system

Abstract

With the spread of autonomous driving technology, we provide a monitoring system that can monitor traffic conditions in real time and respond quickly in order to ensure safe and smooth traffic conditions. [Solution] A system comprising: means for monitoring traffic conditions by collecting and integrating data from vehicles; means for analyzing and predicting aggregated data in real time using a generative model; means for issuing commands to a traffic control system based on the predicted results to maintain traffic safety; and means for receiving feedback after system intervention and performing analysis to make improvements.

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, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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] [With the spread of autonomous driving technology, the risk of accidents caused by system failures and unexpected operations is becoming serious. In order to reduce such risks and realize a safe and smooth traffic situation, a monitoring system that can monitor the traffic situation in real time and respond quickly is required. However, there are still not enough systems that can provide optimal control considering the traffic characteristics of a specific area. ]

Means for Solving the Problems

[0005] [This invention provides a system that collects and integrates data from vehicles, analyzes and predicts in real time using a generative model, monitors traffic conditions, and provides appropriate commands to the traffic control system. Furthermore, it has a function to analyze feedback after intervention and continuously improve, enabling control optimized for region-specific traffic patterns, thereby preventing the risk of accidents and congestion.]

[0006] A "vehicle" is [a type of automatically moving machine designed to travel on roads, typically used to transport people or goods].

[0007] "Traffic conditions" refers to the movement, density, and flow of vehicles on roads in a specific area, including traffic volume, congestion, and the presence or absence of accidents.

[0008] A "generative model" is an algorithm or system that learns from large amounts of data and uses that data to make predictions or generate new data.

[0009] "Real-time" refers to a state where data processing and responses occur instantly, with virtually no time delay.

[0010] "Monitoring" is the act of observing a specific object and recording or analyzing its state in order to prevent problems from occurring.

[0011] "Command" refers to [the act or content of giving an instruction to perform a specific action].

[0012] A "traffic control system" is [a collection of technologies or equipment, including traffic signals and traffic guidance systems, that manage the flow of vehicles on roads and ensure safe and efficient traffic].

[0013] "Feedback" is the process of using the results and reactions obtained from a specific action as input again to make further improvements and adjustments.

[0014] "Optimization" refers to the act of [designing or adjusting a system or process to make it as efficient and effective as possible].

[0015] "Prediction" refers to the act of [judging future events or states in advance based on past data or the current state].

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 a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of 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 Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

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

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

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

[0020] In the following embodiments, the numbered 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, the numbered 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 disks (e.g., hard disks), or magnetic tapes, 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, 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 provides a real-time monitoring and control system for optimally operating autonomous driving technology while ensuring traffic safety. The program processing of this system is described below.

[0038] Server-side processing

[0039] The server aggregates traffic information from multiple data sources, including vehicle GPS data, status information from traffic lights, and data from satellite and ground sensors. The collected data is analyzed using generative models to predict traffic conditions and accident potential in real time. Based on these predictions, the server determines necessary interventions. For example, if congestion is predicted near a highway exit, the server adjusts the timing of traffic light control to smooth traffic flow.

[0040] Terminal (vehicle side) processing

[0041] The terminal acquires environmental information in real time from sensors mounted on the vehicle and transmits it to the server. It also receives instructions from the server and controls the vehicle's operation based on these instructions. For example, if it receives an instruction to slow down at a specific intersection, the terminal automatically adjusts the vehicle's speed to maintain safe driving.

[0042] User roles

[0043] Users monitor the system's behavior while driving the vehicle and record any abnormal situations. This helps identify operational problems and contributes to future system improvements.

[0044] This system utilizes models specifically tailored to the traffic conditions of a particular region, enabling optimal control that takes into account region-specific traffic patterns. For example, in urban centers, it can manage peak traffic flow and minimize risks. In this way, it reduces accident risk and ensures smooth traffic flow.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The server receives vehicle GPS data, traffic signal status, and satellite data, integrates them, and stores them in a database.

[0048] Step 2:

[0049] The server inputs stored data into a generative model and analyzes traffic conditions in real time. This analysis predicts the occurrence of traffic congestion and the likelihood of accidents.

[0050] Step 3:

[0051] Based on the analysis results, the server determines the necessary interventions and generates commands to adjust the timing of traffic signal control or direct instructions to vehicles.

[0052] Step 4:

[0053] The terminal adjusts the vehicle's operation based on instructions received from the server. Specifically, it performs actions such as driving at a predetermined speed and selecting a specific route.

[0054] Step 5:

[0055] The user monitors the system's operation and checks for any abnormalities in the vehicle's behavior. If an abnormality occurs, the user records the details and reports them to the server.

[0056] Step 6:

[0057] The server improves the system and enhances the accuracy of the generative model based on user feedback and new data.

[0058] (Example 1)

[0059] 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."

[0060] [Due to the increase in traffic volume accompanying urbanization in recent years, the frequency of traffic congestion and accidents has increased. Against this backdrop, it is necessary to manage traffic flow more efficiently and safely. Conventional traffic systems struggle to grasp the situation in real time and respond quickly, and they do not adequately consider the traffic characteristics of each region. Therefore, a new method is needed to optimize traffic conditions in real time by utilizing vehicle and traffic signal data.]

[0061] 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.

[0062] In this invention, the server includes means for aggregating diverse environmental data, including location information and traffic signal status information acquired from vehicles, to monitor traffic conditions; means for analyzing the aggregated data at high speed using a generation AI model to predict traffic flow and congestion in real time; and means for dynamically controlling the signal system based on the predictions and issuing commands to enhance traffic safety. This makes it possible to optimize traffic flow in real time, prevent accidents and congestion, and achieve safe and efficient traffic management.

[0063] "Location information acquired from the vehicle" refers to data generated by the vehicle's onboard positioning system to determine the vehicle's geographical location.

[0064] "Traffic light status information" refers to information that indicates the current operating status of a traffic signal (e.g., red light, green light, yellow light, etc.).

[0065] "Environmental data" refers to data that includes information about events around a vehicle, such as location information, traffic light status, weather information, and road conditions.

[0066] A "generative AI model" is a program that uses artificial intelligence technology to analyze data and predict future situations.

[0067] "Traffic flow" is a concept that describes the patterns, speeds, and densities of vehicle and pedestrian movement within a specific region or area.

[0068] "Traffic congestion forecasting" is a process that uses collected data to predict potential traffic congestion in the future.

[0069] "Dynamic control of the signal system" refers to appropriately changing the timing of traffic light operation based on real-time data and predictions.

[0070] "Traffic safety" means maintaining a state in the transportation system where accidents and dangers are minimized, and people and goods can safely reach their destinations.

[0071] The system according to this invention mainly consists of a server, a terminal, and a user.

[0072] The server utilizes diverse hardware and software to aggregate environmental data, including location information and traffic signal status information acquired from vehicles. Specifically, it uses a database system to efficiently retrieve and store this information. On the server, a generative AI model is used to analyze the aggregated data. This model is used to evaluate traffic flow in real time and predict future congestion. For example, a generative AI model built using machine learning libraries can rapidly analyze the acquired data and generate predictions.

[0073] The terminal acquires environmental data in real time through various sensors mounted on the vehicle. This includes cameras, LiDAR, radar, etc. This data is processed by a computer within the terminal and transmitted to a server. It also receives commands from the server and plays a role in controlling the vehicle's operation. This control uses the vehicle's control system to maintain safe and efficient driving.

[0074] Users are obligated to monitor the system's behavior while driving and record any abnormalities that occur. Real-time traffic information and system status can be viewed via smartphones or in-car displays. This allows users to respond quickly to problems, maintain records, and provide feedback.

[0075] As a concrete example, when optimizing traffic flow in the city center, the server issues commands to adjust the timing of traffic signal control. This allows for the management of peak traffic flow and the prevention of congestion.

[0076] An example of a prompt might be, "Suggest a way to optimize traffic signal control based on peak traffic conditions in city A." This prompt functions as a question to a generative AI model, seeking methods to provide appropriate solutions to a specific traffic problem.

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

[0078] Step 1:

[0079] The server receives environmental data, including location information and traffic signal status information acquired from vehicles. This data is input directly from GPS devices and signal control interfaces. The input data is stored in a database system and prepared for further analysis.

[0080] Step 2:

[0081] The server takes in accumulated environmental data and supplies it to the AI ​​model. The AI ​​model uses the input data to analyze traffic flow and the likelihood of congestion. In this process, machine learning algorithms are used to extract correlations and patterns from the data. The output is real-time traffic prediction results.

[0082] Step 3:

[0083] The server generates control commands for the signal system based on prediction results obtained from the generated AI model. By dynamically adjusting the timing of the traffic signals, it optimizes traffic flow. These commands are transmitted to each traffic signal via a communication protocol.

[0084] Step 4:

[0085] The terminal collects real-time environmental data from various sensors within the vehicle and transmits it to the server. Simultaneously, it receives control commands from the server and transmits them to the vehicle's control system. This enables specific vehicle actions (such as speed adjustment or lane changes).

[0086] Step 5:

[0087] While driving, users monitor system control commands and traffic forecast information via the in-vehicle display. They record abnormal situations as needed and take manual action when problems occur. This provides feedback for further system improvements.

[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 vehicle driving technology, understanding traffic conditions in real time and performing appropriate control is extremely difficult. To solve this problem, a system is needed that efficiently predicts traffic information and ensures safe operation. Furthermore, if information is not provided to the driver, it may delay emergency response, which is also a crucial issue that needs to be addressed.

[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 monitoring road conditions by collecting and integrating information from vehicles; means for analyzing and predicting the aggregated information in real time using a generated AI model; and means for visually presenting traffic conditions and predicted information through an information display terminal. This allows drivers to immediately grasp traffic information and supports safe driving.

[0093] "Information from the vehicle" refers to data collected through sensors and communication devices installed on the vehicle, including information on location, speed, acceleration, and the surrounding environment.

[0094] "Integration" refers to the process of centrally combining data collected from multiple sources and converting it into a consistent format.

[0095] "Road condition monitoring" means understanding road conditions in real time, such as traffic flow, traffic signal status, road congestion, and the presence or absence of obstacles.

[0096] A "generative AI model" is an algorithm that predicts future states by analyzing large amounts of data and learning patterns.

[0097] "Real-time analysis and prediction" means rapidly processing data in real time and estimating future situations.

[0098] "Issuing commands to traffic controllers" means instructing traffic signals and vehicle operating systems to perform actions according to the situation.

[0099] "Road traffic safety" means preventing traffic accidents from occurring and maintaining a state where traffic flows smoothly and safely.

[0100] An "information display terminal" is a device designed to convey information to users in an easy-to-understand manner, and is used to visually display traffic conditions and forecast information.

[0101] "Visually presenting traffic conditions and forecast information" means displaying current traffic conditions and future forecasts in a format that drivers and users can intuitively understand.

[0102] "Receiving feedback and performing analysis to improve" means evaluating the results of interventions performed by the system and analyzing those results in detail to further improve performance.

[0103] The system used to realize this application supports real-time traffic control in autonomous vehicles. The system integrates input data from sensors mounted on the vehicle and performs analysis and prediction using an AI model generated by a server. This enables rapid response to changes in road conditions.

[0104] The server aggregates vehicle location information, speed, and traffic signal information obtained from satellite data and ground sensors. This includes data from GPS and sensors installed on each vehicle. The aggregated data is analyzed using a generative AI model (e.g., TENSORFLOW®) to calculate traffic flow, congestion predictions, and accident probabilities in real time.

[0105] In particular, the server maintains safe traffic conditions by issuing commands to traffic controllers, such as controlling traffic signals and giving instructions to vehicles, based on these analysis results. In addition to explicit commands, the prediction results are also visually presented to the driver. Information display terminals (e.g., smart glasses or head-mounted displays) play this role, creating a system where users can easily obtain and confirm information.

[0106] For example, when the server predicts a traffic congestion at an intersection, the smart glasses will display the message, "Longer-than-usual traffic light wait times are expected at the intersection. We recommend taking an alternative route." This information is updated in real time, and recommendations based on a generated AI model are continuously produced.

[0107] An example of a prompt message to the generative model would be, "Display the traffic forecast for the next intersection on the screen. The display should include the predicted waiting time and recommended detour route." The server then sends the appropriate data to an external system to assist the user.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The server collects and integrates information from satellite data, ground sensors, and vehicle sensors. This information includes vehicle location, speed, and traffic signal status. The integrated information forms the basis for understanding the overall traffic situation.

[0111] Step 2:

[0112] The server uses a generative AI model to analyze and predict the integrated data. This analysis predicts current traffic flow, congestion trends, and accident likelihood. Based on the input data, the model applies learned traffic patterns to estimate future situations. The output includes locations and times where specific traffic control measures are required.

[0113] Step 3:

[0114] Based on the analysis results, the server issues commands to traffic controllers. These commands include things like the timing of traffic signal changes and speed adjustments for specific vehicles. This helps to alleviate congestion and promote safer traffic flow.

[0115] Step 4:

[0116] The server sends the analysis results and generated commands to the information display terminal. This information is structured as a prompt message and sent in a format that the terminal can display. For example, it might be in the format of, "An increase in traffic light waiting time at intersections is predicted. We recommend taking an alternative route."

[0117] Step 5:

[0118] The terminal displays visual information to the driver through an information display device (e.g., smart glasses). Based on this information, the driver can consider actions such as changing the route or adjusting their speed. By providing this information, the driver is supported in driving more safely and efficiently.

[0119] Step 6:

[0120] Users drive based on the information provided and record any abnormalities they detect. This record is sent to the server as feedback. This feedback is used to improve the system and contribute to improving the accuracy of future traffic predictions.

[0121] 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.

[0122] This invention provides a system that enhances traffic safety by considering not only vehicle operation but also the user's emotional state. The program processing of this system is described below.

[0123] Server-side processing

[0124] The server integrates user sentiment data provided by the sentiment engine with conventional traffic data. The server inputs this composite dataset into a generative model to analyze traffic conditions in real time. This allows for the prediction of safety risks on the road and the sending of appropriate commands to the traffic control system. For example, if a user's stress level is high, the server can issue commands to tighten vehicle speed limits and increase intervention by driver assistance systems.

[0125] Terminal (vehicle side) processing

[0126] The terminal not only adjusts vehicle operation according to instructions received from the server, but also acquires user emotion data from sensors inside the vehicle and transmits it to the server. Specifically, the terminal analyzes the user's tone of voice and facial expressions to estimate their emotional state in real time. This allows the terminal to change the driving mode according to the user's current emotions.

[0127] User roles

[0128] Users must ensure that the emotional engine accurately recognizes their emotional state by optimizing the in-vehicle conditions. For example, clear voice instructions and ensuring their face is clearly visible to the camera will allow the system to analyze emotions more accurately. They are also expected to provide feedback on the driving assistance and interventions the system offers.

[0129] This system further improves driving safety and comfort by considering the user's emotional state when controlling traffic. For example, if fatigue accumulates during long drives, the system can prevent excessive fatigue by enhancing driver assistance functions. Such an integrated system makes it possible to simultaneously improve vehicle operation safety and user peace of mind.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The server receives various data, including vehicle GPS data, traffic signal status, and user sentiment data, and integrates it into a database.

[0133] Step 2:

[0134] The device uses cameras and microphones inside the vehicle to analyze the user's facial expressions and voice tone in real time and estimate the user's emotional state. This estimation result is then sent to a server.

[0135] Step 3:

[0136] The server inputs integrated data into a generative model, analyzing traffic conditions and user emotional states in real time. This allows it to predict potential risks.

[0137] Step 4:

[0138] Based on the analysis results, the server issues commands to the traffic control system, such as adjusting signal timings or limiting vehicle speeds. Furthermore, if it determines that user stress levels are high, it enhances driver assistance.

[0139] Step 5:

[0140] The terminal receives commands from the server and controls the vehicle's movements. At intersections where concessions are required, it automatically slows down and activates the driver assistance system.

[0141] Step 6:

[0142] The user reviews the driving assistance provided by the system and provides feedback to the system as needed. For example, if the user feels the assistance is inappropriate, they record this information and report it to the server.

[0143] Step 7:

[0144] The server collects user feedback and adjusts and improves the generative model algorithm to help improve future models.

[0145] (Example 2)

[0146] 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".

[0147] In modern transportation systems, the impact of drivers' emotional states on traffic safety is often overlooked. Therefore, there is a need for systems that effectively mitigate human errors caused by driver emotional stress and fatigue, and the resulting risk of traffic accidents. Conventional traffic control systems only consider environmental factors and fail to reflect the driver's internal factors; therefore, new methods are needed to improve this.

[0148] 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.

[0149] In this invention, the server includes means for monitoring traffic conditions by collecting and integrating data from vehicles and emotional data; means for analyzing the aggregated data in real time using a generative model and making predictions that take emotional states into account; and means for issuing commands to the vehicle control system based on the predicted results to maintain traffic safety in accordance with emotional states. This enables safe and efficient traffic control by reflecting the emotional state of drivers in real time.

[0150] "Data" refers to a variety of information, including vehicle operation information and user emotional states, which the system uses to analyze traffic conditions.

[0151] "Emotional data" refers to information indicating the driver's emotional state, estimated based on the tone of their voice and facial expressions, and is used in traffic control to ensure safe driving.

[0152] A "generative model" refers to an algorithm used to analyze large amounts of data and predict traffic conditions, and is particularly characterized by its consideration of sentiment data.

[0153] A "traffic control system" refers to a set of technical means used to regulate vehicle operation and maintain road safety, and which function based on commands from a server.

[0154] A "command" refers to a specific operational instruction issued by the server based on the results of its analysis of traffic conditions, which controls the operation of vehicles and driver assistance systems.

[0155] "Feedback" refers to the driver's reaction and opinion to the system's intervention, and is used to improve the system in the future.

[0156] "Intervention" refers to actions taken by the system to influence vehicle operation in response to the driver's emotional state or traffic conditions, and is implemented to improve safety.

[0157] The system of this invention consists of three components: a vehicle terminal, a central server, and the driver. The terminal is installed inside the vehicle and uses sensors such as cameras and microphones to collect emotional data from the driver. This includes a process of analyzing voice tone and facial expressions in real time. This data is then transmitted to the server via secure communication.

[0158] The server integrates emotional data received from terminals with various traffic-related data (e.g., traffic volume, weather, road conditions, etc.). This generates a complex dataset that includes emotional states. The server has a generative AI model implemented, which is designed to analyze the data in real time and predict traffic conditions. Based on this analysis, the server generates commands for the traffic control system. These commands are adjusted according to the driver's emotional state; for example, if high stress is detected, measures such as tightening speed limits are taken.

[0159] Furthermore, users play a crucial role in improving the system's accuracy. Specifically, providing clear voice commands and ensuring their faces are clearly visible to the camera improves the accuracy of emotion data. In addition, providing feedback on the driving assistance and system interventions offered contributes to the system's continuous improvement.

[0160] For example, if a user's stress level increases during prolonged driving, the system will enhance driver assistance functions to reduce user fatigue. An example of this prompt would be, "Explain how user emotional data influences traffic control."

[0161] Such a system is expected to enable traffic control that reflects the driver's emotional state in real time, thereby improving safety and comfort.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The device collects user emotional data using sensors such as cameras and microphones mounted on the vehicle. Specifically, it captures the user's face with a camera and estimates their emotional state in real time using facial expression analysis software. It also analyzes the user's voice tone using a microphone and measures their stress level. It receives video and audio data as input and generates an estimated result of the emotional state as output.

[0165] Step 2:

[0166] The device sends the collected emotional data to the server. The specific input is digital data indicating the user's emotional state, and this data is securely transmitted to the server using data encryption technology. The output after transmission is the server receiving this data.

[0167] Step 3:

[0168] The server integrates sentiment data received from terminals with conventional traffic data. Specifically, it receives traffic volume, weather information, and road condition data obtained from traffic sensors as input, and combines this with the received sentiment data. Through data processing and calculations, it uses this information to create an integrated dataset. The output is the integrated set of data.

[0169] Step 4:

[0170] The server analyzes the integrated dataset using a generative AI model and makes real-time predictions based on traffic conditions and user sentiment. The input is the previously integrated dataset, and the output is the predicted traffic risk and countermeasures. The specific data calculations are performed using AI modeling techniques to calculate a risk score and determine the necessary traffic control actions.

[0171] Step 5:

[0172] Based on the analysis results, the server issues commands to the vehicle control system. The input is the predicted risks and countermeasures, and the output is the specific control command. Specific actions include speed adjustments and enhanced intervention of driver assistance systems to ensure the vehicle complies with the command.

[0173] Step 6:

[0174] The user experiences the vehicle's behavior in response to commands from the terminal and server, and provides feedback to the system. The input in this step is the user's perception and opinions regarding the vehicle's behavior, while the output is the feedback data sent to the system. Specifically, the user evaluates the effectiveness of the driving assistance and suggests improvements.

[0175] (Application Example 2)

[0176] 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".

[0177] Current transportation systems focus on safety measures based on vehicle operation, but they cannot take into account the emotional state of the user. Therefore, there is a challenge in adequately mitigating the risk of accidents caused by emotional factors such as driver stress and fatigue. Furthermore, current systems also struggle to implement appropriate control tailored to local traffic patterns.

[0178] 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.

[0179] In this invention, the server includes means for monitoring traffic conditions by collecting and integrating data from vehicles and user emotional state information; means for analyzing and predicting the aggregated data and emotional state information in real time using a generative model; and means for issuing commands to the traffic control system and the vehicle's driving mode based on the predicted results to maintain traffic safety and comfort. This enables appropriate traffic control in accordance with the user's emotional state, thereby improving traffic safety and comfort.

[0180] "Data from the vehicle" refers to various types of data collected from the vehicle's sensors and communication equipment, including location information, speed, and acceleration.

[0181] "User emotional state information" refers to information indicating emotional states such as stress, excitement, and fatigue, obtained by analyzing the user's voice tone and facial expression data.

[0182] "Monitoring traffic conditions through integration" means combining and analyzing collected vehicle data and emotional state information to understand the current traffic situation.

[0183] A "generative model" is a machine learning or AI algorithm used to analyze traffic conditions and emotional states based on collected data and to predict future states.

[0184] "Analyzing and predicting in real time" is a process of analyzing data as soon as it is collected and quickly predicting future traffic risks.

[0185] "Issuing commands to traffic control systems and vehicle driving modes to maintain traffic safety and comfort" means issuing appropriate commands such as driving assistance and speed adjustments based on predictions to ensure road safety and passenger comfort.

[0186] "Receiving feedback and making improvements" is a process of learning from the effects of system interventions and user responses in order to improve the quality of future control.

[0187] The system implementing this invention aggregates data from vehicles and user emotional state information to improve traffic safety and comfort. The server, terminals, and users play key roles.

[0188] The server integrates data such as location, speed, and acceleration collected from vehicle sensors and communication devices, as well as emotional state information analyzed from the user's voice tone and facial expressions. This information is processed in real time using emotion analysis tools such as Microsoft® Azure® Cognitive Services. Next, a generative AI model is used to analyze this combined data and predict safety risks and traffic congestion. Based on these predictions, the server commands the traffic control system and changes the vehicle's driving mode.

[0189] The terminal (smartphone or in-vehicle device) receives commands from the server and adjusts the vehicle's driving mode. Furthermore, the terminal also plays a role in acquiring the user's emotional state in real time and transmitting it to the server. This supports safe and comfortable driving. The system utilizes the smartphone's camera and microphone to perform facial recognition and voice analysis to recognize the user's emotional state.

[0190] To make the most of this system, users need to properly position their smartphones so that their faces are clearly visible to the camera. They are also expected to provide feedback on the driving assistance provided by the system, contributing to its further improvement. For example, if user fatigue is detected due to prolonged driving, the server will recommend that the driver take a break via the terminal, thereby enhancing the vehicle's driving assistance functions.

[0191] An example of a prompt that fits the generating AI model is, "Based on emotion analysis data, create an action plan to monitor the user's fatigue in real time during long drives and change to an appropriate driving mode." This allows the system to generate appropriate commands that respond to the user's emotions.

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The server receives sensor data such as location, speed, and acceleration transmitted from the vehicle. In addition, it obtains user emotional state information from the terminal. Input data includes vehicle telematics data and user voice tone and facial expression analysis results. The server integrates this data and records it in a database. The output is the integrated dataset.

[0195] Step 2:

[0196] The server uses a generative AI model to analyze and predict traffic conditions and user sentiment in real time, taking an integrated dataset as input. Sentiment analysis is performed using Azure Cognitive Services. Data calculations assess safety risks and congestion potential. The output includes the predicted level of safety risk and appropriate corrective actions.

[0197] Step 3:

[0198] The server generates appropriate commands for the traffic control system and the vehicle's driving mode based on predicted results. These commands include speed adjustments and enhancements to driver assistance functions. During this process, prompts are input to the generating AI model to construct an appropriate action plan. The output is a specific command for the vehicle and the traffic control system.

[0199] Step 4:

[0200] The terminal adjusts the vehicle's driving mode as needed, following commands received from the server. Specific actions include reducing the vehicle's speed and activating driver assistance functions. The terminal also continuously monitors the user's emotional state and sends new data back to the server. The output consists of the adjusted driving mode and updated emotional state information.

[0201] Step 5:

[0202] Users provide feedback on the system's driving assistance features and their experience with the system. This feedback is sent to the server via a terminal. This information is stored on the server as data for future system improvements. The output is qualitative data representing user feedback.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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".

[0219] This invention provides a real-time monitoring and control system for optimally operating autonomous driving technology while ensuring traffic safety. The program processing of this system is described below.

[0220] Server-side processing

[0221] The server aggregates traffic information from multiple data sources, including vehicle GPS data, status information from traffic lights, and data from satellite and ground sensors. The collected data is analyzed using generative models to predict traffic conditions and accident potential in real time. Based on these predictions, the server determines necessary interventions. For example, if congestion is predicted near a highway exit, the server adjusts the timing of traffic light control to smooth traffic flow.

[0222] Terminal (vehicle side) processing

[0223] The terminal acquires environmental information in real time from sensors mounted on the vehicle and transmits it to the server. It also receives instructions from the server and controls the vehicle's operation based on these instructions. For example, if it receives an instruction to slow down at a specific intersection, the terminal automatically adjusts the vehicle's speed to maintain safe driving.

[0224] User roles

[0225] Users monitor the system's behavior while driving the vehicle and record any abnormal situations. This helps identify operational problems and contributes to future system improvements.

[0226] This system utilizes models specifically tailored to the traffic conditions of a particular region, enabling optimal control that takes into account region-specific traffic patterns. For example, in urban centers, it can manage peak traffic flow and minimize risks. In this way, it reduces accident risk and ensures smooth traffic flow.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The server receives vehicle GPS data, traffic signal status, and satellite data, integrates them, and stores them in a database.

[0230] Step 2:

[0231] The server inputs stored data into a generative model and analyzes traffic conditions in real time. This analysis predicts the occurrence of traffic congestion and the likelihood of accidents.

[0232] Step 3:

[0233] Based on the analysis results, the server determines the necessary interventions and generates commands to adjust the timing of traffic signal control or direct instructions to vehicles.

[0234] Step 4:

[0235] The terminal adjusts the vehicle's operation based on instructions received from the server. Specifically, it performs actions such as driving at a predetermined speed and selecting a specific route.

[0236] Step 5:

[0237] The user monitors the system's operation and checks for any abnormalities in the vehicle's behavior. If an abnormality occurs, the user records the details and reports them to the server.

[0238] Step 6:

[0239] The server improves the system and enhances the accuracy of the generative model based on user feedback and new data.

[0240] (Example 1)

[0241] 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."

[0242] [Due to the increase in traffic volume accompanying urbanization in recent years, the frequency of traffic congestion and accidents has increased. Against this backdrop, it is necessary to manage traffic flow more efficiently and safely. Conventional traffic systems struggle to grasp the situation in real time and respond quickly, and they do not adequately consider the traffic characteristics of each region. Therefore, a new method is needed to optimize traffic conditions in real time by utilizing vehicle and traffic signal data.]

[0243] 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.

[0244] In this invention, the server includes means for aggregating diverse environmental data, including location information and traffic signal status information acquired from vehicles, to monitor traffic conditions; means for analyzing the aggregated data at high speed using a generation AI model to predict traffic flow and congestion in real time; and means for dynamically controlling the signal system based on the predictions and issuing commands to enhance traffic safety. This makes it possible to optimize traffic flow in real time, prevent accidents and congestion, and achieve safe and efficient traffic management.

[0245] "Location information acquired from the vehicle" refers to data generated by the vehicle's onboard positioning system to determine the vehicle's geographical location.

[0246] "Traffic light status information" refers to information that indicates the current operating status of a traffic signal (e.g., red light, green light, yellow light, etc.).

[0247] "Environmental data" refers to data that includes information about events around a vehicle, such as location information, traffic light status, weather information, and road conditions.

[0248] A "generative AI model" is a program that uses artificial intelligence technology to analyze data and predict future situations.

[0249] "Traffic flow" is a concept that describes the patterns, speeds, and densities of vehicle and pedestrian movement within a specific region or area.

[0250] "Traffic congestion forecasting" is a process that uses collected data to predict potential traffic congestion in the future.

[0251] "Dynamic control of the signal system" refers to appropriately changing the timing of traffic light operation based on real-time data and predictions.

[0252] "Traffic safety" means maintaining a state in the transportation system where accidents and dangers are minimized, and people and goods can safely reach their destinations.

[0253] The system according to this invention mainly consists of a server, a terminal, and a user.

[0254] The server utilizes diverse hardware and software to aggregate environmental data, including location information and traffic signal status information acquired from vehicles. Specifically, it uses a database system to efficiently retrieve and store this information. On the server, a generative AI model is used to analyze the aggregated data. This model is used to evaluate traffic flow in real time and predict future congestion. For example, a generative AI model built using machine learning libraries can rapidly analyze the acquired data and generate predictions.

[0255] The terminal acquires environmental data in real time through various sensors mounted on the vehicle. This includes cameras, LiDAR, radar, etc. This data is processed by a computer within the terminal and transmitted to a server. It also receives commands from the server and plays a role in controlling the vehicle's operation. This control uses the vehicle's control system to maintain safe and efficient driving.

[0256] Users are obligated to monitor the system's behavior while driving and record any abnormalities that occur. Real-time traffic information and system status can be viewed via smartphones or in-car displays. This allows users to respond quickly to problems, maintain records, and provide feedback.

[0257] As a concrete example, when optimizing traffic flow in the city center, the server issues commands to adjust the timing of traffic signal control. This allows for the management of peak traffic flow and the prevention of congestion.

[0258] An example of a prompt might be, "Suggest a way to optimize traffic signal control based on peak traffic conditions in city A." This prompt functions as a question to a generative AI model, seeking methods to provide appropriate solutions to a specific traffic problem.

[0259] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0260] Step 1:

[0261] The server receives environmental data, including location information and traffic signal status information acquired from vehicles. This data is input directly from GPS devices and signal control interfaces. The input data is stored in a database system and prepared for further analysis.

[0262] Step 2:

[0263] The server takes in accumulated environmental data and supplies it to the AI ​​model. The AI ​​model uses the input data to analyze traffic flow and the likelihood of congestion. In this process, machine learning algorithms are used to extract correlations and patterns from the data. The output is real-time traffic prediction results.

[0264] Step 3:

[0265] The server generates control commands for the signal system based on prediction results obtained from the generated AI model. By dynamically adjusting the timing of the traffic signals, it optimizes traffic flow. These commands are transmitted to each traffic signal via a communication protocol.

[0266] Step 4:

[0267] The terminal collects real-time environmental data from various sensors within the vehicle and transmits it to the server. Simultaneously, it receives control commands from the server and transmits them to the vehicle's control system. This enables specific vehicle actions (such as speed adjustment or lane changes).

[0268] Step 5:

[0269] While driving, users monitor system control commands and traffic forecast information via the in-vehicle display. They record abnormal situations as needed and take manual action when problems occur. This provides feedback for further system improvements.

[0270] (Application Example 1)

[0271] 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."

[0272] In autonomous vehicle driving technology, understanding traffic conditions in real time and performing appropriate control is extremely difficult. To solve this problem, a system is needed that efficiently predicts traffic information and ensures safe operation. Furthermore, if information is not provided to the driver, it may delay emergency response, which is also a crucial issue that needs to be addressed.

[0273] 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.

[0274] In this invention, the server includes means for monitoring road conditions by collecting and integrating information from vehicles; means for analyzing and predicting the aggregated information in real time using a generated AI model; and means for visually presenting traffic conditions and predicted information through an information display terminal. This allows drivers to immediately grasp traffic information and supports safe driving.

[0275] "Information from the vehicle" refers to data collected through sensors and communication devices installed on the vehicle, including information on location, speed, acceleration, and the surrounding environment.

[0276] "Integration" refers to the process of centrally combining data collected from multiple sources and converting it into a consistent format.

[0277] "Road condition monitoring" means understanding road conditions in real time, such as traffic flow, traffic signal status, road congestion, and the presence or absence of obstacles.

[0278] A "generative AI model" is an algorithm that predicts future states by analyzing large amounts of data and learning patterns.

[0279] "Real-time analysis and prediction" means rapidly processing data in real time and estimating future situations.

[0280] "Issuing commands to traffic controllers" means instructing traffic signals and vehicle operating systems to perform actions according to the situation.

[0281] "Road traffic safety" means preventing traffic accidents from occurring and maintaining a state where traffic flows smoothly and safely.

[0282] An "information display terminal" is a device designed to convey information to users in an easy-to-understand manner, and is used to visually display traffic conditions and forecast information.

[0283] "Visually presenting traffic conditions and prediction information" means displaying the current traffic conditions and future prediction results in a form that can be intuitively understood by drivers and users.

[0284] "Receiving feedback and performing analysis for improvement" means evaluating the intervention results of the system and analyzing the results in detail for further performance improvement.

[0285] The system for realizing this application example supports real-time traffic control in autonomous vehicles. The system integrates input data from sensors mounted on the vehicle and performs analysis and prediction using an AI model generated by the server. This enables rapid response to changes in road conditions.

[0286] The server aggregates vehicle position information, speed, traffic signal information, etc. obtained from satellite data and ground sensors. For this, data such as GPS and data from sensors mounted on each vehicle are used, for example. The aggregated data is analyzed using a generated AI model (e.g., TensorFlow), and traffic flow, traffic jam prediction, the possibility of accidents, etc. are calculated in real-time.

[0287] In particular, based on this analysis result, the server issues commands to traffic controllers such as traffic signal control and instructions to vehicles to maintain a safe traffic situation. Also, apart from explicit commands, the prediction results are visually presented to the driver. An information display terminal (e.g., smart glasses or a head-mounted display) plays this role, and it is a mechanism that enables users to easily acquire and confirm information.

[0288] As a specific example, when the server predicts vehicle concentration at an intersection, the smart glasses display "The signal waiting time at the intersection is expected to be longer than usual. A detour route is recommended." This information is updated in real-time, and recommendations based on the generated AI model are sequentially generated.

[0289] An example of a prompt message to the generative model would be, "Display the traffic forecast for the next intersection on the screen. The display should include the predicted waiting time and recommended detour route." The server then sends the appropriate data to an external system to assist the user.

[0290] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0291] Step 1:

[0292] The server collects and integrates information from satellite data, ground sensors, and vehicle sensors. This information includes vehicle location, speed, and traffic signal status. The integrated information forms the basis for understanding the overall traffic situation.

[0293] Step 2:

[0294] The server uses a generative AI model to analyze and predict the integrated data. This analysis predicts current traffic flow, congestion trends, and accident likelihood. Based on the input data, the model applies learned traffic patterns to estimate future situations. The output includes locations and times where specific traffic control measures are required.

[0295] Step 3:

[0296] Based on the analysis results, the server issues commands to traffic controllers. These commands include things like the timing of traffic signal changes and speed adjustments for specific vehicles. This helps to alleviate congestion and promote safer traffic flow.

[0297] Step 4:

[0298] The server sends the analysis results and generated commands to the information display terminal. This information is structured as a prompt message and sent in a format that the terminal can display. For example, it might be in the format of, "An increase in traffic light waiting time at intersections is predicted. We recommend taking an alternative route."

[0299] Step 5:

[0300] The terminal presents visual information to the driver through an information display terminal (e.g., smart glasses). Based on this information, the driver can consider responses such as route changes and speed adjustments. By presenting the information, the driver is assisted in driving more safely and efficiently.

[0301] Step 6:

[0302] The user drives based on the provided information and records the situation if an abnormality is recognized. This record is transmitted to the server as feedback later. The feedback is used to improve the system, leading to an improvement in future traffic prediction accuracy.

[0303] 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.

[0304] The present invention provides a system that considers not only the operation of the vehicle but also the emotional state of the user in order to enhance traffic safety. The program processing of this system will be described below.

[0305] Server-side processing

[0306] The server integrates the user's emotion data provided from the emotion engine in addition to the conventional traffic data. The server inputs this composite dataset into a generation model and analyzes the traffic situation in real time. Thereby, the safety risks on the road are predicted, and appropriate commands are sent to the traffic control system. For example, when the stress level of the user is high, the server can issue a command to strengthen the speed limit of the vehicle and increase the intervention of the driving support system.

[0307] Terminal (vehicle side) processing

[0308] The terminal not only adjusts vehicle operation according to instructions received from the server, but also acquires user emotion data from sensors inside the vehicle and transmits it to the server. Specifically, the terminal analyzes the user's tone of voice and facial expressions to estimate their emotional state in real time. This allows the terminal to change the driving mode according to the user's current emotions.

[0309] User roles

[0310] Users must ensure that the emotional engine accurately recognizes their emotional state by optimizing the in-vehicle conditions. For example, clear voice instructions and ensuring their face is clearly visible to the camera will allow the system to analyze emotions more accurately. They are also expected to provide feedback on the driving assistance and interventions the system offers.

[0311] This system further improves driving safety and comfort by considering the user's emotional state when controlling traffic. For example, if fatigue accumulates during long drives, the system can prevent excessive fatigue by enhancing driver assistance functions. Such an integrated system makes it possible to simultaneously improve vehicle operation safety and user peace of mind.

[0312] The following describes the processing flow.

[0313] Step 1:

[0314] The server receives various data, including vehicle GPS data, traffic signal status, and user sentiment data, and integrates it into a database.

[0315] Step 2:

[0316] The device uses cameras and microphones inside the vehicle to analyze the user's facial expressions and voice tone in real time and estimate the user's emotional state. This estimation result is then sent to a server.

[0317] Step 3:

[0318] The server inputs integrated data into a generative model, analyzing traffic conditions and user emotional states in real time. This allows it to predict potential risks.

[0319] Step 4:

[0320] Based on the analysis results, the server issues commands to the traffic control system, such as adjusting signal timings or limiting vehicle speeds. Furthermore, if it determines that user stress levels are high, it enhances driver assistance.

[0321] Step 5:

[0322] The terminal receives commands from the server and controls the vehicle's movements. At intersections where concessions are required, it automatically slows down and activates the driver assistance system.

[0323] Step 6:

[0324] The user reviews the driving assistance provided by the system and provides feedback to the system as needed. For example, if the user feels the assistance is inappropriate, they record this information and report it to the server.

[0325] Step 7:

[0326] The server collects user feedback and adjusts and improves the generative model algorithm to help improve future models.

[0327] (Example 2)

[0328] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0329] In modern transportation systems, the impact of drivers' emotional states on traffic safety is often overlooked. Therefore, there is a need for systems that effectively mitigate human errors caused by driver emotional stress and fatigue, and the resulting risk of traffic accidents. Conventional traffic control systems only consider environmental factors and fail to reflect the driver's internal factors; therefore, new methods are needed to improve this.

[0330] 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.

[0331] In this invention, the server includes means for monitoring traffic conditions by collecting and integrating data from vehicles and emotional data; means for analyzing the aggregated data in real time using a generative model and making predictions that take emotional states into account; and means for issuing commands to the vehicle control system based on the predicted results to maintain traffic safety in accordance with emotional states. This enables safe and efficient traffic control by reflecting the emotional state of drivers in real time.

[0332] "Data" refers to a variety of information, including vehicle operation information and user emotional states, which the system uses to analyze traffic conditions.

[0333] "Emotional data" refers to information indicating the driver's emotional state, estimated based on the tone of their voice and facial expressions, and is used in traffic control to ensure safe driving.

[0334] A "generative model" refers to an algorithm used to analyze large amounts of data and predict traffic conditions, and is particularly characterized by its consideration of sentiment data.

[0335] A "traffic control system" refers to a set of technical means used to regulate vehicle operation and maintain road safety, and which function based on commands from a server.

[0336] A "command" refers to a specific operational instruction issued by the server based on the results of its analysis of traffic conditions, which controls the operation of vehicles and driver assistance systems.

[0337] "Feedback" refers to the driver's reaction and opinion to the system's intervention, and is used to improve the system in the future.

[0338] "Intervention" refers to actions taken by the system to influence vehicle operation in response to the driver's emotional state or traffic conditions, and is implemented to improve safety.

[0339] The system of this invention consists of three components: a vehicle terminal, a central server, and the driver. The terminal is installed inside the vehicle and uses sensors such as cameras and microphones to collect emotional data from the driver. This includes a process of analyzing voice tone and facial expressions in real time. This data is then transmitted to the server via secure communication.

[0340] The server integrates emotional data received from terminals with various traffic-related data (e.g., traffic volume, weather, road conditions, etc.). This generates a complex dataset that includes emotional states. The server has a generative AI model implemented, which is designed to analyze the data in real time and predict traffic conditions. Based on this analysis, the server generates commands for the traffic control system. These commands are adjusted according to the driver's emotional state; for example, if high stress is detected, measures such as tightening speed limits are taken.

[0341] Furthermore, users play a crucial role in improving the system's accuracy. Specifically, providing clear voice commands and ensuring their faces are clearly visible to the camera improves the accuracy of emotion data. In addition, providing feedback on the driving assistance and system interventions offered contributes to the system's continuous improvement.

[0342] For example, if a user's stress level increases during prolonged driving, the system will enhance driver assistance functions to reduce user fatigue. An example of this prompt would be, "Explain how user emotional data influences traffic control."

[0343] Such a system is expected to enable traffic control that reflects the driver's emotional state in real time, thereby improving safety and comfort.

[0344] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0345] Step 1:

[0346] The device collects user emotional data using sensors such as cameras and microphones mounted on the vehicle. Specifically, it captures the user's face with a camera and estimates their emotional state in real time using facial expression analysis software. It also analyzes the user's voice tone using a microphone and measures their stress level. It receives video and audio data as input and generates an estimated result of the emotional state as output.

[0347] Step 2:

[0348] The device sends the collected emotional data to the server. The specific input is digital data indicating the user's emotional state, and this data is securely transmitted to the server using data encryption technology. The output after transmission is the server receiving this data.

[0349] Step 3:

[0350] The server integrates sentiment data received from terminals with conventional traffic data. Specifically, it receives traffic volume, weather information, and road condition data obtained from traffic sensors as input, and combines this with the received sentiment data. Through data processing and calculations, it uses this information to create an integrated dataset. The output is the integrated set of data.

[0351] Step 4:

[0352] The server analyzes the integrated dataset using a generative AI model and makes real-time predictions based on traffic conditions and user sentiment. The input is the previously integrated dataset, and the output is the predicted traffic risk and countermeasures. The specific data calculations are performed using AI modeling techniques to calculate a risk score and determine the necessary traffic control actions.

[0353] Step 5:

[0354] Based on the analysis results, the server issues commands to the vehicle control system. The input is the predicted risks and countermeasures, and the output is the specific control command. Specific actions include speed adjustments and enhanced intervention of driver assistance systems to ensure the vehicle complies with the command.

[0355] Step 6:

[0356] The user experiences the vehicle's behavior in response to commands from the terminal and server, and provides feedback to the system. The input in this step is the user's perception and opinions regarding the vehicle's behavior, while the output is the feedback data sent to the system. Specifically, the user evaluates the effectiveness of the driving assistance and suggests improvements.

[0357] (Application Example 2)

[0358] 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."

[0359] Current transportation systems focus on safety measures based on vehicle operation, but they cannot take into account the emotional state of the user. Therefore, there is a challenge in adequately mitigating the risk of accidents caused by emotional factors such as driver stress and fatigue. Furthermore, current systems also struggle to implement appropriate control tailored to local traffic patterns.

[0360] 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.

[0361] In this invention, the server includes means for monitoring traffic conditions by collecting and integrating data from vehicles and user emotional state information; means for analyzing and predicting the aggregated data and emotional state information in real time using a generative model; and means for issuing commands to the traffic control system and the vehicle's driving mode based on the predicted results to maintain traffic safety and comfort. This enables appropriate traffic control in accordance with the user's emotional state, thereby improving traffic safety and comfort.

[0362] "Data from the vehicle" refers to various types of data collected from the vehicle's sensors and communication equipment, including location information, speed, and acceleration.

[0363] "User emotional state information" refers to information indicating emotional states such as stress, excitement, and fatigue, obtained by analyzing the user's voice tone and facial expression data.

[0364] "Monitoring traffic conditions through integration" means combining and analyzing collected vehicle data and emotional state information to understand the current traffic situation.

[0365] A "generative model" is a machine learning or AI algorithm used to analyze traffic conditions and emotional states based on collected data and to predict future states.

[0366] "Analyzing and predicting in real time" is a process of analyzing data as soon as it is collected and quickly predicting future traffic risks.

[0367] "Issuing commands to traffic control systems and vehicle driving modes to maintain traffic safety and comfort" means issuing appropriate commands such as driving assistance and speed adjustments based on predictions to ensure road safety and passenger comfort.

[0368] "Receiving feedback and making improvements" is a process of learning from the effects of system interventions and user responses in order to improve the quality of future control.

[0369] The system implementing this invention aggregates data from vehicles and user emotional state information to improve traffic safety and comfort. The server, terminals, and users play key roles.

[0370] The server integrates data such as location, speed, and acceleration collected from vehicle sensors and communication devices, along with emotional state information analyzed from the user's voice tone and facial expressions. This information is processed in real time using emotion analysis tools such as Microsoft Azure Cognitive Services. Next, a generative AI model is used to analyze this combined data and predict safety risks and traffic congestion. Based on these predictions, the server commands the traffic control system and instructs changes to the vehicle's driving mode.

[0371] The terminal (smartphone or in-vehicle device) receives commands from the server and adjusts the vehicle's driving mode. Furthermore, the terminal also plays a role in acquiring the user's emotional state in real time and transmitting it to the server. This supports safe and comfortable driving. The system utilizes the smartphone's camera and microphone to perform facial recognition and voice analysis to recognize the user's emotional state.

[0372] To make the most of this system, users need to properly position their smartphones so that their faces are clearly visible to the camera. They are also expected to provide feedback on the driving assistance provided by the system, contributing to its further improvement. For example, if user fatigue is detected due to prolonged driving, the server will recommend that the driver take a break via the terminal, thereby enhancing the vehicle's driving assistance functions.

[0373] An example of a prompt that fits the generating AI model is, "Based on emotion analysis data, create an action plan to monitor the user's fatigue in real time during long drives and change to an appropriate driving mode." This allows the system to generate appropriate commands that respond to the user's emotions.

[0374] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0375] Step 1:

[0376] The server receives sensor data such as location, speed, and acceleration transmitted from the vehicle. In addition, it obtains user emotional state information from the terminal. Input data includes vehicle telematics data and user voice tone and facial expression analysis results. The server integrates this data and records it in a database. The output is the integrated dataset.

[0377] Step 2:

[0378] The server uses a generative AI model to analyze and predict traffic conditions and user sentiment in real time, taking an integrated dataset as input. Sentiment analysis is performed using Azure Cognitive Services. Data calculations assess safety risks and congestion potential. The output includes the predicted level of safety risk and appropriate corrective actions.

[0379] Step 3:

[0380] The server generates appropriate commands for the traffic control system and the vehicle's driving mode based on predicted results. These commands include speed adjustments and enhancements to driver assistance functions. During this process, prompts are input to the generating AI model to construct an appropriate action plan. The output is a specific command for the vehicle and the traffic control system.

[0381] Step 4:

[0382] The terminal adjusts the vehicle's driving mode as needed, following commands received from the server. Specific actions include reducing the vehicle's speed and activating driver assistance functions. The terminal also continuously monitors the user's emotional state and sends new data back to the server. The output consists of the adjusted driving mode and updated emotional state information.

[0383] Step 5:

[0384] Users provide feedback on the system's driving assistance features and their experience with the system. This feedback is sent to the server via a terminal. This information is stored on the server as data for future system improvements. The output is qualitative data representing user feedback.

[0385] 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.

[0386] 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.

[0387] 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.

[0388] [Third Embodiment]

[0389] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0390] 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.

[0391] 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).

[0392] 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.

[0393] 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.

[0394] 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).

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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".

[0401] This invention provides a real-time monitoring and control system for optimally operating autonomous driving technology while ensuring traffic safety. The program processing of this system is described below.

[0402] Server-side processing

[0403] The server aggregates traffic information from multiple data sources, including vehicle GPS data, status information from traffic lights, and data from satellite and ground sensors. The collected data is analyzed using generative models to predict traffic conditions and accident potential in real time. Based on these predictions, the server determines necessary interventions. For example, if congestion is predicted near a highway exit, the server adjusts the timing of traffic light control to smooth traffic flow.

[0404] Terminal (vehicle side) processing

[0405] The terminal acquires environmental information in real time from sensors mounted on the vehicle and transmits it to the server. It also receives instructions from the server and controls the vehicle's operation based on these instructions. For example, if it receives an instruction to slow down at a specific intersection, the terminal automatically adjusts the vehicle's speed to maintain safe driving.

[0406] User roles

[0407] Users monitor the system's behavior while driving the vehicle and record any abnormal situations. This helps identify operational problems and contributes to future system improvements.

[0408] This system utilizes models specifically tailored to the traffic conditions of a particular region, enabling optimal control that takes into account region-specific traffic patterns. For example, in urban centers, it can manage peak traffic flow and minimize risks. In this way, it reduces accident risk and ensures smooth traffic flow.

[0409] The following describes the processing flow.

[0410] Step 1:

[0411] The server receives vehicle GPS data, traffic signal status, and satellite data, integrates them, and stores them in a database.

[0412] Step 2:

[0413] The server inputs stored data into a generative model and analyzes traffic conditions in real time. This analysis predicts the occurrence of traffic congestion and the likelihood of accidents.

[0414] Step 3:

[0415] Based on the analysis results, the server determines the necessary interventions and generates commands to adjust the timing of traffic signal control or direct instructions to vehicles.

[0416] Step 4:

[0417] The terminal adjusts the vehicle's operation based on instructions received from the server. Specifically, it performs actions such as driving at a predetermined speed and selecting a specific route.

[0418] Step 5:

[0419] The user monitors the system's operation and checks for any abnormalities in the vehicle's behavior. If an abnormality occurs, the user records the details and reports them to the server.

[0420] Step 6:

[0421] The server improves the system and enhances the accuracy of the generative model based on user feedback and new data.

[0422] (Example 1)

[0423] 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."

[0424] [Due to the increase in traffic volume accompanying urbanization in recent years, the frequency of traffic congestion and accidents has increased. Against this backdrop, it is necessary to manage traffic flow more efficiently and safely. Conventional traffic systems struggle to grasp the situation in real time and respond quickly, and they do not adequately consider the traffic characteristics of each region. Therefore, a new method is needed to optimize traffic conditions in real time by utilizing vehicle and traffic signal data.]

[0425] 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.

[0426] In this invention, the server includes means for aggregating diverse environmental data, including location information and traffic signal status information acquired from vehicles, to monitor traffic conditions; means for analyzing the aggregated data at high speed using a generation AI model to predict traffic flow and congestion in real time; and means for dynamically controlling the signal system based on the predictions and issuing commands to enhance traffic safety. This makes it possible to optimize traffic flow in real time, prevent accidents and congestion, and achieve safe and efficient traffic management.

[0427] "Location information acquired from the vehicle" refers to data generated by the vehicle's onboard positioning system to determine the vehicle's geographical location.

[0428] "Traffic light status information" refers to information that indicates the current operating status of a traffic signal (e.g., red light, green light, yellow light, etc.).

[0429] "Environmental data" refers to data that includes information about events around a vehicle, such as location information, traffic light status, weather information, and road conditions.

[0430] A "generative AI model" is a program that uses artificial intelligence technology to analyze data and predict future situations.

[0431] "Traffic flow" is a concept that describes the patterns, speeds, and densities of vehicle and pedestrian movement within a specific region or area.

[0432] "Traffic congestion forecasting" is a process that uses collected data to predict potential traffic congestion in the future.

[0433] "Dynamic control of the signal system" refers to appropriately changing the timing of traffic light operation based on real-time data and predictions.

[0434] "Traffic safety" means maintaining a state in the transportation system where accidents and dangers are minimized, and people and goods can safely reach their destinations.

[0435] The system according to this invention mainly consists of a server, a terminal, and a user.

[0436] The server utilizes diverse hardware and software to aggregate environmental data, including location information and traffic signal status information acquired from vehicles. Specifically, it uses a database system to efficiently retrieve and store this information. On the server, a generative AI model is used to analyze the aggregated data. This model is used to evaluate traffic flow in real time and predict future congestion. For example, a generative AI model built using machine learning libraries can rapidly analyze the acquired data and generate predictions.

[0437] The terminal acquires environmental data in real time through various sensors mounted on the vehicle. This includes cameras, LiDAR, radar, etc. This data is processed by a computer within the terminal and transmitted to a server. It also receives commands from the server and plays a role in controlling the vehicle's operation. This control uses the vehicle's control system to maintain safe and efficient driving.

[0438] Users are obligated to monitor the system's behavior while driving and record any abnormalities that occur. Real-time traffic information and system status can be viewed via smartphones or in-car displays. This allows users to respond quickly to problems, maintain records, and provide feedback.

[0439] As a concrete example, when optimizing traffic flow in the city center, the server issues commands to adjust the timing of traffic signal control. This allows for the management of peak traffic flow and the prevention of congestion.

[0440] An example of a prompt might be, "Suggest a way to optimize traffic signal control based on peak traffic conditions in city A." This prompt functions as a question to a generative AI model, seeking methods to provide appropriate solutions to a specific traffic problem.

[0441] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0442] Step 1:

[0443] The server receives environmental data, including location information and traffic signal status information acquired from vehicles. This data is input directly from GPS devices and signal control interfaces. The input data is stored in a database system and prepared for further analysis.

[0444] Step 2:

[0445] The server takes in accumulated environmental data and supplies it to the AI ​​model. The AI ​​model uses the input data to analyze traffic flow and the likelihood of congestion. In this process, machine learning algorithms are used to extract correlations and patterns from the data. The output is real-time traffic prediction results.

[0446] Step 3:

[0447] The server generates control commands for the signal system based on prediction results obtained from the generated AI model. By dynamically adjusting the timing of the traffic signals, it optimizes traffic flow. These commands are transmitted to each traffic signal via a communication protocol.

[0448] Step 4:

[0449] The terminal collects real-time environmental data from various sensors within the vehicle and transmits it to the server. Simultaneously, it receives control commands from the server and transmits them to the vehicle's control system. This enables specific vehicle actions (such as speed adjustment or lane changes).

[0450] Step 5:

[0451] While driving, users monitor system control commands and traffic forecast information via the in-vehicle display. They record abnormal situations as needed and take manual action when problems occur. This provides feedback for further system improvements.

[0452] (Application Example 1)

[0453] 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."

[0454] In autonomous vehicle driving technology, understanding traffic conditions in real time and performing appropriate control is extremely difficult. To solve this problem, a system is needed that efficiently predicts traffic information and ensures safe operation. Furthermore, insufficient information provided to drivers can lead to delays in emergency responses, which is another important issue that needs to be addressed.

[0455] 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.

[0456] In this invention, the server includes means for monitoring road conditions by collecting and integrating information from vehicles; means for analyzing and predicting the aggregated information in real time using a generated AI model; and means for visually presenting traffic conditions and predicted information through an information display terminal. This allows drivers to immediately grasp traffic information and supports safe driving.

[0457] "Information from the vehicle" refers to data collected through sensors and communication devices installed on the vehicle, including information on location, speed, acceleration, and the surrounding environment.

[0458] "Integration" refers to the process of centrally combining data collected from multiple sources and converting it into a consistent format.

[0459] "Road condition monitoring" means understanding road conditions in real time, such as traffic flow, traffic signal status, road congestion, and the presence or absence of obstacles.

[0460] A "generative AI model" is an algorithm that predicts future states by analyzing large amounts of data and learning patterns.

[0461] "Real-time analysis and prediction" means rapidly processing data in real time and estimating future situations.

[0462] "Issuing commands to traffic controllers" means instructing traffic signals and vehicle operating systems to perform actions according to the situation.

[0463] "Road traffic safety" means preventing traffic accidents from occurring and maintaining a state where traffic flows smoothly and safely.

[0464] An "information display terminal" is a device designed to convey information to users in an easy-to-understand manner, and is used to visually display traffic conditions and forecast information.

[0465] "Visually presenting traffic conditions and forecast information" means displaying current traffic conditions and future forecasts in a format that drivers and users can intuitively understand.

[0466] "Receiving feedback and performing analysis to improve" means evaluating the results of interventions performed by the system and analyzing those results in detail to further improve performance.

[0467] The system used to realize this application supports real-time traffic control in autonomous vehicles. The system integrates input data from sensors mounted on the vehicle and performs analysis and prediction using an AI model generated by a server. This enables rapid response to changes in road conditions.

[0468] The server aggregates vehicle location information, speed, and traffic signal information obtained from satellite data and ground sensors. This includes data from GPS and sensors installed on each vehicle. The aggregated data is analyzed using a generative AI model (e.g., TensorFlow) to calculate traffic flow, congestion predictions, and accident probabilities in real time.

[0469] In particular, the server maintains safe traffic conditions by issuing commands to traffic controllers, such as controlling traffic signals and giving instructions to vehicles, based on these analysis results. In addition to explicit commands, the prediction results are also visually presented to the driver. Information display terminals (e.g., smart glasses or head-mounted displays) play this role, creating a system where users can easily obtain and confirm information.

[0470] For example, when the server predicts a traffic congestion at an intersection, the smart glasses will display the message, "Longer-than-usual traffic light wait times are expected at the intersection. We recommend taking an alternative route." This information is updated in real time, and recommendations based on a generated AI model are continuously produced.

[0471] An example of a prompt message to the generative model would be, "Display the traffic forecast for the next intersection on the screen. The display should include the predicted waiting time and recommended detour route." The server then sends the appropriate data to an external system to assist the user.

[0472] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0473] Step 1:

[0474] The server collects and integrates information from satellite data, ground sensors, and vehicle sensors. This information includes vehicle location, speed, and traffic signal status. The integrated information forms the basis for understanding the overall traffic situation.

[0475] Step 2:

[0476] The server uses a generative AI model to analyze and predict the integrated data. This analysis predicts current traffic flow, congestion trends, and accident likelihood. Based on the input data, the model applies learned traffic patterns to estimate future situations. The output includes locations and times where specific traffic control measures are required.

[0477] Step 3:

[0478] Based on the analysis results, the server issues commands to traffic controllers. These commands include things like the timing of traffic signal changes and speed adjustments for specific vehicles. This helps to alleviate congestion and promote safer traffic flow.

[0479] Step 4:

[0480] The server sends the analysis results and generated commands to the information display terminal. This information is structured as a prompt message and sent in a format that the terminal can display. For example, it might be in the format of, "An increase in traffic light waiting time at intersections is predicted. We recommend taking an alternative route."

[0481] Step 5:

[0482] The terminal displays visual information to the driver through an information display device (e.g., smart glasses). Based on this information, the driver can consider actions such as changing the route or adjusting their speed. By providing this information, the driver is supported in driving more safely and efficiently.

[0483] Step 6:

[0484] Users drive based on the information provided and record any abnormalities they detect. This record is sent to the server as feedback. This feedback is used to improve the system and contribute to improving the accuracy of future traffic predictions.

[0485] 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.

[0486] This invention provides a system that enhances traffic safety by considering not only vehicle operation but also the user's emotional state. The program processing of this system is described below.

[0487] Server-side processing

[0488] The server integrates user sentiment data provided by the sentiment engine with conventional traffic data. The server inputs this composite dataset into a generative model to analyze traffic conditions in real time. This allows for the prediction of safety risks on the road and the sending of appropriate commands to the traffic control system. For example, if a user's stress level is high, the server can issue commands to tighten vehicle speed limits and increase intervention by driver assistance systems.

[0489] Terminal (vehicle side) processing

[0490] The terminal not only adjusts vehicle operation according to instructions received from the server, but also acquires user emotion data from sensors inside the vehicle and transmits it to the server. Specifically, the terminal analyzes the user's tone of voice and facial expressions to estimate their emotional state in real time. This allows the terminal to change the driving mode according to the user's current emotions.

[0491] User roles

[0492] Users must ensure that the emotional engine accurately recognizes their emotional state by optimizing the in-vehicle conditions. For example, clear voice instructions and ensuring their face is clearly visible to the camera will allow the system to analyze emotions more accurately. They are also expected to provide feedback on the driving assistance and interventions the system offers.

[0493] This system further improves driving safety and comfort by considering the user's emotional state when controlling traffic. For example, if fatigue accumulates during long drives, the system can prevent excessive fatigue by enhancing driver assistance functions. Such an integrated system makes it possible to simultaneously improve vehicle operation safety and user peace of mind.

[0494] The following describes the processing flow.

[0495] Step 1:

[0496] The server receives various data, including vehicle GPS data, traffic signal status, and user sentiment data, and integrates it into a database.

[0497] Step 2:

[0498] The device uses cameras and microphones inside the vehicle to analyze the user's facial expressions and voice tone in real time and estimate the user's emotional state. This estimation result is then sent to a server.

[0499] Step 3:

[0500] The server inputs integrated data into a generative model, analyzing traffic conditions and user emotional states in real time. This allows it to predict potential risks.

[0501] Step 4:

[0502] Based on the analysis results, the server issues commands to the traffic control system, such as adjusting signal timings or limiting vehicle speeds. Furthermore, if it determines that user stress levels are high, it enhances driver assistance.

[0503] Step 5:

[0504] The terminal receives commands from the server and controls the vehicle's movements. At intersections where concessions are required, it automatically slows down and activates the driver assistance system.

[0505] Step 6:

[0506] The user reviews the driving assistance provided by the system and provides feedback to the system as needed. For example, if the user feels the assistance is inappropriate, they record this information and report it to the server.

[0507] Step 7:

[0508] The server collects user feedback and adjusts and improves the generative model algorithm to help improve future models.

[0509] (Example 2)

[0510] 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."

[0511] In modern transportation systems, the impact of drivers' emotional states on traffic safety is often overlooked. Therefore, there is a need for systems that effectively mitigate human errors caused by driver emotional stress and fatigue, and the resulting risk of traffic accidents. Conventional traffic control systems only consider environmental factors and fail to reflect the driver's internal factors; therefore, new methods are needed to improve this.

[0512] 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.

[0513] In this invention, the server includes means for monitoring traffic conditions by collecting and integrating data from vehicles and emotional data; means for analyzing the aggregated data in real time using a generative model and making predictions that take emotional states into account; and means for issuing commands to the vehicle control system based on the predicted results to maintain traffic safety in accordance with emotional states. This enables safe and efficient traffic control by reflecting the emotional state of drivers in real time.

[0514] "Data" refers to a variety of information, including vehicle operation information and user emotional states, which the system uses to analyze traffic conditions.

[0515] "Emotional data" refers to information indicating the driver's emotional state, estimated based on the tone of their voice and facial expressions, and is used in traffic control to ensure safe driving.

[0516] A "generative model" refers to an algorithm used to analyze large amounts of data and predict traffic conditions, and is particularly characterized by its consideration of sentiment data.

[0517] A "traffic control system" refers to a set of technical means used to regulate vehicle operation and maintain road safety, and which function based on commands from a server.

[0518] A "command" refers to a specific operational instruction issued by the server based on the results of its analysis of traffic conditions, which controls the operation of vehicles and driver assistance systems.

[0519] "Feedback" refers to the driver's reaction and opinion to the system's intervention, and is used to improve the system in the future.

[0520] "Intervention" refers to actions taken by the system to influence vehicle operation in response to the driver's emotional state or traffic conditions, and is implemented to improve safety.

[0521] The system of this invention consists of three components: a vehicle terminal, a central server, and the driver. The terminal is installed inside the vehicle and uses sensors such as cameras and microphones to collect emotional data from the driver. This includes a process of analyzing voice tone and facial expressions in real time. This data is then transmitted to the server via secure communication.

[0522] The server integrates emotional data received from terminals with various traffic-related data (e.g., traffic volume, weather, road conditions, etc.). This generates a complex dataset that includes emotional states. The server has a generative AI model implemented, which is designed to analyze the data in real time and predict traffic conditions. Based on this analysis, the server generates commands for the traffic control system. These commands are adjusted according to the driver's emotional state; for example, if high stress is detected, measures such as tightening speed limits are taken.

[0523] Furthermore, users play a crucial role in improving the system's accuracy. Specifically, providing clear voice commands and ensuring their faces are clearly visible to the camera improves the accuracy of emotion data. In addition, providing feedback on the driving assistance and system interventions offered contributes to the system's continuous improvement.

[0524] For example, if a user's stress level increases during prolonged driving, the system will enhance driver assistance functions to reduce user fatigue. An example of this prompt would be, "Explain how user emotional data influences traffic control."

[0525] Such a system is expected to enable traffic control that reflects the driver's emotional state in real time, thereby improving safety and comfort.

[0526] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0527] Step 1:

[0528] The device collects user emotional data using sensors such as cameras and microphones mounted on the vehicle. Specifically, it captures the user's face with a camera and estimates their emotional state in real time using facial expression analysis software. It also analyzes the user's voice tone using a microphone and measures their stress level. It receives video and audio data as input and generates an estimated result of the emotional state as output.

[0529] Step 2:

[0530] The device sends the collected emotional data to the server. The specific input is digital data indicating the user's emotional state, and this data is securely transmitted to the server using data encryption technology. The output after transmission is the server receiving this data.

[0531] Step 3:

[0532] The server integrates sentiment data received from terminals with conventional traffic data. Specifically, it receives traffic volume, weather information, and road condition data obtained from traffic sensors as input, and combines this with the received sentiment data. Through data processing and calculations, it uses this information to create an integrated dataset. The output is the integrated set of data.

[0533] Step 4:

[0534] The server analyzes the integrated dataset using a generative AI model and makes real-time predictions based on traffic conditions and user sentiment. The input is the previously integrated dataset, and the output is the predicted traffic risk and countermeasures. The specific data calculations are performed using AI modeling techniques to calculate a risk score and determine the necessary traffic control actions.

[0535] Step 5:

[0536] Based on the analysis results, the server issues commands to the vehicle control system. The input is the predicted risks and countermeasures, and the output is the specific control command. Specific actions include speed adjustments and enhanced intervention of driver assistance systems to ensure the vehicle complies with the command.

[0537] Step 6:

[0538] The user experiences the vehicle's behavior in response to commands from the terminal and server, and provides feedback to the system. The input in this step is the user's perception and opinions regarding the vehicle's behavior, while the output is the feedback data sent to the system. Specifically, the user evaluates the effectiveness of the driving assistance and suggests improvements.

[0539] (Application Example 2)

[0540] 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."

[0541] Current transportation systems focus on safety measures based on vehicle operation, but they cannot take into account the emotional state of the user. Therefore, there is a challenge in adequately mitigating the risk of accidents caused by emotional factors such as driver stress and fatigue. Furthermore, current systems also struggle to implement appropriate control tailored to local traffic patterns.

[0542] 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.

[0543] In this invention, the server includes means for monitoring traffic conditions by collecting and integrating data from vehicles and user emotional state information; means for analyzing and predicting the aggregated data and emotional state information in real time using a generative model; and means for issuing commands to the traffic control system and the vehicle's driving mode based on the predicted results to maintain traffic safety and comfort. This enables appropriate traffic control in accordance with the user's emotional state, thereby improving traffic safety and comfort.

[0544] "Data from the vehicle" refers to various types of data collected from the vehicle's sensors and communication equipment, including location information, speed, and acceleration.

[0545] "User emotional state information" refers to information indicating emotional states such as stress, excitement, and fatigue, obtained by analyzing the user's voice tone and facial expression data.

[0546] "Monitoring traffic conditions through integration" means combining and analyzing collected vehicle data and emotional state information to understand the current traffic situation.

[0547] A "generative model" is a machine learning or AI algorithm used to analyze traffic conditions and emotional states based on collected data and to predict future states.

[0548] "Analyzing and predicting in real time" is a process of analyzing data as soon as it is collected and quickly predicting future traffic risks.

[0549] "Issuing commands to traffic control systems and vehicle driving modes to maintain traffic safety and comfort" means issuing appropriate commands such as driving assistance and speed adjustments based on predictions to ensure road safety and passenger comfort.

[0550] "Receiving feedback and making improvements" is a process of learning from the effects of system interventions and user responses in order to improve the quality of future control.

[0551] The system implementing this invention aggregates data from vehicles and user emotional state information to improve traffic safety and comfort. The server, terminals, and users play key roles.

[0552] The server integrates data such as location, speed, and acceleration collected from vehicle sensors and communication devices, along with emotional state information analyzed from the user's voice tone and facial expressions. This information is processed in real time using emotion analysis tools such as Microsoft Azure Cognitive Services. Next, a generative AI model is used to analyze this combined data and predict safety risks and traffic congestion. Based on these predictions, the server commands the traffic control system and instructs changes to the vehicle's driving mode.

[0553] The terminal (smartphone or in-vehicle device) receives commands from the server and adjusts the vehicle's driving mode. Furthermore, the terminal also plays a role in acquiring the user's emotional state in real time and transmitting it to the server. This supports safe and comfortable driving. The system utilizes the smartphone's camera and microphone to perform facial recognition and voice analysis to recognize the user's emotional state.

[0554] To make the most of this system, users need to properly position their smartphones so that their faces are clearly visible to the camera. They are also expected to provide feedback on the driving assistance provided by the system, contributing to its further improvement. For example, if user fatigue is detected due to prolonged driving, the server will recommend that the driver take a break via the terminal, thereby enhancing the vehicle's driving assistance functions.

[0555] An example of a prompt that fits the generating AI model is, "Based on emotion analysis data, create an action plan to monitor the user's fatigue in real time during long drives and change to an appropriate driving mode." This allows the system to generate appropriate commands that respond to the user's emotions.

[0556] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0557] Step 1:

[0558] The server receives sensor data such as location, speed, and acceleration transmitted from the vehicle. In addition, it obtains user emotional state information from the terminal. Input data includes vehicle telematics data and user voice tone and facial expression analysis results. The server integrates this data and records it in a database. The output is the integrated dataset.

[0559] Step 2:

[0560] The server uses a generative AI model to analyze and predict traffic conditions and user sentiment in real time, taking an integrated dataset as input. Sentiment analysis is performed using Azure Cognitive Services. Data calculations assess safety risks and congestion potential. The output includes the predicted level of safety risk and appropriate corrective actions.

[0561] Step 3:

[0562] The server generates appropriate commands for the traffic control system and the vehicle's driving mode based on predicted results. These commands include speed adjustments and enhancements to driver assistance functions. During this process, prompts are input to the generating AI model to construct an appropriate action plan. The output is a specific command for the vehicle and the traffic control system.

[0563] Step 4:

[0564] The terminal adjusts the vehicle's driving mode as needed, following commands received from the server. Specific actions include reducing the vehicle's speed and activating driver assistance functions. The terminal also continuously monitors the user's emotional state and sends new data back to the server. The output consists of the adjusted driving mode and updated emotional state information.

[0565] Step 5:

[0566] Users provide feedback on the system's driving assistance features and their experience with the system. This feedback is sent to the server via a terminal. This information is stored on the server as data for future system improvements. The output is qualitative data representing user feedback.

[0567] 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.

[0568] 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.

[0569] 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.

[0570] [Fourth Embodiment]

[0571] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0572] 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.

[0573] 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).

[0574] 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.

[0575] 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.

[0576] 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).

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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".

[0584] This invention provides a real-time monitoring and control system for optimally operating autonomous driving technology while ensuring traffic safety. The program processing of this system is described below.

[0585] Server-side processing

[0586] The server aggregates traffic information from multiple data sources, including vehicle GPS data, status information from traffic lights, and data from satellite and ground sensors. The collected data is analyzed using generative models to predict traffic conditions and accident potential in real time. Based on these predictions, the server determines necessary interventions. For example, if congestion is predicted near a highway exit, the server adjusts the timing of traffic light control to smooth traffic flow.

[0587] Terminal (vehicle side) processing

[0588] The terminal acquires environmental information in real time from sensors mounted on the vehicle and transmits it to the server. It also receives instructions from the server and controls the vehicle's operation based on these instructions. For example, if it receives an instruction to slow down at a specific intersection, the terminal automatically adjusts the vehicle's speed to maintain safe driving.

[0589] User roles

[0590] Users monitor the system's behavior while driving the vehicle and record any abnormal situations. This helps identify operational problems and contributes to future system improvements.

[0591] This system utilizes models specifically tailored to the traffic conditions of a particular region, enabling optimal control that takes into account region-specific traffic patterns. For example, in urban centers, it can manage peak traffic flow and minimize risks. In this way, it reduces accident risk and ensures smooth traffic flow.

[0592] The following describes the processing flow.

[0593] Step 1:

[0594] The server receives vehicle GPS data, traffic signal status, and satellite data, integrates them, and stores them in a database.

[0595] Step 2:

[0596] The server inputs stored data into a generative model and analyzes traffic conditions in real time. This analysis predicts the occurrence of traffic congestion and the likelihood of accidents.

[0597] Step 3:

[0598] Based on the analysis results, the server determines the necessary interventions and generates commands to adjust the timing of traffic signal control or direct instructions to vehicles.

[0599] Step 4:

[0600] The terminal adjusts the vehicle's operation based on instructions received from the server. Specifically, it performs actions such as driving at a predetermined speed and selecting a specific route.

[0601] Step 5:

[0602] The user monitors the system's operation and checks for any abnormalities in the vehicle's behavior. If an abnormality occurs, the user records the details and reports them to the server.

[0603] Step 6:

[0604] The server improves the system and enhances the accuracy of the generative model based on user feedback and new data.

[0605] (Example 1)

[0606] 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".

[0607] [Due to the increase in traffic volume accompanying urbanization in recent years, the frequency of traffic congestion and accidents has increased. Against this backdrop, it is necessary to manage traffic flow more efficiently and safely. Conventional traffic systems struggle to grasp the situation in real time and respond quickly, and they do not adequately consider the traffic characteristics of each region. Therefore, a new method is needed to optimize traffic conditions in real time by utilizing vehicle and traffic signal data.]

[0608] 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.

[0609] In this invention, the server includes means for aggregating diverse environmental data, including location information and traffic signal status information acquired from vehicles, to monitor traffic conditions; means for analyzing the aggregated data at high speed using a generation AI model to predict traffic flow and congestion in real time; and means for dynamically controlling the signal system based on the predictions and issuing commands to enhance traffic safety. This makes it possible to optimize traffic flow in real time, prevent accidents and congestion, and achieve safe and efficient traffic management.

[0610] "Location information acquired from the vehicle" refers to data generated by the vehicle's onboard positioning system to determine the vehicle's geographical location.

[0611] "Traffic light status information" refers to information that indicates the current operating status of a traffic signal (e.g., red light, green light, yellow light, etc.).

[0612] "Environmental data" refers to data that includes information about events around a vehicle, such as location information, traffic light status, weather information, and road conditions.

[0613] A "generative AI model" is a program that uses artificial intelligence technology to analyze data and predict future situations.

[0614] "Traffic flow" is a concept that describes the patterns, speeds, and densities of vehicle and pedestrian movement within a specific region or area.

[0615] "Traffic congestion forecasting" is a process that uses collected data to predict potential traffic congestion in the future.

[0616] "Dynamic control of the signal system" refers to appropriately changing the timing of traffic light operation based on real-time data and predictions.

[0617] "Traffic safety" means maintaining a state in the transportation system where accidents and dangers are minimized, and people and goods can safely reach their destinations.

[0618] The system according to this invention mainly consists of a server, a terminal, and a user.

[0619] The server utilizes diverse hardware and software to aggregate environmental data, including location information and traffic signal status information acquired from vehicles. Specifically, it uses a database system to efficiently retrieve and store this information. On the server, a generative AI model is used to analyze the aggregated data. This model is used to evaluate traffic flow in real time and predict future congestion. For example, a generative AI model built using machine learning libraries can rapidly analyze the acquired data and generate predictions.

[0620] The terminal acquires environmental data in real time through various sensors mounted on the vehicle. This includes cameras, LiDAR, radar, etc. This data is processed by a computer within the terminal and transmitted to a server. It also receives commands from the server and plays a role in controlling the vehicle's operation. This control uses the vehicle's control system to maintain safe and efficient driving.

[0621] Users are obligated to monitor the system's behavior while driving and record any abnormalities that occur. Real-time traffic information and system status can be viewed via smartphones or in-car displays. This allows users to respond quickly to problems, maintain records, and provide feedback.

[0622] As a concrete example, when optimizing traffic flow in the city center, the server issues commands to adjust the timing of traffic signal control. This allows for the management of peak traffic flow and the prevention of congestion.

[0623] An example of a prompt might be, "Suggest a way to optimize traffic signal control based on peak traffic conditions in city A." This prompt functions as a question to a generative AI model, seeking methods to provide appropriate solutions to a specific traffic problem.

[0624] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0625] Step 1:

[0626] The server receives environmental data, including location information and traffic signal status information acquired from vehicles. This data is input directly from GPS devices and signal control interfaces. The input data is stored in a database system and prepared for further analysis.

[0627] Step 2:

[0628] The server takes in accumulated environmental data and supplies it to the AI ​​model. The AI ​​model uses the input data to analyze traffic flow and the likelihood of congestion. In this process, machine learning algorithms are used to extract correlations and patterns from the data. The output is real-time traffic prediction results.

[0629] Step 3:

[0630] The server generates control commands for the signal system based on prediction results obtained from the generated AI model. By dynamically adjusting the timing of the traffic signals, it optimizes traffic flow. These commands are transmitted to each traffic signal via a communication protocol.

[0631] Step 4:

[0632] The terminal collects real-time environmental data from various sensors within the vehicle and transmits it to the server. Simultaneously, it receives control commands from the server and transmits them to the vehicle's control system. This enables specific vehicle actions (such as speed adjustment or lane changes).

[0633] Step 5:

[0634] While driving, users monitor system control commands and traffic forecast information via the in-vehicle display. They record abnormal situations as needed and take manual action when problems occur. This provides feedback for further system improvements.

[0635] (Application Example 1)

[0636] 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".

[0637] In autonomous vehicle driving technology, understanding traffic conditions in real time and performing appropriate control is extremely difficult. To solve this problem, a system is needed that efficiently predicts traffic information and ensures safe operation. Furthermore, if information is not provided to the driver, it may delay emergency response, which is also a crucial issue that needs to be addressed.

[0638] 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.

[0639] In this invention, the server includes means for monitoring road conditions by collecting and integrating information from vehicles; means for analyzing and predicting the aggregated information in real time using a generated AI model; and means for visually presenting traffic conditions and predicted information through an information display terminal. This allows drivers to immediately grasp traffic information and supports safe driving.

[0640] "Information from the vehicle" refers to data collected through sensors and communication devices installed on the vehicle, including information on location, speed, acceleration, and the surrounding environment.

[0641] "Integration" refers to the process of centrally combining data collected from multiple sources and converting it into a consistent format.

[0642] "Road condition monitoring" means understanding road conditions in real time, such as traffic flow, traffic signal status, road congestion, and the presence or absence of obstacles.

[0643] A "generative AI model" is an algorithm that predicts future states by analyzing large amounts of data and learning patterns.

[0644] "Real-time analysis and prediction" means rapidly processing data in real time and estimating future situations.

[0645] "Issuing commands to traffic controllers" means instructing traffic signals and vehicle operating systems to perform actions according to the situation.

[0646] "Road traffic safety" means preventing traffic accidents from occurring and maintaining a state where traffic flows smoothly and safely.

[0647] An "information display terminal" is a device designed to convey information to users in an easy-to-understand manner, and is used to visually display traffic conditions and forecast information.

[0648] "Visually presenting traffic conditions and forecast information" means displaying current traffic conditions and future forecasts in a format that drivers and users can intuitively understand.

[0649] "Receiving feedback and performing analysis to improve" means evaluating the results of interventions performed by the system and analyzing those results in detail to further improve performance.

[0650] The system used to realize this application supports real-time traffic control in autonomous vehicles. The system integrates input data from sensors mounted on the vehicle and performs analysis and prediction using an AI model generated by a server. This enables rapid response to changes in road conditions.

[0651] The server aggregates vehicle location information, speed, and traffic signal information obtained from satellite data and ground sensors. This includes data from GPS and sensors installed on each vehicle. The aggregated data is analyzed using a generative AI model (e.g., TensorFlow) to calculate traffic flow, congestion predictions, and accident probabilities in real time.

[0652] In particular, the server maintains safe traffic conditions by issuing commands to traffic controllers, such as controlling traffic signals and giving instructions to vehicles, based on these analysis results. In addition to explicit commands, the prediction results are also visually presented to the driver. Information display terminals (e.g., smart glasses or head-mounted displays) play this role, creating a system where users can easily obtain and confirm information.

[0653] For example, when the server predicts a traffic congestion at an intersection, the smart glasses will display the message, "Longer-than-usual traffic light wait times are expected at the intersection. We recommend taking an alternative route." This information is updated in real time, and recommendations based on a generated AI model are continuously produced.

[0654] An example of a prompt message to the generative model would be, "Display the traffic forecast for the next intersection on the screen. The display should include the predicted waiting time and recommended detour route." The server then sends the appropriate data to an external system to assist the user.

[0655] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0656] Step 1:

[0657] The server collects and integrates information from satellite data, ground sensors, and vehicle sensors. This information includes vehicle location, speed, and traffic signal status. The integrated information forms the basis for understanding the overall traffic situation.

[0658] Step 2:

[0659] The server uses a generative AI model to analyze and predict the integrated data. This analysis predicts current traffic flow, congestion trends, and accident likelihood. Based on the input data, the model applies learned traffic patterns to estimate future situations. The output includes locations and times where specific traffic control measures are required.

[0660] Step 3:

[0661] Based on the analysis results, the server issues commands to traffic controllers. These commands include things like the timing of traffic signal changes and speed adjustments for specific vehicles. This helps to alleviate congestion and promote safer traffic flow.

[0662] Step 4:

[0663] The server sends the analysis results and generated commands to the information display terminal. This information is structured as a prompt message and sent in a format that the terminal can display. For example, it might be in the format of, "An increase in traffic light waiting time at intersections is predicted. We recommend taking an alternative route."

[0664] Step 5:

[0665] The terminal displays visual information to the driver through an information display device (e.g., smart glasses). Based on this information, the driver can consider actions such as changing the route or adjusting their speed. By providing this information, the driver is supported in driving more safely and efficiently.

[0666] Step 6:

[0667] Users drive based on the information provided and record any abnormalities they detect. This record is sent to the server as feedback. This feedback is used to improve the system and contribute to improving the accuracy of future traffic predictions.

[0668] 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.

[0669] This invention provides a system that enhances traffic safety by considering not only vehicle operation but also the user's emotional state. The program processing of this system is described below.

[0670] Server-side processing

[0671] The server integrates user sentiment data provided by the sentiment engine with conventional traffic data. The server inputs this composite dataset into a generative model to analyze traffic conditions in real time. This allows for the prediction of safety risks on the road and the sending of appropriate commands to the traffic control system. For example, if a user's stress level is high, the server can issue commands to tighten vehicle speed limits and increase intervention by driver assistance systems.

[0672] Terminal (vehicle side) processing

[0673] The terminal not only adjusts vehicle operation according to instructions received from the server, but also acquires user emotion data from sensors inside the vehicle and transmits it to the server. Specifically, the terminal analyzes the user's tone of voice and facial expressions to estimate their emotional state in real time. This allows the terminal to change the driving mode according to the user's current emotions.

[0674] User roles

[0675] Users must ensure that the emotional engine accurately recognizes their emotional state by optimizing the in-vehicle conditions. For example, clear voice instructions and ensuring their face is clearly visible to the camera will allow the system to analyze emotions more accurately. They are also expected to provide feedback on the driving assistance and interventions the system offers.

[0676] This system further improves driving safety and comfort by considering the user's emotional state when controlling traffic. For example, if fatigue accumulates during long drives, the system can prevent excessive fatigue by enhancing driver assistance functions. Such an integrated system makes it possible to simultaneously improve vehicle operation safety and user peace of mind.

[0677] The following describes the processing flow.

[0678] Step 1:

[0679] The server receives various data, including vehicle GPS data, traffic signal status, and user sentiment data, and integrates it into a database.

[0680] Step 2:

[0681] The device uses cameras and microphones inside the vehicle to analyze the user's facial expressions and voice tone in real time and estimate the user's emotional state. This estimation result is then sent to a server.

[0682] Step 3:

[0683] The server inputs integrated data into a generative model, analyzing traffic conditions and user emotional states in real time. This allows it to predict potential risks.

[0684] Step 4:

[0685] Based on the analysis results, the server issues commands to the traffic control system, such as adjusting signal timings or limiting vehicle speeds. Furthermore, if it determines that user stress levels are high, it enhances driver assistance.

[0686] Step 5:

[0687] The terminal receives commands from the server and controls the vehicle's movements. At intersections where concessions are required, it automatically slows down and activates the driver assistance system.

[0688] Step 6:

[0689] The user reviews the driving assistance provided by the system and provides feedback to the system as needed. For example, if the user feels the assistance is inappropriate, they record this information and report it to the server.

[0690] Step 7:

[0691] The server collects user feedback and adjusts and improves the generative model algorithm to help improve future models.

[0692] (Example 2)

[0693] 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".

[0694] In modern transportation systems, the impact of drivers' emotional states on traffic safety is often overlooked. Therefore, there is a need for systems that effectively mitigate human errors caused by driver emotional stress and fatigue, and the resulting risk of traffic accidents. Conventional traffic control systems only consider environmental factors and fail to reflect the driver's internal factors; therefore, new methods are needed to improve this.

[0695] 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.

[0696] In this invention, the server includes means for monitoring traffic conditions by collecting and integrating data from vehicles and emotional data; means for analyzing the aggregated data in real time using a generative model and making predictions that take emotional states into account; and means for issuing commands to the vehicle control system based on the predicted results to maintain traffic safety in accordance with emotional states. This enables safe and efficient traffic control by reflecting the emotional state of drivers in real time.

[0697] "Data" refers to a variety of information, including vehicle operation information and user emotional states, which the system uses to analyze traffic conditions.

[0698] "Emotional data" refers to information indicating the driver's emotional state, estimated based on the tone of their voice and facial expressions, and is used in traffic control to ensure safe driving.

[0699] A "generative model" refers to an algorithm used to analyze large amounts of data and predict traffic conditions, and is particularly characterized by its consideration of sentiment data.

[0700] A "traffic control system" refers to a set of technical means used to regulate vehicle operation and maintain road safety, and which function based on commands from a server.

[0701] A "command" refers to a specific operational instruction issued by the server based on the results of its analysis of traffic conditions, which controls the operation of vehicles and driver assistance systems.

[0702] "Feedback" refers to the driver's reaction and opinion to the system's intervention, and is used to improve the system in the future.

[0703] "Intervention" refers to actions taken by the system to influence vehicle operation in response to the driver's emotional state or traffic conditions, and is implemented to improve safety.

[0704] The system of this invention consists of three components: a vehicle terminal, a central server, and the driver. The terminal is installed inside the vehicle and uses sensors such as cameras and microphones to collect emotional data from the driver. This includes a process of analyzing voice tone and facial expressions in real time. This data is then transmitted to the server via secure communication.

[0705] The server integrates emotional data received from terminals with various traffic-related data (e.g., traffic volume, weather, road conditions, etc.). This generates a complex dataset that includes emotional states. The server has a generative AI model implemented, which is designed to analyze the data in real time and predict traffic conditions. Based on this analysis, the server generates commands for the traffic control system. These commands are adjusted according to the driver's emotional state; for example, if high stress is detected, measures such as tightening speed limits are taken.

[0706] Furthermore, users play a crucial role in improving the system's accuracy. Specifically, providing clear voice commands and ensuring their faces are clearly visible to the camera improves the accuracy of emotion data. In addition, providing feedback on the driving assistance and system interventions offered contributes to the system's continuous improvement.

[0707] For example, if a user's stress level increases during prolonged driving, the system will enhance driver assistance functions to reduce user fatigue. An example of this prompt would be, "Explain how user emotional data influences traffic control."

[0708] Such a system is expected to enable traffic control that reflects the driver's emotional state in real time, thereby improving safety and comfort.

[0709] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0710] Step 1:

[0711] The device collects user emotional data using sensors such as cameras and microphones mounted on the vehicle. Specifically, it captures the user's face with a camera and estimates their emotional state in real time using facial expression analysis software. It also analyzes the user's voice tone using a microphone and measures their stress level. It receives video and audio data as input and generates an estimated result of the emotional state as output.

[0712] Step 2:

[0713] The device sends the collected emotional data to the server. The specific input is digital data indicating the user's emotional state, and this data is securely transmitted to the server using data encryption technology. The output after transmission is the server receiving this data.

[0714] Step 3:

[0715] The server integrates sentiment data received from terminals with conventional traffic data. Specifically, it receives traffic volume, weather information, and road condition data obtained from traffic sensors as input, and combines this with the received sentiment data. Through data processing and calculations, it uses this information to create an integrated dataset. The output is the integrated set of data.

[0716] Step 4:

[0717] The server analyzes the integrated dataset using a generative AI model and makes real-time predictions based on traffic conditions and user sentiment. The input is the previously integrated dataset, and the output is the predicted traffic risk and countermeasures. The specific data calculations are performed using AI modeling techniques to calculate a risk score and determine the necessary traffic control actions.

[0718] Step 5:

[0719] Based on the analysis results, the server issues commands to the vehicle control system. The input is the predicted risks and countermeasures, and the output is the specific control command. Specific actions include speed adjustments and enhanced intervention of driver assistance systems to ensure the vehicle complies with the command.

[0720] Step 6:

[0721] The user experiences the vehicle's behavior in response to commands from the terminal and server, and provides feedback to the system. The input in this step is the user's perception and opinions regarding the vehicle's behavior, while the output is the feedback data sent to the system. Specifically, the user evaluates the effectiveness of the driving assistance and suggests improvements.

[0722] (Application Example 2)

[0723] 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".

[0724] Current transportation systems focus on safety measures based on vehicle operation, but they cannot take into account the emotional state of the user. Therefore, there is a challenge in adequately mitigating the risk of accidents caused by emotional factors such as driver stress and fatigue. Furthermore, current systems also struggle to implement appropriate control tailored to local traffic patterns.

[0725] 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.

[0726] In this invention, the server includes means for monitoring traffic conditions by collecting and integrating data from vehicles and user emotional state information; means for analyzing and predicting the aggregated data and emotional state information in real time using a generative model; and means for issuing commands to the traffic control system and the vehicle's driving mode based on the predicted results to maintain traffic safety and comfort. This enables appropriate traffic control in accordance with the user's emotional state, thereby improving traffic safety and comfort.

[0727] "Data from the vehicle" refers to various types of data collected from the vehicle's sensors and communication equipment, including location information, speed, and acceleration.

[0728] "User emotional state information" refers to information indicating emotional states such as stress, excitement, and fatigue, obtained by analyzing the user's voice tone and facial expression data.

[0729] "Monitoring traffic conditions through integration" means combining and analyzing collected vehicle data and emotional state information to understand the current traffic situation.

[0730] A "generative model" is a machine learning or AI algorithm used to analyze traffic conditions and emotional states based on collected data and to predict future states.

[0731] "Analyzing and predicting in real time" is a process of analyzing data as soon as it is collected and quickly predicting future traffic risks.

[0732] "Issuing commands to traffic control systems and vehicle driving modes to maintain traffic safety and comfort" means issuing appropriate commands such as driving assistance and speed adjustments based on predictions to ensure road safety and passenger comfort.

[0733] "Receiving feedback and making improvements" is a process of learning from the effects of system interventions and user responses in order to improve the quality of future control.

[0734] The system implementing this invention aggregates data from vehicles and user emotional state information to improve traffic safety and comfort. The server, terminals, and users play key roles.

[0735] The server integrates data such as location, speed, and acceleration collected from vehicle sensors and communication devices, along with emotional state information analyzed from the user's voice tone and facial expressions. This information is processed in real time using emotion analysis tools such as Microsoft Azure Cognitive Services. Next, a generative AI model is used to analyze this combined data and predict safety risks and traffic congestion. Based on these predictions, the server commands the traffic control system and instructs changes to the vehicle's driving mode.

[0736] The terminal (smartphone or in-vehicle device) receives commands from the server and adjusts the vehicle's driving mode. Furthermore, the terminal also plays a role in acquiring the user's emotional state in real time and transmitting it to the server. This supports safe and comfortable driving. The system utilizes the smartphone's camera and microphone to perform facial recognition and voice analysis to recognize the user's emotional state.

[0737] To make the most of this system, users need to properly position their smartphones so that their faces are clearly visible to the camera. They are also expected to provide feedback on the driving assistance provided by the system, contributing to its further improvement. For example, if user fatigue is detected due to prolonged driving, the server will recommend that the driver take a break via the terminal, thereby enhancing the vehicle's driving assistance functions.

[0738] An example of a prompt that fits the generating AI model is, "Based on emotion analysis data, create an action plan to monitor the user's fatigue in real time during long drives and change to an appropriate driving mode." This allows the system to generate appropriate commands that respond to the user's emotions.

[0739] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0740] Step 1:

[0741] The server receives sensor data such as location, speed, and acceleration transmitted from the vehicle. In addition, it obtains user emotional state information from the terminal. Input data includes vehicle telematics data and user voice tone and facial expression analysis results. The server integrates this data and records it in a database. The output is the integrated dataset.

[0742] Step 2:

[0743] The server uses a generative AI model to analyze and predict traffic conditions and user sentiment in real time, taking an integrated dataset as input. Sentiment analysis is performed using Azure Cognitive Services. Data calculations assess safety risks and congestion potential. The output includes the predicted level of safety risk and appropriate corrective actions.

[0744] Step 3:

[0745] The server generates appropriate commands for the traffic control system and the vehicle's driving mode based on predicted results. These commands include speed adjustments and enhancements to driver assistance functions. During this process, prompts are input to the generating AI model to construct an appropriate action plan. The output is a specific command for the vehicle and the traffic control system.

[0746] Step 4:

[0747] The terminal adjusts the vehicle's driving mode as needed, following commands received from the server. Specific actions include reducing the vehicle's speed and activating driver assistance functions. The terminal also continuously monitors the user's emotional state and sends new data back to the server. The output consists of the adjusted driving mode and updated emotional state information.

[0748] Step 5:

[0749] Users provide feedback on the system's driving assistance features and their experience with the system. This feedback is sent to the server via a terminal. This information is stored on the server as data for future system improvements. The output is qualitative data representing user feedback.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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."

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0771] The following is further disclosed regarding the embodiments described above.

[0772] (Claim 1)

[0773] [Means of monitoring traffic conditions by collecting and integrating data from vehicles,

[0774] [Means for analyzing and predicting aggregated data in real time using generative models,

[0775] [Means of issuing commands to the traffic control system based on predicted results to maintain traffic safety,

[0776] [Means of receiving feedback after system intervention and conducting analysis to make improvements,

[0777] A system that includes this.

[0778] (Claim 2)

[0779] The system according to claim 1, which provides control optimized for region-specific traffic patterns by using a region-specific generative model.

[0780] (Claim 3)

[0781] [The system according to claim 1, which prevents accidents and traffic congestion by performing predictive interventions using real-time data from vehicles.]

[0782] "Example 1"

[0783] (Claim 1)

[0784] [A means of monitoring traffic conditions by aggregating diverse environmental data, including location information and traffic signal status information acquired from vehicles,]

[0785] [A method for rapidly analyzing the aggregated data using a generative AI model and predicting traffic flow and congestion in real time,

[0786] [Means for dynamically controlling the signal system based on the above predictions and issuing commands to enhance traffic safety,

[0787] [Means for verifying traffic conditions after intervention and conducting analysis to improve system performance,

[0788] A system that includes this.

[0789] (Claim 2)

[0790] [The system according to claim 1, which uses a specialized generation model based on traffic data across multiple regions to realize control that corresponds to region-specific traffic patterns.

[0791] (Claim 3)

[0792] The system according to claim 1, which effectively reduces accidents and congestion by making use of real-time information from vehicle and environmental sensors to perform predictive interventions against future traffic risks.

[0793] "Application Example 1"

[0794] (Claim 1)

[0795] [Means of monitoring road conditions by collecting and integrating information from vehicles,

[0796] [Means for analyzing and predicting aggregated information in real time using a generative AI model,

[0797] [A means of issuing commands to traffic controllers based on predicted results to maintain road traffic safety,

[0798] [Means of visually presenting traffic conditions and forecast information through information display terminals,

[0799] [Means of receiving feedback after system intervention and performing analysis to make improvements,

[0800] A system that includes this.

[0801] (Claim 2)

[0802] The system according to claim 1, which provides control optimized for road patterns specific to a region by using a region-specific generative AI model.

[0803] (Claim 3)

[0804] [The system according to claim 1, which prevents accidents and road congestion by making predictive interventions using real-time information from vehicles.

[0805] "Example 2 of combining an emotion engine"

[0806] (Claim 1)

[0807] [Means of monitoring traffic conditions by collecting and integrating data from vehicles and emotional data,

[0808] [A means of analyzing aggregated data in real time using a generative model and making predictions that take emotional states into account,

[0809] [A means of issuing commands to the vehicle control system based on predicted results and maintaining traffic safety in accordance with emotional state,

[0810] [Means of receiving feedback after system intervention, analyzing it, and improving autonomous driving assistance,]

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, which provides control optimized for region-specific traffic patterns and user emotional states by using a generative model specific to each region.

[0814] (Claim 3)

[0815] [The system according to claim 1, which prevents human error and traffic congestion by performing predictive interventions using vehicle and user emotion data.]

[0816] "Application example 2 when combining with an emotional engine"

[0817] (Claim 1)

[0818] [A means of monitoring traffic conditions by collecting and integrating data from vehicles and user emotional state information,

[0819] [Means for analyzing and predicting aggregated data and emotional state information in real time using generative models,

[0820] [Means for issuing commands to the traffic control system and vehicle driving modes based on predicted results, to maintain traffic safety and comfort,

[0821] [Methods for receiving feedback after system intervention, analyzing it while considering the user's emotional state, and making improvements]

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, which provides control optimized for region-specific traffic patterns and user emotional states by using a generative model specific to each region.

[0825] (Claim 3)

[0826] [The system according to claim 1, which uses real-time data of vehicles and users to perform predictive interventions, thereby preventing accidents and traffic congestion in accordance with emotional states.] [Explanation of Symbols]

[0827] 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 monitoring traffic conditions by collecting and integrating data from vehicles, A means of analyzing and predicting aggregated data in real time using a generative model, Based on predicted results, a means of issuing commands to the traffic control system to maintain traffic safety, A means of receiving feedback after system intervention and conducting analysis to make improvements, A system that includes this.

2. The system according to claim 1, which provides control optimized for region-specific traffic patterns by using a region-specific generative model.

3. The system according to claim 1, which prevents accidents and traffic congestion by making predictive interventions using real-time data from vehicles.

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