System

Abstract

A system is provided.SOLUTION: A system comprising: a communication infrastructure installed in a signal; a data acquisition device that acquires traffic video data; a data acquisition device that acquires weather data; a data aggregation device that aggregates data from the data acquisition devices via a high-speed communication network; an analysis unit that analyzes data transmitted from the data aggregation device; and a control device that controls a signal based on an analysis result of the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Current traffic systems entail many social costs, including traffic congestion, traffic accidents, traffic violation enforcement, and road maintenance. Furthermore, progress in developing communication infrastructure to support the widespread adoption of autonomous driving technology has been slow. This has placed a heavy burden on police and local governments, creating a need for efficient traffic management. To address these issues, a system is needed that can collect and analyze traffic information in real time and dynamically control traffic lights. [Means for solving the problem]

[0005] The present invention provides a system that includes a communication infrastructure installed at traffic lights, a data acquisition device that acquires traffic video data, a data acquisition device that acquires weather data, a data aggregation device that aggregates these data, analysis means that analyzes the aggregated data, and a control device that controls the traffic lights based on the analysis results. Specifically, the analysis means includes means for identifying traffic volume, traffic accidents, and vehicles committing traffic violations, as well as means for predicting road icing and snow accumulation. The control device has a function to dynamically change the lighting status of traffic lights to alleviate traffic congestion and respond to traffic accidents. This provides a system that efficiently solves various issues facing a transportation-related society.

[0006] "Communications infrastructure" refers to technical equipment installed at traffic lights that transmits and receives data using high-speed communications networks such as 5G.

[0007] "Data acquisition devices" are devices such as cameras and sensors that acquire traffic video data and weather data in real time.

[0008] The "data aggregating device" is a device that temporarily aggregates data transmitted from the data acquiring devices and sends the data to the analyzing means.

[0009] "Analysis means" refers to AI or programs that process aggregated data and analyze traffic volume, traffic accidents, traffic violations, and predictions of road freezing and snow accumulation.

[0010] A "controller" is a technical device for dynamically changing the lighting state and operation of a traffic light based on the results of the analysis means.

[0011] A "traffic light" is a device that controls various red, yellow, and green signals installed to manage traffic on roads.

[0012] "Traffic volume" is data that indicates the number of vehicles and pedestrians passing through a particular road within a certain period of time.

[0013] "Traffic accidents" refer to accidents that occur on roads between vehicles or between vehicles and pedestrians, and the data includes the location, scale, and impact of the accident.

[0014] "Traffic violation vehicles" is data used to identify vehicles that do not follow the rules of the Road Traffic Act, such as speeding or ignoring traffic signals.

[0015] "Freezing and snowfall forecast" is data that analyzes the risk of road freezing and snowfall based on meteorological data.

[0016] "Dynamic change" refers to the operation of instantly changing the lighting time and pattern of traffic lights based on data acquired in real time according to the situation. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The present invention is a system consisting of a communication infrastructure installed at traffic lights, a data acquisition device that acquires traffic video data, a data acquisition device that acquires weather data, a data aggregation device that aggregates these data, an analysis means that analyzes the aggregated data, and a control device that controls traffic lights based on the analysis results.This system will improve the efficiency of traffic management and solve problems such as traffic accidents, congestion, violation enforcement, and road condition management.

[0039] System Configuration

[0040] Communication infrastructure (terminals)

[0041] The communication infrastructure installed in the traffic lights uses high-speed communication networks such as 5G to transmit data from the data acquisition devices to a central traffic control server.

[0042] Data acquisition device (terminal)

[0043] The system includes cameras that capture traffic video data and weather sensors that capture meteorological data such as temperature, humidity, and wind speed.

[0044] Data aggregation device (terminal)

[0045] The data sent from the data acquisition devices is temporarily aggregated and sent to an analysis means, which then transmits the data in real time to a central traffic control server using 5G communications.

[0046] Analysis method (server)

[0047] The traffic control server receives the data, and the AI ​​analysis engine analyzes traffic volume, traffic accidents, traffic violations, and the risk of road icy and snow accumulation. Specifically, video data is used to recognize license plates and identify speeding violations and traffic accidents. Weather data is used to analyze the risk of icy and snow accumulation.

[0048] Control device (terminal)

[0049] Based on the results of the analysis, the lighting status and operation of traffic lights are dynamically changed. For example, the green light time of a traffic light is extended during times of heavy traffic, and an emergency response signal is set in the event of a traffic accident.

[0050] Natural language explanation of program processing

[0051] 1. Data collection and upload

[0052] Cameras (terminals) capture real-time traffic video data from traffic light locations, while weather sensors (terminals) measure temperature, humidity, wind speed, and other data. This data is temporarily aggregated and packaged via 5G base stations (terminals), and then uploaded to a central traffic control server via 5G communications.

[0053] 2. Data Analysis

[0054] The traffic control server (server) receives the uploaded data, and the analysis means (server) analyzes traffic volume, traffic accidents, vehicles violating traffic rules, and the risk of road icing and snow accumulation. For example, camera footage can be used to read license plates using OCR technology to identify vehicles speeding. It can also analyze the risk of road icing based on weather data.

[0055] 3. Dynamic Signal Control

[0056] Based on the results of the analysis, the traffic light control system (server) determines the lighting status of traffic lights. For example, if there is congestion, the green light duration of the traffic light will be extended appropriately to smooth the flow of traffic. Also, if a traffic accident occurs, priority signals will be set for traffic lights on the route so that emergency vehicles can arrive at the scene quickly.

[0057] Specific examples

[0058] Consider the case where the system of the present invention is installed at traffic lights installed in the center of City A. During the morning rush hour, a camera captures video data of the intersection at 30 frames per second and collects weather data of a temperature of 2°C and humidity of 80%. This data is transmitted in real time to a traffic control server via a 5G base station.

[0059] The traffic control server's analytics analyzed the video data and identified 20 vehicles in a traffic jam. Furthermore, license plate recognition technology was used to identify speeding vehicles. Weather data analysis revealed a high risk of road surface freezing due to low temperatures.

[0060] Based on this, the traffic light control system will carry out appropriate signal control, such as extending the green light time to ease congestion, changing the signal to prevent freezing, and setting priority signals to allow emergency vehicles to pass in the event of a traffic accident.

[0061] Public safety and local government officials can remotely check the system's data dashboard and implement traffic restrictions and enforcement based on real-time traffic conditions and weather forecasts. This system will efficiently solve a variety of problems facing society's transportation system.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] The camera (terminal) captures images of intersections and road conditions in real time. The camera generates video data at a rate of 30 frames per second to obtain traffic video data.

[0065] Step 2:

[0066] The weather sensor (terminal) measures weather data such as temperature, humidity, wind speed, etc. The weather data is acquired every minute and temporarily stored in the sensor's internal memory.

[0067] Step 3:

[0068] The 5G base station (terminal) aggregates data received from cameras and weather sensors, and packages the aggregated data for transmission to a central traffic control server via 5G communications.

[0069] Step 4:

[0070] The traffic control server (server) receives data sent from 5G base stations and stores it in a database. The received data includes video data and weather data.

[0071] Step 5:

[0072] The AI ​​analysis engine (server) analyzes the received video data. Specifically, it recognizes license plates, detects speeding violations, and identifies traffic accidents. It detects vehicles in the video and reads license plates using OCR technology. It also calculates the distance the vehicle travels between frames and calculates its speed to identify speeding violations.

[0073] Step 6:

[0074] The AI ​​analysis engine (server) analyzes weather data and predicts the risk of freezing and snow accumulation. It determines the possibility of freezing based on temperature and humidity data, and uses wind speed data to predict changes in snow accumulation and road conditions.

[0075] Step 7:

[0076] The traffic light control system (server) determines the lighting status of traffic lights based on the analysis results of the AI ​​analysis engine. When congestion occurs, the green light will be kept on for an extended period to smooth traffic flow. In addition, when a traffic accident occurs, priority signals will be set for emergency vehicles.

[0077] Step 8:

[0078] The traffic light (terminal) receives control instructions from the traffic light control system and changes the lighting status of the signal, thereby alleviating traffic congestion and responding to emergencies.

[0079] Step 9:

[0080] The traffic control server (server) provides analysis results and real-time data as a dashboard, which displays traffic congestion status, traffic accident information, weather forecasts, and more.

[0081] Step 10:

[0082] Public safety and local government officials (users) can remotely access the dashboard to view real-time traffic and weather data, and can remotely implement traffic restrictions and enforcement actions as needed.

[0083] Example 1

[0084] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0085] Conventional traffic management systems have difficulty controlling signals in real time according to traffic volume and weather conditions, resulting in problems such as traffic congestion, traffic accidents, and delays in identifying violating vehicles. They also fail to properly manage the risk of road ice and snow accumulation, which causes frequent traffic accidents and congestion. An effective system to solve these issues is needed.

[0086] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0087] In this invention, the server includes a communication infrastructure installed in the traffic control device, a data acquisition device that acquires traffic video data, a data acquisition device that acquires weather data, a data aggregation device that aggregates data from the data acquisition devices via a high-speed communication network, analysis means that analyzes data transmitted from the data aggregation device, and a control device that controls the traffic control device based on the analysis results of the analysis means. This enables traffic signal control in real time according to traffic volume and weather conditions, thereby easing traffic congestion, preventing traffic accidents, identifying violating vehicles, and appropriately managing the risk of road icing and snow accumulation.

[0088] "Communications infrastructure" refers to equipment installed in traffic control devices that includes a high-speed communications network for transmitting and receiving data.

[0089] The "data acquisition device" is a device for acquiring traffic video data and weather data, and includes cameras, weather sensors, etc.

[0090] The "data aggregating device" is a device that temporarily stores data acquired from a data acquiring device, packages the data, and transmits the data to an analyzing means.

[0091] The "analysis means" is a device or system that analyzes the data sent from the data aggregation device and identifies traffic volume, traffic accidents, vehicles violating traffic laws, and the risk of road icing or snow accumulation.

[0092] The "control device" is a device that dynamically changes the lighting state of traffic lights based on the analysis results of the analysis means, thereby easing traffic congestion and responding to traffic accidents.

[0093] "Traffic volume" is the number of vehicles passing through a particular point in a particular time period.

[0094] A "traffic accident" is an incident caused by traffic troubles, such as collisions between vehicles or single-vehicle accidents.

[0095] A "traffic violation vehicle" is a vehicle that violates traffic laws. This includes violations such as speeding and illegal parking.

[0096] "Freezing" is a phenomenon in which the temperature drops and the road surface freezes over.

[0097] "Snow accumulation" is a phenomenon in which the temperature drops and snow accumulates on roads and surfaces.

[0098] "OCR technology" is an abbreviation for optical character recognition technology, and is a technology for reading character information from image data.

[0099] A "priority signal" is a signal state that is set to allow emergency vehicles and vehicles requiring priority passage to pass more easily.

[0100] This invention is a system comprising a communication infrastructure installed in a traffic control device, a data acquisition device for acquiring traffic video data, a data acquisition device for acquiring weather data, a data aggregation device for aggregating these data, an analysis means for analyzing the aggregated data, and a control device for controlling the traffic control device based on the analysis results.

[0101] Communication infrastructure (terminals)

[0102] The communication infrastructure will be installed in the traffic control device and will transmit data received from the data acquisition device to a central traffic control server using a high-speed communication network. Specifically, 5G communication technology will be used.

[0103] Data acquisition device (terminal)

[0104] The data acquisition device includes cameras that capture traffic video data and weather sensors that capture meteorological data. The cameras capture real-time images of traffic conditions and vehicle movements at intersections and major roads. For example, video data is collected at 30 frames per second. The weather sensors measure temperature, humidity, wind speed, etc. and update the data every five seconds.

[0105] Data aggregation device (terminal)

[0106] The data aggregation device temporarily stores and packages the data sent from the data acquisition device. This sorts out inconsistencies in the data and makes it available for analysis in real time. For example, traffic video data and weather data are sorted in chronological order.

[0107] Analysis method (server)

[0108] The analysis means is installed on the traffic control server, which receives the transmitted data and performs initial processing. Specifically, the AI ​​analysis engine analyzes the video data and recognizes vehicle license plates using OCR technology. This makes it possible to identify speeding vehicles and illegally parked vehicles. Weather data is also analyzed to assess the risk of road icy conditions and snow accumulation.

[0109] Control device (terminal)

[0110] The control device dynamically changes the lighting status of the traffic lights based on the results of the analysis means, for example, by extending the green lighting time of the traffic lights during times of heavy traffic, or by setting priority signals for emergency vehicles in the event of a traffic accident.

[0111] Specific examples

[0112] For example, consider the case where this system is installed in the city center of City A. During the morning rush hour, a camera captures video data of an intersection at 30 frames per second and collects weather data such as a temperature of 2°C and humidity of 80%. The data aggregator organizes this data and transmits it to a traffic control server via 5G communications.

[0113] The traffic control server's analytics analyzes the video data and identifies a traffic jam of 20 vehicles. It then uses license plate recognition technology to identify speeding vehicles. Weather data analysis reveals a high risk of road icing due to low temperatures.

[0114] Based on this, the traffic light control system will carry out appropriate signal control. Specifically, it will extend the time that the green light remains on to alleviate congestion. In addition, in the event of a traffic accident, it will set priority signals so that emergency vehicles can arrive at the scene quickly.

[0115] Prompt Sentence Examples

[0116] 1. Please explain the specific steps by which the traffic control server's analytical means identifies traffic accidents from the uploaded data.

[0117] 2. What are the detailed steps for how the traffic light control system changes the operation of traffic lights when it detects congestion?

[0118] This system enables real-time traffic management, which can efficiently alleviate traffic congestion and prevent traffic accidents. It also enables road management according to weather conditions, providing a safer traffic environment. Public safety and local government officials can remotely view the system's data dashboard and use it as reference information for traffic restrictions and enforcement.

[0119] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0120] Step 1:

[0121] Data collection devices capture traffic footage and weather data.

[0122] Input: Real-time video data from the camera (terminal), weather data (temperature, humidity, wind speed) from the weather sensor.

[0123] How it works: The camera captures images of intersections and major roads at 30 frames per second, and the weather sensor measures temperature, humidity, and wind speed every 5 seconds.

[0124] Output: Video data and weather data.

[0125] Step 2:

[0126] The communication infrastructure receives the data acquired from the data acquisition device.

[0127] Input: Video data and meteorological data transmitted from the data acquisition device.

[0128] Specific operation: Data transmitted from the data acquisition device is temporarily stored via the communication infrastructure.

[0129] Output: Temporarily saved video data and weather data.

[0130] Step 3:

[0131] The data aggregation device acquires data from the communication infrastructure and packages it.

[0132] Input: Temporarily stored video data and weather data.

[0133] Specific operation: The data aggregator sorts out data inconsistencies and packages the data in chronological order.

[0134] Output: Packaged data.

[0135] Step 4:

[0136] The data aggregator transmits the packaged data to the traffic control server.

[0137] Input: Packaged data.

[0138] Specific operation: The data aggregation device uses 5G communications to send packaged data to the traffic control server in real time.

[0139] Output: Data sent to traffic control server.

[0140] Step 5:

[0141] The traffic control server receives the transmitted data and performs initial processing.

[0142] Input: The data sent.

[0143] Specific operation: The traffic control server checks for data inconsistencies and corrects the data as necessary.

[0144] Output: Organized data.

[0145] Step 6:

[0146] The analysis means analyzes the video data and the meteorological data.

[0147] Input: Organized video and weather data.

[0148] Specific operation: Analyzes video data using an AI analysis engine, performs OCR recognition of license plates and vehicle movement analysis, and evaluates the risk of road icy conditions and snow accumulation based on weather data.

[0149] Output: Analysis results (traffic accidents, violating vehicles, risk of freezing, etc.).

[0150] Step 7:

[0151] The control device dynamically changes the lighting status of the traffic light based on the analysis results.

[0152] Input: Analysis results.

[0153] Specific actions: When traffic volume is heavy, the green light will stay on for an extended period of time, and when a traffic accident occurs, traffic lights will be set to give priority to emergency vehicles.

[0154] Output: Changed traffic light status.

[0155] Step 8:

[0156] Users can remotely view the system's data dashboard and implement traffic restrictions and enforcement.

[0157] Input: Data dashboard information.

[0158] Specific operation: Public safety and local government officials can check traffic and weather conditions on the dashboard and remotely issue instructions on how to respond.

[0159] Output: Traffic restrictions and enforcement actions that were applied.

[0160] In this way, each step performs data processing and calculations based on the input to obtain the output of the next step.

[0161] (Application example 1)

[0162] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0163] Conventional traffic management systems have difficulty utilizing acquired traffic and weather data in real time, making it difficult to respond quickly to changes in traffic conditions. Furthermore, there is a lack of means to notify automated driving vehicles of traffic signal status and traffic forecast information, making it difficult to ensure smooth traffic flow. This makes it necessary to respond quickly to traffic congestion and traffic accidents.

[0164] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0165] In this invention, the server includes means for providing real-time traffic conditions using acquired traffic video data and weather data, means for providing traffic alerts and forecasts to autonomous vehicles based on analysis results, means for identifying vehicles violating traffic laws, and means for providing priority signal notifications to autonomous vehicles. This enables the use of traffic data in real time, enabling the prompt provision of information to autonomous vehicles and ensuring smooth traffic flow.

[0166] A "communication infrastructure" is a device installed at traffic lights that sends and receives data using a high-speed communication network.

[0167] A "data acquisition device" is a device installed to acquire traffic video data and weather data.

[0168] The "data aggregating device" is a device that temporarily aggregates data acquired from the data acquiring device and transmits the data to the data analyzing means.

[0169] "Analysis means" refers to a means for analyzing the aggregated data and obtaining useful information about traffic and weather conditions.

[0170] The "controller" is a device that controls traffic lights based on the analysis results of the analyzing means, thereby ensuring smooth traffic flow and safety.

[0171] "Traffic video data" is video data showing the traffic conditions on roads and intersections.

[0172] "Weather data" refers to data related to weather conditions (temperature, humidity, wind speed, etc.).

[0173] "Means for providing traffic conditions in real time" refers to means for analyzing acquired traffic video data and weather data and instantly providing information on current traffic conditions.

[0174] "Means for providing traffic alerts and predictions" means means for providing warnings and predictions about future traffic conditions and risks based on analyzed data.

[0175] The "means for identifying vehicles violating traffic regulations" refers to a means for analyzing traffic video data and identifying vehicles violating regulations.

[0176] A "means for providing a priority signal notification" is a means for notifying an autonomous vehicle of the priority signal status of a particular traffic light.

[0177] The system of the present invention includes a communication infrastructure installed at traffic lights, a data acquisition device that acquires traffic video data and weather data, a data aggregation device that aggregates the data, an analysis means that analyzes the data, a control device that controls the traffic lights based on the analysis results, and a means for providing real-time traffic conditions and traffic alerts and forecasts for autonomous vehicles.

[0178] Hardware and software used

[0179] The server is equipped with a communications infrastructure that utilizes a high-speed communications network (such as 5G), and cameras and weather sensors are connected to the data acquisition devices. The data acquired from these data acquisition devices is aggregated on the server via a data aggregation device.

[0180] The analysis method runs an AI analysis engine on a server to analyze traffic volume, traffic accidents, vehicles violating traffic rules, and the risk of road icy conditions and snow accumulation. This analysis method includes computer vision technology for video analysis and weather models for weather data analysis.

[0181] Data processing and calculation

[0182] The server analyzes the acquired traffic video data using computer vision technology (e.g., OpenCV) to identify traffic volume. It also identifies vehicles violating traffic rules using license plate recognition (e.g., OCR technology). Weather data analysis uses weather models (e.g., ARIMA models) to predict the risk of road icing and snow accumulation.

[0183] The control device dynamically changes the lighting status of traffic lights based on the analysis results, for example by extending the green light time depending on traffic volume or setting an emergency response signal in the event of a traffic accident.

[0184] Specific examples

[0185] For example, if the system of the present invention is installed at traffic lights installed in the center of City A, a camera will capture video data of the intersection in real time during the morning rush hour and collect weather data such as a temperature of 2°C and humidity of 80%. This data will be transmitted in real time to a traffic control server via a 5G base station.

[0186] The traffic control server's analytics analyzes the video data and identifies 20 vehicles in a traffic jam. License plate recognition technology also identifies three speeding vehicles. Weather data analysis reveals a high risk of road icing due to low temperatures.

[0187] Based on this, the traffic light control system will carry out appropriate signal control. Specifically, it will extend the green light time to alleviate congestion and change the traffic lights to prevent freezing. In addition, in the event of a traffic accident, priority signals will be set for traffic lights on the route so that emergency vehicles can arrive at the scene quickly. Public safety and local government officials can remotely check the system's data dashboard and implement traffic restrictions and enforcement remotely based on real-time traffic conditions and weather forecasts.

[0188] Prompt Sentence Examples

[0189] We are designing a smartphone application that displays traffic volume, weather data, and congestion information based on real-time traffic data from a traffic light control system. The following features are required:

[0190] 1. Real-time streaming of traffic video data.

[0191] 2. Predictive alerts for traffic jams and accidents.

[0192] 3. Aggregate daily traffic and weather data and generate reports.

[0193] 4. Notification of changes in traffic light operation.

[0194] Please generate source code using Python, including instructions for using the API and displaying data.

[0195] The above is an embodiment of the invention.

[0196] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0197] Step 1:

[0198] Cameras and weather sensors (terminals) capture traffic video data and weather data in real time. The cameras capture images of roads and intersections, while the weather sensors capture weather data such as temperature, humidity, and wind speed, and send them to the data acquisition device.

[0199] Input: Traffic video data, weather data

[0200] Output: Raw data obtained

[0201] Step 2:

[0202] The data acquisition device (terminal) transmits the acquired traffic video data and weather data to the data aggregation device via the 5G network. Here, the video data is transmitted in a stream format, and the weather data is transmitted as numerical data.

[0203] Input: Raw data acquired

[0204] Output: Stream format video data, numerical data format weather data

[0205] Step 3:

[0206] The data aggregating device (terminal) temporarily stores the received traffic video data and weather data, packages them, and transmits them to the analyzing means.

[0207] Input: Stream format video data, numerical data format weather data

[0208] Output: Packaged data

[0209] Step 4:

[0210] The analysis means (server) receives the packaged data and analyzes the traffic video data using computer vision technology (e.g., OpenCV) to measure traffic volume. Weather data is analyzed using weather models (e.g., ARIMA models) to analyze the risk of road icing and snow accumulation. Furthermore, license plate recognition (e.g., OCR technology) is used to identify vehicles violating traffic laws.

[0211] Input: Packaged traffic video data, weather data

[0212] Output: Analyzed traffic condition data, weather risk data, and violating vehicle data

[0213] Step 5:

[0214] The analysis means (server) provides real-time traffic conditions based on the analysis results and generates traffic alerts and predictions for autonomous vehicles.

[0215] Input: Analyzed traffic condition data, weather risk data, violating vehicle data

[0216] Output: Traffic reports, traffic alerts, forecast data

[0217] Step 6:

[0218] The control device (server) dynamically changes the lighting status of traffic lights based on the analysis results. Specifically, it extends the green light time when traffic volume is heavy, sets an emergency response signal when a traffic accident occurs, and provides priority signal notifications to autonomous vehicles.

[0219] Inputs: Traffic reports, traffic alerts, and forecast data

[0220] Output: Dynamically changed traffic light control information, priority signal notification

[0221] Step 7:

[0222] Users (public safety and local government officials) can remotely check the system's data dashboard and remotely implement traffic restrictions and enforcement based on real-time traffic conditions and weather forecasts.

[0223] Input: Dynamically changed traffic light control information, priority signal notification

[0224] Output: Traffic regulation and enforcement implementation plan

[0225] The above is the flow of processing of the system program for realizing the application example.

[0226] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0227] The present invention further improves the efficiency of traffic management and social safety by combining a system consisting of a communication infrastructure installed at traffic lights, a data acquisition device that acquires traffic video data, a data acquisition device that acquires weather data, a data aggregation device that aggregates this data, analysis means that analyzes the aggregated data, and a control device that controls traffic lights based on the analysis results with an emotion engine that recognizes user emotions.

[0228] System Configuration

[0229] Communication infrastructure (terminals)

[0230] The communication infrastructure installed in the traffic lights uses high-speed communication networks such as 5G to transmit data from the data acquisition devices to a central traffic control server.

[0231] Data acquisition device (terminal)

[0232] The system includes cameras that capture traffic video data and weather sensors that capture meteorological data such as temperature, humidity, and wind speed.

[0233] Data aggregation device (terminal)

[0234] The data sent from the data acquisition devices is temporarily aggregated and sent to an analysis means, which then transmits the data in real time to a central traffic control server using 5G communications.

[0235] Analysis method (server)

[0236] The traffic control server receives the data, and the AI ​​analysis engine analyzes traffic volume, traffic accidents, traffic violations, and the risk of road icy and snow accumulation. Specifically, video data is used to recognize license plates and identify speeding violations and traffic accidents. Weather data is used to analyze the risk of icy and snow accumulation.

[0237] Control device (terminal)

[0238] Based on the results of the analysis, the lighting status and operation of traffic lights are dynamically changed. For example, the green light time of a traffic light is extended during times of heavy traffic, and an emergency response signal is set in the event of a traffic accident.

[0239] Emotion engine (server)

[0240] The emotion engine analyzes the user's facial expressions and voice to recognize their emotions. The analyzed emotion data is fed back to the traffic management system, which then personalizes traffic light control and system notifications to match the user's emotions.

[0241] Natural language explanation of program processing

[0242] 1. Data collection and upload

[0243] Cameras (terminals) will capture real-time traffic video data from traffic light locations, and weather sensors (terminals) will measure data such as temperature, humidity, and wind speed. Additionally, microphones and cameras will be installed to capture the user's facial expressions and voice. This data will be temporarily aggregated and packaged via a 5G base station (terminal), and then uploaded to a central traffic control server via 5G communications.

[0244] 2. Data Analysis

[0245] The traffic control server (server) receives the uploaded data, and the analysis means (server) analyzes traffic volume, traffic accidents, vehicles violating traffic rules, and the risk of road icing and snow accumulation. For example, camera footage can be used to read license plates using OCR technology to identify vehicles speeding. It can also analyze the risk of road icing based on weather data.

[0246] 3. Emotion analysis

[0247] The emotion engine (server) analyzes the acquired facial expression and voice data to identify the user's emotions. For example, it uses facial expression analysis technology to recognize whether the user is stressed or calm. It also infers emotions from the tone and volume of the voice.

[0248] 4. Dynamic Signal Control

[0249] Based on the results of the analysis means and the emotion engine, the traffic light control system (server) determines the lighting status of traffic lights. For example, if there is congestion, the green light duration of the traffic light will be extended appropriately to smooth traffic flow. Also, in the event of a traffic accident, priority signals will be set for traffic lights on the route so that emergency vehicles can arrive at the scene quickly. Based on the analysis results of the emotion engine, if the user is feeling stressed, the notification method will be adjusted, such as providing emergency notifications as a priority.

[0250] Specific examples

[0251] Consider the case where the system of the present invention is installed at traffic lights installed in the center of City A. During the morning rush hour, a camera captures video data of the intersection at 30 frames per second and collects weather data with a temperature of 2°C and humidity of 80%. Furthermore, the system captures the facial expressions and voices of users near the intersection to generate emotion data. This data is then transmitted in real time to a traffic control server via a 5G base station.

[0252] The traffic control server's analytics analyzed the video data and identified 20 vehicles in a traffic jam. Furthermore, license plate recognition technology was used to identify speeding vehicles. Weather data analysis revealed a high risk of road surface freezing due to low temperatures.

[0253] The emotion engine analyzes users' facial expressions and voices to identify when some users are stressed, and this information is fed back to the traffic management system to adjust the notification method.

[0254] Based on this, the traffic light control system will carry out appropriate signal control. Specifically, it will extend the green light time to alleviate congestion and change the signal to prevent freezing. In addition, in the event of a traffic accident, it will set priority signals to allow emergency vehicles to pass. Furthermore, it will prioritize emergency notifications for users who are feeling stressed, encouraging them to take prompt action.

[0255] Public safety and local government officials can remotely check the system's data dashboard and implement traffic restrictions and enforcement based on real-time traffic conditions, weather forecasts, and emotion data. This system will efficiently solve various problems facing society with traffic and will also enable responses that are sensitive to user emotions.

[0256] The processing flow will be explained below.

[0257] Step 1:

[0258] The camera (terminal) captures images of intersections and road conditions in real time. The camera generates video data at a rate of 30 frames per second to obtain traffic video data.

[0259] Step 2:

[0260] The weather sensor (terminal) measures weather data such as temperature, humidity, wind speed, etc. The weather data is acquired every minute and temporarily stored in the sensor's internal memory.

[0261] Step 3:

[0262] The emotion engine camera and microphone (terminal) capture the user's facial expressions and voice. This generates emotion data. The facial expression data and voice data are temporarily stored in memory for emotion analysis.

[0263] Step 4:

[0264] The 5G base station (terminal) aggregates data received from cameras, weather sensors, and the emotion engine camera and microphone, and packages the aggregated data for transmission to a central traffic control server via 5G communications.

[0265] Step 5:

[0266] The traffic control server (server) receives data transmitted from 5G base stations and stores it in a database. The received data includes video data, weather data, and emotion data.

[0267] Step 6:

[0268] The AI ​​analysis engine (server) analyzes the received video data. Specifically, it recognizes license plates, detects speeding violations, and identifies traffic accidents. It detects vehicles in the video and reads license plates using OCR technology. It also calculates the distance the vehicle travels between frames and calculates its speed to identify speeding violations.

[0269] Step 7:

[0270] The AI ​​analysis engine (server) analyzes weather data and predicts the risk of freezing and snow accumulation. It determines the possibility of freezing based on temperature and humidity data, and uses wind speed data to predict changes in snow accumulation and road conditions.

[0271] Step 8:

[0272] The emotion engine (server) analyzes facial expression and voice data to identify the user's emotions. For example, it uses facial expression analysis technology to recognize whether the user is stressed or calm. It also infers emotions from the tone and volume of the voice.

[0273] Step 9:

[0274] The traffic light control system (server) determines the lighting status of traffic lights based on the results of the AI ​​analysis engine and emotion engine. When congestion occurs, the green light duration is extended to smooth traffic flow. In addition, when a traffic accident occurs, priority signals are set for emergency vehicles. Based on the analysis results of the emotion engine, notification methods are adjusted, such as prioritizing emergency notifications if the user is feeling stressed.

[0275] Step 10:

[0276] The traffic light (terminal) receives control instructions from the traffic light control system and changes the lighting status of the signal, thereby alleviating traffic congestion and responding to emergencies.

[0277] Step 11:

[0278] The traffic control server (server) provides analysis results and real-time data as a dashboard, which displays traffic congestion status, traffic accident information, weather forecasts, and emotion data.

[0279] Step 12:

[0280] Public safety and local government officials (users) can remotely access the dashboard to view real-time traffic conditions, weather data, and emotion data, and can remotely implement traffic restrictions and enforcement actions as needed.

[0281] Example 2

[0282] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0283] Conventional traffic management systems are limited to control based on traffic volume and weather conditions, and have difficulty responding dynamically to the user's emotional state. As a result, they have not been able to fully reduce user stress or improve safety in the event of a traffic accident or traffic congestion. Furthermore, they have not been able to fully optimize traffic signal control by taking real-time traffic and weather conditions into account.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0285] In this invention, the server includes a means for identifying traffic volume and traffic accidents, a means for identifying vehicles committing traffic violations, a means for predicting road icing and snow accumulation, and a means for analyzing user emotions. This enables optimal traffic light control based on traffic and weather conditions in real time, and also enables dynamic responses based on user emotions. This is expected to reduce traffic accidents and traffic congestion, as well as reduce user stress and improve social safety.

[0286] The "communication infrastructure" is a platform installed at traffic lights that transmits and receives data using a high-speed communication network.

[0287] A "data acquisition device" is a device that acquires traffic video data, weather data, and the user's facial expressions and voice in real time.

[0288] The "data aggregating device" is a device that temporarily aggregates data acquired from each data acquisition device and transmits the data to the analyzing means via a high-speed communication network.

[0289] "Analysis means" refers to a means of analyzing aggregated data to assess traffic volume, traffic accidents, vehicles violating traffic laws, and the risk of road icing and snow accumulation.

[0290] The "control device" is a device that dynamically controls the lighting state of a traffic light based on the analysis result of the analysis means.

[0291] The "emotion engine" is an engine that analyzes the user's facial expressions and voice to identify their emotional state.

[0292] "Traffic video data" refers to video data that captures traffic conditions at traffic lights and intersections.

[0293] "Weather data" refers to data indicating weather conditions such as temperature, humidity, and wind speed.

[0294] "User emotion data" refers to data relating to the user's emotional state analyzed from their facial expressions and voice.

[0295] "High-speed communication network" refers to a communication network with high bandwidth for transmitting and receiving large amounts of data in real time, and includes technologies such as 5G.

[0296] MODE FOR CARRYING OUT THE INVENTION

[0297] The present invention relates to a traffic management system, which is a system including a communication infrastructure installed at traffic lights, a data acquisition device that acquires traffic video data, a data acquisition device that acquires weather data, a data aggregation device that aggregates this data, analysis means that analyzes the aggregated data, a control device that controls traffic lights based on the analysis results, and an emotion engine that analyzes user emotions.

[0298] System configuration

[0299] Communication infrastructure (terminals)

[0300] The communication infrastructure installed in the traffic lights uses high-speed communication networks such as 5G to transmit data from the data acquisition devices to a central traffic control server.

[0301] Data acquisition device (terminal)

[0302] It includes cameras that capture traffic video data, weather sensors that capture weather data, and cameras and microphones that capture the user's facial expressions and voice. The cameras capture real-time images of intersections, road traffic conditions, and vehicle movements, while the weather sensors capture weather data such as temperature, humidity, and wind speed. Cameras and microphones that capture the user's facial expressions and voice are also installed.

[0303] Data aggregation device (terminal)

[0304] Traffic video data, weather data, and user emotion data sent from the data acquisition device are temporarily aggregated and sent to an analysis means, which then transmits the data in real time to a central traffic control server using 5G communications.

[0305] Analysis method (server)

[0306] The traffic control server receives the data, and an AI analysis engine analyzes traffic volume, traffic accidents, vehicles violating traffic rules, and the risk of road icy and snow accumulation. Specifically, video data is used to recognize license plates and identify speeding violations and traffic accidents. Weather data is also used to analyze the risk of icy and snow accumulation.

[0307] Control device (terminal)

[0308] Based on the results of the analysis, the lighting status and operation of traffic lights are dynamically changed. For example, the green light duration of a traffic light is extended during times of heavy traffic, and an emergency response signal is set in the event of a traffic accident. This smooths traffic flow and improves safety.

[0309] Emotion engine (server)

[0310] The emotion engine recognizes emotions by analyzing the user's facial expressions and voice. The analyzed emotion data is fed back to the traffic management system, which then personalizes traffic light control and system notifications to match the user's emotions. For example, if the user is feeling stressed, important notifications will be given priority.

[0311] Specific examples

[0312] Consider the case where the system of the present invention is installed at traffic lights installed in the center of City A. During the morning rush hour, the terminal camera captures video data of the intersection at 30 frames per second and collects weather data of a temperature of 2°C and humidity of 80%. Furthermore, it captures the facial expressions and voices of users near the intersection and generates emotion data. This data is transmitted in real time to a traffic control server via a 5G base station.

[0313] The traffic control server's analytics analyzes video data and identifies 20 vehicles in a traffic jam. Furthermore, license plate recognition technology is used to identify speeding vehicles. Weather data analysis reveals a high risk of road icy conditions due to low temperatures. An emotion engine analyzes users' facial expressions and voices and identifies that some users are stressed. This information is fed back to the traffic management system, which adjusts notification methods.

[0314] Based on this, the traffic light control system will carry out appropriate signal control. Specifically, it will extend the green light time to alleviate congestion and change the signal to prevent freezing. In addition, in the event of a traffic accident, it will set priority signals to allow emergency vehicles to pass. Furthermore, it will prioritize emergency notifications for users who are feeling stressed, encouraging them to take prompt action.

[0315] Prompt Sentence Examples

[0316] Below are some example prompts to input to a generative AI model:

[0317] "Please explain in detail how your real-time traffic management system works using a 5G communication network. I'd particularly like to know how you analyze traffic video data, weather data, and user emotion data."

[0318] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0319] Specific processing steps of the process

[0320] Step 1: Data collection

[0321] Specific explanation: The terminal uses data acquisition devices installed at intersections and various parts of the traffic system to collect traffic video data, weather data, and the user's facial expressions and voice.

[0322] Input: Traffic video data (camera), weather data (weather sensor), user facial expressions and voice (microphone and camera)

[0323] Data calculation and processing: Traffic video data and user emotion data are captured frame by frame, and weather data is measured at regular intervals. The collected raw data is converted into a basic data format.

[0324] Output: Packaged traffic video data, weather data, emotion data

[0325] How it works: The camera captures video at 30 frames per second, the weather sensor measures weather data every minute, the microphone captures the user's voice in real time, and the facial expression camera captures images to analyze the user's facial expressions.

[0326] Step 2: Data aggregation

[0327] Specific explanation: A 5G base station installed on the terminal temporarily aggregates data from each data acquisition device and transmits it to a traffic management server.

[0328] Input: Raw data from different data acquisition devices

[0329] Data calculation and processing: Collected data is collected and packaged into a single packet. Time stamps are used to ensure data integrity.

[0330] Output: Packaged data packets

[0331] Specific operation: The 5G base station receives data sent from each terminal and transmits it in packet format to the traffic management server without delay.

[0332] Step 3: Upload data

[0333] Specific explanation: Data packets collected from terminals are uploaded to a traffic control server in real time using 5G communications.

[0334] Input: Packaged data packet

[0335] Data Calculation and Processing: Data packets are transmitted to the traffic control server over a high-speed network with minimal delay.

[0336] Output: Data packets to the traffic control server

[0337] Specific operation: The 5G base station uses high-speed communication technology to transmit packaged data packets to the traffic control server.

[0338] Step 4: Analyze traffic data

[0339] Specific Description: The traffic control server analyzes the uploaded traffic video data, weather data, and user emotion data.

[0340] Input: Traffic video data, weather data, user emotion data

[0341] Data calculation and processing: The AI ​​analysis engine uses OCR technology to analyze license plates based on video data, identify traffic volume and traffic accidents, and analyze weather data to assess the risk of icy roads and snow accumulation.

[0342] Output: Analyzed traffic conditions, traffic violations, and weather risks

[0343] How it works: The traffic control server uses AI models to read license plates frame by frame, analyze vehicle speeds and movements, and identify traffic violations and accidents. Weather data analysis determines risks when certain conditions are met.

[0344] Step 5: Analyze the sentiment data

[0345] Specific explanation: The emotion engine, which is the server, analyzes the user's facial expression data and voice data to identify the user's emotions.

[0346] Input: User's facial expression data, voice data

[0347] Data Computation and Processing: Deep learning models are used to analyze facial expressions and vocal tone, rate, and volume to identify emotions.

[0348] Output: Parsed emotion data

[0349] Specific operation: The emotion engine uses facial expression analysis technology to classify the user's emotions and identifies emotions by analyzing voice characteristics.

[0350] Step 6: Dynamic signal control

[0351] Specific explanation: The traffic light control system, which is the server, controls the traffic lights based on the analysis results.

[0352] Input: Analyzed traffic conditions, traffic violations, weather risks, and emotion data

[0353] Data calculation and processing: Automatically adjusts traffic light duration and signal patterns according to traffic congestion and accident situations. Adjusts notification methods based on emotion data.

[0354] Output: Adapted traffic light control, notification method

[0355] Specific operation: The traffic light control system performs optimal traffic management by extending the green light duration, setting priority signals, and changing notification content based on the user's emotions.

[0356] (Application example 2)

[0357] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0358] Increased traffic accidents, traffic congestion, and worsening road conditions due to bad weather have created a need for more efficient and safer traffic management. Furthermore, as autonomous vehicles are increasingly being introduced, systems are needed to enable these vehicles to operate more safely and efficiently. Furthermore, traffic management systems that take into account the emotional state of road users can affect traffic conditions.

[0359] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0360] In this invention, the server includes means for acquiring traffic video data, means for acquiring weather data, means for acquiring and analyzing user emotion data, means for aggregating this data through a high-speed communication network, means for controlling traffic lights based on the analysis results, and means for optimizing the behavior of autonomous vehicles, which enables more efficient traffic management and improved safety, safe operation of autonomous vehicles, and flexible responses that take into account the emotional states of traffic users.

[0361] The "communications infrastructure" is an infrastructure facility that uses a high-speed communications network installed at traffic lights to transfer data sent from data acquisition devices to a central traffic control server.

[0362] "Data acquisition device" refers to a device that includes a camera or sensor for acquiring traffic video data or weather data in real time.

[0363] The "data aggregating device" is a device that temporarily aggregates data transmitted from the data acquiring devices and sends the data to the analyzing means.

[0364] The "analysis means" is a server that receives data sent from the data aggregation device and includes an AI analysis engine for analyzing traffic volume, traffic accidents, vehicles violating traffic laws, and the risk of road icing and snow accumulation.

[0365] The "control device" is a device for controlling the behavior of traffic lights and autonomous vehicles based on the analysis results of the analysis means.

[0366] The "emotion engine" is an analytical engine that analyzes the user's facial expressions and voice to recognize emotions and provides feedback to the traffic management system.

[0367] "Traffic volume" is an index that indicates the number of vehicles passing through a specific point, and is a criterion for determining whether or not there is traffic congestion.

[0368] "Traffic accidents" refer to accidents that occur on roads and disrupt the flow of traffic, such as collisions between vehicles or contact between vehicles and people.

[0369] A "traffic violation vehicle" is a vehicle that does not comply with traffic regulations and commits violations such as speeding or running red lights.

[0370] "Risk of road freezing and snow accumulation" is an indicator that shows the possibility of roads freezing or snow accumulation based on meteorological data such as temperature, humidity, and snowfall.

[0371] "User emotion data" is data that indicates the emotional state of the user analyzed from their facial expressions and voice.

[0372] A "high-speed communication network" is a network that uses high-bandwidth communication technologies such as 5G, enabling high-speed, large-capacity data communication.

[0373] The present invention relates to a system that improves the efficiency and safety of traffic management based on the environment in which an autonomous vehicle is traveling and the emotional state of the user.

[0374] System Configuration

[0375] Communication infrastructure (terminals)

[0376] The devices are installed at traffic lights and transmit data from the data acquisition devices to a central traffic control server via a high-speed communication network.

[0377] Data acquisition device (terminal)

[0378] It includes a camera for capturing traffic video data and a weather sensor for capturing weather data. The camera captures traffic conditions at intersections and roads and vehicle movements in real time, while the weather sensor captures weather data such as temperature, humidity, and wind speed. It also includes a camera and microphone for capturing the user's facial expressions and voice.

[0379] Data aggregation device (terminal)

[0380] The data transmitted from the data acquisition devices is temporarily collected and sent to an analysis means, which transmits the data in real time to a central traffic control server using a high-speed communication network.

[0381] Analysis method (server)

[0382] The traffic control server receives the data, and the AI ​​analysis engine analyzes traffic volume, traffic accidents, traffic violations, and the risk of road icy and snow accumulation. Specifically, video data is used to analyze vehicle movements and identify speeding violations and traffic accidents. Weather data is used to analyze the risk of icy and snow accumulation.

[0383] Emotion engine (server)

[0384] The emotion engine recognizes emotions by analyzing the user's facial expressions and voice. The analyzed emotion data is fed back to the traffic management system, which then personalizes traffic light control and system notifications to match the user's emotions.

[0385] Control device (terminal)

[0386] Based on the results of the analysis method and emotion engine, the lighting status of traffic lights and the behavior of autonomous vehicles are dynamically changed. Specifically, the system extends traffic lights during congestion, sends emergency signals to respond to traffic accidents, controls signals when there is a high risk of roads freezing due to low temperatures, and adjusts notification methods based on the user's emotional state.

[0387] Program processing description

[0388] Cameras (terminals) capture traffic video data in real time from traffic light locations, and weather sensors (terminals) measure data such as temperature, humidity, and wind speed. In addition, cameras and microphones capture the user's facial expressions and voice to collect emotional data in real time. This data is temporarily aggregated in a data aggregation device via a high-speed communication network and then uploaded to a central traffic control server.

[0389] The traffic control server receives the uploaded data and analyzes traffic volume, traffic accidents, traffic violations, and the risk of road icing and snow accumulation. Using an AI analysis engine, it can analyze vehicle movements from camera footage and identify speeding vehicles using license plate recognition technology. Additionally, weather data is analyzed to manage road maintenance risks.

[0390] The emotion engine (server) uses facial expression analysis technology to identify the user's emotions. Specifically, it recognizes whether the user is feeling stressed or calm, and feeds the analysis results back to the traffic management system. This makes it possible to adjust the notification method according to the user's level of stress.

[0391] As a concrete example, consider a traffic management system in City A. During the morning rush hour, a camera captures video data of an intersection at 30 frames per second, and a weather sensor collects data on a temperature of 2 degrees and humidity of 80%. Furthermore, the facial expressions and voices of users near the intersection are analyzed to generate emotion data. This data is transmitted in real time to a traffic control server via a high-speed communication network.

[0392] Example prompt sentence:

[0393] "During the morning rush hour, please simulate a system that analyzes traffic video data, weather data, and user emotion data to ensure safe and efficient driving. Specifically, please execute driving behavior and signal control that takes into account traffic congestion, the risk of road surfaces freezing due to low temperatures, and user stress."

[0394] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0395] Step 1: Data collection

[0396] The device collects traffic video data, weather data, and user emotion data. Traffic video data is captured in real time by a camera, recording vehicle movements and traffic conditions as video data. Weather data is obtained using a weather sensor to acquire temperature, humidity, wind speed, etc. User emotion data is obtained by capturing the user's facial expressions and voice using an facial camera and microphone.

[0397] Input: traffic video, weather data, facial expression data, audio data

[0398] Output: A set of captured real-time data

[0399] Step 2: Data aggregation

[0400] The data collected by the terminals is temporarily sent to a data aggregator, which then consolidates and packages traffic video data, weather data, facial expression data, and voice data before forwarding it to a central traffic control server. The data is transmitted in real time using a high-speed communication network.

[0401] Input: A set of real-time data

[0402] Output: Packaged aggregated data

[0403] Step 3: Data analysis

[0404] The server receives the aggregated data and uses an AI analysis engine to analyze traffic volume, traffic accidents, vehicles violating traffic rules, and the risk of road icy roads and snow accumulation. By analyzing traffic video data, the system recognizes vehicle movements and license plates and identifies vehicles violating traffic rules and traffic accidents. It also predicts the risk of road icy roads and snow accumulation based on weather data.

[0405] Input: Aggregated data

[0406] Output: Traffic situation analysis results, weather risk analysis results

[0407] Step 4: Sentiment Analysis

[0408] The server uses an emotion engine to analyze facial expression and voice data to identify the user's emotions. Facial expression analysis and voice analysis technologies are used to determine the user's stress level and satisfaction.

[0409] Input: facial expression data, voice data

[0410] Output: User's emotional state

[0411] Step 5: Traffic light control

[0412] The server dynamically changes the lighting status of traffic lights based on the results of the analysis method and emotion engine. Specifically, it extends the green light time when traffic volume is heavy, sets an emergency response signal when a traffic accident occurs, and adjusts signal control when there is a risk of freezing due to low temperatures.

[0413] Input: Traffic situation analysis results, weather risk analysis results, user emotional state

[0414] Output: Dynamically changed traffic light status

[0415] Step 6: Adjusting the behavior of the autonomous vehicle

[0416] The server optimizes the behavior of the self-driving vehicle based on the results of the analysis and emotion engine, such as changing the route when traffic is congested, adjusting the speed when it is cold, and changing the driving mode when the user is feeling stressed.

[0417] Input: Traffic situation analysis results, weather risk analysis results, user emotional state

[0418] Output: Optimized autonomous vehicle driving modes

[0419] Step 7: Feedback and Notification

[0420] The server provides appropriate feedback and notifications based on the analysis results of the traffic management system and the user's emotional state. For example, it notifies the user of traffic congestion information and emergency response information. It also provides personalized notifications according to the user's stress level.

[0421] Input: Analysis results, user's emotional state

[0422] Output: Personalized feedback and notifications

[0423] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0424] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0425] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0426] [Second embodiment]

[0427] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0428] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0429] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0430] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0431] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0432] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0433] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0434] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0435] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0436] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0437] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0438] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0439] The present invention is a system consisting of a communication infrastructure installed at traffic lights, a data acquisition device that acquires traffic video data, a data acquisition device that acquires weather data, a data aggregation device that aggregates these data, an analysis means that analyzes the aggregated data, and a control device that controls traffic lights based on the analysis results.This system will improve the efficiency of traffic management and solve problems such as traffic accidents, congestion, violation enforcement, and road condition management.

[0440] System Configuration

[0441] Communication infrastructure (terminals)

[0442] The communication infrastructure installed in the traffic lights uses high-speed communication networks such as 5G to transmit data from the data acquisition devices to a central traffic control server.

[0443] Data acquisition device (terminal)

[0444] The system includes cameras that capture traffic video data and weather sensors that capture meteorological data such as temperature, humidity, and wind speed.

[0445] Data aggregation device (terminal)

[0446] The data sent from the data acquisition devices is temporarily aggregated and sent to an analysis means, which then transmits the data in real time to a central traffic control server using 5G communications.

[0447] Analysis method (server)

[0448] The traffic control server receives the data, and the AI ​​analysis engine analyzes traffic volume, traffic accidents, traffic violations, and the risk of road icy and snow accumulation. Specifically, video data is used to recognize license plates and identify speeding violations and traffic accidents. Weather data is used to analyze the risk of icy and snow accumulation.

[0449] Control device (terminal)

[0450] Based on the results of the analysis, the lighting status and operation of traffic lights are dynamically changed. For example, the green light time of a traffic light is extended during times of heavy traffic, and an emergency response signal is set in the event of a traffic accident.

[0451] Natural language explanation of program processing

[0452] 1. Data collection and upload

[0453] Cameras (terminals) capture real-time traffic video data from traffic light locations, while weather sensors (terminals) measure temperature, humidity, wind speed, and other data. This data is temporarily aggregated and packaged via 5G base stations (terminals), and then uploaded to a central traffic control server via 5G communications.

[0454] 2. Data Analysis

[0455] The traffic control server (server) receives the uploaded data, and the analysis means (server) analyzes traffic volume, traffic accidents, vehicles violating traffic rules, and the risk of road icing and snow accumulation. For example, camera footage can be used to read license plates using OCR technology to identify vehicles speeding. It can also analyze the risk of road icing based on weather data.

[0456] 3. Dynamic Signal Control

[0457] Based on the results of the analysis, the traffic light control system (server) determines the lighting status of traffic lights. For example, if there is congestion, the green light duration of the traffic light will be extended appropriately to smooth the flow of traffic. Also, if a traffic accident occurs, priority signals will be set for traffic lights on the route so that emergency vehicles can arrive at the scene quickly.

[0458] Specific examples

[0459] Consider the case where the system of the present invention is installed at traffic lights installed in the center of City A. During the morning rush hour, a camera captures video data of the intersection at 30 frames per second and collects weather data of a temperature of 2°C and humidity of 80%. This data is transmitted in real time to a traffic control server via a 5G base station.

[0460] The traffic control server's analytics analyzed the video data and identified 20 vehicles in a traffic jam. Furthermore, license plate recognition technology was used to identify speeding vehicles. Weather data analysis revealed a high risk of road surface freezing due to low temperatures.

[0461] Based on this, the traffic light control system will carry out appropriate signal control, such as extending the green light time to ease congestion, changing the signal to prevent freezing, and setting priority signals to allow emergency vehicles to pass in the event of a traffic accident.

[0462] Public safety and local government officials can remotely check the system's data dashboard and implement traffic restrictions and enforcement based on real-time traffic conditions and weather forecasts. This system will efficiently solve a variety of problems facing society's transportation system.

[0463] The processing flow will be explained below.

[0464] Step 1:

[0465] The camera (terminal) captures images of intersections and road conditions in real time. The camera generates video data at a rate of 30 frames per second to obtain traffic video data.

[0466] Step 2:

[0467] The weather sensor (terminal) measures weather data such as temperature, humidity, wind speed, etc. The weather data is acquired every minute and temporarily stored in the sensor's internal memory.

[0468] Step 3:

[0469] The 5G base station (terminal) aggregates data received from cameras and weather sensors, and packages the aggregated data for transmission to a central traffic control server via 5G communications.

[0470] Step 4:

[0471] The traffic control server (server) receives data sent from 5G base stations and stores it in a database. The received data includes video data and weather data.

[0472] Step 5:

[0473] The AI ​​analysis engine (server) analyzes the received video data. Specifically, it recognizes license plates, detects speeding violations, and identifies traffic accidents. It detects vehicles in the video and reads license plates using OCR technology. It also calculates the distance the vehicle travels between frames and calculates its speed to identify speeding violations.

[0474] Step 6:

[0475] The AI ​​analysis engine (server) analyzes weather data and predicts the risk of freezing and snow accumulation. It determines the possibility of freezing based on temperature and humidity data, and uses wind speed data to predict changes in snow accumulation and road conditions.

[0476] Step 7:

[0477] The traffic light control system (server) determines the lighting status of traffic lights based on the analysis results of the AI ​​analysis engine. When congestion occurs, the green light will be kept on for an extended period to smooth traffic flow. In addition, when a traffic accident occurs, priority signals will be set for emergency vehicles.

[0478] Step 8:

[0479] The traffic light (terminal) receives control instructions from the traffic light control system and changes the lighting status of the signal, thereby alleviating traffic congestion and responding to emergencies.

[0480] Step 9:

[0481] The traffic control server (server) provides analysis results and real-time data as a dashboard, which displays traffic congestion status, traffic accident information, weather forecasts, and more.

[0482] Step 10:

[0483] Public safety and local government officials (users) can remotely access the dashboard to view real-time traffic and weather data, and can remotely implement traffic restrictions and enforcement actions as needed.

[0484] Example 1

[0485] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0486] Conventional traffic management systems have difficulty controlling signals in real time according to traffic volume and weather conditions, resulting in problems such as traffic congestion, traffic accidents, and delays in identifying violating vehicles. They also fail to properly manage the risk of road ice and snow accumulation, which causes frequent traffic accidents and congestion. An effective system to solve these issues is needed.

[0487] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0488] In this invention, the server includes a communication infrastructure installed in the traffic control device, a data acquisition device that acquires traffic video data, a data acquisition device that acquires weather data, a data aggregation device that aggregates data from the data acquisition devices via a high-speed communication network, analysis means that analyzes data transmitted from the data aggregation device, and a control device that controls the traffic control device based on the analysis results of the analysis means. This enables traffic signal control in real time according to traffic volume and weather conditions, thereby easing traffic congestion, preventing traffic accidents, identifying violating vehicles, and appropriately managing the risk of road icing and snow accumulation.

[0489] "Communications infrastructure" refers to equipment installed in traffic control devices that includes a high-speed communications network for transmitting and receiving data.

[0490] The "data acquisition device" is a device for acquiring traffic video data and weather data, and includes cameras, weather sensors, etc.

[0491] The "data aggregating device" is a device that temporarily stores data acquired from a data acquiring device, packages the data, and transmits the data to an analyzing means.

[0492] The "analysis means" is a device or system that analyzes the data sent from the data aggregation device and identifies traffic volume, traffic accidents, vehicles violating traffic laws, and the risk of road icing or snow accumulation.

[0493] The "control device" is a device that dynamically changes the lighting state of traffic lights based on the analysis results of the analysis means, thereby easing traffic congestion and responding to traffic accidents.

[0494] "Traffic volume" is the number of vehicles passing through a particular point in a particular time period.

[0495] A "traffic accident" is an incident caused by traffic troubles, such as collisions between vehicles or single-vehicle accidents.

[0496] A "traffic violation vehicle" is a vehicle that violates traffic laws. This includes violations such as speeding and illegal parking.

[0497] "Freezing" is a phenomenon in which the temperature drops and the road surface freezes over.

[0498] "Snow accumulation" is a phenomenon in which the temperature drops and snow accumulates on roads and surfaces.

[0499] "OCR technology" is an abbreviation for optical character recognition technology, and is a technology for reading character information from image data.

[0500] A "priority signal" is a signal state that is set to allow emergency vehicles and vehicles requiring priority passage to pass more easily.

[0501] This invention is a system comprising a communication infrastructure installed in a traffic control device, a data acquisition device for acquiring traffic video data, a data acquisition device for acquiring weather data, a data aggregation device for aggregating these data, an analysis means for analyzing the aggregated data, and a control device for controlling the traffic control device based on the analysis results.

[0502] Communication infrastructure (terminals)

[0503] The communication infrastructure will be installed in the traffic control device and will transmit data received from the data acquisition device to a central traffic control server using a high-speed communication network. Specifically, 5G communication technology will be used.

[0504] Data acquisition device (terminal)

[0505] The data acquisition device includes cameras that capture traffic video data and weather sensors that capture meteorological data. The cameras capture real-time images of traffic conditions and vehicle movements at intersections and major roads. For example, video data is collected at 30 frames per second. The weather sensors measure temperature, humidity, wind speed, etc. and update the data every five seconds.

[0506] Data aggregation device (terminal)

[0507] The data aggregation device temporarily stores and packages the data sent from the data acquisition device. This sorts out inconsistencies in the data and makes it available for analysis in real time. For example, traffic video data and weather data are sorted in chronological order.

[0508] Analysis method (server)

[0509] The analysis means is installed on the traffic control server, which receives the transmitted data and performs initial processing. Specifically, the AI ​​analysis engine analyzes the video data and recognizes vehicle license plates using OCR technology. This makes it possible to identify speeding vehicles and illegally parked vehicles. Weather data is also analyzed to assess the risk of road icy conditions and snow accumulation.

[0510] Control device (terminal)

[0511] The control device dynamically changes the lighting status of the traffic lights based on the results of the analysis means, for example, by extending the green lighting time of the traffic lights during times of heavy traffic, or by setting priority signals for emergency vehicles in the event of a traffic accident.

[0512] Specific examples

[0513] For example, consider the case where this system is installed in the city center of City A. During the morning rush hour, a camera captures video data of an intersection at 30 frames per second and collects weather data such as a temperature of 2°C and humidity of 80%. The data aggregator organizes this data and transmits it to a traffic control server via 5G communications.

[0514] The traffic control server's analytics analyzes the video data and identifies a traffic jam of 20 vehicles. It then uses license plate recognition technology to identify speeding vehicles. Weather data analysis reveals a high risk of road icing due to low temperatures.

[0515] Based on this, the traffic light control system will carry out appropriate signal control. Specifically, it will extend the time that the green light remains on to alleviate congestion. In addition, in the event of a traffic accident, it will set priority signals so that emergency vehicles can arrive at the scene quickly.

[0516] Prompt Sentence Examples

[0517] 1. Please explain the specific steps by which the traffic control server's analytical means identifies traffic accidents from the uploaded data.

[0518] 2. What are the detailed steps for how the traffic light control system changes the operation of traffic lights when it detects congestion?

[0519] This system enables real-time traffic management, which can efficiently alleviate traffic congestion and prevent traffic accidents. It also enables road management according to weather conditions, providing a safer traffic environment. Public safety and local government officials can remotely view the system's data dashboard and use it as reference information for traffic restrictions and enforcement.

[0520] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0521] Step 1:

[0522] Data collection devices capture traffic footage and weather data.

[0523] Input: Real-time video data from the camera (terminal), weather data (temperature, humidity, wind speed) from the weather sensor.

[0524] How it works: The camera captures images of intersections and major roads at 30 frames per second, and the weather sensor measures temperature, humidity, and wind speed every 5 seconds.

[0525] Output: Video data and weather data.

[0526] Step 2:

[0527] The communication infrastructure receives the data acquired from the data acquisition device.

[0528] Input: Video data and meteorological data transmitted from the data acquisition device.

[0529] Specific operation: Data transmitted from the data acquisition device is temporarily stored via the communication infrastructure.

[0530] Output: Temporarily saved video data and weather data.

[0531] Step 3:

[0532] The data aggregation device acquires data from the communication infrastructure and packages it.

[0533] Input: Temporarily stored video data and weather data.

[0534] Specific operation: The data aggregator sorts out data inconsistencies and packages the data in chronological order.

[0535] Output: Packaged data.

[0536] Step 4:

[0537] The data aggregator transmits the packaged data to the traffic control server.

[0538] Input: Packaged data.

[0539] Specific operation: The data aggregation device uses 5G communications to send packaged data to the traffic control server in real time.

[0540] Output: Data sent to traffic control server.

[0541] Step 5:

[0542] The traffic control server receives the transmitted data and performs initial processing.

[0543] Input: The data sent.

[0544] Specific operation: The traffic control server checks for data inconsistencies and corrects the data as necessary.

[0545] Output: Organized data.

[0546] Step 6:

[0547] The analysis means analyzes the video data and the meteorological data.

[0548] Input: Organized video and weather data.

[0549] Specific operation: Analyzes video data using an AI analysis engine, performs OCR recognition of license plates and vehicle movement analysis, and evaluates the risk of road icy conditions and snow accumulation based on weather data.

[0550] Output: Analysis results (traffic accidents, violating vehicles, risk of freezing, etc.).

[0551] Step 7:

[0552] The control device dynamically changes the lighting status of the traffic light based on the analysis results.

[0553] Input: Analysis results.

[0554] Specific actions: When traffic volume is heavy, the green light will stay on for an extended period of time, and when a traffic accident occurs, traffic lights will be set to give priority to emergency vehicles.

[0555] Output: Changed traffic light status.

[0556] Step 8:

[0557] Users can remotely view the system's data dashboard and implement traffic restrictions and enforcement.

[0558] Input: Data dashboard information.

[0559] Specific operation: Public safety and local government officials can check traffic and weather conditions on the dashboard and remotely issue instructions on how to respond.

[0560] Output: Traffic restrictions and enforcement actions that were applied.

[0561] In this way, each step performs data processing and calculations based on the input to obtain the output of the next step.

[0562] (Application example 1)

[0563] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0564] Conventional traffic management systems have difficulty utilizing acquired traffic and weather data in real time, making it difficult to respond quickly to changes in traffic conditions. Furthermore, there is a lack of means to notify automated driving vehicles of traffic signal status and traffic forecast information, making it difficult to ensure smooth traffic flow. This makes it necessary to respond quickly to traffic congestion and traffic accidents.

[0565] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0566] In this invention, the server includes means for providing real-time traffic conditions using acquired traffic video data and weather data, means for providing traffic alerts and forecasts to autonomous vehicles based on analysis results, means for identifying vehicles violating traffic laws, and means for providing priority signal notifications to autonomous vehicles. This enables the use of traffic data in real time, enabling the prompt provision of information to autonomous vehicles and ensuring smooth traffic flow.

[0567] A "communication infrastructure" is a device installed at traffic lights that sends and receives data using a high-speed communication network.

[0568] A "data acquisition device" is a device installed to acquire traffic video data and weather data.

[0569] The "data aggregating device" is a device that temporarily aggregates data acquired from the data acquiring device and transmits the data to the data analyzing means.

[0570] "Analysis means" refers to a means for analyzing the aggregated data and obtaining useful information about traffic and weather conditions.

[0571] The "controller" is a device that controls traffic lights based on the analysis results of the analyzing means, thereby ensuring smooth traffic flow and safety.

[0572] "Traffic video data" is video data showing the traffic conditions on roads and intersections.

[0573] "Weather data" refers to data related to weather conditions (temperature, humidity, wind speed, etc.).

[0574] "Means for providing traffic conditions in real time" refers to means for analyzing acquired traffic video data and weather data and instantly providing information on current traffic conditions.

[0575] "Means for providing traffic alerts and predictions" means means for providing warnings and predictions about future traffic conditions and risks based on analyzed data.

[0576] The "means for identifying vehicles violating traffic regulations" refers to a means for analyzing traffic video data and identifying vehicles violating regulations.

[0577] A "means for providing a priority signal notification" is a means for notifying an autonomous vehicle of the priority signal status of a particular traffic light.

[0578] The system of the present invention includes a communication infrastructure installed at traffic lights, a data acquisition device that acquires traffic video data and weather data, a data aggregation device that aggregates the data, an analysis means that analyzes the data, a control device that controls the traffic lights based on the analysis results, and a means for providing real-time traffic conditions and traffic alerts and forecasts for autonomous vehicles.

[0579] Hardware and software used

[0580] The server is equipped with a communications infrastructure that utilizes a high-speed communications network (such as 5G), and cameras and weather sensors are connected to the data acquisition devices. The data acquired from these data acquisition devices is aggregated on the server via a data aggregation device.

[0581] The analysis method runs an AI analysis engine on a server to analyze traffic volume, traffic accidents, vehicles violating traffic rules, and the risk of road icy conditions and snow accumulation. This analysis method includes computer vision technology for video analysis and weather models for weather data analysis.

[0582] Data processing and calculation

[0583] The server analyzes the acquired traffic video data using computer vision technology (e.g., OpenCV) to identify traffic volume. It also identifies vehicles violating traffic rules using license plate recognition (e.g., OCR technology). Weather data analysis uses weather models (e.g., ARIMA models) to predict the risk of road icing and snow accumulation.

[0584] The control device dynamically changes the lighting status of traffic lights based on the analysis results, for example by extending the green light time depending on traffic volume or setting an emergency response signal in the event of a traffic accident.

[0585] Specific examples

[0586] For example, if the system of the present invention is installed at traffic lights installed in the center of City A, a camera will capture video data of the intersection in real time during the morning rush hour and collect weather data such as a temperature of 2°C and humidity of 80%. This data will be transmitted in real time to a traffic control server via a 5G base station.

[0587] The traffic control server's analytics analyzes the video data and identifies 20 vehicles in a traffic jam. License plate recognition technology also identifies three speeding vehicles. Weather data analysis reveals a high risk of road icing due to low temperatures.

[0588] Based on this, the traffic light control system will carry out appropriate signal control. Specifically, it will extend the green light time to alleviate congestion and change the traffic lights to prevent freezing. In addition, in the event of a traffic accident, priority signals will be set for traffic lights on the route so that emergency vehicles can arrive at the scene quickly. Public safety and local government officials can remotely check the system's data dashboard and implement traffic restrictions and enforcement remotely based on real-time traffic conditions and weather forecasts.

[0589] Prompt Sentence Examples

[0590] We are designing a smartphone application that displays traffic volume, weather data, and congestion information based on real-time traffic data from a traffic light control system. The following features are required:

[0591] 1. Real-time streaming of traffic video data.

[0592] 2. Predictive alerts for traffic jams and accidents.

[0593] 3. Aggregate daily traffic and weather data and generate reports.

[0594] 4. Notification of changes in traffic light operation.

[0595] Please generate source code using Python, including instructions for using the API and displaying data.

[0596] The above is an embodiment of the invention.

[0597] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0598] Step 1:

[0599] Cameras and weather sensors (terminals) capture traffic video data and weather data in real time. The cameras capture images of roads and intersections, while the weather sensors capture weather data such as temperature, humidity, and wind speed, and send them to the data acquisition device.

[0600] Input: Traffic video data, weather data

[0601] Output: Raw data obtained

[0602] Step 2:

[0603] The data acquisition device (terminal) transmits the acquired traffic video data and weather data to the data aggregation device via the 5G network. Here, the video data is transmitted in a stream format, and the weather data is transmitted as numerical data.

[0604] Input: Raw data acquired

[0605] Output: Stream format video data, numerical data format weather data

[0606] Step 3:

[0607] The data aggregating device (terminal) temporarily stores the received traffic video data and weather data, packages them, and transmits them to the analyzing means.

[0608] Input: Stream format video data, numerical data format weather data

[0609] Output: Packaged data

[0610] Step 4:

[0611] The analysis means (server) receives the packaged data and analyzes the traffic video data using computer vision technology (e.g., OpenCV) to measure traffic volume. Weather data is analyzed using weather models (e.g., ARIMA models) to analyze the risk of road icing and snow accumulation. Furthermore, license plate recognition (e.g., OCR technology) is used to identify vehicles violating traffic laws.

[0612] Input: Packaged traffic video data, weather data

[0613] Output: Analyzed traffic condition data, weather risk data, and violating vehicle data

[0614] Step 5:

[0615] The analysis means (server) provides real-time traffic conditions based on the analysis results and generates traffic alerts and predictions for autonomous vehicles.

[0616] Input: Analyzed traffic condition data, weather risk data, violating vehicle data

[0617] Output: Traffic reports, traffic alerts, forecast data

[0618] Step 6:

[0619] The control device (server) dynamically changes the lighting status of traffic lights based on the analysis results. Specifically, it extends the green light time when traffic volume is heavy, sets an emergency response signal when a traffic accident occurs, and provides priority signal notifications to autonomous vehicles.

[0620] Inputs: Traffic reports, traffic alerts, and forecast data

[0621] Output: Dynamically changed traffic light control information, priority signal notification

[0622] Step 7:

[0623] Users (public safety and local government officials) can remotely check the system's data dashboard and remotely implement traffic restrictions and enforcement based on real-time traffic conditions and weather forecasts.

[0624] Input: Dynamically changed traffic light control information, priority signal notification

[0625] Output: Traffic regulation and enforcement implementation plan

[0626] The above is the flow of processing of the system program for realizing the application example.

[0627] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0628] The present invention further improves the efficiency of traffic management and social safety by combining a system consisting of a communication infrastructure installed at traffic lights, a data acquisition device that acquires traffic video data, a data acquisition device that acquires weather data, a data aggregation device that aggregates this data, analysis means that analyzes the aggregated data, and a control device that controls traffic lights based on the analysis results with an emotion engine that recognizes user emotions.

[0629] System Configuration

[0630] Communication infrastructure (terminals)

[0631] The communication infrastructure installed in the traffic lights uses high-speed communication networks such as 5G to transmit data from the data acquisition devices to a central traffic control server.

[0632] Data acquisition device (terminal)

[0633] The system includes cameras that capture traffic video data and weather sensors that capture meteorological data such as temperature, humidity, and wind speed.

[0634] Data aggregation device (terminal)

[0635] The data sent from the data acquisition devices is temporarily aggregated and sent to an analysis means, which then transmits the data in real time to a central traffic control server using 5G communications.

[0636] Analysis method (server)

[0637] The traffic control server receives the data, and the AI ​​analysis engine analyzes traffic volume, traffic accidents, traffic violations, and the risk of road icy and snow accumulation. Specifically, video data is used to recognize license plates and identify speeding violations and traffic accidents. Weather data is used to analyze the risk of icy and snow accumulation.

[0638] Control device (terminal)

[0639] Based on the results of the analysis, the lighting status and operation of traffic lights are dynamically changed. For example, the green light time of a traffic light is extended during times of heavy traffic, and an emergency response signal is set in the event of a traffic accident.

[0640] Emotion engine (server)

[0641] The emotion engine analyzes the user's facial expressions and voice to recognize their emotions. The analyzed emotion data is fed back to the traffic management system, which then personalizes traffic light control and system notifications to match the user's emotions.

[0642] Natural language explanation of program processing

[0643] 1. Data collection and upload

[0644] Cameras (terminals) will capture real-time traffic video data from traffic light locations, and weather sensors (terminals) will measure data such as temperature, humidity, and wind speed. Additionally, microphones and cameras will be installed to capture the user's facial expressions and voice. This data will be temporarily aggregated and packaged via a 5G base station (terminal), and then uploaded to a central traffic control server via 5G communications.

[0645] 2. Data Analysis

[0646] The traffic control server (server) receives the uploaded data, and the analysis means (server) analyzes traffic volume, traffic accidents, vehicles violating traffic rules, and the risk of road icing and snow accumulation. For example, camera footage can be used to read license plates using OCR technology to identify vehicles speeding. It can also analyze the risk of road icing based on weather data.

[0647] 3. Emotion analysis

[0648] The emotion engine (server) analyzes the acquired facial expression and voice data to identify the user's emotions. For example, it uses facial expression analysis technology to recognize whether the user is stressed or calm. It also infers emotions from the tone and volume of the voice.

[0649] 4. Dynamic Signal Control

[0650] Based on the results of the analysis means and the emotion engine, the traffic light control system (server) determines the lighting status of traffic lights. For example, if there is congestion, the green light duration of the traffic light will be extended appropriately to smooth traffic flow. Also, in the event of a traffic accident, priority signals will be set for traffic lights on the route so that emergency vehicles can arrive at the scene quickly. Based on the analysis results of the emotion engine, if the user is feeling stressed, the notification method will be adjusted, such as providing emergency notifications as a priority.

[0651] Specific examples

[0652] Consider the case where the system of the present invention is installed at traffic lights installed in the center of City A. During the morning rush hour, a camera captures video data of the intersection at 30 frames per second and collects weather data with a temperature of 2°C and humidity of 80%. Furthermore, the system captures the facial expressions and voices of users near the intersection to generate emotion data. This data is then transmitted in real time to a traffic control server via a 5G base station.

[0653] The traffic control server's analytics analyzed the video data and identified 20 vehicles in a traffic jam. Furthermore, license plate recognition technology was used to identify speeding vehicles. Weather data analysis revealed a high risk of road surface freezing due to low temperatures.

[0654] The emotion engine analyzes users' facial expressions and voices to identify when some users are stressed, and this information is fed back to the traffic management system to adjust the notification method.

[0655] Based on this, the traffic light control system will carry out appropriate signal control. Specifically, it will extend the green light time to alleviate congestion and change the signal to prevent freezing. In addition, in the event of a traffic accident, it will set priority signals to allow emergency vehicles to pass. Furthermore, it will prioritize emergency notifications for users who are feeling stressed, encouraging them to take prompt action.

[0656] Public safety and local government officials can remotely check the system's data dashboard and implement traffic restrictions and enforcement based on real-time traffic conditions, weather forecasts, and emotion data. This system will efficiently solve various problems facing society with traffic and will also enable responses that are sensitive to user emotions.

[0657] The processing flow will be explained below.

[0658] Step 1:

[0659] The camera (terminal) captures images of intersections and road conditions in real time. The camera generates video data at a rate of 30 frames per second to obtain traffic video data.

[0660] Step 2:

[0661] The weather sensor (terminal) measures weather data such as temperature, humidity, wind speed, etc. The weather data is acquired every minute and temporarily stored in the sensor's internal memory.

[0662] Step 3:

[0663] The emotion engine camera and microphone (terminal) capture the user's facial expressions and voice. This generates emotion data. The facial expression data and voice data are temporarily stored in memory for emotion analysis.

[0664] Step 4:

[0665] The 5G base station (terminal) aggregates data received from cameras, weather sensors, and the emotion engine camera and microphone, and packages the aggregated data for transmission to a central traffic control server via 5G communications.

[0666] Step 5:

[0667] The traffic control server (server) receives data transmitted from 5G base stations and stores it in a database. The received data includes video data, weather data, and emotion data.

[0668] Step 6:

[0669] The AI ​​analysis engine (server) analyzes the received video data. Specifically, it recognizes license plates, detects speeding violations, and identifies traffic accidents. It detects vehicles in the video and reads license plates using OCR technology. It also calculates the distance the vehicle travels between frames and calculates its speed to identify speeding violations.

[0670] Step 7:

[0671] The AI ​​analysis engine (server) analyzes weather data and predicts the risk of freezing and snow accumulation. It determines the possibility of freezing based on temperature and humidity data, and uses wind speed data to predict changes in snow accumulation and road conditions.

[0672] Step 8:

[0673] The emotion engine (server) analyzes facial expression and voice data to identify the user's emotions. For example, it uses facial expression analysis technology to recognize whether the user is stressed or calm. It also infers emotions from the tone and volume of the voice.

[0674] Step 9:

[0675] The traffic light control system (server) determines the lighting status of traffic lights based on the results of the AI ​​analysis engine and emotion engine. When congestion occurs, the green light duration is extended to smooth traffic flow. In addition, when a traffic accident occurs, priority signals are set for emergency vehicles. Based on the analysis results of the emotion engine, notification methods are adjusted, such as prioritizing emergency notifications if the user is feeling stressed.

[0676] Step 10:

[0677] The traffic light (terminal) receives control instructions from the traffic light control system and changes the lighting status of the signal, thereby alleviating traffic congestion and responding to emergencies.

[0678] Step 11:

[0679] The traffic control server (server) provides analysis results and real-time data as a dashboard, which displays traffic congestion status, traffic accident information, weather forecasts, and emotion data.

[0680] Step 12:

[0681] Public safety and local government officials (users) can remotely access the dashboard to view real-time traffic conditions, weather data, and emotion data, and can remotely implement traffic restrictions and enforcement actions as needed.

[0682] Example 2

[0683] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0684] Conventional traffic management systems are limited to control based on traffic volume and weather conditions, and have difficulty responding dynamically to the user's emotional state. As a result, they have not been able to fully reduce user stress or improve safety in the event of a traffic accident or traffic congestion. Furthermore, they have not been able to fully optimize traffic signal control by taking real-time traffic and weather conditions into account.

[0685] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0686] In this invention, the server includes a means for identifying traffic volume and traffic accidents, a means for identifying vehicles committing traffic violations, a means for predicting road icing and snow accumulation, and a means for analyzing user emotions. This enables optimal traffic light control based on traffic and weather conditions in real time, and also enables dynamic responses based on user emotions. This is expected to reduce traffic accidents and traffic congestion, as well as reduce user stress and improve social safety.

[0687] The "communication infrastructure" is a platform installed at traffic lights that transmits and receives data using a high-speed communication network.

[0688] A "data acquisition device" is a device that acquires traffic video data, weather data, and the user's facial expressions and voice in real time.

[0689] The "data aggregating device" is a device that temporarily aggregates data acquired from each data acquisition device and transmits the data to the analyzing means via a high-speed communication network.

[0690] "Analysis means" refers to a means of analyzing aggregated data to assess traffic volume, traffic accidents, vehicles violating traffic laws, and the risk of road icing and snow accumulation.

[0691] The "control device" is a device that dynamically controls the lighting state of a traffic light based on the analysis result of the analysis means.

[0692] The "emotion engine" is an engine that analyzes the user's facial expressions and voice to identify their emotional state.

[0693] "Traffic video data" refers to video data that captures traffic conditions at traffic lights and intersections.

[0694] "Weather data" refers to data indicating weather conditions such as temperature, humidity, and wind speed.

[0695] "User emotion data" refers to data relating to the user's emotional state analyzed from their facial expressions and voice.

[0696] "High-speed communication network" refers to a communication network with high bandwidth for transmitting and receiving large amounts of data in real time, and includes technologies such as 5G.

[0697] MODE FOR CARRYING OUT THE INVENTION

[0698] The present invention relates to a traffic management system, which is a system including a communication infrastructure installed at traffic lights, a data acquisition device that acquires traffic video data, a data acquisition device that acquires weather data, a data aggregation device that aggregates this data, analysis means that analyzes the aggregated data, a control device that controls traffic lights based on the analysis results, and an emotion engine that analyzes user emotions.

[0699] System configuration

[0700] Communication infrastructure (terminals)

[0701] The communication infrastructure installed in the traffic lights uses high-speed communication networks such as 5G to transmit data from the data acquisition devices to a central traffic control server.

[0702] Data acquisition device (terminal)

[0703] It includes cameras that capture traffic video data, weather sensors that capture weather data, and cameras and microphones that capture the user's facial expressions and voice. The cameras capture real-time images of intersections, road traffic conditions, and vehicle movements, while the weather sensors capture weather data such as temperature, humidity, and wind speed. Cameras and microphones that capture the user's facial expressions and voice are also installed.

[0704] Data aggregation device (terminal)

[0705] Traffic video data, weather data, and user emotion data sent from the data acquisition device are temporarily aggregated and sent to an analysis means, which then transmits the data in real time to a central traffic control server using 5G communications.

[0706] Analysis method (server)

[0707] The traffic control server receives the data, and an AI analysis engine analyzes traffic volume, traffic accidents, vehicles violating traffic rules, and the risk of road icy and snow accumulation. Specifically, video data is used to recognize license plates and identify speeding violations and traffic accidents. Weather data is also used to analyze the risk of icy and snow accumulation.

[0708] Control device (terminal)

[0709] Based on the results of the analysis, the lighting status and operation of traffic lights are dynamically changed. For example, the green light duration of a traffic light is extended during times of heavy traffic, and an emergency response signal is set in the event of a traffic accident. This smooths traffic flow and improves safety.

[0710] Emotion engine (server)

[0711] The emotion engine recognizes emotions by analyzing the user's facial expressions and voice. The analyzed emotion data is fed back to the traffic management system, which then personalizes traffic light control and system notifications to match the user's emotions. For example, if the user is feeling stressed, important notifications will be given priority.

[0712] Specific examples

[0713] Consider the case where the system of the present invention is installed at traffic lights installed in the center of City A. During the morning rush hour, the terminal camera captures video data of the intersection at 30 frames per second and collects weather data of a temperature of 2°C and humidity of 80%. Furthermore, it captures the facial expressions and voices of users near the intersection and generates emotion data. This data is transmitted in real time to a traffic control server via a 5G base station.

[0714] The traffic control server's analytics analyzes video data and identifies 20 vehicles in a traffic jam. Furthermore, license plate recognition technology is used to identify speeding vehicles. Weather data analysis reveals a high risk of road icy conditions due to low temperatures. An emotion engine analyzes users' facial expressions and voices and identifies that some users are stressed. This information is fed back to the traffic management system, which adjusts notification methods.

[0715] Based on this, the traffic light control system will carry out appropriate signal control. Specifically, it will extend the green light time to alleviate congestion and change the signal to prevent freezing. In addition, in the event of a traffic accident, it will set priority signals to allow emergency vehicles to pass. Furthermore, it will prioritize emergency notifications for users who are feeling stressed, encouraging them to take prompt action.

[0716] Prompt Sentence Examples

[0717] Below are some example prompts to input to a generative AI model:

[0718] "Please explain in detail how your real-time traffic management system works using a 5G communication network. I'd particularly like to know how you analyze traffic video data, weather data, and user emotion data."

[0719] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0720] Specific processing steps of the process

[0721] Step 1: Data collection

[0722] Specific explanation: The terminal uses data acquisition devices installed at intersections and various parts of the traffic system to collect traffic video data, weather data, and the user's facial expressions and voice.

[0723] Input: Traffic video data (camera), weather data (weather sensor), user facial expressions and voice (microphone and camera)

[0724] Data calculation and processing: Traffic video data and user emotion data are captured frame by frame, and weather data is measured at regular intervals. The collected raw data is converted into a basic data format.

[0725] Output: Packaged traffic video data, weather data, emotion data

[0726] How it works: The camera captures video at 30 frames per second, the weather sensor measures weather data every minute, the microphone captures the user's voice in real time, and the facial expression camera captures images to analyze the user's facial expressions.

[0727] Step 2: Data aggregation

[0728] Specific explanation: A 5G base station installed on the terminal temporarily aggregates data from each data acquisition device and transmits it to a traffic management server.

[0729] Input: Raw data from different data acquisition devices

[0730] Data calculation and processing: Collected data is collected and packaged into a single packet. Time stamps are used to ensure data integrity.

[0731] Output: Packaged data packets

[0732] Specific operation: The 5G base station receives data sent from each terminal and transmits it in packet format to the traffic management server without delay.

[0733] Step 3: Upload data

[0734] Specific explanation: Data packets collected from terminals are uploaded to a traffic control server in real time using 5G communications.

[0735] Input: Packaged data packet

[0736] Data Calculation and Processing: Data packets are transmitted to the traffic control server over a high-speed network with minimal delay.

[0737] Output: Data packets to the traffic control server

[0738] Specific operation: The 5G base station uses high-speed communication technology to transmit packaged data packets to the traffic control server.

[0739] Step 4: Analyze traffic data

[0740] Specific Description: The traffic control server analyzes the uploaded traffic video data, weather data, and user emotion data.

[0741] Input: Traffic video data, weather data, user emotion data

[0742] Data calculation and processing: The AI ​​analysis engine uses OCR technology to analyze license plates based on video data, identify traffic volume and traffic accidents, and analyze weather data to assess the risk of icy roads and snow accumulation.

[0743] Output: Analyzed traffic conditions, traffic violations, and weather risks

[0744] How it works: The traffic control server uses AI models to read license plates frame by frame, analyze vehicle speeds and movements, and identify traffic violations and accidents. Weather data analysis determines risks when certain conditions are met.

[0745] Step 5: Analyze the sentiment data

[0746] Specific explanation: The emotion engine, which is the server, analyzes the user's facial expression data and voice data to identify the user's emotions.

[0747] Input: User's facial expression data, voice data

[0748] Data Computation and Processing: Deep learning models are used to analyze facial expressions and vocal tone, rate, and volume to identify emotions.

[0749] Output: Parsed emotion data

[0750] Specific operation: The emotion engine uses facial expression analysis technology to classify the user's emotions and identifies emotions by analyzing voice characteristics.

[0751] Step 6: Dynamic signal control

[0752] Specific explanation: The traffic light control system, which is the server, controls the traffic lights based on the analysis results.

[0753] Input: Analyzed traffic conditions, traffic violations, weather risks, and emotion data

[0754] Data calculation and processing: Automatically adjusts traffic light duration and signal patterns according to traffic congestion and accident situations. Adjusts notification methods based on emotion data.

[0755] Output: Adapted traffic light control, notification method

[0756] Specific operation: The traffic light control system performs optimal traffic management by extending the green light duration, setting priority signals, and changing notification content based on the user's emotions.

[0757] (Application example 2)

[0758] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0759] Increased traffic accidents, traffic congestion, and worsening road conditions due to bad weather have created a need for more efficient and safer traffic management. Furthermore, as autonomous vehicles are increasingly being introduced, systems are needed to enable these vehicles to operate more safely and efficiently. Furthermore, traffic management systems that take into account the emotional state of road users can affect traffic conditions.

[0760] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0761] In this invention, the server includes means for acquiring traffic video data, means for acquiring weather data, means for acquiring and analyzing user emotion data, means for aggregating this data through a high-speed communication network, means for controlling traffic lights based on the analysis results, and means for optimizing the behavior of autonomous vehicles, which enables more efficient traffic management and improved safety, safe operation of autonomous vehicles, and flexible responses that take into account the emotional states of traffic users.

[0762] The "communications infrastructure" is an infrastructure facility that uses a high-speed communications network installed at traffic lights to transfer data sent from data acquisition devices to a central traffic control server.

[0763] "Data acquisition device" refers to a device that includes a camera or sensor for acquiring traffic video data or weather data in real time.

[0764] The "data aggregating device" is a device that temporarily aggregates data transmitted from the data acquiring devices and sends the data to the analyzing means.

[0765] The "analysis means" is a server that receives data sent from the data aggregation device and includes an AI analysis engine for analyzing traffic volume, traffic accidents, vehicles violating traffic laws, and the risk of road icing and snow accumulation.

[0766] The "control device" is a device for controlling the behavior of traffic lights and autonomous vehicles based on the analysis results of the analysis means.

[0767] The "emotion engine" is an analytical engine that analyzes the user's facial expressions and voice to recognize emotions and provides feedback to the traffic management system.

[0768] "Traffic volume" is an index that indicates the number of vehicles passing through a specific point, and is a criterion for determining whether or not there is traffic congestion.

[0769] "Traffic accidents" refer to accidents that occur on roads and disrupt the flow of traffic, such as collisions between vehicles or contact between vehicles and people.

[0770] A "traffic violation vehicle" is a vehicle that does not comply with traffic regulations and commits violations such as speeding or running red lights.

[0771] "Risk of road freezing and snow accumulation" is an indicator that shows the possibility of roads freezing or snow accumulation based on meteorological data such as temperature, humidity, and snowfall.

[0772] "User emotion data" is data that indicates the emotional state of the user analyzed from their facial expressions and voice.

[0773] A "high-speed communication network" is a network that uses high-bandwidth communication technologies such as 5G, enabling high-speed, large-capacity data communication.

[0774] The present invention relates to a system that improves the efficiency and safety of traffic management based on the environment in which an autonomous vehicle is traveling and the emotional state of the user.

[0775] System Configuration

[0776] Communication infrastructure (terminals)

[0777] The devices are installed at traffic lights and transmit data from the data acquisition devices to a central traffic control server via a high-speed communication network.

[0778] Data acquisition device (terminal)

[0779] It includes a camera for capturing traffic video data and a weather sensor for capturing weather data. The camera captures traffic conditions at intersections and roads and vehicle movements in real time, while the weather sensor captures weather data such as temperature, humidity, and wind speed. It also includes a camera and microphone for capturing the user's facial expressions and voice.

[0780] Data aggregation device (terminal)

[0781] The data transmitted from the data acquisition devices is temporarily collected and sent to an analysis means, which transmits the data in real time to a central traffic control server using a high-speed communication network.

[0782] Analysis method (server)

[0783] The traffic control server receives the data, and the AI ​​analysis engine analyzes traffic volume, traffic accidents, traffic violations, and the risk of road icy and snow accumulation. Specifically, video data is used to analyze vehicle movements and identify speeding violations and traffic accidents. Weather data is used to analyze the risk of icy and snow accumulation.

[0784] Emotion engine (server)

[0785] The emotion engine recognizes emotions by analyzing the user's facial expressions and voice. The analyzed emotion data is fed back to the traffic management system, which then personalizes traffic light control and system notifications to match the user's emotions.

[0786] Control device (terminal)

[0787] Based on the results of the analysis method and emotion engine, the lighting status of traffic lights and the behavior of autonomous vehicles are dynamically changed. Specifically, the system extends traffic lights during congestion, sends emergency signals to respond to traffic accidents, controls signals when there is a high risk of roads freezing due to low temperatures, and adjusts notification methods based on the user's emotional state.

[0788] Program processing description

[0789] Cameras (terminals) capture traffic video data in real time from traffic light locations, and weather sensors (terminals) measure data such as temperature, humidity, and wind speed. In addition, cameras and microphones capture the user's facial expressions and voice to collect emotional data in real time. This data is temporarily aggregated in a data aggregation device via a high-speed communication network and then uploaded to a central traffic control server.

[0790] The traffic control server receives the uploaded data and analyzes traffic volume, traffic accidents, traffic violations, and the risk of road icing and snow accumulation. Using an AI analysis engine, it can analyze vehicle movements from camera footage and identify speeding vehicles using license plate recognition technology. Additionally, weather data is analyzed to manage road maintenance risks.

[0791] The emotion engine (server) uses facial expression analysis technology to identify the user's emotions. Specifically, it recognizes whether the user is feeling stressed or calm, and feeds the analysis results back to the traffic management system. This makes it possible to adjust the notification method according to the user's level of stress.

[0792] As a concrete example, consider a traffic management system in City A. During the morning rush hour, a camera captures video data of an intersection at 30 frames per second, and a weather sensor collects data on a temperature of 2 degrees and humidity of 80%. Furthermore, the facial expressions and voices of users near the intersection are analyzed to generate emotion data. This data is transmitted in real time to a traffic control server via a high-speed communication network.

[0793] Example prompt sentence:

[0794] "During the morning rush hour, please simulate a system that analyzes traffic video data, weather data, and user emotion data to ensure safe and efficient driving. Specifically, please execute driving behavior and signal control that takes into account traffic congestion, the risk of road surfaces freezing due to low temperatures, and user stress."

[0795] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0796] Step 1: Data collection

[0797] The device collects traffic video data, weather data, and user emotion data. Traffic video data is captured in real time by a camera, recording vehicle movements and traffic conditions as video data. Weather data is obtained using a weather sensor to acquire temperature, humidity, wind speed, etc. User emotion data is obtained by capturing the user's facial expressions and voice using an facial camera and microphone.

[0798] Input: traffic video, weather data, facial expression data, audio data

[0799] Output: A set of captured real-time data

[0800] Step 2: Data aggregation

[0801] The data collected by the terminals is temporarily sent to a data aggregator, which then consolidates and packages traffic video data, weather data, facial expression data, and voice data before forwarding it to a central traffic control server. The data is transmitted in real time using a high-speed communication network.

[0802] Input: A set of real-time data

[0803] Output: Packaged aggregated data

[0804] Step 3: Data analysis

[0805] The server receives the aggregated data and uses an AI analysis engine to analyze traffic volume, traffic accidents, vehicles violating traffic rules, and the risk of road icy roads and snow accumulation. By analyzing traffic video data, the system recognizes vehicle movements and license plates and identifies vehicles violating traffic rules and traffic accidents. It also predicts the risk of road icy roads and snow accumulation based on weather data.

[0806] Input: Aggregated data

[0807] Output: Traffic situation analysis results, weather risk analysis results

[0808] Step 4: Sentiment Analysis

[0809] The server uses an emotion engine to analyze facial expression and voice data to identify the user's emotions. Facial expression analysis and voice analysis technologies are used to determine the user's stress level and satisfaction.

[0810] Input: facial expression data, voice data

[0811] Output: User's emotional state

[0812] Step 5: Traffic light control

[0813] The server dynamically changes the lighting status of traffic lights based on the results of the analysis method and emotion engine. Specifically, it extends the green light time when traffic volume is heavy, sets an emergency response signal when a traffic accident occurs, and adjusts signal control when there is a risk of freezing due to low temperatures.

[0814] Input: Traffic situation analysis results, weather risk analysis results, user emotional state

[0815] Output: Dynamically changed traffic light status

[0816] Step 6: Adjusting the behavior of the autonomous vehicle

[0817] The server optimizes the behavior of the self-driving vehicle based on the results of the analysis and emotion engine, such as changing the route when traffic is congested, adjusting the speed when it is cold, and changing the driving mode when the user is feeling stressed.

[0818] Input: Traffic situation analysis results, weather risk analysis results, user emotional state

[0819] Output: Optimized autonomous vehicle driving modes

[0820] Step 7: Feedback and Notification

[0821] The server provides appropriate feedback and notifications based on the analysis results of the traffic management system and the user's emotional state. For example, it notifies the user of traffic congestion information and emergency response information. It also provides personalized notifications according to the user's stress level.

[0822] Input: Analysis results, user's emotional state

[0823] Output: Personalized feedback and notifications

[0824] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0825] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0826] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0827] [Third embodiment]

[0828] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0829] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0830] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0831] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0832] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0833] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0834] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0835] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0836] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0837] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0838] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0839] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0840] The present invention is a system consisting of a communication infrastructure installed at traffic lights, a data acquisition device that acquires traffic video data, a data acquisition device that acquires weather data, a data aggregation device that aggregates these data, an analysis means that analyzes the aggregated data, and a control device that controls traffic lights based on the analysis results.This system will improve the efficiency of traffic management and solve problems such as traffic accidents, congestion, violation enforcement, and road condition management.

[0841] System Configuration

[0842] Communication infrastructure (terminals)

[0843] The communication infrastructure installed in the traffic lights uses high-speed communication networks such as 5G to transmit data from the data acquisition devices to a central traffic control server.

[0844] Data acquisition device (terminal)

[0845] The system includes cameras that capture traffic video data and weather sensors that capture meteorological data such as temperature, humidity, and wind speed.

[0846] Data aggregation device (terminal)

[0847] The data sent from the data acquisition devices is temporarily aggregated and sent to an analysis means, which then transmits the data in real time to a central traffic control server using 5G communications.

[0848] Analysis method (server)

[0849] The traffic control server receives the data, and the AI ​​analysis engine analyzes traffic volume, traffic accidents, traffic violations, and the risk of road icy and snow accumulation. Specifically, video data is used to recognize license plates and identify speeding violations and traffic accidents. Weather data is used to analyze the risk of icy and snow accumulation.

[0850] Control device (terminal)

[0851] Based on the results of the analysis, the lighting status and operation of traffic lights are dynamically changed. For example, the green light time of a traffic light is extended during times of heavy traffic, and an emergency response signal is set in the event of a traffic accident.

[0852] Natural language explanation of program processing

[0853] 1. Data collection and upload

[0854] Cameras (terminals) capture real-time traffic video data from traffic light locations, while weather sensors (terminals) measure temperature, humidity, wind speed, and other data. This data is temporarily aggregated and packaged via 5G base stations (terminals), and then uploaded to a central traffic control server via 5G communications.

[0855] 2. Data Analysis

[0856] The traffic control server (server) receives the uploaded data, and the analysis means (server) analyzes traffic volume, traffic accidents, vehicles violating traffic rules, and the risk of road icing and snow accumulation. For example, camera footage can be used to read license plates using OCR technology to identify vehicles speeding. It can also analyze the risk of road icing based on weather data.

[0857] 3. Dynamic Signal Control

[0858] Based on the results of the analysis, the traffic light control system (server) determines the lighting status of traffic lights. For example, if there is congestion, the green light duration of the traffic light will be extended appropriately to smooth the flow of traffic. Also, if a traffic accident occurs, priority signals will be set for traffic lights on the route so that emergency vehicles can arrive at the scene quickly.

[0859] Specific examples

[0860] Consider the case where the system of the present invention is installed at traffic lights installed in the center of City A. During the morning rush hour, a camera captures video data of the intersection at 30 frames per second and collects weather data of a temperature of 2°C and humidity of 80%. This data is transmitted in real time to a traffic control server via a 5G base station.

[0861] The traffic control server's analytics analyzed the video data and identified 20 vehicles in a traffic jam. Furthermore, license plate recognition technology was used to identify speeding vehicles. Weather data analysis revealed a high risk of road surface freezing due to low temperatures.

[0862] Based on this, the traffic light control system will carry out appropriate signal control, such as extending the green light time to ease congestion, changing the signal to prevent freezing, and setting priority signals to allow emergency vehicles to pass in the event of a traffic accident.

[0863] Public safety and local government officials can remotely check the system's data dashboard and implement traffic restrictions and enforcement based on real-time traffic conditions and weather forecasts. This system will efficiently solve a variety of problems facing society's transportation system.

[0864] The processing flow will be explained below.

[0865] Step 1:

[0866] The camera (terminal) captures images of intersections and road conditions in real time. The camera generates video data at a rate of 30 frames per second to obtain traffic video data.

[0867] Step 2:

[0868] The weather sensor (terminal) measures weather data such as temperature, humidity, wind speed, etc. The weather data is acquired every minute and temporarily stored in the sensor's internal memory.

[0869] Step 3:

[0870] The 5G base station (terminal) aggregates data received from cameras and weather sensors, and packages the aggregated data for transmission to a central traffic control server via 5G communications.

[0871] Step 4:

[0872] The traffic control server (server) receives data sent from 5G base stations and stores it in a database. The received data includes video data and weather data.

[0873] Step 5:

[0874] The AI ​​analysis engine (server) analyzes the received video data. Specifically, it recognizes license plates, detects speeding violations, and identifies traffic accidents. It detects vehicles in the video and reads license plates using OCR technology. It also calculates the distance the vehicle travels between frames and calculates its speed to identify speeding violations.

[0875] Step 6:

[0876] The AI ​​analysis engine (server) analyzes weather data and predicts the risk of freezing and snow accumulation. It determines the possibility of freezing based on temperature and humidity data, and uses wind speed data to predict changes in snow accumulation and road conditions.

[0877] Step 7:

[0878] The traffic light control system (server) determines the lighting status of traffic lights based on the analysis results of the AI ​​analysis engine. When congestion occurs, the green light will be kept on for an extended period to smooth traffic flow. In addition, when a traffic accident occurs, priority signals will be set for emergency vehicles.

[0879] Step 8:

[0880] The traffic light (terminal) receives control instructions from the traffic light control system and changes the lighting status of the signal, thereby alleviating traffic congestion and responding to emergencies.

[0881] Step 9:

[0882] The traffic control server (server) provides analysis results and real-time data as a dashboard, which displays traffic congestion status, traffic accident information, weather forecasts, and more.

[0883] Step 10:

[0884] Public safety and local government officials (users) can remotely access the dashboard to view real-time traffic and weather data, and can remotely implement traffic restrictions and enforcement actions as needed.

[0885] Example 1

[0886] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0887] Conventional traffic management systems have difficulty controlling signals in real time according to traffic volume and weather conditions, resulting in problems such as traffic congestion, traffic accidents, and delays in identifying violating vehicles. They also fail to properly manage the risk of road ice and snow accumulation, which causes frequent traffic accidents and congestion. An effective system to solve these issues is needed.

[0888] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0889] In this invention, the server includes a communication infrastructure installed in the traffic control device, a data acquisition device that acquires traffic video data, a data acquisition device that acquires weather data, a data aggregation device that aggregates data from the data acquisition devices via a high-speed communication network, analysis means that analyzes data transmitted from the data aggregation device, and a control device that controls the traffic control device based on the analysis results of the analysis means. This enables traffic signal control in real time according to traffic volume and weather conditions, thereby easing traffic congestion, preventing traffic accidents, identifying violating vehicles, and appropriately managing the risk of road icing and snow accumulation.

[0890] "Communications infrastructure" refers to equipment installed in traffic control devices that includes a high-speed communications network for transmitting and receiving data.

[0891] The "data acquisition device" is a device for acquiring traffic video data and weather data, and includes cameras, weather sensors, etc.

[0892] The "data aggregating device" is a device that temporarily stores data acquired from a data acquiring device, packages the data, and transmits the data to an analyzing means.

[0893] The "analysis means" is a device or system that analyzes the data sent from the data aggregation device and identifies traffic volume, traffic accidents, vehicles violating traffic laws, and the risk of road icing or snow accumulation.

[0894] The "control device" is a device that dynamically changes the lighting state of traffic lights based on the analysis results of the analysis means, thereby easing traffic congestion and responding to traffic accidents.

[0895] "Traffic volume" is the number of vehicles passing through a particular point in a particular time period.

[0896] A "traffic accident" is an incident caused by traffic troubles, such as collisions between vehicles or single-vehicle accidents.

[0897] A "traffic violation vehicle" is a vehicle that violates traffic laws. This includes violations such as speeding and illegal parking.

[0898] "Freezing" is a phenomenon in which the temperature drops and the road surface freezes over.

[0899] "Snow accumulation" is a phenomenon in which the temperature drops and snow accumulates on roads and surfaces.

[0900] "OCR technology" is an abbreviation for optical character recognition technology, and is a technology for reading character information from image data.

[0901] A "priority signal" is a signal state that is set to allow emergency vehicles and vehicles requiring priority passage to pass more easily.

[0902] This invention is a system comprising a communication infrastructure installed in a traffic control device, a data acquisition device for acquiring traffic video data, a data acquisition device for acquiring weather data, a data aggregation device for aggregating these data, an analysis means for analyzing the aggregated data, and a control device for controlling the traffic control device based on the analysis results.

[0903] Communication infrastructure (terminals)

[0904] The communication infrastructure will be installed in the traffic control device and will transmit data received from the data acquisition device to a central traffic control server using a high-speed communication network. Specifically, 5G communication technology will be used.

[0905] Data acquisition device (terminal)

[0906] The data acquisition device includes cameras that capture traffic video data and weather sensors that capture meteorological data. The cameras capture real-time images of traffic conditions and vehicle movements at intersections and major roads. For example, video data is collected at 30 frames per second. The weather sensors measure temperature, humidity, wind speed, etc. and update the data every five seconds.

[0907] Data aggregation device (terminal)

[0908] The data aggregation device temporarily stores and packages the data sent from the data acquisition device. This sorts out inconsistencies in the data and makes it available for analysis in real time. For example, traffic video data and weather data are sorted in chronological order.

[0909] Analysis method (server)

[0910] The analysis means is installed on the traffic control server, which receives the transmitted data and performs initial processing. Specifically, the AI ​​analysis engine analyzes the video data and recognizes vehicle license plates using OCR technology. This makes it possible to identify speeding vehicles and illegally parked vehicles. Weather data is also analyzed to assess the risk of road icy conditions and snow accumulation.

[0911] Control device (terminal)

[0912] The control device dynamically changes the lighting status of the traffic lights based on the results of the analysis means, for example, by extending the green lighting time of the traffic lights during times of heavy traffic, or by setting priority signals for emergency vehicles in the event of a traffic accident.

[0913] Specific examples

[0914] For example, consider the case where this system is installed in the city center of City A. During the morning rush hour, a camera captures video data of an intersection at 30 frames per second and collects weather data such as a temperature of 2°C and humidity of 80%. The data aggregator organizes this data and transmits it to a traffic control server via 5G communications.

[0915] The traffic control server's analytics analyzes the video data and identifies a traffic jam of 20 vehicles. It then uses license plate recognition technology to identify speeding vehicles. Weather data analysis reveals a high risk of road icing due to low temperatures.

[0916] Based on this, the traffic light control system will carry out appropriate signal control. Specifically, it will extend the time that the green light remains on to alleviate congestion. In addition, in the event of a traffic accident, it will set priority signals so that emergency vehicles can arrive at the scene quickly.

[0917] Prompt Sentence Examples

[0918] 1. Please explain the specific steps by which the traffic control server's analytical means identifies traffic accidents from the uploaded data.

[0919] 2. What are the detailed steps for how the traffic light control system changes the operation of traffic lights when it detects congestion?

[0920] This system enables real-time traffic management, which can efficiently alleviate traffic congestion and prevent traffic accidents. It also enables road management according to weather conditions, providing a safer traffic environment. Public safety and local government officials can remotely view the system's data dashboard and use it as reference information for traffic restrictions and enforcement.

[0921] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0922] Step 1:

[0923] Data collection devices capture traffic footage and weather data.

[0924] Input: Real-time video data from the camera (terminal), weather data (temperature, humidity, wind speed) from the weather sensor.

[0925] How it works: The camera captures images of intersections and major roads at 30 frames per second, and the weather sensor measures temperature, humidity, and wind speed every 5 seconds.

[0926] Output: Video data and weather data.

[0927] Step 2:

[0928] The communication infrastructure receives the data acquired from the data acquisition device.

[0929] Input: Video data and meteorological data transmitted from the data acquisition device.

[0930] Specific operation: Data transmitted from the data acquisition device is temporarily stored via the communication infrastructure.

[0931] Output: Temporarily saved video data and weather data.

[0932] Step 3:

[0933] The data aggregation device acquires data from the communication infrastructure and packages it.

[0934] Input: Temporarily stored video data and weather data.

[0935] Specific operation: The data aggregator sorts out data inconsistencies and packages the data in chronological order.

[0936] Output: Packaged data.

[0937] Step 4:

[0938] The data aggregator transmits the packaged data to the traffic control server.

[0939] Input: Packaged data.

[0940] Specific operation: The data aggregation device uses 5G communications to send packaged data to the traffic control server in real time.

[0941] Output: Data sent to traffic control server.

[0942] Step 5:

[0943] The traffic control server receives the transmitted data and performs initial processing.

[0944] Input: The data sent.

[0945] Specific operation: The traffic control server checks for data inconsistencies and corrects the data as necessary.

[0946] Output: Organized data.

[0947] Step 6:

[0948] The analysis means analyzes the video data and the meteorological data.

[0949] Input: Organized video and weather data.

[0950] Specific operation: Analyzes video data using an AI analysis engine, performs OCR recognition of license plates and vehicle movement analysis, and evaluates the risk of road icy conditions and snow accumulation based on weather data.

[0951] Output: Analysis results (traffic accidents, violating vehicles, risk of freezing, etc.).

[0952] Step 7:

[0953] The control device dynamically changes the lighting status of the traffic light based on the analysis results.

[0954] Input: Analysis results.

[0955] Specific actions: When traffic volume is heavy, the green light will stay on for an extended period of time, and when a traffic accident occurs, traffic lights will be set to give priority to emergency vehicles.

[0956] Output: Changed traffic light status.

[0957] Step 8:

[0958] Users can remotely view the system's data dashboard and implement traffic restrictions and enforcement.

[0959] Input: Data dashboard information.

[0960] Specific operation: Public safety and local government officials can check traffic and weather conditions on the dashboard and remotely issue instructions on how to respond.

[0961] Output: Traffic restrictions and enforcement actions that were applied.

[0962] In this way, each step performs data processing and calculations based on the input to obtain the output of the next step.

[0963] (Application example 1)

[0964] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0965] Conventional traffic management systems have difficulty utilizing acquired traffic and weather data in real time, making it difficult to respond quickly to changes in traffic conditions. Furthermore, there is a lack of means to notify automated driving vehicles of traffic signal status and traffic forecast information, making it difficult to ensure smooth traffic flow. This makes it necessary to respond quickly to traffic congestion and traffic accidents.

[0966] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0967] In this invention, the server includes means for providing real-time traffic conditions using acquired traffic video data and weather data, means for providing traffic alerts and forecasts to autonomous vehicles based on analysis results, means for identifying vehicles violating traffic laws, and means for providing priority signal notifications to autonomous vehicles. This enables the use of traffic data in real time, enabling the prompt provision of information to autonomous vehicles and ensuring smooth traffic flow.

[0968] A "communication infrastructure" is a device installed at traffic lights that sends and receives data using a high-speed communication network.

[0969] A "data acquisition device" is a device installed to acquire traffic video data and weather data.

[0970] The "data aggregating device" is a device that temporarily aggregates data acquired from the data acquiring device and transmits the data to the data analyzing means.

[0971] "Analysis means" refers to a means for analyzing the aggregated data and obtaining useful information about traffic and weather conditions.

[0972] The "controller" is a device that controls traffic lights based on the analysis results of the analyzing means, thereby ensuring smooth traffic flow and safety.

[0973] "Traffic video data" is video data showing the traffic conditions on roads and intersections.

[0974] "Weather data" refers to data related to weather conditions (temperature, humidity, wind speed, etc.).

[0975] "Means for providing traffic conditions in real time" refers to means for analyzing acquired traffic video data and weather data and instantly providing information on current traffic conditions.

[0976] "Means for providing traffic alerts and predictions" means means for providing warnings and predictions about future traffic conditions and risks based on analyzed data.

[0977] The "means for identifying vehicles violating traffic regulations" refers to a means for analyzing traffic video data and identifying vehicles violating regulations.

[0978] A "means for providing a priority signal notification" is a means for notifying an autonomous vehicle of the priority signal status of a particular traffic light.

[0979] The system of the present invention includes a communication infrastructure installed at traffic lights, a data acquisition device that acquires traffic video data and weather data, a data aggregation device that aggregates the data, an analysis means that analyzes the data, a control device that controls the traffic lights based on the analysis results, and a means for providing real-time traffic conditions and traffic alerts and forecasts for autonomous vehicles.

[0980] Hardware and software used

[0981] The server is equipped with a communications infrastructure that utilizes a high-speed communications network (such as 5G), and cameras and weather sensors are connected to the data acquisition devices. The data acquired from these data acquisition devices is aggregated on the server via a data aggregation device.

[0982] The analysis method runs an AI analysis engine on a server to analyze traffic volume, traffic accidents, vehicles violating traffic rules, and the risk of road icy conditions and snow accumulation. This analysis method includes computer vision technology for video analysis and weather models for weather data analysis.

[0983] Data processing and calculation

[0984] The server analyzes the acquired traffic video data using computer vision technology (e.g., OpenCV) to identify traffic volume. It also identifies vehicles violating traffic rules using license plate recognition (e.g., OCR technology). Weather data analysis uses weather models (e.g., ARIMA models) to predict the risk of road icing and snow accumulation.

[0985] The control device dynamically changes the lighting status of traffic lights based on the analysis results, for example by extending the green light time depending on traffic volume or setting an emergency response signal in the event of a traffic accident.

[0986] Specific examples

[0987] For example, if the system of the present invention is installed at traffic lights installed in the center of City A, a camera will capture video data of the intersection in real time during the morning rush hour and collect weather data such as a temperature of 2°C and humidity of 80%. This data will be transmitted in real time to a traffic control server via a 5G base station.

[0988] The traffic control server's analytics analyzes the video data and identifies 20 vehicles in a traffic jam. License plate recognition technology also identifies three speeding vehicles. Weather data analysis reveals a high risk of road icing due to low temperatures.

[0989] Based on this, the traffic light control system will carry out appropriate signal control. Specifically, it will extend the green light time to alleviate congestion and change the traffic lights to prevent freezing. In addition, in the event of a traffic accident, priority signals will be set for traffic lights on the route so that emergency vehicles can arrive at the scene quickly. Public safety and local government officials can remotely check the system's data dashboard and implement traffic restrictions and enforcement remotely based on real-time traffic conditions and weather forecasts.

[0990] Prompt Sentence Examples

[0991] We are designing a smartphone application that displays traffic volume, weather data, and congestion information based on real-time traffic data from a traffic light control system. The following features are required:

[0992] 1. Real-time streaming of traffic video data.

[0993] 2. Predictive alerts for traffic jams and accidents.

[0994] 3. Aggregate daily traffic and weather data and generate reports.

[0995] 4. Notification of changes in traffic light operation.

[0996] Please generate source code using Python, including instructions for using the API and displaying data.

[0997] The above is an embodiment of the invention.

[0998] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0999] Step 1:

[1000] Cameras and weather sensors (terminals) capture traffic video data and weather data in real time. The cameras capture images of roads and intersections, while the weather sensors capture weather data such as temperature, humidity, and wind speed, and send them to the data acquisition device.

[1001] Input: Traffic video data, weather data

[1002] Output: Raw data obtained

[1003] Step 2:

[1004] The data acquisition device (terminal) transmits the acquired traffic video data and weather data to the data aggregation device via the 5G network. Here, the video data is transmitted in a stream format, and the weather data is transmitted as numerical data.

[1005] Input: Raw data acquired

[1006] Output: Stream format video data, numerical data format weather data

[1007] Step 3:

[1008] The data aggregating device (terminal) temporarily stores the received traffic video data and weather data, packages them, and transmits them to the analyzing means.

[1009] Input: Stream format video data, numerical data format weather data

[1010] Output: Packaged data

[1011] Step 4:

[1012] The analysis means (server) receives the packaged data and analyzes the traffic video data using computer vision technology (e.g., OpenCV) to measure traffic volume. Weather data is analyzed using weather models (e.g., ARIMA models) to analyze the risk of road icing and snow accumulation. Furthermore, license plate recognition (e.g., OCR technology) is used to identify vehicles violating traffic laws.

[1013] Input: Packaged traffic video data, weather data

[1014] Output: Analyzed traffic condition data, weather risk data, and violating vehicle data

[1015] Step 5:

[1016] The analysis means (server) provides real-time traffic conditions based on the analysis results and generates traffic alerts and predictions for autonomous vehicles.

[1017] Input: Analyzed traffic condition data, weather risk data, violating vehicle data

[1018] Output: Traffic reports, traffic alerts, forecast data

[1019] Step 6:

[1020] The control device (server) dynamically changes the lighting status of traffic lights based on the analysis results. Specifically, it extends the green light time when traffic volume is heavy, sets an emergency response signal when a traffic accident occurs, and provides priority signal notifications to autonomous vehicles.

[1021] Inputs: Traffic reports, traffic alerts, and forecast data

[1022] Output: Dynamically changed traffic light control information, priority signal notification

[1023] Step 7:

[1024] Users (public safety and local government officials) can remotely check the system's data dashboard and remotely implement traffic restrictions and enforcement based on real-time traffic conditions and weather forecasts.

[1025] Input: Dynamically changed traffic light control information, priority signal notification

[1026] Output: Traffic regulation and enforcement implementation plan

[1027] The above is the flow of processing of the system program for realizing the application example.

[1028] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1029] The present invention further improves the efficiency of traffic management and social safety by combining a system consisting of a communication infrastructure installed at traffic lights, a data acquisition device that acquires traffic video data, a data acquisition device that acquires weather data, a data aggregation device that aggregates this data, analysis means that analyzes the aggregated data, and a control device that controls traffic lights based on the analysis results with an emotion engine that recognizes user emotions.

[1030] System Configuration

[1031] Communication infrastructure (terminals)

[1032] The communication infrastructure installed in the traffic lights uses high-speed communication networks such as 5G to transmit data from the data acquisition devices to a central traffic control server.

[1033] Data acquisition device (terminal)

[1034] The system includes cameras that capture traffic video data and weather sensors that capture meteorological data such as temperature, humidity, and wind speed.

[1035] Data aggregation device (terminal)

[1036] The data sent from the data acquisition devices is temporarily aggregated and sent to an analysis means, which then transmits the data in real time to a central traffic control server using 5G communications.

[1037] Analysis method (server)

[1038] The traffic control server receives the data, and the AI ​​analysis engine analyzes traffic volume, traffic accidents, traffic violations, and the risk of road icy and snow accumulation. Specifically, video data is used to recognize license plates and identify speeding violations and traffic accidents. Weather data is used to analyze the risk of icy and snow accumulation.

[1039] Control device (terminal)

[1040] Based on the results of the analysis, the lighting status and operation of traffic lights are dynamically changed. For example, the green light time of a traffic light is extended during times of heavy traffic, and an emergency response signal is set in the event of a traffic accident.

[1041] Emotion engine (server)

[1042] The emotion engine analyzes the user's facial expressions and voice to recognize their emotions. The analyzed emotion data is fed back to the traffic management system, which then personalizes traffic light control and system notifications to match the user's emotions.

[1043] Natural language explanation of program processing

[1044] 1. Data collection and upload

[1045] Cameras (terminals) will capture real-time traffic video data from traffic light locations, and weather sensors (terminals) will measure data such as temperature, humidity, and wind speed. Additionally, microphones and cameras will be installed to capture the user's facial expressions and voice. This data will be temporarily aggregated and packaged via a 5G base station (terminal), and then uploaded to a central traffic control server via 5G communications.

[1046] 2. Data Analysis

[1047] The traffic control server (server) receives the uploaded data, and the analysis means (server) analyzes traffic volume, traffic accidents, vehicles violating traffic rules, and the risk of road icing and snow accumulation. For example, camera footage can be used to read license plates using OCR technology to identify vehicles speeding. It can also analyze the risk of road icing based on weather data.

[1048] 3. Emotion analysis

[1049] The emotion engine (server) analyzes the acquired facial expression and voice data to identify the user's emotions. For example, it uses facial expression analysis technology to recognize whether the user is stressed or calm. It also infers emotions from the tone and volume of the voice.

[1050] 4. Dynamic Signal Control

[1051] Based on the results of the analysis means and the emotion engine, the traffic light control system (server) determines the lighting status of traffic lights. For example, if there is congestion, the green light duration of the traffic light will be extended appropriately to smooth traffic flow. Also, in the event of a traffic accident, priority signals will be set for traffic lights on the route so that emergency vehicles can arrive at the scene quickly. Based on the analysis results of the emotion engine, if the user is feeling stressed, the notification method will be adjusted, such as providing emergency notifications as a priority.

[1052] Specific examples

[1053] Consider the case where the system of the present invention is installed at traffic lights installed in the center of City A. During the morning rush hour, a camera captures video data of the intersection at 30 frames per second and collects weather data with a temperature of 2°C and humidity of 80%. Furthermore, the system captures the facial expressions and voices of users near the intersection to generate emotion data. This data is then transmitted in real time to a traffic control server via a 5G base station.

[1054] The traffic control server's analytics analyzed the video data and identified 20 vehicles in a traffic jam. Furthermore, license plate recognition technology was used to identify speeding vehicles. Weather data analysis revealed a high risk of road surface freezing due to low temperatures.

[1055] The emotion engine analyzes users' facial expressions and voices to identify when some users are stressed, and this information is fed back to the traffic management system to adjust the notification method.

[1056] Based on this, the traffic light control system will carry out appropriate signal control. Specifically, it will extend the green light time to alleviate congestion and change the signal to prevent freezing. In addition, in the event of a traffic accident, it will set priority signals to allow emergency vehicles to pass. Furthermore, it will prioritize emergency notifications for users who are feeling stressed, encouraging them to take prompt action.

[1057] Public safety and local government officials can remotely check the system's data dashboard and implement traffic restrictions and enforcement based on real-time traffic conditions, weather forecasts, and emotion data. This system will efficiently solve various problems facing society with traffic and will also enable responses that are sensitive to user emotions.

[1058] The processing flow will be explained below.

[1059] Step 1:

[1060] The camera (terminal) captures images of intersections and road conditions in real time. The camera generates video data at a rate of 30 frames per second to obtain traffic video data.

[1061] Step 2:

[1062] The weather sensor (terminal) measures weather data such as temperature, humidity, wind speed, etc. The weather data is acquired every minute and temporarily stored in the sensor's internal memory.

[1063] Step 3:

[1064] The emotion engine camera and microphone (terminal) capture the user's facial expressions and voice. This generates emotion data. The facial expression data and voice data are temporarily stored in memory for emotion analysis.

[1065] Step 4:

[1066] The 5G base station (terminal) aggregates data received from cameras, weather sensors, and the emotion engine camera and microphone, and packages the aggregated data for transmission to a central traffic control server via 5G communications.

[1067] Step 5:

[1068] The traffic control server (server) receives data transmitted from 5G base stations and stores it in a database. The received data includes video data, weather data, and emotion data.

[1069] Step 6:

[1070] The AI ​​analysis engine (server) analyzes the received video data. Specifically, it recognizes license plates, detects speeding violations, and identifies traffic accidents. It detects vehicles in the video and reads license plates using OCR technology. It also calculates the distance the vehicle travels between frames and calculates its speed to identify speeding violations.

[1071] Step 7:

[1072] The AI ​​analysis engine (server) analyzes weather data and predicts the risk of freezing and snow accumulation. It determines the possibility of freezing based on temperature and humidity data, and uses wind speed data to predict changes in snow accumulation and road conditions.

[1073] Step 8:

[1074] The emotion engine (server) analyzes facial expression and voice data to identify the user's emotions. For example, it uses facial expression analysis technology to recognize whether the user is stressed or calm. It also infers emotions from the tone and volume of the voice.

[1075] Step 9:

[1076] The traffic light control system (server) determines the lighting status of traffic lights based on the results of the AI ​​analysis engine and emotion engine. When congestion occurs, the green light duration is extended to smooth traffic flow. In addition, when a traffic accident occurs, priority signals are set for emergency vehicles. Based on the analysis results of the emotion engine, notification methods are adjusted, such as prioritizing emergency notifications if the user is feeling stressed.

[1077] Step 10:

[1078] The traffic light (terminal) receives control instructions from the traffic light control system and changes the lighting status of the signal, thereby alleviating traffic congestion and responding to emergencies.

[1079] Step 11:

[1080] The traffic control server (server) provides analysis results and real-time data as a dashboard, which displays traffic congestion status, traffic accident information, weather forecasts, and emotion data.

[1081] Step 12:

[1082] Public safety and local government officials (users) can remotely access the dashboard to view real-time traffic conditions, weather data, and emotion data, and can remotely implement traffic restrictions and enforcement actions as needed.

[1083] Example 2

[1084] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1085] Conventional traffic management systems are limited to control based on traffic volume and weather conditions, and have difficulty responding dynamically to the user's emotional state. As a result, they have not been able to fully reduce user stress or improve safety in the event of a traffic accident or traffic congestion. Furthermore, they have not been able to fully optimize traffic signal control by taking real-time traffic and weather conditions into account.

[1086] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1087] In this invention, the server includes a means for identifying traffic volume and traffic accidents, a means for identifying vehicles committing traffic violations, a means for predicting road icing and snow accumulation, and a means for analyzing user emotions. This enables optimal traffic light control based on traffic and weather conditions in real time, and also enables dynamic responses based on user emotions. This is expected to reduce traffic accidents and traffic congestion, as well as reduce user stress and improve social safety.

[1088] The "communication infrastructure" is a platform installed at traffic lights that transmits and receives data using a high-speed communication network.

[1089] A "data acquisition device" is a device that acquires traffic video data, weather data, and the user's facial expressions and voice in real time.

[1090] The "data aggregating device" is a device that temporarily aggregates data acquired from each data acquisition device and transmits the data to the analyzing means via a high-speed communication network.

[1091] "Analysis means" refers to a means of analyzing aggregated data to assess traffic volume, traffic accidents, vehicles violating traffic laws, and the risk of road icing and snow accumulation.

[1092] The "control device" is a device that dynamically controls the lighting state of a traffic light based on the analysis result of the analysis means.

[1093] The "emotion engine" is an engine that analyzes the user's facial expressions and voice to identify their emotional state.

[1094] "Traffic video data" refers to video data that captures traffic conditions at traffic lights and intersections.

[1095] "Weather data" refers to data indicating weather conditions such as temperature, humidity, and wind speed.

[1096] "User emotion data" refers to data relating to the user's emotional state analyzed from their facial expressions and voice.

[1097] "High-speed communication network" refers to a communication network with high bandwidth for transmitting and receiving large amounts of data in real time, and includes technologies such as 5G.

[1098] MODE FOR CARRYING OUT THE INVENTION

[1099] The present invention relates to a traffic management system, which is a system including a communication infrastructure installed at traffic lights, a data acquisition device that acquires traffic video data, a data acquisition device that acquires weather data, a data aggregation device that aggregates this data, analysis means that analyzes the aggregated data, a control device that controls traffic lights based on the analysis results, and an emotion engine that analyzes user emotions.

[1100] System configuration

[1101] Communication infrastructure (terminals)

[1102] The communication infrastructure installed in the traffic lights uses high-speed communication networks such as 5G to transmit data from the data acquisition devices to a central traffic control server.

[1103] Data acquisition device (terminal)

[1104] It includes cameras that capture traffic video data, weather sensors that capture weather data, and cameras and microphones that capture the user's facial expressions and voice. The cameras capture real-time images of intersections, road traffic conditions, and vehicle movements, while the weather sensors capture weather data such as temperature, humidity, and wind speed. Cameras and microphones that capture the user's facial expressions and voice are also installed.

[1105] Data aggregation device (terminal)

[1106] Traffic video data, weather data, and user emotion data sent from the data acquisition device are temporarily aggregated and sent to an analysis means, which then transmits the data in real time to a central traffic control server using 5G communications.

[1107] Analysis method (server)

[1108] The traffic control server receives the data, and an AI analysis engine analyzes traffic volume, traffic accidents, vehicles violating traffic rules, and the risk of road icy and snow accumulation. Specifically, video data is used to recognize license plates and identify speeding violations and traffic accidents. Weather data is also used to analyze the risk of icy and snow accumulation.

[1109] Control device (terminal)

[1110] Based on the results of the analysis, the lighting status and operation of traffic lights are dynamically changed. For example, the green light duration of a traffic light is extended during times of heavy traffic, and an emergency response signal is set in the event of a traffic accident. This smooths traffic flow and improves safety.

[1111] Emotion engine (server)

[1112] The emotion engine recognizes emotions by analyzing the user's facial expressions and voice. The analyzed emotion data is fed back to the traffic management system, which then personalizes traffic light control and system notifications to match the user's emotions. For example, if the user is feeling stressed, important notifications will be given priority.

[1113] Specific examples

[1114] Consider the case where the system of the present invention is installed at traffic lights installed in the center of City A. During the morning rush hour, the terminal camera captures video data of the intersection at 30 frames per second and collects weather data of a temperature of 2°C and humidity of 80%. Furthermore, it captures the facial expressions and voices of users near the intersection and generates emotion data. This data is transmitted in real time to a traffic control server via a 5G base station.

[1115] The traffic control server's analytics analyzes video data and identifies 20 vehicles in a traffic jam. Furthermore, license plate recognition technology is used to identify speeding vehicles. Weather data analysis reveals a high risk of road icy conditions due to low temperatures. An emotion engine analyzes users' facial expressions and voices and identifies that some users are stressed. This information is fed back to the traffic management system, which adjusts notification methods.

[1116] Based on this, the traffic light control system will carry out appropriate signal control. Specifically, it will extend the green light time to alleviate congestion and change the signal to prevent freezing. In addition, in the event of a traffic accident, it will set priority signals to allow emergency vehicles to pass. Furthermore, it will prioritize emergency notifications for users who are feeling stressed, encouraging them to take prompt action.

[1117] Prompt Sentence Examples

[1118] Below are some example prompts to input to a generative AI model:

[1119] "Please explain in detail how your real-time traffic management system works using a 5G communication network. I'd particularly like to know how you analyze traffic video data, weather data, and user emotion data."

[1120] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1121] Specific processing steps of the process

[1122] Step 1: Data collection

[1123] Specific explanation: The terminal uses data acquisition devices installed at intersections and various parts of the traffic system to collect traffic video data, weather data, and the user's facial expressions and voice.

[1124] Input: Traffic video data (camera), weather data (weather sensor), user facial expressions and voice (microphone and camera)

[1125] Data calculation and processing: Traffic video data and user emotion data are captured frame by frame, and weather data is measured at regular intervals. The collected raw data is converted into a basic data format.

[1126] Output: Packaged traffic video data, weather data, emotion data

[1127] How it works: The camera captures video at 30 frames per second, the weather sensor measures weather data every minute, the microphone captures the user's voice in real time, and the facial expression camera captures images to analyze the user's facial expressions.

[1128] Step 2: Data aggregation

[1129] Specific explanation: A 5G base station installed on the terminal temporarily aggregates data from each data acquisition device and transmits it to a traffic management server.

[1130] Input: Raw data from different data acquisition devices

[1131] Data calculation and processing: Collected data is collected and packaged into a single packet. Time stamps are used to ensure data integrity.

[1132] Output: Packaged data packets

[1133] Specific operation: The 5G base station receives data sent from each terminal and transmits it in packet format to the traffic management server without delay.

[1134] Step 3: Upload data

[1135] Specific explanation: Data packets collected from terminals are uploaded to a traffic control server in real time using 5G communications.

[1136] Input: Packaged data packet

[1137] Data Calculation and Processing: Data packets are transmitted to the traffic control server over a high-speed network with minimal delay.

[1138] Output: Data packets to the traffic control server

[1139] Specific operation: The 5G base station uses high-speed communication technology to transmit packaged data packets to the traffic control server.

[1140] Step 4: Analyze traffic data

[1141] Specific Description: The traffic control server analyzes the uploaded traffic video data, weather data, and user emotion data.

[1142] Input: Traffic video data, weather data, user emotion data

[1143] Data calculation and processing: The AI ​​analysis engine uses OCR technology to analyze license plates based on video data, identify traffic volume and traffic accidents, and analyze weather data to assess the risk of icy roads and snow accumulation.

[1144] Output: Analyzed traffic conditions, traffic violations, and weather risks

[1145] How it works: The traffic control server uses AI models to read license plates frame by frame, analyze vehicle speeds and movements, and identify traffic violations and accidents. Weather data analysis determines risks when certain conditions are met.

[1146] Step 5: Analyze the sentiment data

[1147] Specific explanation: The emotion engine, which is the server, analyzes the user's facial expression data and voice data to identify the user's emotions.

[1148] Input: User's facial expression data, voice data

[1149] Data Computation and Processing: Deep learning models are used to analyze facial expressions and vocal tone, rate, and volume to identify emotions.

[1150] Output: Parsed emotion data

[1151] Specific operation: The emotion engine uses facial expression analysis technology to classify the user's emotions and identifies emotions by analyzing voice characteristics.

[1152] Step 6: Dynamic signal control

[1153] Specific explanation: The traffic light control system, which is the server, controls the traffic lights based on the analysis results.

[1154] Input: Analyzed traffic conditions, traffic violations, weather risks, and emotion data

[1155] Data calculation and processing: Automatically adjusts traffic light duration and signal patterns according to traffic congestion and accident situations. Adjusts notification methods based on emotion data.

[1156] Output: Adapted traffic light control, notification method

[1157] Specific operation: The traffic light control system performs optimal traffic management by extending the green light duration, setting priority signals, and changing notification content based on the user's emotions.

[1158] (Application example 2)

[1159] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1160] Increased traffic accidents, traffic congestion, and worsening road conditions due to bad weather have created a need for more efficient and safer traffic management. Furthermore, as autonomous vehicles are increasingly being introduced, systems are needed to enable these vehicles to operate more safely and efficiently. Furthermore, traffic management systems that take into account the emotional state of road users can affect traffic conditions.

[1161] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1162] In this invention, the server includes means for acquiring traffic video data, means for acquiring weather data, means for acquiring and analyzing user emotion data, means for aggregating this data through a high-speed communication network, means for controlling traffic lights based on the analysis results, and means for optimizing the behavior of autonomous vehicles, which enables more efficient traffic management and improved safety, safe operation of autonomous vehicles, and flexible responses that take into account the emotional states of traffic users.

[1163] The "communications infrastructure" is an infrastructure facility that uses a high-speed communications network installed at traffic lights to transfer data sent from data acquisition devices to a central traffic control server.

[1164] "Data acquisition device" refers to a device that includes a camera or sensor for acquiring traffic video data or weather data in real time.

[1165] The "data aggregating device" is a device that temporarily aggregates data transmitted from the data acquiring devices and sends the data to the analyzing means.

[1166] The "analysis means" is a server that receives data sent from the data aggregation device and includes an AI analysis engine for analyzing traffic volume, traffic accidents, vehicles violating traffic laws, and the risk of road icing and snow accumulation.

[1167] The "control device" is a device for controlling the behavior of traffic lights and autonomous vehicles based on the analysis results of the analysis means.

[1168] The "emotion engine" is an analytical engine that analyzes the user's facial expressions and voice to recognize emotions and provides feedback to the traffic management system.

[1169] "Traffic volume" is an index that indicates the number of vehicles passing through a specific point, and is a criterion for determining whether or not there is traffic congestion.

[1170] "Traffic accidents" refer to accidents that occur on roads and disrupt the flow of traffic, such as collisions between vehicles or contact between vehicles and people.

[1171] A "traffic violation vehicle" is a vehicle that does not comply with traffic regulations and commits violations such as speeding or running red lights.

[1172] "Risk of road freezing and snow accumulation" is an indicator that shows the possibility of roads freezing or snow accumulation based on meteorological data such as temperature, humidity, and snowfall.

[1173] "User emotion data" is data that indicates the emotional state of the user analyzed from their facial expressions and voice.

[1174] A "high-speed communication network" is a network that uses high-bandwidth communication technologies such as 5G, enabling high-speed, large-capacity data communication.

[1175] The present invention relates to a system that improves the efficiency and safety of traffic management based on the environment in which an autonomous vehicle is traveling and the emotional state of the user.

[1176] System Configuration

[1177] Communication infrastructure (terminals)

[1178] The devices are installed at traffic lights and transmit data from the data acquisition devices to a central traffic control server via a high-speed communication network.

[1179] Data acquisition device (terminal)

[1180] It includes a camera for capturing traffic video data and a weather sensor for capturing weather data. The camera captures traffic conditions at intersections and roads and vehicle movements in real time, while the weather sensor captures weather data such as temperature, humidity, and wind speed. It also includes a camera and microphone for capturing the user's facial expressions and voice.

[1181] Data aggregation device (terminal)

[1182] The data transmitted from the data acquisition devices is temporarily collected and sent to an analysis means, which transmits the data in real time to a central traffic control server using a high-speed communication network.

[1183] Analysis method (server)

[1184] The traffic control server receives the data, and the AI ​​analysis engine analyzes traffic volume, traffic accidents, traffic violations, and the risk of road icy and snow accumulation. Specifically, video data is used to analyze vehicle movements and identify speeding violations and traffic accidents. Weather data is used to analyze the risk of icy and snow accumulation.

[1185] Emotion engine (server)

[1186] The emotion engine recognizes emotions by analyzing the user's facial expressions and voice. The analyzed emotion data is fed back to the traffic management system, which then personalizes traffic light control and system notifications to match the user's emotions.

[1187] Control device (terminal)

[1188] Based on the results of the analysis method and emotion engine, the lighting status of traffic lights and the behavior of autonomous vehicles are dynamically changed. Specifically, the system extends traffic lights during congestion, sends emergency signals to respond to traffic accidents, controls signals when there is a high risk of roads freezing due to low temperatures, and adjusts notification methods based on the user's emotional state.

[1189] Program processing description

[1190] Cameras (terminals) capture traffic video data in real time from traffic light locations, and weather sensors (terminals) measure data such as temperature, humidity, and wind speed. In addition, cameras and microphones capture the user's facial expressions and voice to collect emotional data in real time. This data is temporarily aggregated in a data aggregation device via a high-speed communication network and then uploaded to a central traffic control server.

[1191] The traffic control server receives the uploaded data and analyzes traffic volume, traffic accidents, traffic violations, and the risk of road icing and snow accumulation. Using an AI analysis engine, it can analyze vehicle movements from camera footage and identify speeding vehicles using license plate recognition technology. Additionally, weather data is analyzed to manage road maintenance risks.

[1192] The emotion engine (server) uses facial expression analysis technology to identify the user's emotions. Specifically, it recognizes whether the user is feeling stressed or calm, and feeds the analysis results back to the traffic management system. This makes it possible to adjust the notification method according to the user's level of stress.

[1193] As a concrete example, consider a traffic management system in City A. During the morning rush hour, a camera captures video data of an intersection at 30 frames per second, and a weather sensor collects data on a temperature of 2 degrees and humidity of 80%. Furthermore, the facial expressions and voices of users near the intersection are analyzed to generate emotion data. This data is transmitted in real time to a traffic control server via a high-speed communication network.

[1194] Example prompt sentence:

[1195] "During the morning rush hour, please simulate a system that analyzes traffic video data, weather data, and user emotion data to ensure safe and efficient driving. Specifically, please execute driving behavior and signal control that takes into account traffic congestion, the risk of road surfaces freezing due to low temperatures, and user stress."

[1196] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1197] Step 1: Data collection

[1198] The device collects traffic video data, weather data, and user emotion data. Traffic video data is captured in real time by a camera, recording vehicle movements and traffic conditions as video data. Weather data is obtained using a weather sensor to acquire temperature, humidity, wind speed, etc. User emotion data is obtained by capturing the user's facial expressions and voice using an facial camera and microphone.

[1199] Input: traffic video, weather data, facial expression data, audio data

[1200] Output: A set of captured real-time data

[1201] Step 2: Data aggregation

[1202] The data collected by the terminals is temporarily sent to a data aggregator, which then consolidates and packages traffic video data, weather data, facial expression data, and voice data before forwarding it to a central traffic control server. The data is transmitted in real time using a high-speed communication network.

[1203] Input: A set of real-time data

[1204] Output: Packaged aggregated data

[1205] Step 3: Data analysis

[1206] The server receives the aggregated data and uses an AI analysis engine to analyze traffic volume, traffic accidents, vehicles violating traffic rules, and the risk of road icy roads and snow accumulation. By analyzing traffic video data, the system recognizes vehicle movements and license plates and identifies vehicles violating traffic rules and traffic accidents. It also predicts the risk of road icy roads and snow accumulation based on weather data.

[1207] Input: Aggregated data

[1208] Output: Traffic situation analysis results, weather risk analysis results

[1209] Step 4: Sentiment Analysis

[1210] The server uses an emotion engine to analyze facial expression and voice data to identify the user's emotions. Facial expression analysis and voice analysis technologies are used to determine the user's stress level and satisfaction.

[1211] Input: facial expression data, voice data

[1212] Output: User's emotional state

[1213] Step 5: Traffic light control

[1214] The server dynamically changes the lighting status of traffic lights based on the results of the analysis method and emotion engine. Specifically, it extends the green light time when traffic volume is heavy, sets an emergency response signal when a traffic accident occurs, and adjusts signal control when there is a risk of freezing due to low temperatures.

[1215] Input: Traffic situation analysis results, weather risk analysis results, user emotional state

[1216] Output: Dynamically changed traffic light status

[1217] Step 6: Adjusting the behavior of the autonomous vehicle

[1218] The server optimizes the behavior of the self-driving vehicle based on the results of the analysis and emotion engine, such as changing the route when traffic is congested, adjusting the speed when it is cold, and changing the driving mode when the user is feeling stressed.

[1219] Input: Traffic situation analysis results, weather risk analysis results, user emotional state

[1220] Output: Optimized autonomous vehicle driving modes

[1221] Step 7: Feedback and Notification

[1222] The server provides appropriate feedback and notifications based on the analysis results of the traffic management system and the user's emotional state. For example, it notifies the user of traffic congestion information and emergency response information. It also provides personalized notifications according to the user's stress level.

[1223] Input: Analysis results, user's emotional state

[1224] Output: Personalized feedback and notifications

[1225] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1226] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1227] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1228] [Fourth embodiment]

[1229] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1230] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1231] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1232] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1233] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1234] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1235] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1236] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1237] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1238] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1239] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1240] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1241] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1242] The present invention is a system consisting of a communication infrastructure installed at traffic lights, a data acquisition device that acquires traffic video data, a data acquisition device that acquires weather data, a data aggregation device that aggregates these data, an analysis means that analyzes the aggregated data, and a control device that controls traffic lights based on the analysis results.This system will improve the efficiency of traffic management and solve problems such as traffic accidents, congestion, violation enforcement, and road condition management.

[1243] System Configuration

[1244] Communication infrastructure (terminals)

[1245] The communication infrastructure installed in the traffic lights uses high-speed communication networks such as 5G to transmit data from the data acquisition devices to a central traffic control server.

[1246] Data acquisition device (terminal)

[1247] The system includes cameras that capture traffic video data and weather sensors that capture meteorological data such as temperature, humidity, and wind speed.

[1248] Data aggregation device (terminal)

[1249] The data sent from the data acquisition devices is temporarily aggregated and sent to an analysis means, which then transmits the data in real time to a central traffic control server using 5G communications.

[1250] Analysis method (server)

[1251] The traffic control server receives the data, and the AI ​​analysis engine analyzes traffic volume, traffic accidents, traffic violations, and the risk of road icy and snow accumulation. Specifically, video data is used to recognize license plates and identify speeding violations and traffic accidents. Weather data is used to analyze the risk of icy and snow accumulation.

[1252] Control device (terminal)

[1253] Based on the results of the analysis, the lighting status and operation of traffic lights are dynamically changed. For example, the green light time of a traffic light is extended during times of heavy traffic, and an emergency response signal is set in the event of a traffic accident.

[1254] Natural language explanation of program processing

[1255] 1. Data collection and upload

[1256] Cameras (terminals) capture real-time traffic video data from traffic light locations, while weather sensors (terminals) measure temperature, humidity, wind speed, and other data. This data is temporarily aggregated and packaged via 5G base stations (terminals), and then uploaded to a central traffic control server via 5G communications.

[1257] 2. Data Analysis

[1258] The traffic control server (server) receives the uploaded data, and the analysis means (server) analyzes traffic volume, traffic accidents, vehicles violating traffic rules, and the risk of road icing and snow accumulation. For example, camera footage can be used to read license plates using OCR technology to identify vehicles speeding. It can also analyze the risk of road icing based on weather data.

[1259] 3. Dynamic Signal Control

[1260] Based on the results of the analysis, the traffic light control system (server) determines the lighting status of traffic lights. For example, if there is congestion, the green light duration of the traffic light will be extended appropriately to smooth the flow of traffic. Also, if a traffic accident occurs, priority signals will be set for traffic lights on the route so that emergency vehicles can arrive at the scene quickly.

[1261] Specific examples

[1262] Consider the case where the system of the present invention is installed at traffic lights installed in the center of City A. During the morning rush hour, a camera captures video data of the intersection at 30 frames per second and collects weather data of a temperature of 2°C and humidity of 80%. This data is transmitted in real time to a traffic control server via a 5G base station.

[1263] The traffic control server's analytics analyzed the video data and identified 20 vehicles in a traffic jam. Furthermore, license plate recognition technology was used to identify speeding vehicles. Weather data analysis revealed a high risk of road surface freezing due to low temperatures.

[1264] Based on this, the traffic light control system will carry out appropriate signal control, such as extending the green light time to ease congestion, changing the signal to prevent freezing, and setting priority signals to allow emergency vehicles to pass in the event of a traffic accident.

[1265] Public safety and local government officials can remotely check the system's data dashboard and implement traffic restrictions and enforcement based on real-time traffic conditions and weather forecasts. This system will efficiently solve a variety of problems facing society's transportation system.

[1266] The processing flow will be explained below.

[1267] Step 1:

[1268] The camera (terminal) captures images of intersections and road conditions in real time. The camera generates video data at a rate of 30 frames per second to obtain traffic video data.

[1269] Step 2:

[1270] The weather sensor (terminal) measures weather data such as temperature, humidity, wind speed, etc. The weather data is acquired every minute and temporarily stored in the sensor's internal memory.

[1271] Step 3:

[1272] The 5G base station (terminal) aggregates data received from cameras and weather sensors, and packages the aggregated data for transmission to a central traffic control server via 5G communications.

[1273] Step 4:

[1274] The traffic control server (server) receives data sent from 5G base stations and stores it in a database. The received data includes video data and weather data.

[1275] Step 5:

[1276] The AI ​​analysis engine (server) analyzes the received video data. Specifically, it recognizes license plates, detects speeding violations, and identifies traffic accidents. It detects vehicles in the video and reads license plates using OCR technology. It also calculates the distance the vehicle travels between frames and calculates its speed to identify speeding violations.

[1277] Step 6:

[1278] The AI ​​analysis engine (server) analyzes weather data and predicts the risk of freezing and snow accumulation. It determines the possibility of freezing based on temperature and humidity data, and uses wind speed data to predict changes in snow accumulation and road conditions.

[1279] Step 7:

[1280] The traffic light control system (server) determines the lighting status of traffic lights based on the analysis results of the AI ​​analysis engine. When congestion occurs, the green light will be kept on for an extended period to smooth traffic flow. In addition, when a traffic accident occurs, priority signals will be set for emergency vehicles.

[1281] Step 8:

[1282] The traffic light (terminal) receives control instructions from the traffic light control system and changes the lighting status of the signal, thereby alleviating traffic congestion and responding to emergencies.

[1283] Step 9:

[1284] The traffic control server (server) provides analysis results and real-time data as a dashboard, which displays traffic congestion status, traffic accident information, weather forecasts, and more.

[1285] Step 10:

[1286] Public safety and local government officials (users) can remotely access the dashboard to view real-time traffic and weather data, and can remotely implement traffic restrictions and enforcement actions as needed.

[1287] Example 1

[1288] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1289] Conventional traffic management systems have difficulty controlling signals in real time according to traffic volume and weather conditions, resulting in problems such as traffic congestion, traffic accidents, and delays in identifying violating vehicles. They also fail to properly manage the risk of road ice and snow accumulation, which causes frequent traffic accidents and congestion. An effective system to solve these issues is needed.

[1290] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1291] In this invention, the server includes a communication infrastructure installed in the traffic control device, a data acquisition device that acquires traffic video data, a data acquisition device that acquires weather data, a data aggregation device that aggregates data from the data acquisition devices via a high-speed communication network, analysis means that analyzes data transmitted from the data aggregation device, and a control device that controls the traffic control device based on the analysis results of the analysis means. This enables traffic signal control in real time according to traffic volume and weather conditions, thereby easing traffic congestion, preventing traffic accidents, identifying violating vehicles, and appropriately managing the risk of road icing and snow accumulation.

[1292] "Communications infrastructure" refers to equipment installed in traffic control devices that includes a high-speed communications network for transmitting and receiving data.

[1293] The "data acquisition device" is a device for acquiring traffic video data and weather data, and includes cameras, weather sensors, etc.

[1294] The "data aggregating device" is a device that temporarily stores data acquired from a data acquiring device, packages the data, and transmits the data to an analyzing means.

[1295] The "analysis means" is a device or system that analyzes the data sent from the data aggregation device and identifies traffic volume, traffic accidents, vehicles violating traffic laws, and the risk of road icing or snow accumulation.

[1296] The "control device" is a device that dynamically changes the lighting state of traffic lights based on the analysis results of the analysis means, thereby easing traffic congestion and responding to traffic accidents.

[1297] "Traffic volume" is the number of vehicles passing through a particular point in a particular time period.

[1298] A "traffic accident" is an incident caused by traffic troubles, such as collisions between vehicles or single-vehicle accidents.

[1299] A "traffic violation vehicle" is a vehicle that violates traffic laws. This includes violations such as speeding and illegal parking.

[1300] "Freezing" is a phenomenon in which the temperature drops and the road surface freezes over.

[1301] "Snow accumulation" is a phenomenon in which the temperature drops and snow accumulates on roads and surfaces.

[1302] "OCR technology" is an abbreviation for optical character recognition technology, and is a technology for reading character information from image data.

[1303] A "priority signal" is a signal state that is set to allow emergency vehicles and vehicles requiring priority passage to pass more easily.

[1304] This invention is a system comprising a communication infrastructure installed in a traffic control device, a data acquisition device for acquiring traffic video data, a data acquisition device for acquiring weather data, a data aggregation device for aggregating these data, an analysis means for analyzing the aggregated data, and a control device for controlling the traffic control device based on the analysis results.

[1305] Communication infrastructure (terminals)

[1306] The communication infrastructure will be installed in the traffic control device and will transmit data received from the data acquisition device to a central traffic control server using a high-speed communication network. Specifically, 5G communication technology will be used.

[1307] Data acquisition device (terminal)

[1308] The data acquisition device includes cameras that capture traffic video data and weather sensors that capture meteorological data. The cameras capture real-time images of traffic conditions and vehicle movements at intersections and major roads. For example, video data is collected at 30 frames per second. The weather sensors measure temperature, humidity, wind speed, etc. and update the data every five seconds.

[1309] Data aggregation device (terminal)

[1310] The data aggregation device temporarily stores and packages the data sent from the data acquisition device. This sorts out inconsistencies in the data and makes it available for analysis in real time. For example, traffic video data and weather data are sorted in chronological order.

[1311] Analysis method (server)

[1312] The analysis means is installed on the traffic control server, which receives the transmitted data and performs initial processing. Specifically, the AI ​​analysis engine analyzes the video data and recognizes vehicle license plates using OCR technology. This makes it possible to identify speeding vehicles and illegally parked vehicles. Weather data is also analyzed to assess the risk of road icy conditions and snow accumulation.

[1313] Control device (terminal)

[1314] The control device dynamically changes the lighting status of the traffic lights based on the results of the analysis means, for example, by extending the green lighting time of the traffic lights during times of heavy traffic, or by setting priority signals for emergency vehicles in the event of a traffic accident.

[1315] Specific examples

[1316] For example, consider the case where this system is installed in the city center of City A. During the morning rush hour, a camera captures video data of an intersection at 30 frames per second and collects weather data such as a temperature of 2°C and humidity of 80%. The data aggregator organizes this data and transmits it to a traffic control server via 5G communications.

[1317] The traffic control server's analytics analyzes the video data and identifies a traffic jam of 20 vehicles. It then uses license plate recognition technology to identify speeding vehicles. Weather data analysis reveals a high risk of road icing due to low temperatures.

[1318] Based on this, the traffic light control system will carry out appropriate signal control. Specifically, it will extend the time that the green light remains on to alleviate congestion. In addition, in the event of a traffic accident, it will set priority signals so that emergency vehicles can arrive at the scene quickly.

[1319] Prompt Sentence Examples

[1320] 1. Please explain the specific steps by which the traffic control server's analytical means identifies traffic accidents from the uploaded data.

[1321] 2. What are the detailed steps for how the traffic light control system changes the operation of traffic lights when it detects congestion?

[1322] This system enables real-time traffic management, which can efficiently alleviate traffic congestion and prevent traffic accidents. It also enables road management according to weather conditions, providing a safer traffic environment. Public safety and local government officials can remotely view the system's data dashboard and use it as reference information for traffic restrictions and enforcement.

[1323] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1324] Step 1:

[1325] Data collection devices capture traffic footage and weather data.

[1326] Input: Real-time video data from the camera (terminal), weather data (temperature, humidity, wind speed) from the weather sensor.

[1327] How it works: The camera captures images of intersections and major roads at 30 frames per second, and the weather sensor measures temperature, humidity, and wind speed every 5 seconds.

[1328] Output: Video data and weather data.

[1329] Step 2:

[1330] The communication infrastructure receives the data acquired from the data acquisition device.

[1331] Input: Video data and meteorological data transmitted from the data acquisition device.

[1332] Specific operation: Data transmitted from the data acquisition device is temporarily stored via the communication infrastructure.

[1333] Output: Temporarily saved video data and weather data.

[1334] Step 3:

[1335] The data aggregation device acquires data from the communication infrastructure and packages it.

[1336] Input: Temporarily stored video data and weather data.

[1337] Specific operation: The data aggregator sorts out data inconsistencies and packages the data in chronological order.

[1338] Output: Packaged data.

[1339] Step 4:

[1340] The data aggregator transmits the packaged data to the traffic control server.

[1341] Input: Packaged data.

[1342] Specific operation: The data aggregation device uses 5G communications to send packaged data to the traffic control server in real time.

[1343] Output: Data sent to traffic control server.

[1344] Step 5:

[1345] The traffic control server receives the transmitted data and performs initial processing.

[1346] Input: The data sent.

[1347] Specific operation: The traffic control server checks for data inconsistencies and corrects the data as necessary.

[1348] Output: Organized data.

[1349] Step 6:

[1350] The analysis means analyzes the video data and the meteorological data.

[1351] Input: Organized video and weather data.

[1352] Specific operation: Analyzes video data using an AI analysis engine, performs OCR recognition of license plates and vehicle movement analysis, and evaluates the risk of road icy conditions and snow accumulation based on weather data.

[1353] Output: Analysis results (traffic accidents, violating vehicles, risk of freezing, etc.).

[1354] Step 7:

[1355] The control device dynamically changes the lighting status of the traffic light based on the analysis results.

[1356] Input: Analysis results.

[1357] Specific actions: When traffic volume is heavy, the green light will stay on for an extended period of time, and when a traffic accident occurs, traffic lights will be set to give priority to emergency vehicles.

[1358] Output: Changed traffic light status.

[1359] Step 8:

[1360] Users can remotely view the system's data dashboard and implement traffic restrictions and enforcement.

[1361] Input: Data dashboard information.

[1362] Specific operation: Public safety and local government officials can check traffic and weather conditions on the dashboard and remotely issue instructions on how to respond.

[1363] Output: Traffic restrictions and enforcement actions that were applied.

[1364] In this way, each step performs data processing and calculations based on the input to obtain the output of the next step.

[1365] (Application example 1)

[1366] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1367] Conventional traffic management systems have difficulty utilizing acquired traffic and weather data in real time, making it difficult to respond quickly to changes in traffic conditions. Furthermore, there is a lack of means to notify automated driving vehicles of traffic signal status and traffic forecast information, making it difficult to ensure smooth traffic flow. This makes it necessary to respond quickly to traffic congestion and traffic accidents.

[1368] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1369] In this invention, the server includes means for providing real-time traffic conditions using acquired traffic video data and weather data, means for providing traffic alerts and forecasts to autonomous vehicles based on analysis results, means for identifying vehicles violating traffic laws, and means for providing priority signal notifications to autonomous vehicles. This enables the use of traffic data in real time, enabling the prompt provision of information to autonomous vehicles and ensuring smooth traffic flow.

[1370] A "communication infrastructure" is a device installed at traffic lights that sends and receives data using a high-speed communication network.

[1371] A "data acquisition device" is a device installed to acquire traffic video data and weather data.

[1372] The "data aggregating device" is a device that temporarily aggregates data acquired from the data acquiring device and transmits the data to the data analyzing means.

[1373] "Analysis means" refers to a means for analyzing the aggregated data and obtaining useful information about traffic and weather conditions.

[1374] The "controller" is a device that controls traffic lights based on the analysis results of the analyzing means, thereby ensuring smooth traffic flow and safety.

[1375] "Traffic video data" is video data showing the traffic conditions on roads and intersections.

[1376] "Weather data" refers to data related to weather conditions (temperature, humidity, wind speed, etc.).

[1377] "Means for providing traffic conditions in real time" refers to means for analyzing acquired traffic video data and weather data and instantly providing information on current traffic conditions.

[1378] "Means for providing traffic alerts and predictions" means means for providing warnings and predictions about future traffic conditions and risks based on analyzed data.

[1379] The "means for identifying vehicles violating traffic regulations" refers to a means for analyzing traffic video data and identifying vehicles violating regulations.

[1380] A "means for providing a priority signal notification" is a means for notifying an autonomous vehicle of the priority signal status of a particular traffic light.

[1381] The system of the present invention includes a communication infrastructure installed at traffic lights, a data acquisition device that acquires traffic video data and weather data, a data aggregation device that aggregates the data, an analysis means that analyzes the data, a control device that controls the traffic lights based on the analysis results, and a means for providing real-time traffic conditions and traffic alerts and forecasts for autonomous vehicles.

[1382] Hardware and software used

[1383] The server is equipped with a communications infrastructure that utilizes a high-speed communications network (such as 5G), and cameras and weather sensors are connected to the data acquisition devices. The data acquired from these data acquisition devices is aggregated on the server via a data aggregation device.

[1384] The analysis method runs an AI analysis engine on a server to analyze traffic volume, traffic accidents, vehicles violating traffic rules, and the risk of road icy conditions and snow accumulation. This analysis method includes computer vision technology for video analysis and weather models for weather data analysis.

[1385] Data processing and calculation

[1386] The server analyzes the acquired traffic video data using computer vision technology (e.g., OpenCV) to identify traffic volume. It also identifies vehicles violating traffic rules using license plate recognition (e.g., OCR technology). Weather data analysis uses weather models (e.g., ARIMA models) to predict the risk of road icing and snow accumulation.

[1387] The control device dynamically changes the lighting status of traffic lights based on the analysis results, for example by extending the green light time depending on traffic volume or setting an emergency response signal in the event of a traffic accident.

[1388] Specific examples

[1389] For example, if the system of the present invention is installed at traffic lights installed in the center of City A, a camera will capture video data of the intersection in real time during the morning rush hour and collect weather data such as a temperature of 2°C and humidity of 80%. This data will be transmitted in real time to a traffic control server via a 5G base station.

[1390] The traffic control server's analytics analyzes the video data and identifies 20 vehicles in a traffic jam. License plate recognition technology also identifies three speeding vehicles. Weather data analysis reveals a high risk of road icing due to low temperatures.

[1391] Based on this, the traffic light control system will carry out appropriate signal control. Specifically, it will extend the green light time to alleviate congestion and change the traffic lights to prevent freezing. In addition, in the event of a traffic accident, priority signals will be set for traffic lights on the route so that emergency vehicles can arrive at the scene quickly. Public safety and local government officials can remotely check the system's data dashboard and implement traffic restrictions and enforcement remotely based on real-time traffic conditions and weather forecasts.

[1392] Prompt Sentence Examples

[1393] We are designing a smartphone application that displays traffic volume, weather data, and congestion information based on real-time traffic data from a traffic light control system. The following features are required:

[1394] 1. Real-time streaming of traffic video data.

[1395] 2. Predictive alerts for traffic jams and accidents.

[1396] 3. Aggregate daily traffic and weather data and generate reports.

[1397] 4. Notification of changes in traffic light operation.

[1398] Please generate source code using Python, including instructions for using the API and displaying data.

[1399] The above is an embodiment of the invention.

[1400] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1401] Step 1:

[1402] Cameras and weather sensors (terminals) capture traffic video data and weather data in real time. The cameras capture images of roads and intersections, while the weather sensors capture weather data such as temperature, humidity, and wind speed, and send them to the data acquisition device.

[1403] Input: Traffic video data, weather data

[1404] Output: Raw data obtained

[1405] Step 2:

[1406] The data acquisition device (terminal) transmits the acquired traffic video data and weather data to the data aggregation device via the 5G network. Here, the video data is transmitted in a stream format, and the weather data is transmitted as numerical data.

[1407] Input: Raw data acquired

[1408] Output: Stream format video data, numerical data format weather data

[1409] Step 3:

[1410] The data aggregating device (terminal) temporarily stores the received traffic video data and weather data, packages them, and transmits them to the analyzing means.

[1411] Input: Stream format video data, numerical data format weather data

[1412] Output: Packaged data

[1413] Step 4:

[1414] The analysis means (server) receives the packaged data and analyzes the traffic video data using computer vision technology (e.g., OpenCV) to measure traffic volume. Weather data is analyzed using weather models (e.g., ARIMA models) to analyze the risk of road icing and snow accumulation. Furthermore, license plate recognition (e.g., OCR technology) is used to identify vehicles violating traffic laws.

[1415] Input: Packaged traffic video data, weather data

[1416] Output: Analyzed traffic condition data, weather risk data, and violating vehicle data

[1417] Step 5:

[1418] The analysis means (server) provides real-time traffic conditions based on the analysis results and generates traffic alerts and predictions for autonomous vehicles.

[1419] Input: Analyzed traffic condition data, weather risk data, violating vehicle data

[1420] Output: Traffic reports, traffic alerts, forecast data

[1421] Step 6:

[1422] The control device (server) dynamically changes the lighting status of traffic lights based on the analysis results. Specifically, it extends the green light time when traffic volume is heavy, sets an emergency response signal when a traffic accident occurs, and provides priority signal notifications to autonomous vehicles.

[1423] Inputs: Traffic reports, traffic alerts, and forecast data

[1424] Output: Dynamically changed traffic light control information, priority signal notification

[1425] Step 7:

[1426] Users (public safety and local government officials) can remotely check the system's data dashboard and remotely implement traffic restrictions and enforcement based on real-time traffic conditions and weather forecasts.

[1427] Input: Dynamically changed traffic light control information, priority signal notification

[1428] Output: Traffic regulation and enforcement implementation plan

[1429] The above is the flow of processing of the system program for realizing the application example.

[1430] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1431] The present invention further improves the efficiency of traffic management and social safety by combining a system consisting of a communication infrastructure installed at traffic lights, a data acquisition device that acquires traffic video data, a data acquisition device that acquires weather data, a data aggregation device that aggregates this data, analysis means that analyzes the aggregated data, and a control device that controls traffic lights based on the analysis results with an emotion engine that recognizes user emotions.

[1432] System Configuration

[1433] Communication infrastructure (terminals)

[1434] The communication infrastructure installed in the traffic lights uses high-speed communication networks such as 5G to transmit data from the data acquisition devices to a central traffic control server.

[1435] Data acquisition device (terminal)

[1436] The system includes cameras that capture traffic video data and weather sensors that capture meteorological data such as temperature, humidity, and wind speed.

[1437] Data aggregation device (terminal)

[1438] The data sent from the data acquisition devices is temporarily aggregated and sent to an analysis means, which then transmits the data in real time to a central traffic control server using 5G communications.

[1439] Analysis method (server)

[1440] The traffic control server receives the data, and the AI ​​analysis engine analyzes traffic volume, traffic accidents, traffic violations, and the risk of road icy and snow accumulation. Specifically, video data is used to recognize license plates and identify speeding violations and traffic accidents. Weather data is used to analyze the risk of icy and snow accumulation.

[1441] Control device (terminal)

[1442] Based on the results of the analysis, the lighting status and operation of traffic lights are dynamically changed. For example, the green light time of a traffic light is extended during times of heavy traffic, and an emergency response signal is set in the event of a traffic accident.

[1443] Emotion engine (server)

[1444] The emotion engine analyzes the user's facial expressions and voice to recognize their emotions. The analyzed emotion data is fed back to the traffic management system, which then personalizes traffic light control and system notifications to match the user's emotions.

[1445] Natural language explanation of program processing

[1446] 1. Data collection and upload

[1447] Cameras (terminals) will capture real-time traffic video data from traffic light locations, and weather sensors (terminals) will measure data such as temperature, humidity, and wind speed. Additionally, microphones and cameras will be installed to capture the user's facial expressions and voice. This data will be temporarily aggregated and packaged via a 5G base station (terminal), and then uploaded to a central traffic control server via 5G communications.

[1448] 2. Data Analysis

[1449] The traffic control server (server) receives the uploaded data, and the analysis means (server) analyzes traffic volume, traffic accidents, vehicles violating traffic rules, and the risk of road icing and snow accumulation. For example, camera footage can be used to read license plates using OCR technology to identify vehicles speeding. It can also analyze the risk of road icing based on weather data.

[1450] 3. Emotion analysis

[1451] The emotion engine (server) analyzes the acquired facial expression and voice data to identify the user's emotions. For example, it uses facial expression analysis technology to recognize whether the user is stressed or calm. It also infers emotions from the tone and volume of the voice.

[1452] 4. Dynamic Signal Control

[1453] Based on the results of the analysis means and the emotion engine, the traffic light control system (server) determines the lighting status of traffic lights. For example, if there is congestion, the green light duration of the traffic light will be extended appropriately to smooth traffic flow. Also, in the event of a traffic accident, priority signals will be set for traffic lights on the route so that emergency vehicles can arrive at the scene quickly. Based on the analysis results of the emotion engine, if the user is feeling stressed, the notification method will be adjusted, such as providing emergency notifications as a priority.

[1454] Specific examples

[1455] Consider the case where the system of the present invention is installed at traffic lights installed in the center of City A. During the morning rush hour, a camera captures video data of the intersection at 30 frames per second and collects weather data with a temperature of 2°C and humidity of 80%. Furthermore, the system captures the facial expressions and voices of users near the intersection to generate emotion data. This data is then transmitted in real time to a traffic control server via a 5G base station.

[1456] The traffic control server's analytics analyzed the video data and identified 20 vehicles in a traffic jam. Furthermore, license plate recognition technology was used to identify speeding vehicles. Weather data analysis revealed a high risk of road surface freezing due to low temperatures.

[1457] The emotion engine analyzes users' facial expressions and voices to identify when some users are stressed, and this information is fed back to the traffic management system to adjust the notification method.

[1458] Based on this, the traffic light control system will carry out appropriate signal control. Specifically, it will extend the green light time to alleviate congestion and change the signal to prevent freezing. In addition, in the event of a traffic accident, it will set priority signals to allow emergency vehicles to pass. Furthermore, it will prioritize emergency notifications for users who are feeling stressed, encouraging them to take prompt action.

[1459] Public safety and local government officials can remotely check the system's data dashboard and implement traffic restrictions and enforcement based on real-time traffic conditions, weather forecasts, and emotion data. This system will efficiently solve various problems facing society with traffic and will also enable responses that are sensitive to user emotions.

[1460] The processing flow will be explained below.

[1461] Step 1:

[1462] The camera (terminal) captures images of intersections and road conditions in real time. The camera generates video data at a rate of 30 frames per second to obtain traffic video data.

[1463] Step 2:

[1464] The weather sensor (terminal) measures weather data such as temperature, humidity, wind speed, etc. The weather data is acquired every minute and temporarily stored in the sensor's internal memory.

[1465] Step 3:

[1466] The emotion engine camera and microphone (terminal) capture the user's facial expressions and voice. This generates emotion data. The facial expression data and voice data are temporarily stored in memory for emotion analysis.

[1467] Step 4:

[1468] The 5G base station (terminal) aggregates data received from cameras, weather sensors, and the emotion engine camera and microphone, and packages the aggregated data for transmission to a central traffic control server via 5G communications.

[1469] Step 5:

[1470] The traffic control server (server) receives data transmitted from 5G base stations and stores it in a database. The received data includes video data, weather data, and emotion data.

[1471] Step 6:

[1472] The AI ​​analysis engine (server) analyzes the received video data. Specifically, it recognizes license plates, detects speeding violations, and identifies traffic accidents. It detects vehicles in the video and reads license plates using OCR technology. It also calculates the distance the vehicle travels between frames and calculates its speed to identify speeding violations.

[1473] Step 7:

[1474] The AI ​​analysis engine (server) analyzes weather data and predicts the risk of freezing and snow accumulation. It determines the possibility of freezing based on temperature and humidity data, and uses wind speed data to predict changes in snow accumulation and road conditions.

[1475] Step 8:

[1476] The emotion engine (server) analyzes facial expression and voice data to identify the user's emotions. For example, it uses facial expression analysis technology to recognize whether the user is stressed or calm. It also infers emotions from the tone and volume of the voice.

[1477] Step 9:

[1478] The traffic light control system (server) determines the lighting status of traffic lights based on the results of the AI ​​analysis engine and emotion engine. When congestion occurs, the green light duration is extended to smooth traffic flow. In addition, when a traffic accident occurs, priority signals are set for emergency vehicles. Based on the analysis results of the emotion engine, notification methods are adjusted, such as prioritizing emergency notifications if the user is feeling stressed.

[1479] Step 10:

[1480] The traffic light (terminal) receives control instructions from the traffic light control system and changes the lighting status of the signal, thereby alleviating traffic congestion and responding to emergencies.

[1481] Step 11:

[1482] The traffic control server (server) provides analysis results and real-time data as a dashboard, which displays traffic congestion status, traffic accident information, weather forecasts, and emotion data.

[1483] Step 12:

[1484] Public safety and local government officials (users) can remotely access the dashboard to view real-time traffic conditions, weather data, and emotion data, and can remotely implement traffic restrictions and enforcement actions as needed.

[1485] Example 2

[1486] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1487] Conventional traffic management systems are limited to control based on traffic volume and weather conditions, and have difficulty responding dynamically to the user's emotional state. As a result, they have not been able to fully reduce user stress or improve safety in the event of a traffic accident or traffic congestion. Furthermore, they have not been able to fully optimize traffic signal control by taking real-time traffic and weather conditions into account.

[1488] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1489] In this invention, the server includes a means for identifying traffic volume and traffic accidents, a means for identifying vehicles committing traffic violations, a means for predicting road icing and snow accumulation, and a means for analyzing user emotions. This enables optimal traffic light control based on traffic and weather conditions in real time, and also enables dynamic responses based on user emotions. This is expected to reduce traffic accidents and traffic congestion, as well as reduce user stress and improve social safety.

[1490] The "communication infrastructure" is a platform installed at traffic lights that transmits and receives data using a high-speed communication network.

[1491] A "data acquisition device" is a device that acquires traffic video data, weather data, and the user's facial expressions and voice in real time.

[1492] The "data aggregating device" is a device that temporarily aggregates data acquired from each data acquisition device and transmits the data to the analyzing means via a high-speed communication network.

[1493] "Analysis means" refers to a means of analyzing aggregated data to assess traffic volume, traffic accidents, vehicles violating traffic laws, and the risk of road icing and snow accumulation.

[1494] The "control device" is a device that dynamically controls the lighting state of a traffic light based on the analysis result of the analysis means.

[1495] The "emotion engine" is an engine that analyzes the user's facial expressions and voice to identify their emotional state.

[1496] "Traffic video data" refers to video data that captures traffic conditions at traffic lights and intersections.

[1497] "Weather data" refers to data indicating weather conditions such as temperature, humidity, and wind speed.

[1498] "User emotion data" refers to data relating to the user's emotional state analyzed from their facial expressions and voice.

[1499] "High-speed communication network" refers to a communication network with high bandwidth for transmitting and receiving large amounts of data in real time, and includes technologies such as 5G.

[1500] MODE FOR CARRYING OUT THE INVENTION

[1501] The present invention relates to a traffic management system, which is a system including a communication infrastructure installed at traffic lights, a data acquisition device that acquires traffic video data, a data acquisition device that acquires weather data, a data aggregation device that aggregates this data, analysis means that analyzes the aggregated data, a control device that controls traffic lights based on the analysis results, and an emotion engine that analyzes user emotions.

[1502] System configuration

[1503] Communication infrastructure (terminals)

[1504] The communication infrastructure installed in the traffic lights uses high-speed communication networks such as 5G to transmit data from the data acquisition devices to a central traffic control server.

[1505] Data acquisition device (terminal)

[1506] It includes cameras that capture traffic video data, weather sensors that capture weather data, and cameras and microphones that capture the user's facial expressions and voice. The cameras capture real-time images of intersections, road traffic conditions, and vehicle movements, while the weather sensors capture weather data such as temperature, humidity, and wind speed. Cameras and microphones that capture the user's facial expressions and voice are also installed.

[1507] Data aggregation device (terminal)

[1508] Traffic video data, weather data, and user emotion data sent from the data acquisition device are temporarily aggregated and sent to an analysis means, which then transmits the data in real time to a central traffic control server using 5G communications.

[1509] Analysis method (server)

[1510] The traffic control server receives the data, and an AI analysis engine analyzes traffic volume, traffic accidents, vehicles violating traffic rules, and the risk of road icy and snow accumulation. Specifically, video data is used to recognize license plates and identify speeding violations and traffic accidents. Weather data is also used to analyze the risk of icy and snow accumulation.

[1511] Control device (terminal)

[1512] Based on the results of the analysis, the lighting status and operation of traffic lights are dynamically changed. For example, the green light duration of a traffic light is extended during times of heavy traffic, and an emergency response signal is set in the event of a traffic accident. This smooths traffic flow and improves safety.

[1513] Emotion engine (server)

[1514] The emotion engine recognizes emotions by analyzing the user's facial expressions and voice. The analyzed emotion data is fed back to the traffic management system, which then personalizes traffic light control and system notifications to match the user's emotions. For example, if the user is feeling stressed, important notifications will be given priority.

[1515] Specific examples

[1516] Consider the case where the system of the present invention is installed at traffic lights installed in the center of City A. During the morning rush hour, the terminal camera captures video data of the intersection at 30 frames per second and collects weather data of a temperature of 2°C and humidity of 80%. Furthermore, it captures the facial expressions and voices of users near the intersection and generates emotion data. This data is transmitted in real time to a traffic control server via a 5G base station.

[1517] The traffic control server's analytics analyzes video data and identifies 20 vehicles in a traffic jam. Furthermore, license plate recognition technology is used to identify speeding vehicles. Weather data analysis reveals a high risk of road icy conditions due to low temperatures. An emotion engine analyzes users' facial expressions and voices and identifies that some users are stressed. This information is fed back to the traffic management system, which adjusts notification methods.

[1518] Based on this, the traffic light control system will carry out appropriate signal control. Specifically, it will extend the green light time to alleviate congestion and change the signal to prevent freezing. In addition, in the event of a traffic accident, it will set priority signals to allow emergency vehicles to pass. Furthermore, it will prioritize emergency notifications for users who are feeling stressed, encouraging them to take prompt action.

[1519] Prompt Sentence Examples

[1520] Below are some example prompts to input to a generative AI model:

[1521] "Please explain in detail how your real-time traffic management system works using a 5G communication network. I'd particularly like to know how you analyze traffic video data, weather data, and user emotion data."

[1522] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1523] Specific processing steps of the process

[1524] Step 1: Data collection

[1525] Specific explanation: The terminal uses data acquisition devices installed at intersections and various parts of the traffic system to collect traffic video data, weather data, and the user's facial expressions and voice.

[1526] Input: Traffic video data (camera), weather data (weather sensor), user facial expressions and voice (microphone and camera)

[1527] Data calculation and processing: Traffic video data and user emotion data are captured frame by frame, and weather data is measured at regular intervals. The collected raw data is converted into a basic data format.

[1528] Output: Packaged traffic video data, weather data, emotion data

[1529] How it works: The camera captures video at 30 frames per second, the weather sensor measures weather data every minute, the microphone captures the user's voice in real time, and the facial expression camera captures images to analyze the user's facial expressions.

[1530] Step 2: Data aggregation

[1531] Specific explanation: A 5G base station installed on the terminal temporarily aggregates data from each data acquisition device and transmits it to a traffic management server.

[1532] Input: Raw data from different data acquisition devices

[1533] Data calculation and processing: Collected data is collected and packaged into a single packet. Time stamps are used to ensure data integrity.

[1534] Output: Packaged data packets

[1535] Specific operation: The 5G base station receives data sent from each terminal and transmits it in packet format to the traffic management server without delay.

[1536] Step 3: Upload data

[1537] Specific explanation: Data packets collected from terminals are uploaded to a traffic control server in real time using 5G communications.

[1538] Input: Packaged data packet

[1539] Data Calculation and Processing: Data packets are transmitted to the traffic control server over a high-speed network with minimal delay.

[1540] Output: Data packets to the traffic control server

[1541] Specific operation: The 5G base station uses high-speed communication technology to transmit packaged data packets to the traffic control server.

[1542] Step 4: Analyze traffic data

[1543] Specific Description: The traffic control server analyzes the uploaded traffic video data, weather data, and user emotion data.

[1544] Input: Traffic video data, weather data, user emotion data

[1545] Data calculation and processing: The AI ​​analysis engine uses OCR technology to analyze license plates based on video data, identify traffic volume and traffic accidents, and analyze weather data to assess the risk of icy roads and snow accumulation.

[1546] Output: Analyzed traffic conditions, traffic violations, and weather risks

[1547] How it works: The traffic control server uses AI models to read license plates frame by frame, analyze vehicle speeds and movements, and identify traffic violations and accidents. Weather data analysis determines risks when certain conditions are met.

[1548] Step 5: Analyze the sentiment data

[1549] Specific explanation: The emotion engine, which is the server, analyzes the user's facial expression data and voice data to identify the user's emotions.

[1550] Input: User's facial expression data, voice data

[1551] Data Computation and Processing: Deep learning models are used to analyze facial expressions and vocal tone, rate, and volume to identify emotions.

[1552] Output: Parsed emotion data

[1553] Specific operation: The emotion engine uses facial expression analysis technology to classify the user's emotions and identifies emotions by analyzing voice characteristics.

[1554] Step 6: Dynamic signal control

[1555] Specific explanation: The traffic light control system, which is the server, controls the traffic lights based on the analysis results.

[1556] Input: Analyzed traffic conditions, traffic violations, weather risks, and emotion data

[1557] Data calculation and processing: Automatically adjusts traffic light duration and signal patterns according to traffic congestion and accident situations. Adjusts notification methods based on emotion data.

[1558] Output: Adapted traffic light control, notification method

[1559] Specific operation: The traffic light control system performs optimal traffic management by extending the green light duration, setting priority signals, and changing notification content based on the user's emotions.

[1560] (Application example 2)

[1561] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1562] Increased traffic accidents, traffic congestion, and worsening road conditions due to bad weather have created a need for more efficient and safer traffic management. Furthermore, as autonomous vehicles are increasingly being introduced, systems are needed to enable these vehicles to operate more safely and efficiently. Furthermore, traffic management systems that take into account the emotional state of road users can affect traffic conditions.

[1563] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1564] In this invention, the server includes means for acquiring traffic video data, means for acquiring weather data, means for acquiring and analyzing user emotion data, means for aggregating this data through a high-speed communication network, means for controlling traffic lights based on the analysis results, and means for optimizing the behavior of autonomous vehicles, which enables more efficient traffic management and improved safety, safe operation of autonomous vehicles, and flexible responses that take into account the emotional states of traffic users.

[1565] The "communications infrastructure" is an infrastructure facility that uses a high-speed communications network installed at traffic lights to transfer data sent from data acquisition devices to a central traffic control server.

[1566] "Data acquisition device" refers to a device that includes a camera or sensor for acquiring traffic video data or weather data in real time.

[1567] The "data aggregating device" is a device that temporarily aggregates data transmitted from the data acquiring devices and sends the data to the analyzing means.

[1568] The "analysis means" is a server that receives data sent from the data aggregation device and includes an AI analysis engine for analyzing traffic volume, traffic accidents, vehicles violating traffic laws, and the risk of road icing and snow accumulation.

[1569] The "control device" is a device for controlling the behavior of traffic lights and autonomous vehicles based on the analysis results of the analysis means.

[1570] The "emotion engine" is an analytical engine that analyzes the user's facial expressions and voice to recognize emotions and provides feedback to the traffic management system.

[1571] "Traffic volume" is an index that indicates the number of vehicles passing through a specific point, and is a criterion for determining whether or not there is traffic congestion.

[1572] "Traffic accidents" refer to accidents that occur on roads and disrupt the flow of traffic, such as collisions between vehicles or contact between vehicles and people.

[1573] A "traffic violation vehicle" is a vehicle that does not comply with traffic regulations and commits violations such as speeding or running red lights.

[1574] "Risk of road freezing and snow accumulation" is an indicator that shows the possibility of roads freezing or snow accumulation based on meteorological data such as temperature, humidity, and snowfall.

[1575] "User emotion data" is data that indicates the emotional state of the user analyzed from their facial expressions and voice.

[1576] A "high-speed communication network" is a network that uses high-bandwidth communication technologies such as 5G, enabling high-speed, large-capacity data communication.

[1577] The present invention relates to a system that improves the efficiency and safety of traffic management based on the environment in which an autonomous vehicle is traveling and the emotional state of the user.

[1578] System Configuration

[1579] Communication infrastructure (terminals)

[1580] The devices are installed at traffic lights and transmit data from the data acquisition devices to a central traffic control server via a high-speed communication network.

[1581] Data acquisition device (terminal)

[1582] It includes a camera for capturing traffic video data and a weather sensor for capturing weather data. The camera captures traffic conditions at intersections and roads and vehicle movements in real time, while the weather sensor captures weather data such as temperature, humidity, and wind speed. It also includes a camera and microphone for capturing the user's facial expressions and voice.

[1583] Data aggregation device (terminal)

[1584] The data transmitted from the data acquisition devices is temporarily collected and sent to an analysis means, which transmits the data in real time to a central traffic control server using a high-speed communication network.

[1585] Analysis method (server)

[1586] The traffic control server receives the data, and the AI ​​analysis engine analyzes traffic volume, traffic accidents, traffic violations, and the risk of road icy and snow accumulation. Specifically, video data is used to analyze vehicle movements and identify speeding violations and traffic accidents. Weather data is used to analyze the risk of icy and snow accumulation.

[1587] Emotion engine (server)

[1588] The emotion engine recognizes emotions by analyzing the user's facial expressions and voice. The analyzed emotion data is fed back to the traffic management system, which then personalizes traffic light control and system notifications to match the user's emotions.

[1589] Control device (terminal)

[1590] Based on the results of the analysis method and emotion engine, the lighting status of traffic lights and the behavior of autonomous vehicles are dynamically changed. Specifically, the system extends traffic lights during congestion, sends emergency signals to respond to traffic accidents, controls signals when there is a high risk of roads freezing due to low temperatures, and adjusts notification methods based on the user's emotional state.

[1591] Program processing description

[1592] Cameras (terminals) capture traffic video data in real time from traffic light locations, and weather sensors (terminals) measure data such as temperature, humidity, and wind speed. In addition, cameras and microphones capture the user's facial expressions and voice to collect emotional data in real time. This data is temporarily aggregated in a data aggregation device via a high-speed communication network and then uploaded to a central traffic control server.

[1593] The traffic control server receives the uploaded data and analyzes traffic volume, traffic accidents, traffic violations, and the risk of road icing and snow accumulation. Using an AI analysis engine, it can analyze vehicle movements from camera footage and identify speeding vehicles using license plate recognition technology. Additionally, weather data is analyzed to manage road maintenance risks.

[1594] The emotion engine (server) uses facial expression analysis technology to identify the user's emotions. Specifically, it recognizes whether the user is feeling stressed or calm, and feeds the analysis results back to the traffic management system. This makes it possible to adjust the notification method according to the user's level of stress.

[1595] As a concrete example, consider a traffic management system in City A. During the morning rush hour, a camera captures video data of an intersection at 30 frames per second, and a weather sensor collects data on a temperature of 2 degrees and humidity of 80%. Furthermore, the facial expressions and voices of users near the intersection are analyzed to generate emotion data. This data is transmitted in real time to a traffic control server via a high-speed communication network.

[1596] Example prompt sentence:

[1597] "During the morning rush hour, please simulate a system that analyzes traffic video data, weather data, and user emotion data to ensure safe and efficient driving. Specifically, please execute driving behavior and signal control that takes into account traffic congestion, the risk of road surfaces freezing due to low temperatures, and user stress."

[1598] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1599] Step 1: Data collection

[1600] The device collects traffic video data, weather data, and user emotion data. Traffic video data is captured in real time by a camera, recording vehicle movements and traffic conditions as video data. Weather data is obtained using a weather sensor to acquire temperature, humidity, wind speed, etc. User emotion data is obtained by capturing the user's facial expressions and voice using an facial camera and microphone.

[1601] Input: traffic video, weather data, facial expression data, audio data

[1602] Output: A set of captured real-time data

[1603] Step 2: Data aggregation

[1604] The data collected by the terminals is temporarily sent to a data aggregator, which then consolidates and packages traffic video data, weather data, facial expression data, and voice data before forwarding it to a central traffic control server. The data is transmitted in real time using a high-speed communication network.

[1605] Input: A set of real-time data

[1606] Output: Packaged aggregated data

[1607] Step 3: Data analysis

[1608] The server receives the aggregated data and uses an AI analysis engine to analyze traffic volume, traffic accidents, vehicles violating traffic rules, and the risk of road icy roads and snow accumulation. By analyzing traffic video data, the system recognizes vehicle movements and license plates and identifies vehicles violating traffic rules and traffic accidents. It also predicts the risk of road icy roads and snow accumulation based on weather data.

[1609] Input: Aggregated data

[1610] Output: Traffic situation analysis results, weather risk analysis results

[1611] Step 4: Sentiment Analysis

[1612] The server uses an emotion engine to analyze facial expression and voice data to identify the user's emotions. Facial expression analysis and voice analysis technologies are used to determine the user's stress level and satisfaction.

[1613] Input: facial expression data, voice data

[1614] Output: User's emotional state

[1615] Step 5: Traffic light control

[1616] The server dynamically changes the lighting status of traffic lights based on the results of the analysis method and emotion engine. Specifically, it extends the green light time when traffic volume is heavy, sets an emergency response signal when a traffic accident occurs, and adjusts signal control when there is a risk of freezing due to low temperatures.

[1617] Input: Traffic situation analysis results, weather risk analysis results, user emotional state

[1618] Output: Dynamically changed traffic light status

[1619] Step 6: Adjusting the behavior of the autonomous vehicle

[1620] The server optimizes the behavior of the self-driving vehicle based on the results of the analysis and emotion engine, such as changing the route when traffic is congested, adjusting the speed when it is cold, and changing the driving mode when the user is feeling stressed.

[1621] Input: Traffic situation analysis results, weather risk analysis results, user emotional state

[1622] Output: Optimized autonomous vehicle driving modes

[1623] Step 7: Feedback and Notification

[1624] The server provides appropriate feedback and notifications based on the analysis results of the traffic management system and the user's emotional state. For example, it notifies the user of traffic congestion information and emergency response information. It also provides personalized notifications according to the user's stress level.

[1625] Input: Analysis results, user's emotional state

[1626] Output: Personalized feedback and notifications

[1627] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1628] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1629] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1630] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1631] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1632] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1633] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1634] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1635] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1636] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1637] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing...

Claims

1. The communication infrastructure installed in the traffic lights, a data acquisition device for acquiring traffic video data; a data acquisition device for acquiring meteorological data; a data aggregator that aggregates data from the data acquisition devices via a high-speed communication network; analysis means for analyzing data transmitted from the data aggregating device; a control device that controls a traffic light based on the analysis result of the analysis means; A system including:

2. The analyzing means includes: a means for identifying traffic volume and traffic accidents; a means for identifying a traffic violating vehicle; 2. The system of claim 1, further comprising means for predicting ice and snow accumulation on roads.

3. 2. The system according to claim 1, wherein the control device includes means for dynamically changing the lighting state of traffic lights to alleviate traffic congestion and respond to traffic accidents.

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