System

Abstract

A system is provided.SOLUTION: A system, comprising: a plurality of traffic controllers; means for collecting traffic data; means for transmitting the collected traffic data to a central processing unit; the central processing unit performing a traffic analysis based on the collected traffic data; means for generating an efficient signal pattern based on a traffic analysis result; means for simulating the generated signal pattern; means for determining an optimal signal pattern based on a simulation result and transmitting the optimal signal pattern to the plurality of traffic controllers; and means for controlling traffic signals according to the optimal signal pattern.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] Conventional traffic signal control systems switch signals based on fixed programs, which means they cannot flexibly respond to fluctuations in traffic volume. This results in frequent traffic congestion and makes it difficult for emergency vehicles to pass quickly. Unnecessary signal stops also increase energy consumption and have a negative impact on the environment. To solve these issues, a system is needed that can collect and analyze traffic data in real time and automatically generate optimal signal patterns. [Means for solving the problem]

[0005] The present invention provides a system in which multiple traffic control devices collect traffic data and transmit that data to a central processing unit. The central processing unit performs traffic analysis based on the collected traffic data and generates efficient signal patterns based on the analysis results. Furthermore, the system has the ability to simulate these signal patterns in advance using digital twin technology. Once the optimal signal pattern is determined, it is transmitted to each traffic control device, allowing signals to be flexibly controlled according to actual traffic conditions. This reduces traffic congestion, allows emergency vehicles to pass more quickly, reduces energy consumption, and reduces environmental impact.

[0006] A "traffic control device" is a device that includes a traffic signal and has the data collection and communication functions necessary to control the traffic signal.

[0007] "Traffic data" refers to various information necessary to indicate traffic conditions, such as the number of vehicles, their speed, road occupancy rate, and vehicle type.

[0008] "Central Processing Unit" refers to the central processing unit that integrates and analyzes collected traffic data, generates signal patterns, and transmits the results to the traffic control device.

[0009] "Traffic analysis" is the process of evaluating traffic flow, congestion, and other characteristics based on collected traffic data.

[0010] A "signal pattern" is a setting that determines the order and timing of signal changes displayed by a traffic signal.

[0011] "Digital twin technology" is a technology that creates a digital model of a physical system and uses that model to perform simulations.

[0012] "Simulation" refers to the act of virtually recreating on a computer and evaluating how a changed signal pattern will affect actual traffic conditions.

[0013] A "traffic jam" is a situation where there are too many cars on the road and they cannot move forward, causing the flow of traffic to slow down.

[0014] An "emergency vehicle" is a motor vehicle that is permitted to travel for emergency purposes, such as an ambulance, fire engine, or police vehicle. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The present invention realizes a series of processes in a traffic control system, in which multiple traffic control devices (terminals) collect traffic data in real time, a central processing unit (server) performs traffic analysis based on that data, and automatically generates optimal signal patterns. Specific embodiments are described below with examples.

[0037] System configuration

[0038] The main components of this system are as follows:

[0039] Traffic control device (terminal)

[0040] Central Processing Unit (Server)

[0041] network

[0042] Traffic control devices (terminals) are installed at each intersection and use sensors to collect traffic data in real time, which is then transmitted to a central processing unit (server) via a network.

[0043] Program processing

[0044] The specific operations of the server and the terminal will be explained step by step below.

[0045] Traffic data collection

[0046] The terminals use built-in sensors to collect traffic data such as the number of vehicles, their speeds, vehicle types, road occupancy rates, etc. For example, a terminal installed at an intersection counts the number of vehicles passing by every second and transmits this information to a central processing unit (server) in real time.

[0047] Traffic data analysis

[0048] The server receives traffic data from multiple devices and stores it in a database. It then uses AI algorithms to perform traffic analysis. For example, it can analyze the phenomenon of a sudden increase in vehicle traffic at a particular intersection and identify the cause of congestion.

[0049] Signal pattern generation and simulation

[0050] The server generates optimal signal patterns based on the analysis results. These patterns are then simulated using digital twin technology to evaluate how the modified patterns will affect actual traffic conditions. For example, it is possible to confirm in advance how traffic flow and emergency vehicle transit times will be reduced if a new signal pattern is applied.

[0051] Applying signal patterns

[0052] Based on the results of the simulation, the server determines the most effective signal pattern and sends it to each device. The device then controls the signals according to the received signal pattern, thereby optimizing traffic flow. For example, at intersection A, which was causing congestion, the red light time could be shortened and the green light time extended, alleviating congestion.

[0053] Traffic situation monitoring and feedback

[0054] The server continues to monitor traffic conditions in real time after the signal pattern change and makes further adjustments as needed, ensuring optimal traffic flow at all times. For example, signal patterns can be changed instantly in response to unexpected accidents or emergency vehicles passing by.

[0055] Specific examples

[0056] Traffic jams frequently occurred at intersection A in a certain city during the morning rush hour. Conventional fixed programs could not resolve this congestion, but by introducing this system, the following steps were implemented:

[0057] 1. The terminal collected traffic data at intersection A every second and sent it to the server.

[0058] 2. The server performed traffic analysis based on the collected data and determined that the cause of the traffic jam was the length of the red light.

[0059] 3. The server generated a new traffic light pattern that shortened the red light time by 5 seconds and extended the green light time by 8 seconds, and performed a simulation using digital twin technology.

[0060] 4. Since the simulation was successful, the optimal signal pattern was sent to the terminal and implemented at intersection A.

[0061] 5. Users saw that traffic congestion had eased and used this data for urban planning.

[0062] In this way, the traffic control system of the present invention can alleviate traffic congestion, allow emergency vehicles to pass quickly, reduce energy consumption, and reduce the environmental load.

[0063] The processing flow will be explained below.

[0064] Step 1: Collecting traffic data

[0065] The device uses sensors to collect real-time traffic data, such as the number of vehicles, their speed, vehicle type, and road occupancy rate, every second. This data is temporarily stored on the device.

[0066] Step 2: Sending traffic data

[0067] The terminals transmit the collected traffic data to a central processing unit (server) via a network. The transmission interval is real-time, so the latest data is updated every second.

[0068] Step 3: Aggregating traffic data

[0069] The server receives traffic data sent from multiple devices and stores it in a database, enabling real-time centralized management of traffic conditions across the city.

[0070] Step 4: Analyze traffic data

[0071] The server analyzes the collected traffic data using AI algorithms. Specifically, it identifies traffic flow and congestion factors based on past and current data. It also analyzes traffic fluctuation patterns due to time of day and specific events.

[0072] Step 5: Generate signal patterns

[0073] The server generates optimal signal patterns based on the results of traffic analysis, taking into consideration factors such as easing traffic congestion, shortening transit times for emergency vehicles, and improving energy efficiency.

[0074] Step 6: Simulation with the digital twin

[0075] The server then uses digital twin technology to simulate the generated traffic signal patterns. Specifically, it applies the traffic signal patterns in a virtual environment and evaluates the resulting traffic flow, waiting times, accident risk, and other factors.

[0076] Step 7: Determine the optimal pattern

[0077] The server determines the most effective traffic light pattern based on the simulation results, taking into account the positive effects shown by the simulation (e.g., reduced congestion, shorter transit times for emergency vehicles).

[0078] Step 8: Sending a signal pattern

[0079] The server then transmits the optimal signal pattern to each terminal in real time, and the pattern is immediately reflected in signal control.

[0080] Step 9: Applying the signal pattern

[0081] The terminal controls the traffic light based on the received new signal pattern, for example, shortening the red light time and extending the green light time.

[0082] Step 10: Traffic monitoring

[0083] The server continues to monitor traffic conditions in real time after the signal pattern changes to ensure that the changes are having the intended effect and make further adjustments if necessary.

[0084] Step 11: Data Feedback

[0085] The server analyzes traffic data and generates a report on the effectiveness of the system. This data is used for future urban planning and further system optimization. Users (urban planners) can use this data to consider improvements and new plans.

[0086] Example 1

[0087] 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."

[0088] Conventional traffic control systems rely on fixed programs to control traffic signals, lacking the flexibility to respond to real-time changes. This makes it difficult to respond quickly to traffic congestion or the passage of emergency vehicles, making it difficult to improve traffic efficiency or reduce energy consumption. Furthermore, the use of simulation technology was insufficient, making it difficult to verify the effectiveness of the generated signal patterns in advance.

[0089] 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.

[0090] In this invention, the server includes means for collecting traffic data, means for performing traffic analysis based on the traffic data, means for generating efficient signal patterns based on the results of the traffic analysis, means for simulating the generated signal patterns using digital twin technology, and means for monitoring traffic conditions after the signal patterns are applied and providing feedback. This enables flexible signal control in accordance with traffic conditions in real time, thereby realizing the alleviation of traffic congestion, the rapid passage of emergency vehicles, reduction of energy consumption, and reduction of environmental impact.

[0091] A "terminal" is a device or system that collects and transmits traffic data to a server.

[0092] "Server" refers to a central processing unit that receives traffic data sent from terminals and analyzes and processes it.

[0093] "Traffic data" refers to information about traffic, such as the number of vehicles, their speeds, vehicle types, and road occupancy.

[0094] "Traffic analysis" is the process of analyzing traffic conditions and patterns based on collected traffic data.

[0095] "Signal pattern" refers to the timing and sequence of traffic signals set at traffic lights at intersections and other locations.

[0096] "Digital twin technology" is a technology that creates virtual models of physical systems and performs simulations and data analysis.

[0097] "Simulation" is a method of virtually recreating real-world traffic conditions and evaluating the impact of changes to traffic signal patterns in advance.

[0098] "Monitoring" is the activity of observing traffic conditions in real time and collecting data.

[0099] "Feedback" is the process of adjusting system settings and signal patterns based on monitoring results.

[0100] "Real-time" means that data is processed and results are reflected almost instantly.

[0101] "Traffic signal" refers to a signal facility installed to control the movement of vehicles and pedestrians on a road.

[0102] The present invention realizes a series of processes in a traffic control system in which multiple terminals collect traffic data in real time, a server performs traffic analysis based on that data, and automatically generates optimal signal patterns. Each component of this system and its specific operation are described in detail below.

[0103] System Components

[0104] The main components of this system are as follows:

[0105] Terminal

[0106] server

[0107] network

[0108] Terminals are installed at each intersection and use sensors to collect traffic data in real time, which is then sent to a server via a network.

[0109] Hardware and software used

[0110] 1. Terminal: A device for collecting traffic data, specifically a device with built-in sensors (e.g., vehicle counting sensors and speed sensors) and a communication module.

[0111] 2. Server: Serves as the central processing unit, analyzing traffic data, generating, simulating, and applying traffic signal patterns. Software used includes MySQL and PostgreSQL (database systems), Python's Scikit-learn (analysis algorithms), TensorFlow and PyTorch (deep learning models), and Unity and AnyLogic (digital twin technologies).

[0112] 3. Network: An internet connection or dedicated communication network that enables data communication between the device and the server.

[0113] Specific examples

[0114] At intersection A in a certain city, traffic jams frequently occurred during the morning rush hour. By introducing this system, the following problems were solved:

[0115] Traffic data collection: The device collected traffic data at intersection A every second and sent it to the server.

[0116] Traffic analysis: The server analyzed the traffic situation based on the collected data and determined that the cause of the congestion was the length of the red light.

[0117] Generation and simulation of traffic light patterns: The server generated traffic light patterns that shortened the red light time by 5 seconds and extended the green light time by 8 seconds, and simulated them using digital twin technology.

[0118] Applying the signal pattern: Since the simulation was successful, the optimal signal pattern was sent to the terminal and implemented at intersection A.

[0119] Traffic condition monitoring and feedback: The server monitored traffic conditions after the new signal patterns were applied and confirmed that congestion had been alleviated.

[0120] The system has optimized traffic flow, easing traffic congestion, allowing emergency vehicles to pass through more quickly, reducing energy consumption, and easing the environmental impact.

[0121] Prompt Sentence Examples

[0122] Below are examples of specific prompt sentences to input into the generative AI model.

[0123] "Input real-time traffic data for a specific intersection and create an AI model to generate optimal traffic light patterns."

[0124] "Design an algorithm for signal pattern simulation using digital twin technology in a traffic control system."

[0125] This will provide guidelines for developing more detailed and specific traffic control system programs.

[0126] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0127] Processing flow

[0128] Step 1: Collecting traffic data

[0129] The device uses built-in sensors to collect real-time traffic data such as the number of vehicles at intersections, their speeds, vehicle types, and road occupancy rates. For example, the device counts the number of vehicles passing by every second and stores this information in its internal memory.

[0130] Input: Traffic sensor data

[0131] Output: Collected traffic data

[0132] Step 2: Sending traffic data

[0133] The terminal sends the collected traffic data to the server via the network. Specifically, the terminal uses the HTTP protocol to send a POST request to the server API in JSON format for the collected data.

[0134] Input: Collected traffic data

[0135] Output: Traffic data sent to the server

[0136] Step 3: Save traffic data

[0137] The server stores the traffic data received from the terminal in a database. For example, the server uses a database system such as MySQL or PostgreSQL and inserts the data into the database.

[0138] Input: Traffic data sent from the device

[0139] Output: Traffic data stored in a database

[0140] Step 4: Traffic analysis

[0141] The server analyzes the stored traffic data using AI algorithms, for example, using Python's Scikit-learn library to learn from the traffic data and identify congested areas and their causes.

[0142] Input: Traffic data stored in a database

[0143] Output: Analysis results

[0144] Step 5: Generate signal patterns

[0145] The server generates optimal signal patterns based on the analysis results, for example, by automatically generating new signal patterns using deep learning models (using TensorFlow or PyTorch).

[0146] Input: Analysis results

[0147] Output: Generated signal pattern

[0148] Step 6: Simulate the signal pattern

[0149] The server then uses digital twin technology to simulate the generated traffic light patterns, such as Unity or AnyLogic, to evaluate the impact of the traffic light patterns on actual traffic conditions.

[0150] Input: Generated signal pattern

[0151] Output: Simulation results

[0152] Step 7: Applying the signal pattern

[0153] The server transmits the signal pattern determined to be optimal through simulation to each terminal, and the terminal controls the signal in real time according to the received signal pattern.

[0154] Input: Signal pattern based on simulation results

[0155] Output: Signal control at the intersection with the new signal pattern applied

[0156] Step 8: Traffic monitoring and feedback

[0157] The server monitors traffic conditions in real time after the signal patterns are applied and readjusts the signal patterns as necessary, thereby maintaining optimal traffic flow at all times.

[0158] Input: Traffic data (after applying signal patterns)

[0159] Output: Signal pattern readjustment instructions

[0160] By executing each step continuously, the traffic control system is optimized in real time, reducing traffic congestion and improving traffic flow.

[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] In modern transportation systems, simply collecting traffic data and generating optimal signal patterns is not enough to eliminate traffic congestion or allow emergency vehicles to pass quickly. Furthermore, for autonomous vehicles to be used effectively, it is necessary to utilize real-time traffic data and provide optimal driving routes. Systems that can solve these issues and improve traffic efficiency and safety are needed.

[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 multiple traffic control devices to collect traffic data, means for transmitting the collected traffic data to a central processing unit, means for the central processing unit to perform traffic analysis based on the collected traffic data, means for generating efficient signal patterns based on the traffic analysis results, means for simulating the generated signal patterns, means for determining optimal signal patterns based on the simulation results and transmitting them to the multiple traffic control devices, and means for controlling traffic signals in accordance with the optimal signal patterns.This makes it possible to improve traffic efficiency and the safety of autonomous vehicles by equipping autonomous vehicles with means for receiving real-time traffic data from traffic control devices and monitoring surrounding traffic conditions, means for proposing optimal routes based on the traffic data, and means for providing appropriate instructions to autonomous vehicles when an emergency vehicle is approaching.

[0166] A "traffic control device" is a device that collects traffic data in real time and transmits it to a central processing unit.

[0167] "Traffic data" refers to information about traffic conditions, such as the number of vehicles, their speeds, vehicle types, and road occupancy rates.

[0168] The "central processing unit" is a device that analyzes traffic data sent from multiple traffic control devices and generates optimal signal patterns.

[0169] A "signal pattern" refers to the timing and time distribution of red, green, and yellow traffic lights.

[0170] "Digital twin technology" is a technology that reproduces real-world traffic conditions in a virtual environment and simulates and evaluates traffic light patterns.

[0171] An "autonomous vehicle" is a vehicle that can automatically control its driving based on traffic data and signal patterns.

[0172] An "optimal route" is a route that allows a vehicle to reach its destination efficiently and safely, based on traffic data.

[0173] "Emergency vehicles" are vehicles that should have priority in an emergency, such as ambulances, fire engines, and police vehicles.

[0174] System Overview

[0175] This invention proposes a system that improves traffic efficiency and safety by coordinating automated driving vehicles and traffic control systems. The system includes the following main components:

[0176] Traffic control device

[0177] Autonomous vehicles

[0178] Central Processing Unit (Server)

[0179] network

[0180] Program processing details

[0181] Traffic data collection and transmission

[0182] The traffic control system collects traffic data such as the number of vehicles, their speed, vehicle type, and road occupancy rate through sensors installed at each intersection, and transmits the collected data to a central processing unit (server) in real time.

[0183] Traffic data analysis and signal pattern generation

[0184] The server receives traffic data from multiple traffic control devices and stores it in a database. It then uses AI algorithms to analyze traffic and generate optimal signal patterns. The effectiveness of these signal patterns is evaluated through simulations using digital twin technology.

[0185] Autonomous Vehicle Handling

[0186] The autonomous vehicle receives real-time traffic data from the traffic control device and constantly monitors the surrounding traffic conditions. Based on the received traffic data, the server proposes the optimal driving route for the vehicle. When an emergency vehicle approaches, the server provides appropriate instructions to the autonomous vehicle.

[0187] Specific hardware and software

[0188] Hardware

[0189] Onboard computers in autonomous vehicles

[0190] GPS Modules

[0191] Communication module (LTE / 5G)

[0192] Sensors installed at each intersection

[0193] software

[0194] Python

[0195] HTTP request library (requests)

[0196] Use cases and generative AI models

[0197] One example of how this system can be used is to suggest optimal routes for autonomous vehicles during morning rush hour.

[0198] Specific examples

[0199] At intersection A in a certain city, traffic jams frequently occur during the morning rush hour. By implementing this system, the following steps were implemented:

[0200] 1. The traffic control device collected traffic data at intersection A every second and sent it to the server.

[0201] 2. The server performed traffic analysis based on the collected data and determined that the cause of the traffic jam was the length of the red light.

[0202] 3. The server generated a new traffic light pattern that shortened the red light time by 5 seconds and extended the green light time by 8 seconds, and performed a simulation using digital twin technology.

[0203] 4. Since the simulation was successful, the optimal signal pattern was sent to the terminal and implemented at intersection A.

[0204] 5. Users saw that traffic congestion had eased and used this data for urban planning.

[0205] Prompt Sentence Examples

[0206] "Generate the optimal traffic light pattern for morning rush hour based on traffic data for intersection A. The data includes the number of vehicles, their speeds, vehicle types, and road occupancy."

[0207] This system will help alleviate traffic congestion, allow emergency vehicles to pass through more quickly, reduce energy consumption, and lighten the burden on the environment.

[0208] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0209] Step 1:

[0210] Traffic data collection and transmission

[0211] The terminal collects real-time traffic data such as the number of vehicles, their speed, vehicle type, and road occupancy rate from sensors installed at intersections. This data is collected every second and sent to a server via a network. The input data is electrical signal data output by the sensors, and the output is traffic data in JSON format sent to the server.

[0212] Step 2:

[0213] Traffic data analysis

[0214] The server receives traffic data sent from multiple traffic control devices in bulk and stores it in a database. Next, it performs traffic analysis using AI algorithms. In the analysis step, the collected traffic situation data is used as input data, and a series of data processing and data calculations (for example, counting the number of vehicles and calculating average speeds) are performed. The output is an analysis of the causes of traffic congestion and prediction data.

[0215] Step 3:

[0216] Signal pattern generation

[0217] The server automatically generates the optimal traffic light pattern based on the analysis results. The input in this step is the traffic analysis results, and the output is the newly generated traffic light pattern. Specifically, it calculates the time allocation of red and green lights.

[0218] Step 4:

[0219] Simulation and Evaluation

[0220] The generated signal patterns are simulated in a virtual environment using digital twin technology. This simulation evaluates how the signal patterns affect real-world traffic conditions. The input data is the signal patterns generated in the previous step, and the output data is the evaluation results based on the simulation.

[0221] Step 5:

[0222] Applying and transmitting signal patterns

[0223] The server determines the optimal signal pattern based on the simulation results and sends it to each terminal. The input at this stage is the simulation results, and the output is the data of the final signal pattern. The actual traffic lights are controlled according to the signal pattern sent to the terminal.

[0224] Step 6:

[0225] Providing data to autonomous vehicles

[0226] The signal patterns and traffic condition data sent from the terminal are provided to the autonomous vehicle in real time. The vehicle monitors the surrounding traffic conditions based on the received traffic data and is then suggested the optimal driving route. The input is data from the traffic control device, and the output is driving instruction data used by the autonomous driving system.

[0227] Step 7:

[0228] Emergency response

[0229] When an emergency vehicle approaches, the server issues instructions to the autonomous vehicle for a rapid response, including the emergency vehicle's location data and emergency response instructions. The input is the emergency vehicle's location information, and the output is data on avoidance and response instructions for the autonomous vehicle.

[0230] These steps will concretely improve traffic efficiency and the safety of autonomous vehicles.

[0231] 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.

[0232] The present invention realizes a series of processes in a traffic control system, in which user emotion data is collected by an emotion engine and the data is incorporated into traffic analysis to generate signal patterns. Specific embodiments are described below with examples.

[0233] System configuration

[0234] The main components of this system are as follows:

[0235] Traffic control device (terminal)

[0236] Central Processing Unit (Server)

[0237] Emotion Engine

[0238] network

[0239] Traffic control devices (terminals) are installed at each intersection and use sensors to collect traffic data in real time. The emotion engine has the function of collecting and analyzing user emotion data. The collected data is sent to a central processing unit (server) via a network.

[0240] Program processing

[0241] The specific operations of the server, terminal, and user will be explained step by step below.

[0242] Traffic data collection

[0243] The device uses sensors to collect real-time traffic data, such as the number of vehicles, their speed, vehicle type, and road occupancy rate, every second. This data is temporarily stored on the device.

[0244] Collecting Emotional Data

[0245] The emotion engine collects user emotional data in real time based on biometric information, facial expression analysis, etc. For example, it analyzes the user's facial expressions using an in-car camera to evaluate the level of stress and irritation.

[0246] Transmission of traffic and emotion data

[0247] The terminal transmits the collected traffic data and emotion data collected by the emotion engine to a central processing unit (server) via a network. The transmission interval is real-time, so the latest data is updated every second.

[0248] Aggregation of traffic and emotion data

[0249] The server receives all the data sent from multiple devices and emotion engines and stores it in a database, enabling real-time centralized management of traffic conditions and users' emotional states across the city.

[0250] Traffic and emotion data analysis

[0251] The server uses AI algorithms to analyze the collected traffic and emotion data. Specifically, it identifies traffic flow, factors behind congestion, and patterns of user emotion change based on past and current data. It also analyzes the correlation between traffic and emotion based on time of day and specific events.

[0252] Signal pattern generation and simulation

[0253] The server then generates optimal traffic light patterns based on the analysis results. These patterns take into consideration factors such as easing traffic congestion, shortening transit times for emergency vehicles, improving energy efficiency, and reducing user stress. The generated traffic light patterns are then simulated using digital twin technology to evaluate how the changed patterns will affect actual traffic conditions and user emotions.

[0254] Applying signal patterns

[0255] The server determines the most effective traffic signal pattern based on the simulation results and sends it to each device. The device then controls the traffic signals according to the received signal pattern, thereby optimizing traffic flow.

[0256] Monitoring traffic conditions and user emotions

[0257] The server continues to monitor traffic conditions and users' emotional state in real time after the signal pattern change and makes further adjustments as needed, thereby always maintaining optimal traffic flow and user comfort.

[0258] Specific examples

[0259] Traffic jams frequently occurred at intersection A in a certain city during the morning rush hour. Conventional fixed programs could not resolve this congestion, but by introducing this system, the following steps were implemented:

[0260] 1. The terminal collected traffic data at intersection A every second and sent it to the server.

[0261] 2. The emotion engine analyzed the facial expressions of the user inside the car, measured their stress level, and sent the data to the server.

[0262] 3. The server performed traffic analysis and sentiment analysis based on the collected data to identify the causes of traffic congestion and factors that cause user stress.

[0263] 4. The server generated a new traffic light pattern that shortened the red light time by 5 seconds and extended the green light time by 8 seconds, and performed a simulation using digital twin technology.

[0264] 5. The simulation was successful, so the optimal signal pattern was sent to the terminal and implemented at intersection A.

[0265] 6. As traffic congestion eased, users saw their stress levels drop, and they used this data for urban planning.

[0266] In this way, the traffic control system of the present invention can alleviate traffic congestion, allow emergency vehicles to pass quickly, reduce energy consumption, ease environmental load, and improve user comfort.

[0267] The processing flow will be explained below.

[0268] Step 1: Collecting traffic data

[0269] The device collects real-time traffic data using sensors installed at each intersection, specifically information such as the number of vehicles, speed, vehicle type, and road occupancy rate, which is acquired every second and temporarily stored in its internal memory.

[0270] Step 2: Collecting emotion data

[0271] The emotion engine collects biometric information such as facial expressions, voice, and heart rate from the user interface and camera, and analyzes this information to obtain emotional data about the user. For example, it can analyze video footage from an in-car camera and quantify the user's stress level and frustration.

[0272] Step 3: Sending traffic and emotion data

[0273] The terminals and emotion engines each send the collected traffic data and emotion data to a central processing unit (server) via a network. This transmission is done in real time, with the latest data being sent from each terminal to the server every second.

[0274] Step 4: Aggregating traffic and sentiment data

[0275] The server receives all the data sent from multiple devices and emotion engines and stores it in a database. By aggregating this data, it is possible to centrally manage traffic conditions and users' emotional states throughout the city in real time.

[0276] Step 5: Analyzing traffic and emotion data

[0277] The server uses AI algorithms to analyze the collected traffic and emotion data. Specifically, it compares it with past data to identify traffic flow, causes of congestion, and patterns of user emotion change. It also analyzes correlations between traffic and emotion based on time of day or specific events.

[0278] Step 6: Generate and simulate signal patterns

[0279] The server generates optimal signal patterns based on the results of traffic and emotion analysis. These signal patterns are then simulated in a virtual environment using digital twin technology to evaluate how the modified patterns affect actual traffic conditions and user emotions.

[0280] Step 7: Determine the optimal pattern

[0281] Based on the simulation results, the server determines the most effective traffic signal pattern, which aims to reduce traffic congestion, shorten transit times for emergency vehicles, improve energy efficiency, and reduce user stress.

[0282] Step 8: Sending a signal pattern

[0283] The server then transmits the determined optimal signal pattern to each terminal in real time, so that the signal pattern is immediately applied.

[0284] Step 9: Applying the signal pattern

[0285] The device then controls the traffic lights based on the new signal pattern received, for example by shortening the red light time and extending the green light time.

[0286] Step 10: Monitoring traffic conditions and user emotions

[0287] The server continues to monitor traffic conditions and user sentiment in real time after the signal pattern change, allowing it to assess whether the changes are having the desired effect and make further adjustments if necessary.

[0288] Step 11: Data Feedback

[0289] The server generates a report based on the traffic conditions and user sentiment data and provides the results as feedback to urban planners (users), who can use the data to improve future transportation planning and systems.

[0290] Example 2

[0291] 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."

[0292] Conventional traffic control systems generate signal patterns based solely on traffic data, making it difficult to achieve optimal traffic signal control that takes into account the user's emotional state. Traffic congestion, in particular, increases user stress, ultimately affecting driving safety and comfort. Therefore, there is a demand for a system that can collect user emotional data in real time and incorporate it into traffic analysis to achieve more efficient and user-friendly traffic signal control.

[0293] 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.

[0294] In this invention, the server includes means for performing analysis based on traffic data and emotion data, means for generating efficient signal patterns based on the analysis results, and means for monitoring traffic conditions and emotion data in real time and making further adjustments. This makes it possible to take the collected user emotion data into account in traffic analysis and generate more optimal signal patterns.

[0295] A "terminal" is a part of a traffic control device, and is a device that is installed at an intersection or the like to collect traffic data using sensors and collect user emotion data using an emotion engine.

[0296] The "server" is a central processing unit that centrally manages traffic data and emotion data transmitted from multiple terminals, analyzes this data, performs simulations, and generates and transmits signal patterns.

[0297] "Traffic data" refers to data collected in real time that indicates traffic conditions, such as the number of vehicles, their speeds, vehicle types, and road occupancy rates.

[0298] "Emotion data" is data that indicates the emotional state of the user, analyzed based on the user's biometric information, facial expressions, and the like.

[0299] "Signal pattern" refers to data that indicates the time allocation of red and green traffic lights and the timing of their switching.

[0300] "Analysis" refers to the process of using AI algorithms and other methods to identify traffic flow, factors behind congestion, and patterns of user emotion change based on collected traffic and emotion data.

[0301] "Digital twin technology" is a technology that digitally reproduces and simulates physical systems and environments.

[0302] "Simulation" refers to the process of using digital twin technology to evaluate in advance how the generated signal patterns will affect actual traffic conditions and user emotions.

[0303] "Monitoring" refers to the process of monitoring traffic conditions and the user's emotional state in real time after a traffic light pattern change and making adjustments as necessary.

[0304] This invention realizes a series of processes in a traffic control system, in which user emotion data is collected by an emotion engine and that data is incorporated into traffic analysis to generate signal patterns. Specific embodiments of this system are described in detail below.

[0305] System configuration

[0306] The system consists of the following main components:

[0307] Traffic control device (terminal)

[0308] Central Processing Unit (Server)

[0309] Emotion Engine

[0310] network

[0311] Terminals are installed at each intersection and use sensors to collect traffic data in real time. The emotion engine has the function of collecting and analyzing user emotion data. The collected data is sent to a central processing unit (server) via a network.

[0312] Functions and Roles

[0313] Terminal

[0314] The device uses sensors to collect traffic data in real time. Specifically, information such as the number of vehicles, their speed, vehicle type, and road occupancy rate is acquired every second and temporarily stored in its internal memory. The device's built-in emotion engine also collects user emotional data using the user's biometric information and in-car cameras. This allows the device to evaluate the user's stress level and irritability in real time. The device then transmits the collected traffic data and emotional data to a server via the network.

[0315] server

[0316] The server receives traffic and emotion data sent from multiple devices and stores it in a database. It then analyzes the collected data to identify traffic flow, factors behind congestion, and patterns of user emotion change. The server generates optimal signal patterns based on the analysis results and simulates them using digital twin technology. Based on the simulation results, it determines the most effective signal pattern and sends it back to the device. The server then monitors traffic conditions and users' emotional states in real time after the signal pattern change and makes further adjustments as necessary.

[0317] User

[0318] Users are the targets of emotional data collection by the device's emotion engine, and benefit from optimal traffic signals based on the analysis results of traffic data. In particular, users can experience benefits such as reduced congestion and stress due to the optimized signal patterns.

[0319] Specific examples

[0320] Traffic jams frequently occur at intersection A in a certain city during the morning rush hour. Conventional fixed programs could not resolve this congestion, but by introducing this system, the following steps are implemented:

[0321] 1. The terminal collects traffic data at intersection A every second and sends it to the server.

[0322] 2. The emotion engine analyzes the facial expressions of the user inside the car, measures the level of stress, and sends the data to the server.

[0323] 3. The server performs traffic analysis and sentiment analysis based on the collected data to identify the causes of traffic congestion and factors that cause user stress.

[0324] 4. The server generates a new traffic light pattern that shortens the red light time by 5 seconds and extends the green light time by 8 seconds, and runs a simulation using digital twin technology.

[0325] 5. Since the simulation was successful, the optimal signal pattern is sent to the terminal and implemented at intersection A.

[0326] 6. Once the user confirms that traffic congestion has been alleviated and that their stress levels have also decreased, they can use this data for urban planning.

[0327] Prompt Sentence Examples

[0328] Please provide a concrete example of traffic data collected by the system's terminals. Also, please explain how the emotion engine analyzes user emotions.

[0329] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0330] Step 1:

[0331] The device uses sensors to collect traffic data in real time.

[0332] Input: Information such as number of vehicles, speed, type of vehicle, and road occupancy from sensors installed at each intersection.

[0333] Specific operation: The device acquires data from the sensor every second and temporarily stores it in its internal memory.

[0334] Output: Collected traffic data (number of vehicles, speed, type of vehicle, road occupancy, etc.).

[0335] Step 2:

[0336] The terminal uses an emotion engine to collect emotion data of the user.

[0337] Input: In-car camera footage, user biometric information.

[0338] Specific operation: The camera captures the user's face, and the emotion engine analyzes the facial expression and evaluates the level of stress, irritation, etc.

[0339] Output: Collected emotional data (stress level, irritability, etc.).

[0340] Step 3:

[0341] The terminal transmits the collected traffic data and emotion data to a server via a network.

[0342] Input: Traffic and emotion data stored on the device.

[0343] What it does: It splits data into packets and sends them over the network to the server, automatically every second.

[0344] Output: Traffic and emotion data sent to the server.

[0345] Step 4:

[0346] The server receives the traffic data and emotion data transmitted from the multiple terminals and stores them in a database.

[0347] Input: Data packets sent from each device.

[0348] Specific operation: The server analyzes the received data and stores it in the corresponding database.

[0349] Output: Stored traffic and emotion data.

[0350] Step 5:

[0351] The server analyzes the collected traffic data and emotion data.

[0352] Input: Traffic and emotion data stored in a database.

[0353] Specific operation: Using an AI algorithm, traffic data and emotion data are compared and analyzed to identify the causes of traffic congestion and patterns of user emotion changes.

[0354] Output: Analysis results (factors of traffic congestion, patterns of user emotion changes, etc.).

[0355] Step 6:

[0356] The server generates an efficient signal pattern based on the analysis results.

[0357] Input: Analysis results from AI algorithm.

[0358] Specific operation: Based on the analysis results, the server generates traffic light patterns to ease traffic congestion and reduce user stress.

[0359] Output: The new signal pattern.

[0360] Step 7:

[0361] The server simulates the generated signal patterns using digital twin technology.

[0362] Input: The generated signal pattern.

[0363] Specific behavior: Using digital twin technology, we simulate how traffic light patterns affect real-world traffic conditions and user emotions.

[0364] Output: Simulation results.

[0365] Step 8:

[0366] Based on the simulation results, the server determines the most effective signal pattern and transmits it to the terminal.

[0367] Input: Simulation results.

[0368] Specific operation: The optimal signal pattern is selected from the simulation results and transmitted to each terminal.

[0369] Output: The optimal signal pattern sent to the device.

[0370] Step 9:

[0371] The terminal controls the signal according to the received signal pattern.

[0372] Input: The optimal signal pattern sent by the server.

[0373] Specific operation: The device controls the traffic light appropriately based on the signal pattern received.

[0374] Output: The modified signal pattern.

[0375] Step 10:

[0376] The server monitors the traffic conditions and the user's emotional state in real time after the signal pattern change and makes further adjustments as necessary.

[0377] Input: Modified traffic and emotion data.

[0378] How it works: The server continuously receives and analyzes new data and adjusts the signal pattern again if improvements are needed.

[0379] Output: constantly optimized traffic signals and the user's emotional state.

[0380] (Application example 2)

[0381] 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."

[0382] Current traffic control systems control signal patterns based solely on traffic data, and are therefore unable to consider users' emotions or stress levels. This leaves traffic congestion unresolved and users unable to enjoy a comfortable driving environment. Furthermore, it is difficult to efficiently simulate traffic analysis results and generate highly accurate signal patterns.

[0383] 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.

[0384] In this invention, the server includes means for collecting and transmitting traffic data and emotion data, means for performing traffic analysis and emotion analysis based on the traffic data and emotion data, and means for generating efficient traffic signal patterns based on the results of the traffic analysis and emotion analysis, thereby making it possible to effectively alleviate traffic congestion while taking into account the emotional state of the user and provide a comfortable driving environment for the user.

[0385] A "traffic control device" is a device that has the function of collecting and transmitting traffic data.

[0386] "Traffic data" refers to information that indicates traffic conditions, including the number of vehicles, their speeds, vehicle types, road occupancy rates, and so on.

[0387] "Emotion data" is information that indicates the user's emotional state based on biometric information and facial expression analysis.

[0388] A "central processing unit" is a device that receives data sent from multiple terminals and analyzes and processes it in a unified manner.

[0389] "Emotion analysis" is a means of analyzing collected emotion data to assess the user's emotional state.

[0390] "Traffic analysis" is a method for identifying traffic flow and congestion factors based on collected traffic data.

[0391] A "signal pattern" is a schedule or algorithm that determines how traffic signals are controlled.

[0392] "Digital twin technology" is a technology that reproduces real-world physical objects and systems in a virtual environment and performs simulations.

[0393] "Simulation" is a method for virtually verifying the actual effects of a generated signal pattern.

[0394] The "optimal signal pattern" is the most effective method of traffic signal control, taking into consideration factors such as easing traffic congestion, shortening the transit time for emergency vehicles, improving energy efficiency, and reducing user stress.

[0395] The present invention realizes a series of processes in a traffic control system, in which user emotion data is collected by an emotion engine and the data is taken into account in traffic analysis to generate traffic signal patterns. Specific embodiments are described in detail below.

[0396] System configuration

[0397] The main components of this system are as follows:

[0398] Traffic control device (terminal)

[0399] Central Processing Unit (Server)

[0400] Emotion Engine

[0401] network

[0402] Traffic control devices (terminals) are installed at each intersection and use sensors to collect traffic data in real time. The emotion engine has the function of analyzing user emotion data. The collected data is sent to a central processing unit (server) via a network.

[0403] Collecting emotion and traffic data

[0404] The server includes means for collecting and transmitting both emotion data and traffic data, and means for performing traffic analysis and emotion analysis.

[0405] Hardware and software used

[0406] On-board computer: A computer system installed in a self-driving car, such as NVIDIA DRIVE.

[0407] In-car camera: A camera that captures passengers' facial expressions, such as the Logitech C920.

[0408] Sensors: LIDAR, cameras, radar, etc. are used to collect traffic data.

[0409] Network module: A network device for sending and receiving data.

[0410] Emotion engine: Uses software such as the Emotion AI SDK to analyze user emotional data.

[0411] Traffic analysis algorithm: An algorithm that analyzes traffic data using Python and TensorFlow.

[0412] Real-time database: Firebase Realtime Database, etc., is used.

[0413] Data processing procedures

[0414] 1. Traffic and emotion data collection: In-vehicle sensors collect information on surrounding traffic conditions in real time, and the Emotion AI SDK extracts emotion data from the user's facial expressions.

[0415] 2. Data transmission: The onboard computer sends the collected data to the Firebase Realtime Database.

[0416] 3. Analysis: The server analyzes the data using Python and TensorFlow to generate traffic and sentiment analysis results.

[0417] 4. Signal pattern generation: Based on the analysis results, efficient signal patterns are generated and simulated using digital twin technology.

[0418] 5. Application of optimal signal patterns: Based on the simulation results, the optimal signal patterns are sent to the onboard computer and reflected in actual traffic signal control.

[0419] Specific examples

[0420] Traffic jams frequently occurred at intersection A in a certain city during the morning rush hour. Conventional fixed programs could not resolve this congestion, but by introducing this system, the following steps were implemented:

[0421] 1. The traffic control device (terminal) collected traffic data at intersection A every second and sent it to the server.

[0422] 2. The emotion engine analyzed the facial expressions of the user inside the car, measured their stress level, and sent the data to the server.

[0423] 3. The server performed traffic analysis and sentiment analysis based on the collected data to identify the causes of traffic congestion and factors that cause user stress.

[0424] 4. The server generated a new traffic light pattern that shortened the red light time by 5 seconds and extended the green light time by 8 seconds, and performed a simulation using digital twin technology.

[0425] 5. The simulation was successful, so the optimal signal pattern was sent to the terminal and implemented at intersection A.

[0426] 6. It was confirmed that traffic congestion was alleviated and users' stress levels also decreased.

[0427] Below is an example of a prompt sentence to input to the generative AI model.

[0428] Analyze the current traffic situation based on the collected emotion data and traffic data, and generate the optimal driving route and speed pattern. If the emotion data has a high stress score, select a gentler route, and if there is traffic congestion, prioritize avoiding it. Provide the output format in JSON.

[0429] Emotion data sample: {'stress_score': 75}

[0430] Traffic data sample: {'vehicle_speed': 60, 'traffic_density': 'high'}

[0431] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0432] Step 1:

[0433] An on-board computer collects traffic and emotion data through in-car cameras and sensors.

[0434] Input: Raw data from interior cameras, LIDAR, cameras, and radar

[0435] Output: Processed data such as stress score, vehicle speed, and traffic density

[0436] How it works: The onboard camera captures passengers' facial expressions, which are then analyzed by the Emotion AI SDK to calculate a stress score. Sensors collect data on vehicle speed and traffic density and process it in real time.

[0437] Step 2:

[0438] The on-board computer sends collected emotion and traffic data to the Firebase Realtime Database in real time.

[0439] Input: Processed data obtained in step 1

[0440] Output: Data stored in Firebase Realtime Database

[0441] Specific operation: Emotion data and traffic data are sent to the Firebase Realtime Database via NVIDIA DRIVE's network function.

[0442] Step 3:

[0443] The server receives data from the Firebase Realtime Database and performs traffic analysis and sentiment analysis.

[0444] Input: Sentiment and traffic data stored in Firebase Realtime Database

[0445] Output: Analysis results (traffic flow, causes of congestion, patterns of user emotion changes)

[0446] How it works: Using Python and TensorFlow, the server analyzes traffic and emotion data and compares it with past and current data to identify trends.

[0447] Step 4:

[0448] The server generates efficient signal patterns based on the analysis results and performs simulations using digital twin technology.

[0449] Input: Analysis results obtained in step 3

[0450] Output: Simulation results and optimal signal patterns

[0451] Specific operation: Using a generative AI model, generate optimal signal patterns, perform simulations using digital twin technology, and evaluate the simulation results.

[0452] Step 5:

[0453] The server determines the optimal signal pattern based on the simulation results and sends it back to the on-board computer.

[0454] Input: Simulation results obtained in step 4

[0455] Output: Optimal signal pattern

[0456] Specific operation: The optimal signal pattern is determined and transmitted to the vehicle's onboard computer via the network.

[0457] Step 6:

[0458] An on-board computer controls the traffic lights according to the received signal patterns.

[0459] Input: Optimal signal pattern sent in step 5

[0460] Output: Actual traffic light control

[0461] Specific operation: The on-board computer controls traffic signals based on the optimal signal pattern and adjusts to actual traffic conditions.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] [Second embodiment]

[0466] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0467] 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.

[0468] 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).

[0469] 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.

[0470] 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.

[0471] 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).

[0472] 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.

[0473] 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.

[0474] 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.

[0475] 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.

[0476] 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.

[0477] 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."

[0478] The present invention realizes a series of processes in a traffic control system, in which multiple traffic control devices (terminals) collect traffic data in real time, a central processing unit (server) performs traffic analysis based on that data, and automatically generates optimal signal patterns. Specific embodiments are described below with examples.

[0479] System configuration

[0480] The main components of this system are as follows:

[0481] Traffic control device (terminal)

[0482] Central Processing Unit (Server)

[0483] network

[0484] Traffic control devices (terminals) are installed at each intersection and use sensors to collect traffic data in real time, which is then transmitted to a central processing unit (server) via a network.

[0485] Program processing

[0486] The specific operations of the server and the terminal will be explained step by step below.

[0487] Traffic data collection

[0488] The terminals use built-in sensors to collect traffic data such as the number of vehicles, their speeds, vehicle types, road occupancy rates, etc. For example, a terminal installed at an intersection counts the number of vehicles passing by every second and transmits this information to a central processing unit (server) in real time.

[0489] Traffic data analysis

[0490] The server receives traffic data from multiple devices and stores it in a database. It then uses AI algorithms to perform traffic analysis. For example, it can analyze the phenomenon of a sudden increase in vehicle traffic at a particular intersection and identify the cause of congestion.

[0491] Signal pattern generation and simulation

[0492] The server generates optimal signal patterns based on the analysis results. These patterns are then simulated using digital twin technology to evaluate how the modified patterns will affect actual traffic conditions. For example, it is possible to confirm in advance how traffic flow and emergency vehicle transit times will be reduced if a new signal pattern is applied.

[0493] Applying signal patterns

[0494] Based on the results of the simulation, the server determines the most effective signal pattern and sends it to each device. The device then controls the signals according to the received signal pattern, thereby optimizing traffic flow. For example, at intersection A, which was causing congestion, the red light time could be shortened and the green light time extended, alleviating congestion.

[0495] Traffic situation monitoring and feedback

[0496] The server continues to monitor traffic conditions in real time after the signal pattern change and makes further adjustments as needed, ensuring optimal traffic flow at all times. For example, signal patterns can be changed instantly in response to unexpected accidents or emergency vehicles passing by.

[0497] Specific examples

[0498] Traffic jams frequently occurred at intersection A in a certain city during the morning rush hour. Conventional fixed programs could not resolve this congestion, but by introducing this system, the following steps were implemented:

[0499] 1. The terminal collected traffic data at intersection A every second and sent it to the server.

[0500] 2. The server performed traffic analysis based on the collected data and determined that the cause of the traffic jam was the length of the red light.

[0501] 3. The server generated a new traffic light pattern that shortened the red light time by 5 seconds and extended the green light time by 8 seconds, and performed a simulation using digital twin technology.

[0502] 4. Since the simulation was successful, the optimal signal pattern was sent to the terminal and implemented at intersection A.

[0503] 5. Users saw that traffic congestion had eased and used this data for urban planning.

[0504] In this way, the traffic control system of the present invention can alleviate traffic congestion, allow emergency vehicles to pass quickly, reduce energy consumption, and reduce the environmental load.

[0505] The processing flow will be explained below.

[0506] Step 1: Collecting traffic data

[0507] The device uses sensors to collect real-time traffic data, such as the number of vehicles, their speed, vehicle type, and road occupancy rate, every second. This data is temporarily stored on the device.

[0508] Step 2: Sending traffic data

[0509] The terminals transmit the collected traffic data to a central processing unit (server) via a network. The transmission interval is real-time, so the latest data is updated every second.

[0510] Step 3: Aggregating traffic data

[0511] The server receives traffic data sent from multiple devices and stores it in a database, enabling real-time centralized management of traffic conditions across the city.

[0512] Step 4: Analyze traffic data

[0513] The server analyzes the collected traffic data using AI algorithms. Specifically, it identifies traffic flow and congestion factors based on past and current data. It also analyzes traffic fluctuation patterns due to time of day and specific events.

[0514] Step 5: Generate signal patterns

[0515] The server generates optimal signal patterns based on the results of traffic analysis, taking into consideration factors such as easing traffic congestion, shortening transit times for emergency vehicles, and improving energy efficiency.

[0516] Step 6: Simulation with the digital twin

[0517] The server then uses digital twin technology to simulate the generated traffic signal patterns. Specifically, it applies the traffic signal patterns in a virtual environment and evaluates the resulting traffic flow, waiting times, accident risk, and other factors.

[0518] Step 7: Determine the optimal pattern

[0519] The server determines the most effective traffic light pattern based on the simulation results, taking into account the positive effects shown by the simulation (e.g., reduced congestion, shorter transit times for emergency vehicles).

[0520] Step 8: Sending a signal pattern

[0521] The server then transmits the optimal signal pattern to each terminal in real time, and the pattern is immediately reflected in signal control.

[0522] Step 9: Applying the signal pattern

[0523] The terminal controls the traffic light based on the received new signal pattern, for example, shortening the red light time and extending the green light time.

[0524] Step 10: Traffic monitoring

[0525] The server continues to monitor traffic conditions in real time after the signal pattern changes to ensure that the changes are having the intended effect and make further adjustments if necessary.

[0526] Step 11: Data Feedback

[0527] The server analyzes traffic data and generates a report on the effectiveness of the system. This data is used for future urban planning and further system optimization. Users (urban planners) can use this data to consider improvements and new plans.

[0528] Example 1

[0529] 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."

[0530] Conventional traffic control systems rely on fixed programs to control traffic signals, lacking the flexibility to respond to real-time changes. This makes it difficult to respond quickly to traffic congestion or the passage of emergency vehicles, making it difficult to improve traffic efficiency or reduce energy consumption. Furthermore, the use of simulation technology was insufficient, making it difficult to verify the effectiveness of the generated signal patterns in advance.

[0531] 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.

[0532] In this invention, the server includes means for collecting traffic data, means for performing traffic analysis based on the traffic data, means for generating efficient signal patterns based on the results of the traffic analysis, means for simulating the generated signal patterns using digital twin technology, and means for monitoring traffic conditions after the signal patterns are applied and providing feedback. This enables flexible signal control in accordance with traffic conditions in real time, thereby realizing the alleviation of traffic congestion, the rapid passage of emergency vehicles, reduction of energy consumption, and reduction of environmental impact.

[0533] A "terminal" is a device or system that collects and transmits traffic data to a server.

[0534] "Server" refers to a central processing unit that receives traffic data sent from terminals and analyzes and processes it.

[0535] "Traffic data" refers to information about traffic, such as the number of vehicles, their speeds, vehicle types, and road occupancy.

[0536] "Traffic analysis" is the process of analyzing traffic conditions and patterns based on collected traffic data.

[0537] "Signal pattern" refers to the timing and sequence of traffic signals set at traffic lights at intersections and other locations.

[0538] "Digital twin technology" is a technology that creates virtual models of physical systems and performs simulations and data analysis.

[0539] "Simulation" is a method of virtually recreating real-world traffic conditions and evaluating the impact of changes to traffic signal patterns in advance.

[0540] "Monitoring" is the activity of observing traffic conditions in real time and collecting data.

[0541] "Feedback" is the process of adjusting system settings and signal patterns based on monitoring results.

[0542] "Real-time" means that data is processed and results are reflected almost instantly.

[0543] "Traffic signal" refers to a signal facility installed to control the movement of vehicles and pedestrians on a road.

[0544] The present invention realizes a series of processes in a traffic control system in which multiple terminals collect traffic data in real time, a server performs traffic analysis based on that data, and automatically generates optimal signal patterns. Each component of this system and its specific operation are described in detail below.

[0545] System Components

[0546] The main components of this system are as follows:

[0547] Terminal

[0548] server

[0549] network

[0550] Terminals are installed at each intersection and use sensors to collect traffic data in real time, which is then sent to a server via a network.

[0551] Hardware and software used

[0552] 1. Terminal: A device for collecting traffic data, specifically a device with built-in sensors (e.g., vehicle counting sensors and speed sensors) and a communication module.

[0553] 2. Server: Serves as the central processing unit, analyzing traffic data, generating, simulating, and applying traffic signal patterns. Software used includes MySQL and PostgreSQL (database systems), Python's Scikit-learn (analysis algorithms), TensorFlow and PyTorch (deep learning models), and Unity and AnyLogic (digital twin technologies).

[0554] 3. Network: An internet connection or dedicated communication network that enables data communication between the device and the server.

[0555] Specific examples

[0556] At intersection A in a certain city, traffic jams frequently occurred during the morning rush hour. By introducing this system, the following problems were solved:

[0557] Traffic data collection: The device collected traffic data at intersection A every second and sent it to the server.

[0558] Traffic analysis: The server analyzed the traffic situation based on the collected data and determined that the cause of the congestion was the length of the red light.

[0559] Generation and simulation of traffic light patterns: The server generated traffic light patterns that shortened the red light time by 5 seconds and extended the green light time by 8 seconds, and simulated them using digital twin technology.

[0560] Applying the signal pattern: Since the simulation was successful, the optimal signal pattern was sent to the terminal and implemented at intersection A.

[0561] Traffic condition monitoring and feedback: The server monitored traffic conditions after the new signal patterns were applied and confirmed that congestion had been alleviated.

[0562] The system has optimized traffic flow, easing traffic congestion, allowing emergency vehicles to pass through more quickly, reducing energy consumption, and easing the environmental impact.

[0563] Prompt Sentence Examples

[0564] Below are examples of specific prompt sentences to input into the generative AI model.

[0565] "Input real-time traffic data for a specific intersection and create an AI model to generate optimal traffic light patterns."

[0566] "Design an algorithm for signal pattern simulation using digital twin technology in a traffic control system."

[0567] This will provide guidelines for developing more detailed and specific traffic control system programs.

[0568] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0569] Processing flow

[0570] Step 1: Collecting traffic data

[0571] The device uses built-in sensors to collect real-time traffic data such as the number of vehicles at intersections, their speeds, vehicle types, and road occupancy rates. For example, the device counts the number of vehicles passing by every second and stores this information in its internal memory.

[0572] Input: Traffic sensor data

[0573] Output: Collected traffic data

[0574] Step 2: Sending traffic data

[0575] The terminal sends the collected traffic data to the server via the network. Specifically, the terminal uses the HTTP protocol to send a POST request to the server API in JSON format for the collected data.

[0576] Input: Collected traffic data

[0577] Output: Traffic data sent to the server

[0578] Step 3: Save traffic data

[0579] The server stores the traffic data received from the terminal in a database. For example, the server uses a database system such as MySQL or PostgreSQL and inserts the data into the database.

[0580] Input: Traffic data sent from the device

[0581] Output: Traffic data stored in a database

[0582] Step 4: Traffic analysis

[0583] The server analyzes the stored traffic data using AI algorithms, for example, using Python's Scikit-learn library to learn from the traffic data and identify congested areas and their causes.

[0584] Input: Traffic data stored in a database

[0585] Output: Analysis results

[0586] Step 5: Generate signal patterns

[0587] The server generates optimal signal patterns based on the analysis results, for example, by automatically generating new signal patterns using deep learning models (using TensorFlow or PyTorch).

[0588] Input: Analysis results

[0589] Output: Generated signal pattern

[0590] Step 6: Simulate the signal pattern

[0591] The server then uses digital twin technology to simulate the generated traffic light patterns, such as Unity or AnyLogic, to evaluate the impact of the traffic light patterns on actual traffic conditions.

[0592] Input: Generated signal pattern

[0593] Output: Simulation results

[0594] Step 7: Applying the signal pattern

[0595] The server transmits the signal pattern determined to be optimal through simulation to each terminal, and the terminal controls the signal in real time according to the received signal pattern.

[0596] Input: Signal pattern based on simulation results

[0597] Output: Signal control at the intersection with the new signal pattern applied

[0598] Step 8: Traffic monitoring and feedback

[0599] The server monitors traffic conditions in real time after the signal patterns are applied and readjusts the signal patterns as necessary, thereby maintaining optimal traffic flow at all times.

[0600] Input: Traffic data (after applying signal patterns)

[0601] Output: Signal pattern readjustment instructions

[0602] By executing each step continuously, the traffic control system is optimized in real time, reducing traffic congestion and improving traffic flow.

[0603] (Application example 1)

[0604] 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."

[0605] In modern transportation systems, simply collecting traffic data and generating optimal signal patterns is not enough to eliminate traffic congestion or allow emergency vehicles to pass quickly. Furthermore, for autonomous vehicles to be used effectively, it is necessary to utilize real-time traffic data and provide optimal driving routes. Systems that can solve these issues and improve traffic efficiency and safety are needed.

[0606] 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.

[0607] In this invention, the server includes means for multiple traffic control devices to collect traffic data, means for transmitting the collected traffic data to a central processing unit, means for the central processing unit to perform traffic analysis based on the collected traffic data, means for generating efficient signal patterns based on the traffic analysis results, means for simulating the generated signal patterns, means for determining optimal signal patterns based on the simulation results and transmitting them to the multiple traffic control devices, and means for controlling traffic signals in accordance with the optimal signal patterns.This makes it possible to improve traffic efficiency and the safety of autonomous vehicles by equipping autonomous vehicles with means for receiving real-time traffic data from traffic control devices and monitoring surrounding traffic conditions, means for proposing optimal routes based on the traffic data, and means for providing appropriate instructions to autonomous vehicles when an emergency vehicle is approaching.

[0608] A "traffic control device" is a device that collects traffic data in real time and transmits it to a central processing unit.

[0609] "Traffic data" refers to information about traffic conditions, such as the number of vehicles, their speeds, vehicle types, and road occupancy rates.

[0610] The "central processing unit" is a device that analyzes traffic data sent from multiple traffic control devices and generates optimal signal patterns.

[0611] A "signal pattern" refers to the timing and time distribution of red, green, and yellow traffic lights.

[0612] "Digital twin technology" is a technology that reproduces real-world traffic conditions in a virtual environment and simulates and evaluates traffic light patterns.

[0613] An "autonomous vehicle" is a vehicle that can automatically control its driving based on traffic data and signal patterns.

[0614] An "optimal route" is a route that allows a vehicle to reach its destination efficiently and safely, based on traffic data.

[0615] "Emergency vehicles" are vehicles that should have priority in an emergency, such as ambulances, fire engines, and police vehicles.

[0616] System Overview

[0617] This invention proposes a system that improves traffic efficiency and safety by coordinating automated driving vehicles and traffic control systems. The system includes the following main components:

[0618] Traffic control device

[0619] Autonomous vehicles

[0620] Central Processing Unit (Server)

[0621] network

[0622] Program processing details

[0623] Traffic data collection and transmission

[0624] The traffic control system collects traffic data such as the number of vehicles, their speed, vehicle type, and road occupancy rate through sensors installed at each intersection, and transmits the collected data to a central processing unit (server) in real time.

[0625] Traffic data analysis and signal pattern generation

[0626] The server receives traffic data from multiple traffic control devices and stores it in a database. It then uses AI algorithms to analyze traffic and generate optimal signal patterns. The effectiveness of these signal patterns is evaluated through simulations using digital twin technology.

[0627] Autonomous Vehicle Handling

[0628] The autonomous vehicle receives real-time traffic data from the traffic control device and constantly monitors the surrounding traffic conditions. Based on the received traffic data, the server proposes the optimal driving route for the vehicle. When an emergency vehicle approaches, the server provides appropriate instructions to the autonomous vehicle.

[0629] Specific hardware and software

[0630] Hardware

[0631] Onboard computers in autonomous vehicles

[0632] GPS Modules

[0633] Communication module (LTE / 5G)

[0634] Sensors installed at each intersection

[0635] software

[0636] Python

[0637] HTTP request library (requests)

[0638] Use cases and generative AI models

[0639] One example of how this system can be used is to suggest optimal routes for autonomous vehicles during morning rush hour.

[0640] Specific examples

[0641] At intersection A in a certain city, traffic jams frequently occur during the morning rush hour. By implementing this system, the following steps were implemented:

[0642] 1. The traffic control device collected traffic data at intersection A every second and sent it to the server.

[0643] 2. The server performed traffic analysis based on the collected data and determined that the cause of the traffic jam was the length of the red light.

[0644] 3. The server generated a new traffic light pattern that shortened the red light time by 5 seconds and extended the green light time by 8 seconds, and performed a simulation using digital twin technology.

[0645] 4. Since the simulation was successful, the optimal signal pattern was sent to the terminal and implemented at intersection A.

[0646] 5. Users saw that traffic congestion had eased and used this data for urban planning.

[0647] Prompt Sentence Examples

[0648] "Generate the optimal traffic light pattern for morning rush hour based on traffic data for intersection A. The data includes the number of vehicles, their speeds, vehicle types, and road occupancy."

[0649] This system will help alleviate traffic congestion, allow emergency vehicles to pass through more quickly, reduce energy consumption, and lighten the burden on the environment.

[0650] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0651] Step 1:

[0652] Traffic data collection and transmission

[0653] The terminal collects real-time traffic data such as the number of vehicles, their speed, vehicle type, and road occupancy rate from sensors installed at intersections. This data is collected every second and sent to a server via a network. The input data is electrical signal data output by the sensors, and the output is traffic data in JSON format sent to the server.

[0654] Step 2:

[0655] Traffic data analysis

[0656] The server receives traffic data sent from multiple traffic control devices in bulk and stores it in a database. Next, it performs traffic analysis using AI algorithms. In the analysis step, the collected traffic situation data is used as input data, and a series of data processing and data calculations (for example, counting the number of vehicles and calculating average speeds) are performed. The output is an analysis of the causes of traffic congestion and prediction data.

[0657] Step 3:

[0658] Signal pattern generation

[0659] The server automatically generates the optimal traffic light pattern based on the analysis results. The input in this step is the traffic analysis results, and the output is the newly generated traffic light pattern. Specifically, it calculates the time allocation of red and green lights.

[0660] Step 4:

[0661] Simulation and Evaluation

[0662] The generated signal patterns are simulated in a virtual environment using digital twin technology. This simulation evaluates how the signal patterns affect real-world traffic conditions. The input data is the signal patterns generated in the previous step, and the output data is the evaluation results based on the simulation.

[0663] Step 5:

[0664] Applying and transmitting signal patterns

[0665] The server determines the optimal signal pattern based on the simulation results and sends it to each terminal. The input at this stage is the simulation results, and the output is the data of the final signal pattern. The actual traffic lights are controlled according to the signal pattern sent to the terminal.

[0666] Step 6:

[0667] Providing data to autonomous vehicles

[0668] The signal patterns and traffic condition data sent from the terminal are provided to the autonomous vehicle in real time. The vehicle monitors the surrounding traffic conditions based on the received traffic data and is then suggested the optimal driving route. The input is data from the traffic control device, and the output is driving instruction data used by the autonomous driving system.

[0669] Step 7:

[0670] Emergency response

[0671] When an emergency vehicle approaches, the server issues instructions to the autonomous vehicle for a rapid response, including the emergency vehicle's location data and emergency response instructions. The input is the emergency vehicle's location information, and the output is data on avoidance and response instructions for the autonomous vehicle.

[0672] These steps will concretely improve traffic efficiency and the safety of autonomous vehicles.

[0673] 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.

[0674] The present invention realizes a series of processes in a traffic control system, in which user emotion data is collected by an emotion engine and the data is incorporated into traffic analysis to generate signal patterns. Specific embodiments are described below with examples.

[0675] System configuration

[0676] The main components of this system are as follows:

[0677] Traffic control device (terminal)

[0678] Central Processing Unit (Server)

[0679] Emotion Engine

[0680] network

[0681] Traffic control devices (terminals) are installed at each intersection and use sensors to collect traffic data in real time. The emotion engine has the function of collecting and analyzing user emotion data. The collected data is sent to a central processing unit (server) via a network.

[0682] Program processing

[0683] The specific operations of the server, terminal, and user will be explained step by step below.

[0684] Traffic data collection

[0685] The device uses sensors to collect real-time traffic data, such as the number of vehicles, their speed, vehicle type, and road occupancy rate, every second. This data is temporarily stored on the device.

[0686] Collecting Emotional Data

[0687] The emotion engine collects user emotional data in real time based on biometric information, facial expression analysis, etc. For example, it analyzes the user's facial expressions using an in-car camera to evaluate the level of stress and irritation.

[0688] Transmission of traffic and emotion data

[0689] The terminal transmits the collected traffic data and emotion data collected by the emotion engine to a central processing unit (server) via a network. The transmission interval is real-time, so the latest data is updated every second.

[0690] Aggregation of traffic and emotion data

[0691] The server receives all the data sent from multiple devices and emotion engines and stores it in a database, enabling real-time centralized management of traffic conditions and users' emotional states across the city.

[0692] Traffic and emotion data analysis

[0693] The server uses AI algorithms to analyze the collected traffic and emotion data. Specifically, it identifies traffic flow, factors behind congestion, and patterns of user emotion change based on past and current data. It also analyzes the correlation between traffic and emotion based on time of day and specific events.

[0694] Signal pattern generation and simulation

[0695] The server then generates optimal traffic light patterns based on the analysis results. These patterns take into consideration factors such as easing traffic congestion, shortening transit times for emergency vehicles, improving energy efficiency, and reducing user stress. The generated traffic light patterns are then simulated using digital twin technology to evaluate how the changed patterns will affect actual traffic conditions and user emotions.

[0696] Applying signal patterns

[0697] The server determines the most effective traffic signal pattern based on the simulation results and sends it to each device. The device then controls the traffic signals according to the received signal pattern, thereby optimizing traffic flow.

[0698] Monitoring traffic conditions and user emotions

[0699] The server continues to monitor traffic conditions and users' emotional state in real time after the signal pattern change and makes further adjustments as needed, thereby always maintaining optimal traffic flow and user comfort.

[0700] Specific examples

[0701] Traffic jams frequently occurred at intersection A in a certain city during the morning rush hour. Conventional fixed programs could not resolve this congestion, but by introducing this system, the following steps were implemented:

[0702] 1. The terminal collected traffic data at intersection A every second and sent it to the server.

[0703] 2. The emotion engine analyzed the facial expressions of the user inside the car, measured their stress level, and sent the data to the server.

[0704] 3. The server performed traffic analysis and sentiment analysis based on the collected data to identify the causes of traffic congestion and factors that cause user stress.

[0705] 4. The server generated a new traffic light pattern that shortened the red light time by 5 seconds and extended the green light time by 8 seconds, and performed a simulation using digital twin technology.

[0706] 5. The simulation was successful, so the optimal signal pattern was sent to the terminal and implemented at intersection A.

[0707] 6. As traffic congestion eased, users saw their stress levels drop, and they used this data for urban planning.

[0708] In this way, the traffic control system of the present invention can alleviate traffic congestion, allow emergency vehicles to pass quickly, reduce energy consumption, ease environmental load, and improve user comfort.

[0709] The processing flow will be explained below.

[0710] Step 1: Collecting traffic data

[0711] The device collects real-time traffic data using sensors installed at each intersection, specifically information such as the number of vehicles, speed, vehicle type, and road occupancy rate, which is acquired every second and temporarily stored in its internal memory.

[0712] Step 2: Collecting emotion data

[0713] The emotion engine collects biometric information such as facial expressions, voice, and heart rate from the user interface and camera, and analyzes this information to obtain emotional data about the user. For example, it can analyze video footage from an in-car camera and quantify the user's stress level and frustration.

[0714] Step 3: Sending traffic and emotion data

[0715] The terminals and emotion engines each send the collected traffic data and emotion data to a central processing unit (server) via a network. This transmission is done in real time, with the latest data being sent from each terminal to the server every second.

[0716] Step 4: Aggregating traffic and sentiment data

[0717] The server receives all the data sent from multiple devices and emotion engines and stores it in a database. By aggregating this data, it is possible to centrally manage traffic conditions and users' emotional states throughout the city in real time.

[0718] Step 5: Analyzing traffic and emotion data

[0719] The server uses AI algorithms to analyze the collected traffic and emotion data. Specifically, it compares it with past data to identify traffic flow, causes of congestion, and patterns of user emotion change. It also analyzes correlations between traffic and emotion based on time of day or specific events.

[0720] Step 6: Generate and simulate signal patterns

[0721] The server generates optimal signal patterns based on the results of traffic and emotion analysis. These signal patterns are then simulated in a virtual environment using digital twin technology to evaluate how the modified patterns affect actual traffic conditions and user emotions.

[0722] Step 7: Determine the optimal pattern

[0723] Based on the simulation results, the server determines the most effective traffic signal pattern, which aims to reduce traffic congestion, shorten transit times for emergency vehicles, improve energy efficiency, and reduce user stress.

[0724] Step 8: Sending a signal pattern

[0725] The server then transmits the determined optimal signal pattern to each terminal in real time, so that the signal pattern is immediately applied.

[0726] Step 9: Applying the signal pattern

[0727] The device then controls the traffic lights based on the new signal pattern received, for example by shortening the red light time and extending the green light time.

[0728] Step 10: Monitoring traffic conditions and user emotions

[0729] The server continues to monitor traffic conditions and user sentiment in real time after the signal pattern change, allowing it to assess whether the changes are having the desired effect and make further adjustments if necessary.

[0730] Step 11: Data Feedback

[0731] The server generates a report based on the traffic conditions and user sentiment data and provides the results as feedback to urban planners (users), who can use the data to improve future transportation planning and systems.

[0732] Example 2

[0733] 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."

[0734] Conventional traffic control systems generate signal patterns based solely on traffic data, making it difficult to achieve optimal traffic signal control that takes into account the user's emotional state. Traffic congestion, in particular, increases user stress, ultimately affecting driving safety and comfort. Therefore, there is a demand for a system that can collect user emotional data in real time and incorporate it into traffic analysis to achieve more efficient and user-friendly traffic signal control.

[0735] 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.

[0736] In this invention, the server includes means for performing analysis based on traffic data and emotion data, means for generating efficient signal patterns based on the analysis results, and means for monitoring traffic conditions and emotion data in real time and making further adjustments. This makes it possible to take the collected user emotion data into account in traffic analysis and generate more optimal signal patterns.

[0737] A "terminal" is a part of a traffic control device, and is a device that is installed at an intersection or the like to collect traffic data using sensors and collect user emotion data using an emotion engine.

[0738] The "server" is a central processing unit that centrally manages traffic data and emotion data transmitted from multiple terminals, analyzes this data, performs simulations, and generates and transmits signal patterns.

[0739] "Traffic data" refers to data collected in real time that indicates traffic conditions, such as the number of vehicles, their speeds, vehicle types, and road occupancy rates.

[0740] "Emotion data" is data that indicates the emotional state of the user, analyzed based on the user's biometric information, facial expressions, and the like.

[0741] "Signal pattern" refers to data that indicates the time allocation of red and green traffic lights and the timing of their switching.

[0742] "Analysis" refers to the process of using AI algorithms and other methods to identify traffic flow, factors behind congestion, and patterns of user emotion change based on collected traffic and emotion data.

[0743] "Digital twin technology" is a technology that digitally reproduces and simulates physical systems and environments.

[0744] "Simulation" refers to the process of using digital twin technology to evaluate in advance how the generated signal patterns will affect actual traffic conditions and user emotions.

[0745] "Monitoring" refers to the process of monitoring traffic conditions and the user's emotional state in real time after a traffic light pattern change and making adjustments as necessary.

[0746] This invention realizes a series of processes in a traffic control system, in which user emotion data is collected by an emotion engine and that data is incorporated into traffic analysis to generate signal patterns. Specific embodiments of this system are described in detail below.

[0747] System configuration

[0748] The system consists of the following main components:

[0749] Traffic control device (terminal)

[0750] Central Processing Unit (Server)

[0751] Emotion Engine

[0752] network

[0753] Terminals are installed at each intersection and use sensors to collect traffic data in real time. The emotion engine has the function of collecting and analyzing user emotion data. The collected data is sent to a central processing unit (server) via a network.

[0754] Functions and Roles

[0755] Terminal

[0756] The device uses sensors to collect traffic data in real time. Specifically, information such as the number of vehicles, their speed, vehicle type, and road occupancy rate is acquired every second and temporarily stored in its internal memory. The device's built-in emotion engine also collects user emotional data using the user's biometric information and in-car cameras. This allows the device to evaluate the user's stress level and irritability in real time. The device then transmits the collected traffic data and emotional data to a server via the network.

[0757] server

[0758] The server receives traffic and emotion data sent from multiple devices and stores it in a database. It then analyzes the collected data to identify traffic flow, factors behind congestion, and patterns of user emotion change. The server generates optimal signal patterns based on the analysis results and simulates them using digital twin technology. Based on the simulation results, it determines the most effective signal pattern and sends it back to the device. The server then monitors traffic conditions and users' emotional states in real time after the signal pattern change and makes further adjustments as necessary.

[0759] User

[0760] Users are the targets of emotional data collection by the device's emotion engine, and benefit from optimal traffic signals based on the analysis results of traffic data. In particular, users can experience benefits such as reduced congestion and stress due to the optimized signal patterns.

[0761] Specific examples

[0762] Traffic jams frequently occur at intersection A in a certain city during the morning rush hour. Conventional fixed programs could not resolve this congestion, but by introducing this system, the following steps are implemented:

[0763] 1. The terminal collects traffic data at intersection A every second and sends it to the server.

[0764] 2. The emotion engine analyzes the facial expressions of the user inside the car, measures the level of stress, and sends the data to the server.

[0765] 3. The server performs traffic analysis and sentiment analysis based on the collected data to identify the causes of traffic congestion and factors that cause user stress.

[0766] 4. The server generates a new traffic light pattern that shortens the red light time by 5 seconds and extends the green light time by 8 seconds, and runs a simulation using digital twin technology.

[0767] 5. Since the simulation was successful, the optimal signal pattern is sent to the terminal and implemented at intersection A.

[0768] 6. Once the user confirms that traffic congestion has been alleviated and that their stress levels have also decreased, they can use this data for urban planning.

[0769] Prompt Sentence Examples

[0770] Please provide a concrete example of traffic data collected by the system's terminals. Also, please explain how the emotion engine analyzes user emotions.

[0771] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0772] Step 1:

[0773] The device uses sensors to collect traffic data in real time.

[0774] Input: Information such as number of vehicles, speed, type of vehicle, and road occupancy from sensors installed at each intersection.

[0775] Specific operation: The device acquires data from the sensor every second and temporarily stores it in its internal memory.

[0776] Output: Collected traffic data (number of vehicles, speed, type of vehicle, road occupancy, etc.).

[0777] Step 2:

[0778] The terminal uses an emotion engine to collect emotion data of the user.

[0779] Input: In-car camera footage, user biometric information.

[0780] Specific operation: The camera captures the user's face, and the emotion engine analyzes the facial expression and evaluates the level of stress, irritation, etc.

[0781] Output: Collected emotional data (stress level, irritability, etc.).

[0782] Step 3:

[0783] The terminal transmits the collected traffic data and emotion data to a server via a network.

[0784] Input: Traffic and emotion data stored on the device.

[0785] What it does: It splits data into packets and sends them over the network to the server, automatically every second.

[0786] Output: Traffic and emotion data sent to the server.

[0787] Step 4:

[0788] The server receives the traffic data and emotion data transmitted from the multiple terminals and stores them in a database.

[0789] Input: Data packets sent from each device.

[0790] Specific operation: The server analyzes the received data and stores it in the corresponding database.

[0791] Output: Stored traffic and emotion data.

[0792] Step 5:

[0793] The server analyzes the collected traffic data and emotion data.

[0794] Input: Traffic and emotion data stored in a database.

[0795] Specific operation: Using an AI algorithm, traffic data and emotion data are compared and analyzed to identify the causes of traffic congestion and patterns of user emotion changes.

[0796] Output: Analysis results (factors of traffic congestion, patterns of user emotion changes, etc.).

[0797] Step 6:

[0798] The server generates an efficient signal pattern based on the analysis results.

[0799] Input: Analysis results from AI algorithm.

[0800] Specific operation: Based on the analysis results, the server generates traffic light patterns to ease traffic congestion and reduce user stress.

[0801] Output: The new signal pattern.

[0802] Step 7:

[0803] The server simulates the generated signal patterns using digital twin technology.

[0804] Input: The generated signal pattern.

[0805] Specific behavior: Using digital twin technology, we simulate how traffic light patterns affect real-world traffic conditions and user emotions.

[0806] Output: Simulation results.

[0807] Step 8:

[0808] Based on the simulation results, the server determines the most effective signal pattern and transmits it to the terminal.

[0809] Input: Simulation results.

[0810] Specific operation: The optimal signal pattern is selected from the simulation results and transmitted to each terminal.

[0811] Output: The optimal signal pattern sent to the device.

[0812] Step 9:

[0813] The terminal controls the signal according to the received signal pattern.

[0814] Input: The optimal signal pattern sent by the server.

[0815] Specific operation: The device controls the traffic light appropriately based on the signal pattern received.

[0816] Output: The modified signal pattern.

[0817] Step 10:

[0818] The server monitors the traffic conditions and the user's emotional state in real time after the signal pattern change and makes further adjustments as necessary.

[0819] Input: Modified traffic and emotion data.

[0820] How it works: The server continuously receives and analyzes new data and adjusts the signal pattern again if improvements are needed.

[0821] Output: constantly optimized traffic signals and the user's emotional state.

[0822] (Application example 2)

[0823] 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."

[0824] Current traffic control systems control signal patterns based solely on traffic data, and are therefore unable to consider users' emotions or stress levels. This leaves traffic congestion unresolved and users unable to enjoy a comfortable driving environment. Furthermore, it is difficult to efficiently simulate traffic analysis results and generate highly accurate signal patterns.

[0825] 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.

[0826] In this invention, the server includes means for collecting and transmitting traffic data and emotion data, means for performing traffic analysis and emotion analysis based on the traffic data and emotion data, and means for generating efficient traffic signal patterns based on the results of the traffic analysis and emotion analysis, thereby making it possible to effectively alleviate traffic congestion while taking into account the emotional state of the user and provide a comfortable driving environment for the user.

[0827] A "traffic control device" is a device that has the function of collecting and transmitting traffic data.

[0828] "Traffic data" refers to information that indicates traffic conditions, including the number of vehicles, their speeds, vehicle types, road occupancy rates, and so on.

[0829] "Emotion data" is information that indicates the user's emotional state based on biometric information and facial expression analysis.

[0830] A "central processing unit" is a device that receives data sent from multiple terminals and analyzes and processes it in a unified manner.

[0831] "Emotion analysis" is a means of analyzing collected emotion data to assess the user's emotional state.

[0832] "Traffic analysis" is a method for identifying traffic flow and congestion factors based on collected traffic data.

[0833] A "signal pattern" is a schedule or algorithm that determines how traffic signals are controlled.

[0834] "Digital twin technology" is a technology that reproduces real-world physical objects and systems in a virtual environment and performs simulations.

[0835] "Simulation" is a method for virtually verifying the actual effects of a generated signal pattern.

[0836] The "optimal signal pattern" is the most effective method of traffic signal control, taking into consideration factors such as easing traffic congestion, shortening the transit time for emergency vehicles, improving energy efficiency, and reducing user stress.

[0837] The present invention realizes a series of processes in a traffic control system, in which user emotion data is collected by an emotion engine and the data is taken into account in traffic analysis to generate traffic signal patterns. Specific embodiments are described in detail below.

[0838] System configuration

[0839] The main components of this system are as follows:

[0840] Traffic control device (terminal)

[0841] Central Processing Unit (Server)

[0842] Emotion Engine

[0843] network

[0844] Traffic control devices (terminals) are installed at each intersection and use sensors to collect traffic data in real time. The emotion engine has the function of analyzing user emotion data. The collected data is sent to a central processing unit (server) via a network.

[0845] Collecting emotion and traffic data

[0846] The server includes means for collecting and transmitting both emotion data and traffic data, and means for performing traffic analysis and emotion analysis.

[0847] Hardware and software used

[0848] On-board computer: A computer system installed in a self-driving car, such as NVIDIA DRIVE.

[0849] In-car camera: A camera that captures passengers' facial expressions, such as the Logitech C920.

[0850] Sensors: LIDAR, cameras, radar, etc. are used to collect traffic data.

[0851] Network module: A network device for sending and receiving data.

[0852] Emotion engine: Uses software such as the Emotion AI SDK to analyze user emotional data.

[0853] Traffic analysis algorithm: An algorithm that analyzes traffic data using Python and TensorFlow.

[0854] Real-time database: Firebase Realtime Database, etc., is used.

[0855] Data processing procedures

[0856] 1. Traffic and emotion data collection: In-vehicle sensors collect information on surrounding traffic conditions in real time, and the Emotion AI SDK extracts emotion data from the user's facial expressions.

[0857] 2. Data transmission: The onboard computer sends the collected data to the Firebase Realtime Database.

[0858] 3. Analysis: The server analyzes the data using Python and TensorFlow to generate traffic and sentiment analysis results.

[0859] 4. Signal pattern generation: Based on the analysis results, efficient signal patterns are generated and simulated using digital twin technology.

[0860] 5. Application of optimal signal patterns: Based on the simulation results, the optimal signal patterns are sent to the onboard computer and reflected in actual traffic signal control.

[0861] Specific examples

[0862] Traffic jams frequently occurred at intersection A in a certain city during the morning rush hour. Conventional fixed programs could not resolve this congestion, but by introducing this system, the following steps were implemented:

[0863] 1. The traffic control device (terminal) collected traffic data at intersection A every second and sent it to the server.

[0864] 2. The emotion engine analyzed the facial expressions of the user inside the car, measured their stress level, and sent the data to the server.

[0865] 3. The server performed traffic analysis and sentiment analysis based on the collected data to identify the causes of traffic congestion and factors that cause user stress.

[0866] 4. The server generated a new traffic light pattern that shortened the red light time by 5 seconds and extended the green light time by 8 seconds, and performed a simulation using digital twin technology.

[0867] 5. The simulation was successful, so the optimal signal pattern was sent to the terminal and implemented at intersection A.

[0868] 6. It was confirmed that traffic congestion was alleviated and users' stress levels also decreased.

[0869] Below is an example of a prompt sentence to input to the generative AI model.

[0870] Analyze the current traffic situation based on the collected emotion data and traffic data, and generate the optimal driving route and speed pattern. If the emotion data has a high stress score, select a gentler route, and if there is traffic congestion, prioritize avoiding it. Provide the output format in JSON.

[0871] Emotion data sample: {'stress_score': 75}

[0872] Traffic data sample: {'vehicle_speed': 60, 'traffic_density': 'high'}

[0873] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0874] Step 1:

[0875] An on-board computer collects traffic and emotion data through in-car cameras and sensors.

[0876] Input: Raw data from interior cameras, LIDAR, cameras, and radar

[0877] Output: Processed data such as stress score, vehicle speed, and traffic density

[0878] How it works: The onboard camera captures passengers' facial expressions, which are then analyzed by the Emotion AI SDK to calculate a stress score. Sensors collect data on vehicle speed and traffic density and process it in real time.

[0879] Step 2:

[0880] The on-board computer sends collected emotion and traffic data to the Firebase Realtime Database in real time.

[0881] Input: Processed data obtained in step 1

[0882] Output: Data stored in Firebase Realtime Database

[0883] Specific operation: Emotion data and traffic data are sent to the Firebase Realtime Database via NVIDIA DRIVE's network function.

[0884] Step 3:

[0885] The server receives data from the Firebase Realtime Database and performs traffic analysis and sentiment analysis.

[0886] Input: Sentiment and traffic data stored in Firebase Realtime Database

[0887] Output: Analysis results (traffic flow, causes of congestion, patterns of user emotion changes)

[0888] How it works: Using Python and TensorFlow, the server analyzes traffic and emotion data and compares it with past and current data to identify trends.

[0889] Step 4:

[0890] The server generates efficient signal patterns based on the analysis results and performs simulations using digital twin technology.

[0891] Input: Analysis results obtained in step 3

[0892] Output: Simulation results and optimal signal patterns

[0893] Specific operation: Using a generative AI model, generate optimal signal patterns, perform simulations using digital twin technology, and evaluate the simulation results.

[0894] Step 5:

[0895] The server determines the optimal signal pattern based on the simulation results and sends it back to the on-board computer.

[0896] Input: Simulation results obtained in step 4

[0897] Output: Optimal signal pattern

[0898] Specific operation: The optimal signal pattern is determined and transmitted to the vehicle's onboard computer via the network.

[0899] Step 6:

[0900] An on-board computer controls the traffic lights according to the received signal patterns.

[0901] Input: Optimal signal pattern sent in step 5

[0902] Output: Actual traffic light control

[0903] Specific operation: The on-board computer controls traffic signals based on the optimal signal pattern and adjusts to actual traffic conditions.

[0904] 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.

[0905] 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.

[0906] 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.

[0907] [Third embodiment]

[0908] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0909] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0910] 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).

[0911] 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.

[0912] 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.

[0913] 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).

[0914] 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.

[0915] 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.

[0916] 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.

[0917] 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.

[0918] 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.

[0919] 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."

[0920] The present invention realizes a series of processes in a traffic control system, in which multiple traffic control devices (terminals) collect traffic data in real time, a central processing unit (server) performs traffic analysis based on that data, and automatically generates optimal signal patterns. Specific embodiments are described below with examples.

[0921] System configuration

[0922] The main components of this system are as follows:

[0923] Traffic control device (terminal)

[0924] Central Processing Unit (Server)

[0925] network

[0926] Traffic control devices (terminals) are installed at each intersection and use sensors to collect traffic data in real time, which is then transmitted to a central processing unit (server) via a network.

[0927] Program processing

[0928] The specific operations of the server and the terminal will be explained step by step below.

[0929] Traffic data collection

[0930] The terminals use built-in sensors to collect traffic data such as the number of vehicles, their speeds, vehicle types, road occupancy rates, etc. For example, a terminal installed at an intersection counts the number of vehicles passing by every second and transmits this information to a central processing unit (server) in real time.

[0931] Traffic data analysis

[0932] The server receives traffic data from multiple devices and stores it in a database. It then uses AI algorithms to perform traffic analysis. For example, it can analyze the phenomenon of a sudden increase in vehicle traffic at a particular intersection and identify the cause of congestion.

[0933] Signal pattern generation and simulation

[0934] The server generates optimal signal patterns based on the analysis results. These patterns are then simulated using digital twin technology to evaluate how the modified patterns will affect actual traffic conditions. For example, it is possible to confirm in advance how traffic flow and emergency vehicle transit times will be reduced if a new signal pattern is applied.

[0935] Applying signal patterns

[0936] Based on the results of the simulation, the server determines the most effective signal pattern and sends it to each device. The device then controls the signals according to the received signal pattern, thereby optimizing traffic flow. For example, at intersection A, which was causing congestion, the red light time could be shortened and the green light time extended, alleviating congestion.

[0937] Traffic situation monitoring and feedback

[0938] The server continues to monitor traffic conditions in real time after the signal pattern change and makes further adjustments as needed, ensuring optimal traffic flow at all times. For example, signal patterns can be changed instantly in response to unexpected accidents or emergency vehicles passing by.

[0939] Specific examples

[0940] Traffic jams frequently occurred at intersection A in a certain city during the morning rush hour. Conventional fixed programs could not resolve this congestion, but by introducing this system, the following steps were implemented:

[0941] 1. The terminal collected traffic data at intersection A every second and sent it to the server.

[0942] 2. The server performed traffic analysis based on the collected data and determined that the cause of the traffic jam was the length of the red light.

[0943] 3. The server generated a new traffic light pattern that shortened the red light time by 5 seconds and extended the green light time by 8 seconds, and performed a simulation using digital twin technology.

[0944] 4. Since the simulation was successful, the optimal signal pattern was sent to the terminal and implemented at intersection A.

[0945] 5. Users saw that traffic congestion had eased and used this data for urban planning.

[0946] In this way, the traffic control system of the present invention can alleviate traffic congestion, allow emergency vehicles to pass quickly, reduce energy consumption, and reduce the environmental load.

[0947] The processing flow will be explained below.

[0948] Step 1: Collecting traffic data

[0949] The device uses sensors to collect real-time traffic data, such as the number of vehicles, their speed, vehicle type, and road occupancy rate, every second. This data is temporarily stored on the device.

[0950] Step 2: Sending traffic data

[0951] The terminals transmit the collected traffic data to a central processing unit (server) via a network. The transmission interval is real-time, so the latest data is updated every second.

[0952] Step 3: Aggregating traffic data

[0953] The server receives traffic data sent from multiple devices and stores it in a database, enabling real-time centralized management of traffic conditions across the city.

[0954] Step 4: Analyze traffic data

[0955] The server analyzes the collected traffic data using AI algorithms. Specifically, it identifies traffic flow and congestion factors based on past and current data. It also analyzes traffic fluctuation patterns due to time of day and specific events.

[0956] Step 5: Generate signal patterns

[0957] The server generates optimal signal patterns based on the results of traffic analysis, taking into consideration factors such as easing traffic congestion, shortening transit times for emergency vehicles, and improving energy efficiency.

[0958] Step 6: Simulation with the digital twin

[0959] The server then uses digital twin technology to simulate the generated traffic signal patterns. Specifically, it applies the traffic signal patterns in a virtual environment and evaluates the resulting traffic flow, waiting times, accident risk, and other factors.

[0960] Step 7: Determine the optimal pattern

[0961] The server determines the most effective traffic light pattern based on the simulation results, taking into account the positive effects shown by the simulation (e.g., reduced congestion, shorter transit times for emergency vehicles).

[0962] Step 8: Sending a signal pattern

[0963] The server then transmits the optimal signal pattern to each terminal in real time, and the pattern is immediately reflected in signal control.

[0964] Step 9: Applying the signal pattern

[0965] The terminal controls the traffic light based on the received new signal pattern, for example, shortening the red light time and extending the green light time.

[0966] Step 10: Traffic monitoring

[0967] The server continues to monitor traffic conditions in real time after the signal pattern changes to ensure that the changes are having the intended effect and make further adjustments if necessary.

[0968] Step 11: Data Feedback

[0969] The server analyzes traffic data and generates a report on the effectiveness of the system. This data is used for future urban planning and further system optimization. Users (urban planners) can use this data to consider improvements and new plans.

[0970] Example 1

[0971] 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."

[0972] Conventional traffic control systems rely on fixed programs to control traffic signals, lacking the flexibility to respond to real-time changes. This makes it difficult to respond quickly to traffic congestion or the passage of emergency vehicles, making it difficult to improve traffic efficiency or reduce energy consumption. Furthermore, the use of simulation technology was insufficient, making it difficult to verify the effectiveness of the generated signal patterns in advance.

[0973] 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.

[0974] In this invention, the server includes means for collecting traffic data, means for performing traffic analysis based on the traffic data, means for generating efficient signal patterns based on the results of the traffic analysis, means for simulating the generated signal patterns using digital twin technology, and means for monitoring traffic conditions after the signal patterns are applied and providing feedback. This enables flexible signal control in accordance with traffic conditions in real time, thereby realizing the alleviation of traffic congestion, the rapid passage of emergency vehicles, reduction of energy consumption, and reduction of environmental impact.

[0975] A "terminal" is a device or system that collects and transmits traffic data to a server.

[0976] "Server" refers to a central processing unit that receives traffic data sent from terminals and analyzes and processes it.

[0977] "Traffic data" refers to information about traffic, such as the number of vehicles, their speeds, vehicle types, and road occupancy.

[0978] "Traffic analysis" is the process of analyzing traffic conditions and patterns based on collected traffic data.

[0979] "Signal pattern" refers to the timing and sequence of traffic signals set at traffic lights at intersections and other locations.

[0980] "Digital twin technology" is a technology that creates virtual models of physical systems and performs simulations and data analysis.

[0981] "Simulation" is a method of virtually recreating real-world traffic conditions and evaluating the impact of changes to traffic signal patterns in advance.

[0982] "Monitoring" is the activity of observing traffic conditions in real time and collecting data.

[0983] "Feedback" is the process of adjusting system settings and signal patterns based on monitoring results.

[0984] "Real-time" means that data is processed and results are reflected almost instantly.

[0985] "Traffic signal" refers to a signal facility installed to control the movement of vehicles and pedestrians on a road.

[0986] The present invention realizes a series of processes in a traffic control system in which multiple terminals collect traffic data in real time, a server performs traffic analysis based on that data, and automatically generates optimal signal patterns. Each component of this system and its specific operation are described in detail below.

[0987] System Components

[0988] The main components of this system are as follows:

[0989] Terminal

[0990] server

[0991] network

[0992] Terminals are installed at each intersection and use sensors to collect traffic data in real time, which is then sent to a server via a network.

[0993] Hardware and software used

[0994] 1. Terminal: A device for collecting traffic data, specifically a device with built-in sensors (e.g., vehicle counting sensors and speed sensors) and a communication module.

[0995] 2. Server: Serves as the central processing unit, analyzing traffic data, generating, simulating, and applying traffic signal patterns. Software used includes MySQL and PostgreSQL (database systems), Python's Scikit-learn (analysis algorithms), TensorFlow and PyTorch (deep learning models), and Unity and AnyLogic (digital twin technologies).

[0996] 3. Network: An internet connection or dedicated communication network that enables data communication between the device and the server.

[0997] Specific examples

[0998] At intersection A in a certain city, traffic jams frequently occurred during the morning rush hour. By introducing this system, the following problems were solved:

[0999] Traffic data collection: The device collected traffic data at intersection A every second and sent it to the server.

[1000] Traffic analysis: The server analyzed the traffic situation based on the collected data and determined that the cause of the congestion was the length of the red light.

[1001] Generation and simulation of traffic light patterns: The server generated traffic light patterns that shortened the red light time by 5 seconds and extended the green light time by 8 seconds, and simulated them using digital twin technology.

[1002] Applying the signal pattern: Since the simulation was successful, the optimal signal pattern was sent to the terminal and implemented at intersection A.

[1003] Traffic condition monitoring and feedback: The server monitored traffic conditions after the new signal patterns were applied and confirmed that congestion had been alleviated.

[1004] The system has optimized traffic flow, easing traffic congestion, allowing emergency vehicles to pass through more quickly, reducing energy consumption, and easing the environmental impact.

[1005] Prompt Sentence Examples

[1006] Below are examples of specific prompt sentences to input into the generative AI model.

[1007] "Input real-time traffic data for a specific intersection and create an AI model to generate optimal traffic light patterns."

[1008] "Design an algorithm for signal pattern simulation using digital twin technology in a traffic control system."

[1009] This will provide guidelines for developing more detailed and specific traffic control system programs.

[1010] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1011] Processing flow

[1012] Step 1: Collecting traffic data

[1013] The device uses built-in sensors to collect real-time traffic data such as the number of vehicles at intersections, their speeds, vehicle types, and road occupancy rates. For example, the device counts the number of vehicles passing by every second and stores this information in its internal memory.

[1014] Input: Traffic sensor data

[1015] Output: Collected traffic data

[1016] Step 2: Sending traffic data

[1017] The terminal sends the collected traffic data to the server via the network. Specifically, the terminal uses the HTTP protocol to send a POST request to the server API in JSON format for the collected data.

[1018] Input: Collected traffic data

[1019] Output: Traffic data sent to the server

[1020] Step 3: Save traffic data

[1021] The server stores the traffic data received from the terminal in a database. For example, the server uses a database system such as MySQL or PostgreSQL and inserts the data into the database.

[1022] Input: Traffic data sent from the device

[1023] Output: Traffic data stored in a database

[1024] Step 4: Traffic analysis

[1025] The server analyzes the stored traffic data using AI algorithms, for example, using Python's Scikit-learn library to learn from the traffic data and identify congested areas and their causes.

[1026] Input: Traffic data stored in a database

[1027] Output: Analysis results

[1028] Step 5: Generate signal patterns

[1029] The server generates optimal signal patterns based on the analysis results, for example, by automatically generating new signal patterns using deep learning models (using TensorFlow or PyTorch).

[1030] Input: Analysis results

[1031] Output: Generated signal pattern

[1032] Step 6: Simulate the signal pattern

[1033] The server then uses digital twin technology to simulate the generated traffic light patterns, such as Unity or AnyLogic, to evaluate the impact of the traffic light patterns on actual traffic conditions.

[1034] Input: Generated signal pattern

[1035] Output: Simulation results

[1036] Step 7: Applying the signal pattern

[1037] The server transmits the signal pattern determined to be optimal through simulation to each terminal, and the terminal controls the signal in real time according to the received signal pattern.

[1038] Input: Signal pattern based on simulation results

[1039] Output: Signal control at the intersection with the new signal pattern applied

[1040] Step 8: Traffic monitoring and feedback

[1041] The server monitors traffic conditions in real time after the signal patterns are applied and readjusts the signal patterns as necessary, thereby maintaining optimal traffic flow at all times.

[1042] Input: Traffic data (after applying signal patterns)

[1043] Output: Signal pattern readjustment instructions

[1044] By executing each step continuously, the traffic control system is optimized in real time, reducing traffic congestion and improving traffic flow.

[1045] (Application example 1)

[1046] 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."

[1047] In modern transportation systems, simply collecting traffic data and generating optimal signal patterns is not enough to eliminate traffic congestion or allow emergency vehicles to pass quickly. Furthermore, for autonomous vehicles to be used effectively, it is necessary to utilize real-time traffic data and provide optimal driving routes. Systems that can solve these issues and improve traffic efficiency and safety are needed.

[1048] 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.

[1049] In this invention, the server includes means for multiple traffic control devices to collect traffic data, means for transmitting the collected traffic data to a central processing unit, means for the central processing unit to perform traffic analysis based on the collected traffic data, means for generating efficient signal patterns based on the traffic analysis results, means for simulating the generated signal patterns, means for determining optimal signal patterns based on the simulation results and transmitting them to the multiple traffic control devices, and means for controlling traffic signals in accordance with the optimal signal patterns.This makes it possible to improve traffic efficiency and the safety of autonomous vehicles by equipping autonomous vehicles with means for receiving real-time traffic data from traffic control devices and monitoring surrounding traffic conditions, means for proposing optimal routes based on the traffic data, and means for providing appropriate instructions to autonomous vehicles when an emergency vehicle is approaching.

[1050] A "traffic control device" is a device that collects traffic data in real time and transmits it to a central processing unit.

[1051] "Traffic data" refers to information about traffic conditions, such as the number of vehicles, their speeds, vehicle types, and road occupancy rates.

[1052] The "central processing unit" is a device that analyzes traffic data sent from multiple traffic control devices and generates optimal signal patterns.

[1053] A "signal pattern" refers to the timing and time distribution of red, green, and yellow traffic lights.

[1054] "Digital twin technology" is a technology that reproduces real-world traffic conditions in a virtual environment and simulates and evaluates traffic light patterns.

[1055] An "autonomous vehicle" is a vehicle that can automatically control its driving based on traffic data and signal patterns.

[1056] An "optimal route" is a route that allows a vehicle to reach its destination efficiently and safely, based on traffic data.

[1057] "Emergency vehicles" are vehicles that should have priority in an emergency, such as ambulances, fire engines, and police vehicles.

[1058] System Overview

[1059] This invention proposes a system that improves traffic efficiency and safety by coordinating automated driving vehicles and traffic control systems. The system includes the following main components:

[1060] Traffic control device

[1061] Autonomous vehicles

[1062] Central Processing Unit (Server)

[1063] network

[1064] Program processing details

[1065] Traffic data collection and transmission

[1066] The traffic control system collects traffic data such as the number of vehicles, their speed, vehicle type, and road occupancy rate through sensors installed at each intersection, and transmits the collected data to a central processing unit (server) in real time.

[1067] Traffic data analysis and signal pattern generation

[1068] The server receives traffic data from multiple traffic control devices and stores it in a database. It then uses AI algorithms to analyze traffic and generate optimal signal patterns. The effectiveness of these signal patterns is evaluated through simulations using digital twin technology.

[1069] Autonomous Vehicle Handling

[1070] The autonomous vehicle receives real-time traffic data from the traffic control device and constantly monitors the surrounding traffic conditions. Based on the received traffic data, the server proposes the optimal driving route for the vehicle. When an emergency vehicle approaches, the server provides appropriate instructions to the autonomous vehicle.

[1071] Specific hardware and software

[1072] Hardware

[1073] Onboard computers in autonomous vehicles

[1074] GPS Modules

[1075] Communication module (LTE / 5G)

[1076] Sensors installed at each intersection

[1077] software

[1078] Python

[1079] HTTP request library (requests)

[1080] Use cases and generative AI models

[1081] One example of how this system can be used is to suggest optimal routes for autonomous vehicles during morning rush hour.

[1082] Specific examples

[1083] At intersection A in a certain city, traffic jams frequently occur during the morning rush hour. By implementing this system, the following steps were implemented:

[1084] 1. The traffic control device collected traffic data at intersection A every second and sent it to the server.

[1085] 2. The server performed traffic analysis based on the collected data and determined that the cause of the traffic jam was the length of the red light.

[1086] 3. The server generated a new traffic light pattern that shortened the red light time by 5 seconds and extended the green light time by 8 seconds, and performed a simulation using digital twin technology.

[1087] 4. Since the simulation was successful, the optimal signal pattern was sent to the terminal and implemented at intersection A.

[1088] 5. Users saw that traffic congestion had eased and used this data for urban planning.

[1089] Prompt Sentence Examples

[1090] "Generate the optimal traffic light pattern for morning rush hour based on traffic data for intersection A. The data includes the number of vehicles, their speeds, vehicle types, and road occupancy."

[1091] This system will help alleviate traffic congestion, allow emergency vehicles to pass through more quickly, reduce energy consumption, and lighten the burden on the environment.

[1092] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1093] Step 1:

[1094] Traffic data collection and transmission

[1095] The terminal collects real-time traffic data such as the number of vehicles, their speed, vehicle type, and road occupancy rate from sensors installed at intersections. This data is collected every second and sent to a server via a network. The input data is electrical signal data output by the sensors, and the output is traffic data in JSON format sent to the server.

[1096] Step 2:

[1097] Traffic data analysis

[1098] The server receives traffic data sent from multiple traffic control devices in bulk and stores it in a database. Next, it performs traffic analysis using AI algorithms. In the analysis step, the collected traffic situation data is used as input data, and a series of data processing and data calculations (for example, counting the number of vehicles and calculating average speeds) are performed. The output is an analysis of the causes of traffic congestion and prediction data.

[1099] Step 3:

[1100] Signal pattern generation

[1101] The server automatically generates the optimal traffic light pattern based on the analysis results. The input in this step is the traffic analysis results, and the output is the newly generated traffic light pattern. Specifically, it calculates the time allocation of red and green lights.

[1102] Step 4:

[1103] Simulation and Evaluation

[1104] The generated signal patterns are simulated in a virtual environment using digital twin technology. This simulation evaluates how the signal patterns affect real-world traffic conditions. The input data is the signal patterns generated in the previous step, and the output data is the evaluation results based on the simulation.

[1105] Step 5:

[1106] Applying and transmitting signal patterns

[1107] The server determines the optimal signal pattern based on the simulation results and sends it to each terminal. The input at this stage is the simulation results, and the output is the data of the final signal pattern. The actual traffic lights are controlled according to the signal pattern sent to the terminal.

[1108] Step 6:

[1109] Providing data to autonomous vehicles

[1110] The signal patterns and traffic condition data sent from the terminal are provided to the autonomous vehicle in real time. The vehicle monitors the surrounding traffic conditions based on the received traffic data and is then suggested the optimal driving route. The input is data from the traffic control device, and the output is driving instruction data used by the autonomous driving system.

[1111] Step 7:

[1112] Emergency response

[1113] When an emergency vehicle approaches, the server issues instructions to the autonomous vehicle for a rapid response, including the emergency vehicle's location data and emergency response instructions. The input is the emergency vehicle's location information, and the output is data on avoidance and response instructions for the autonomous vehicle.

[1114] These steps will concretely improve traffic efficiency and the safety of autonomous vehicles.

[1115] 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.

[1116] The present invention realizes a series of processes in a traffic control system, in which user emotion data is collected by an emotion engine and the data is incorporated into traffic analysis to generate signal patterns. Specific embodiments are described below with examples.

[1117] System configuration

[1118] The main components of this system are as follows:

[1119] Traffic control device (terminal)

[1120] Central Processing Unit (Server)

[1121] Emotion Engine

[1122] network

[1123] Traffic control devices (terminals) are installed at each intersection and use sensors to collect traffic data in real time. The emotion engine has the function of collecting and analyzing user emotion data. The collected data is sent to a central processing unit (server) via a network.

[1124] Program processing

[1125] The specific operations of the server, terminal, and user will be explained step by step below.

[1126] Traffic data collection

[1127] The device uses sensors to collect real-time traffic data, such as the number of vehicles, their speed, vehicle type, and road occupancy rate, every second. This data is temporarily stored on the device.

[1128] Collecting Emotional Data

[1129] The emotion engine collects user emotional data in real time based on biometric information, facial expression analysis, etc. For example, it analyzes the user's facial expressions using an in-car camera to evaluate the level of stress and irritation.

[1130] Transmission of traffic and emotion data

[1131] The terminal transmits the collected traffic data and emotion data collected by the emotion engine to a central processing unit (server) via a network. The transmission interval is real-time, so the latest data is updated every second.

[1132] Aggregation of traffic and emotion data

[1133] The server receives all the data sent from multiple devices and emotion engines and stores it in a database, enabling real-time centralized management of traffic conditions and users' emotional states across the city.

[1134] Traffic and emotion data analysis

[1135] The server uses AI algorithms to analyze the collected traffic and emotion data. Specifically, it identifies traffic flow, factors behind congestion, and patterns of user emotion change based on past and current data. It also analyzes the correlation between traffic and emotion based on time of day and specific events.

[1136] Signal pattern generation and simulation

[1137] The server then generates optimal traffic light patterns based on the analysis results. These patterns take into consideration factors such as easing traffic congestion, shortening transit times for emergency vehicles, improving energy efficiency, and reducing user stress. The generated traffic light patterns are then simulated using digital twin technology to evaluate how the changed patterns will affect actual traffic conditions and user emotions.

[1138] Applying signal patterns

[1139] The server determines the most effective traffic signal pattern based on the simulation results and sends it to each device. The device then controls the traffic signals according to the received signal pattern, thereby optimizing traffic flow.

[1140] Monitoring traffic conditions and user emotions

[1141] The server continues to monitor traffic conditions and users' emotional state in real time after the signal pattern change and makes further adjustments as needed, thereby always maintaining optimal traffic flow and user comfort.

[1142] Specific examples

[1143] Traffic jams frequently occurred at intersection A in a certain city during the morning rush hour. Conventional fixed programs could not resolve this congestion, but by introducing this system, the following steps were implemented:

[1144] 1. The terminal collected traffic data at intersection A every second and sent it to the server.

[1145] 2. The emotion engine analyzed the facial expressions of the user inside the car, measured their stress level, and sent the data to the server.

[1146] 3. The server performed traffic analysis and sentiment analysis based on the collected data to identify the causes of traffic congestion and factors that cause user stress.

[1147] 4. The server generated a new traffic light pattern that shortened the red light time by 5 seconds and extended the green light time by 8 seconds, and performed a simulation using digital twin technology.

[1148] 5. The simulation was successful, so the optimal signal pattern was sent to the terminal and implemented at intersection A.

[1149] 6. As traffic congestion eased, users saw their stress levels drop, and they used this data for urban planning.

[1150] In this way, the traffic control system of the present invention can alleviate traffic congestion, allow emergency vehicles to pass quickly, reduce energy consumption, ease environmental load, and improve user comfort.

[1151] The processing flow will be explained below.

[1152] Step 1: Collecting traffic data

[1153] The device collects real-time traffic data using sensors installed at each intersection, specifically information such as the number of vehicles, speed, vehicle type, and road occupancy rate, which is acquired every second and temporarily stored in its internal memory.

[1154] Step 2: Collecting emotion data

[1155] The emotion engine collects biometric information such as facial expressions, voice, and heart rate from the user interface and camera, and analyzes this information to obtain emotional data about the user. For example, it can analyze video footage from an in-car camera and quantify the user's stress level and frustration.

[1156] Step 3: Sending traffic and emotion data

[1157] The terminals and emotion engines each send the collected traffic data and emotion data to a central processing unit (server) via a network. This transmission is done in real time, with the latest data being sent from each terminal to the server every second.

[1158] Step 4: Aggregating traffic and sentiment data

[1159] The server receives all the data sent from multiple devices and emotion engines and stores it in a database. By aggregating this data, it is possible to centrally manage traffic conditions and users' emotional states throughout the city in real time.

[1160] Step 5: Analyzing traffic and emotion data

[1161] The server uses AI algorithms to analyze the collected traffic and emotion data. Specifically, it compares it with past data to identify traffic flow, causes of congestion, and patterns of user emotion change. It also analyzes correlations between traffic and emotion based on time of day or specific events.

[1162] Step 6: Generate and simulate signal patterns

[1163] The server generates optimal signal patterns based on the results of traffic and emotion analysis. These signal patterns are then simulated in a virtual environment using digital twin technology to evaluate how the modified patterns affect actual traffic conditions and user emotions.

[1164] Step 7: Determine the optimal pattern

[1165] Based on the simulation results, the server determines the most effective traffic signal pattern, which aims to reduce traffic congestion, shorten transit times for emergency vehicles, improve energy efficiency, and reduce user stress.

[1166] Step 8: Sending a signal pattern

[1167] The server then transmits the determined optimal signal pattern to each terminal in real time, so that the signal pattern is immediately applied.

[1168] Step 9: Applying the signal pattern

[1169] The device then controls the traffic lights based on the new signal pattern received, for example by shortening the red light time and extending the green light time.

[1170] Step 10: Monitoring traffic conditions and user emotions

[1171] The server continues to monitor traffic conditions and user sentiment in real time after the signal pattern change, allowing it to assess whether the changes are having the desired effect and make further adjustments if necessary.

[1172] Step 11: Data Feedback

[1173] The server generates a report based on the traffic conditions and user sentiment data and provides the results as feedback to urban planners (users), who can use the data to improve future transportation planning and systems.

[1174] Example 2

[1175] 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."

[1176] Conventional traffic control systems generate signal patterns based solely on traffic data, making it difficult to achieve optimal traffic signal control that takes into account the user's emotional state. Traffic congestion, in particular, increases user stress, ultimately affecting driving safety and comfort. Therefore, there is a demand for a system that can collect user emotional data in real time and incorporate it into traffic analysis to achieve more efficient and user-friendly traffic signal control.

[1177] 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.

[1178] In this invention, the server includes means for performing analysis based on traffic data and emotion data, means for generating efficient signal patterns based on the analysis results, and means for monitoring traffic conditions and emotion data in real time and making further adjustments. This makes it possible to take the collected user emotion data into account in traffic analysis and generate more optimal signal patterns.

[1179] A "terminal" is a part of a traffic control device, and is a device that is installed at an intersection or the like to collect traffic data using sensors and collect user emotion data using an emotion engine.

[1180] The "server" is a central processing unit that centrally manages traffic data and emotion data transmitted from multiple terminals, analyzes this data, performs simulations, and generates and transmits signal patterns.

[1181] "Traffic data" refers to data collected in real time that indicates traffic conditions, such as the number of vehicles, their speeds, vehicle types, and road occupancy rates.

[1182] "Emotion data" is data that indicates the emotional state of the user, analyzed based on the user's biometric information, facial expressions, and the like.

[1183] "Signal pattern" refers to data that indicates the time allocation of red and green traffic lights and the timing of their switching.

[1184] "Analysis" refers to the process of using AI algorithms and other methods to identify traffic flow, factors behind congestion, and patterns of user emotion change based on collected traffic and emotion data.

[1185] "Digital twin technology" is a technology that digitally reproduces and simulates physical systems and environments.

[1186] "Simulation" refers to the process of using digital twin technology to evaluate in advance how the generated signal patterns will affect actual traffic conditions and user emotions.

[1187] "Monitoring" refers to the process of monitoring traffic conditions and the user's emotional state in real time after a traffic light pattern change and making adjustments as necessary.

[1188] This invention realizes a series of processes in a traffic control system, in which user emotion data is collected by an emotion engine and that data is incorporated into traffic analysis to generate signal patterns. Specific embodiments of this system are described in detail below.

[1189] System configuration

[1190] The system consists of the following main components:

[1191] Traffic control device (terminal)

[1192] Central Processing Unit (Server)

[1193] Emotion Engine

[1194] network

[1195] Terminals are installed at each intersection and use sensors to collect traffic data in real time. The emotion engine has the function of collecting and analyzing user emotion data. The collected data is sent to a central processing unit (server) via a network.

[1196] Functions and Roles

[1197] Terminal

[1198] The device uses sensors to collect traffic data in real time. Specifically, information such as the number of vehicles, their speed, vehicle type, and road occupancy rate is acquired every second and temporarily stored in its internal memory. The device's built-in emotion engine also collects user emotional data using the user's biometric information and in-car cameras. This allows the device to evaluate the user's stress level and irritability in real time. The device then transmits the collected traffic data and emotional data to a server via the network.

[1199] server

[1200] The server receives traffic and emotion data sent from multiple devices and stores it in a database. It then analyzes the collected data to identify traffic flow, factors behind congestion, and patterns of user emotion change. The server generates optimal signal patterns based on the analysis results and simulates them using digital twin technology. Based on the simulation results, it determines the most effective signal pattern and sends it back to the device. The server then monitors traffic conditions and users' emotional states in real time after the signal pattern change and makes further adjustments as necessary.

[1201] User

[1202] Users are the targets of emotional data collection by the device's emotion engine, and benefit from optimal traffic signals based on the analysis results of traffic data. In particular, users can experience benefits such as reduced congestion and stress due to the optimized signal patterns.

[1203] Specific examples

[1204] Traffic jams frequently occur at intersection A in a certain city during the morning rush hour. Conventional fixed programs could not resolve this congestion, but by introducing this system, the following steps are implemented:

[1205] 1. The terminal collects traffic data at intersection A every second and sends it to the server.

[1206] 2. The emotion engine analyzes the facial expressions of the user inside the car, measures the level of stress, and sends the data to the server.

[1207] 3. The server performs traffic analysis and sentiment analysis based on the collected data to identify the causes of traffic congestion and factors that cause user stress.

[1208] 4. The server generates a new traffic light pattern that shortens the red light time by 5 seconds and extends the green light time by 8 seconds, and runs a simulation using digital twin technology.

[1209] 5. Since the simulation was successful, the optimal signal pattern is sent to the terminal and implemented at intersection A.

[1210] 6. Once the user confirms that traffic congestion has been alleviated and that their stress levels have also decreased, they can use this data for urban planning.

[1211] Prompt Sentence Examples

[1212] Please provide a concrete example of traffic data collected by the system's terminals. Also, please explain how the emotion engine analyzes user emotions.

[1213] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1214] Step 1:

[1215] The device uses sensors to collect traffic data in real time.

[1216] Input: Information such as number of vehicles, speed, type of vehicle, and road occupancy from sensors installed at each intersection.

[1217] Specific operation: The device acquires data from the sensor every second and temporarily stores it in its internal memory.

[1218] Output: Collected traffic data (number of vehicles, speed, type of vehicle, road occupancy, etc.).

[1219] Step 2:

[1220] The terminal uses an emotion engine to collect emotion data of the user.

[1221] Input: In-car camera footage, user biometric information.

[1222] Specific operation: The camera captures the user's face, and the emotion engine analyzes the facial expression and evaluates the level of stress, irritation, etc.

[1223] Output: Collected emotional data (stress level, irritability, etc.).

[1224] Step 3:

[1225] The terminal transmits the collected traffic data and emotion data to a server via a network.

[1226] Input: Traffic and emotion data stored on the device.

[1227] What it does: It splits data into packets and sends them over the network to the server, automatically every second.

[1228] Output: Traffic and emotion data sent to the server.

[1229] Step 4:

[1230] The server receives the traffic data and emotion data transmitted from the multiple terminals and stores them in a database.

[1231] Input: Data packets sent from each device.

[1232] Specific operation: The server analyzes the received data and stores it in the corresponding database.

[1233] Output: Stored traffic and emotion data.

[1234] Step 5:

[1235] The server analyzes the collected traffic data and emotion data.

[1236] Input: Traffic and emotion data stored in a database.

[1237] Specific operation: Using an AI algorithm, traffic data and emotion data are compared and analyzed to identify the causes of traffic congestion and patterns of user emotion changes.

[1238] Output: Analysis results (factors of traffic congestion, patterns of user emotion changes, etc.).

[1239] Step 6:

[1240] The server generates an efficient signal pattern based on the analysis results.

[1241] Input: Analysis results from AI algorithm.

[1242] Specific operation: Based on the analysis results, the server generates traffic light patterns to ease traffic congestion and reduce user stress.

[1243] Output: The new signal pattern.

[1244] Step 7:

[1245] The server simulates the generated signal patterns using digital twin technology.

[1246] Input: The generated signal pattern.

[1247] Specific behavior: Using digital twin technology, we simulate how traffic light patterns affect real-world traffic conditions and user emotions.

[1248] Output: Simulation results.

[1249] Step 8:

[1250] Based on the simulation results, the server determines the most effective signal pattern and transmits it to the terminal.

[1251] Input: Simulation results.

[1252] Specific operation: The optimal signal pattern is selected from the simulation results and transmitted to each terminal.

[1253] Output: The optimal signal pattern sent to the device.

[1254] Step 9:

[1255] The terminal controls the signal according to the received signal pattern.

[1256] Input: The optimal signal pattern sent by the server.

[1257] Specific operation: The device controls the traffic light appropriately based on the signal pattern received.

[1258] Output: The modified signal pattern.

[1259] Step 10:

[1260] The server monitors the traffic conditions and the user's emotional state in real time after the signal pattern change and makes further adjustments as necessary.

[1261] Input: Modified traffic and emotion data.

[1262] How it works: The server continuously receives and analyzes new data and adjusts the signal pattern again if improvements are needed.

[1263] Output: constantly optimized traffic signals and the user's emotional state.

[1264] (Application example 2)

[1265] 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."

[1266] Current traffic control systems control signal patterns based solely on traffic data, and are therefore unable to consider users' emotions or stress levels. This leaves traffic congestion unresolved and users unable to enjoy a comfortable driving environment. Furthermore, it is difficult to efficiently simulate traffic analysis results and generate highly accurate signal patterns.

[1267] 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.

[1268] In this invention, the server includes means for collecting and transmitting traffic data and emotion data, means for performing traffic analysis and emotion analysis based on the traffic data and emotion data, and means for generating efficient traffic signal patterns based on the results of the traffic analysis and emotion analysis, thereby making it possible to effectively alleviate traffic congestion while taking into account the emotional state of the user and provide a comfortable driving environment for the user.

[1269] A "traffic control device" is a device that has the function of collecting and transmitting traffic data.

[1270] "Traffic data" refers to information that indicates traffic conditions, including the number of vehicles, their speeds, vehicle types, road occupancy rates, and so on.

[1271] "Emotion data" is information that indicates the user's emotional state based on biometric information and facial expression analysis.

[1272] A "central processing unit" is a device that receives data sent from multiple terminals and analyzes and processes it in a unified manner.

[1273] "Emotion analysis" is a means of analyzing collected emotion data to assess the user's emotional state.

[1274] "Traffic analysis" is a method for identifying traffic flow and congestion factors based on collected traffic data.

[1275] A "signal pattern" is a schedule or algorithm that determines how traffic signals are controlled.

[1276] "Digital twin technology" is a technology that reproduces real-world physical objects and systems in a virtual environment and performs simulations.

[1277] "Simulation" is a method for virtually verifying the actual effects of a generated signal pattern.

[1278] The "optimal signal pattern" is the most effective method of traffic signal control, taking into consideration factors such as easing traffic congestion, shortening the transit time for emergency vehicles, improving energy efficiency, and reducing user stress.

[1279] The present invention realizes a series of processes in a traffic control system, in which user emotion data is collected by an emotion engine and the data is taken into account in traffic analysis to generate traffic signal patterns. Specific embodiments are described in detail below.

[1280] System configuration

[1281] The main components of this system are as follows:

[1282] Traffic control device (terminal)

[1283] Central Processing Unit (Server)

[1284] Emotion Engine

[1285] network

[1286] Traffic control devices (terminals) are installed at each intersection and use sensors to collect traffic data in real time. The emotion engine has the function of analyzing user emotion data. The collected data is sent to a central processing unit (server) via a network.

[1287] Collecting emotion and traffic data

[1288] The server includes means for collecting and transmitting both emotion data and traffic data, and means for performing traffic analysis and emotion analysis.

[1289] Hardware and software used

[1290] On-board computer: A computer system installed in a self-driving car, such as NVIDIA DRIVE.

[1291] In-car camera: A camera that captures passengers' facial expressions, such as the Logitech C920.

[1292] Sensors: LIDAR, cameras, radar, etc. are used to collect traffic data.

[1293] Network module: A network device for sending and receiving data.

[1294] Emotion engine: Uses software such as the Emotion AI SDK to analyze user emotional data.

[1295] Traffic analysis algorithm: An algorithm that analyzes traffic data using Python and TensorFlow.

[1296] Real-time database: Firebase Realtime Database, etc., is used.

[1297] Data processing procedures

[1298] 1. Traffic and emotion data collection: In-vehicle sensors collect information on surrounding traffic conditions in real time, and the Emotion AI SDK extracts emotion data from the user's facial expressions.

[1299] 2. Data transmission: The onboard computer sends the collected data to the Firebase Realtime Database.

[1300] 3. Analysis: The server analyzes the data using Python and TensorFlow to generate traffic and sentiment analysis results.

[1301] 4. Signal pattern generation: Based on the analysis results, efficient signal patterns are generated and simulated using digital twin technology.

[1302] 5. Application of optimal signal patterns: Based on the simulation results, the optimal signal patterns are sent to the onboard computer and reflected in actual traffic signal control.

[1303] Specific examples

[1304] Traffic jams frequently occurred at intersection A in a certain city during the morning rush hour. Conventional fixed programs could not resolve this congestion, but by introducing this system, the following steps were implemented:

[1305] 1. The traffic control device (terminal) collected traffic data at intersection A every second and sent it to the server.

[1306] 2. The emotion engine analyzed the facial expressions of the user inside the car, measured their stress level, and sent the data to the server.

[1307] 3. The server performed traffic analysis and sentiment analysis based on the collected data to identify the causes of traffic congestion and factors that cause user stress.

[1308] 4. The server generated a new traffic light pattern that shortened the red light time by 5 seconds and extended the green light time by 8 seconds, and performed a simulation using digital twin technology.

[1309] 5. The simulation was successful, so the optimal signal pattern was sent to the terminal and implemented at intersection A.

[1310] 6. It was confirmed that traffic congestion was alleviated and users' stress levels also decreased.

[1311] Below is an example of a prompt sentence to input to the generative AI model.

[1312] Analyze the current traffic situation based on the collected emotion data and traffic data, and generate the optimal driving route and speed pattern. If the emotion data has a high stress score, select a gentler route, and if there is traffic congestion, prioritize avoiding it. Provide the output format in JSON.

[1313] Emotion data sample: {'stress_score': 75}

[1314] Traffic data sample: {'vehicle_speed': 60, 'traffic_density': 'high'}

[1315] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1316] Step 1:

[1317] An on-board computer collects traffic and emotion data through in-car cameras and sensors.

[1318] Input: Raw data from interior cameras, LIDAR, cameras, and radar

[1319] Output: Processed data such as stress score, vehicle speed, and traffic density

[1320] How it works: The onboard camera captures passengers' facial expressions, which are then analyzed by the Emotion AI SDK to calculate a stress score. Sensors collect data on vehicle speed and traffic density and process it in real time.

[1321] Step 2:

[1322] The on-board computer sends collected emotion and traffic data to the Firebase Realtime Database in real time.

[1323] Input: Processed data obtained in step 1

[1324] Output: Data stored in Firebase Realtime Database

[1325] Specific operation: Emotion data and traffic data are sent to the Firebase Realtime Database via NVIDIA DRIVE's network function.

[1326] Step 3:

[1327] The server receives data from the Firebase Realtime Database and performs traffic analysis and sentiment analysis.

[1328] Input: Sentiment and traffic data stored in Firebase Realtime Database

[1329] Output: Analysis results (traffic flow, causes of congestion, patterns of user emotion changes)

[1330] How it works: Using Python and TensorFlow, the server analyzes traffic and emotion data and compares it with past and current data to identify trends.

[1331] Step 4:

[1332] The server generates efficient signal patterns based on the analysis results and performs simulations using digital twin technology.

[1333] Input: Analysis results obtained in step 3

[1334] Output: Simulation results and optimal signal patterns

[1335] Specific operation: Using a generative AI model, generate optimal signal patterns, perform simulations using digital twin technology, and evaluate the simulation results.

[1336] Step 5:

[1337] The server determines the optimal signal pattern based on the simulation results and sends it back to the on-board computer.

[1338] Input: Simulation results obtained in step 4

[1339] Output: Optimal signal pattern

[1340] Specific operation: The optimal signal pattern is determined and transmitted to the vehicle's onboard computer via the network.

[1341] Step 6:

[1342] An on-board computer controls the traffic lights according to the received signal patterns.

[1343] Input: Optimal signal pattern sent in step 5

[1344] Output: Actual traffic light control

[1345] Specific operation: The on-board computer controls traffic signals based on the optimal signal pattern and adjusts to actual traffic conditions.

[1346] 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.

[1347] 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.

[1348] 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.

[1349] [Fourth embodiment]

[1350] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1351] 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.

[1352] 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).

[1353] 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.

[1354] 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.

[1355] 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).

[1356] 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.

[1357] 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.

[1358] 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.

[1359] 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.

[1360] 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.

[1361] 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.

[1362] 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."

[1363] The present invention realizes a series of processes in a traffic control system, in which multiple traffic control devices (terminals) collect traffic data in real time, a central processing unit (server) performs traffic analysis based on that data, and automatically generates optimal signal patterns. Specific embodiments are described below with examples.

[1364] System configuration

[1365] The main components of this system are as follows:

[1366] Traffic control device (terminal)

[1367] Central Processing Unit (Server)

[1368] network

[1369] Traffic control devices (terminals) are installed at each intersection and use sensors to collect traffic data in real time, which is then transmitted to a central processing unit (server) via a network.

[1370] Program processing

[1371] The specific operations of the server and the terminal will be explained step by step below.

[1372] Traffic data collection

[1373] The terminals use built-in sensors to collect traffic data such as the number of vehicles, their speeds, vehicle types, road occupancy rates, etc. For example, a terminal installed at an intersection counts the number of vehicles passing by every second and transmits this information to a central processing unit (server) in real time.

[1374] Traffic data analysis

[1375] The server receives traffic data from multiple devices and stores it in a database. It then uses AI algorithms to perform traffic analysis. For example, it can analyze the phenomenon of a sudden increase in vehicle traffic at a particular intersection and identify the cause of congestion.

[1376] Signal pattern generation and simulation

[1377] The server generates optimal signal patterns based on the analysis results. These patterns are then simulated using digital twin technology to evaluate how the modified patterns will affect actual traffic conditions. For example, it is possible to confirm in advance how traffic flow and emergency vehicle transit times will be reduced if a new signal pattern is applied.

[1378] Applying signal patterns

[1379] Based on the results of the simulation, the server determines the most effective signal pattern and sends it to each device. The device then controls the signals according to the received signal pattern, thereby optimizing traffic flow. For example, at intersection A, which was causing congestion, the red light time could be shortened and the green light time extended, alleviating congestion.

[1380] Traffic situation monitoring and feedback

[1381] The server continues to monitor traffic conditions in real time after the signal pattern change and makes further adjustments as needed, ensuring optimal traffic flow at all times. For example, signal patterns can be changed instantly in response to unexpected accidents or emergency vehicles passing by.

[1382] Specific examples

[1383] Traffic jams frequently occurred at intersection A in a certain city during the morning rush hour. Conventional fixed programs could not resolve this congestion, but by introducing this system, the following steps were implemented:

[1384] 1. The terminal collected traffic data at intersection A every second and sent it to the server.

[1385] 2. The server performed traffic analysis based on the collected data and determined that the cause of the traffic jam was the length of the red light.

[1386] 3. The server generated a new traffic light pattern that shortened the red light time by 5 seconds and extended the green light time by 8 seconds, and performed a simulation using digital twin technology.

[1387] 4. Since the simulation was successful, the optimal signal pattern was sent to the terminal and implemented at intersection A.

[1388] 5. Users saw that traffic congestion had eased and used this data for urban planning.

[1389] In this way, the traffic control system of the present invention can alleviate traffic congestion, allow emergency vehicles to pass quickly, reduce energy consumption, and reduce the environmental load.

[1390] The processing flow will be explained below.

[1391] Step 1: Collecting traffic data

[1392] The device uses sensors to collect real-time traffic data, such as the number of vehicles, their speed, vehicle type, and road occupancy rate, every second. This data is temporarily stored on the device.

[1393] Step 2: Sending traffic data

[1394] The terminals transmit the collected traffic data to a central processing unit (server) via a network. The transmission interval is real-time, so the latest data is updated every second.

[1395] Step 3: Aggregating traffic data

[1396] The server receives traffic data sent from multiple devices and stores it in a database, enabling real-time centralized management of traffic conditions across the city.

[1397] Step 4: Analyze traffic data

[1398] The server analyzes the collected traffic data using AI algorithms. Specifically, it identifies traffic flow and congestion factors based on past and current data. It also analyzes traffic fluctuation patterns due to time of day and specific events.

[1399] Step 5: Generate signal patterns

[1400] The server generates optimal signal patterns based on the results of traffic analysis, taking into consideration factors such as easing traffic congestion, shortening transit times for emergency vehicles, and improving energy efficiency.

[1401] Step 6: Simulation with the digital twin

[1402] The server then uses digital twin technology to simulate the generated traffic signal patterns. Specifically, it applies the traffic signal patterns in a virtual environment and evaluates the resulting traffic flow, waiting times, accident risk, and other factors.

[1403] Step 7: Determine the optimal pattern

[1404] The server determines the most effective traffic light pattern based on the simulation results, taking into account the positive effects shown by the simulation (e.g., reduced congestion, shorter transit times for emergency vehicles).

[1405] Step 8: Sending a signal pattern

[1406] The server then transmits the optimal signal pattern to each terminal in real time, and the pattern is immediately reflected in signal control.

[1407] Step 9: Applying the signal pattern

[1408] The terminal controls the traffic light based on the received new signal pattern, for example, shortening the red light time and extending the green light time.

[1409] Step 10: Traffic monitoring

[1410] The server continues to monitor traffic conditions in real time after the signal pattern changes to ensure that the changes are having the intended effect and make further adjustments if necessary.

[1411] Step 11: Data Feedback

[1412] The server analyzes traffic data and generates a report on the effectiveness of the system. This data is used for future urban planning and further system optimization. Users (urban planners) can use this data to consider improvements and new plans.

[1413] Example 1

[1414] 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."

[1415] Conventional traffic control systems rely on fixed programs to control traffic signals, lacking the flexibility to respond to real-time changes. This makes it difficult to respond quickly to traffic congestion or the passage of emergency vehicles, making it difficult to improve traffic efficiency or reduce energy consumption. Furthermore, the use of simulation technology was insufficient, making it difficult to verify the effectiveness of the generated signal patterns in advance.

[1416] 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.

[1417] In this invention, the server includes means for collecting traffic data, means for performing traffic analysis based on the traffic data, means for generating efficient signal patterns based on the results of the traffic analysis, means for simulating the generated signal patterns using digital twin technology, and means for monitoring traffic conditions after the signal patterns are applied and providing feedback. This enables flexible signal control in accordance with traffic conditions in real time, thereby realizing the alleviation of traffic congestion, the rapid passage of emergency vehicles, reduction of energy consumption, and reduction of environmental impact.

[1418] A "terminal" is a device or system that collects and transmits traffic data to a server.

[1419] "Server" refers to a central processing unit that receives traffic data sent from terminals and analyzes and processes it.

[1420] "Traffic data" refers to information about traffic, such as the number of vehicles, their speeds, vehicle types, and road occupancy.

[1421] "Traffic analysis" is the process of analyzing traffic conditions and patterns based on collected traffic data.

[1422] "Signal pattern" refers to the timing and sequence of traffic signals set at traffic lights at intersections and other locations.

[1423] "Digital twin technology" is a technology that creates virtual models of physical systems and performs simulations and data analysis.

[1424] "Simulation" is a method of virtually recreating real-world traffic conditions and evaluating the impact of changes to traffic signal patterns in advance.

[1425] "Monitoring" is the activity of observing traffic conditions in real time and collecting data.

[1426] "Feedback" is the process of adjusting system settings and signal patterns based on monitoring results.

[1427] "Real-time" means that data is processed and results are reflected almost instantly.

[1428] "Traffic signal" refers to a signal facility installed to control the movement of vehicles and pedestrians on a road.

[1429] The present invention realizes a series of processes in a traffic control system in which multiple terminals collect traffic data in real time, a server performs traffic analysis based on that data, and automatically generates optimal signal patterns. Each component of this system and its specific operation are described in detail below.

[1430] System Components

[1431] The main components of this system are as follows:

[1432] Terminal

[1433] server

[1434] network

[1435] Terminals are installed at each intersection and use sensors to collect traffic data in real time, which is then sent to a server via a network.

[1436] Hardware and software used

[1437] 1. Terminal: A device for collecting traffic data, specifically a device with built-in sensors (e.g., vehicle counting sensors and speed sensors) and a communication module.

[1438] 2. Server: Serves as the central processing unit, analyzing traffic data, generating, simulating, and applying traffic signal patterns. Software used includes MySQL and PostgreSQL (database systems), Python's Scikit-learn (analysis algorithms), TensorFlow and PyTorch (deep learning models), and Unity and AnyLogic (digital twin technologies).

[1439] 3. Network: An internet connection or dedicated communication network that enables data communication between the device and the server.

[1440] Specific examples

[1441] At intersection A in a certain city, traffic jams frequently occurred during the morning rush hour. By introducing this system, the following problems were solved:

[1442] Traffic data collection: The device collected traffic data at intersection A every second and sent it to the server.

[1443] Traffic analysis: The server analyzed the traffic situation based on the collected data and determined that the cause of the congestion was the length of the red light.

[1444] Generation and simulation of traffic light patterns: The server generated traffic light patterns that shortened the red light time by 5 seconds and extended the green light time by 8 seconds, and simulated them using digital twin technology.

[1445] Applying the signal pattern: Since the simulation was successful, the optimal signal pattern was sent to the terminal and implemented at intersection A.

[1446] Traffic condition monitoring and feedback: The server monitored traffic conditions after the new signal patterns were applied and confirmed that congestion had been alleviated.

[1447] The system has optimized traffic flow, easing traffic congestion, allowing emergency vehicles to pass through more quickly, reducing energy consumption, and easing the environmental impact.

[1448] Prompt Sentence Examples

[1449] Below are examples of specific prompt sentences to input into the generative AI model.

[1450] "Input real-time traffic data for a specific intersection and create an AI model to generate optimal traffic light patterns."

[1451] "Design an algorithm for signal pattern simulation using digital twin technology in a traffic control system."

[1452] This will provide guidelines for developing more detailed and specific traffic control system programs.

[1453] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1454] Processing flow

[1455] Step 1: Collecting traffic data

[1456] The device uses built-in sensors to collect real-time traffic data such as the number of vehicles at intersections, their speeds, vehicle types, and road occupancy rates. For example, the device counts the number of vehicles passing by every second and stores this information in its internal memory.

[1457] Input: Traffic sensor data

[1458] Output: Collected traffic data

[1459] Step 2: Sending traffic data

[1460] The terminal sends the collected traffic data to the server via the network. Specifically, the terminal uses the HTTP protocol to send a POST request to the server API in JSON format for the collected data.

[1461] Input: Collected traffic data

[1462] Output: Traffic data sent to the server

[1463] Step 3: Save traffic data

[1464] The server stores the traffic data received from the terminal in a database. For example, the server uses a database system such as MySQL or PostgreSQL and inserts the data into the database.

[1465] Input: Traffic data sent from the device

[1466] Output: Traffic data stored in a database

[1467] Step 4: Traffic analysis

[1468] The server analyzes the stored traffic data using AI algorithms, for example, using Python's Scikit-learn library to learn from the traffic data and identify congested areas and their causes.

[1469] Input: Traffic data stored in a database

[1470] Output: Analysis results

[1471] Step 5: Generate signal patterns

[1472] The server generates optimal signal patterns based on the analysis results, for example, by automatically generating new signal patterns using deep learning models (using TensorFlow or PyTorch).

[1473] Input: Analysis results

[1474] Output: Generated signal pattern

[1475] Step 6: Simulate the signal pattern

[1476] The server then uses digital twin technology to simulate the generated traffic light patterns, such as Unity or AnyLogic, to evaluate the impact of the traffic light patterns on actual traffic conditions.

[1477] Input: Generated signal pattern

[1478] Output: Simulation results

[1479] Step 7: Applying the signal pattern

[1480] The server transmits the signal pattern determined to be optimal through simulation to each terminal, and the terminal controls the signal in real time according to the received signal pattern.

[1481] Input: Signal pattern based on simulation results

[1482] Output: Signal control at the intersection with the new signal pattern applied

[1483] Step 8: Traffic monitoring and feedback

[1484] The server monitors traffic conditions in real time after the signal patterns are applied and readjusts the signal patterns as necessary, thereby maintaining optimal traffic flow at all times.

[1485] Input: Traffic data (after applying signal patterns)

[1486] Output: Signal pattern readjustment instructions

[1487] By executing each step continuously, the traffic control system is optimized in real time, reducing traffic congestion and improving traffic flow.

[1488] (Application example 1)

[1489] 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."

[1490] In modern transportation systems, simply collecting traffic data and generating optimal signal patterns is not enough to eliminate traffic congestion or allow emergency vehicles to pass quickly. Furthermore, for autonomous vehicles to be used effectively, it is necessary to utilize real-time traffic data and provide optimal driving routes. Systems that can solve these issues and improve traffic efficiency and safety are needed.

[1491] 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.

[1492] In this invention, the server includes means for multiple traffic control devices to collect traffic data, means for transmitting the collected traffic data to a central processing unit, means for the central processing unit to perform traffic analysis based on the collected traffic data, means for generating efficient signal patterns based on the traffic analysis results, means for simulating the generated signal patterns, means for determining optimal signal patterns based on the simulation results and transmitting them to the multiple traffic control devices, and means for controlling traffic signals in accordance with the optimal signal patterns.This makes it possible to improve traffic efficiency and the safety of autonomous vehicles by equipping autonomous vehicles with means for receiving real-time traffic data from traffic control devices and monitoring surrounding traffic conditions, means for proposing optimal routes based on the traffic data, and means for providing appropriate instructions to autonomous vehicles when an emergency vehicle is approaching.

[1493] A "traffic control device" is a device that collects traffic data in real time and transmits it to a central processing unit.

[1494] "Traffic data" refers to information about traffic conditions, such as the number of vehicles, their speeds, vehicle types, and road occupancy rates.

[1495] The "central processing unit" is a device that analyzes traffic data sent from multiple traffic control devices and generates optimal signal patterns.

[1496] A "signal pattern" refers to the timing and time distribution of red, green, and yellow traffic lights.

[1497] "Digital twin technology" is a technology that reproduces real-world traffic conditions in a virtual environment and simulates and evaluates traffic light patterns.

[1498] An "autonomous vehicle" is a vehicle that can automatically control its driving based on traffic data and signal patterns.

[1499] An "optimal route" is a route that allows a vehicle to reach its destination efficiently and safely, based on traffic data.

[1500] "Emergency vehicles" are vehicles that should have priority in an emergency, such as ambulances, fire engines, and police vehicles.

[1501] System Overview

[1502] This invention proposes a system that improves traffic efficiency and safety by coordinating automated driving vehicles and traffic control systems. The system includes the following main components:

[1503] Traffic control device

[1504] Autonomous vehicles

[1505] Central Processing Unit (Server)

[1506] network

[1507] Program processing details

[1508] Traffic data collection and transmission

[1509] The traffic control system collects traffic data such as the number of vehicles, their speed, vehicle type, and road occupancy rate through sensors installed at each intersection, and transmits the collected data to a central processing unit (server) in real time.

[1510] Traffic data analysis and signal pattern generation

[1511] The server receives traffic data from multiple traffic control devices and stores it in a database. It then uses AI algorithms to analyze traffic and generate optimal signal patterns. The effectiveness of these signal patterns is evaluated through simulations using digital twin technology.

[1512] Autonomous Vehicle Handling

[1513] The autonomous vehicle receives real-time traffic data from the traffic control device and constantly monitors the surrounding traffic conditions. Based on the received traffic data, the server proposes the optimal driving route for the vehicle. When an emergency vehicle approaches, the server provides appropriate instructions to the autonomous vehicle.

[1514] Specific hardware and software

[1515] Hardware

[1516] Onboard computers in autonomous vehicles

[1517] GPS Modules

[1518] Communication module (LTE / 5G)

[1519] Sensors installed at each intersection

[1520] software

[1521] Python

[1522] HTTP request library (requests)

[1523] Use cases and generative AI models

[1524] One example of how this system can be used is to suggest optimal routes for autonomous vehicles during morning rush hour.

[1525] Specific examples

[1526] At intersection A in a certain city, traffic jams frequently occur during the morning rush hour. By implementing this system, the following steps were implemented:

[1527] 1. The traffic control device collected traffic data at intersection A every second and sent it to the server.

[1528] 2. The server performed traffic analysis based on the collected data and determined that the cause of the traffic jam was the length of the red light.

[1529] 3. The server generated a new traffic light pattern that shortened the red light time by 5 seconds and extended the green light time by 8 seconds, and performed a simulation using digital twin technology.

[1530] 4. Since the simulation was successful, the optimal signal pattern was sent to the terminal and implemented at intersection A.

[1531] 5. Users saw that traffic congestion had eased and used this data for urban planning.

[1532] Prompt Sentence Examples

[1533] "Generate the optimal traffic light pattern for morning rush hour based on traffic data for intersection A. The data includes the number of vehicles, their speeds, vehicle types, and road occupancy."

[1534] This system will help alleviate traffic congestion, allow emergency vehicles to pass through more quickly, reduce energy consumption, and lighten the burden on the environment.

[1535] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1536] Step 1:

[1537] Traffic data collection and transmission

[1538] The terminal collects real-time traffic data such as the number of vehicles, their speed, vehicle type, and road occupancy rate from sensors installed at intersections. This data is collected every second and sent to a server via a network. The input data is electrical signal data output by the sensors, and the output is traffic data in JSON format sent to the server.

[1539] Step 2:

[1540] Traffic data analysis

[1541] The server receives traffic data sent from multiple traffic control devices in bulk and stores it in a database. Next, it performs traffic analysis using AI algorithms. In the analysis step, the collected traffic situation data is used as input data, and a series of data processing and data calculations (for example, counting the number of vehicles and calculating average speeds) are performed. The output is an analysis of the causes of traffic congestion and prediction data.

[1542] Step 3:

[1543] Signal pattern generation

[1544] The server automatically generates the optimal traffic light pattern based on the analysis results. The input in this step is the traffic analysis results, and the output is the newly generated traffic light pattern. Specifically, it calculates the time allocation of red and green lights.

[1545] Step 4:

[1546] Simulation and Evaluation

[1547] The generated signal patterns are simulated in a virtual environment using digital twin technology. This simulation evaluates how the signal patterns affect real-world traffic conditions. The input data is the signal patterns generated in the previous step, and the output data is the evaluation results based on the simulation.

[1548] Step 5:

[1549] Applying and transmitting signal patterns

[1550] The server determines the optimal signal pattern based on the simulation results and sends it to each terminal. The input at this stage is the simulation results, and the output is the data of the final signal pattern. The actual traffic lights are controlled according to the signal pattern sent to the terminal.

[1551] Step 6:

[1552] Providing data to autonomous vehicles

[1553] The signal patterns and traffic condition data sent from the terminal are provided to the autonomous vehicle in real time. The vehicle monitors the surrounding traffic conditions based on the received traffic data and is then suggested the optimal driving route. The input is data from the traffic control device, and the output is driving instruction data used by the autonomous driving system.

[1554] Step 7:

[1555] Emergency response

[1556] When an emergency vehicle approaches, the server issues instructions to the autonomous vehicle for a rapid response, including the emergency vehicle's location data and emergency response instructions. The input is the emergency vehicle's location information, and the output is data on avoidance and response instructions for the autonomous vehicle.

[1557] These steps will concretely improve traffic efficiency and the safety of autonomous vehicles.

[1558] 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.

[1559] The present invention realizes a series of processes in a traffic control system, in which user emotion data is collected by an emotion engine and the data is incorporated into traffic analysis to generate signal patterns. Specific embodiments are described below with examples.

[1560] System configuration

[1561] The main components of this system are as follows:

[1562] Traffic control device (terminal)

[1563] Central Processing Unit (Server)

[1564] Emotion Engine

[1565] network

[1566] Traffic control devices (terminals) are installed at each intersection and use sensors to collect traffic data in real time. The emotion engine has the function of collecting and analyzing user emotion data. The collected data is sent to a central processing unit (server) via a network.

[1567] Program processing

[1568] The specific operations of the server, terminal, and user will be explained step by step below.

[1569] Traffic data collection

[1570] The device uses sensors to collect real-time traffic data, such as the number of vehicles, their speed, vehicle type, and road occupancy rate, every second. This data is temporarily stored on the device.

[1571] Collecting Emotional Data

[1572] The emotion engine collects user emotional data in real time based on biometric information, facial expression analysis, etc. For example, it analyzes the user's facial expressions using an in-car camera to evaluate the level of stress and irritation.

[1573] Transmission of traffic and emotion data

[1574] The terminal transmits the collected traffic data and emotion data collected by the emotion engine to a central processing unit (server) via a network. The transmission interval is real-time, so the latest data is updated every second.

[1575] Aggregation of traffic and emotion data

[1576] The server receives all the data sent from multiple devices and emotion engines and stores it in a database, enabling real-time centralized management of traffic conditions and users' emotional states across the city.

[1577] Traffic and emotion data analysis

[1578] The server uses AI algorithms to analyze the collected traffic and emotion data. Specifically, it identifies traffic flow, factors behind congestion, and patterns of user emotion change based on past and current data. It also analyzes the correlation between traffic and emotion based on time of day and specific events.

[1579] Signal pattern generation and simulation

[1580] The server then generates optimal traffic light patterns based on the analysis results. These patterns take into consideration factors such as easing traffic congestion, shortening transit times for emergency vehicles, improving energy efficiency, and reducing user stress. The generated traffic light patterns are then simulated using digital twin technology to evaluate how the changed patterns will affect actual traffic conditions and user emotions.

[1581] Applying signal patterns

[1582] The server determines the most effective traffic signal pattern based on the simulation results and sends it to each device. The device then controls the traffic signals according to the received signal pattern, thereby optimizing traffic flow.

[1583] Monitoring traffic conditions and user emotions

[1584] The server continues to monitor traffic conditions and users' emotional state in real time after the signal pattern change and makes further adjustments as needed, thereby always maintaining optimal traffic flow and user comfort.

[1585] Specific examples

[1586] Traffic jams frequently occurred at intersection A in a certain city during the morning rush hour. Conventional fixed programs could not resolve this congestion, but by introducing this system, the following steps were implemented:

[1587] 1. The terminal collected traffic data at intersection A every second and sent it to the server.

[1588] 2. The emotion engine analyzed the facial expressions of the user inside the car, measured their stress level, and sent the data to the server.

[1589] 3. The server performed traffic analysis and sentiment analysis based on the collected data to identify the causes of traffic congestion and factors that cause user stress.

[1590] 4. The server generated a new traffic light pattern that shortened the red light time by 5 seconds and extended the green light time by 8 seconds, and performed a simulation using digital twin technology.

[1591] 5. The simulation was successful, so the optimal signal pattern was sent to the terminal and implemented at intersection A.

[1592] 6. As traffic congestion eased, users saw their stress levels drop, and they used this data for urban planning.

[1593] In this way, the traffic control system of the present invention can alleviate traffic congestion, allow emergency vehicles to pass quickly, reduce energy consumption, ease environmental load, and improve user comfort.

[1594] The processing flow will be explained below.

[1595] Step 1: Collecting traffic data

[1596] The device collects real-time traffic data using sensors installed at each intersection, specifically information such as the number of vehicles, speed, vehicle type, and road occupancy rate, which is acquired every second and temporarily stored in its internal memory.

[1597] Step 2: Collecting emotion data

[1598] The emotion engine collects biometric information such as facial expressions, voice, and heart rate from the user interface and camera, and analyzes this information to obtain emotional data about the user. For example, it can analyze video footage from an in-car camera and quantify the user's stress level and frustration.

[1599] Step 3: Sending traffic and emotion data

[1600] The terminals and emotion engines each send the collected traffic data and emotion data to a central processing unit (server) via a network. This transmission is done in real time, with the latest data being sent from each terminal to the server every second.

[1601] Step 4: Aggregating traffic and sentiment data

[1602] The server receives all the data sent from multiple devices and emotion engines and stores it in a database. By aggregating this data, it is possible to centrally manage traffic conditions and users' emotional states throughout the city in real time.

[1603] Step 5: Analyzing traffic and emotion data

[1604] The server uses AI algorithms to analyze the collected traffic and emotion data. Specifically, it compares it with past data to identify traffic flow, causes of congestion, and patterns of user emotion change. It also analyzes correlations between traffic and emotion based on time of day or specific events.

[1605] Step 6: Generate and simulate signal patterns

[1606] The server generates optimal signal patterns based on the results of traffic and emotion analysis. These signal patterns are then simulated in a virtual environment using digital twin technology to evaluate how the modified patterns affect actual traffic conditions and user emotions.

[1607] Step 7: Determine the optimal pattern

[1608] Based on the simulation results, the server determines the most effective traffic signal pattern, which aims to reduce traffic congestion, shorten transit times for emergency vehicles, improve energy efficiency, and reduce user stress.

[1609] Step 8: Sending a signal pattern

[1610] The server then transmits the determined optimal signal pattern to each terminal in real time, so that the signal pattern is immediately applied.

[1611] Step 9: Applying the signal pattern

[1612] The device then controls the traffic lights based on the new signal pattern received, for example by shortening the red light time and extending the green light time.

[1613] Step 10: Monitoring traffic conditions and user emotions

[1614] The server continues to monitor traffic conditions and user sentiment in real time after the signal pattern change, allowing it to assess whether the changes are having the desired effect and make further adjustments if necessary.

[1615] Step 11: Data Feedback

[1616] The server generates a report based on the traffic conditions and user sentiment data and provides the results as feedback to urban planners (users), who can use the data to improve future transportation planning and systems.

[1617] Example 2

[1618] 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."

[1619] Conventional traffic control systems generate signal patterns based solely on traffic data, making it difficult to achieve optimal traffic signal control that takes into account the user's emotional state. Traffic congestion, in particular, increases user stress, ultimately affecting driving safety and comfort. Therefore, there is a demand for a system that can collect user emotional data in real time and incorporate it into traffic analysis to achieve more efficient and user-friendly traffic signal control.

[1620] 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.

[1621] In this invention, the server includes means for performing analysis based on traffic data and emotion data, means for generating efficient signal patterns based on the analysis results, and means for monitoring traffic conditions and emotion data in real time and making further adjustments. This makes it possible to take the collected user emotion data into account in traffic analysis and generate more optimal signal patterns.

[1622] A "terminal" is a part of a traffic control device, and is a device that is installed at an intersection or the like to collect traffic data using sensors and collect user emotion data using an emotion engine.

[1623] The "server" is a central processing unit that centrally manages traffic data and emotion data transmitted from multiple terminals, analyzes this data, performs simulations, and generates and transmits signal patterns.

[1624] "Traffic data" refers to data collected in real time that indicates traffic conditions, such as the number of vehicles, their speeds, vehicle types, and road occupancy rates.

[1625] "Emotion data" is data that indicates the emotional state of the user, analyzed based on the user's biometric information, facial expressions, and the like.

[1626] "Signal pattern" refers to data that indicates the time allocation of red and green traffic lights and the timing of their switching.

[1627] "Analysis" refers to the process of using AI algorithms and other methods to identify traffic flow, factors behind congestion, and patterns of user emotion change based on collected traffic and emotion data.

[1628] "Digital twin technology" is a technology that digitally reproduces and simulates physical systems and environments.

[1629] "Simulation" refers to the process of using digital twin technology to evaluate in advance how the generated signal patterns will affect actual traffic conditions and user emotions.

[1630] "Monitoring" refers to the process of monitoring traffic conditions and the user's emotional state in real time after a traffic light pattern change and making adjustments as necessary.

[1631] This invention realizes a series of processes in a traffic control system, in which user emotion data is collected by an emotion engine and that data is incorporated into traffic analysis to generate signal patterns. Specific embodiments of this system are described in detail below.

[1632] System configuration

[1633] The system consists of the following main components:

[1634] Traffic control device (terminal)

[1635] Central Processing Unit (Server)

[1636] Emotion Engine

[1637] network

[1638] Terminals are installed at each intersection and use sensors to collect traffic data in real time. The emotion engine has the function of collecting and analyzing user emotion data. The collected data is sent to a central processing unit (server) via a network.

[1639] Functions and Roles

[1640] Terminal

[1641] The device uses sensors to collect traffic data in real time. Specifically, information such as the number of vehicles, their speed, vehicle type, and road occupancy rate is acquired every second and temporarily stored in its internal memory. The device's built-in emotion engine also collects user emotional data using the user's biometric information and in-car cameras. This allows the device to evaluate the user's stress level and irritability in real time. The device then transmits the collected traffic data and emotional data to a server via the network.

[1642] server

[1643] The server receives traffic and emotion data sent from multiple devices and stores it in a database. It then analyzes the collected data to identify traffic flow, factors behind congestion, and patterns of user emotion change. The server generates optimal signal patterns based on the analysis results and simulates them using digital twin technology. Based on the simulation results, it determines the most effective signal pattern and sends it back to the device. The server then monitors traffic conditions and users' emotional states in real time after the signal pattern change and makes further adjustments as necessary.

[1644] User

[1645] Users are the targets of emotional data collection by the device's emotion engine, and benefit from optimal traffic signals based on the analysis results of traffic data. In particular, users can experience benefits such as reduced congestion and stress due to the optimized signal patterns.

[1646] Specific examples

[1647] Traffic jams frequently occur at intersection A in a certain city during the morning rush hour. Conventional fixed programs could not resolve this congestion, but by introducing this system, the following steps are implemented:

[1648] 1. The terminal collects traffic data at intersection A every second and sends it to the server.

[1649] 2. The emotion engine analyzes the facial expressions of the user inside the car, measures the level of stress, and sends the data to the server.

[1650] 3. The server performs traffic analysis and sentiment analysis based on the collected data to identify the causes of traffic congestion and factors that cause user stress.

[1651] 4. The server generates a new traffic light pattern that shortens the red light time by 5 seconds and extends the green light time by 8 seconds, and runs a simulation using digital twin technology.

[1652] 5. Since the simulation was successful, the optimal signal pattern is sent to the terminal and implemented at intersection A.

[1653] 6. Once the user confirms that traffic congestion has been alleviated and that their stress levels have also decreased, they can use this data for urban planning.

[1654] Prompt Sentence Examples

[1655] Please provide a concrete example of traffic data collected by the system's terminals. Also, please explain how the emotion engine analyzes user emotions.

[1656] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1657] Step 1:

[1658] The device uses sensors to collect traffic data in real time.

[1659] Input: Information such as number of vehicles, speed, type of vehicle, and road occupancy from sensors installed at each intersection.

[1660] Specific operation: The device acquires data from the sensor every second and temporarily stores it in its internal memory.

[1661] Output: Collected traffic data (number of vehicles, speed, type of vehicle, road occupancy, etc.).

[1662] Step 2:

[1663] The terminal uses an emotion engine to collect emotion data of the user.

[1664] Input: In-car camera footage, user biometric information.

[1665] Specific operation: The camera captures the user's face, and the emotion engine analyzes the facial expression and evaluates the level of stress, irritation, etc.

[1666] Output: Collected emotional data (stress level, irritability, etc.).

[1667] Step 3:

[1668] The terminal transmits the collected traffic data and emotion data to a server via a network.

[1669] Input: Traffic and emotion data stored on the device.

[1670] What it does: It splits data into packets and sends them over the network to the server, automatically every second.

[1671] Output: Traffic and emotion data sent to the server.

[1672] Step 4:

[1673] The server receives the traffic data and emotion data transmitted from the multiple terminals and stores them in a database.

[1674] Input: Data packets sent from each device.

[1675] Specific operation: The server analyzes the received data and stores it in the corresponding database.

[1676] Output: Stored traffic and emotion data.

[1677] Step 5:

[1678] The server analyzes the collected traffic data and emotion data.

[1679] Input: Traffic and emotion data stored in a database.

[1680] Specific operation: Using an AI algorithm, traffic data and emotion data are compared and analyzed to identify the causes of traffic congestion and patterns of user emotion changes.

[1681] Output: Analysis results (factors of traffic congestion, patterns of user emotion changes, etc.).

[1682] Step 6:

[1683] The server generates an efficient signal pattern based on the analysis results.

[1684] Input: Analysis results from AI algorithm.

[1685] Specific operation: Based on the analysis results, the server generates traffic light patterns to ease traffic congestion and reduce user stress.

[1686] Output: The new signal pattern.

[1687] Step 7:

[1688] The server simulates the generated signal patterns using digital twin technology.

[1689] Input: The generated signal pattern.

[1690] Specific behavior: Using digital twin technology, we simulate how traffic light patterns affect real-world traffic conditions and user emotions.

[1691] Output: Simulation results.

[1692] Step 8:

[1693] Based on the simulation results, the server determines the most effective signal pattern and transmits it to the terminal.

[1694] Input: Simulation results.

[1695] Specific operation: The optimal signal pattern is selected from the simulation results and transmitted to each terminal.

[1696] Output: The optimal signal pattern sent to the device.

[1697] Step 9:

[1698] The terminal controls the signal according to the received signal pattern.

[1699] Input: The optimal signal pattern sent by the server.

[1700] Specific operation: The device controls the traffic light appropriately based on the signal pattern received.

[1701] Output: The modified signal pattern.

[1702] Step 10:

[1703] The server monitors the traffic conditions and the user's emotional state in real time after the signal pattern change and makes further adjustments as necessary.

[1704] Input: Modified traffic and emotion data.

[1705] How it works: The server continuously receives and analyzes new data and adjusts the signal pattern again if improvements are needed.

[1706] Output: constantly optimized traffic signals and the user's emotional state.

[1707] (Application example 2)

[1708] 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."

[1709] Current traffic control systems control signal patterns based solely on traffic data, and are therefore unable to consider users' emotions or stress levels. This leaves traffic congestion unresolved and users unable to enjoy a comfortable driving environment. Furthermore, it is difficult to efficiently simulate traffic analysis results and generate highly accurate signal patterns.

[1710] 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.

[1711] In this invention, the server includes means for collecting and transmitting traffic data and emotion data, means for performing traffic analysis and emotion analysis based on the traffic data and emotion data, and means for generating efficient traffic signal patterns based on the results of the traffic analysis and emotion analysis, thereby making it possible to effectively alleviate traffic congestion while taking into account the emotional state of the user and provide a comfortable driving environment for the user.

[1712] A "traffic control device" is a device that has the function of collecting and transmitting traffic data.

[1713] "Traffic data" refers to information that indicates traffic conditions, including the number of vehicles, their speeds, vehicle types, road occupancy rates, and so on.

[1714] "Emotion data" is information that indicates the user's emotional state based on biometric information and facial expression analysis.

[1715] A "central processing unit" is a device that receives data sent from multiple terminals and analyzes and processes it in a unified manner.

[1716] "Emotion analysis" is a means of analyzing collected emotion data to assess the user's emotional state.

[1717] "Traffic analysis" is a method for identifying traffic flow and congestion factors based on collected traffic data.

[1718] A "signal pattern" is a schedule or algorithm that determines how traffic signals are controlled.

[1719] "Digital twin technology" is a technology that reproduces real-world physical objects and systems in a virtual environment and performs simulations.

[1720] "Simulation" is a method for virtually verifying the actual effects of a generated signal pattern.

[1721] The "optimal signal pattern" is the most effective method of traffic signal control, taking into consideration factors such as easing traffic congestion, shortening the transit time for emergency vehicles, improving energy efficiency, and reducing user stress.

[1722] The present invention realizes a series of processes in a traffic control system, in which user emotion data is collected by an emotion engine and the data is taken into account in traffic analysis to generate traffic signal patterns. Specific embodiments are described in detail below.

[1723] System configuration

[1724] The main components of this system are as follows:

[1725] Traffic control device (terminal)

[1726] Central Processing Unit (Server)

[1727] Emotion Engine

[1728] network

[1729] Traffic control devices (terminals) are installed at each intersection and use sensors to collect traffic data in real time. The emotion engine has the function of analyzing user emotion data. The collected data is sent to a central processing unit (server) via a network.

[1730] Collecting emotion and traffic data

[1731] The server includes means for collecting and transmitting both emotion data and traffic data, and means for performing traffic analysis and emotion analysis.

[1732] Hardware and software used

[1733] On-board computer: A computer system installed in a self-driving car, such as NVIDIA DRIVE.

[1734] In-car camera: A camera that captures passengers' facial expressions, such as the Logitech C920.

[1735] Sensors: LIDAR, cameras, radar, etc. are used to collect traffic data.

[1736] Network module: A network device for sending and receiving data.

[1737] Emotion engine: Uses software such as the Emotion AI SDK to analyze user emotional data.

[1738] Traffic analysis algorithm: An algorithm that analyzes traffic data using Python and TensorFlow.

[1739] Real-time database: Firebase Realtime Database, etc., is used.

[1740] Data processing procedures

[1741] 1. Traffic and emotion data collection: In-vehicle sensors collect information on surrounding traffic conditions in real time, and the Emotion AI SDK extracts emotion data from the user's facial expressions.

[1742] 2. Data transmission: The onboard computer sends the collected data to the Firebase Realtime Database.

[1743] 3. Analysis: The server analyzes the data using Python and TensorFlow to generate traffic and sentiment analysis results.

[1744] 4. Signal pattern generation: Based on the analysis results, efficient signal patterns are generated and simulated using digital twin technology.

[1745] 5. Application of optimal signal patterns: Based on the simulation results, the optimal signal patterns are sent to the onboard computer and reflected in actual traffic signal control.

[1746] Specific examples

[1747] Traffic jams frequently occurred at intersection A in a certain city during the morning rush hour. Conventional fixed programs could not resolve this congestion, but by introducing this system, the following steps were implemented:

[1748] 1. The traffic control device (terminal) collected traffic data at intersection A every second and sent it to the server.

[1749] 2. The emotion engine analyzed the facial expressions of the user inside the car, measured their stress level, and sent the data to the server.

[1750] 3. The server performed traffic analysis and sentiment analysis based on the collected data to identify the causes of traffic congestion and factors that cause user stress.

[1751] 4. The server generated a new traffic light pattern that shortened the red light time by 5 seconds and extended the green light time by 8 seconds, and performed a simulation using digital twin technology.

[1752] 5. The simulation was successful, so the optimal signal pattern was sent to the terminal and implemented at intersection A.

[1753] 6. It was confirmed that traffic congestion was alleviated and users' stress levels also decreased.

[1754] Below is an example of a prompt sentence to input to the generative AI model.

[1755] Analyze the current traffic situation based on the collected emotion data and traffic data, and generate the optimal driving route and speed pattern. If the emotion data has a high stress score, select a gentler route, and if there is traffic congestion, prioritize avoiding it. Provide the output format in JSON.

[1756] Emotion data sample: {'stress_score': 75}

[1757] Traffic data sample: {'vehicle_speed': 60, 'traffic_density': 'high'}

[1758] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1759] Step 1:

[1760] An on-board computer collects traffic and emotion data through in-car cameras and sensors.

[1761] Input: Raw data from interior cameras, LIDAR, cameras, and radar

[1762] Output: Processed data such as stress score, vehicle speed, and traffic density

[1763] How it works: The onboard camera captures passengers' facial expressions, which are then analyzed by the Emotion AI SDK to calculate a stress score. Sensors collect data on vehicle speed and traffic density and process it in real time.

[1764] Step 2:

[1765] The on-board computer sends collected emotion and traffic data to the Firebase Realtime Database in real time.

[1766] Input: Processed data obtained in step 1

[1767] Output: Data stored in Firebase Realtime Database

[1768] Specific operation: Emotion data and traffic data are sent to the Firebase Realtime Database via NVIDIA DRIVE's network function.

[1769] Step 3:

[1770] The server receives data from the Firebase Realtime Database and performs traffic analysis and sentiment analysis.

[1771] Input: Sentiment and traffic data stored in Firebase Realtime Database

[1772] Output: Analysis results (traffic flow, causes of congestion, patterns of user emotion changes)

[1773] How it works: Using Python and TensorFlow, the server analyzes traffic and emotion data and compares it with past and current data to identify trends.

[1774] Step 4:

[1775] The server generates efficient signal patterns based on the analysis results and performs simulations using digital twin technology.

[1776] Input: Analysis results obtained in step 3

[1777] Output: Simulation results and optimal signal patterns

[1778] Specific operation: Using a generative AI model, generate optimal signal patterns, perform simulations using digital twin technology, and evaluate the simulation results.

[1779] Step 5:

[1780] The server determines the optimal signal pattern based on the simulation results and sends it back to the on-board computer.

[1781] Input: Simulation results obtained in step 4

[1782] Output: Optimal signal pattern

[1783] Specific operation: The optimal signal pattern is determined and transmitted to the vehicle's onboard computer via the network.

[1784] Step 6:

[1785] An on-board computer controls the traffic lights according to the received signal patterns.

[1786] Input: Optimal signal pattern sent in step 5

[1787] Output: Actual traffic light control

[1788] Specific operation: The on-board computer controls traffic signals based on the optimal signal pattern and adjusts to actual traffic conditions.

[1789] 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.

[1790] 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.

[1791] 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.

[1792] 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.

[1793] 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.

[1794] 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.

[1795] 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).

[1796] 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.

[1797] 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."

[1798] 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.

[1799] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1800] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1801] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1802] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1803] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1804] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1805] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1806] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1807] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1808] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1809] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1810] The following is further disclosed regarding the above embodiment.

[1811] (Claim 1)

[1812] a plurality of traffic control devices, each of which includes a means for collecting traffic data;

[1813] means for transmitting the collected traffic data to a central processing unit;

[1814] a means for the central processing unit to perform traffic analysis based on the collected traffic data;

[1815] means for generating an efficient signal pattern based on the traffic analysis results;

[1816] means for simulating the generated signal pattern;

[1817] a means for determining an optimal signal pattern based on the simulation results and transmitting the optimal signal pattern to a plurality of traffic control devices;

[1818] means for controlling traffic signals according to an optimal signal pattern;

[1819] A system including:

[1820] (Claim 2)

[1821] 10. The system of claim 1, further comprising means for simulating traffic analysis results using digital twin technology.

[1822] (Claim 3)

[1823] 10. The system according to claim 1, further comprising means for analyzing traffic characteristics by time period and location based on the collected traffic data.

[1824] "Example 1"

[1825] (Claim 1)

[1826] A means for collecting traffic data by a plurality of terminals;

[1827] means for transmitting the collected traffic data to a server;

[1828] A means for the server to perform traffic analysis based on the collected traffic data;

[1829] means for generating an efficient signal pattern based on the traffic analysis results;

[1830] A means for simulating the generated signal pattern using digital twin technology;

[1831] means for determining an optimal signal pattern based on the simulation results and transmitting the optimal signal pattern to a plurality of terminals;

[1832] means for controlling traffic signals according to an optimal signal pattern;

[1833] A means of monitoring and providing feedback on traffic conditions in real time;

[1834] A system including:

[1835] (Claim 2)

[1836] 10. The system of claim 1, further comprising means for simulating traffic analysis results using digital twin technology.

[1837] (Claim 3)

[1838] 10. The system according to claim 1, further comprising means for analyzing traffic characteristics by time period and location based on the collected traffic data.

[1839] "Application Example 1"

[1840] (Claim 1)

[1841] a plurality of traffic control devices, each of which includes a means for collecting traffic data;

[1842] means for transmitting the collected traffic data to a central processing unit;

[1843] a means for the central processing unit to perform traffic analysis based on the collected traffic data;

[1844] means for generating an efficient signal pattern based on the traffic analysis results;

[1845] means for simulating the generated signal pattern;

[1846] a means for determining an optimal signal pattern based on the simulation results and transmitting the optimal signal pattern to a plurality of traffic control devices;

[1847] means for controlling traffic signals according to an optimal signal pattern;

[1848] a means for the autonomous vehicle to receive real-time traffic data from a traffic control device and monitor surrounding traffic conditions;

[1849] A means of suggesting optimal routes based on traffic data;

[1850] means for providing appropriate instructions to the automated vehicle when an emergency vehicle is approaching; and

[1851] A system including:

[1852] (Claim 2)

[1853] 10. The system of claim 1, further comprising means for simulating traffic analysis results using digital twin technology.

[1854] (Claim 3)

[1855] 10. The system according to claim 1, further comprising means for analyzing traffic characteristics by time period and location based on the collected traffic data.

[1856] "Example 2: Combining Emotion Engines"

[1857] (Claim 1)

[1858] a means for collecting traffic data by the terminal;

[1859] A terminal includes means for collecting emotion data of a user;

[1860] means for transmitting the collected traffic data and emotion data to a server;

[1861] A server performs analysis based on the collected traffic data and emotion data;

[1862] means for generating an efficient signal pattern based on the analysis results;

[1863] A means for simulating the generated signal pattern using digital twin technology;

[1864] means for determining an optimal signal pattern based on the simulation results and transmitting the optimal signal pattern to the terminal;

[1865] means for controlling traffic signals according to an optimal signal pattern;

[1866] A means of monitoring traffic conditions and sentiment data in real time and making further adjustments;

[1867] A system including:

[1868] (Claim 2)

[1869] 10. The system of claim 1, further comprising means for simulating signal patterns using digital twin technology.

[1870] (Claim 3)

[1871] 10. The system of claim 1, further comprising means for analyzing traffic characteristics by time period and location based on the collected traffic data and emotion data.

[1872] "Application example 2 when combining emotion engines"

[1873] (Claim 1)

[1874] a plurality of traffic control devices, each of which includes a means for collecting traffic data;

[1875] means for transmitting the collected traffic data and emotion data to a central processing unit;

[1876] A central processing unit performs traffic analysis and emotion analysis based on the collected traffic data and emotion data;

[1877] means for generating an efficient signal pattern based on the traffic analysis result and the sentiment analysis result;

[1878] means for simulating the generated signal pattern;

[1879] a means for determining an optimal signal pattern based on the simulation results and transmitting the optimal signal pattern to a plurality of traffic control devices;

[1880] means for controlling traffic signals according to an optimal signal pattern;

[1881] A system including:

[1882] (Claim 2)

[1883] 10. The system of claim 1, further comprising means for simulating traffic analysis results using digital twin technology.

[1884] (Claim 3)

[1885] 10. The system of claim 1, further comprising means for analyzing traffic and emotion characteristics by time period and location based on the collected traffic data and emotion data. [Explanation of symbols]

[1886] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a plurality of traffic control devices, each of which includes a means for collecting traffic data; means for transmitting the collected traffic data to a central processing unit; a means for the central processing unit to perform traffic analysis based on the collected traffic data; means for generating an efficient signal pattern based on the traffic analysis results; means for simulating the generated signal pattern; a means for determining an optimal signal pattern based on the simulation results and transmitting the optimal signal pattern to a plurality of traffic control devices; means for controlling traffic signals according to an optimal signal pattern; A system including:

2. The system of claim 1 , further comprising means for simulating traffic analysis results using digital twin technology.

3. The system according to claim 1, further comprising means for analyzing traffic characteristics by time period and location based on the collected traffic data.

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