1114] The device uses various sensors installed in the vehicle to collect driving data and physiological data of the driver in real time. Specifically, it acquires driving data such as speed, braking operation, accelerator operation, steering angle, etc. It also collects physiological data such as heart rate, body temperature, interior temperature, and voice, as well as in-vehicle environmental data.

[1115] Emotional state assessment by emotion engine

[1116] Subject: Emotion Engine

[1117] The emotion engine analyzes the driver's facial expression, tone of voice, heart rate variability patterns, etc. to determine the driver's emotional state (e.g., stress, impatience, relaxation, etc.) and transmits this emotion data to the server.

[1118] Evaluation of driving skills and fatigue

[1119] Subject: Server

[1120] The server analyzes the collected driving and physiological data to evaluate driving skills and fatigue levels. Driving skills are evaluated and scored based on data such as the accuracy of driving operations, speed, and frequency of braking. Fatigue levels are measured based on heart rate and body temperature fluctuations, and the fatigue level is calculated.

[1121] Proposal for transition to autonomous driving mode taking into account emotional state

[1122] Subject: Server

[1123] Based on the evaluation results, the server will suggest the appropriate timing to switch to autonomous driving mode. If the driving skill score is low, the fatigue level is high, or the emotional state is unstable, the server will generate a message such as "We suggest switching to autonomous driving mode because your fatigue level is high" or "We suggest switching to autonomous driving mode because your stress level is increasing" and send it to the device.

[1124] Providing advice using natural language processing models

[1125] Subject: Server

[1126] The server sends the collected data to a natural language processing (NLP) model, which then generates safe driving advice for the driver based on the analysis results, such as "Please be careful not to drive too fast."

[1127] Context-sensitive communication

[1128] Subject: Autonomous Driving System

[1129] The autonomous driving system detects the surrounding situation from sensor data and communicates as necessary. When the conditions are met (for example, when there is a pedestrian at a crosswalk), the system generates a voice message such as "There is a pedestrian at the crosswalk, please be careful" and notifies those around.

[1130] Specific examples

[1131] Example 1: High fatigue and unstable emotional state

[1132] The device monitors the driver's heart rate and body temperature, and if these exceed the standard values ​​and the emotion engine detects a high stress level, the server notifies the driver, "Due to high levels of fatigue and stress, we suggest switching to autonomous driving mode."

[1133] Example 2: Safe driving advice

[1134] While driving, the device detects frequent use of the brakes. The server analyzes the situation and provides advice such as, "Because you are braking suddenly a lot, please be careful to maintain a safe distance from other vehicles."

[1135] Example 3: When there is a pedestrian on the crosswalk

[1136] The autonomous driving system uses sensors to detect pedestrians approaching a crosswalk, and generates a voice message to alert those around it, saying, "There is a pedestrian on the crosswalk. Please be careful."

[1137] Through these steps, the system of the present invention enhances driver safety and establishes proper communication with surrounding people, thereby improving the reliability and acceptability of the overall transportation system.

[1138] The processing flow will be explained below.

[1139] Step 1:

[1140] Subject: Device

[1141] The device collects driving data and physiological data from various sensors installed in the vehicle. For example, it obtains the vehicle's speed from a speed sensor and the frequency of brake use from a brake pedal sensor. Furthermore, it obtains the driver's physiological data (heart rate, body temperature) using a heart rate sensor and a body temperature sensor.

[1142] Step 2:

[1143] Subject: Emotion Engine

[1144] The emotion engine analyzes the driver's facial expressions, voice, and heart rate fluctuation patterns to recognize the driver's emotional state. For example, facial expression analysis technology can determine whether the driver is stressed, and voice tone can determine whether the driver is irritated. This emotional data is sent to the server.

[1145] Step 3:

[1146] Subject: Device

[1147] The device preprocesses the driving and physiological data collected, removes noise from the data, and converts it into an analyzable format. The preprocessed data is then sent to the server.

[1148] Step 4:

[1149] Subject: Server

[1150] The server analyzes the pre-processed driving data and physiological data to calculate a driving skill score and fatigue level. Driving skill is evaluated based on data such as steering and speed fluctuations, while fatigue level is measured based on fluctuations in heart rate and body temperature.

[1151] Step 5:

[1152] Subject: Server

[1153] The server analyzes the emotion data sent from the emotion engine and combines it with the driving skill and fatigue data to make an overall evaluation. For example, if the driving skill is poor, the fatigue level is high, and the emotional state is stressed, the overall score will be low.

[1154] Step 6:

[1155] Subject: Server

[1156] Based on the evaluation results, the server generates a message such as "Your driving skills are low, and your fatigue and stress levels are high. We suggest you switch to autonomous driving mode," and sends it to the device.

[1157] Step 7:

[1158] Subject: Device

[1159] The device notifies the driver (user) of the proposal message received from the server, using a screen display and audio alert to inform the driver that "Due to high levels of fatigue and stress, we propose switching to autonomous driving mode."

[1160] Step 8:

[1161] Subject: Server

[1162] The server inputs driving and physiological data into a natural language processing model to generate specific advice on safe driving, such as "Please be careful to maintain a safe distance as there are many instances of sudden braking."

[1163] Step 9:

[1164] Subject: Device

[1165] The device receives safe driving advice from the server and provides it to the driver, allowing the driver to receive specific advice via screen displays and audio alerts.

[1166] Step 10:

[1167] Subject: Autonomous Driving System

[1168] The autonomous driving system monitors the surrounding situation and communicates with people around it as needed. For example, if a pedestrian is detected at a crosswalk, it will generate a voice message saying, "There is a pedestrian at the crosswalk. Please be careful."

[1169] Step 11:

[1170] Subject: User

[1171] The user (driver) decides whether to accept the proposal to switch to autonomous driving mode. If the proposal is accepted, the system switches to autonomous driving mode. If not accepted, the system continues to collect data and provide safe driving advice.

[1172] Example 2

[1173] 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."

[1174] Conventional driver assistance systems were able to collect driving and physiological data to assess driving skills and fatigue levels, but they lacked the functionality to consider the driver's emotional state and suggest transitioning to autonomous driving mode at the appropriate time. As a result, the system was unable to provide appropriate driving assistance due to a lack of consideration for the driver's emotional state, such as stress or impatience. Furthermore, the system lacked the functionality to provide specific advice on safe driving based on the collected data. Furthermore, the system lacked the functionality to communicate in response to the surrounding situation, making it difficult to improve the reliability and acceptability of the overall transportation system.

[1175] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1176] In this invention, the server includes a means for collecting driving data, a means for acquiring physiological data and in-vehicle environmental data of the driver, a means for analyzing the driver's emotional state, a means for evaluating the driver's driving skill and fatigue state from the driving data and the physiological data, and a means for proposing transition to autonomous driving mode based on the evaluation results and taking the driver's emotional state into consideration. This makes it possible to comprehensively evaluate the driver's driving skill, fatigue state, and emotional state and to propose transition to autonomous driving mode at an appropriate time. Furthermore, it is possible to provide specific advice on safe driving using a natural language processing model and communicate according to the surrounding situation, thereby improving the reliability and acceptability of the overall transportation system.

[1177] "Driving data" refers to data relating to driving operations such as vehicle speed, braking operation, accelerator operation, and steering angle, as well as vehicle behavior.

[1178] "Physiological data" is data relating to the driver's heart rate, body temperature, breathing patterns and other physiological conditions.

[1179] "In-vehicle environment data" refers to data relating to the environment inside the vehicle, such as the temperature, humidity, and sound environment inside the vehicle.

[1180] "Means of evaluation" refers to the process of analyzing collected driving data and physiological data and quantifying and evaluating driving skills and fatigue levels.

[1181] "Means for suggesting transition to automated driving mode" refers to a process for generating a message to recommend to the driver that they switch to automated driving mode based on the results of an evaluation of their driving skills, fatigue state, and emotional state.

[1182] A "natural language processing model" is an algorithm or computer program that analyzes collected data and generates specific advice for drivers in natural language.

[1183] "Means for analyzing emotional state" refers to a process for analyzing the driver's facial expression, tone of voice, heart rate fluctuation patterns, etc. to determine the driver's emotional state (stress, impatience, relaxation, etc.).

[1184] "Means for detecting surrounding conditions" refers to the process by which an automated driving system uses external sensors to monitor the surrounding environment and detect the location and movement of pedestrians and other vehicles.

[1185] "Means of communication" refers to the process of notifying the driver and those around them of necessary information and warnings as audio or visual messages depending on the detected surrounding conditions.

[1186] This invention combines a system that collects driving data and physiological data, analyzes them to evaluate driving skills and fatigue levels, and suggests switching to autonomous driving mode at the appropriate time with an emotion engine that recognizes the user's emotions. It also aims to increase social acceptance by providing safe driving advice to drivers using a natural language processing model and communicating appropriately according to the surrounding situation.

[1187] Driving and physiological data collection

[1188] Subject: Device

[1189] The device collects driving data and the driver's physiological data in real time using various sensors installed in the vehicle, such as speed sensors, brake sensors, accelerator sensors, and steering angle sensors. It also collects physiological data such as the driver's heart rate, body temperature, interior temperature, and voice using a heart rate monitor, thermometer, interior temperature sensor, and microphone, as well as in-vehicle environmental data.

[1190] Specific examples

[1191] Speed ​​sensor: Get the current speed of the vehicle

[1192] Heart rate monitor: Monitors the driver's heart rate fluctuations

[1193] Emotional state analysis using emotion engine

[1194] Subject: Emotion Engine

[1195] The emotion engine analyzes facial expression data, voice data, and data from a heart rate monitor acquired from a camera and microphone to determine the driver's emotional state, which can include stress, impatience, relaxation, etc. This emotional data is then sent to a server.

[1196] Specific examples

[1197] Analyzing the driver's stress level from facial expressions captured on camera

[1198] Determine your relaxation state using heart rate fluctuation patterns

[1199] Sending data to the server

[1200] Subject: Device

[1201] The device sends the collected and analyzed data to a server, including driving data, physiological data, and emotional data.

[1202] Evaluation of driving skills and fatigue

[1203] Subject: Server

[1204] The server analyzes all the data sent and evaluates the driver's driving skills and fatigue level, including scoring the driver's driving skills based on the accuracy of driving maneuvers, speed, frequency of sudden braking, etc., and calculating the driver's fatigue level based on fluctuations in heart rate and body temperature.

[1205] Specific examples

[1206] Low driving skill score based on frequent hard braking and sudden acceleration

[1207] Increased heart rate and body temperature indicate high levels of fatigue.

[1208] Proposal for transition to autonomous driving mode

[1209] Subject: Server

[1210] Based on the evaluation results, the server will suggest the appropriate timing to switch to autonomous driving mode. If the driving skill score is low, the fatigue level is high, or the emotional state is unstable, the server will generate a message such as "Due to high fatigue level, we suggest switching to autonomous driving mode" or "Due to increasing stress, we suggest switching to autonomous driving mode" and send it to the device.

[1211] Providing advice using natural language processing models

[1212] Subject: Server

[1213] The server uses natural language processing (NLP) models to analyze the collected data and generate specific safe driving advice for the driver, such as "Please be careful not to drive too fast."

[1214] Specific examples

[1215] The advice is "Please be careful to maintain a safe distance from other vehicles due to frequent sudden braking."

[1216] Context-sensitive communication

[1217] Subject: Autonomous Driving System

[1218] The autonomous driving system detects the surrounding situation using various sensors and communicates by generating audio and visual messages as needed. For example, if there is a pedestrian at a crosswalk, it will generate a warning message such as "There is a pedestrian at the crosswalk, please be careful."

[1219] Specific examples

[1220] If there is a pedestrian on the crosswalk, a warning message will be generated saying "There is a pedestrian on the crosswalk, please be careful."

[1221] This invention makes it possible to comprehensively evaluate a driver's driving skill, fatigue level, and emotional state, and provide appropriate safe driving support. In addition, by communicating in accordance with the surrounding situation, the reliability and acceptability of the entire transportation system can be improved.

[1222] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1223] Step 1: Collect driving and physiological data

[1224] Subject: Device

[1225] Inputs: Speed ​​sensor, brake sensor, accelerator sensor, steering angle sensor, heart rate monitor, thermometer, interior temperature sensor, microphone

[1226] Specific operation: The terminal uses various sensors installed in the vehicle to collect driving data and physiological data of the driver. The speed sensor measures the vehicle's speed in real time, the brake sensor detects brake pedal operation status, the heart rate monitor obtains the driver's heart rate, and the thermometer records the driver's body temperature. This data is integrated to understand the driver's condition while driving.

[1227] Output: A set of driving and physiological data

[1228] Step 2: Analyze emotional state

[1229] Subject: Emotion Engine

[1230] Input: facial expression data, voice data, heart rate data

[1231] Specific operation: The emotion engine analyzes facial expression data, voice data, and heart rate data acquired from the camera and microphone. It analyzes the driver's facial expressions captured by the camera to determine their emotional state, such as stress or impatience. It also analyzes the voice data to infer emotions from the tone and speed of the driver's voice. This determines the driver's emotional state.

[1232] Output: Emotional state data

[1233] Step 3: Sending data to the server

[1234] Subject: Device

[1235] Input: driving data, physiological data, emotional state data

[1236] How it works: The device sends collected and analyzed data to a server, including real-time collected driving data, physiological data, and emotional state data. The data is transmitted using a secure protocol.

[1237] Output: Notify the server that data has been sent

[1238] Step 4: Evaluate driving skills and fatigue

[1239] Subject: Server

[1240] Input: driving data, physiological data, emotional state data

[1241] Specific operation: The server analyzes the received data and evaluates the driver's driving skills and fatigue level. Driving skills are quantified based on the accuracy of driving operations, speed, frequency of sudden braking, etc. Fatigue level is calculated based on fluctuations in heart rate and body temperature. If the evaluation result exceeds a certain standard, the system proceeds to the next step.

[1242] Output: Driving skill evaluation score, fatigue state evaluation score

[1243] Step 5: Proposal to transition to autonomous driving mode

[1244] Subject: Server

[1245] Input: Driving skill evaluation score, fatigue state evaluation score, emotional state data

[1246] Specific operation: Based on the evaluation results, the server determines the appropriate timing to switch to autonomous driving mode. If the driving skill score is low, the fatigue level is high, or the emotional state is unstable, a message suggesting switching to autonomous driving mode will be generated and sent to the device.

[1247] Output: Message proposing to switch to autonomous driving mode

[1248] Step 6: Providing safe driving advice

[1249] Subject: Server

[1250] Input: driving data, physiological data, natural language processing model

[1251] Specific operation: The server uses a natural language processing (NLP) model to analyze the collected data and generate specific safe driving advice for the driver. For example, if the speed is too high or there are frequent sudden braking, specific instructions and advice will be generated in natural language and sent to the device.

[1252] Output: Safe driving advice

[1253] Step 7: Communicate in context

[1254] Subject: Autonomous Driving System

[1255] Input: Sensor data (e.g., environmental monitoring results)

[1256] Specific operation: The autonomous driving system uses various sensors to monitor the surrounding situation and generates audio and visual messages to notify the driver and those around the vehicle as needed. For example, if there is a pedestrian at a crosswalk, the system will generate a message such as "There is a pedestrian at the crosswalk, please be careful" and notify the driver through a speaker.

[1257] Output: Warning message to the surroundings

[1258] (Application example 2)

[1259] 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."

[1260] Conventional automated driving systems provide means to evaluate driving skills and fatigue levels, but they have issues in taking the driver's emotional state into account. It is also necessary to closely monitor the driver's stress and fatigue and suggest transitioning to automated driving mode at the appropriate time. Furthermore, advice on safe driving is provided without taking the driver's emotional state into consideration, which has led to issues with the acceptability and effectiveness of the advice. There is a need to design a system that solves these issues and significantly improves driver safety and comfort.

[1261] 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.

[1262] In this invention, the server includes means for collecting driving data, means for acquiring physiological data and in-vehicle environmental data of the driver, means for evaluating the emotional state of the driver, means for evaluating the driving skill and fatigue state from the driving data and the physiological data, means for proposing a transition to an autonomous driving mode based on the evaluation results, and means for appropriate communication based on the emotional state. This makes it possible to comprehensively evaluate the driver's driving skill, fatigue state, and emotional state, thereby significantly improving the safety and comfort of the driver.

[1263] "Driving data" refers to information such as speed, braking operation, accelerator operation, and steering angle that occurs when a driver operates a vehicle.

[1264] "Physiological data" refers to information that indicates the driver's physical condition and physiological responses, such as the driver's heart rate, body temperature, and tone of voice.

[1265] "In-vehicle environment data" refers to information indicating the environmental conditions inside the vehicle, such as the temperature, sound, and lighting inside the vehicle.

[1266] "Emotional state" refers to the driver's emotional state, such as stress, impatience, or relaxation, which can be obtained by analyzing the driver's facial expression, tone of voice, heart rate variability pattern, etc.

[1267] "Autonomous driving mode" refers to a mode in which the vehicle drives automatically, without requiring driver operation.

[1268] A "natural language processing model" refers to an artificial intelligence technology for analyzing and understanding human language, and is used to generate advice on safe driving.

[1269] "Communication" refers to the act of sharing information with the driver and people around, and includes notifications via audio and display.

[1270] "Evaluation results" refer to the results of driving skills and fatigue state calculated based on an analysis of driving data, physiological data, and emotional state.

[1271] This invention is a system that collects and analyzes driving data, physiological data, and emotional state, evaluates the driver's driving skill and fatigue level, and suggests transitioning to autonomous driving mode at the appropriate time. Furthermore, it can provide advice on safe driving using a natural language processing model and communicate according to the surrounding situation. The specific configuration and operation of this system are described below.

[1272] System configuration

[1273] The system includes the following hardware and software:

[1274] Terminal: A device used to collect and display data, such as a smartphone.

[1275] Vehicle sensors: Various sensors for collecting driving data such as speed, braking, accelerating, and steering angle, as well as physiological data such as heart rate, body temperature, interior temperature, and voice.

[1276] Server: A cloud server that analyzes and evaluates data and provides appropriate advice and suggestions to the driver.

[1277] Natural Language Processing Library: An NLP library for generating safe driving advice.

[1278] Emotion engine: An engine for analyzing the driver's emotional state.

[1279] Data collection and analysis

[1280] The device uses various sensors installed in the vehicle to collect real-time driving data and physiological data of the driver, including speed, braking operation, accelerator operation, steering angle, heart rate, body temperature, interior temperature, and voice. In addition, the emotion engine analyzes the driver's facial expressions, tone of voice, heart rate variability patterns, etc. to determine the driver's emotional state.

[1281] Data evaluation

[1282] The server analyzes the collected driving and physiological data to evaluate driving skills and fatigue levels. Driving skills are evaluated and scored based on data such as the accuracy of driving operations, speed, and frequency of braking. Fatigue levels are measured based on heart rate and body temperature fluctuations, and the fatigue level is calculated. These evaluation results, along with emotional states, are stored on the server.

[1283] Proposal for transition to autonomous driving mode

[1284] Based on the evaluation results, the server will suggest switching to autonomous driving mode at an appropriate time. If the driving skill score is low, the fatigue level is high, or the emotional state is unstable, the server will generate a message suggesting switching to autonomous driving mode and send it to the device.

[1285] Providing advice and communicating

[1286] The server sends the collected data to a natural language processing model and generates safe driving advice for the driver based on the analysis results. For example, a message such as "Please be careful not to drive too fast" is provided to the driver. The autonomous driving system also detects the surrounding situation from sensor data and generates voice messages such as "There is a pedestrian on the crosswalk, please be careful" to notify those around it as necessary.

[1287] Specific examples

[1288] Example 1: High fatigue and unstable emotional state

[1289] The device monitors the driver's heart rate and body temperature, and if these exceed the standard values ​​or the emotion engine detects a high stress level, the server notifies the driver, "Due to high levels of fatigue and stress, we suggest switching to autonomous driving mode."

[1290] Example 2: Safe driving advice

[1291] If the device detects that the driver is using the brakes frequently while driving, the server will analyze the situation and provide advice such as, "There are many sudden braking attempts, so please be careful to maintain a safe distance from other vehicles."

[1292] Example prompt sentence:

[1293] The data collected during driving is analyzed based on the following prompts, and suggestions and advice are provided to the driver.

[1294] "Driving data: {driving_data}, Physiological data: {physiological_data}, Emotional state: {emotional_state}"

[1295] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1296] Step 1:

[1297] Data collection

[1298] Subject: Device

[1299] The terminal collects driving data and the driver's physiological data in real time through various sensors installed in the vehicle. Specifically, it acquires driving data such as speed, braking operation, accelerator operation, and steering angle, as well as physiological data such as heart rate, body temperature, interior temperature, and voice. The input is real-time data from the various sensors, and the output is the collected driving data and physiological data.

[1300] Step 2:

[1301] Emotional state assessment

[1302] Subject: Emotion Engine

[1303] The physiological data collected by the device is sent to the emotion engine to evaluate the driver's emotional state. Specifically, facial expressions, tone of voice, heart rate variability patterns, etc. are analyzed to determine the driver's emotional state, such as stress, impatience, or relaxation. The input is physiological data, and the output is the evaluation result of the emotional state.

[1304] Step 3:

[1305] Evaluation of driving skills and fatigue

[1306] Subject: Server

[1307] The server analyzes the collected driving data and physiological data to evaluate driving skills and fatigue levels. Specifically, it scores driving skills based on data such as the accuracy of driving operations, speed, and braking frequency, and calculates fatigue levels based on heart rate and body temperature fluctuations. The inputs are driving data and physiological data, and the outputs are driving skill scores and fatigue levels.

[1308] Step 4:

[1309] Proposal for transition to autonomous driving mode

[1310] Subject: Server

[1311] The server determines the appropriate timing to switch to autonomous driving mode based on the evaluation results. If the driving skill score is low, the fatigue level is high, or the emotional state is unstable, the server generates a message suggesting that "switching to autonomous driving mode is appropriate" and sends it to the terminal. The inputs are the driving skill score, fatigue level, and emotional state, and the output is a message suggesting switching to autonomous driving mode.

[1312] Step 5:

[1313] Providing safe driving advice

[1314] Subject: Server

[1315] The server sends the collected data to a natural language processing model and generates safe driving advice for the driver based on the analysis results. Specifically, it generates messages such as "Please be careful not to drive too fast" and provides them to the driver via their device. The input is driving data and physiological data, and the output is a safe driving advice message.

[1316] Step 6:

[1317] Context-sensitive communication

[1318] Subject: Autonomous Driving System

[1319] The autonomous driving system analyzes surrounding sensor data and communicates as needed. For example, if there is a pedestrian at a crosswalk, it generates a voice message such as "Please be careful, there is a pedestrian at the crosswalk" to notify people around. The input is the surrounding sensor data, and the output is the appropriate communication message.

[1320] 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.

[1321] 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.

[1322] 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.

[1323] [Fourth embodiment]

[1324] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1325] 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.

[1326] 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).

[1327] 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.

[1328] 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.

[1329] 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).

[1330] 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.

[1331] 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.

[1332] 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.

[1333] 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.

[1334] 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.

[1335] 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.

[1336] 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."

[1337] This system collects driving data and the driver's physiological data, analyzes them to evaluate driving skills and fatigue levels, and suggests switching to autonomous driving mode at the appropriate time. It also aims to increase social acceptance by providing safe driving advice to drivers using natural language processing models and communicating appropriately according to the surrounding situation.

[1338] As a specific embodiment for implementing this system, the following processing is performed.

[1339] Driving and physiological data collection

[1340] Subject: Device

[1341] The device uses various sensors installed in the vehicle to collect driving data and physiological data of the driver in real time. Specifically, it acquires driving data such as speed, braking operation, accelerator operation, steering angle, etc. It also collects physiological data such as heart rate, body temperature, interior temperature, and voice, as well as in-vehicle environmental data.

[1342] Evaluation of driving skills and fatigue

[1343] Subject: Server

[1344] The server analyzes the collected driving and physiological data to evaluate driving skills and fatigue levels. Driving skills are evaluated and scored based on data such as the accuracy of driving operations, speed, and frequency of braking. Fatigue levels are measured based on heart rate and body temperature fluctuations, and the fatigue level is calculated.

[1345] Proposal for transition to autonomous driving mode

[1346] Subject: Server

[1347] Based on the evaluation results, the server will suggest the appropriate timing to switch to autonomous driving mode. For example, if the driver's fatigue level is high, the server will generate a message saying, "Due to high fatigue level, we suggest switching to autonomous driving mode," and notify the driver. A similar suggestion will also be made if the driver's driving skills are low.

[1348] Providing advice using natural language processing models

[1349] Subject: Server

[1350] The server sends the collected data to a natural language processing (NLP) model, which then generates safe driving advice for the driver based on the analysis results, such as "Please be careful not to drive too fast."

[1351] Context-sensitive communication

[1352] Subject: Autonomous Driving System

[1353] The autonomous driving system detects the surrounding situation from sensor data and communicates as necessary. When the conditions are met (for example, when there is a pedestrian at a crosswalk), the system generates a voice message to notify those around it, such as "There is a pedestrian at the crosswalk, please be careful."

[1354] Specific examples

[1355] Example 1: High fatigue

[1356] The device monitors the driver's heart rate and body temperature, and if these exceed the standard values, the server notifies the driver, "You are highly fatigued, so we suggest switching to autonomous driving mode."

[1357] Example 2: Safe driving advice

[1358] While driving, the device detects frequent use of the brakes. The server analyzes the situation and provides advice such as, "Because you are braking suddenly a lot, please be careful to maintain a safe distance from other vehicles."

[1359] Example 3: When there is a pedestrian on the crosswalk

[1360] The autonomous driving system uses sensors to detect pedestrians approaching a crosswalk, and generates a voice message to alert those around it, saying, "There is a pedestrian on the crosswalk. Please be careful."

[1361] As described above, the present invention is a system that supports safe and comfortable driving for drivers and can also increase social acceptance of automated driving technology.

[1362] The processing flow will be explained below.

[1363] Step 1:

[1364] Subject: Device

[1365] The device collects driving data and physiological data. Specifically, it obtains driving data such as speed, braking, and steering angle from various sensors built into the vehicle. It also simultaneously collects physiological data such as the driver's heart rate and interior temperature using heart rate and temperature sensors.

[1366] Step 2:

[1367] Subject: Device

[1368] The collected driving and physiological data is preprocessed, for example, by removing noise from the data and converting it into an analyzable format. The preprocessed data is then sent to the server, where it is packaged appropriately according to the data format and protocol.

[1369] Step 3:

[1370] Subject: Server

[1371] The server analyzes the received data, calculates a driving skill score from the driving data, and measures fatigue level from physiological data. For example, driving skill is calculated based on the accuracy and smoothness of steering, while fatigue level is evaluated based on heart rate fluctuations and body temperature rise.

[1372] Step 4:

[1373] Subject: Server

[1374] Based on the evaluation results, the server will suggest switching to autonomous driving mode. If the driving skill score is low or the fatigue level is high, the server will generate a message such as "Due to high fatigue level, we suggest switching to autonomous driving mode" and send it to the device.

[1375] Step 5:

[1376] Subject: Device

[1377] The device notifies the driver of the proposal message received from the server. The notification method may be a screen display or an audio alert. The driver (user) who receives this notification decides whether to approve the transition to autonomous driving mode.

[1378] Step 6:

[1379] Subject: Server

[1380] The server inputs driving data and physiological data into a natural language processing model to generate advice on safe driving. For example, if frequent sudden braking is detected, the server generates advice such as, "There are many sudden braking incidents, so please be careful to maintain a safe distance between vehicles."

[1381] Step 7:

[1382] Subject: Device

[1383] The device receives safe driving advice from the server and provides it to the driver, also via screen display and audio alerts, to encourage the driver to be more careful.

[1384] Step 8:

[1385] Subject: Autonomous Driving System

[1386] The autonomous driving system uses sensors to monitor the surroundings and communicates with people around it as needed. For example, if a pedestrian is detected at a crosswalk, the system will generate a voice message saying, "There is a pedestrian at the crosswalk. Please be careful."

[1387] Step 9:

[1388] Subject: User

[1389] The user (driver) decides whether to accept the autonomous driving mode suggestion. If accepted, the system switches to autonomous driving mode. If not accepted, the system continues to collect data and provide safe driving advice.

[1390] Through these steps, the system of the present invention enhances driver safety and establishes proper communication with surrounding people, thereby improving the reliability and acceptability of the overall transportation system.

[1391] Example 1

[1392] 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."

[1393] Conventional driver assistance systems have had difficulty accurately assessing the driver's level of fatigue and driving skill in real time and suggesting transition to autonomous driving mode at the appropriate time. They also lacked the ability to provide detailed advice on safe driving and communicate in accordance with the surrounding situation. As a result, they were unable to adequately support the driver in safe and comfortable driving, and social acceptance of autonomous driving technology was low.

[1394] 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.

[1395] In this invention, the server includes means for collecting driving data and physiological data of the driver, means for performing noise removal and outlier correction as preprocessing of the data, means for evaluating driving skill and fatigue state from the driving data and the physiological data, means for proposing transition to autonomous driving mode based on the evaluation results, means for transmitting the driving data and the physiological data to a natural language processing model, means for generating advice on safe driving from the natural language processing model, means for detecting surrounding conditions and determining the need for communication, and means for detecting surrounding conditions in real time with sensors and generating and notifying voice messages. This makes it possible to accurately evaluate the driver's condition and driving skill in real time, provide advice on safe driving, and further communicate with those around the driver as needed.

[1396] "Driving data" refers to data related to driving behavior such as vehicle speed, braking operation, accelerator operation, and steering angle.

[1397] "Physiological data" refers to data that indicates the physiological state of the driver, such as heart rate and body temperature.

[1398] "In-vehicle environment data" is data indicating environmental conditions such as temperature, humidity, and sound inside the vehicle.

[1399] "Driving skill" is an indicator that shows the accuracy and safety of a driver's driving behavior.

[1400] "Fatigue state" is an index that indicates the degree of mental and physical fatigue of the driver.

[1401] "Autonomous driving mode" is a mode in which the system automatically performs driving operations of the vehicle.

[1402] The "evaluation results" are the results of the driving skills and fatigue state analyzed from the collected data.

[1403] A "natural language processing model" is an algorithm that analyzes collected data and generates advice in natural language.

[1404] "Safe driving advice" is a specific suggestion to encourage drivers to drive safely.

[1405] "Surrounding conditions" is data that indicates the surrounding environment and conditions of the vehicle.

[1406] "Communication" refers to the act of conveying information to the driver and people around them by voice or message.

[1407] A "sensor" is a device used to collect data from a vehicle or driver.

[1408] "Noise reduction" is the process of removing unwanted noise from collected data.

[1409] "Abnormal value correction" is a process for correcting abnormal values ​​contained in collected data.

[1410] This system collects driving data and the driver's physiological data, analyzes them to evaluate driving skills and fatigue levels, and suggests switching to autonomous driving mode at the appropriate time. It also aims to provide safe driving advice to the driver using a natural language processing model and communicate appropriately according to the surrounding situation.

[1411] Hardware and Software Configuration

[1412] The system uses the following hardware and software:

[1413] Device: A group of sensors installed in the vehicle (speed sensor, brake sensor, accelerator sensor, steering angle sensor, heart rate sensor, body temperature sensor, etc.)

[1414] Server: A computer that analyzes collected data, evaluates driving skills and fatigue, and proposes transitioning to autonomous driving mode.

[1415] Natural language processing model (NLP model): an algorithm for generating safe driving advice from collected data

[1416] Sensors: Cameras, LIDAR, and radar for real-time detection of surrounding conditions

[1417] Data processing and calculation

[1418] The device uses sensors to collect driving data and the driver's physiological data in real time. This data is recorded at regular intervals and sent to a server, where it is first preprocessed by noise removal and outlier correction. The driving data and physiological data are then input into a machine learning algorithm to evaluate driving skill and fatigue state.

[1419] Driving skill assessment: Driving skills are scored based on the accuracy of driving operations, speed, frequency of braking, etc.

[1420] Fatigue assessment: Calculate fatigue level from fluctuations in heart rate and body temperature.

[1421] Based on the analysis results, the server will suggest the appropriate timing to switch to autonomous driving mode. For example, if the driver's fatigue level is high, the server will generate a message to notify the driver, such as "Due to high fatigue level, we suggest switching to autonomous driving mode." The autonomous driving system also detects the surrounding situation using data from sensors and generates voice messages to notify those around it as necessary.

[1422] Specific examples

[1423] Example 1: High fatigue

[1424] The device monitors the driver's heart rate and body temperature, and if these exceed the standard values, the server notifies the driver, "You are highly fatigued, so we suggest switching to autonomous driving mode."

[1425] Example 2: Safe driving advice

[1426] While driving, the device detects frequent use of the brakes. The server analyzes the situation and provides advice such as, "Because you are braking suddenly a lot, please be careful to maintain a safe distance from other vehicles."

[1427] Example 3: When there is a pedestrian on the crosswalk

[1428] The autonomous driving system uses sensors to detect pedestrians approaching a crosswalk, and generates a voice message to alert those around it, saying, "There is a pedestrian on the crosswalk. Please be careful."

[1429] Prompt Sentence Examples

[1430] "Please explain the system that uses driving and physiological data to assess the driver's fatigue level and suggest transitioning to autonomous driving mode. Please also provide details on the specific steps and the sensors and algorithms used."

[1431] This allows the present invention to accurately evaluate the driver's condition and driving skills in real time, provide advice on safe driving, and communicate with those around the vehicle as needed.

[1432] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1433] Step 1:

[1434] Data collection

[1435] Input: Data from various sensors installed in the vehicle

[1436] Operation: The device activates speed sensors, brake sensors, accelerator sensors, steering angle sensors, heart rate sensors, body temperature sensors, etc. to collect driving data and driver physiological data in real time.

[1437] Data processing: The collected data is recorded at regular time intervals.

[1438] Output: The collected driving and physiological data are stored on the device and sent to the server.

[1439] Step 2:

[1440] Data Preprocessing

[1441] Input: Driving data and physiological data sent from the device

[1442] How it works: The server performs noise removal and outlier correction on the received data before analyzing it.

[1443] Data calculations: Data cleaning algorithms are used to remove noise and correct outliers.

[1444] Output: A preprocessed, clean dataset

[1445] Step 3:

[1446] Evaluation of driving skills and fatigue

[1447] Input: Preprocessed driving and physiological data

[1448] How it works: The server inputs data into a machine learning algorithm to assess driving skill and fatigue state.

[1449] Data calculation: For driving skills, the system calculates a skill score by analyzing the accuracy of driving operations, speed, frequency of braking, etc. For fatigue, the system calculates the fatigue level by analyzing fluctuations in heart rate and body temperature.

[1450] Output: Driving skill score and fatigue level

[1451] Step 4:

[1452] Proposal for transition to autonomous driving mode

[1453] Input: Driving skill score and fatigue level

[1454] Operation: Based on the evaluation results, the server determines whether to suggest to the driver to switch to autonomous driving mode.

[1455] Data calculation: For example, if the fatigue level is high, a message such as "Due to high fatigue level, we suggest switching to autonomous driving mode" is generated.

[1456] Output: A message is generated and notified to the driver via the terminal.

[1457] Step 5:

[1458] Providing safe driving advice

[1459] Input: Preprocessed driving and physiological data

[1460] How it works: The server sends these data to a natural language processing (NLP) model.

[1461] Data Computation: The NLP model analyzes the data and generates safe driving advice for the driver.

[1462] Output: The generated advice is notified to the driver via the terminal.

[1463] Step 6:

[1464] Context-sensitive communication

[1465] Input: Surroundings data from sensors installed in the vehicle

[1466] How it works: The autonomous driving system uses sensors (cameras, LIDAR, radar) to detect the surroundings in real time.

[1467] Data processing: When certain conditions are met (for example, when there is a pedestrian in a crosswalk), a voice message is generated to notify the driver.

[1468] Output: The generated message is transmitted to the surroundings through the vehicle's speakers.

[1469] (Application example 1)

[1470] 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."

[1471] Conventional driver assistance systems are limited to simply monitoring driving data and the driver's physiological data, and are limited in their ability to evaluate driving skills and fatigue levels in real time and provide appropriate advice. Furthermore, they lack the ability to appropriately transition to autonomous driving mode depending on the surrounding conditions or provide specific safe driving advice to the driver, making it impossible to fully guarantee the driver's safety and comfort. Furthermore, there are also challenges such as difficulty in smoothly communicating with the driver and providing appropriate advice.

[1472] 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.

[1473] In this invention, the server includes a means for collecting driving data, a means for acquiring physiological data and in-vehicle environmental data of the driver, a means for evaluating the driving skill and fatigue state from the driving data and the physiological data, a means for suggesting switching to an autonomous driving mode based on the evaluation results, a means for generating safe driving advice for the driver using a natural language processing model, and a means for notifying the driver of the generated advice by voice or text. This allows the driver to receive a real-time evaluation of their driving situation and receive a suggestion to switch to an autonomous driving mode at an appropriate time, enabling safe and efficient driving. Furthermore, by utilizing the natural language processing model, specific and effective safe driving advice is provided to the driver, improving the driver's safety and comfort. Furthermore, by appropriately communicating with people around the driver according to the surrounding situation, a good harmony between the driver and the surrounding environment can be achieved.

[1474] "Driving data" refers to information related to the driver's driving behavior, such as vehicle speed, braking operation, accelerator operation, and steering angle.

[1475] "Physiological data" is information that indicates the physical condition of the driver, such as the driver's heart rate and body temperature.

[1476] "In-vehicle environment data" is information indicating the environmental conditions inside the vehicle, such as the temperature, humidity, and sound level inside the vehicle.

[1477] "Driving skill" is an evaluation of the driver's accuracy of driving operations, judgment, reaction speed, etc.

[1478] The "fatigue state" indicates the degree of fatigue of the driver and is calculated based on physiological data.

[1479] "Autonomous driving mode" is a mode in which the vehicle performs driving operations autonomously.

[1480] A "natural language processing model" is an algorithm or system for analyzing and understanding human language.

[1481] "Safe driving advice" is information that encourages drivers to drive properly and take precautions.

[1482] A "sensor" is a device that senses physical or environmental information and converts it into digital data.

[1483] "Notification" refers to the act and means of conveying information to the driver.

[1484] "Real-time" means that processing and communication occurs almost instantly, with little delay.

[1485] "Surrounding conditions" refers to information about pedestrians, other vehicles, traffic signals, and the like around the vehicle.

[1486] "Communication" is the act and means of transmitting information to each other.

[1487] This system collects and analyzes driving data and driver physiological data to evaluate driving skills and fatigue levels, and provides specific advice for safe driving. This system is designed to improve driver safety and comfort.

[1488] Driving and physiological data collection

[1489] The device collects driving data and physiological data of the driver in real time using multiple sensors installed in the vehicle. Specifically, driving data is acquired from the speedometer, brake operation sensor, accelerator operation sensor, steering angle sensor, etc. In addition, physiological data of the driver and in-vehicle environmental data are collected from the heart rate sensor, body temperature sensor, in-vehicle temperature sensor, microphone, etc.

[1490] Data analysis and evaluation

[1491] The server analyzes the collected driving and physiological data to evaluate driving skills and fatigue levels. Driving skills are scored based on data such as the accuracy of driving operations, speed, and frequency of braking. Fatigue levels are evaluated and quantified based on fluctuations in heart rate and body temperature.

[1492] Proposal for transition to autonomous driving mode

[1493] Based on the evaluation results, the server will suggest the appropriate timing to switch to autonomous driving mode. For example, if the driver's fatigue level is high, the server will generate a message such as "Due to high fatigue level, we suggest switching to autonomous driving mode" and notify the driver via the device.

[1494] Providing advice using natural language processing models

[1495] The server sends the collected data to a natural language processing (NLP) model and generates safe driving advice for the driver based on the analysis results. For example, if the frequency of sudden braking is high, a message such as "There are many sudden braking incidents, so please be careful to maintain a safe distance between vehicles" will be generated.

[1496] Context-sensitive communication

[1497] The autonomous driving system uses sensors to detect the surrounding situation and communicates by voice or text as necessary. For example, if there is a pedestrian in a crosswalk, the system will generate a voice message such as "There is a pedestrian in the crosswalk, please be careful" to notify the driver and people around.

[1498] Specific example explanation

[1499] Example 1: High fatigue

[1500] The driver's heart rate and body temperature are monitored by the terminal, and if these exceed the standard values, the server notifies the driver, "Due to high fatigue level, we suggest switching to autonomous driving mode."

[1501] Example 2: Safe driving advice

[1502] If the device detects that the driver is using the brakes frequently while driving, the server will analyze the situation and provide advice such as, "There are many sudden braking attempts, so please be careful to maintain a safe distance from other vehicles."

[1503] Example 3: When there is a pedestrian on the crosswalk

[1504] The autonomous driving system uses sensors to detect pedestrians on the crosswalk and generates a voice message saying, "There is a pedestrian on the crosswalk. Please be careful," to notify those around.

[1505] Prompt Sentence Examples

[1506] For example, the following prompt sentences can be input into a generative AI model and used:

[1507] User input: "I've been feeling tired a lot lately while driving. What should I do?"

[1508] Generative AI: "If you feel tired, it's important to take regular breaks while driving. Also, try to avoid long periods of driving and consider driving in autonomous mode."

[1509] In this way, the present invention can provide comprehensive assistance to the driver and provide a safer and more comfortable driving environment.

[1510] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1511] Step 1:

[1512] The device collects driving data and the driver's physiological data in real time through sensors installed in the vehicle (speedometer, brake operation sensor, accelerator operation sensor, steering angle sensor, heart rate sensor, body temperature sensor, interior temperature sensor, microphone). This collected data indicates the vehicle's driving situation and the driver's physical condition, and the device stores this as digital data.

[1513] Input: Data from various sensors installed in the vehicle

[1514] Output: Driving data and driver physiological data

[1515] Step 2:

[1516] The device transmits the collected driving and physiological data to a server, where the data is transmitted along with location and time information while maintaining real-time performance, and is converted into a format that can be analyzed by the server.

[1517] Input: Driving data and driver physiological data

[1518] Output: Driving and physiological data sent to the server

[1519] Step 3:

[1520] The server analyzes the received driving data and physiological data to evaluate driving skills and fatigue levels. Specifically, it scores driving skills based on the accuracy of driving operations, speed, frequency of braking, etc., and quantifies fatigue levels from fluctuations in heart rate and body temperature. This generates an evaluation result based on each data.

[1521] Input: Driving data and driver physiological data sent to the server

[1522] Output: Evaluation results of driving skills and fatigue state

[1523] Step 4:

[1524] Based on the evaluation results, the server will suggest the appropriate timing to switch to autonomous driving mode. For example, if the driver's fatigue level is high, the server will generate a message saying, "Due to high fatigue level, we suggest switching to autonomous driving mode," and notify the driver of this message via the device.

[1525] Input: Evaluation results of driving skills and fatigue state

[1526] Output: Message proposing to switch to autonomous driving mode

[1527] Step 5:

[1528] The server sends the collected data to a natural language processing (NLP) model and generates safe driving advice for the driver based on the analysis results. Specifically, if the frequency of sudden braking is high, a message such as "There are many sudden braking incidents, so please be careful to maintain a safe distance between vehicles" is generated and provided to the driver via the terminal.

[1529] Input: Driving and physiological data

[1530] Output: Safe driving advice

[1531] Step 6:

[1532] The device receives input from the driver and sends it to the server. The server uses a natural language processing model to generate a response and provides it to the driver via the device. For example, in response to an input such as, "I often feel tired while driving recently. What should I do?", the server responds, "If you feel tired, it is important to take appropriate breaks while driving. Also, try to avoid driving for long periods of time and consider driving in autonomous driving mode."

[1533] Input: Input from the driver

[1534] Output: Response from the generative AI model

[1535] Step 7:

[1536] The autonomous driving system uses sensors installed around the vehicle to detect the surrounding situation. For example, if there is a pedestrian in a crosswalk, the system will detect this and generate a voice message such as "There is a pedestrian in the crosswalk, please be careful" to notify the driver and people around.

[1537] Input: Vehicle surroundings data

[1538] Output: Messages depending on the surroundings

[1539] Step 8:

[1540] The device provides all generated messages and notifications to the driver, allowing the driver to receive safe driving advice in real time and suggestions for switching to autonomous driving mode at the same time, improving driver safety and comfort.

[1541] Input: Generated messages and notifications

[1542] Output: Notification to the driver

[1543] 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.

[1544] This invention combines a system that collects driving data and physiological data, analyzes them to evaluate driving skills and fatigue levels, and suggests switching to autonomous driving mode at the appropriate time with an emotion engine that recognizes the user's emotions. It also aims to increase social acceptance by providing safe driving advice to drivers using a natural language processing model and communicating appropriately according to the surrounding situation.

[1545] As a specific embodiment for implementing this system, the following processing is performed.

[1546] Driving and physiological data collection

[1547] Subject: Device

[1548] The device uses various sensors installed in the vehicle to collect driving data and physiological data of the driver in real time. Specifically, it acquires driving data such as speed, braking operation, accelerator operation, steering angle, etc. It also collects physiological data such as heart rate, body temperature, interior temperature, and voice, as well as in-vehicle environmental data.

[1549] Emotional state assessment by emotion engine

[1550] Subject: Emotion Engine

[1551] The emotion engine analyzes the driver's facial expression, tone of voice, heart rate variability patterns, etc. to determine the driver's emotional state (e.g., stress, impatience, relaxation, etc.) and transmits this emotion data to the server.

[1552] Evaluation of driving skills and fatigue

[1553] Subject: Server

[1554] The server analyzes the collected driving and physiological data to evaluate driving skills and fatigue levels. Driving skills are evaluated and scored based on data such as the accuracy of driving operations, speed, and frequency of braking. Fatigue levels are measured based on heart rate and body temperature fluctuations, and the fatigue level is calculated.

[1555] Proposal for transition to autonomous driving mode taking into account emotional state

[1556] Subject: Server

[1557] Based on the evaluation results, the server will suggest the appropriate timing to switch to autonomous driving mode. If the driving skill score is low, the fatigue level is high, or the emotional state is unstable, the server will generate a message such as "We suggest switching to autonomous driving mode because your fatigue level is high" or "We suggest switching to autonomous driving mode because your stress level is increasing" and send it to the device.

[1558] Providing advice using natural language processing models

[1559] Subject: Server

[1560] The server sends the collected data to a natural language processing (NLP) model, which then generates safe driving advice for the driver based on the analysis results, such as "Please be careful not to drive too fast."

[1561] Context-sensitive communication

[1562] Subject: Autonomous Driving System

[1563] The autonomous driving system detects the surrounding situation from sensor data and communicates as necessary. When the conditions are met (for example, when there is a pedestrian at a crosswalk), the system generates a voice message such as "There is a pedestrian at the crosswalk, please be careful" and notifies those around.

[1564] Specific examples

[1565] Example 1: High fatigue and unstable emotional state

[1566] The device monitors the driver's heart rate and body temperature, and if these exceed the standard values ​​and the emotion engine detects a high stress level, the server notifies the driver, "Due to high levels of fatigue and stress, we suggest switching to autonomous driving mode."

[1567] Example 2: Safe driving advice

[1568] While driving, the device detects frequent use of the brakes. The server analyzes the situation and provides advice such as, "Because you are braking suddenly a lot, please be careful to maintain a safe distance from other vehicles."

[1569] Example 3: When there is a pedestrian on the crosswalk

[1570] The autonomous driving system uses sensors to detect pedestrians approaching a crosswalk, and generates a voice message to alert those around it, saying, "There is a pedestrian on the crosswalk. Please be careful."

[1571] Through these steps, the system of the present invention enhances driver safety and establishes proper communication with surrounding people, thereby improving the reliability and acceptability of the overall transportation system.

[1572] The processing flow will be explained below.

[1573] Step 1:

[1574] Subject: Device

[1575] The device collects driving data and physiological data from various sensors installed in the vehicle. For example, it obtains the vehicle's speed from a speed sensor and the frequency of brake use from a brake pedal sensor. Furthermore, it obtains the driver's physiological data (heart rate, body temperature) using a heart rate sensor and a body temperature sensor.

[1576] Step 2:

[1577] Subject: Emotion Engine

[1578] The emotion engine analyzes the driver's facial expressions, voice, and heart rate fluctuation patterns to recognize the driver's emotional state. For example, facial expression analysis technology can determine whether the driver is stressed, and voice tone can determine whether the driver is irritated. This emotional data is sent to the server.

[1579] Step 3:

[1580] Subject: Device

[1581] The device preprocesses the driving and physiological data collected, removes noise from the data, and converts it into an analyzable format. The preprocessed data is then sent to the server.

[1582] Step 4:

[1583] Subject: Server

[1584] The server analyzes the pre-processed driving data and physiological data to calculate a driving skill score and fatigue level. Driving skill is evaluated based on data such as steering and speed fluctuations, while fatigue level is measured based on fluctuations in heart rate and body temperature.

[1585] Step 5:

[1586] Subject: Server

[1587] The server analyzes the emotion data sent from the emotion engine and combines it with the driving skill and fatigue data to make an overall evaluation. For example, if the driving skill is poor, the fatigue level is high, and the emotional state is stressed, the overall score will be low.

[1588] Step 6:

[1589] Subject: Server

[1590] Based on the evaluation results, the server generates a message such as "Your driving skills are low, and your fatigue and stress levels are high. We suggest you switch to autonomous driving mode," and sends it to the device.

[1591] Step 7:

[1592] Subject: Device

[1593] The device notifies the driver (user) of the proposal message received from the server, using a screen display and audio alert to inform the driver that "Due to high levels of fatigue and stress, we propose switching to autonomous driving mode."

[1594] Step 8:

[1595] Subject: Server

[1596] The server inputs driving and physiological data into a natural language processing model to generate specific advice on safe driving, such as "Please be careful to maintain a safe distance as there are many instances of sudden braking."

[1597] Step 9:

[1598] Subject: Device

[1599] The device receives safe driving advice from the server and provides it to the driver, allowing the driver to receive specific advice via screen displays and audio alerts.

[1600] Step 10:

[1601] Subject: Autonomous Driving System

[1602] The autonomous driving system monitors the surrounding situation and communicates with people around it as needed. For example, if a pedestrian is detected at a crosswalk, it will generate a voice message saying, "There is a pedestrian at the crosswalk. Please be careful."

[1603] Step 11:

[1604] Subject: User

[1605] The user (driver) decides whether to accept the proposal to switch to autonomous driving mode. If the proposal is accepted, the system switches to autonomous driving mode. If not accepted, the system continues to collect data and provide safe driving advice.

[1606] Example 2

[1607] 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."

[1608] Conventional driver assistance systems were able to collect driving and physiological data to assess driving skills and fatigue levels, but they lacked the functionality to consider the driver's emotional state and suggest transitioning to autonomous driving mode at the appropriate time. As a result, the system was unable to provide appropriate driving assistance due to a lack of consideration for the driver's emotional state, such as stress or impatience. Furthermore, the system lacked the functionality to provide specific advice on safe driving based on the collected data. Furthermore, the system lacked the functionality to communicate in response to the surrounding situation, making it difficult to improve the reliability and acceptability of the overall transportation system.

[1609] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1610] In this invention, the server includes a means for collecting driving data, a means for acquiring physiological data and in-vehicle environmental data of the driver, a means for analyzing the driver's emotional state, a means for evaluating the driver's driving skill and fatigue state from the driving data and the physiological data, and a means for proposing transition to autonomous driving mode based on the evaluation results and taking the driver's emotional state into consideration. This makes it possible to comprehensively evaluate the driver's driving skill, fatigue state, and emotional state and to propose transition to autonomous driving mode at an appropriate time. Furthermore, it is possible to provide specific advice on safe driving using a natural language processing model and communicate according to the surrounding situation, thereby improving the reliability and acceptability of the overall transportation system.

[1611] "Driving data" refers to data relating to driving operations such as vehicle speed, braking operation, accelerator operation, and steering angle, as well as vehicle behavior.

[1612] "Physiological data" is data relating to the driver's heart rate, body temperature, breathing patterns and other physiological conditions.

[1613] "In-vehicle environment data" refers to data relating to the environment inside the vehicle, such as the temperature, humidity, and sound environment inside the vehicle.

[1614] "Means of evaluation" refers to the process of analyzing collected driving data and physiological data and quantifying and evaluating driving skills and fatigue levels.

[1615] "Means for suggesting transition to automated driving mode" refers to a process for generating a message to recommend to the driver that they switch to automated driving mode based on the results of an evaluation of their driving skills, fatigue state, and emotional state.

[1616] A "natural language processing model" is an algorithm or computer program that analyzes collected data and generates specific advice for drivers in natural language.

[1617] "Means for analyzing emotional state" refers to a process for analyzing the driver's facial expression, tone of voice, heart rate fluctuation patterns, etc. to determine the driver's emotional state (stress, impatience, relaxation, etc.).

[1618] "Means for detecting surrounding conditions" refers to the process by which an automated driving system uses external sensors to monitor the surrounding environment and detect the location and movement of pedestrians and other vehicles.

[1619] "Means of communication" refers to the process of notifying the driver and those around them of necessary information and warnings as audio or visual messages depending on the detected surrounding conditions.

[1620] This invention combines a system that collects driving data and physiological data, analyzes them to evaluate driving skills and fatigue levels, and suggests switching to autonomous driving mode at the appropriate time with an emotion engine that recognizes the user's emotions. It also aims to increase social acceptance by providing safe driving advice to drivers using a natural language processing model and communicating appropriately according to the surrounding situation.

[1621] Driving and physiological data collection

[1622] Subject: Device

[1623] The device collects driving data and the driver's physiological data in real time using various sensors installed in the vehicle, such as speed sensors, brake sensors, accelerator sensors, and steering angle sensors. It also collects physiological data such as the driver's heart rate, body temperature, interior temperature, and voice using a heart rate monitor, thermometer, interior temperature sensor, and microphone, as well as in-vehicle environmental data.

[1624] Specific examples

[1625] Speed ​​sensor: Get the current speed of the vehicle

[1626] Heart rate monitor: Monitors the driver's heart rate fluctuations

[1627] Emotional state analysis using emotion engine

[1628] Subject: Emotion Engine

[1629] The emotion engine analyzes facial expression data, voice data, and data from a heart rate monitor acquired from a camera and microphone to determine the driver's emotional state, which can include stress, impatience, relaxation, etc. This emotional data is then sent to a server.

[1630] Specific examples

[1631] Analyzing the driver's stress level from facial expressions captured on camera

[1632] Determine your relaxation state using heart rate fluctuation patterns

[1633] Sending data to the server

[1634] Subject: Device

[1635] The device sends the collected and analyzed data to a server, including driving data, physiological data, and emotional data.

[1636] Evaluation of driving skills and fatigue

[1637] Subject: Server

[1638] The server analyzes all the data sent and evaluates the driver's driving skills and fatigue level, including scoring the driver's driving skills based on the accuracy of driving maneuvers, speed, frequency of sudden braking, etc., and calculating the driver's fatigue level based on fluctuations in heart rate and body temperature.

[1639] Specific examples

[1640] Low driving skill score based on frequent hard braking and sudden acceleration

[1641] Increased heart rate and body temperature indicate high levels of fatigue.

[1642] Proposal for transition to autonomous driving mode

[1643] Subject: Server

[1644] Based on the evaluation results, the server will suggest the appropriate timing to switch to autonomous driving mode. If the driving skill score is low, the fatigue level is high, or the emotional state is unstable, the server will generate a message such as "Due to high fatigue level, we suggest switching to autonomous driving mode" or "Due to increasing stress, we suggest switching to autonomous driving mode" and send it to the device.

[1645] Providing advice using natural language processing models

[1646] Subject: Server

[1647] The server uses natural language processing (NLP) models to analyze the collected data and generate specific safe driving advice for the driver, such as "Please be careful not to drive too fast."

[1648] Specific examples

[1649] The advice is "Please be careful to maintain a safe distance from other vehicles due to frequent sudden braking."

[1650] Context-sensitive communication

[1651] Subject: Autonomous Driving System

[1652] The autonomous driving system detects the surrounding situation using various sensors and communicates by generating audio and visual messages as needed. For example, if there is a pedestrian at a crosswalk, it will generate a warning message such as "There is a pedestrian at the crosswalk, please be careful."

[1653] Specific examples

[1654] If there is a pedestrian on the crosswalk, a warning message will be generated saying "There is a pedestrian on the crosswalk, please be careful."

[1655] This invention makes it possible to comprehensively evaluate a driver's driving skill, fatigue level, and emotional state, and provide appropriate safe driving support. In addition, by communicating in accordance with the surrounding situation, the reliability and acceptability of the entire transportation system can be improved.

[1656] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1657] Step 1: Collect driving and physiological data

[1658] Subject: Device

[1659] Inputs: Speed ​​sensor, brake sensor, accelerator sensor, steering angle sensor, heart rate monitor, thermometer, interior temperature sensor, microphone

[1660] Specific operation: The terminal uses various sensors installed in the vehicle to collect driving data and physiological data of the driver. The speed sensor measures the vehicle's speed in real time, the brake sensor detects brake pedal operation status, the heart rate monitor obtains the driver's heart rate, and the thermometer records the driver's body temperature. This data is integrated to understand the driver's condition while driving.

[1661] Output: A set of driving and physiological data

[1662] Step 2: Analyze emotional state

[1663] Subject: Emotion Engine

[1664] Input: facial expression data, voice data, heart rate data

[1665] Specific operation: The emotion engine analyzes facial expression data, voice data, and heart rate data acquired from the camera and microphone. It analyzes the driver's facial expressions captured by the camera to determine their emotional state, such as stress or impatience. It also analyzes the voice data to infer emotions from the tone and speed of the driver's voice. This determines the driver's emotional state.

[1666] Output: Emotional state data

[1667] Step 3: Sending data to the server

[1668] Subject: Device

[1669] Input: driving data, physiological data, emotional state data

[1670] How it works: The device sends collected and analyzed data to a server, including real-time collected driving data, physiological data, and emotional state data. The data is transmitted using a secure protocol.

[1671] Output: Notify the server that data has been sent

[1672] Step 4: Evaluate driving skills and fatigue

[1673] Subject: Server

[1674] Input: driving data, physiological data, emotional state data

[1675] Specific operation: The server analyzes the received data and evaluates the driver's driving skills and fatigue level. Driving skills are quantified based on the accuracy of driving operations, speed, frequency of sudden braking, etc. Fatigue level is calculated based on fluctuations in heart rate and body temperature. If the evaluation result exceeds a certain standard, the system proceeds to the next step.

[1676] Output: Driving skill evaluation score, fatigue state evaluation score

[1677] Step 5: Proposal to transition to autonomous driving mode

[1678] Subject: Server

[1679] Input: Driving skill evaluation score, fatigue state evaluation score, emotional state data

[1680] Specific operation: Based on the evaluation results, the server determines the appropriate timing to switch to autonomous driving mode. If the driving skill score is low, the fatigue level is high, or the emotional state is unstable, a message suggesting switching to autonomous driving mode will be generated and sent to the device.

[1681] Output: Message proposing to switch to autonomous driving mode

[1682] Step 6: Providing safe driving advice

[1683] Subject: Server

[1684] Input: driving data, physiological data, natural language processing model

[1685] Specific operation: The server uses a natural language processing (NLP) model to analyze the collected data and generate specific safe driving advice for the driver. For example, if the speed is too high or there are frequent sudden braking, specific instructions and advice will be generated in natural language and sent to the device.

[1686] Output: Safe driving advice

[1687] Step 7: Communicate in context

[1688] Subject: Autonomous Driving System

[1689] Input: Sensor data (e.g., environmental monitoring results)

[1690] Specific operation: The autonomous driving system uses various sensors to monitor the surrounding situation and generates audio and visual messages to notify the driver and those around the vehicle as needed. For example, if there is a pedestrian at a crosswalk, the system will generate a message such as "There is a pedestrian at the crosswalk, please be careful" and notify the driver through a speaker.

[1691] Output: Warning message to the surroundings

[1692] (Application example 2)

[1693] 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."

[1694] Conventional automated driving systems provide means to evaluate driving skills and fatigue levels, but they have issues in taking the driver's emotional state into account. It is also necessary to closely monitor the driver's stress and fatigue and suggest transitioning to automated driving mode at the appropriate time. Furthermore, advice on safe driving is provided without taking the driver's emotional state into consideration, which has led to issues with the acceptability and effectiveness of the advice. There is a need to design a system that solves these issues and significantly improves driver safety and comfort.

[1695] 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.

[1696] In this invention, the server includes means for collecting driving data, means for acquiring physiological data and in-vehicle environmental data of the driver, means for evaluating the emotional state of the driver, means for evaluating the driving skill and fatigue state from the driving data and the physiological data, means for proposing a transition to an autonomous driving mode based on the evaluation results, and means for appropriate communication based on the emotional state. This makes it possible to comprehensively evaluate the driver's driving skill, fatigue state, and emotional state, thereby significantly improving the safety and comfort of the driver.

[1697] "Driving data" refers to information such as speed, braking operation, accelerator operation, and steering angle that occurs when a driver operates a vehicle.

[1698] "Physiological data" refers to information that indicates the driver's physical condition and physiological responses, such as the driver's heart rate, body temperature, and tone of voice.

[1699] "In-vehicle environment data" refers to information indicating the environmental conditions inside the vehicle, such as the temperature, sound, and lighting inside the vehicle.

[1700] "Emotional state" refers to the driver's emotional state, such as stress, impatience, or relaxation, which can be obtained by analyzing the driver's facial expression, tone of voice, heart rate variability pattern, etc.

[1701] "Autonomous driving mode" refers to a mode in which the vehicle drives automatically, without requiring driver operation.

[1702] A "natural language processing model" refers to an artificial intelligence technology for analyzing and understanding human language, and is used to generate advice on safe driving.

[1703] "Communication" refers to the act of sharing information with the driver and people around, and includes notifications via audio and display.

[1704] "Evaluation results" refer to the results of driving skills and fatigue state calculated based on an analysis of driving data, physiological data, and emotional state.

[1705] This invention is a system that collects and analyzes driving data, physiological data, and emotional state, evaluates the driver's driving skill and fatigue level, and suggests transitioning to autonomous driving mode at the appropriate time. Furthermore, it can provide advice on safe driving using a natural language processing model and communicate according to the surrounding situation. The specific configuration and operation of this system are described below.

[1706] System configuration

[1707] The system includes the following hardware and software:

[1708] Terminal: A device used to collect and display data, such as a smartphone.

[1709] Vehicle sensors: Various sensors for collecting driving data such as speed, braking, accelerating, and steering angle, as well as physiological data such as heart rate, body temperature, interior temperature, and voice.

[1710] Server: A cloud server that analyzes and evaluates data and provides appropriate advice and suggestions to the driver.

[1711] Natural Language Processing Library: An NLP library for generating safe driving advice.

[1712] Emotion engine: An engine for analyzing the driver's emotional state.

[1713] Data collection and analysis

[1714] The device uses various sensors installed in the vehicle to collect real-time driving data and physiological data of the driver, including speed, braking operation, accelerator operation, steering angle, heart rate, body temperature, interior temperature, and voice. In addition, the emotion engine analyzes the driver's facial expressions, tone of voice, heart rate variability patterns, etc. to determine the driver's emotional state.

[1715] Data evaluation

[1716] The server analyzes the collected driving and physiological data to evaluate driving skills and fatigue levels. Driving skills are evaluated and scored based on data such as the accuracy of driving operations, speed, and frequency of braking. Fatigue levels are measured based on heart rate and body temperature fluctuations, and the fatigue level is calculated. These evaluation results, along with emotional states, are stored on the server.

[1717] Proposal for transition to autonomous driving mode

[1718] Based on the evaluation results, the server will suggest switching to autonomous driving mode at an appropriate time. If the driving skill score is low, the fatigue level is high, or the emotional state is unstable, the server will generate a message suggesting switching to autonomous driving mode and send it to the device.

[1719] Providing advice and communicating

[1720] The server sends the collected data to a natural language processing model and generates safe driving advice for the driver based on the analysis results. For example, a message such as "Please be careful not to drive too fast" is provided to the driver. The autonomous driving system also detects the surrounding situation from sensor data and generates voice messages such as "There is a pedestrian on the crosswalk, please be careful" to notify those around it as necessary.

[1721] Specific examples

[1722] Example 1: High fatigue and unstable emotional state

[1723] The device monitors the driver's heart rate and body temperature, and if these exceed the standard values ​​or the emotion engine detects a high stress level, the server notifies the driver, "Due to high levels of fatigue and stress, we suggest switching to autonomous driving mode."

[1724] Example 2: Safe driving advice

[1725] If the device detects that the driver is using the brakes frequently while driving, the server will analyze the situation and provide advice such as, "There are many sudden braking attempts, so please be careful to maintain a safe distance from other vehicles."

[1726] Example prompt sentence:

[1727] The data collected during driving is analyzed based on the following prompts, and suggestions and advice are provided to the driver.

[1728] "Driving data: {driving_data}, Physiological data: {physiological_data}, Emotional state: {emotional_state}"

[1729] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1730] Step 1:

[1731] Data collection

[1732] Subject: Device

[1733] The terminal collects driving data and the driver's physiological data in real time through various sensors installed in the vehicle. Specifically, it acquires driving data such as speed, braking operation, accelerator operation, and steering angle, as well as physiological data such as heart rate, body temperature, interior temperature, and voice. The input is real-time data from the various sensors, and the output is the collected driving data and physiological data.

[1734] Step 2:

[1735] Emotional state assessment

[1736] Subject: Emotion Engine

[1737] The physiological data collected by the device is sent to the emotion engine to evaluate the driver's emotional state. Specifically, facial expressions, tone of voice, heart rate variability patterns, etc. are analyzed to determine the driver's emotional state, such as stress, impatience, or relaxation. The input is physiological data, and the output is the evaluation result of the emotional state.

[1738] Step 3:

[1739] Evaluation of driving skills and fatigue

[1740] Subject: Server

[1741] The server analyzes the collected driving data and physiological data to evaluate driving skills and fatigue levels. Specifically, it scores driving skills based on data such as the accuracy of driving operations, speed, and braking frequency, and calculates fatigue levels based on heart rate and body temperature fluctuations. The inputs are driving data and physiological data, and the outputs are driving skill scores and fatigue levels.

[1742] Step 4:

[1743] Proposal for transition to autonomous driving mode

[1744] Subject: Server

[1745] The server determines the appropriate timing to switch to autonomous driving mode based on the evaluation results. If the driving skill score is low, the fatigue level is high, or the emotional state is unstable, the server generates a message suggesting that "switching to autonomous driving mode is appropriate" and sends it to the terminal. The inputs are the driving skill score, fatigue level, and emotional state, and the output is a message suggesting switching to autonomous driving mode.

[1746] Step 5:

[1747] Providing safe driving advice

[1748] Subject: Server

[1749] The server sends the collected data to a natural language processing model and generates safe driving advice for the driver based on the analysis results. Specifically, it generates messages such as "Please be careful not to drive too fast" and provides them to the driver via their device. The input is driving data and physiological data, and the output is a safe driving advice message.

[1750] Step 6:

[1751] Context-sensitive communication

[1752] Subject: Autonomous Driving System

[1753] The autonomous driving system analyzes surrounding sensor data and communicates as needed. For example, if there is a pedestrian at a crosswalk, it generates a voice message such as "Please be careful, there is a pedestrian at the crosswalk" to notify people around. The input is the surrounding sensor data, and the output is the appropriate communication message.

[1754] 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.

[1755] 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.

[1756] 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.

[1757] 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.

[1758] 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.

[1759] 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.

[1760] 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).

[1761] 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.

[1762] 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."

[1763] 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.

[1764] 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).

[1765] 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.

[1766] 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.

[1767] 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.

[1768] 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.

[1769] 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.

[1770] 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.

[1771] 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.

[1772] 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.

[1773] 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.

[1774] 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.

[1775] The following is further disclosed regarding the above embodiment.

[1776] (Claim 1)

[1777] a means for collecting driving data;

[1778] A means for acquiring physiological data of a driver and in-vehicle environmental data;

[1779] means for evaluating driving skills and fatigue state from the driving data and the physiological data;

[1780] A means for proposing a transition to an autonomous driving mode based on the evaluation result;

[1781] A system including:

[1782] (Claim 2)

[1783] means for transmitting the driving data and the physiological data to a natural language processing model;

[1784] means for generating advice on safe driving from the natural language processing model;

[1785] The system of claim 1 further comprising:

[1786] (Claim 3)

[1787] A means of detecting the surrounding situation and determining the need for communication;

[1788] means for communicating with people around the person in accordance with said need;

[1789] The system of claim 1 further comprising:

[1790] "Example 1"

[1791] (Claim 1)

[1792] a means for collecting driving data;

[1793] A means for acquiring physiological data of a driver and in-vehicle environmental data;

[1794] means for evaluating driving skills and fatigue state from the driving data and the physiological data;

[1795] A means for proposing a transition to an autonomous driving mode based on the evaluation result;

[1796] means for performing noise removal and outlier correction as preprocessing of the data;

[1797] a means for utilizing heart rate and body temperature fluctuations to evaluate the driver's fatigue state;

[1798] A system including:

[1799] (Claim 2)

[1800] means for transmitting the driving data and the physiological data to a natural language processing model;

[1801] The system of claim 1 , further comprising means for generating safe driving advice from the natural language processing model.

[1802] (Claim 3)

[1803] A means of detecting the surrounding situation and determining the need for communication;

[1804] means for communicating with people around the person in accordance with said need;

[1805] A means of detecting the surrounding situation in real time using sensors and generating a voice message to notify the user.

[1806] The system of claim 1 further comprising:

[1807] "Application Example 1"

[1808] (Claim 1)

[1809] a means for collecting driving data;

[1810] A means for acquiring physiological data of a driver and in-vehicle environmental data;

[1811] means for evaluating driving skills and fatigue state from the driving data and the physiological data;

[1812] A means for proposing a transition to an autonomous driving mode based on the evaluation result;

[1813] a means for generating safe driving advice for a driver using a natural language processing model;

[1814] a means for notifying the driver of the generated advice by voice or text;

[1815] A system including:

[1816] (Claim 2)

[1817] means for receiving driver input and generating a response based thereon using a natural language processing model;

[1818] means for providing said response to the driver by voice or text;

[1819] The system of claim 1 further comprising:

[1820] (Claim 3)

[1821] A means to detect the surrounding situation with sensors and communicate appropriately with people around when the conditions are met.

[1822] means for notifying the driver of the content of the communication;

[1823] The system of claim 1 further comprising:

[1824] "Example 2: Combining Emotion Engines"

[1825] (Claim 1)

[1826] a means for collecting driving data;

[1827] A means for acquiring physiological data of a driver and in-vehicle environmental data;

[1828] a means for analyzing an emotional state;

[1829] means for evaluating driving skills and fatigue state from the driving data and the physiological data;

[1830] A means for proposing a transition to an automated driving mode based on the evaluation result and taking into consideration the emotional state of the driver;

[1831] A system including:

[1832] (Claim 2)

[1833] means for transmitting...

Claims

1. a means for collecting driving data; A means for acquiring physiological data of a driver and in-vehicle environmental data; means for evaluating driving skills and fatigue state from the driving data and the physiological data; A means for proposing a transition to an autonomous driving mode based on the evaluation result; A system including:

2. means for transmitting the driving data and the physiological data to a natural language processing model; means for generating advice on safe driving from the natural language processing model; The system of claim 1 further comprising:

3. A means of detecting the surrounding situation and determining the need for communication; means for communicating with people around the person in accordance with said need; The system of claim 1 further comprising:

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