system

Abstract

We provide the system. [Solution] A means of collecting information from multiple sensors mounted on the vehicle, A means of sending the collected information to the server, A means of analyzing information collected on a server and generating driver warnings, A means of providing the generated warning to the driver in audio format, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a problem that novice drivers or those who resume driving after a long time are likely to cause traffic accidents because they cannot obtain accurate and timely information during driving. In particular, there is a problem that important information regarding signals, pedestrians, and other traffic situations is often overlooked, making it difficult to drive safely. Furthermore, when there is no passenger, driving support cannot be received, which becomes a factor for the driver to feel不安.

Means for Solving the Problems

[0005] To address this challenge, we propose a system that collects information using multiple sensors mounted on the vehicle and transmits it to a server. The server analyzes the collected information to generate necessary warnings for the driver and provides this information in real-time via voice. This makes it easier for drivers to obtain important traffic information while driving, thereby reducing the risk of accidents. Furthermore, encryption is applied during data transmission to enhance communication security and foster a sense of security for the driver.

[0006] A "vehicle" is a means of transport designed to travel on roads, and includes automobiles, motorcycles, and other similar vehicles.

[0007] A "sensor" is a device that detects physical phenomena and outputs them as electrical signals.

[0008] "Collecting information" refers to the act of acquiring external data using sensors or other means, and then collectively storing or processing that data.

[0009] A "server" is a computer system that provides data and services to other computers via a network.

[0010] "Analysis" is the process of examining collected data in detail to extract specific patterns or information.

[0011] A "driver" is a person who operates a vehicle and drives it on a public road.

[0012] "Warning" refers to the act of providing information and instructions to drivers to make them aware of the current situation and encourage them to take appropriate action.

[0013] "Audio format" refers to a method of outputting information as sound so that it can be recognized by hearing.

[0014] "Real-time" refers to processing and outputting information with a delay that is almost instantaneous.

[0015] "Encryption" is a method of converting information using a specific algorithm to make it difficult for third parties to decrypt.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiment for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0019] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0020] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention relates to a driver assistance system for safely operating a vehicle. This system consists of various sensors and terminals mounted on the vehicle and a server connected via the internet. Specific embodiments of this system are described below.

[0038] The user's device collects various data from sensors installed in the vehicle. This includes GPS location information, vehicle speed from the speedometer, and surrounding video from cameras. The device is then prepared to transmit this data to a server.

[0039] The terminal ensures a stable network connection and transmits the collected data to the server. During this process, the data is properly encrypted, ensuring the security of the communication. The server processes the received data in real time and analyzes the operating status using a generated AI model.

[0040] Based on the analysis results, the server generates necessary alerts for the driver. For example, it creates warning messages that include information such as when the traffic light is about to turn red or when there are pedestrians or cyclists nearby. This allows drivers to receive important information at any time.

[0041] The generated warning message is sent back to the user's device. The device converts this from text to audio and transmits it to the driver through the car's speakers. By listening to the audio guidance, the driver can make appropriate decisions based on the surrounding situation.

[0042] A concrete example is when a driver approaches an intersection, the server detects a change in traffic light and provides a voice command to the terminal, such as, "The light will turn red in 5 seconds." In this way, the driver can always be aware of the current traffic situation and continue driving safely.

[0043] The system of this invention provides effective support to alleviate driver anxiety and enhance safety.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The terminal collects data such as GPS location information, speed, and camera images from various sensors installed in the vehicle. This data provides a detailed reflection of the vehicle's operating status.

[0047] Step 2:

[0048] The terminal preprocesses the collected data according to various protocols. Preprocessing includes data format conversion, noise reduction, and necessary compression. This ensures optimal data transmission to the server.

[0049] Step 3:

[0050] The terminal sends the pre-processed data to the server via a secure communication protocol. The data is encrypted during this process, ensuring the security of the communication.

[0051] Step 4:

[0052] The server immediately analyzes the received data. Using a generative AI model, it extracts important information related to the driver and analyzes the current traffic situation in detail.

[0053] Step 5:

[0054] Based on the analysis results, the server generates necessary warning messages for the driver. For example, it identifies situations that require immediate action from the driver, such as "The traffic light is turning red" or "There are pedestrians near the intersection."

[0055] Step 6:

[0056] The server encodes the generated alert message and sends it to the user's terminal. Because the message requires immediacy, it is transmitted using the most optimal communication path.

[0057] Step 7:

[0058] The terminal receives messages from the server, converts them into speech using a speech synthesis engine, and transmits them to the driver through the vehicle's speakers. This allows the driver to receive important information without relying on their vision.

[0059] Step 8:

[0060] The user receives voice instructions and adjusts their driving behavior accordingly. This allows for quick responses to changes in traffic conditions, resulting in safer driving.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] When drivers operate a vehicle, it is crucial for them to make appropriate decisions based on traffic conditions and the surrounding environment. However, sudden environmental changes and information overload can cause drivers to miss necessary information. To solve this problem, there is a need to develop a system that provides drivers with relevant information in real time and reduces their mental burden.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes means for acquiring various data from multiple detectors installed in the vehicle, means for appropriately formatting and safely transmitting the acquired data to an information processing device, and means for processing the received data in real time at the information processing device, analyzing the driving situation using a generated AI model, and forming warnings. As a result, the driver can always obtain the latest and most important information, thereby improving safety.

[0066] A "detector" is a device installed in a vehicle to acquire various data in real time, and it has the function of understanding location information and the surrounding situation.

[0067] An "information processing device" is a computer that can receive acquired data and process it in real time. It has the function of analyzing driving conditions using a generated AI model and forming warnings.

[0068] A "generative AI model" is an artificial intelligence model used to analyze driving conditions based on collected data and generate information that should be provided to the driver.

[0069] "Warning" refers to warnings and advice provided to drivers, including information to encourage appropriate judgment depending on the driving situation.

[0070] "Encryption technology" is a means of protecting the security of data during communication. It is a technique that transforms data using a specific algorithm to prevent unauthorized access.

[0071] This invention relates to a support system for drivers to safely operate a vehicle. The system consists of a detector installed in the vehicle, a terminal for transmitting and receiving data, and an information processing device for processing and analyzing the data. A specific embodiment of this system is described below.

[0072] The user's device collects data from various sensors within the vehicle. This data includes location information from GPS sensors, speed from the speedometer, and surrounding images from cameras. This collected data is appropriately formatted for transmission to an information processing device and securely transferred using encryption technology. The device also plays a role in stabilizing the network connection.

[0073] The information processing device, which functions as a server, processes data received from terminals in real time. This utilizes a generative AI model, which analyzes driving conditions and generates necessary warnings for the driver. A specific example of the generative AI model's use is predicting changes in traffic signals at intersections and warning drivers accordingly.

[0074] The generated warnings are provided to the driver via a terminal. The terminal converts the warnings from text to audio and transmits them to the driver in real time through the in-car speaker. This function allows the driver to properly understand the traffic situation and make necessary decisions quickly.

[0075] As a concrete example, the user's device receives a warning message such as "The traffic light is turning red," and converts it into voice to communicate to the driver. An example of a prompt message for the generating AI model could be, "Based on the current vehicle speed and location information, please generate the next necessary warning."

[0076] In this way, the system aims to support drivers in driving safely and improve traffic safety.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The user's device collects data from sensors installed in the vehicle. Specifically, it acquires location information from GPS, speed data from the speedometer, and surrounding video from cameras. This data is necessary to understand the driving situation. The input for this step is raw data from the vehicle's sensors, and the output is a properly formatted dataset.

[0080] Step 2:

[0081] The terminal is prepared to transmit the collected data to the information processing device. The data is structured and secured using encryption technology. After confirming that the network connection is stable, preparations are made to send the data to the server. At this stage, the input is the raw data before processing, and the output is encrypted, transmittable data.

[0082] Step 3:

[0083] The server receives data transmitted from the terminal and processes it in real time. This processing includes analyzing driving conditions using a generative AI model. Specifically, it extracts and analyzes safety-related information from the vehicle's position, speed, and surrounding video footage. The input for this step is encrypted data, and the output is warning data as a result of the analysis.

[0084] Step 4:

[0085] The server generates driver alerts based on analysis results obtained using a generative AI model. It uses prompts to create messages containing useful information for the driver, such as "The traffic light is turning red." The input for this step is the analysis results, and the output is a warning message for the driver.

[0086] Step 5:

[0087] The terminal converts the warning message received from the server from text information into audio information. Furthermore, it transmits this information to the driver via the in-car speaker. By listening to this audio guidance, the driver can respond appropriately to the surrounding traffic conditions. The input for this step is a text message, and the output is an audio interface that the driver can understand.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] In autonomous vehicles, a key challenge is accurately recognizing the surrounding environment and providing safe and timely warnings to the occupants. This allows occupants to understand the vehicle's driving situation and use the vehicle with confidence.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes means for collecting data from multiple detectors mounted on the vehicle, means for transmitting the collected data to a database, means for analyzing the data collected in the database and generating warnings for the driver and passengers, and device means for providing the generated warnings in audio and visual formats. This makes it possible to provide occupants with real-time information about the surrounding environment and improve safety.

[0093] A "detector" is a device installed in a vehicle that collects information about the surrounding environment and the vehicle's condition.

[0094] A "database" is an information storage device used to store collected data and utilize it for subsequent analysis.

[0095] "Analysis" is an information processing process that uses collected data to evaluate the vehicle's driving conditions and surrounding environment, and to generate necessary warnings.

[0096] "Warning" refers to warning information provided to occupants, which should be communicated in audio or visual format depending on the driving situation and surrounding environment.

[0097] An "information display device" is a device or equipment used to communicate generated warnings to crew members in audio or visual format.

[0098] An "information processing device" is an electronic device used to analyze data and deliver the generated information appropriately to the crew.

[0099] The system realizing this invention is built for providing safety information in autonomous vehicles. The server has the function of collecting data in real time from multiple sensors installed in the vehicle and transmitting it to a database via the internet. The collected data is analyzed by a generative AI model hosted on Google® Cloud AI Platform. The analyzed information is generated as a warning for the driver and passengers and is provided in audio and visual formats.

[0100] The server interacts with the database to encrypt data and ensure secure communication. Specifically, it uses the SSL / TLS protocol to guarantee data security. The generated alerts are transmitted to the occupants via smart glasses or smartphone applications. This allows occupants to accurately perceive the surrounding environment of the autonomous vehicle, thereby enhancing safety.

[0101] As a concrete example, if the server detects the presence of a pedestrian at an intersection, it will issue a voice and visual warning via smart glasses stating, "A pedestrian is approaching the intersection." An example of a prompt to be input into the generating AI model is, "Identify hazardous elements from the current intersection situation and create a warning message." This will improve the safety of autonomous driving and provide peace of mind to the occupants.

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The terminal collects data in real time from multiple sensors mounted on the vehicle. The input data includes speed, location information, and surrounding video data. The terminal temporarily stores this data and prepares it for transmission to the server.

[0105] Step 2:

[0106] The device transmits data to the server via the internet. The input is collected sensor data, and the output is a secure data stream to the server. Data transmission is encrypted using the SSL / TLS protocol, ensuring secure and efficient processing.

[0107] Step 3:

[0108] The server stores the received sensor data in a database and inputs it into a generating AI model for analysis. Based on the input data, it identifies multiple elements corresponding to the surrounding environment and vehicle status, and generates warning information as output. Google Cloud AI Platform is used for this analysis.

[0109] Step 4:

[0110] The server sends the generated alert information to the terminal. The input is alert data from the generating AI model, and the output is data converted into a format that can be displayed on the terminal. The server improves responsiveness by optimizing the transmitted data.

[0111] Step 5:

[0112] The device presents the received warning information to the user visually and audibly. Input is optimized data from the server, and output is a warning display on smart glasses or an in-car display. Specifically, the warning content is displayed on the visual display, and at the same time, voice guidance is provided through the speaker to inform the user of their surroundings.

[0113] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0114] This invention is a support system designed to make the driver's driving experience safer and more comfortable, and combines a group of sensors mounted on the vehicle, a user terminal, a server, and an emotion recognition engine. This system adapts its operation according to the driver's emotional state, providing more personalized driving assistance.

[0115] The terminal collects data from the vehicle's sensors, including the vehicle's GPS location, speed, and in-car camera footage. The sensors are also used to provide the driver's voice and facial expression data to the emotion recognition engine. The terminal preprocesses this data and sends it to the server.

[0116] Communication between the terminal and the server is highly encrypted, and security is ensured by using a secure protocol. The server receives this data and analyzes it using a generative AI model. In particular, it monitors and predicts changes in driving conditions in real time to generate optimal warnings.

[0117] In addition, the emotion recognition engine analyzes the driver's emotions based on their heart rate, tone of voice, and facial expressions. If the emotions indicate stress or anxiety, the server creates a corresponding warning message in a calm and soothing voice tone. Conversely, if the driver is calm, it creates instructions in a normal tone.

[0118] The generated message is sent to the terminal, which then presents it to the driver via a speech synthesis engine. For example, it might say, "Relax and continue driving. The traffic light will soon turn red," providing instructions tailored to the driver's current situation.

[0119] This system provides drivers with a sense of mental security while driving and simultaneously encourages their ability to respond quickly to changes in traffic conditions. For example, when approaching an intersection, if the driver is in an excited state, a message is provided encouraging them to calm down. This allows the driver to continue driving safely at their own pace. This invention provides advanced driving assistance that adapts to the driver's mental and physical state.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The device collects environmental data from sensors mounted on the vehicle. This includes GPS location, vehicle speed, in-vehicle audio, and video from a facial recognition camera. The device then processes this data and converts it into a usable format.

[0123] Step 2:

[0124] The terminal extracts emotional information by analyzing the driver's voice tone and facial expressions using a facial recognition camera. The emotion recognition engine determines the degree of stress and relaxation and prepares to send this information, along with other driving data, to the server.

[0125] Step 3:

[0126] The device encrypts all collected data and transmits it to the server via a secure communication channel. This process is performed periodically, allowing the data to be updated in real time.

[0127] Step 4:

[0128] The server analyzes the received data. A generative AI model identifies the current driving situation and approaching hazards, and generates the necessary warnings for the driver. The server also analyzes emotional data and adjusts the message based on the driver's emotional state.

[0129] Step 5:

[0130] The server generates a more subdued warning message depending on the driver's emotional state (e.g., if they are under high stress) and sends it to the user's device. If the driver is calm, the normal tone is maintained.

[0131] Step 6:

[0132] The terminal receives messages from the server and performs speech synthesis. The voice message is played through the speaker, conveying necessary information to the driver. This allows the driver to obtain information without taking their eyes off the road.

[0133] Step 7:

[0134] The user accepts voice instructions provided by the device and adjusts their driving behavior accordingly. In this process, the driver can change their actions in response to warnings and take appropriate measures such as slowing down or stopping as needed.

[0135] (Example 2)

[0136] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0137] In today's driving environment, a driver's attention and mental state significantly impact safe driving. However, current technology lacks the means to effectively analyze a driver's real-time emotional state and provide individualized warnings. As a result, drivers continue to drive while feeling stressed or anxious, increasing the risk of traffic accidents.

[0138] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0139] In this invention, the server includes means for acquiring information from a number of detection devices mounted on the vehicle, means for sending the acquired information to a communication device, and means for analyzing the acquired information, evaluating the driver's emotional state, and generating warnings for the driver. This makes it possible to provide individualized warnings in real time according to the driver's emotional state, thereby realizing a safe and comfortable driving environment.

[0140] A "detection device" refers to devices such as sensors and cameras mounted on a vehicle, which are used to collect physical and environmental data.

[0141] "Information" refers to data acquired from the detection device, which includes digital data such as vehicle location information, speed, and the driver's voice and facial expressions.

[0142] A "communication device" is a device or system used to send and receive information between a terminal and a server, and it plays a role in encrypting data and securely transmitting information.

[0143] "Analysis" is the process of information processing that evaluates the driver's condition based on acquired information and determines the necessary actions.

[0144] "Driver's Attention" refers to voice messages or visual alerts generated by the server, which are instructions or warnings to encourage safe driving.

[0145] "Emotional state" refers to the psychological or mental state inferred from the driver's heart rate, voice tone, and facial expression data, and is a concept that includes stress, anxiety, calmness, etc.

[0146] This invention is a support system designed to make the driver's driving experience safer and more comfortable. It combines a vehicle-mounted detection device, a user terminal, a communication device, and an emotion recognition engine. The system adapts its operation according to the driver's emotional state, providing individually optimized driving assistance.

[0147] terminal

[0148] The terminal collects information provided by the vehicle's detection system. Specific examples include GPS location information, vehicle speed, in-car camera footage, and driver voice and facial expression data. The terminal preprocesses and encrypts this data before transmitting it to the server via a communication device.

[0149] server

[0150] The server analyzes data received from the communication device using a generating AI model. This analysis process assesses the driver's emotional state through their heart rate, tone of voice, and facial expressions, and generates appropriate warnings. If the emotion indicates stress or anxiety, a message with a calming voice tone is created. The software used by the server's emotion recognition engine includes state-of-the-art voice and video analysis algorithms.

[0151] User

[0152] Messages sent to the user's terminal are delivered to the driver in real time via a speech synthesis engine. For example, if the driver is feeling stressed, instructions such as "Relax and continue driving. The traffic light will soon turn red" are presented as voice commands. In this way, providing feedback based on the driver's emotional state helps to support their mental well-being while driving.

[0153] A specific example of a prompt message is the instruction, "Generate a warning message for when the heart rate is high as the driver approaches an intersection," which will provide appropriate warnings to the driver.

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

[0155] Step 1:

[0156] The terminal collects information from detection devices installed in the vehicle. Specifically, it receives GPS location information, speed data, interior camera footage, audio, and facial expression data. The input is the raw data obtained from the detection devices, and collecting this data is the first step. The output is this dataset.

[0157] Step 2:

[0158] The terminal preprocesses the collected data. Specific operations include denoising, data standardization, and normalization. This data processing allows the algorithm to analyze the data more accurately. The input is the raw data obtained in step 1, and the output is the preprocessed data.

[0159] Step 3:

[0160] The terminal encrypts the pre-processed data and sends it to the server using a secure communication protocol, thereby ensuring the security of the information. The input is pre-processed data, and the output is encrypted data sent to the server.

[0161] Step 4:

[0162] The server decrypts the encrypted data received from the terminal and analyzes the data using a generative AI model. This analysis process evaluates the driver's emotional state from their heart rate, voice tone, and facial expression data. The input is encrypted data sent from the terminal, and the output is the analyzed information and the evaluation of the emotional state.

[0163] Step 5:

[0164] The server generates a warning message for the driver based on the analysis results. The generating AI model uses prompts to create the most appropriate message. For example, if the driver is stressed, a message including "Please relax and continue driving" will be generated. The input is the analysis results from step 4, and the output is the warning message.

[0165] Step 6:

[0166] The server sends the generated message to the terminal. The terminal uses a speech synthesis engine to present the message to the driver in voice format. This allows the driver to receive feedback in real time. The input is message data from the server, and the output is voice feedback to the driver.

[0167] (Application Example 2)

[0168] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0169] In autonomous vehicles, providing passengers with appropriate information tailored to their emotional state is essential to ensuring they can travel with peace of mind. However, current technology faces the challenge of being unable to analyze passengers' emotions in real time and provide optimal notifications based on that analysis.

[0170] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0171] In this invention, the server includes means for collecting information from a number of detection devices installed in the vehicle, means for transmitting the collected information to a data processing device, and means for analyzing the information collected by the data processing device and generating notifications for passengers. This makes it possible to provide real-time information that corresponds to the emotional state of passengers.

[0172] The term "vehicle" refers to any mechanical device used as a means of transportation, and especially to those with autonomous driving capabilities.

[0173] A "detection device" is equipment positioned to acquire information from inside and outside a vehicle, and includes sensors such as cameras, microphones, and heart rate monitors.

[0174] "Situation" refers to the collective data indicating the vehicle's operating status and the passengers' emotional state.

[0175] A "data processing device" refers to a computer system and its functions used to analyze and process collected information.

[0176] "Data transmission" refers to the process of transferring information from a data collection point to a processing unit.

[0177] "Notification" refers to information provided to passengers based on analysis results, and includes messages delivered via audio or visual means.

[0178] Encryption is a transformation technology used to ensure the security of information, and it is important for protecting information from unauthorized access.

[0179] "Emotional state" refers to a concrete expression of a passenger's psychological and physiological responses, and its analysis is used to improve passenger comfort.

[0180] This system aims to enhance passenger safety in autonomous vehicles by providing real-time information based on data obtained from numerous detection devices installed within the vehicle. The invented program incorporates intelligent data processing methods based on cloud services such as Azure Cognitive Services.

[0181] The server collects information from detection devices placed inside and outside the vehicle, including video and audio data from cameras and microphones, and biometric information from heart rate sensors. The data processing unit then analyzes the collected information and uses a generative AI model to determine the passengers' emotional state in real time. Specifically, it detects whether passengers are experiencing anxiety or stress and generates optimal notifications based on that.

[0182] The terminal receives notifications from the server and provides information to passengers via a speech synthesis engine and display. Notifications include suggestions for music to help passengers relax and voice messages explaining the features of the route. This makes it possible to provide a comfortable travel experience that is tailored to the passengers' emotions.

[0183] As a concrete example, if a passenger in an autonomous vehicle touring a tourist destination feels anxious, they will receive a message via their device saying, "Please relax. You can see the famous landmarks on the right side of the window." This helps the passenger feel more at ease.

[0184] An example of a prompt statement is, "When passengers are inside an autonomous vehicle, how should their emotions be recognized, and what kind of refresh message should be generated?" Based on this prompt statement, the AI ​​model operates and provides a foundation for generating appropriate notifications.

[0185] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0186] Step 1:

[0187] The server collects situational data from detection devices installed in the vehicle. It receives video data from cameras, audio data from microphones, and biometric data from heart rate sensors as input. This data is processed as foundational information to understand the emotional state of passengers.

[0188] Step 2:

[0189] The server preprocesses the collected situational data. It handles video, audio, and biometric information as input data, and performs data processing such as noise reduction and format standardization. As output, it generates data converted into a format suitable for analysis.

[0190] Step 3:

[0191] The server inputs pre-processed data into a generating AI model to analyze the passengers' emotional states. This analysis predicts the passengers' emotions, such as whether they are anxious or relaxed, based on the input data. The output is an evaluation of their emotional states.

[0192] Step 4:

[0193] The server generates appropriate notifications based on the analysis results. Here, it considers the emotion ratings provided as input and creates audio messages containing relaxation messages and tourist information. The output consists of audio and text information for presentation to passengers.

[0194] Step 5:

[0195] The terminal outputs notifications received from the server as voice messages using a speech synthesis engine. If a display is present, it also displays visual messages. It receives notification information from the server as input and provides it to passengers in the most appropriate format.

[0196] Step 6:

[0197] Users can enjoy their journey with peace of mind by receiving audio and visual notifications from their devices. The expected outcome here is an improvement in passenger safety and satisfaction.

[0198] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0199] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0200] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0204] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0205] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0206] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0207] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0208] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0209] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0210] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0211] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0212] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0213] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0214] This invention relates to a driver assistance system for safely operating a vehicle. This system consists of various sensors and terminals mounted on the vehicle and a server connected via the internet. Specific embodiments of this system are described below.

[0215] The user's device collects various data from sensors installed in the vehicle. This includes location information via GPS, vehicle speed from the speedometer, and surrounding images from cameras. The device is then prepared to transmit this data to a server.

[0216] The terminal ensures a stable network connection and transmits the collected data to the server. During this process, the data is properly encrypted, ensuring the security of the communication. The server processes the received data in real time and analyzes the operating status using a generated AI model.

[0217] Based on the analysis results, the server generates necessary alerts for the driver. For example, it creates warning messages that include information such as when the traffic light is about to turn red or when there are pedestrians or cyclists nearby. This allows drivers to receive important information at any time.

[0218] The generated warning message is sent back to the user's device. The device converts this from text to audio and transmits it to the driver through the car's speakers. By listening to the audio guidance, the driver can make appropriate decisions based on the surrounding situation.

[0219] A concrete example is when a driver approaches an intersection, the server detects a change in traffic light and provides a voice command to the terminal, such as, "The light will turn red in 5 seconds." In this way, the driver can always be aware of the current traffic situation and continue driving safely.

[0220] The system of this invention provides effective support to alleviate driver anxiety and enhance safety.

[0221] The following describes the processing flow.

[0222] Step 1:

[0223] The terminal collects data such as GPS location information, speed, and camera images from various sensors installed in the vehicle. This data provides a detailed reflection of the vehicle's operating status.

[0224] Step 2:

[0225] The terminal preprocesses the collected data according to various protocols. Preprocessing includes data format conversion, noise reduction, and necessary compression. This ensures optimal data transmission to the server.

[0226] Step 3:

[0227] The terminal sends the pre-processed data to the server via a secure communication protocol. The data is encrypted during this process, ensuring the security of the communication.

[0228] Step 4:

[0229] The server immediately analyzes the received data. Using a generative AI model, it extracts important information related to the driver and analyzes the current traffic situation in detail.

[0230] Step 5:

[0231] Based on the analysis results, the server generates necessary warning messages for the driver. For example, it identifies situations that require immediate action from the driver, such as "The traffic light is turning red" or "There are pedestrians near the intersection."

[0232] Step 6:

[0233] The server encodes the generated alert message and sends it to the user's terminal. Because the message requires immediacy, it is transmitted using the most optimal communication path.

[0234] Step 7:

[0235] The terminal receives messages from the server, converts them into speech using a speech synthesis engine, and transmits them to the driver through the vehicle's speakers. This allows the driver to receive important information without relying on their vision.

[0236] Step 8:

[0237] The user receives voice instructions and adjusts their driving behavior accordingly. This allows for quick responses to changes in traffic conditions, resulting in safer driving.

[0238] (Example 1)

[0239] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0240] When drivers operate a vehicle, it is crucial for them to make appropriate decisions based on traffic conditions and the surrounding environment. However, sudden environmental changes and information overload can cause drivers to miss necessary information. To solve this problem, there is a need to develop a system that provides drivers with relevant information in real time and reduces their mental burden.

[0241] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0242] In this invention, the server includes means for acquiring various data from multiple detectors installed in the vehicle, means for appropriately formatting and safely transmitting the acquired data to an information processing device, and means for processing the received data in real time at the information processing device, analyzing the driving situation using a generated AI model, and forming warnings. As a result, the driver can always obtain the latest and most important information, thereby improving safety.

[0243] A "detector" is a device installed in a vehicle to acquire various data in real time, and it has the function of understanding location information and the surrounding situation.

[0244] An "information processing device" is a computer that can receive acquired data and process it in real time. It has the function of analyzing driving conditions using a generated AI model and forming warnings.

[0245] A "generative AI model" is an artificial intelligence model used to analyze driving conditions based on collected data and generate information that should be provided to the driver.

[0246] "Warning" refers to warnings and advice provided to drivers, including information to encourage appropriate judgment depending on the driving situation.

[0247] "Encryption technology" is a means of protecting the security of data during communication. It is a technique that transforms data using a specific algorithm to prevent unauthorized access.

[0248] This invention relates to a support system for drivers to safely operate a vehicle. The system consists of a detector installed in the vehicle, a terminal for transmitting and receiving data, and an information processing device for processing and analyzing the data. A specific embodiment of this system is described below.

[0249] The user's device collects data from various sensors within the vehicle. This data includes location information from GPS sensors, speed from the speedometer, and surrounding images from cameras. This collected data is appropriately formatted for transmission to an information processing device and securely transferred using encryption technology. The device also plays a role in stabilizing the network connection.

[0250] The information processing device, which functions as a server, processes data received from terminals in real time. This utilizes a generative AI model, which analyzes driving conditions and generates necessary warnings for the driver. A specific example of the generative AI model's use is predicting changes in traffic signals at intersections and warning drivers accordingly.

[0251] The generated warnings are provided to the driver via a terminal. The terminal converts the warnings from text to audio and transmits them to the driver in real time through the in-car speakers. This function allows the driver to properly understand the traffic situation and make necessary decisions quickly.

[0252] As a concrete example, the user's device receives a warning message such as "The traffic light is turning red," and converts it into voice to communicate to the driver. An example of a prompt message for the generating AI model could be, "Based on the current vehicle speed and location information, please generate the next necessary warning."

[0253] In this way, the system aims to support drivers in driving safely and improve traffic safety.

[0254] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0255] Step 1:

[0256] The user's device collects data from sensors installed in the vehicle. Specifically, it acquires location information from GPS, speed data from the speedometer, and surrounding video from cameras. This data is necessary to understand the driving situation. The input for this step is raw data from the vehicle's sensors, and the output is a properly formatted dataset.

[0257] Step 2:

[0258] The terminal is prepared to transmit the collected data to the information processing device. The data is structured and secured using encryption technology. After confirming that the network connection is stable, preparations are made to send the data to the server. At this stage, the input is the raw data before processing, and the output is encrypted, transmittable data.

[0259] Step 3:

[0260] The server receives data transmitted from the terminal and processes it in real time. This processing includes analyzing driving conditions using a generative AI model. Specifically, it extracts and analyzes safety-related information from the vehicle's position, speed, and surrounding video footage. The input for this step is encrypted data, and the output is warning data as a result of the analysis.

[0261] Step 4:

[0262] The server generates driver alerts based on analysis results obtained using a generative AI model. It uses prompts to create messages containing useful information for the driver, such as "The traffic light is turning red." The input for this step is the analysis results, and the output is a warning message for the driver.

[0263] Step 5:

[0264] The terminal converts the warning message received from the server from text information into audio information. Furthermore, it transmits this information to the driver via the in-car speaker. By listening to this audio guidance, the driver can respond appropriately to the surrounding traffic conditions. The input for this step is a text message, and the output is an audio interface that the driver can understand.

[0265] (Application Example 1)

[0266] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0267] In autonomous vehicles, a key challenge is accurately recognizing the surrounding environment and providing safe and timely warnings to the occupants. This allows occupants to understand the vehicle's operating status and use the vehicle with confidence.

[0268] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0269] In this invention, the server includes means for collecting data from multiple detectors mounted on the vehicle, means for transmitting the collected data to a database, means for analyzing the data collected in the database and generating warnings for the driver and passengers, and device means for providing the generated warnings in audio and visual formats. This makes it possible to provide occupants with real-time information about the surrounding environment and improve safety.

[0270] A "detector" is a device installed in a vehicle that collects information about the surrounding environment and the vehicle's condition.

[0271] A "database" is an information storage device used to store collected data and utilize it for subsequent analysis.

[0272] "Analysis" is an information processing process that uses collected data to evaluate the vehicle's driving conditions and surrounding environment, and to generate necessary warnings.

[0273] "Warning" refers to warning information provided to occupants, which should be communicated in audio or visual format depending on the driving situation and surrounding environment.

[0274] An "information display device" is a device or equipment used to communicate generated warnings to crew members in audio or visual format.

[0275] An "information processing device" is an electronic device used to analyze data and deliver the generated information appropriately to the crew.

[0276] The system realizing this invention is built for providing safety information in autonomous vehicles. The server has the function of collecting data in real time from multiple sensors installed in the vehicle and transmitting it to a database via the internet. The collected data is analyzed by a generative AI model hosted on the Google Cloud AI Platform. The analyzed information is generated as a warning for the driver and passengers and is provided in audio and visual formats.

[0277] The server interacts with the database to encrypt data and ensure secure communication. Specifically, it uses the SSL / TLS protocol to guarantee data security. The generated alerts are transmitted to the occupants via smart glasses or smartphone applications. This allows occupants to accurately perceive the surrounding environment of the autonomous vehicle, thereby enhancing safety.

[0278] As a concrete example, if the server detects the presence of a pedestrian at an intersection, it will issue a voice and visual warning via smart glasses stating, "A pedestrian is approaching the intersection." An example of a prompt to be input into the generating AI model is, "Identify hazardous elements from the current intersection situation and create a warning message." This will improve the safety of autonomous driving and provide peace of mind to the occupants.

[0279] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0280] Step 1:

[0281] The terminal collects data in real time from multiple sensors mounted on the vehicle. The input data includes speed, location information, and surrounding video data. The terminal temporarily stores this data and prepares it for transmission to the server.

[0282] Step 2:

[0283] The terminal sends data to the server via the Internet. The input is the collected sensor data, and the output is a secure data stream to the server. The data transmission is encrypted using the SSL / TLS protocol and processed securely and efficiently.

[0284] Step 3:

[0285] The server saves the received sensor data in the database and inputs it into the generated AI model for analysis. Based on the input data, it identifies multiple elements according to the surrounding environment and vehicle conditions, and generates warning information as output. Google Cloud AI Platform is used for this analysis.

[0286] Step 4:

[0287] The server sends the generated warning information to the terminal. The input is the warning data from the generated AI model, and the output is the data converted into a format that can be displayed on the terminal. The server enhances the responsiveness by optimizing the transmitted data.

[0288] Step 5:

[0289] Based on the received warning information, the terminal presents it to the user visually and audibly. The input is the optimized data from the server, and the output is a warning display on smart glasses or an in-vehicle display. Specifically, when the warning content is displayed on the visual display, voice guidance is provided through the speaker to inform the user of the surrounding situation.

[0290] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0291] This invention is a support system designed to make the driver's driving experience safer and more comfortable, and combines a group of sensors mounted on the vehicle, a user terminal, a server, and an emotion recognition engine. This system adapts its operation according to the driver's emotional state, providing more personalized driving assistance.

[0292] The terminal collects data from the vehicle's sensors, including the vehicle's GPS location, speed, and in-car camera footage. The sensors are also used to provide the driver's voice and facial expression data to the emotion recognition engine. The terminal preprocesses this data and sends it to the server.

[0293] Communication between the terminal and the server is highly encrypted, and security is ensured by using a secure protocol. The server receives this data and analyzes it using a generative AI model. In particular, it monitors and predicts changes in driving conditions in real time to generate optimal warnings.

[0294] In addition, the emotion recognition engine analyzes the driver's emotions based on their heart rate, tone of voice, and facial expressions. If the emotions indicate stress or anxiety, the server creates a corresponding warning message in a calm and soothing voice tone. Conversely, if the driver is calm, it creates instructions in a normal tone.

[0295] The generated message is sent to the terminal, which then presents it to the driver via a speech synthesis engine. For example, it might say, "Relax and continue driving. The traffic light will soon turn red," providing instructions tailored to the driver's current situation.

[0296] This system provides drivers with a sense of mental security while driving and simultaneously encourages their ability to respond quickly to changes in traffic conditions. For example, when approaching an intersection, if the driver is in an excited state, a message is provided encouraging them to calm down. This allows the driver to continue driving safely at their own pace. This invention provides advanced driving assistance that adapts to the driver's mental and physical state.

[0297] The following describes the processing flow.

[0298] Step 1:

[0299] The device collects environmental data from sensors mounted on the vehicle. This includes GPS location, vehicle speed, in-vehicle audio, and video from a facial recognition camera. The device then processes this data and converts it into a usable format.

[0300] Step 2:

[0301] The terminal extracts emotional information by analyzing the driver's voice tone and facial expressions using a facial recognition camera. The emotion recognition engine determines the degree of stress and relaxation and prepares to send this information, along with other driving data, to the server.

[0302] Step 3:

[0303] The device encrypts all collected data and sends it to the server via a secure communication channel. This process is performed periodically, allowing the data to be updated in real time.

[0304] Step 4:

[0305] The server analyzes the received data. A generative AI model identifies the current driving situation and approaching hazards, and generates the necessary warnings for the driver. The server also analyzes emotional data and adjusts the message based on the driver's emotional state.

[0306] Step 5:

[0307] The server generates an attention message in a gentler tone according to the driver's emotional state (for example, when the stress is high) and sends it to the user's terminal. When the driver is calm, the normal tone is maintained.

[0308] Step 6:

[0309] The terminal receives the message from the server and performs speech synthesis. The voice message is played from the speaker to convey the necessary information to the driver. Thereby, the driver can obtain information without taking his eyes off the road.

[0310] Step 7:

[0311] The user accepts the voice instruction provided by the terminal and adjusts the driving behavior. At this time, the driver can change his behavior according to the attention call and take corresponding measures such as reducing the speed or stopping if necessary.

[0312] (Example 2)

[0313] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0314] In the modern vehicle driving environment, the driver's attention and mental state have a great impact on safe driving. However, current technologies lack means to effectively analyze the driver's real-time emotional state and provide individual attention calls. As a result, there is a problem that the risk of traffic accidents increases when the driver continues to drive in a state of stress or anxiety.

[0315] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0316] In this invention, the server includes means for acquiring information from a number of detection devices mounted on the vehicle, means for sending the acquired information to a communication device, and means for analyzing the acquired information, evaluating the driver's emotional state, and generating warnings for the driver. This makes it possible to provide individualized warnings in real time according to the driver's emotional state, thereby realizing a safe and comfortable driving environment.

[0317] A "detection device" refers to devices such as sensors and cameras mounted on a vehicle, which are used to collect physical and environmental data.

[0318] "Information" refers to data acquired from the detection device, which includes digital data such as vehicle location information, speed, and the driver's voice and facial expressions.

[0319] A "communication device" is a device or system used to send and receive information between a terminal and a server, and it plays a role in encrypting data and securely transmitting information.

[0320] "Analysis" is the process of information processing that evaluates the driver's condition based on acquired information and determines the necessary actions.

[0321] "Driver's Attention" refers to voice messages or visual alerts generated by the server, which are instructions or warnings to encourage safe driving.

[0322] "Emotional state" refers to the psychological or mental state inferred from the driver's heart rate, voice tone, and facial expression data, and is a concept that includes stress, anxiety, calmness, etc.

[0323] This invention is a support system designed to make the driver's driving experience safer and more comfortable. It combines a vehicle-mounted detection device, a user terminal, a communication device, and an emotion recognition engine. The system adapts its operation according to the driver's emotional state, providing individually optimized driving assistance.

[0324] terminal

[0325] The terminal collects information provided by the vehicle's detection system. Specific examples include GPS location information, vehicle speed, in-car camera footage, and driver voice and facial expression data. The terminal preprocesses and encrypts this data before transmitting it to the server via a communication device.

[0326] server

[0327] The server analyzes data received from the communication device using a generating AI model. This analysis process assesses the driver's emotional state through their heart rate, tone of voice, and facial expressions, and generates appropriate warnings. If the emotion indicates stress or anxiety, a message with a calming voice tone is created. The software used by the server's emotion recognition engine includes state-of-the-art voice and video analysis algorithms.

[0328] User

[0329] Messages sent to the user's terminal are delivered to the driver in real time via a speech synthesis engine. For example, if the driver is feeling stressed, instructions such as "Relax and continue driving. The traffic light will soon turn red" are presented as voice commands. In this way, providing feedback based on the driver's emotional state helps to support their mental well-being while driving.

[0330] A specific example of a prompt message is the instruction, "Generate a warning message for when the heart rate is high as the driver approaches an intersection," which will provide appropriate warnings to the driver.

[0331] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0332] Step 1:

[0333] The terminal collects information from detection devices installed in the vehicle. Specifically, it receives GPS location information, speed data, interior camera footage, audio, and facial expression data. The input is the raw data obtained from the detection devices, and collecting this data is the first step. The output is this dataset.

[0334] Step 2:

[0335] The terminal preprocesses the collected data. Specific operations include denoising, data standardization, and normalization. This data processing allows the algorithm to analyze the data more accurately. The input is the raw data obtained in step 1, and the output is the preprocessed data.

[0336] Step 3:

[0337] The terminal encrypts the pre-processed data and sends it to the server using a secure communication protocol, thereby ensuring the security of the information. The input is pre-processed data, and the output is encrypted data sent to the server.

[0338] Step 4:

[0339] The server decrypts the encrypted data received from the terminal and analyzes the data using a generative AI model. This analysis process evaluates the driver's emotional state from their heart rate, voice tone, and facial expression data. The input is encrypted data sent from the terminal, and the output is the analyzed information and the evaluation of the emotional state.

[0340] Step 5:

[0341] The server generates a warning message for the driver based on the analysis results. The generating AI model uses prompts to create the most appropriate message. For example, if the driver is stressed, a message including "Please relax and continue driving" will be generated. The input is the analysis results from step 4, and the output is the warning message.

[0342] Step 6:

[0343] The server sends the generated message to the terminal. The terminal uses a speech synthesis engine to present the message to the driver in voice format. This allows the driver to receive feedback in real time. The input is message data from the server, and the output is voice feedback to the driver.

[0344] (Application Example 2)

[0345] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0346] In autonomous vehicles, providing passengers with appropriate information tailored to their emotional state is essential to ensuring they can travel with peace of mind. However, current technology faces the challenge of being unable to analyze passengers' emotions in real time and provide optimal notifications based on that analysis.

[0347] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0348] In this invention, the server includes means for collecting information from a number of detection devices installed in the vehicle, means for transmitting the collected information to a data processing device, and means for analyzing the information collected by the data processing device and generating notifications for passengers. This makes it possible to provide real-time information that corresponds to the emotional state of passengers.

[0349] The term "vehicle" refers to any mechanical device used as a means of transportation, and especially to those with autonomous driving capabilities.

[0350] A "detection device" is equipment positioned to acquire information from inside and outside a vehicle, and includes sensors such as cameras, microphones, and heart rate monitors.

[0351] "Situation" refers to the collective data indicating the vehicle's operating status and the passengers' emotional state.

[0352] A "data processing device" refers to a computer system and its functions used to analyze and process collected information.

[0353] "Data transmission" refers to the process of transferring information from a data collection point to a processing unit.

[0354] "Notification" refers to information provided to passengers based on analysis results, and includes messages delivered via audio or visual means.

[0355] Encryption is a transformation technology used to ensure the security of information, and it is important for protecting information from unauthorized access.

[0356] "Emotional state" refers to a concrete expression of a passenger's psychological and physiological responses, and its analysis is used to improve passenger comfort.

[0357] This system aims to enhance passenger safety in autonomous vehicles by providing real-time information based on data from numerous detection devices installed within the vehicle. The invented program employs intelligent data processing methods based on cloud-based technologies such as Azure Cognitive Services.

[0358] The server collects information from detection devices placed inside and outside the vehicle, including video and audio data from cameras and microphones, and biometric information from heart rate sensors. The data processing unit then analyzes the collected information and uses a generative AI model to determine the passengers' emotional state in real time. Specifically, it detects whether passengers are experiencing anxiety or stress and generates optimal notifications based on that.

[0359] The terminal receives notifications from the server and provides information to passengers via a speech synthesis engine and display. Notifications include suggestions for music to help passengers relax and voice messages explaining the features of the route. This makes it possible to provide a comfortable travel experience that is tailored to the passengers' emotions.

[0360] As a concrete example, if a passenger in an autonomous vehicle tours a tourist destination feels anxious, they will receive a message via their device stating, "Please relax. You can see the sights on the right side of the window." This helps the passenger feel more at ease.

[0361] An example of a prompt statement is, "When passengers are inside an autonomous vehicle, how should their emotions be recognized, and what kind of refresh message should be generated?" Based on this prompt statement, the AI ​​model operates and provides a foundation for generating appropriate notifications.

[0362] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0363] Step 1:

[0364] The server collects situational data from detection devices installed in the vehicle. It receives video data from cameras, audio data from microphones, and biometric data from heart rate sensors as input. This data is processed as foundational information to understand the emotional state of passengers.

[0365] Step 2:

[0366] The server preprocesses the collected situational data. It handles video, audio, and biometric information as input data, and performs data processing such as noise reduction and format standardization. As output, it generates data converted into a format suitable for analysis.

[0367] Step 3:

[0368] The server inputs pre-processed data into a generating AI model to analyze the passengers' emotional states. This analysis predicts the passengers' emotions, such as whether they are anxious or relaxed, based on the input data. The output is an evaluation of their emotional states.

[0369] Step 4:

[0370] The server generates appropriate notifications based on the analysis results. Here, it considers the emotion ratings provided as input and creates audio messages containing relaxation messages and tourist information. The output consists of audio and text information for presentation to passengers.

[0371] Step 5:

[0372] The terminal outputs notifications received from the server as voice messages using a speech synthesis engine. If a display is present, it also displays visual messages. It receives notification information from the server as input and provides it to passengers in the most appropriate format.

[0373] Step 6:

[0374] Users can enjoy their journey with peace of mind by receiving audio and visual notifications from their devices. The expected outcome here is an improvement in passenger safety and satisfaction.

[0375] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0376] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0377] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0378] [Third Embodiment]

[0379] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0380] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0381] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0382] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0383] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0384] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0385] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0386] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0387] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0388] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0389] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0390] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0391] This invention relates to a driver assistance system for safely operating a vehicle. This system consists of various sensors and terminals mounted on the vehicle and a server connected via the internet. Specific embodiments of this system are described below.

[0392] The user's device collects various data from sensors installed in the vehicle. This includes GPS location information, vehicle speed from the speedometer, and surrounding video from cameras. The device is then prepared to transmit this data to a server.

[0393] The terminal ensures a stable network connection and transmits the collected data to the server. During this process, the data is properly encrypted, ensuring the security of the communication. The server processes the received data in real time and analyzes the operating status using a generated AI model.

[0394] Based on the analysis results, the server generates necessary alerts for the driver. For example, it creates warning messages that include information such as when the traffic light is about to turn red or when there are pedestrians or cyclists nearby. This allows drivers to receive important information at any time.

[0395] The generated warning message is sent back to the user's device. The device converts this from text to audio and transmits it to the driver through the car's speakers. By listening to the audio guidance, the driver can make appropriate decisions based on the surrounding situation.

[0396] A concrete example is when a driver approaches an intersection, the server detects a change in traffic light and provides a voice command to the terminal, such as, "The light will turn red in 5 seconds." In this way, the driver can always be aware of the current traffic situation and continue driving safely.

[0397] The system of this invention provides effective support to alleviate driver anxiety and enhance safety.

[0398] The following describes the processing flow.

[0399] Step 1:

[0400] The terminal collects data such as GPS location information, speed, and camera images from various sensors installed in the vehicle. This data provides a detailed reflection of the vehicle's operating status.

[0401] Step 2:

[0402] The terminal preprocesses the collected data according to various protocols. Preprocessing includes data format conversion, noise reduction, and necessary compression. This ensures optimal data transmission to the server.

[0403] Step 3:

[0404] The terminal sends the pre-processed data to the server via a secure communication protocol. The data is encrypted during this process, ensuring the security of the communication.

[0405] Step 4:

[0406] The server immediately analyzes the received data. Using a generative AI model, it extracts important information related to the driver and analyzes the current traffic situation in detail.

[0407] Step 5:

[0408] Based on the analysis results, the server generates necessary warning messages for the driver. For example, it identifies situations that require immediate action from the driver, such as "The traffic light is turning red" or "There are pedestrians near the intersection."

[0409] Step 6:

[0410] The server encodes the generated alert message and sends it to the user's terminal. Because the message requires immediacy, it is transmitted using the most optimal communication path.

[0411] Step 7:

[0412] The terminal receives messages from the server, converts them into speech using a speech synthesis engine, and transmits them to the driver through the vehicle's speakers. This allows the driver to receive important information without relying on their vision.

[0413] Step 8:

[0414] The user receives voice instructions and adjusts their driving behavior accordingly. This allows for quick responses to changes in traffic conditions, resulting in safer driving.

[0415] (Example 1)

[0416] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0417] When drivers operate a vehicle, it is crucial for them to make appropriate decisions based on traffic conditions and the surrounding environment. However, sudden environmental changes and information overload can cause drivers to miss necessary information. To solve this problem, there is a need to develop a system that provides drivers with relevant information in real time and reduces their mental burden.

[0418] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0419] In this invention, the server includes means for acquiring various data from multiple detectors installed in the vehicle, means for appropriately formatting and safely transmitting the acquired data to an information processing device, and means for processing the received data in real time at the information processing device, analyzing the driving situation using a generated AI model, and forming warnings. As a result, the driver can always obtain the latest and most important information, thereby improving safety.

[0420] A "detector" is a device installed in a vehicle to acquire various data in real time, and it has the function of understanding location information and the surrounding situation.

[0421] An "information processing device" is a computer that can receive acquired data and process it in real time. It has the function of analyzing driving conditions using a generated AI model and forming warnings.

[0422] A "generative AI model" is an artificial intelligence model used to analyze driving conditions based on collected data and generate information that should be provided to the driver.

[0423] "Warning" refers to warnings and advice provided to drivers, including information to encourage appropriate judgment depending on the driving situation.

[0424] "Encryption technology" is a means of protecting the security of data during communication. It is a technique that transforms data using a specific algorithm to prevent unauthorized access.

[0425] This invention relates to a support system for drivers to safely operate a vehicle. The system consists of a detector installed in the vehicle, a terminal for transmitting and receiving data, and an information processing device for processing and analyzing the data. A specific embodiment of this system is described below.

[0426] The user's device collects data from various sensors within the vehicle. This data includes location information from GPS sensors, speed from the speedometer, and surrounding images from cameras. This collected data is appropriately formatted for transmission to an information processing device and securely transferred using encryption technology. The device also plays a role in stabilizing the network connection.

[0427] The information processing device, which functions as a server, processes data received from terminals in real time. This utilizes a generative AI model, which analyzes driving conditions and generates necessary warnings for the driver. A specific example of the generative AI model's use is predicting changes in traffic signals at intersections and warning drivers accordingly.

[0428] The generated warnings are provided to the driver via a terminal. The terminal converts the warnings from text to audio and transmits them to the driver in real time through the in-car speaker. This function allows the driver to properly understand the traffic situation and make necessary decisions quickly.

[0429] As a concrete example, the user's device receives a warning message such as "The traffic light is turning red," and converts it into voice to communicate to the driver. An example of a prompt message for the generating AI model could be, "Based on the current vehicle speed and location information, please generate the next necessary warning."

[0430] In this way, the system aims to support drivers in driving safely and improve traffic safety.

[0431] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0432] Step 1:

[0433] The user's device collects data from sensors installed in the vehicle. Specifically, it acquires location information from GPS, speed data from the speedometer, and surrounding video from cameras. This data is necessary to understand the driving situation. The input for this step is raw data from the vehicle's sensors, and the output is a properly formatted dataset.

[0434] Step 2:

[0435] The terminal is prepared to transmit the collected data to the information processing device. The data is structured and secured using encryption technology. After confirming that the network connection is stable, preparations are made to send the data to the server. At this stage, the input is the raw data before processing, and the output is encrypted, transmittable data.

[0436] Step 3:

[0437] The server receives data transmitted from the terminal and processes it in real time. This processing includes analyzing driving conditions using a generative AI model. Specifically, it extracts and analyzes safety-related information from the vehicle's position, speed, and surrounding video footage. The input for this step is encrypted data, and the output is warning data as a result of the analysis.

[0438] Step 4:

[0439] The server generates driver alerts based on analysis results obtained using a generative AI model. It uses prompts to create messages containing useful information for the driver, such as "The traffic light is turning red." The input for this step is the analysis results, and the output is a warning message for the driver.

[0440] Step 5:

[0441] The terminal converts the warning message received from the server from text information into audio information. Furthermore, it transmits this information to the driver via the in-car speaker. By listening to this audio guidance, the driver can respond appropriately to the surrounding traffic conditions. The input for this step is a text message, and the output is an audio interface that the driver can understand.

[0442] (Application Example 1)

[0443] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0444] In autonomous vehicles, a key challenge is accurately recognizing the surrounding environment and providing safe and timely warnings to the occupants. This allows occupants to understand the vehicle's driving situation and use the vehicle with confidence.

[0445] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0446] In this invention, the server includes means for collecting data from multiple detectors mounted on the vehicle, means for transmitting the collected data to a database, means for analyzing the data collected in the database and generating warnings for the driver and passengers, and device means for providing the generated warnings in audio and visual formats. This makes it possible to provide occupants with real-time information about the surrounding environment and improve safety.

[0447] A "detector" is a device installed in a vehicle that collects information about the surrounding environment and the vehicle's condition.

[0448] A "database" is an information storage device used to store collected data and utilize it for subsequent analysis.

[0449] "Analysis" is an information processing process that uses collected data to evaluate the vehicle's driving conditions and surrounding environment, and to generate necessary warnings.

[0450] "Warning" refers to warning information provided to occupants, which should be communicated in audio or visual format depending on the driving situation and surrounding environment.

[0451] An "information display device" is a device or equipment used to communicate generated warnings to crew members in audio or visual format.

[0452] An "information processing device" is an electronic device used to analyze data and deliver the generated information appropriately to the crew.

[0453] The system realizing this invention is built for providing safety information in autonomous vehicles. The server has the function of collecting data in real time from multiple sensors installed in the vehicle and transmitting it to a database via the internet. The collected data is analyzed by a generative AI model hosted on the Google Cloud AI Platform. The analyzed information is generated as a warning for the driver and passengers and is provided in audio and visual formats.

[0454] The server interacts with the database to encrypt data and ensure secure communication. Specifically, it uses the SSL / TLS protocol to guarantee data security. The generated alerts are transmitted to the occupants via smart glasses or smartphone applications. This allows occupants to accurately perceive the surrounding environment of the autonomous vehicle, thereby enhancing safety.

[0455] As a concrete example, if the server detects the presence of a pedestrian at an intersection, it will issue a voice and visual warning via smart glasses stating, "A pedestrian is approaching the intersection." An example of a prompt to be input into the generating AI model is, "Identify hazardous elements from the current intersection situation and create a warning message." This will improve the safety of autonomous driving and provide peace of mind to the occupants.

[0456] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0457] Step 1:

[0458] The terminal collects data in real time from multiple sensors mounted on the vehicle. The input data includes speed, location information, and surrounding video data. The terminal temporarily stores this data and prepares it for transmission to the server.

[0459] Step 2:

[0460] The device transmits data to the server via the internet. The input is collected sensor data, and the output is a secure data stream to the server. Data transmission is encrypted using the SSL / TLS protocol, ensuring secure and efficient processing.

[0461] Step 3:

[0462] The server stores the received sensor data in a database and inputs it into a generating AI model for analysis. Based on the input data, it identifies multiple elements corresponding to the surrounding environment and vehicle status, and generates warning information as output. Google Cloud AI Platform is used for this analysis.

[0463] Step 4:

[0464] The server sends the generated alert information to the terminal. The input is alert data from the generating AI model, and the output is data converted into a format that can be displayed on the terminal. The server improves responsiveness by optimizing the transmitted data.

[0465] Step 5:

[0466] The device presents the received warning information to the user visually and audibly. Input is optimized data from the server, and output is a warning display on smart glasses or an in-car display. Specifically, the warning content is displayed on the visual display, and at the same time, voice guidance is provided through the speaker to inform the user of their surroundings.

[0467] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0468] This invention is a support system designed to make the driver's driving experience safer and more comfortable, and combines a group of sensors mounted on the vehicle, a user terminal, a server, and an emotion recognition engine. This system adapts its operation according to the driver's emotional state, providing more personalized driving assistance.

[0469] The terminal collects data from the vehicle's sensors, including the vehicle's GPS location, speed, and in-car camera footage. The sensors are also used to provide the driver's voice and facial expression data to the emotion recognition engine. The terminal preprocesses this data and sends it to the server.

[0470] Communication between the terminal and the server is highly encrypted, and security is ensured by using a secure protocol. The server receives this data and analyzes it using a generative AI model. In particular, it monitors and predicts changes in driving conditions in real time to generate optimal warnings.

[0471] In addition, the emotion recognition engine analyzes the driver's emotions based on their heart rate, tone of voice, and facial expressions. If the emotions indicate stress or anxiety, the server creates a corresponding warning message in a calm and soothing voice tone. Conversely, if the driver is calm, it creates instructions in a normal tone.

[0472] The generated message is sent to the terminal, which then presents it to the driver via a speech synthesis engine. For example, it might say, "Relax and continue driving. The traffic light will soon turn red," providing instructions tailored to the driver's current situation.

[0473] This system provides drivers with a sense of mental security while driving and simultaneously encourages their ability to respond quickly to changes in traffic conditions. For example, when approaching an intersection, if the driver is in an excited state, a message is provided encouraging them to calm down. This allows the driver to continue driving safely at their own pace. This invention provides advanced driving assistance that adapts to the driver's mental and physical state.

[0474] The following describes the processing flow.

[0475] Step 1:

[0476] The device collects environmental data from sensors mounted on the vehicle. This includes GPS location, vehicle speed, in-vehicle audio, and video from a facial recognition camera. The device then processes this data and converts it into a usable format.

[0477] Step 2:

[0478] The terminal extracts emotional information by analyzing the driver's voice tone and facial expressions using a facial recognition camera. The emotion recognition engine determines the degree of stress and relaxation and prepares to send this information, along with other driving data, to the server.

[0479] Step 3:

[0480] The device encrypts all collected data and transmits it to the server via a secure communication channel. This process is performed periodically, allowing the data to be updated in real time.

[0481] Step 4:

[0482] The server analyzes the received data. A generative AI model identifies the current driving situation and approaching hazards, and generates the necessary warnings for the driver. The server also analyzes emotional data and adjusts the message based on the driver's emotional state.

[0483] Step 5:

[0484] The server generates a more subdued warning message depending on the driver's emotional state (e.g., if they are under high stress) and sends it to the user's device. If the driver is calm, the normal tone is maintained.

[0485] Step 6:

[0486] The terminal receives messages from the server and performs speech synthesis. The voice message is played through the speaker, conveying necessary information to the driver. This allows the driver to obtain information without taking their eyes off the road.

[0487] Step 7:

[0488] The user accepts voice instructions provided by the device and adjusts their driving behavior accordingly. In this process, the driver can change their actions in response to warnings and take appropriate measures such as slowing down or stopping as needed.

[0489] (Example 2)

[0490] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0491] In today's driving environment, a driver's attention and mental state significantly impact safe driving. However, current technology lacks the means to effectively analyze a driver's real-time emotional state and provide individualized warnings. As a result, drivers continue to drive while feeling stressed or anxious, increasing the risk of traffic accidents.

[0492] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0493] In this invention, the server includes means for acquiring information from a number of detection devices mounted on the vehicle, means for sending the acquired information to a communication device, and means for analyzing the acquired information, evaluating the driver's emotional state, and generating warnings for the driver. This makes it possible to provide individualized warnings in real time according to the driver's emotional state, thereby realizing a safe and comfortable driving environment.

[0494] A "detection device" refers to devices such as sensors and cameras mounted on a vehicle, which are used to collect physical and environmental data.

[0495] "Information" refers to data acquired from the detection device, which includes digital data such as vehicle location information, speed, and the driver's voice and facial expressions.

[0496] A "communication device" is a device or system used to send and receive information between a terminal and a server, and it plays a role in encrypting data and securely transmitting information.

[0497] "Analysis" is the process of information processing that evaluates the driver's condition based on acquired information and determines the necessary actions.

[0498] "Driver's Attention" refers to voice messages or visual alerts generated by the server, which are instructions or warnings to encourage safe driving.

[0499] "Emotional state" refers to the psychological or mental state inferred from the driver's heart rate, voice tone, and facial expression data, and is a concept that includes stress, anxiety, calmness, etc.

[0500] This invention is a support system designed to make the driver's driving experience safer and more comfortable. It combines a vehicle-mounted detection device, a user terminal, a communication device, and an emotion recognition engine. The system adapts its operation according to the driver's emotional state, providing individually optimized driving assistance.

[0501] terminal

[0502] The terminal collects information provided by the vehicle's detection system. Specific examples include GPS location information, vehicle speed, in-car camera footage, and driver voice and facial expression data. The terminal preprocesses and encrypts this data before transmitting it to the server via a communication device.

[0503] server

[0504] The server analyzes data received from the communication device using a generating AI model. This analysis process assesses the driver's emotional state through their heart rate, tone of voice, and facial expressions, and generates appropriate warnings. If the emotion indicates stress or anxiety, a message with a calming voice tone is created. The software used by the server's emotion recognition engine includes state-of-the-art voice and video analysis algorithms.

[0505] User

[0506] Messages sent to the user's terminal are delivered to the driver in real time via a speech synthesis engine. For example, if the driver is feeling stressed, instructions such as "Relax and continue driving. The traffic light will soon turn red" are presented as voice commands. In this way, providing feedback based on the driver's emotional state helps to support their mental well-being while driving.

[0507] A specific example of a prompt message is the instruction, "Generate a warning message for when the heart rate is high as the driver approaches an intersection," which will provide appropriate warnings to the driver.

[0508] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0509] Step 1:

[0510] The terminal collects information from detection devices installed in the vehicle. Specifically, it receives GPS location information, speed data, interior camera footage, audio, and facial expression data. The input is the raw data obtained from the detection devices, and collecting this data is the first step. The output is this dataset.

[0511] Step 2:

[0512] The terminal preprocesses the collected data. Specific operations include denoising, data standardization, and normalization. This data processing allows the algorithm to analyze the data more accurately. The input is the raw data obtained in step 1, and the output is the preprocessed data.

[0513] Step 3:

[0514] The terminal encrypts the pre-processed data and sends it to the server using a secure communication protocol, thereby ensuring the security of the information. The input is pre-processed data, and the output is encrypted data sent to the server.

[0515] Step 4:

[0516] The server decrypts the encrypted data received from the terminal and analyzes the data using a generative AI model. This analysis process evaluates the driver's emotional state from their heart rate, voice tone, and facial expression data. The input is encrypted data sent from the terminal, and the output is the analyzed information and the evaluation of the emotional state.

[0517] Step 5:

[0518] The server generates a warning message for the driver based on the analysis results. The generating AI model uses prompts to create the most appropriate message. For example, if the driver is stressed, a message including "Please relax and continue driving" will be generated. The input is the analysis results from step 4, and the output is the warning message.

[0519] Step 6:

[0520] The server sends the generated message to the terminal. The terminal uses a speech synthesis engine to present the message to the driver in voice format. This allows the driver to receive feedback in real time. The input is message data from the server, and the output is voice feedback to the driver.

[0521] (Application Example 2)

[0522] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0523] In autonomous vehicles, providing passengers with appropriate information tailored to their emotional state is essential to ensuring they can travel with peace of mind. However, current technology faces the challenge of being unable to analyze passengers' emotions in real time and provide optimal notifications based on that analysis.

[0524] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0525] In this invention, the server includes means for collecting information from a number of detection devices installed in the vehicle, means for transmitting the collected information to a data processing device, and means for analyzing the information collected by the data processing device and generating notifications for passengers. This makes it possible to provide real-time information that corresponds to the emotional state of passengers.

[0526] The term "vehicle" refers to any mechanical device used as a means of transportation, and especially to those with autonomous driving capabilities.

[0527] A "detection device" is equipment positioned to acquire information from inside and outside a vehicle, and includes sensors such as cameras, microphones, and heart rate monitors.

[0528] "Situation" refers to the collective data indicating the vehicle's operating status and the passengers' emotional state.

[0529] A "data processing device" refers to a computer system and its functions used to analyze and process collected information.

[0530] "Data transmission" refers to the process of transferring information from a data collection point to a processing unit.

[0531] "Notification" refers to information provided to passengers based on analysis results, and includes messages delivered via audio or visual means.

[0532] Encryption is a transformation technology used to ensure the security of information, and it is important for protecting information from unauthorized access.

[0533] "Emotional state" refers to a concrete expression of a passenger's psychological and physiological responses, and its analysis is used to improve passenger comfort.

[0534] This system aims to enhance passenger safety in autonomous vehicles by providing real-time information based on data from numerous detection devices installed within the vehicle. The invented program employs intelligent data processing methods based on cloud-based technologies such as Azure Cognitive Services.

[0535] The server collects information from detection devices placed inside and outside the vehicle, including video and audio data from cameras and microphones, and biometric information from heart rate sensors. The data processing unit then analyzes the collected information and uses a generative AI model to determine the passengers' emotional state in real time. Specifically, it detects whether passengers are experiencing anxiety or stress and generates optimal notifications based on that.

[0536] The terminal receives notifications from the server and provides information to passengers via a speech synthesis engine and display. Notifications include suggestions for music to help passengers relax and voice messages explaining the features of the route. This makes it possible to provide a comfortable travel experience that is tailored to the passengers' emotions.

[0537] As a concrete example, if a passenger in an autonomous vehicle tours a tourist destination feels anxious, they will receive a message via their device stating, "Please relax. You can see the sights on the right side of the window." This helps the passenger feel more at ease.

[0538] An example of a prompt statement is, "When passengers are inside an autonomous vehicle, how should their emotions be recognized, and what kind of refresh message should be generated?" Based on this prompt statement, the AI ​​model operates and provides a foundation for generating appropriate notifications.

[0539] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0540] Step 1:

[0541] The server collects situational data from detection devices installed in the vehicle. It receives video data from cameras, audio data from microphones, and biometric data from heart rate sensors as input. This data is processed as foundational information to understand the emotional state of passengers.

[0542] Step 2:

[0543] The server preprocesses the collected situational data. It handles video, audio, and biometric information as input data, and performs data processing such as noise reduction and format standardization. As output, it generates data converted into a format suitable for analysis.

[0544] Step 3:

[0545] The server inputs pre-processed data into a generating AI model to analyze the passengers' emotional states. This analysis predicts the passengers' emotions, such as whether they are anxious or relaxed, based on the input data. The output is an evaluation of their emotional states.

[0546] Step 4:

[0547] The server generates appropriate notifications based on the analysis results. Here, it considers the emotion ratings provided as input and creates audio messages containing relaxation messages and tourist information. The output consists of audio and text information for presentation to passengers.

[0548] Step 5:

[0549] The terminal outputs notifications received from the server as voice messages using a speech synthesis engine. If a display is present, it also displays visual messages. It receives notification information from the server as input and provides it to passengers in the most appropriate format.

[0550] Step 6:

[0551] Users can enjoy their journey with peace of mind by receiving audio and visual notifications from their devices. The expected outcome here is an improvement in passenger safety and satisfaction.

[0552] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0553] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0554] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0555] [Fourth Embodiment]

[0556] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0557] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0558] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0559] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0560] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0561] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0562] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0563] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0564] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0565] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0566] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0567] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0568] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0569] This invention relates to a driver assistance system for safely operating a vehicle. This system consists of various sensors and terminals mounted on the vehicle and a server connected via the internet. Specific embodiments of this system are described below.

[0570] The user's device collects various data from sensors installed in the vehicle. This includes GPS location information, vehicle speed from the speedometer, and surrounding video from cameras. The device is then prepared to transmit this data to a server.

[0571] The terminal ensures a stable network connection and transmits the collected data to the server. During this process, the data is properly encrypted, ensuring the security of the communication. The server processes the received data in real time and analyzes the operating status using a generated AI model.

[0572] Based on the analysis results, the server generates necessary alerts for the driver. For example, it creates warning messages that include information such as when the traffic light is about to turn red or when there are pedestrians or cyclists nearby. This allows drivers to receive important information at any time.

[0573] The generated warning message is sent back to the user's device. The device converts this from text to audio and transmits it to the driver through the car's speakers. By listening to the audio guidance, the driver can make appropriate decisions based on the surrounding situation.

[0574] A concrete example is when a driver approaches an intersection, the server detects a change in traffic light and provides a voice command to the terminal, such as, "The light will turn red in 5 seconds." In this way, the driver can always be aware of the current traffic situation and continue driving safely.

[0575] The system of this invention provides effective support to alleviate driver anxiety and enhance safety.

[0576] The following describes the processing flow.

[0577] Step 1:

[0578] The terminal collects data such as GPS location information, speed, and camera images from various sensors installed in the vehicle. This data provides a detailed reflection of the vehicle's operating status.

[0579] Step 2:

[0580] The terminal preprocesses the collected data according to various protocols. Preprocessing includes data format conversion, noise reduction, and necessary compression. This ensures optimal data transmission to the server.

[0581] Step 3:

[0582] The terminal sends the pre-processed data to the server via a secure communication protocol. The data is encrypted during this process, ensuring the security of the communication.

[0583] Step 4:

[0584] The server immediately analyzes the received data. Using a generative AI model, it extracts important information related to the driver and analyzes the current traffic situation in detail.

[0585] Step 5:

[0586] Based on the analysis results, the server generates necessary warning messages for the driver. For example, it identifies situations that require immediate action from the driver, such as "The traffic light is turning red" or "There are pedestrians near the intersection."

[0587] Step 6:

[0588] The server encodes the generated alert message and sends it to the user's terminal. Because the message requires immediacy, it is transmitted using the most optimal communication path.

[0589] Step 7:

[0590] The terminal receives messages from the server, converts them into speech using a speech synthesis engine, and transmits them to the driver through the vehicle's speakers. This allows the driver to receive important information without relying on their vision.

[0591] Step 8:

[0592] The user receives voice instructions and adjusts their driving behavior accordingly. This allows for quick responses to changes in traffic conditions, resulting in safer driving.

[0593] (Example 1)

[0594] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0595] When drivers operate a vehicle, it is crucial for them to make appropriate decisions based on traffic conditions and the surrounding environment. However, sudden environmental changes and information overload can cause drivers to miss necessary information. To solve this problem, there is a need to develop a system that provides drivers with relevant information in real time and reduces their mental burden.

[0596] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0597] In this invention, the server includes means for acquiring various data from multiple detectors installed in the vehicle, means for appropriately formatting and safely transmitting the acquired data to an information processing device, and means for processing the received data in real time at the information processing device, analyzing the driving situation using a generated AI model, and forming warnings. As a result, the driver can always obtain the latest and most important information, thereby improving safety.

[0598] A "detector" is a device installed in a vehicle to acquire various data in real time, and it has the function of understanding location information and the surrounding situation.

[0599] An "information processing device" is a computer that can receive acquired data and process it in real time. It has the function of analyzing driving conditions using a generated AI model and forming warnings.

[0600] A "generative AI model" is an artificial intelligence model used to analyze driving conditions based on collected data and generate information that should be provided to the driver.

[0601] "Warning" refers to warnings and advice provided to drivers, including information to encourage appropriate judgment depending on the driving situation.

[0602] "Encryption technology" is a means of protecting the security of data during communication. It is a technique that transforms data using a specific algorithm to prevent unauthorized access.

[0603] This invention relates to a support system for drivers to safely operate a vehicle. The system consists of a detector installed in the vehicle, a terminal for transmitting and receiving data, and an information processing device for processing and analyzing the data. A specific embodiment of this system is described below.

[0604] The user's device collects data from various sensors within the vehicle. This data includes location information from GPS sensors, speed from the speedometer, and surrounding images from cameras. This collected data is appropriately formatted for transmission to an information processing device and securely transferred using encryption technology. The device also plays a role in stabilizing the network connection.

[0605] The information processing device, which functions as a server, processes data received from terminals in real time. This utilizes a generative AI model, which analyzes driving conditions and generates necessary warnings for the driver. A specific example of the generative AI model's use is predicting changes in traffic signals at intersections and warning drivers accordingly.

[0606] The generated warnings are provided to the driver via a terminal. The terminal converts the warnings from text to audio and transmits them to the driver in real time through the in-car speaker. This function allows the driver to properly understand the traffic situation and make necessary decisions quickly.

[0607] As a concrete example, the user's device receives a warning message such as "The traffic light is turning red," and converts it into voice to communicate to the driver. An example of a prompt message for the generating AI model could be, "Based on the current vehicle speed and location information, please generate the next necessary warning."

[0608] In this way, the system aims to support drivers in driving safely and improve traffic safety.

[0609] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0610] Step 1:

[0611] The user's device collects data from sensors installed in the vehicle. Specifically, it acquires location information from GPS, speed data from the speedometer, and surrounding video from cameras. This data is necessary to understand the driving situation. The input for this step is raw data from the vehicle's sensors, and the output is a properly formatted dataset.

[0612] Step 2:

[0613] The terminal is prepared to transmit the collected data to the information processing device. The data is structured and secured using encryption technology. After confirming that the network connection is stable, preparations are made to send the data to the server. At this stage, the input is the raw data before processing, and the output is encrypted, transmittable data.

[0614] Step 3:

[0615] The server receives data transmitted from the terminal and processes it in real time. This processing includes analyzing driving conditions using a generative AI model. Specifically, it extracts and analyzes safety-related information from the vehicle's position, speed, and surrounding video footage. The input for this step is encrypted data, and the output is warning data as a result of the analysis.

[0616] Step 4:

[0617] The server generates driver alerts based on analysis results obtained using a generative AI model. It uses prompts to create messages containing useful information for the driver, such as "The traffic light is turning red." The input for this step is the analysis results, and the output is a warning message for the driver.

[0618] Step 5:

[0619] The terminal converts the warning message received from the server from text information into audio information. Furthermore, it transmits this information to the driver via the in-car speaker. By listening to this audio guidance, the driver can respond appropriately to the surrounding traffic conditions. The input for this step is a text message, and the output is an audio interface that the driver can understand.

[0620] (Application Example 1)

[0621] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0622] In autonomous vehicles, a key challenge is accurately recognizing the surrounding environment and providing safe and timely warnings to the occupants. This allows occupants to understand the vehicle's driving situation and use the vehicle with confidence.

[0623] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0624] In this invention, the server includes means for collecting data from multiple detectors mounted on the vehicle, means for transmitting the collected data to a database, means for analyzing the data collected in the database and generating warnings for the driver and passengers, and device means for providing the generated warnings in audio and visual formats. This makes it possible to provide occupants with real-time information about the surrounding environment and improve safety.

[0625] A "detector" is a device installed in a vehicle that collects information about the surrounding environment and the vehicle's condition.

[0626] A "database" is an information storage device used to store collected data and utilize it for subsequent analysis.

[0627] "Analysis" is an information processing process that uses collected data to evaluate the vehicle's driving conditions and surrounding environment, and to generate necessary warnings.

[0628] "Warning" refers to warning information provided to occupants, which should be communicated in audio or visual format depending on the driving situation and surrounding environment.

[0629] An "information display device" is a device or equipment used to communicate generated warnings to crew members in audio or visual format.

[0630] An "information processing device" is an electronic device used to analyze data and deliver the generated information appropriately to the crew.

[0631] The system realizing this invention is built for providing safety information in autonomous vehicles. The server has the function of collecting data in real time from multiple sensors installed in the vehicle and transmitting it to a database via the internet. The collected data is analyzed by a generative AI model hosted on the Google Cloud AI Platform. The analyzed information is generated as a warning for the driver and passengers and is provided in audio and visual formats.

[0632] The server interacts with the database to encrypt data and ensure secure communication. Specifically, it uses the SSL / TLS protocol to guarantee data security. The generated alerts are transmitted to the occupants via smart glasses or smartphone applications. This allows occupants to accurately perceive the surrounding environment of the autonomous vehicle, thereby enhancing safety.

[0633] As a concrete example, if the server detects the presence of a pedestrian at an intersection, it will issue a voice and visual warning via smart glasses stating, "A pedestrian is approaching the intersection." An example of a prompt to be input into the generating AI model is, "Identify hazardous elements from the current intersection situation and create a warning message." This will improve the safety of autonomous driving and provide peace of mind to the occupants.

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

[0635] Step 1:

[0636] The terminal collects data in real time from multiple sensors mounted on the vehicle. The input data includes speed, location information, and surrounding video data. The terminal temporarily stores this data and prepares it for transmission to the server.

[0637] Step 2:

[0638] The device transmits data to the server via the internet. The input is collected sensor data, and the output is a secure data stream to the server. Data transmission is encrypted using the SSL / TLS protocol, ensuring secure and efficient processing.

[0639] Step 3:

[0640] The server stores the received sensor data in a database and inputs it into a generating AI model for analysis. Based on the input data, it identifies multiple elements corresponding to the surrounding environment and vehicle status, and generates warning information as output. Google Cloud AI Platform is used for this analysis.

[0641] Step 4:

[0642] The server sends the generated alert information to the terminal. The input is alert data from the generating AI model, and the output is data converted into a format that can be displayed on the terminal. The server improves responsiveness by optimizing the transmitted data.

[0643] Step 5:

[0644] The device presents the received warning information to the user visually and audibly. Input is optimized data from the server, and output is a warning display on smart glasses or an in-car display. Specifically, the warning content is displayed on the visual display, and at the same time, voice guidance is provided through the speaker to inform the user of their surroundings.

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

[0646] This invention is a support system designed to make the driver's driving experience safer and more comfortable, and combines a group of sensors mounted on the vehicle, a user terminal, a server, and an emotion recognition engine. This system adapts its operation according to the driver's emotional state, providing more personalized driving assistance.

[0647] The terminal collects data from the vehicle's sensors, including the vehicle's GPS location, speed, and in-car camera footage. The sensors are also used to provide the driver's voice and facial expression data to the emotion recognition engine. The terminal preprocesses this data and sends it to the server.

[0648] Communication between the terminal and the server is highly encrypted, and security is ensured by using a secure protocol. The server receives this data and analyzes it using a generative AI model. In particular, it monitors and predicts changes in driving conditions in real time to generate optimal warnings.

[0649] In addition, the emotion recognition engine analyzes the driver's emotions based on their heart rate, tone of voice, and facial expressions. If the emotions indicate stress or anxiety, the server creates a corresponding warning message in a calm and soothing voice tone. Conversely, if the driver is calm, it creates instructions in a normal tone.

[0650] The generated message is sent to the terminal, which then presents it to the driver via a speech synthesis engine. For example, it might say, "Relax and continue driving. The traffic light will soon turn red," providing instructions tailored to the driver's current situation.

[0651] This system provides drivers with a sense of mental security while driving and simultaneously encourages their ability to respond quickly to changes in traffic conditions. For example, when approaching an intersection, if the driver is in an excited state, a message is provided encouraging them to calm down. This allows the driver to continue driving safely at their own pace. This invention provides advanced driving assistance that adapts to the driver's mental and physical state.

[0652] The following describes the processing flow.

[0653] Step 1:

[0654] The device collects environmental data from sensors mounted on the vehicle. This includes GPS location, vehicle speed, in-vehicle audio, and video from a facial recognition camera. The device then processes this data and converts it into a usable format.

[0655] Step 2:

[0656] The terminal extracts emotional information by analyzing the driver's voice tone and facial expressions using a facial recognition camera. The emotion recognition engine determines the degree of stress and relaxation and prepares to send this information, along with other driving data, to the server.

[0657] Step 3:

[0658] The device encrypts all collected data and transmits it to the server via a secure communication channel. This process is performed periodically, allowing the data to be updated in real time.

[0659] Step 4:

[0660] The server analyzes the received data. A generative AI model identifies the current driving situation and approaching hazards, and generates the necessary warnings for the driver. The server also analyzes emotional data and adjusts the message based on the driver's emotional state.

[0661] Step 5:

[0662] The server generates a more subdued warning message depending on the driver's emotional state (e.g., if they are under high stress) and sends it to the user's device. If the driver is calm, the normal tone is maintained.

[0663] Step 6:

[0664] The terminal receives messages from the server and performs speech synthesis. The voice message is played through the speaker, conveying necessary information to the driver. This allows the driver to obtain information without taking their eyes off the road.

[0665] Step 7:

[0666] The user accepts voice instructions provided by the device and adjusts their driving behavior accordingly. In this process, the driver can change their actions in response to warnings and take appropriate measures such as slowing down or stopping as needed.

[0667] (Example 2)

[0668] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0669] In today's driving environment, a driver's attention and mental state significantly impact safe driving. However, current technology lacks the means to effectively analyze a driver's real-time emotional state and provide individualized warnings. As a result, drivers continue to drive while feeling stressed or anxious, increasing the risk of traffic accidents.

[0670] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0671] In this invention, the server includes means for acquiring information from a number of detection devices mounted on the vehicle, means for sending the acquired information to a communication device, and means for analyzing the acquired information, evaluating the driver's emotional state, and generating warnings for the driver. This makes it possible to provide individualized warnings in real time according to the driver's emotional state, thereby realizing a safe and comfortable driving environment.

[0672] A "detection device" refers to devices such as sensors and cameras mounted on a vehicle, which are used to collect physical and environmental data.

[0673] "Information" refers to data acquired from the detection device, which includes digital data such as vehicle location information, speed, and the driver's voice and facial expressions.

[0674] A "communication device" is a device or system used to send and receive information between a terminal and a server, and it plays a role in encrypting data and securely transmitting information.

[0675] "Analysis" is the process of information processing that evaluates the driver's condition based on acquired information and determines the necessary actions.

[0676] "Driver's Attention" refers to voice messages or visual alerts generated by the server, which are instructions or warnings to encourage safe driving.

[0677] "Emotional state" refers to the psychological or mental state inferred from the driver's heart rate, voice tone, and facial expression data, and is a concept that includes stress, anxiety, calmness, etc.

[0678] This invention is a support system designed to make the driver's driving experience safer and more comfortable. It combines a vehicle-mounted detection device, a user terminal, a communication device, and an emotion recognition engine. The system adapts its operation according to the driver's emotional state, providing individually optimized driving assistance.

[0679] terminal

[0680] The terminal collects information provided by the vehicle's detection system. Specific examples include GPS location information, vehicle speed, in-car camera footage, and driver voice and facial expression data. The terminal preprocesses and encrypts this data before transmitting it to the server via a communication device.

[0681] server

[0682] The server analyzes data received from the communication device using a generating AI model. This analysis process assesses the driver's emotional state through their heart rate, tone of voice, and facial expressions, and generates appropriate warnings. If the emotion indicates stress or anxiety, a message with a calming voice tone is created. The software used by the server's emotion recognition engine includes state-of-the-art voice and video analysis algorithms.

[0683] User

[0684] Messages sent to the user's terminal are delivered to the driver in real time via a speech synthesis engine. For example, if the driver is feeling stressed, instructions such as "Relax and continue driving. The traffic light will soon turn red" are presented as voice commands. In this way, providing feedback based on the driver's emotional state helps to support their mental well-being while driving.

[0685] A specific example of a prompt message is the instruction, "Generate a warning message for when the heart rate is high as the driver approaches an intersection," which will provide appropriate warnings to the driver.

[0686] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0687] Step 1:

[0688] The terminal collects information from detection devices installed in the vehicle. Specifically, it receives GPS location information, speed data, interior camera footage, audio, and facial expression data. The input is the raw data obtained from the detection devices, and collecting this data is the first step. The output is this dataset.

[0689] Step 2:

[0690] The terminal preprocesses the collected data. Specific operations include denoising, data standardization, and normalization. This data processing allows the algorithm to analyze the data more accurately. The input is the raw data obtained in step 1, and the output is the preprocessed data.

[0691] Step 3:

[0692] The terminal encrypts the pre-processed data and sends it to the server using a secure communication protocol, thereby ensuring the security of the information. The input is pre-processed data, and the output is encrypted data sent to the server.

[0693] Step 4:

[0694] The server decrypts the encrypted data received from the terminal and analyzes the data using a generative AI model. This analysis process evaluates the driver's emotional state from their heart rate, voice tone, and facial expression data. The input is encrypted data sent from the terminal, and the output is the analyzed information and the evaluation of the emotional state.

[0695] Step 5:

[0696] The server generates a warning message for the driver based on the analysis results. The generating AI model uses prompts to create the most appropriate message. For example, if the driver is stressed, a message including "Please relax and continue driving" will be generated. The input is the analysis results from step 4, and the output is the warning message.

[0697] Step 6:

[0698] The server sends the generated message to the terminal. The terminal uses a speech synthesis engine to present the message to the driver in voice format. This allows the driver to receive feedback in real time. The input is message data from the server, and the output is voice feedback to the driver.

[0699] (Application Example 2)

[0700] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0701] In autonomous vehicles, providing passengers with appropriate information tailored to their emotional state is essential to ensuring they can travel with peace of mind. However, current technology faces the challenge of being unable to analyze passengers' emotions in real time and provide optimal notifications based on that analysis.

[0702] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0703] In this invention, the server includes means for collecting information from a number of detection devices installed in the vehicle, means for transmitting the collected information to a data processing device, and means for analyzing the information collected by the data processing device and generating notifications for passengers. This makes it possible to provide real-time information that corresponds to the emotional state of passengers.

[0704] The term "vehicle" refers to any mechanical device used as a means of transportation, and especially to those with autonomous driving capabilities.

[0705] A "detection device" is equipment positioned to acquire information from inside and outside a vehicle, and includes sensors such as cameras, microphones, and heart rate monitors.

[0706] "Situation" refers to the collective data indicating the vehicle's operating status and the passengers' emotional state.

[0707] A "data processing device" refers to a computer system and its functions used to analyze and process collected information.

[0708] "Data transmission" refers to the process of transferring information from a data collection point to a processing unit.

[0709] "Notification" refers to information provided to passengers based on analysis results, and includes messages delivered via audio or visual means.

[0710] Encryption is a transformation technology used to ensure the security of information, and it is important for protecting information from unauthorized access.

[0711] "Emotional state" refers to a concrete expression of a passenger's psychological and physiological responses, and its analysis is used to improve passenger comfort.

[0712] This system aims to enhance passenger safety in autonomous vehicles by providing real-time information based on data from numerous detection devices installed within the vehicle. The invented program employs intelligent data processing methods based on cloud-based technologies such as Azure Cognitive Services.

[0713] The server collects information from detection devices placed inside and outside the vehicle, including video and audio data from cameras and microphones, and biometric information from heart rate sensors. The data processing unit then analyzes the collected information and uses a generative AI model to determine the passengers' emotional state in real time. Specifically, it detects whether passengers are experiencing anxiety or stress and generates optimal notifications based on that.

[0714] The terminal receives notifications from the server and provides information to passengers via a speech synthesis engine and display. Notifications include suggestions for music to help passengers relax and voice messages explaining the features of the route. This makes it possible to provide a comfortable travel experience that is tailored to the passengers' emotions.

[0715] As a concrete example, if a passenger in an autonomous vehicle touring a tourist destination feels anxious, they will receive a message via their device saying, "Please relax. You can see the famous landmarks on the right side of the window." This helps the passenger feel more at ease.

[0716] An example of a prompt statement is, "When passengers are inside an autonomous vehicle, how should their emotions be recognized, and what kind of refresh message should be generated?" Based on this prompt statement, the AI ​​model operates and provides a foundation for generating appropriate notifications.

[0717] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0718] Step 1:

[0719] The server collects situational data from detection devices installed in the vehicle. It receives video data from cameras, audio data from microphones, and biometric data from heart rate sensors as input. This data is processed as foundational information to understand the emotional state of passengers.

[0720] Step 2:

[0721] The server preprocesses the collected situational data. It handles video, audio, and biometric information as input data, and performs data processing such as noise reduction and format standardization. As output, it generates data converted into a format suitable for analysis.

[0722] Step 3:

[0723] The server inputs pre-processed data into a generating AI model to analyze the passengers' emotional states. This analysis predicts the passengers' emotions, such as whether they are anxious or relaxed, based on the input data. The output is an evaluation of their emotional states.

[0724] Step 4:

[0725] The server generates appropriate notifications based on the analysis results. Here, it considers the emotion ratings provided as input and creates audio messages containing relaxation messages and tourist information. The output consists of audio and text information for presentation to passengers.

[0726] Step 5:

[0727] The terminal outputs notifications received from the server as voice messages using a speech synthesis engine. If a display is present, it also displays visual messages. It receives notification information from the server as input and provides it to passengers in the most appropriate format.

[0728] Step 6:

[0729] Users can enjoy their journey with peace of mind by receiving audio and visual notifications from their devices. The expected outcome here is an improvement in passenger safety and satisfaction.

[0730] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0731] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0732] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0733] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0734] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0735] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0736] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0737] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0738] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0739] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0740] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0741] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0742] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0743] 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.

[0744] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0745] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0746] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0747] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0748] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0749] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0750] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0751] The following is further disclosed regarding the embodiments described above.

[0752] (Claim 1)

[0753] A means of collecting information from multiple sensors mounted on the vehicle,

[0754] A means of sending the collected information to the server,

[0755] A means of analyzing information collected on a server and generating driver warnings,

[0756] A means of providing the generated warning to the driver in audio format,

[0757] A system that includes this.

[0758] (Claim 2)

[0759] The system according to claim 1, which optimizes the generated warning in real time based on the driver's current location and speed information.

[0760] (Claim 3)

[0761] The system according to claim 1, which encrypts and transmits data in order to securely transmit data between a server and a terminal.

[0762] "Example 1"

[0763] (Claim 1)

[0764] A means of acquiring various data from multiple detectors installed on a vehicle,

[0765] In order to transmit the acquired data to an information processing device, a means is provided to properly format the data and transmit it securely.

[0766] A means of processing data received by an information processing device in real time, analyzing the driving situation using a generated AI model, and forming a warning,

[0767] A means of transmitting the generated warning to the driver as auditory information,

[0768] A system that includes this.

[0769] (Claim 2)

[0770] The system according to claim 1, which optimizes the generated warnings in real time based on the driver's current location and speed information, thereby prompting the driver to make appropriate decisions in response to the situation.

[0771] (Claim 3)

[0772] The system according to claim 1, which uses encryption technology to protect data communication in order to securely transmit data between an information processing device and a terminal.

[0773] "Application Example 1"

[0774] (Claim 1)

[0775] A means of collecting data from multiple detectors mounted on a vehicle,

[0776] A means of sending the collected data to a database,

[0777] A means of analyzing data collected in a database and generating warnings for drivers and passengers,

[0778] A device means that provides the generated alert in audio and visual formats,

[0779] A system that includes this.

[0780] (Claim 2)

[0781] The system according to claim 1, which optimizes the generated alert in real time based on the occupant's current position and speed and provides it via an information display device.

[0782] (Claim 3)

[0783] The system according to claim 1, which encrypts and transmits information in order to securely transmit data between a database and an information processing device.

[0784] "Example 2 of combining an emotion engine"

[0785] (Claim 1)

[0786] A means of acquiring information from numerous detection devices mounted on a vehicle,

[0787] A means of sending the acquired information to a communication device,

[0788] A means of analyzing information acquired by a communication device and generating warnings for the driver,

[0789] A means of presenting the generated warning to the driver in audio format,

[0790] A means of analyzing the driver's emotional state and generating attention corresponding to that emotion,

[0791] A system that includes this.

[0792] (Claim 2)

[0793] The system according to claim 1, which optimizes the generated attention in real time based on the driver's location information and speed information.

[0794] (Claim 3)

[0795] The system according to claim 1, which encrypts and transmits information in order to securely transmit information between a communication device and a terminal.

[0796] "Application example 2 when combining with an emotional engine"

[0797] (Claim 1)

[0798] A means of collecting information from numerous detection devices installed in the vehicle,

[0799] A means for transmitting the collected information to a data processing device,

[0800] A means for analyzing the situation collected by a data processing device and generating notifications for passengers,

[0801] Means for providing the generated notification to passengers in audio and visual formats,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, which instantly optimizes generated notifications based on the passenger's emotional state and vehicle operation information.

[0805] (Claim 3)

[0806] The system according to claim 1, which encrypts and transmits information in order to securely transmit information between a data processing device and a terminal. [Explanation of Symbols]

[0807] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting information from multiple sensors mounted on the vehicle, A means of sending the collected information to the server, A means of analyzing information collected on a server and generating driver warnings, A means of providing the generated warning to the driver in audio format, A system that includes this.

2. The system according to claim 1, which optimizes the generated warning in real time based on the driver's current location and speed information.

3. The system according to claim 1, which encrypts and transmits data in order to securely transmit data between a server and a terminal.

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