System

Abstract

A system is provided.SOLUTION: A system comprising: means for real-time analysis of traffic information collected from cameras and sensors; means for generating a traffic simulation using a generative AI model; means for calculating optimal traffic routes and signal light timing; means for sending instructions to signal lights and digital signage based on the calculation results; means for continuously monitoring and optimizing traffic conditions; and means for notifying users of traffic information.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 management systems have struggled to effectively solve problems such as traffic congestion, regulatory violations, and frequent traffic accidents. Manual traffic management by humans has limited the ability to analyze data in real time and respond immediately, limiting the efficiency and safety of traffic. Furthermore, traffic signal timing adjustments at each intersection and road are uniform, resulting in insufficient optimization of traffic flow at specific locations or times of day. [Means for solving the problem]

[0005] The present invention addresses the aforementioned challenges with a system that includes a means for analyzing traffic data collected from cameras and sensors in real time, a means for generating traffic simulations using a generative AI model, a means for calculating optimal traffic routes and traffic light timings, a means for sending instructions to traffic lights and digital signs based on the calculation results, a means for continuously monitoring and optimizing traffic conditions, and a means for notifying users of traffic information. Specifically, the system achieves real-time traffic situation analysis and optimization by including a means for preprocessing data collected from cameras and sensors, a means for inputting the preprocessed data into a generative AI model, and a means for calculating optimal traffic routes and traffic light timings using an optimization algorithm. Furthermore, the system enables multifaceted traffic management by including ancillary digital sign management and a means for analyzing and improving traffic patterns at specific times and locations. This is expected to reduce traffic congestion, regulatory violations, and traffic accidents.

[0006] The "cameras" are devices installed at urban intersections and major roads to capture the movements of vehicles and pedestrians.

[0007] A "sensor" is a device that measures traffic volume, vehicle speed, and environmental data.

[0008] "Traffic Data" means information about traffic conditions collected from cameras and sensors, including the number, speed, and type of vehicles, and pedestrian movement.

[0009] "Real-time analysis" is the process of instantly processing collected data to understand current traffic conditions.

[0010] A "generative AI model" is an artificial intelligence model that generates simulations based on input data and calculates optimal traffic routes and traffic light timings.

[0011] "Traffic simulation" is a method of recreating virtual traffic conditions based on traffic data and visually displaying traffic flow.

[0012] An "optimal transportation route" is a route that avoids traffic congestion and reaches a destination most efficiently.

[0013] "Traffic light timing" is the combination of times that a traffic light displays red, yellow, and green lights.

[0014] "Digital signs" are electronic signs used to display traffic information and instructions.

[0015] "Monitoring" refers to the activity of continuously observing traffic conditions and collecting necessary data.

[0016] An "optimization algorithm" is a computational method for searching for the best solution, and is used to calculate traffic routes and traffic light timings.

[0017] "Feedback" is the process of adjusting input data and parameters based on the system's output.

[0018] "User" refers to the end user who receives traffic information through a dedicated app or information bulletin board.

[0019] A "notification" is an act by which the system conveys specific information to the user. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] The present invention is a system for optimizing urban traffic management in real time. This system uses multiple cameras and sensors, generative AI models, and optimization algorithms to reduce traffic congestion and accidents. Specific embodiments of the system are described below.

[0042] System configuration

[0043] The system includes the following components:

[0044] 1. Cameras and Sensors

[0045] 2. Data collection and analysis server

[0046] 3. Server that runs the optimization algorithm

[0047] 4. Traffic lights and digital signs that receive instructions

[0048] 5. Terminal that notifies users of traffic information

[0049] System Operation

[0050] Data collection from cameras and sensors

[0051] The devices collect data in real time from cameras and sensors installed at intersections, major roads, and pedestrian areas throughout the city.

[0052] For example, cameras installed on major roads capture the number and speed of vehicles and detect pedestrian movement, while sensors measure weather and road conditions.

[0053] Data analysis by server

[0054] The server immediately preprocesses the collected data to remove noise, impute missing values, and standardize the data, then inputs this preprocessed data into the generative AI model.

[0055] As a specific example, if there is a sudden increase in traffic volume at a particular intersection, the noise-removed data can be used to assess the risk of congestion at that point.

[0056] Traffic simulation generation

[0057] The server uses a generative AI model to generate traffic simulations, which use a 3D model of the city to visualize vehicle and pedestrian movements in real time.

[0058] As a concrete example, a simulation is performed at a specific intersection to visually confirm which route can avoid the congestion.

[0059] Calculating optimal traffic routes and traffic light timings

[0060] The server uses an optimization algorithm to calculate optimal traffic routes and traffic light timings based on analysis results and simulations.

[0061] As a specific example, a genetic algorithm is used to re-adjust the timing of traffic lights at an intersection to improve overall traffic flow.

[0062] Sending and executing instructions

[0063] The server sends instructions to traffic lights and digital signs based on the calculated results, which changes the traffic light cycle and the display content of digital signs in real time.

[0064] As a specific example, the green light time at a particular intersection may be extended to allow priority traffic to certain lanes.

[0065] Real-time monitoring and feedback

[0066] The server continuously monitors traffic conditions, reanalyzing and optimizing the system based on new data collected, and taking immediate action if traffic conditions change.

[0067] As a concrete example, if a sudden accident occurs, the information will be analyzed immediately and instructions for a new optimal route will be sent to traffic lights and digital signs.

[0068] Traffic information notification to users

[0069] Users receive traffic information via a dedicated app or information board, including current traffic conditions, optimal detour routes, and predicted congestion information.

[0070] As a specific example, the system will notify the user's smartphone in real time of the "optimal route from the current location to the destination" and the "congestion status at specific intersections."

[0071] In this way, the traffic management system according to the present invention enables real-time optimization of traffic conditions and rapid response, thereby keeping traffic flow smooth.

[0072] The processing flow will be explained below.

[0073] Step 1:

[0074] The devices collect data in real time from cameras and sensors installed at intersections and major roads across the city.

[0075] Specifically, cameras capture the number and speed of vehicles, and sensors measure weather and road conditions.

[0076] Step 2:

[0077] The terminals transmit the collected traffic data to a central server.

[0078] Specifically, the terminal packetizes data at set intervals and sends it to the server using a secure communication protocol.

[0079] Step 3:

[0080] Preprocessing data received by the server.

[0081] Specific operations include noise removal, data integration, missing value completion, and data standardization.

[0082] Step 4:

[0083] The server inputs the preprocessed data into the generative AI model.

[0084] Specifically, the formatted data is converted into the input format for the AI ​​model and supplied to the model.

[0085] Step 5:

[0086] The server generates a traffic simulation using a generative AI model.

[0087] Specifically, it works by reflecting real-time data in a 3D model of the city and virtually recreating traffic movements.

[0088] Step 6:

[0089] The server calculates the optimal traffic route and traffic light timing based on the simulation results.

[0090] Specifically, the system uses genetic algorithms and linear programming to search for and calculate optimal solutions.

[0091] Step 7:

[0092] The server sends the calculated optimal traffic light timing instructions to the traffic lights and digital signs.

[0093] Specifically, the traffic light communicates with the server via an API to change the traffic light cycle.

[0094] Step 8:

[0095] The server continuously monitors traffic conditions.

[0096] Specifically, new data is collected and analyzed to be reflected in the system.

[0097] Step 9:

[0098] The server runs the feedback loop as needed.

[0099] Specifically, the data is analyzed and optimized again, and the instructions on traffic lights and digital signs are updated.

[0100] Step 10:

[0101] Users receive traffic information through a dedicated app or information board.

[0102] Specifically, it notifies smartphone apps and web portals of real-time traffic information and optimal routes.

[0103] Example 1

[0104] 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."

[0105] In urban traffic management, traffic congestion and accidents occur frequently, and smoother traffic flow is required. To address this, a more efficient system that realizes real-time traffic optimization is needed, as conventional traffic management systems have limitations in real-time adaptability and accuracy.

[0106] 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.

[0107] In this invention, the server includes means for pre-processing traffic data collected from cameras and sensors in real time, means for using the pre-processed data to input into a generative AI model to generate a traffic simulation, and means for calculating optimal traffic routes and traffic light timings using an optimization algorithm, thereby enabling analysis and optimization of traffic conditions in real time.

[0108] "Cameras and sensors" are devices that collect visual and non-visual data and are installed to monitor and measure traffic conditions.

[0109] "Traffic data" is a general term for information collected to describe traffic conditions, such as the number, speed, and location of vehicles, pedestrian movement, weather conditions, and road conditions.

[0110] "Preprocessing" refers to the process of converting collected raw data into a format that is easy to analyze by performing processes such as noise removal, missing value completion, and standardization.

[0111] A "generative AI model" is a mathematical model that uses machine learning technology to generate traffic simulations and predict future traffic conditions.

[0112] "Traffic simulation" is a virtual traffic flow model based on real traffic conditions, and is a means of visualizing the movement of vehicles and pedestrians, the effects of traffic lights, etc.

[0113] An "optimization algorithm" is a computational method used to calculate optimal traffic routes and traffic light timings under given conditions.

[0114] "Traffic lights and digital signs" means signal control devices and information display electronic billboards installed for traffic management purposes.

[0115] "Traffic information" refers to information used to keep traffic flowing smoothly, such as real-time traffic conditions, congestion forecasts, and optimal detour routes.

[0116] "User" means any person or entity that uses the system to receive traffic information.

[0117] This invention is a system for optimizing urban traffic management in real time. This system uses multiple cameras and sensors, generative AI models, and optimization algorithms to reduce traffic congestion and accidents. It also automatically manages traffic signals and digital signs, and notifies users of traffic information. Specific aspects of the system are described below.

[0118] System configuration

[0119] The system consists of the following components:

[0120] 1. Cameras and Sensors

[0121] 2. Data collection and analysis server

[0122] 3. Server that runs the optimization algorithm

[0123] 4. Traffic lights and digital signs that receive instructions

[0124] 5. Terminal that notifies users of traffic information

[0125] Data collection from cameras and sensors

[0126] The devices collect real-time data from cameras and sensors installed at intersections, major roads, and pedestrian areas throughout the city. Specifically, cameras installed on major roads capture the number of vehicles, their speeds, and pedestrian movements, while weather sensors collect data such as outside temperature, precipitation, and wind speed.

[0127] Data Preprocessing

[0128] The server preprocesses the collected data, applying algorithms for noise removal, missing value imputation, and standardization to convert it into a format that is easier to analyze. For example, it removes outliers and estimates missing data to improve the accuracy of the data.

[0129] Generative AI model inputs

[0130] The server then inputs the pre-processed data into a generative AI model, which is used to predict future traffic conditions based on vehicle counts, speeds, weather information, pedestrian data, and more.

[0131] Traffic simulation generation

[0132] The server generates traffic simulations using generative AI models. The simulations run on a 3D model of the city and graphically visualize the movements of vehicles and pedestrians. For example, the simulation can predict whether a particular intersection will experience congestion.

[0133] Calculating optimal traffic routes and traffic light timings

[0134] The server uses optimization algorithms to calculate optimal traffic routes and traffic light timings based on analysis results and simulations. For example, it uses genetic algorithms to adjust traffic light cycles and perform calculations to maximize traffic flow.

[0135] Sending and executing instructions

[0136] Based on the results of the calculations, the server sends instructions to traffic lights and digital signs, which can change the traffic light cycle and sign display in real time, for example extending the green light time at an intersection or giving priority to certain lanes.

[0137] Real-time monitoring and feedback

[0138] The server continuously monitors traffic conditions, reanalyzing and optimizing them based on new data. If traffic conditions change, it immediately formulates countermeasures and sends instructions to traffic lights and digital signs again.

[0139] Traffic information notification to users

[0140] Users receive the latest traffic information through a dedicated app or information board. Notifications include current traffic conditions, optimal detour routes, and predicted congestion information. For example, users' smartphone apps can receive real-time notifications of the "optimal route from their current location to their destination" and "congestion status at specific intersections."

[0141] This system will enable the optimization of urban traffic conditions in real time, ensuring smooth traffic flow.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Step 1:

[0144] The device collects data from cameras and sensors installed at intersections, major roads, and pedestrian areas within the city. It takes in the number of vehicles, their speed, weather information, and pedestrian movements as input, and provides raw data as output. Specifically, cameras installed on major roads capture the number and speed of vehicles once per second, while sensors measure temperature and precipitation.

[0145] Step 2:

[0146] The server preprocesses the raw data. As input, it receives raw data from cameras and sensors, performs noise removal, missing value imputation, and data standardization. As output, it generates preprocessed clean data. Specifically, it filters out outliers and imputes missing data using a mean imputation algorithm.

[0147] Step 3:

[0148] The server inputs the preprocessed data into the generative AI model. The clean data is provided as input to the AI ​​model, and a traffic simulation is generated as output. Specifically, the number of vehicles, speed, and weather information are input into the AI ​​model to predict future traffic conditions.

[0149] Step 4:

[0150] The server runs an optimization algorithm based on the generated traffic simulation. It takes in traffic simulation data as input and applies the optimization algorithm to calculate the optimal traffic route and traffic light timing. The optimal route and traffic light timing are obtained as output. Specifically, it uses a genetic algorithm to perform calculations to adjust the traffic light cycle at intersections.

[0151] Step 5:

[0152] The server sends instructions to traffic lights and digital signs based on the calculation results. As input, it receives optimal traffic light timing and route information, and sends execution instructions to traffic lights and digital signs. As output, the status of the traffic lights and signs that received the instructions is changed in real time. Specific actions include issuing an instruction to the traffic lights to extend the green light and setting priority for certain lanes.

[0153] Step 6:

[0154] The server continuously monitors traffic conditions and reanalyzes and optimizes based on newly collected data. It performs necessary reanalysis based on continuously acquired traffic data as input. It generates new optimization results as output. Specifically, it captures information about accidents and congestion and retransmits detour route instructions to traffic lights and digital signs.

[0155] Step 7:

[0156] Users receive the latest traffic information through a dedicated app or information board. The system receives traffic information from the server as input and displays real-time traffic conditions, detour routes, congestion forecasts, etc. as output. Specific operations include notifying the user's smartphone of the "optimal route from the current location to the destination" and the "congestion status of specific intersections."

[0157] (Application example 1)

[0158] 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."

[0159] Urban traffic management is becoming increasingly complex, and efficient traffic management is difficult, especially in areas where traffic congestion and accidents are frequent. In particular, the introduction of autonomous vehicles has increased the need for traffic management systems that enable these vehicles to select optimal routes in real time and safely reach their destinations. In order to solve this problem, the present invention aims to provide an optimal traffic management system for autonomous vehicles to operate efficiently and safely.

[0160] 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.

[0161] In this invention, the server includes means for analyzing traffic data collected from cameras and sensors in real time, means for generating traffic simulations using a generative AI model, means for calculating optimal traffic routes and traffic light timings, means for sending instructions to traffic lights and digital signs based on the calculation results, means for continuously monitoring and optimizing traffic conditions, means for notifying users of traffic information, means for collecting data from cameras and sensors equipped in autonomous vehicles and analyzing the data in real time, means for calculating optimal routes based on the analysis results and sending instructions to the autonomous vehicles, and means for continuously monitoring traffic conditions while the autonomous vehicles are moving and updating the routes as necessary. This enables autonomous vehicles to monitor urban traffic conditions in real time, receive instructions on optimal routes and traffic light timings, and operate safely and efficiently.

[0162] A "camera" is a device for taking images and recording them as data.

[0163] A "sensor" is a device that detects a physical quantity and converts it into an electrical signal.

[0164] "Real time" refers to the instantaneous capture and processing of ongoing events.

[0165] "Analysis" is the process of investigating and examining collected data to extract meaning.

[0166] A "generative AI model" is an artificial intelligence model that can generate new samples from data.

[0167] "Traffic simulation" is the process of simulating traffic movements on a computer.

[0168] An "optimal route" is the route to a destination that is the most efficient in terms of time, distance, etc.

[0169] A "traffic light" is a device that emits a light signal to control traffic flow.

[0170] A "digital sign" is a sign that displays information using a digital display.

[0171] "Monitoring" refers to the continuous observation and recording of specific data or conditions.

[0172] "Optimization" refers to adjusting processes and systems to make the most efficient use of resources.

[0173] A "user" is a person or device that uses a system or service.

[0174] An "autonomous vehicle" is a vehicle that uses artificial intelligence and sensors to drive and move automatically.

[0175] "Analysis results" refers to the specific information and conclusions obtained through the analysis.

[0176] "Sending instructions" refers to conveying orders for a particular action or behavior.

[0177] This invention is a system that enables autonomous vehicles to monitor urban traffic conditions in real time and optimize operation. This system is composed of a combination of cameras, sensors, generative AI models, traffic simulation, optimization algorithms, monitoring functions, and information notification functions.

[0178] System configuration

[0179] The system includes the following components:

[0180] 1. Cameras and sensors: These are equipped on autonomous vehicles and collect real-time information on surrounding traffic conditions, vehicle counts, pedestrian movement, weather conditions, and more.

[0181] 2. Server: Analyzes collected data in real time and generates traffic simulations using generative AI models.

[0182] 3. Optimization algorithm: Based on the simulation results, the optimal traffic routes and traffic light timings are calculated.

[0183] 4. Instruction sending device: Sends instructions to traffic lights and digital signs based on the calculation results.

[0184] 5. Monitoring device: Continuously monitors traffic conditions and optimizes the entire system as needed.

[0185] 6. User device: Drivers and autonomous vehicles receive information on optimal traffic routes and predicted congestion.

[0186] System processing procedure

[0187] 1. Data Collection:

[0188] Cameras and sensors capture the surrounding traffic conditions and send the data to a server in real time.

[0189] 2. Data Analysis:

[0190] The server preprocesses the collected data, removing noise and standardizing the data.

[0191] 3. Simulation generation:

[0192] The pre-processed data is fed into a generative AI model to generate a city-wide traffic simulation.

[0193] 4. Run the optimization algorithm:

[0194] Based on the simulation results, the optimal route and traffic light timing are calculated.

[0195] 5. Send instructions:

[0196] Optimal routes and traffic light timings are transmitted in real time to autonomous vehicles and digital signs.

[0197] 6. Real-time monitoring:

[0198] Continuously monitor traffic conditions and update routes and traffic light timings as needed.

[0199] 7. Notice to Users:

[0200] The user or vehicle system receives optimal operational information through a dedicated app.

[0201] Hardware and software used

[0202] Cameras and sensors: devices that capture images and convert physical quantities into electrical signals (e.g., LIDAR, radar)

[0203] Server: Data analysis, AI model execution, optimization algorithm execution (e.g. TensorFlow model)

[0204] Generative AI models: AI that generates new examples (e.g., GANs, generative models)

[0205] Cloud platform: Integrated management of data transmission, analysis, and optimization processes (e.g., AWS, Google Cloud)

[0206] Specific examples

[0207] For example, if there is a sudden increase in traffic volume at an intersection, cameras and sensors collect that information, which is then instantly analyzed by a server. A generative AI model then runs a simulation based on that data, and an optimization algorithm calculates the optimal route and traffic light timing. This information is then sent in real time to the autonomous vehicle, which then selects the optimal route and drives accordingly.

[0208] Prompt Sentence Examples

[0209] "What is the best route for travel based on current vehicle count, weather, and road conditions? Analyze the last 10 minutes of data at major intersections to find a route that avoids traffic jams."

[0210] The present invention enables autonomous vehicles to monitor traffic conditions in real time and operate safely and efficiently.

[0211] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0212] Step 1:

[0213] Data collection

[0214] The device collects data from cameras and sensors installed in the autonomous vehicle, specifically using LIDAR and radar to capture the surrounding traffic conditions, and transmits the data in real time.

[0215] Input: Real-time traffic data (number of vehicles, location, speed, weather, etc.)

[0216] Output: Raw data collected

[0217] Step 2:

[0218] Data Preprocessing

[0219] The server preprocesses the collected raw data, removing noise, filling in missing data, and standardizing the data. Specifically, it uses a scaler to standardize the data and applies a filter to remove noise.

[0220] Input: Raw data collected

[0221] Output: Preprocessed data (denoised and in standardized format)

[0222] Step 3:

[0223] Simulation Generation

[0224] The server inputs the preprocessed data into a generative AI model to generate a traffic simulation. The model uses the data to predict traffic trends across the city and visualizes them as a 3D model. Specifically, the traffic simulation is generated using frameworks such as TensorFlow.

[0225] Input: Preprocessed data

[0226] Output: Generated traffic simulation (traffic trends throughout the city)

[0227] Step 4:

[0228] Calculating optimal routes and traffic light timings

[0229] The server calculates optimal traffic routes and traffic light timings based on the simulation results obtained from the generative AI model. A specific example is solving optimization problems using genetic algorithms.

[0230] Input: Traffic simulation results

[0231] Output: Optimal traffic routes and traffic light timings

[0232] Step 5:

[0233] Sending instructions

[0234] The server calculates optimal routes and traffic light timings and sends them as instructions to autonomous vehicles and digital signs, allowing them to optimally operate in real time.

[0235] Input: Optimal traffic routes and traffic light timings

[0236] Output: Instructions received by autonomous vehicles and digital signs

[0237] Step 6:

[0238] Real-time monitoring

[0239] The device continuously monitors traffic conditions, and the server analyzes new data and updates the entire system as needed. Specifically, if a new situation arises, the data is re-collected and the process described above is repeated.

[0240] Input: Real-time new traffic data

[0241] Output: Updated optimal route and traffic light timings

[0242] Step 7:

[0243] User Notifications

[0244] Users receive optimal travel information through a dedicated app, which displays real-time traffic conditions, optimal routes, predicted congestion information, and more.

[0245] Input: Optimal route and traffic information from the server

[0246] Output: Traffic information received by the user through the app

[0247] For example, the prompt is:

[0248] "What is the best route for travel based on current vehicle count, weather, and road conditions? Analyze the last 10 minutes of data at major intersections to find a route that avoids traffic jams."

[0249] 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.

[0250] The present invention combines a system that analyzes traffic data collected from cameras and sensors in real time, generates traffic simulations using generative AI models, and calculates optimal traffic routes and traffic light timings with an emotion engine that recognizes user emotions. Specific embodiments of the system are described below.

[0251] System configuration

[0252] The system includes the following components:

[0253] 1. Cameras and Sensors

[0254] 2. Data collection and analysis server

[0255] 3. Servers that use generative AI models

[0256] 4. Server that runs the optimization algorithm

[0257] 5. Traffic lights and digital signs that receive instructions

[0258] 6. User Device with Emotion Engine

[0259] 7. Terminals that notify users of traffic information

[0260] System Operation

[0261] Data collection from cameras and sensors

[0262] The devices collect real-time data from cameras and sensors installed at intersections, major roads, and pedestrian areas throughout the city. For example, cameras installed on major roads capture the number and speed of vehicles, while sensors measure weather and road conditions.

[0263] Data analysis by server

[0264] The server immediately preprocesses the collected data to remove noise, fill in missing values, and standardize the data. This preprocessed data is then input into a generative AI model. For example, if there is a sudden increase in traffic volume at a particular intersection, the noise-removed data can be used to evaluate the risk of congestion at that point.

[0265] Traffic simulation generation

[0266] The server generates traffic simulations using generative AI models. The simulations use a 3D model of the city to visualize vehicle and pedestrian movements in real time. For example, a simulation of a specific intersection can be performed to visually confirm which route will avoid congestion.

[0267] Calculating optimal traffic routes and traffic light timings

[0268] The server uses optimization algorithms to calculate optimal traffic routes and traffic light timings based on analysis results and simulations. For example, a genetic algorithm is used to readjust the timing of traffic lights at a certain intersection to improve overall traffic flow.

[0269] Sending and executing instructions

[0270] The server sends instructions to traffic lights and digital signs based on the calculated results, which changes the traffic light cycle and digital sign display in real time. For example, it extends the green light time at certain intersections and allows priority traffic to certain lanes.

[0271] Real-time monitoring and feedback

[0272] The server continuously monitors traffic conditions, reanalyzing and optimizing them based on new data collected. If there are changes in traffic conditions, countermeasures are taken immediately. For example, if a sudden accident occurs, the information is analyzed immediately and new optimal route instructions are sent to traffic lights and digital signs.

[0273] Recognizing user emotions with an emotion engine

[0274] An emotion engine built into the user's device recognizes emotions in real time from the user's facial expressions, voice, pulse rate, etc. For example, if the user is feeling impatient or angry, the emotion engine will detect that emotional state.

[0275] Traffic information notification to users

[0276] The server adjusts and notifies traffic information and optimal routes based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, the server will suggest a less congested route and select a route that will allow them to relax.

[0277] In this way, the traffic management system of the present invention enables optimization of traffic conditions in real time, rapid response, and provision of customized traffic information according to the user's emotions, thereby maintaining smooth and comfortable traffic flow.

[0278] The processing flow will be explained below.

[0279] Step 1:

[0280] The devices collect data in real time from cameras and sensors installed at intersections and major roads across the city.

[0281] Specifically, cameras capture the number and speed of vehicles, and sensors measure weather and road conditions.

[0282] Step 2:

[0283] The terminals transmit the collected traffic data to a central server.

[0284] Specifically, the terminal packetizes data at set intervals and sends it to the server using a secure communication protocol.

[0285] Step 3:

[0286] Preprocessing data received by the server.

[0287] Specific operations include noise removal, data integration, missing value completion, and data standardization.

[0288] Step 4:

[0289] The server inputs the preprocessed data into the generative AI model.

[0290] Specifically, the formatted data is converted into the input format for the AI ​​model and supplied to the model.

[0291] Step 5:

[0292] The server generates a traffic simulation using a generative AI model.

[0293] Specifically, it works by reflecting real-time data in a 3D model of the city and virtually recreating traffic movements.

[0294] Step 6:

[0295] The server calculates the optimal traffic route and traffic light timing based on the simulation results.

[0296] Specifically, the system uses genetic algorithms and linear programming to search for and calculate optimal solutions.

[0297] Step 7:

[0298] The server sends the calculated optimal traffic light timing instructions to the traffic lights and digital signs.

[0299] Specifically, the traffic light communicates with the server via an API to change the traffic light cycle.

[0300] Step 8:

[0301] The server continuously monitors traffic conditions.

[0302] Specifically, new data is collected and analyzed to be reflected in the system.

[0303] Step 9:

[0304] The server runs the feedback loop as needed.

[0305] Specifically, the data is analyzed and optimized again, and the instructions on traffic lights and digital signs are updated.

[0306] Step 10:

[0307] The emotion engine built into the user's device recognizes emotions in real time from the user's facial expressions, voice, pulse rate, etc.

[0308] Specifically, it uses facial recognition technology and biometric sensors to determine whether the user is feeling anxiety or anger.

[0309] Step 11:

[0310] The server adjusts and notifies traffic information and optimal routes based on the user's emotions recognized by the emotion engine.

[0311] Specifically, if the user is feeling stressed, the system suggests a less congested route and selects a route that allows the user to relax.

[0312] Step 12:

[0313] Users receive traffic information through a dedicated app or information board.

[0314] Specifically, it notifies smartphone apps and web portals of real-time traffic information and optimal routes.

[0315] Example 2

[0316] 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."

[0317] Current urban traffic management systems lack the ability to analyze traffic data in real time and provide optimal routes, increasing the risk of traffic congestion and accidents. Furthermore, they lack the means to provide traffic information that takes into account the user's emotional state, and there is a lack of means to improve the user experience. This makes it difficult to maintain smooth traffic flow while reducing user stress.

[0318] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for analyzing traffic data collected from cameras and sensors in real time, a means for generating a traffic simulation using a generative AI model, and a means for calculating optimal traffic routes and traffic light timings. This enables real-time traffic data analysis and the presentation of optimal traffic routes.

[0319] Furthermore, the server includes means for continuously monitoring and optimizing statistical data, means for notifying users of traffic information, means for recognizing users' emotions in real time, and means for adjusting traffic information based on the users' emotions, thereby enabling the provision of customized traffic information according to the users' emotional state while maintaining smooth traffic flow.

[0320] "Traffic data" refers to information such as the number of vehicles, their speed, weather, and road conditions collected from cameras and sensors.

[0321] "Real-time analysis" refers to the immediate processing of traffic data collected from cameras and sensors to analyze current traffic conditions.

[0322] A "generative AI model" is a type of artificial intelligence used to simulate future traffic conditions based on collected data.

[0323] "Traffic simulation" is the process of virtually recreating traffic conditions across an entire city using generative AI models.

[0324] An "optimization algorithm" is a mathematical method for calculating optimal traffic routes and traffic light timings based on given data and conditions.

[0325] A "traffic light" is a lighting device installed to control traffic at intersections and major roads.

[0326] "Information display device" means a digital sign or other display device used to display traffic conditions or instructions.

[0327] "Monitoring" is the process of continuously collecting and analyzing traffic data.

[0328] "Feedback" refers to adjusting system settings and traffic light timing based on data obtained through monitoring.

[0329] The "emotion engine" is a technology that recognizes emotions in real time from a user's facial expressions, voice, pulse rate, etc.

[0330] "User" refers to a vehicle driver or pedestrian using the system.

[0331] "Customized traffic information" is information that provides optimal traffic routes and notification content according to the user's emotional state.

[0332] The present invention combines a system that analyzes traffic data collected from cameras and sensors in real time, generates traffic simulations using generative AI models, and calculates optimal traffic routes and traffic light timings with an emotion engine that recognizes user emotions. Specific embodiments of the system are described below.

[0333] System configuration

[0334] The system includes the following components:

[0335] 1. Cameras and Sensors

[0336] 2. Data collection and analysis server

[0337] 3. Servers that use generative AI models

[0338] 4. Server that runs the optimization algorithm

[0339] 5. Signals and information display devices that receive instructions

[0340] 6. User Device with Emotion Engine

[0341] 7. Terminals that notify users of traffic information

[0342] System Operation

[0343] Data collection from cameras and sensors

[0344] The devices collect real-time data from cameras and sensors installed at intersections, major roads, and pedestrian areas throughout the city. For example, cameras installed on major roads capture the number and speed of vehicles, while sensors measure weather and road conditions.

[0345] Data analysis by server

[0346] The server immediately preprocesses the collected data, removing noise, filling in missing values, and standardizing the data. For example, if there is a sudden increase in traffic volume at a particular intersection, the noise-removed data can be used to evaluate the risk of congestion at that point.

[0347] Traffic simulation generation

[0348] The server generates traffic simulations using generative AI models. The simulations use a 3D model of the city to visualize vehicle and pedestrian movements in real time. For example, a simulation of a specific intersection can be performed to visually confirm which route will avoid congestion.

[0349] Calculating optimal traffic routes and traffic light timings

[0350] The server uses optimization algorithms to calculate optimal traffic routes and traffic light timings based on analysis results and simulations. For example, a genetic algorithm is used to readjust the timing of traffic lights at a certain intersection to improve overall traffic flow.

[0351] Sending and executing instructions

[0352] The server sends instructions to traffic lights and information display devices based on the calculated results. This allows the traffic light cycle and the information displayed on the information display devices to be changed in real time. For example, the green light time at a specific intersection can be extended, and priority traffic can be given to certain lanes.

[0353] Real-time monitoring and feedback

[0354] The server continuously monitors traffic conditions, reanalyzing and optimizing them based on new data collected. If there are changes in traffic conditions, countermeasures are taken immediately. For example, if a sudden accident occurs, the information is analyzed immediately and new optimal route instructions are sent to traffic lights and information display devices.

[0355] Recognizing user emotions with an emotion engine

[0356] An emotion engine built into the user's device recognizes emotions in real time from the user's facial expressions, voice, pulse rate, etc. For example, if the user is feeling impatient or angry, the emotion engine will detect that emotional state.

[0357] Traffic information notification to users

[0358] The server adjusts and notifies traffic information and optimal routes based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, the server will suggest a less congested route and select a route that will allow them to relax.

[0359] Prompt Sentence Examples

[0360] "If the user is stressed, suggest the best route to avoid the congestion. For example, imagine a scenario where it's Friday evening and you're stuck in traffic going through a particular intersection."

[0361] In this way, the traffic management system of the present invention enables optimization of traffic conditions in real time, rapid response, and provision of customized traffic information according to the user's emotions, thereby maintaining smooth and comfortable traffic flow.

[0362] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0363] Step 1:

[0364] The device collects real-time data from cameras and sensors installed at each intersection, major road, and pedestrian area within the city. This data includes the number and speed of vehicles, weather, road conditions, etc. For example, cameras installed on major roads capture the number and speed of vehicles every second, while sensors continuously measure changes in weather and road conditions. This data collection is stored directly in a database and can be accessed for subsequent processing steps.

[0365] Step 2:

[0366] The server receives the data collected in step 1 and performs noise removal, missing value imputation, and data standardization. This preprocessing is essential to improve the accuracy of data analysis. The input data is the raw data collected in step 1, and the output is clean data with noise removed and missing values ​​imputed. For example, outliers are removed and missing weather data is imputed from past data.

[0367] Step 3:

[0368] The server inputs the preprocessed data into a generative AI model to generate a traffic simulation. The input data is the clean data from Step 2, and the output is a 3D traffic model of the entire city. Specifically, it can simulate vehicle and pedestrian movements at specific intersections in real time and predict future traffic congestion.

[0369] Step 4:

[0370] The server uses an optimization algorithm to calculate traffic routes and traffic light timings based on the simulation results obtained from the generative AI model. The input data are the simulation results from Step 3, and the output is the optimal traffic route and traffic light timing settings. Specifically, it evaluates multiple possible traffic light timing settings and finds the part that maximizes overall traffic efficiency.

[0371] Step 5:

[0372] The server sends instructions to traffic lights and information display devices based on the optimal traffic route and traffic light timing settings. The input data is the calculation results from step 4, and the output is the traffic light timing settings and updates to the information display devices. Specifically, the server controls the traffic lights to extend the green light time for specific lanes and allow priority traffic.

[0373] Step 6:

[0374] The server continuously monitors traffic conditions and reanalyzes and optimizes the system based on newly collected data. The input data is traffic data updated in real time, and the output is the latest optimization results. For example, if a traffic accident occurs, the information is analyzed immediately and new optimal route instructions are sent to traffic lights and information display devices.

[0375] Step 7:

[0376] The emotion engine built into the user's device recognizes emotions in real time from the user's facial expressions, voice, pulse rate, etc. The input data is the user's emotional indicators, and the output is the detected emotional state. Specifically, it detects when the user is feeling impatient or anger while in the car.

[0377] Step 8:

[0378] The server recognizes the user's emotions and adjusts and notifies traffic information and optimal routes based on those emotions. The input data is the user's emotional state detected in step 7 and real-time traffic data, and the output is adjusted traffic information notified to the user. For example, if the user is feeling stressed, a notification is sent suggesting an optimal route that avoids traffic jams.

[0379] This enables the system to optimize traffic conditions in real time, respond quickly, and provide customized traffic information according to the user's emotions.

[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] In modern transportation systems, traffic congestion and accidents occur frequently, often hindering efficient travel. In particular, increased traffic at specific intersections or during certain times of the day causes congestion, necessitating the optimization of traffic flow. Furthermore, because the driver's emotional state affects driving safety and efficiency, it is necessary to reduce driver stress and provide a comfortable driving environment. However, conventional traffic management systems have difficulty analyzing data in real time and proposing optimal routes, and they do not propose routes that take driver emotions into account.

[0383] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing traffic data collected from cameras and sensors in real time, means for generating a traffic simulation using a generative AI model, and means for calculating optimal traffic routes and traffic light timings. This makes it possible to analyze traffic conditions in real time and calculate optimal routes and traffic light timings. In addition, the server includes means for analyzing data collected from cameras and sensors of an autonomous vehicle in real time and updating traffic information, means equipped with an emotion engine that recognizes the driver's emotions, and means for proposing optimal traffic routes according to the driver's emotional state, making it possible to propose routes that adapt to the driver's emotional state and reduce stress.

[0384] "Cameras and sensors" are devices used to collect information on traffic conditions, vehicles, pedestrians, etc. in real time.

[0385] "Real-time analysis means" refers to technology for processing and analyzing collected data immediately.

[0386] A "generative AI model" is an artificial intelligence model used to generate traffic simulations and predict future traffic patterns based on past data and current conditions.

[0387] The "means for generating traffic simulations" refers to the use of collected traffic data to virtually reproduce the movement of the entire traffic system and calculate optimal routes and traffic light timings.

[0388] "Means for calculating optimal traffic routes and traffic light timings" refers to technology that analyzes traffic flow and congestion conditions to determine routes that allow vehicles to travel efficiently and traffic light control timings.

[0389] "Means for sending instructions to traffic lights and digital signs" refers to means for transmitting calculated optimal traffic control information to traffic lights and digital signs and for carrying out actual traffic control.

[0390] "Means for continuously monitoring and optimizing traffic conditions" refers to technology that constantly monitors collected traffic data and performs optimization in real time as necessary.

[0391] "Means for notifying users of traffic information" refers to technology for informing users of calculated optimal routes and traffic information.

[0392] "Autonomous vehicle cameras and sensors" means devices installed on autonomous vehicles that sense surrounding traffic conditions and collect data.

[0393] The "emotion engine" is a technology that analyzes the driver's facial expressions, voice, pulse rate, etc. to recognize their current emotional state.

[0394] The "means for suggesting optimal transportation routes according to emotional state" is a technology for suggesting optimal routes to reduce driver stress based on the recognized emotional state.

[0395] This system analyzes traffic data in real time, generates traffic simulations using generative AI models, and calculates optimal traffic routes and traffic signal timings. In addition, by incorporating an emotion engine that recognizes the user's emotions, it is possible to suggest optimal routes that reduce the user's stress.

[0396] System configuration

[0397] The system includes the following components:

[0398] 1. Cameras and Sensors

[0399] 2. Data collection and analysis server

[0400] 3. Servers that use generative AI models

[0401] 4. Server that runs the optimization algorithm

[0402] 5. Traffic lights and digital signs that receive instructions

[0403] 6. User Device with Emotion Engine

[0404] 7. Terminals that notify users of traffic information

[0405] System Operation

[0406] Data collection from cameras and sensors

[0407] Cameras and sensors will be installed at intersections, major roads, and pedestrian areas throughout the city to collect real-time data. For example, cameras on major roads will capture the number and speed of vehicles, while sensors will measure weather and road conditions.

[0408] Data analysis

[0409] The server immediately preprocesses the collected data to remove noise, fill in missing values, and standardize the data. This preprocessed data is then input into a generative AI model. For example, if there is a sudden increase in traffic volume at a particular intersection, the noise-removed data is used to evaluate the risk of congestion at that point.

[0410] Traffic simulation generation

[0411] The server generates traffic simulations using generative AI models. The simulations use a 3D model of the city to visualize vehicle and pedestrian movements in real time. For example, a simulation can be run at a specific intersection to visually confirm which route will avoid congestion.

[0412] Calculating optimal traffic routes and traffic light timings

[0413] The server uses optimization algorithms to calculate optimal traffic routes and traffic light timings based on analysis and simulation results. For example, a genetic algorithm can be used to readjust the timing of traffic lights at a given intersection to improve overall traffic flow.

[0414] Sending and executing instructions

[0415] Based on the calculated results, the server sends instructions to traffic lights and digital signs, which change the traffic light cycle and digital sign display in real time, for example, extending the green light time at a particular intersection or allowing priority traffic in certain lanes.

[0416] Recognizing user emotions with an emotion engine

[0417] An emotion engine built into the user device recognizes emotions in real time from the user's facial expressions, voice, pulse rate, etc. For example, if the user is feeling impatient or angry, the emotion engine will detect that emotional state.

[0418] Traffic information notification to users

[0419] The server adjusts and notifies traffic information and optimal routes based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, it will suggest a less congested route and select a relaxing route.

[0420] Specific examples

[0421] For example, if you enter a prompt such as "Analyze the current traffic situation, and if the driver is feeling stressed, choose the optimal route for the self-driving vehicle to relax," the system will collect and analyze traffic data, recognize the user's emotional state using its emotion engine, and suggest the optimal route.

[0422] In this way, the traffic management system of the present invention enables optimization of traffic conditions in real time, rapid response, and provision of customized traffic information according to the user's emotions, thereby maintaining smooth and comfortable traffic flow.

[0423] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0424] Step 1:

[0425] Traffic data is collected from cameras and sensors. The server collects data in real time from cameras and sensors installed at each intersection, major road, and pedestrian area in the city. The input includes the number of vehicles, speed, weather, and road surface conditions at each location. This data is collected and temporarily stored. The output is the collected raw data.

[0426] Step 2:

[0427] Preprocess the collected data. The server denoises the collected data, imputes missing values, and standardizes the data. This preprocessing generates clean data suitable for analysis. The input includes the raw data collected in step 1. The output is the preprocessed data.

[0428] Step 3:

[0429] The preprocessed data is used to input data into a generative AI model. The server inputs the preprocessed data into the generative AI model to generate a traffic simulation. Specifically, the model predicts future traffic patterns based on past data and current conditions. The input includes the preprocessed data. The output is the traffic simulation results.

[0430] Step 4:

[0431] Calculates optimal traffic routes and traffic light timings. The server runs an optimization algorithm based on traffic simulations to calculate optimal routes and traffic light timings. Specifically, it uses a genetic algorithm to optimize traffic flow. Simulation results are included as inputs. Optimal traffic routes and traffic light timings are obtained as outputs.

[0432] Step 5:

[0433] Based on the calculation results, the server sends instructions to traffic lights and digital signs. The server transmits the calculation results to the traffic lights and digital signs, which then perform the actual traffic control. The inputs include optimal traffic routes and traffic light timings. The output is control information for the traffic lights and digital signs.

[0434] Step 6:

[0435] The emotion engine recognizes the user's emotions. The user device uses the emotion engine to analyze the driver's facial expressions, voice, pulse rate, etc. to recognize their current emotional state. Input includes the user's biometric information. The recognized emotional state is obtained as output.

[0436] Step 7:

[0437] The optimal transportation route is proposed based on the user's emotional state. The server adjusts the traffic information based on the recognized emotional state and notifies the user of the optimal route. The input includes the emotional state recognized in step 6. The output is optimal route information adapted to the emotional state.

[0438] These steps enable optimal traffic information and route suggestions based on the user's emotional state. For example, when the prompt "Analyze the current traffic situation and select the optimal route for the autonomous vehicle to relax if the driver is feeling stressed" is entered, the system will suggest the optimal route through the above steps.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] [Second embodiment]

[0443] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0444] 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.

[0445] 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).

[0446] 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.

[0447] 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.

[0448] 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).

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] 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."

[0455] The present invention is a system for optimizing urban traffic management in real time. This system uses multiple cameras and sensors, generative AI models, and optimization algorithms to reduce traffic congestion and accidents. Specific embodiments of the system are described below.

[0456] System configuration

[0457] The system includes the following components:

[0458] 1. Cameras and Sensors

[0459] 2. Data collection and analysis server

[0460] 3. Server that runs the optimization algorithm

[0461] 4. Traffic lights and digital signs that receive instructions

[0462] 5. Terminal that notifies users of traffic information

[0463] System Operation

[0464] Data collection from cameras and sensors

[0465] The devices collect data in real time from cameras and sensors installed at intersections, major roads, and pedestrian areas throughout the city.

[0466] For example, cameras installed on major roads capture the number and speed of vehicles and detect pedestrian movement, while sensors measure weather and road conditions.

[0467] Data analysis by server

[0468] The server immediately preprocesses the collected data to remove noise, impute missing values, and standardize the data, then inputs this preprocessed data into the generative AI model.

[0469] As a specific example, if there is a sudden increase in traffic volume at a particular intersection, the noise-removed data can be used to assess the risk of congestion at that point.

[0470] Traffic simulation generation

[0471] The server uses a generative AI model to generate traffic simulations, which use a 3D model of the city to visualize vehicle and pedestrian movements in real time.

[0472] As a concrete example, a simulation is performed at a specific intersection to visually confirm which route can avoid the congestion.

[0473] Calculating optimal traffic routes and traffic light timings

[0474] The server uses an optimization algorithm to calculate optimal traffic routes and traffic light timings based on analysis results and simulations.

[0475] As a specific example, a genetic algorithm is used to re-adjust the timing of traffic lights at an intersection to improve overall traffic flow.

[0476] Sending and executing instructions

[0477] The server sends instructions to traffic lights and digital signs based on the calculated results, which changes the traffic light cycle and the display content of digital signs in real time.

[0478] As a specific example, the green light time at a particular intersection may be extended to allow priority traffic to certain lanes.

[0479] Real-time monitoring and feedback

[0480] The server continuously monitors traffic conditions, reanalyzing and optimizing the system based on new data collected, and taking immediate action if traffic conditions change.

[0481] As a concrete example, if a sudden accident occurs, the information will be analyzed immediately and instructions for a new optimal route will be sent to traffic lights and digital signs.

[0482] Traffic information notification to users

[0483] Users receive traffic information via a dedicated app or information board, including current traffic conditions, optimal detour routes, and predicted congestion information.

[0484] As a specific example, the system will notify the user's smartphone in real time of the "optimal route from the current location to the destination" and the "congestion status at specific intersections."

[0485] In this way, the traffic management system according to the present invention enables real-time optimization of traffic conditions and rapid response, thereby keeping traffic flow smooth.

[0486] The processing flow will be explained below.

[0487] Step 1:

[0488] The devices collect data in real time from cameras and sensors installed at intersections and major roads across the city.

[0489] Specifically, cameras capture the number and speed of vehicles, and sensors measure weather and road conditions.

[0490] Step 2:

[0491] The terminals transmit the collected traffic data to a central server.

[0492] Specifically, the terminal packetizes data at set intervals and sends it to the server using a secure communication protocol.

[0493] Step 3:

[0494] Preprocessing data received by the server.

[0495] Specific operations include noise removal, data integration, missing value completion, and data standardization.

[0496] Step 4:

[0497] The server inputs the preprocessed data into the generative AI model.

[0498] Specifically, the formatted data is converted into the input format for the AI ​​model and supplied to the model.

[0499] Step 5:

[0500] The server generates a traffic simulation using a generative AI model.

[0501] Specifically, it works by reflecting real-time data in a 3D model of the city and virtually recreating traffic movements.

[0502] Step 6:

[0503] The server calculates the optimal traffic route and traffic light timing based on the simulation results.

[0504] Specifically, the system uses genetic algorithms and linear programming to search for and calculate optimal solutions.

[0505] Step 7:

[0506] The server sends the calculated optimal traffic light timing instructions to the traffic lights and digital signs.

[0507] Specifically, the traffic light communicates with the server via an API to change the traffic light cycle.

[0508] Step 8:

[0509] The server continuously monitors traffic conditions.

[0510] Specifically, new data is collected and analyzed to be reflected in the system.

[0511] Step 9:

[0512] The server runs the feedback loop as needed.

[0513] Specifically, the data is analyzed and optimized again, and the instructions on traffic lights and digital signs are updated.

[0514] Step 10:

[0515] Users receive traffic information through a dedicated app or information board.

[0516] Specifically, it notifies smartphone apps and web portals of real-time traffic information and optimal routes.

[0517] Example 1

[0518] 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."

[0519] In urban traffic management, traffic congestion and accidents occur frequently, and smoother traffic flow is required. To address this, a more efficient system that realizes real-time traffic optimization is needed, as conventional traffic management systems have limitations in real-time adaptability and accuracy.

[0520] 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.

[0521] In this invention, the server includes means for pre-processing traffic data collected from cameras and sensors in real time, means for using the pre-processed data to input into a generative AI model to generate a traffic simulation, and means for calculating optimal traffic routes and traffic light timings using an optimization algorithm, thereby enabling analysis and optimization of traffic conditions in real time.

[0522] "Cameras and sensors" are devices that collect visual and non-visual data and are installed to monitor and measure traffic conditions.

[0523] "Traffic data" is a general term for information collected to describe traffic conditions, such as the number, speed, and location of vehicles, pedestrian movement, weather conditions, and road conditions.

[0524] "Preprocessing" refers to the process of converting collected raw data into a format that is easy to analyze by performing processes such as noise removal, missing value completion, and standardization.

[0525] A "generative AI model" is a mathematical model that uses machine learning technology to generate traffic simulations and predict future traffic conditions.

[0526] "Traffic simulation" is a virtual traffic flow model based on real traffic conditions, and is a means of visualizing the movement of vehicles and pedestrians, the effects of traffic lights, etc.

[0527] An "optimization algorithm" is a computational method used to calculate optimal traffic routes and traffic light timings under given conditions.

[0528] "Traffic lights and digital signs" means signal control devices and information display electronic billboards installed for traffic management purposes.

[0529] "Traffic information" refers to information used to keep traffic flowing smoothly, such as real-time traffic conditions, congestion forecasts, and optimal detour routes.

[0530] "User" means any person or entity that uses the system to receive traffic information.

[0531] This invention is a system for optimizing urban traffic management in real time. This system uses multiple cameras and sensors, generative AI models, and optimization algorithms to reduce traffic congestion and accidents. It also automatically manages traffic signals and digital signs, and notifies users of traffic information. Specific aspects of the system are described below.

[0532] System configuration

[0533] The system consists of the following components:

[0534] 1. Cameras and Sensors

[0535] 2. Data collection and analysis server

[0536] 3. Server that runs the optimization algorithm

[0537] 4. Traffic lights and digital signs that receive instructions

[0538] 5. Terminal that notifies users of traffic information

[0539] Data collection from cameras and sensors

[0540] The devices collect real-time data from cameras and sensors installed at intersections, major roads, and pedestrian areas throughout the city. Specifically, cameras installed on major roads capture the number of vehicles, their speeds, and pedestrian movements, while weather sensors collect data such as outside temperature, precipitation, and wind speed.

[0541] Data Preprocessing

[0542] The server preprocesses the collected data, applying algorithms for noise removal, missing value imputation, and standardization to convert it into a format that is easier to analyze. For example, it removes outliers and estimates missing data to improve the accuracy of the data.

[0543] Generative AI model inputs

[0544] The server then inputs the pre-processed data into a generative AI model, which is used to predict future traffic conditions based on vehicle counts, speeds, weather information, pedestrian data, and more.

[0545] Traffic simulation generation

[0546] The server generates traffic simulations using generative AI models. The simulations run on a 3D model of the city and graphically visualize the movements of vehicles and pedestrians. For example, the simulation can predict whether a particular intersection will experience congestion.

[0547] Calculating optimal traffic routes and traffic light timings

[0548] The server uses optimization algorithms to calculate optimal traffic routes and traffic light timings based on analysis results and simulations. For example, it uses genetic algorithms to adjust traffic light cycles and perform calculations to maximize traffic flow.

[0549] Sending and executing instructions

[0550] Based on the results of the calculations, the server sends instructions to traffic lights and digital signs, which can change the traffic light cycle and sign display in real time, for example extending the green light time at an intersection or giving priority to certain lanes.

[0551] Real-time monitoring and feedback

[0552] The server continuously monitors traffic conditions, reanalyzing and optimizing them based on new data. If traffic conditions change, it immediately formulates countermeasures and sends instructions to traffic lights and digital signs again.

[0553] Traffic information notification to users

[0554] Users receive the latest traffic information through a dedicated app or information board. Notifications include current traffic conditions, optimal detour routes, and predicted congestion information. For example, users' smartphone apps can receive real-time notifications of the "optimal route from their current location to their destination" and "congestion status at specific intersections."

[0555] This system will enable the optimization of urban traffic conditions in real time, ensuring smooth traffic flow.

[0556] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0557] Step 1:

[0558] The device collects data from cameras and sensors installed at intersections, major roads, and pedestrian areas within the city. It takes in the number of vehicles, their speed, weather information, and pedestrian movements as input, and provides raw data as output. Specifically, cameras installed on major roads capture the number and speed of vehicles once per second, while sensors measure temperature and precipitation.

[0559] Step 2:

[0560] The server preprocesses the raw data. As input, it receives raw data from cameras and sensors, performs noise removal, missing value imputation, and data standardization. As output, it generates preprocessed clean data. Specifically, it filters out outliers and imputes missing data using a mean imputation algorithm.

[0561] Step 3:

[0562] The server inputs the preprocessed data into the generative AI model. The clean data is provided as input to the AI ​​model, and a traffic simulation is generated as output. Specifically, the number of vehicles, speed, and weather information are input into the AI ​​model to predict future traffic conditions.

[0563] Step 4:

[0564] The server runs an optimization algorithm based on the generated traffic simulation. It takes in traffic simulation data as input and applies the optimization algorithm to calculate the optimal traffic route and traffic light timing. The optimal route and traffic light timing are obtained as output. Specifically, it uses a genetic algorithm to perform calculations to adjust the traffic light cycle at intersections.

[0565] Step 5:

[0566] The server sends instructions to traffic lights and digital signs based on the calculation results. As input, it receives optimal traffic light timing and route information, and sends execution instructions to traffic lights and digital signs. As output, the status of the traffic lights and signs that received the instructions is changed in real time. Specific actions include issuing an instruction to the traffic lights to extend the green light and setting priority for certain lanes.

[0567] Step 6:

[0568] The server continuously monitors traffic conditions and reanalyzes and optimizes based on newly collected data. It performs necessary reanalysis based on continuously acquired traffic data as input. It generates new optimization results as output. Specifically, it captures information about accidents and congestion and retransmits detour route instructions to traffic lights and digital signs.

[0569] Step 7:

[0570] Users receive the latest traffic information through a dedicated app or information board. The system receives traffic information from the server as input and displays real-time traffic conditions, detour routes, congestion forecasts, etc. as output. Specific operations include notifying the user's smartphone of the "optimal route from the current location to the destination" and the "congestion status of specific intersections."

[0571] (Application example 1)

[0572] 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."

[0573] Urban traffic management is becoming increasingly complex, and efficient traffic management is difficult, especially in areas where traffic congestion and accidents are frequent. In particular, the introduction of autonomous vehicles has increased the need for traffic management systems that enable these vehicles to select optimal routes in real time and safely reach their destinations. In order to solve this problem, the present invention aims to provide an optimal traffic management system for autonomous vehicles to operate efficiently and safely.

[0574] 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.

[0575] In this invention, the server includes means for analyzing traffic data collected from cameras and sensors in real time, means for generating traffic simulations using a generative AI model, means for calculating optimal traffic routes and traffic light timings, means for sending instructions to traffic lights and digital signs based on the calculation results, means for continuously monitoring and optimizing traffic conditions, means for notifying users of traffic information, means for collecting data from cameras and sensors equipped in autonomous vehicles and analyzing the data in real time, means for calculating optimal routes based on the analysis results and sending instructions to the autonomous vehicles, and means for continuously monitoring traffic conditions while the autonomous vehicles are moving and updating the routes as necessary. This enables autonomous vehicles to monitor urban traffic conditions in real time, receive instructions on optimal routes and traffic light timings, and operate safely and efficiently.

[0576] A "camera" is a device for taking images and recording them as data.

[0577] A "sensor" is a device that detects a physical quantity and converts it into an electrical signal.

[0578] "Real time" refers to the instantaneous capture and processing of ongoing events.

[0579] "Analysis" is the process of investigating and examining collected data to extract meaning.

[0580] A "generative AI model" is an artificial intelligence model that can generate new samples from data.

[0581] "Traffic simulation" is the process of simulating traffic movements on a computer.

[0582] An "optimal route" is the route to a destination that is the most efficient in terms of time, distance, etc.

[0583] A "traffic light" is a device that emits a light signal to control traffic flow.

[0584] A "digital sign" is a sign that displays information using a digital display.

[0585] "Monitoring" refers to the continuous observation and recording of specific data or conditions.

[0586] "Optimization" refers to adjusting processes and systems to make the most efficient use of resources.

[0587] A "user" is a person or device that uses a system or service.

[0588] An "autonomous vehicle" is a vehicle that uses artificial intelligence and sensors to drive and move automatically.

[0589] "Analysis results" refers to the specific information and conclusions obtained through the analysis.

[0590] "Sending instructions" refers to conveying orders for a particular action or behavior.

[0591] This invention is a system that enables autonomous vehicles to monitor urban traffic conditions in real time and optimize operation. This system is composed of a combination of cameras, sensors, generative AI models, traffic simulation, optimization algorithms, monitoring functions, and information notification functions.

[0592] System configuration

[0593] The system includes the following components:

[0594] 1. Cameras and sensors: These are equipped on autonomous vehicles and collect real-time information on surrounding traffic conditions, vehicle counts, pedestrian movement, weather conditions, and more.

[0595] 2. Server: Analyzes collected data in real time and generates traffic simulations using generative AI models.

[0596] 3. Optimization algorithm: Based on the simulation results, the optimal traffic routes and traffic light timings are calculated.

[0597] 4. Instruction sending device: Sends instructions to traffic lights and digital signs based on the calculation results.

[0598] 5. Monitoring device: Continuously monitors traffic conditions and optimizes the entire system as needed.

[0599] 6. User device: Drivers and autonomous vehicles receive information on optimal traffic routes and predicted congestion.

[0600] System processing procedure

[0601] 1. Data Collection:

[0602] Cameras and sensors capture the surrounding traffic conditions and send the data to a server in real time.

[0603] 2. Data Analysis:

[0604] The server preprocesses the collected data, removing noise and standardizing the data.

[0605] 3. Simulation generation:

[0606] The pre-processed data is fed into a generative AI model to generate a city-wide traffic simulation.

[0607] 4. Run the optimization algorithm:

[0608] Based on the simulation results, the optimal route and traffic light timing are calculated.

[0609] 5. Send instructions:

[0610] Optimal routes and traffic light timings are transmitted in real time to autonomous vehicles and digital signs.

[0611] 6. Real-time monitoring:

[0612] Continuously monitor traffic conditions and update routes and traffic light timings as needed.

[0613] 7. Notice to Users:

[0614] The user or vehicle system receives optimal operational information through a dedicated app.

[0615] Hardware and software used

[0616] Cameras and sensors: devices that capture images and convert physical quantities into electrical signals (e.g., LIDAR, radar)

[0617] Server: Data analysis, AI model execution, optimization algorithm execution (e.g. TensorFlow model)

[0618] Generative AI models: AI that generates new examples (e.g., GANs, generative models)

[0619] Cloud platform: Integrated management of data transmission, analysis, and optimization processes (e.g., AWS, Google Cloud)

[0620] Specific examples

[0621] For example, if there is a sudden increase in traffic volume at an intersection, cameras and sensors collect that information, which is then instantly analyzed by a server. A generative AI model then runs a simulation based on that data, and an optimization algorithm calculates the optimal route and traffic light timing. This information is then sent in real time to the autonomous vehicle, which then selects the optimal route and drives accordingly.

[0622] Prompt Sentence Examples

[0623] "What is the best route for travel based on current vehicle count, weather, and road conditions? Analyze the last 10 minutes of data at major intersections to find a route that avoids traffic jams."

[0624] The present invention enables autonomous vehicles to monitor traffic conditions in real time and operate safely and efficiently.

[0625] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0626] Step 1:

[0627] Data collection

[0628] The device collects data from cameras and sensors installed in the autonomous vehicle, specifically using LIDAR and radar to capture the surrounding traffic conditions, and transmits the data in real time.

[0629] Input: Real-time traffic data (number of vehicles, location, speed, weather, etc.)

[0630] Output: Raw data collected

[0631] Step 2:

[0632] Data Preprocessing

[0633] The server preprocesses the collected raw data, removing noise, filling in missing data, and standardizing the data. Specifically, it uses a scaler to standardize the data and applies a filter to remove noise.

[0634] Input: Raw data collected

[0635] Output: Preprocessed data (denoised and in standardized format)

[0636] Step 3:

[0637] Simulation Generation

[0638] The server inputs the preprocessed data into a generative AI model to generate a traffic simulation. The model uses the data to predict traffic trends across the city and visualizes them as a 3D model. Specifically, the traffic simulation is generated using frameworks such as TensorFlow.

[0639] Input: Preprocessed data

[0640] Output: Generated traffic simulation (traffic trends throughout the city)

[0641] Step 4:

[0642] Calculating optimal routes and traffic light timings

[0643] The server calculates optimal traffic routes and traffic light timings based on the simulation results obtained from the generative AI model. A specific example is solving optimization problems using genetic algorithms.

[0644] Input: Traffic simulation results

[0645] Output: Optimal traffic routes and traffic light timings

[0646] Step 5:

[0647] Sending instructions

[0648] The server calculates optimal routes and traffic light timings and sends them as instructions to autonomous vehicles and digital signs, allowing them to optimally operate in real time.

[0649] Input: Optimal traffic routes and traffic light timings

[0650] Output: Instructions received by autonomous vehicles and digital signs

[0651] Step 6:

[0652] Real-time monitoring

[0653] The device continuously monitors traffic conditions, and the server analyzes new data and updates the entire system as needed. Specifically, if a new situation arises, the data is re-collected and the process described above is repeated.

[0654] Input: Real-time new traffic data

[0655] Output: Updated optimal route and traffic light timings

[0656] Step 7:

[0657] User Notifications

[0658] Users receive optimal travel information through a dedicated app, which displays real-time traffic conditions, optimal routes, predicted congestion information, and more.

[0659] Input: Optimal route and traffic information from the server

[0660] Output: Traffic information received by the user through the app

[0661] For example, the prompt is:

[0662] "What is the best route for travel based on current vehicle count, weather, and road conditions? Analyze the last 10 minutes of data at major intersections to find a route that avoids traffic jams."

[0663] 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.

[0664] The present invention combines a system that analyzes traffic data collected from cameras and sensors in real time, generates traffic simulations using generative AI models, and calculates optimal traffic routes and traffic light timings with an emotion engine that recognizes user emotions. Specific embodiments of the system are described below.

[0665] System configuration

[0666] The system includes the following components:

[0667] 1. Cameras and Sensors

[0668] 2. Data collection and analysis server

[0669] 3. Servers that use generative AI models

[0670] 4. Server that runs the optimization algorithm

[0671] 5. Traffic lights and digital signs that receive instructions

[0672] 6. User Device with Emotion Engine

[0673] 7. Terminals that notify users of traffic information

[0674] System Operation

[0675] Data collection from cameras and sensors

[0676] The devices collect real-time data from cameras and sensors installed at intersections, major roads, and pedestrian areas throughout the city. For example, cameras installed on major roads capture the number and speed of vehicles, while sensors measure weather and road conditions.

[0677] Data analysis by server

[0678] The server immediately preprocesses the collected data to remove noise, fill in missing values, and standardize the data. This preprocessed data is then input into a generative AI model. For example, if there is a sudden increase in traffic volume at a particular intersection, the noise-removed data can be used to evaluate the risk of congestion at that point.

[0679] Traffic simulation generation

[0680] The server generates traffic simulations using generative AI models. The simulations use a 3D model of the city to visualize vehicle and pedestrian movements in real time. For example, a simulation of a specific intersection can be performed to visually confirm which route will avoid congestion.

[0681] Calculating optimal traffic routes and traffic light timings

[0682] The server uses optimization algorithms to calculate optimal traffic routes and traffic light timings based on analysis results and simulations. For example, a genetic algorithm is used to readjust the timing of traffic lights at a certain intersection to improve overall traffic flow.

[0683] Sending and executing instructions

[0684] The server sends instructions to traffic lights and digital signs based on the calculated results, which changes the traffic light cycle and digital sign display in real time. For example, it extends the green light time at certain intersections and allows priority traffic to certain lanes.

[0685] Real-time monitoring and feedback

[0686] The server continuously monitors traffic conditions, reanalyzing and optimizing them based on new data collected. If there are changes in traffic conditions, countermeasures are taken immediately. For example, if a sudden accident occurs, the information is analyzed immediately and new optimal route instructions are sent to traffic lights and digital signs.

[0687] Recognizing user emotions with an emotion engine

[0688] An emotion engine built into the user's device recognizes emotions in real time from the user's facial expressions, voice, pulse rate, etc. For example, if the user is feeling impatient or angry, the emotion engine will detect that emotional state.

[0689] Traffic information notification to users

[0690] The server adjusts and notifies traffic information and optimal routes based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, the server will suggest a less congested route and select a route that will allow them to relax.

[0691] In this way, the traffic management system of the present invention enables optimization of traffic conditions in real time, rapid response, and provision of customized traffic information according to the user's emotions, thereby maintaining smooth and comfortable traffic flow.

[0692] The processing flow will be explained below.

[0693] Step 1:

[0694] The devices collect data in real time from cameras and sensors installed at intersections and major roads across the city.

[0695] Specifically, cameras capture the number and speed of vehicles, and sensors measure weather and road conditions.

[0696] Step 2:

[0697] The terminals transmit the collected traffic data to a central server.

[0698] Specifically, the terminal packetizes data at set intervals and sends it to the server using a secure communication protocol.

[0699] Step 3:

[0700] Preprocessing data received by the server.

[0701] Specific operations include noise removal, data integration, missing value completion, and data standardization.

[0702] Step 4:

[0703] The server inputs the preprocessed data into the generative AI model.

[0704] Specifically, the formatted data is converted into the input format for the AI ​​model and supplied to the model.

[0705] Step 5:

[0706] The server generates a traffic simulation using a generative AI model.

[0707] Specifically, it works by reflecting real-time data in a 3D model of the city and virtually recreating traffic movements.

[0708] Step 6:

[0709] The server calculates the optimal traffic route and traffic light timing based on the simulation results.

[0710] Specifically, the system uses genetic algorithms and linear programming to search for and calculate optimal solutions.

[0711] Step 7:

[0712] The server sends the calculated optimal traffic light timing instructions to the traffic lights and digital signs.

[0713] Specifically, the traffic light communicates with the server via an API to change the traffic light cycle.

[0714] Step 8:

[0715] The server continuously monitors traffic conditions.

[0716] Specifically, new data is collected and analyzed to be reflected in the system.

[0717] Step 9:

[0718] The server runs the feedback loop as needed.

[0719] Specifically, the data is analyzed and optimized again, and the instructions on traffic lights and digital signs are updated.

[0720] Step 10:

[0721] The emotion engine built into the user's device recognizes emotions in real time from the user's facial expressions, voice, pulse rate, etc.

[0722] Specifically, it uses facial recognition technology and biometric sensors to determine whether the user is feeling anxiety or anger.

[0723] Step 11:

[0724] The server adjusts and notifies traffic information and optimal routes based on the user's emotions recognized by the emotion engine.

[0725] Specifically, if the user is feeling stressed, the system suggests a less congested route and selects a route that allows the user to relax.

[0726] Step 12:

[0727] Users receive traffic information through a dedicated app or information board.

[0728] Specifically, it notifies smartphone apps and web portals of real-time traffic information and optimal routes.

[0729] Example 2

[0730] 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."

[0731] Current urban traffic management systems lack the ability to analyze traffic data in real time and provide optimal routes, increasing the risk of traffic congestion and accidents. Furthermore, they lack the means to provide traffic information that takes into account the user's emotional state, and there is a lack of means to improve the user experience. This makes it difficult to maintain smooth traffic flow while reducing user stress.

[0732] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for analyzing traffic data collected from cameras and sensors in real time, a means for generating a traffic simulation using a generative AI model, and a means for calculating optimal traffic routes and traffic light timings. This enables real-time traffic data analysis and the presentation of optimal traffic routes.

[0733] Furthermore, the server includes means for continuously monitoring and optimizing statistical data, means for notifying users of traffic information, means for recognizing users' emotions in real time, and means for adjusting traffic information based on the users' emotions, thereby enabling the provision of customized traffic information according to the users' emotional state while maintaining smooth traffic flow.

[0734] "Traffic data" refers to information such as the number of vehicles, their speed, weather, and road conditions collected from cameras and sensors.

[0735] "Real-time analysis" refers to the immediate processing of traffic data collected from cameras and sensors to analyze current traffic conditions.

[0736] A "generative AI model" is a type of artificial intelligence used to simulate future traffic conditions based on collected data.

[0737] "Traffic simulation" is the process of virtually recreating traffic conditions across an entire city using generative AI models.

[0738] An "optimization algorithm" is a mathematical method for calculating optimal traffic routes and traffic light timings based on given data and conditions.

[0739] A "traffic light" is a lighting device installed to control traffic at intersections and major roads.

[0740] "Information display device" means a digital sign or other display device used to display traffic conditions or instructions.

[0741] "Monitoring" is the process of continuously collecting and analyzing traffic data.

[0742] "Feedback" refers to adjusting system settings and traffic light timing based on data obtained through monitoring.

[0743] The "emotion engine" is a technology that recognizes emotions in real time from a user's facial expressions, voice, pulse rate, etc.

[0744] "User" refers to a vehicle driver or pedestrian using the system.

[0745] "Customized traffic information" is information that provides optimal traffic routes and notification content according to the user's emotional state.

[0746] The present invention combines a system that analyzes traffic data collected from cameras and sensors in real time, generates traffic simulations using generative AI models, and calculates optimal traffic routes and traffic light timings with an emotion engine that recognizes user emotions. Specific embodiments of the system are described below.

[0747] System configuration

[0748] The system includes the following components:

[0749] 1. Cameras and Sensors

[0750] 2. Data collection and analysis server

[0751] 3. Servers that use generative AI models

[0752] 4. Server that runs the optimization algorithm

[0753] 5. Signals and information display devices that receive instructions

[0754] 6. User Device with Emotion Engine

[0755] 7. Terminals that notify users of traffic information

[0756] System Operation

[0757] Data collection from cameras and sensors

[0758] The devices collect real-time data from cameras and sensors installed at intersections, major roads, and pedestrian areas throughout the city. For example, cameras installed on major roads capture the number and speed of vehicles, while sensors measure weather and road conditions.

[0759] Data analysis by server

[0760] The server immediately preprocesses the collected data, removing noise, filling in missing values, and standardizing the data. For example, if there is a sudden increase in traffic volume at a particular intersection, the noise-removed data can be used to evaluate the risk of congestion at that point.

[0761] Traffic simulation generation

[0762] The server generates traffic simulations using generative AI models. The simulations use a 3D model of the city to visualize vehicle and pedestrian movements in real time. For example, a simulation of a specific intersection can be performed to visually confirm which route will avoid congestion.

[0763] Calculating optimal traffic routes and traffic light timings

[0764] The server uses optimization algorithms to calculate optimal traffic routes and traffic light timings based on analysis results and simulations. For example, a genetic algorithm is used to readjust the timing of traffic lights at a certain intersection to improve overall traffic flow.

[0765] Sending and executing instructions

[0766] The server sends instructions to traffic lights and information display devices based on the calculated results. This allows the traffic light cycle and the information displayed on the information display devices to be changed in real time. For example, the green light time at a specific intersection can be extended, and priority traffic can be given to certain lanes.

[0767] Real-time monitoring and feedback

[0768] The server continuously monitors traffic conditions, reanalyzing and optimizing them based on new data collected. If there are changes in traffic conditions, countermeasures are taken immediately. For example, if a sudden accident occurs, the information is analyzed immediately and new optimal route instructions are sent to traffic lights and information display devices.

[0769] Recognizing user emotions with an emotion engine

[0770] An emotion engine built into the user's device recognizes emotions in real time from the user's facial expressions, voice, pulse rate, etc. For example, if the user is feeling impatient or angry, the emotion engine will detect that emotional state.

[0771] Traffic information notification to users

[0772] The server adjusts and notifies traffic information and optimal routes based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, the server will suggest a less congested route and select a route that will allow them to relax.

[0773] Prompt Sentence Examples

[0774] "If the user is stressed, suggest the best route to avoid the congestion. For example, imagine a scenario where it's Friday evening and you're stuck in traffic going through a particular intersection."

[0775] In this way, the traffic management system of the present invention enables optimization of traffic conditions in real time, rapid response, and provision of customized traffic information according to the user's emotions, thereby maintaining smooth and comfortable traffic flow.

[0776] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0777] Step 1:

[0778] The device collects real-time data from cameras and sensors installed at each intersection, major road, and pedestrian area within the city. This data includes the number and speed of vehicles, weather, road conditions, etc. For example, cameras installed on major roads capture the number and speed of vehicles every second, while sensors continuously measure changes in weather and road conditions. This data collection is stored directly in a database and can be accessed for subsequent processing steps.

[0779] Step 2:

[0780] The server receives the data collected in step 1 and performs noise removal, missing value imputation, and data standardization. This preprocessing is essential to improve the accuracy of data analysis. The input data is the raw data collected in step 1, and the output is clean data with noise removed and missing values ​​imputed. For example, outliers are removed and missing weather data is imputed from past data.

[0781] Step 3:

[0782] The server inputs the preprocessed data into a generative AI model to generate a traffic simulation. The input data is the clean data from Step 2, and the output is a 3D traffic model of the entire city. Specifically, it can simulate vehicle and pedestrian movements at specific intersections in real time and predict future traffic congestion.

[0783] Step 4:

[0784] The server uses an optimization algorithm to calculate traffic routes and traffic light timings based on the simulation results obtained from the generative AI model. The input data are the simulation results from Step 3, and the output is the optimal traffic route and traffic light timing settings. Specifically, it evaluates multiple possible traffic light timing settings and finds the part that maximizes overall traffic efficiency.

[0785] Step 5:

[0786] The server sends instructions to traffic lights and information display devices based on the optimal traffic route and traffic light timing settings. The input data is the calculation results from step 4, and the output is the traffic light timing settings and updates to the information display devices. Specifically, the server controls the traffic lights to extend the green light time for specific lanes and allow priority traffic.

[0787] Step 6:

[0788] The server continuously monitors traffic conditions and reanalyzes and optimizes the system based on newly collected data. The input data is traffic data updated in real time, and the output is the latest optimization results. For example, if a traffic accident occurs, the information is analyzed immediately and new optimal route instructions are sent to traffic lights and information display devices.

[0789] Step 7:

[0790] The emotion engine built into the user's device recognizes emotions in real time from the user's facial expressions, voice, pulse rate, etc. The input data is the user's emotional indicators, and the output is the detected emotional state. Specifically, it detects when the user is feeling impatient or anger while in the car.

[0791] Step 8:

[0792] The server recognizes the user's emotions and adjusts and notifies traffic information and optimal routes based on those emotions. The input data is the user's emotional state detected in step 7 and real-time traffic data, and the output is adjusted traffic information notified to the user. For example, if the user is feeling stressed, a notification is sent suggesting an optimal route that avoids traffic jams.

[0793] This enables the system to optimize traffic conditions in real time, respond quickly, and provide customized traffic information according to the user's emotions.

[0794] (Application example 2)

[0795] 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."

[0796] In modern transportation systems, traffic congestion and accidents occur frequently, often hindering efficient travel. In particular, increased traffic at specific intersections or during certain times of the day causes congestion, necessitating the optimization of traffic flow. Furthermore, because the driver's emotional state affects driving safety and efficiency, it is necessary to reduce driver stress and provide a comfortable driving environment. However, conventional traffic management systems have difficulty analyzing data in real time and proposing optimal routes, and they do not propose routes that take driver emotions into account.

[0797] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing traffic data collected from cameras and sensors in real time, means for generating a traffic simulation using a generative AI model, and means for calculating optimal traffic routes and traffic light timings. This makes it possible to analyze traffic conditions in real time and calculate optimal routes and traffic light timings. In addition, the server includes means for analyzing data collected from cameras and sensors of an autonomous vehicle in real time and updating traffic information, means equipped with an emotion engine that recognizes the driver's emotions, and means for proposing optimal traffic routes according to the driver's emotional state, making it possible to propose routes that adapt to the driver's emotional state and reduce stress.

[0798] "Cameras and sensors" are devices used to collect information on traffic conditions, vehicles, pedestrians, etc. in real time.

[0799] "Real-time analysis means" refers to technology for processing and analyzing collected data immediately.

[0800] A "generative AI model" is an artificial intelligence model used to generate traffic simulations and predict future traffic patterns based on past data and current conditions.

[0801] The "means for generating traffic simulations" refers to the use of collected traffic data to virtually reproduce the movement of the entire traffic system and calculate optimal routes and traffic light timings.

[0802] "Means for calculating optimal traffic routes and traffic light timings" refers to technology that analyzes traffic flow and congestion conditions to determine routes that allow vehicles to travel efficiently and traffic light control timings.

[0803] "Means for sending instructions to traffic lights and digital signs" refers to means for transmitting calculated optimal traffic control information to traffic lights and digital signs and for carrying out actual traffic control.

[0804] "Means for continuously monitoring and optimizing traffic conditions" refers to technology that constantly monitors collected traffic data and performs optimization in real time as necessary.

[0805] "Means for notifying users of traffic information" refers to technology for informing users of calculated optimal routes and traffic information.

[0806] "Autonomous vehicle cameras and sensors" means devices installed on autonomous vehicles that sense surrounding traffic conditions and collect data.

[0807] The "emotion engine" is a technology that analyzes the driver's facial expressions, voice, pulse rate, etc. to recognize their current emotional state.

[0808] The "means for suggesting optimal transportation routes according to emotional state" is a technology for suggesting optimal routes to reduce driver stress based on the recognized emotional state.

[0809] This system analyzes traffic data in real time, generates traffic simulations using generative AI models, and calculates optimal traffic routes and traffic signal timings. In addition, by incorporating an emotion engine that recognizes the user's emotions, it is possible to suggest optimal routes that reduce the user's stress.

[0810] System configuration

[0811] The system includes the following components:

[0812] 1. Cameras and Sensors

[0813] 2. Data collection and analysis server

[0814] 3. Servers that use generative AI models

[0815] 4. Server that runs the optimization algorithm

[0816] 5. Traffic lights and digital signs that receive instructions

[0817] 6. User Device with Emotion Engine

[0818] 7. Terminals that notify users of traffic information

[0819] System Operation

[0820] Data collection from cameras and sensors

[0821] Cameras and sensors will be installed at intersections, major roads, and pedestrian areas throughout the city to collect real-time data. For example, cameras on major roads will capture the number and speed of vehicles, while sensors will measure weather and road conditions.

[0822] Data analysis

[0823] The server immediately preprocesses the collected data to remove noise, fill in missing values, and standardize the data. This preprocessed data is then input into a generative AI model. For example, if there is a sudden increase in traffic volume at a particular intersection, the noise-removed data is used to evaluate the risk of congestion at that point.

[0824] Traffic simulation generation

[0825] The server generates traffic simulations using generative AI models. The simulations use a 3D model of the city to visualize vehicle and pedestrian movements in real time. For example, a simulation can be run at a specific intersection to visually confirm which route will avoid congestion.

[0826] Calculating optimal traffic routes and traffic light timings

[0827] The server uses optimization algorithms to calculate optimal traffic routes and traffic light timings based on analysis and simulation results. For example, a genetic algorithm can be used to readjust the timing of traffic lights at a given intersection to improve overall traffic flow.

[0828] Sending and executing instructions

[0829] Based on the calculated results, the server sends instructions to traffic lights and digital signs, which change the traffic light cycle and digital sign display in real time, for example, extending the green light time at a particular intersection or allowing priority traffic in certain lanes.

[0830] Recognizing user emotions with an emotion engine

[0831] An emotion engine built into the user device recognizes emotions in real time from the user's facial expressions, voice, pulse rate, etc. For example, if the user is feeling impatient or angry, the emotion engine will detect that emotional state.

[0832] Traffic information notification to users

[0833] The server adjusts and notifies traffic information and optimal routes based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, it will suggest a less congested route and select a relaxing route.

[0834] Specific examples

[0835] For example, if you enter a prompt such as "Analyze the current traffic situation, and if the driver is feeling stressed, choose the optimal route for the self-driving vehicle to relax," the system will collect and analyze traffic data, recognize the user's emotional state using its emotion engine, and suggest the optimal route.

[0836] In this way, the traffic management system of the present invention enables optimization of traffic conditions in real time, rapid response, and provision of customized traffic information according to the user's emotions, thereby maintaining smooth and comfortable traffic flow.

[0837] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0838] Step 1:

[0839] Traffic data is collected from cameras and sensors. The server collects data in real time from cameras and sensors installed at each intersection, major road, and pedestrian area in the city. The input includes the number of vehicles, speed, weather, and road surface conditions at each location. This data is collected and temporarily stored. The output is the collected raw data.

[0840] Step 2:

[0841] Preprocess the collected data. The server denoises the collected data, imputes missing values, and standardizes the data. This preprocessing generates clean data suitable for analysis. The input includes the raw data collected in step 1. The output is the preprocessed data.

[0842] Step 3:

[0843] The preprocessed data is used to input data into a generative AI model. The server inputs the preprocessed data into the generative AI model to generate a traffic simulation. Specifically, the model predicts future traffic patterns based on past data and current conditions. The input includes the preprocessed data. The output is the traffic simulation results.

[0844] Step 4:

[0845] Calculates optimal traffic routes and traffic light timings. The server runs an optimization algorithm based on traffic simulations to calculate optimal routes and traffic light timings. Specifically, it uses a genetic algorithm to optimize traffic flow. Simulation results are included as inputs. Optimal traffic routes and traffic light timings are obtained as outputs.

[0846] Step 5:

[0847] Based on the calculation results, the server sends instructions to traffic lights and digital signs. The server transmits the calculation results to the traffic lights and digital signs, which then perform the actual traffic control. The inputs include optimal traffic routes and traffic light timings. The output is control information for the traffic lights and digital signs.

[0848] Step 6:

[0849] The emotion engine recognizes the user's emotions. The user device uses the emotion engine to analyze the driver's facial expressions, voice, pulse rate, etc. to recognize their current emotional state. Input includes the user's biometric information. The recognized emotional state is obtained as output.

[0850] Step 7:

[0851] The optimal transportation route is proposed based on the user's emotional state. The server adjusts the traffic information based on the recognized emotional state and notifies the user of the optimal route. The input includes the emotional state recognized in step 6. The output is optimal route information adapted to the emotional state.

[0852] These steps enable optimal traffic information and route suggestions based on the user's emotional state. For example, when the prompt "Analyze the current traffic situation and select the optimal route for the autonomous vehicle to relax if the driver is feeling stressed" is entered, the system will suggest the optimal route through the above steps.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] [Third embodiment]

[0857] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0858] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0859] 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).

[0860] 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.

[0861] 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.

[0862] 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).

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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."

[0869] The present invention is a system for optimizing urban traffic management in real time. This system uses multiple cameras and sensors, generative AI models, and optimization algorithms to reduce traffic congestion and accidents. Specific embodiments of the system are described below.

[0870] System configuration

[0871] The system includes the following components:

[0872] 1. Cameras and Sensors

[0873] 2. Data collection and analysis server

[0874] 3. Server that runs the optimization algorithm

[0875] 4. Traffic lights and digital signs that receive instructions

[0876] 5. Terminal that notifies users of traffic information

[0877] System Operation

[0878] Data collection from cameras and sensors

[0879] The devices collect data in real time from cameras and sensors installed at intersections, major roads, and pedestrian areas throughout the city.

[0880] For example, cameras installed on major roads capture the number and speed of vehicles and detect pedestrian movement, while sensors measure weather and road conditions.

[0881] Data analysis by server

[0882] The server immediately preprocesses the collected data to remove noise, impute missing values, and standardize the data, then inputs this preprocessed data into the generative AI model.

[0883] As a specific example, if there is a sudden increase in traffic volume at a particular intersection, the noise-removed data can be used to assess the risk of congestion at that point.

[0884] Traffic simulation generation

[0885] The server uses a generative AI model to generate traffic simulations, which use a 3D model of the city to visualize vehicle and pedestrian movements in real time.

[0886] As a concrete example, a simulation is performed at a specific intersection to visually confirm which route can avoid the congestion.

[0887] Calculating optimal traffic routes and traffic light timings

[0888] The server uses an optimization algorithm to calculate optimal traffic routes and traffic light timings based on analysis results and simulations.

[0889] As a specific example, a genetic algorithm is used to re-adjust the timing of traffic lights at an intersection to improve overall traffic flow.

[0890] Sending and executing instructions

[0891] The server sends instructions to traffic lights and digital signs based on the calculated results, which changes the traffic light cycle and the display content of digital signs in real time.

[0892] As a specific example, the green light time at a particular intersection may be extended to allow priority traffic to certain lanes.

[0893] Real-time monitoring and feedback

[0894] The server continuously monitors traffic conditions, reanalyzing and optimizing the system based on new data collected, and taking immediate action if traffic conditions change.

[0895] As a concrete example, if a sudden accident occurs, the information will be analyzed immediately and instructions for a new optimal route will be sent to traffic lights and digital signs.

[0896] Traffic information notification to users

[0897] Users receive traffic information via a dedicated app or information board, including current traffic conditions, optimal detour routes, and predicted congestion information.

[0898] As a specific example, the system will notify the user's smartphone in real time of the "optimal route from the current location to the destination" and the "congestion status at specific intersections."

[0899] In this way, the traffic management system according to the present invention enables real-time optimization of traffic conditions and rapid response, thereby keeping traffic flow smooth.

[0900] The processing flow will be explained below.

[0901] Step 1:

[0902] The devices collect data in real time from cameras and sensors installed at intersections and major roads across the city.

[0903] Specifically, cameras capture the number and speed of vehicles, and sensors measure weather and road conditions.

[0904] Step 2:

[0905] The terminals transmit the collected traffic data to a central server.

[0906] Specifically, the terminal packetizes data at set intervals and sends it to the server using a secure communication protocol.

[0907] Step 3:

[0908] Preprocessing data received by the server.

[0909] Specific operations include noise removal, data integration, missing value completion, and data standardization.

[0910] Step 4:

[0911] The server inputs the preprocessed data into the generative AI model.

[0912] Specifically, the formatted data is converted into the input format for the AI ​​model and supplied to the model.

[0913] Step 5:

[0914] The server generates a traffic simulation using a generative AI model.

[0915] Specifically, it works by reflecting real-time data in a 3D model of the city and virtually recreating traffic movements.

[0916] Step 6:

[0917] The server calculates the optimal traffic route and traffic light timing based on the simulation results.

[0918] Specifically, the system uses genetic algorithms and linear programming to search for and calculate optimal solutions.

[0919] Step 7:

[0920] The server sends the calculated optimal traffic light timing instructions to the traffic lights and digital signs.

[0921] Specifically, the traffic light communicates with the server via an API to change the traffic light cycle.

[0922] Step 8:

[0923] The server continuously monitors traffic conditions.

[0924] Specifically, new data is collected and analyzed to be reflected in the system.

[0925] Step 9:

[0926] The server runs the feedback loop as needed.

[0927] Specifically, the data is analyzed and optimized again, and the instructions on traffic lights and digital signs are updated.

[0928] Step 10:

[0929] Users receive traffic information through a dedicated app or information board.

[0930] Specifically, it notifies smartphone apps and web portals of real-time traffic information and optimal routes.

[0931] Example 1

[0932] 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."

[0933] In urban traffic management, traffic congestion and accidents occur frequently, and smoother traffic flow is required. To address this, a more efficient system that realizes real-time traffic optimization is needed, as conventional traffic management systems have limitations in real-time adaptability and accuracy.

[0934] 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.

[0935] In this invention, the server includes means for pre-processing traffic data collected from cameras and sensors in real time, means for using the pre-processed data to input into a generative AI model to generate a traffic simulation, and means for calculating optimal traffic routes and traffic light timings using an optimization algorithm, thereby enabling analysis and optimization of traffic conditions in real time.

[0936] "Cameras and sensors" are devices that collect visual and non-visual data and are installed to monitor and measure traffic conditions.

[0937] "Traffic data" is a general term for information collected to describe traffic conditions, such as the number, speed, and location of vehicles, pedestrian movement, weather conditions, and road conditions.

[0938] "Preprocessing" refers to the process of converting collected raw data into a format that is easy to analyze by performing processes such as noise removal, missing value completion, and standardization.

[0939] A "generative AI model" is a mathematical model that uses machine learning technology to generate traffic simulations and predict future traffic conditions.

[0940] "Traffic simulation" is a virtual traffic flow model based on real traffic conditions, and is a means of visualizing the movement of vehicles and pedestrians, the effects of traffic lights, etc.

[0941] An "optimization algorithm" is a computational method used to calculate optimal traffic routes and traffic light timings under given conditions.

[0942] "Traffic lights and digital signs" means signal control devices and information display electronic billboards installed for traffic management purposes.

[0943] "Traffic information" refers to information used to keep traffic flowing smoothly, such as real-time traffic conditions, congestion forecasts, and optimal detour routes.

[0944] "User" means any person or entity that uses the system to receive traffic information.

[0945] This invention is a system for optimizing urban traffic management in real time. This system uses multiple cameras and sensors, generative AI models, and optimization algorithms to reduce traffic congestion and accidents. It also automatically manages traffic signals and digital signs, and notifies users of traffic information. Specific aspects of the system are described below.

[0946] System configuration

[0947] The system consists of the following components:

[0948] 1. Cameras and Sensors

[0949] 2. Data collection and analysis server

[0950] 3. Server that runs the optimization algorithm

[0951] 4. Traffic lights and digital signs that receive instructions

[0952] 5. Terminal that notifies users of traffic information

[0953] Data collection from cameras and sensors

[0954] The devices collect real-time data from cameras and sensors installed at intersections, major roads, and pedestrian areas throughout the city. Specifically, cameras installed on major roads capture the number of vehicles, their speeds, and pedestrian movements, while weather sensors collect data such as outside temperature, precipitation, and wind speed.

[0955] Data Preprocessing

[0956] The server preprocesses the collected data, applying algorithms for noise removal, missing value imputation, and standardization to convert it into a format that is easier to analyze. For example, it removes outliers and estimates missing data to improve the accuracy of the data.

[0957] Generative AI model inputs

[0958] The server then inputs the pre-processed data into a generative AI model, which is used to predict future traffic conditions based on vehicle counts, speeds, weather information, pedestrian data, and more.

[0959] Traffic simulation generation

[0960] The server generates traffic simulations using generative AI models. The simulations run on a 3D model of the city and graphically visualize the movements of vehicles and pedestrians. For example, the simulation can predict whether a particular intersection will experience congestion.

[0961] Calculating optimal traffic routes and traffic light timings

[0962] The server uses optimization algorithms to calculate optimal traffic routes and traffic light timings based on analysis results and simulations. For example, it uses genetic algorithms to adjust traffic light cycles and perform calculations to maximize traffic flow.

[0963] Sending and executing instructions

[0964] Based on the results of the calculations, the server sends instructions to traffic lights and digital signs, which can change the traffic light cycle and sign display in real time, for example extending the green light time at an intersection or giving priority to certain lanes.

[0965] Real-time monitoring and feedback

[0966] The server continuously monitors traffic conditions, reanalyzing and optimizing them based on new data. If traffic conditions change, it immediately formulates countermeasures and sends instructions to traffic lights and digital signs again.

[0967] Traffic information notification to users

[0968] Users receive the latest traffic information through a dedicated app or information board. Notifications include current traffic conditions, optimal detour routes, and predicted congestion information. For example, users' smartphone apps can receive real-time notifications of the "optimal route from their current location to their destination" and "congestion status at specific intersections."

[0969] This system will enable the optimization of urban traffic conditions in real time, ensuring smooth traffic flow.

[0970] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0971] Step 1:

[0972] The device collects data from cameras and sensors installed at intersections, major roads, and pedestrian areas within the city. It takes in the number of vehicles, their speed, weather information, and pedestrian movements as input, and provides raw data as output. Specifically, cameras installed on major roads capture the number and speed of vehicles once per second, while sensors measure temperature and precipitation.

[0973] Step 2:

[0974] The server preprocesses the raw data. As input, it receives raw data from cameras and sensors, performs noise removal, missing value imputation, and data standardization. As output, it generates preprocessed clean data. Specifically, it filters out outliers and imputes missing data using a mean imputation algorithm.

[0975] Step 3:

[0976] The server inputs the preprocessed data into the generative AI model. The clean data is provided as input to the AI ​​model, and a traffic simulation is generated as output. Specifically, the number of vehicles, speed, and weather information are input into the AI ​​model to predict future traffic conditions.

[0977] Step 4:

[0978] The server runs an optimization algorithm based on the generated traffic simulation. It takes in traffic simulation data as input and applies the optimization algorithm to calculate the optimal traffic route and traffic light timing. The optimal route and traffic light timing are obtained as output. Specifically, it uses a genetic algorithm to perform calculations to adjust the traffic light cycle at intersections.

[0979] Step 5:

[0980] The server sends instructions to traffic lights and digital signs based on the calculation results. As input, it receives optimal traffic light timing and route information, and sends execution instructions to traffic lights and digital signs. As output, the status of the traffic lights and signs that received the instructions is changed in real time. Specific actions include issuing an instruction to the traffic lights to extend the green light and setting priority for certain lanes.

[0981] Step 6:

[0982] The server continuously monitors traffic conditions and reanalyzes and optimizes based on newly collected data. It performs necessary reanalysis based on continuously acquired traffic data as input. It generates new optimization results as output. Specifically, it captures information about accidents and congestion and retransmits detour route instructions to traffic lights and digital signs.

[0983] Step 7:

[0984] Users receive the latest traffic information through a dedicated app or information board. The system receives traffic information from the server as input and displays real-time traffic conditions, detour routes, congestion forecasts, etc. as output. Specific operations include notifying the user's smartphone of the "optimal route from the current location to the destination" and the "congestion status of specific intersections."

[0985] (Application example 1)

[0986] 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."

[0987] Urban traffic management is becoming increasingly complex, and efficient traffic management is difficult, especially in areas where traffic congestion and accidents are frequent. In particular, the introduction of autonomous vehicles has increased the need for traffic management systems that enable these vehicles to select optimal routes in real time and safely reach their destinations. In order to solve this problem, the present invention aims to provide an optimal traffic management system for autonomous vehicles to operate efficiently and safely.

[0988] 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.

[0989] In this invention, the server includes means for analyzing traffic data collected from cameras and sensors in real time, means for generating traffic simulations using a generative AI model, means for calculating optimal traffic routes and traffic light timings, means for sending instructions to traffic lights and digital signs based on the calculation results, means for continuously monitoring and optimizing traffic conditions, means for notifying users of traffic information, means for collecting data from cameras and sensors equipped in autonomous vehicles and analyzing the data in real time, means for calculating optimal routes based on the analysis results and sending instructions to the autonomous vehicles, and means for continuously monitoring traffic conditions while the autonomous vehicles are moving and updating the routes as necessary. This enables autonomous vehicles to monitor urban traffic conditions in real time, receive instructions on optimal routes and traffic light timings, and operate safely and efficiently.

[0990] A "camera" is a device for taking images and recording them as data.

[0991] A "sensor" is a device that detects a physical quantity and converts it into an electrical signal.

[0992] "Real time" refers to the instantaneous capture and processing of ongoing events.

[0993] "Analysis" is the process of investigating and examining collected data to extract meaning.

[0994] A "generative AI model" is an artificial intelligence model that can generate new samples from data.

[0995] "Traffic simulation" is the process of simulating traffic movements on a computer.

[0996] An "optimal route" is the route to a destination that is the most efficient in terms of time, distance, etc.

[0997] A "traffic light" is a device that emits a light signal to control traffic flow.

[0998] A "digital sign" is a sign that displays information using a digital display.

[0999] "Monitoring" refers to the continuous observation and recording of specific data or conditions.

[1000] "Optimization" refers to adjusting processes and systems to make the most efficient use of resources.

[1001] A "user" is a person or device that uses a system or service.

[1002] An "autonomous vehicle" is a vehicle that uses artificial intelligence and sensors to drive and move automatically.

[1003] "Analysis results" refers to the specific information and conclusions obtained through the analysis.

[1004] "Sending instructions" refers to conveying orders for a particular action or behavior.

[1005] This invention is a system that enables autonomous vehicles to monitor urban traffic conditions in real time and optimize operation. This system is composed of a combination of cameras, sensors, generative AI models, traffic simulation, optimization algorithms, monitoring functions, and information notification functions.

[1006] System configuration

[1007] The system includes the following components:

[1008] 1. Cameras and sensors: These are equipped on autonomous vehicles and collect real-time information on surrounding traffic conditions, vehicle counts, pedestrian movement, weather conditions, and more.

[1009] 2. Server: Analyzes collected data in real time and generates traffic simulations using generative AI models.

[1010] 3. Optimization algorithm: Based on the simulation results, the optimal traffic routes and traffic light timings are calculated.

[1011] 4. Instruction sending device: Sends instructions to traffic lights and digital signs based on the calculation results.

[1012] 5. Monitoring device: Continuously monitors traffic conditions and optimizes the entire system as needed.

[1013] 6. User device: Drivers and autonomous vehicles receive information on optimal traffic routes and predicted congestion.

[1014] System processing procedure

[1015] 1. Data Collection:

[1016] Cameras and sensors capture the surrounding traffic conditions and send the data to a server in real time.

[1017] 2. Data Analysis:

[1018] The server preprocesses the collected data, removing noise and standardizing the data.

[1019] 3. Simulation generation:

[1020] The pre-processed data is fed into a generative AI model to generate a city-wide traffic simulation.

[1021] 4. Run the optimization algorithm:

[1022] Based on the simulation results, the optimal route and traffic light timing are calculated.

[1023] 5. Send instructions:

[1024] Optimal routes and traffic light timings are transmitted in real time to autonomous vehicles and digital signs.

[1025] 6. Real-time monitoring:

[1026] Continuously monitor traffic conditions and update routes and traffic light timings as needed.

[1027] 7. Notice to Users:

[1028] The user or vehicle system receives optimal operational information through a dedicated app.

[1029] Hardware and software used

[1030] Cameras and sensors: devices that capture images and convert physical quantities into electrical signals (e.g., LIDAR, radar)

[1031] Server: Data analysis, AI model execution, optimization algorithm execution (e.g. TensorFlow model)

[1032] Generative AI models: AI that generates new examples (e.g., GANs, generative models)

[1033] Cloud platform: Integrated management of data transmission, analysis, and optimization processes (e.g., AWS, Google Cloud)

[1034] Specific examples

[1035] For example, if there is a sudden increase in traffic volume at an intersection, cameras and sensors collect that information, which is then instantly analyzed by a server. A generative AI model then runs a simulation based on that data, and an optimization algorithm calculates the optimal route and traffic light timing. This information is then sent in real time to the autonomous vehicle, which then selects the optimal route and drives accordingly.

[1036] Prompt Sentence Examples

[1037] "What is the best route for travel based on current vehicle count, weather, and road conditions? Analyze the last 10 minutes of data at major intersections to find a route that avoids traffic jams."

[1038] The present invention enables autonomous vehicles to monitor traffic conditions in real time and operate safely and efficiently.

[1039] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1040] Step 1:

[1041] Data collection

[1042] The device collects data from cameras and sensors installed in the autonomous vehicle, specifically using LIDAR and radar to capture the surrounding traffic conditions, and transmits the data in real time.

[1043] Input: Real-time traffic data (number of vehicles, location, speed, weather, etc.)

[1044] Output: Raw data collected

[1045] Step 2:

[1046] Data Preprocessing

[1047] The server preprocesses the collected raw data, removing noise, filling in missing data, and standardizing the data. Specifically, it uses a scaler to standardize the data and applies a filter to remove noise.

[1048] Input: Raw data collected

[1049] Output: Preprocessed data (denoised and in standardized format)

[1050] Step 3:

[1051] Simulation Generation

[1052] The server inputs the preprocessed data into a generative AI model to generate a traffic simulation. The model uses the data to predict traffic trends across the city and visualizes them as a 3D model. Specifically, the traffic simulation is generated using frameworks such as TensorFlow.

[1053] Input: Preprocessed data

[1054] Output: Generated traffic simulation (traffic trends throughout the city)

[1055] Step 4:

[1056] Calculating optimal routes and traffic light timings

[1057] The server calculates optimal traffic routes and traffic light timings based on the simulation results obtained from the generative AI model. A specific example is solving optimization problems using genetic algorithms.

[1058] Input: Traffic simulation results

[1059] Output: Optimal traffic routes and traffic light timings

[1060] Step 5:

[1061] Sending instructions

[1062] The server calculates optimal routes and traffic light timings and sends them as instructions to autonomous vehicles and digital signs, allowing them to optimally operate in real time.

[1063] Input: Optimal traffic routes and traffic light timings

[1064] Output: Instructions received by autonomous vehicles and digital signs

[1065] Step 6:

[1066] Real-time monitoring

[1067] The device continuously monitors traffic conditions, and the server analyzes new data and updates the entire system as needed. Specifically, if a new situation arises, the data is re-collected and the process described above is repeated.

[1068] Input: Real-time new traffic data

[1069] Output: Updated optimal route and traffic light timings

[1070] Step 7:

[1071] User Notifications

[1072] Users receive optimal travel information through a dedicated app, which displays real-time traffic conditions, optimal routes, predicted congestion information, and more.

[1073] Input: Optimal route and traffic information from the server

[1074] Output: Traffic information received by the user through the app

[1075] For example, the prompt is:

[1076] "What is the best route for travel based on current vehicle count, weather, and road conditions? Analyze the last 10 minutes of data at major intersections to find a route that avoids traffic jams."

[1077] 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.

[1078] The present invention combines a system that analyzes traffic data collected from cameras and sensors in real time, generates traffic simulations using generative AI models, and calculates optimal traffic routes and traffic light timings with an emotion engine that recognizes user emotions. Specific embodiments of the system are described below.

[1079] System configuration

[1080] The system includes the following components:

[1081] 1. Cameras and Sensors

[1082] 2. Data collection and analysis server

[1083] 3. Servers that use generative AI models

[1084] 4. Server that runs the optimization algorithm

[1085] 5. Traffic lights and digital signs that receive instructions

[1086] 6. User Device with Emotion Engine

[1087] 7. Terminals that notify users of traffic information

[1088] System Operation

[1089] Data collection from cameras and sensors

[1090] The devices collect real-time data from cameras and sensors installed at intersections, major roads, and pedestrian areas throughout the city. For example, cameras installed on major roads capture the number and speed of vehicles, while sensors measure weather and road conditions.

[1091] Data analysis by server

[1092] The server immediately preprocesses the collected data to remove noise, fill in missing values, and standardize the data. This preprocessed data is then input into a generative AI model. For example, if there is a sudden increase in traffic volume at a particular intersection, the noise-removed data can be used to evaluate the risk of congestion at that point.

[1093] Traffic simulation generation

[1094] The server generates traffic simulations using generative AI models. The simulations use a 3D model of the city to visualize vehicle and pedestrian movements in real time. For example, a simulation of a specific intersection can be performed to visually confirm which route will avoid congestion.

[1095] Calculating optimal traffic routes and traffic light timings

[1096] The server uses optimization algorithms to calculate optimal traffic routes and traffic light timings based on analysis results and simulations. For example, a genetic algorithm is used to readjust the timing of traffic lights at a certain intersection to improve overall traffic flow.

[1097] Sending and executing instructions

[1098] The server sends instructions to traffic lights and digital signs based on the calculated results, which changes the traffic light cycle and digital sign display in real time. For example, it extends the green light time at certain intersections and allows priority traffic to certain lanes.

[1099] Real-time monitoring and feedback

[1100] The server continuously monitors traffic conditions, reanalyzing and optimizing them based on new data collected. If there are changes in traffic conditions, countermeasures are taken immediately. For example, if a sudden accident occurs, the information is analyzed immediately and new optimal route instructions are sent to traffic lights and digital signs.

[1101] Recognizing user emotions with an emotion engine

[1102] An emotion engine built into the user's device recognizes emotions in real time from the user's facial expressions, voice, pulse rate, etc. For example, if the user is feeling impatient or angry, the emotion engine will detect that emotional state.

[1103] Traffic information notification to users

[1104] The server adjusts and notifies traffic information and optimal routes based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, the server will suggest a less congested route and select a route that will allow them to relax.

[1105] In this way, the traffic management system of the present invention enables optimization of traffic conditions in real time, rapid response, and provision of customized traffic information according to the user's emotions, thereby maintaining smooth and comfortable traffic flow.

[1106] The processing flow will be explained below.

[1107] Step 1:

[1108] The devices collect data in real time from cameras and sensors installed at intersections and major roads across the city.

[1109] Specifically, cameras capture the number and speed of vehicles, and sensors measure weather and road conditions.

[1110] Step 2:

[1111] The terminals transmit the collected traffic data to a central server.

[1112] Specifically, the terminal packetizes data at set intervals and sends it to the server using a secure communication protocol.

[1113] Step 3:

[1114] Preprocessing data received by the server.

[1115] Specific operations include noise removal, data integration, missing value completion, and data standardization.

[1116] Step 4:

[1117] The server inputs the preprocessed data into the generative AI model.

[1118] Specifically, the formatted data is converted into the input format for the AI ​​model and supplied to the model.

[1119] Step 5:

[1120] The server generates a traffic simulation using a generative AI model.

[1121] Specifically, it works by reflecting real-time data in a 3D model of the city and virtually recreating traffic movements.

[1122] Step 6:

[1123] The server calculates the optimal traffic route and traffic light timing based on the simulation results.

[1124] Specifically, the system uses genetic algorithms and linear programming to search for and calculate optimal solutions.

[1125] Step 7:

[1126] The server sends the calculated optimal traffic light timing instructions to the traffic lights and digital signs.

[1127] Specifically, the traffic light communicates with the server via an API to change the traffic light cycle.

[1128] Step 8:

[1129] The server continuously monitors traffic conditions.

[1130] Specifically, new data is collected and analyzed to be reflected in the system.

[1131] Step 9:

[1132] The server runs the feedback loop as needed.

[1133] Specifically, the data is analyzed and optimized again, and the instructions on traffic lights and digital signs are updated.

[1134] Step 10:

[1135] The emotion engine built into the user's device recognizes emotions in real time from the user's facial expressions, voice, pulse rate, etc.

[1136] Specifically, it uses facial recognition technology and biometric sensors to determine whether the user is feeling anxiety or anger.

[1137] Step 11:

[1138] The server adjusts and notifies traffic information and optimal routes based on the user's emotions recognized by the emotion engine.

[1139] Specifically, if the user is feeling stressed, the system suggests a less congested route and selects a route that allows the user to relax.

[1140] Step 12:

[1141] Users receive traffic information through a dedicated app or information board.

[1142] Specifically, it notifies smartphone apps and web portals of real-time traffic information and optimal routes.

[1143] Example 2

[1144] 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."

[1145] Current urban traffic management systems lack the ability to analyze traffic data in real time and provide optimal routes, increasing the risk of traffic congestion and accidents. Furthermore, they lack the means to provide traffic information that takes into account the user's emotional state, and there is a lack of means to improve the user experience. This makes it difficult to maintain smooth traffic flow while reducing user stress.

[1146] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for analyzing traffic data collected from cameras and sensors in real time, a means for generating a traffic simulation using a generative AI model, and a means for calculating optimal traffic routes and traffic light timings. This enables real-time traffic data analysis and the presentation of optimal traffic routes.

[1147] Furthermore, the server includes means for continuously monitoring and optimizing statistical data, means for notifying users of traffic information, means for recognizing users' emotions in real time, and means for adjusting traffic information based on the users' emotions, thereby enabling the provision of customized traffic information according to the users' emotional state while maintaining smooth traffic flow.

[1148] "Traffic data" refers to information such as the number of vehicles, their speed, weather, and road conditions collected from cameras and sensors.

[1149] "Real-time analysis" refers to the immediate processing of traffic data collected from cameras and sensors to analyze current traffic conditions.

[1150] A "generative AI model" is a type of artificial intelligence used to simulate future traffic conditions based on collected data.

[1151] "Traffic simulation" is the process of virtually recreating traffic conditions across an entire city using generative AI models.

[1152] An "optimization algorithm" is a mathematical method for calculating optimal traffic routes and traffic light timings based on given data and conditions.

[1153] A "traffic light" is a lighting device installed to control traffic at intersections and major roads.

[1154] "Information display device" means a digital sign or other display device used to display traffic conditions or instructions.

[1155] "Monitoring" is the process of continuously collecting and analyzing traffic data.

[1156] "Feedback" refers to adjusting system settings and traffic light timing based on data obtained through monitoring.

[1157] The "emotion engine" is a technology that recognizes emotions in real time from a user's facial expressions, voice, pulse rate, etc.

[1158] "User" refers to a vehicle driver or pedestrian using the system.

[1159] "Customized traffic information" is information that provides optimal traffic routes and notification content according to the user's emotional state.

[1160] The present invention combines a system that analyzes traffic data collected from cameras and sensors in real time, generates traffic simulations using generative AI models, and calculates optimal traffic routes and traffic light timings with an emotion engine that recognizes user emotions. Specific embodiments of the system are described below.

[1161] System configuration

[1162] The system includes the following components:

[1163] 1. Cameras and Sensors

[1164] 2. Data collection and analysis server

[1165] 3. Servers that use generative AI models

[1166] 4. Server that runs the optimization algorithm

[1167] 5. Signals and information display devices that receive instructions

[1168] 6. User Device with Emotion Engine

[1169] 7. Terminals that notify users of traffic information

[1170] System Operation

[1171] Data collection from cameras and sensors

[1172] The devices collect real-time data from cameras and sensors installed at intersections, major roads, and pedestrian areas throughout the city. For example, cameras installed on major roads capture the number and speed of vehicles, while sensors measure weather and road conditions.

[1173] Data analysis by server

[1174] The server immediately preprocesses the collected data, removing noise, filling in missing values, and standardizing the data. For example, if there is a sudden increase in traffic volume at a particular intersection, the noise-removed data can be used to evaluate the risk of congestion at that point.

[1175] Traffic simulation generation

[1176] The server generates traffic simulations using generative AI models. The simulations use a 3D model of the city to visualize vehicle and pedestrian movements in real time. For example, a simulation of a specific intersection can be performed to visually confirm which route will avoid congestion.

[1177] Calculating optimal traffic routes and traffic light timings

[1178] The server uses optimization algorithms to calculate optimal traffic routes and traffic light timings based on analysis results and simulations. For example, a genetic algorithm is used to readjust the timing of traffic lights at a certain intersection to improve overall traffic flow.

[1179] Sending and executing instructions

[1180] The server sends instructions to traffic lights and information display devices based on the calculated results. This allows the traffic light cycle and the information displayed on the information display devices to be changed in real time. For example, the green light time at a specific intersection can be extended, and priority traffic can be given to certain lanes.

[1181] Real-time monitoring and feedback

[1182] The server continuously monitors traffic conditions, reanalyzing and optimizing them based on new data collected. If there are changes in traffic conditions, countermeasures are taken immediately. For example, if a sudden accident occurs, the information is analyzed immediately and new optimal route instructions are sent to traffic lights and information display devices.

[1183] Recognizing user emotions with an emotion engine

[1184] An emotion engine built into the user's device recognizes emotions in real time from the user's facial expressions, voice, pulse rate, etc. For example, if the user is feeling impatient or angry, the emotion engine will detect that emotional state.

[1185] Traffic information notification to users

[1186] The server adjusts and notifies traffic information and optimal routes based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, the server will suggest a less congested route and select a route that will allow them to relax.

[1187] Prompt Sentence Examples

[1188] "If the user is stressed, suggest the best route to avoid the congestion. For example, imagine a scenario where it's Friday evening and you're stuck in traffic going through a particular intersection."

[1189] In this way, the traffic management system of the present invention enables optimization of traffic conditions in real time, rapid response, and provision of customized traffic information according to the user's emotions, thereby maintaining smooth and comfortable traffic flow.

[1190] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1191] Step 1:

[1192] The device collects real-time data from cameras and sensors installed at each intersection, major road, and pedestrian area within the city. This data includes the number and speed of vehicles, weather, road conditions, etc. For example, cameras installed on major roads capture the number and speed of vehicles every second, while sensors continuously measure changes in weather and road conditions. This data collection is stored directly in a database and can be accessed for subsequent processing steps.

[1193] Step 2:

[1194] The server receives the data collected in step 1 and performs noise removal, missing value imputation, and data standardization. This preprocessing is essential to improve the accuracy of data analysis. The input data is the raw data collected in step 1, and the output is clean data with noise removed and missing values ​​imputed. For example, outliers are removed and missing weather data is imputed from past data.

[1195] Step 3:

[1196] The server inputs the preprocessed data into a generative AI model to generate a traffic simulation. The input data is the clean data from Step 2, and the output is a 3D traffic model of the entire city. Specifically, it can simulate vehicle and pedestrian movements at specific intersections in real time and predict future traffic congestion.

[1197] Step 4:

[1198] The server uses an optimization algorithm to calculate traffic routes and traffic light timings based on the simulation results obtained from the generative AI model. The input data are the simulation results from Step 3, and the output is the optimal traffic route and traffic light timing settings. Specifically, it evaluates multiple possible traffic light timing settings and finds the part that maximizes overall traffic efficiency.

[1199] Step 5:

[1200] The server sends instructions to traffic lights and information display devices based on the optimal traffic route and traffic light timing settings. The input data is the calculation results from step 4, and the output is the traffic light timing settings and updates to the information display devices. Specifically, the server controls the traffic lights to extend the green light time for specific lanes and allow priority traffic.

[1201] Step 6:

[1202] The server continuously monitors traffic conditions and reanalyzes and optimizes the system based on newly collected data. The input data is traffic data updated in real time, and the output is the latest optimization results. For example, if a traffic accident occurs, the information is analyzed immediately and new optimal route instructions are sent to traffic lights and information display devices.

[1203] Step 7:

[1204] The emotion engine built into the user's device recognizes emotions in real time from the user's facial expressions, voice, pulse rate, etc. The input data is the user's emotional indicators, and the output is the detected emotional state. Specifically, it detects when the user is feeling impatient or anger while in the car.

[1205] Step 8:

[1206] The server recognizes the user's emotions and adjusts and notifies traffic information and optimal routes based on those emotions. The input data is the user's emotional state detected in step 7 and real-time traffic data, and the output is adjusted traffic information notified to the user. For example, if the user is feeling stressed, a notification is sent suggesting an optimal route that avoids traffic jams.

[1207] This enables the system to optimize traffic conditions in real time, respond quickly, and provide customized traffic information according to the user's emotions.

[1208] (Application example 2)

[1209] 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."

[1210] In modern transportation systems, traffic congestion and accidents occur frequently, often hindering efficient travel. In particular, increased traffic at specific intersections or during certain times of the day causes congestion, necessitating the optimization of traffic flow. Furthermore, because the driver's emotional state affects driving safety and efficiency, it is necessary to reduce driver stress and provide a comfortable driving environment. However, conventional traffic management systems have difficulty analyzing data in real time and proposing optimal routes, and they do not propose routes that take driver emotions into account.

[1211] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing traffic data collected from cameras and sensors in real time, means for generating a traffic simulation using a generative AI model, and means for calculating optimal traffic routes and traffic light timings. This makes it possible to analyze traffic conditions in real time and calculate optimal routes and traffic light timings. In addition, the server includes means for analyzing data collected from cameras and sensors of an autonomous vehicle in real time and updating traffic information, means equipped with an emotion engine that recognizes the driver's emotions, and means for proposing optimal traffic routes according to the driver's emotional state, making it possible to propose routes that adapt to the driver's emotional state and reduce stress.

[1212] "Cameras and sensors" are devices used to collect information on traffic conditions, vehicles, pedestrians, etc. in real time.

[1213] "Real-time analysis means" refers to technology for processing and analyzing collected data immediately.

[1214] A "generative AI model" is an artificial intelligence model used to generate traffic simulations and predict future traffic patterns based on past data and current conditions.

[1215] The "means for generating traffic simulations" refers to the use of collected traffic data to virtually reproduce the movement of the entire traffic system and calculate optimal routes and traffic light timings.

[1216] "Means for calculating optimal traffic routes and traffic light timings" refers to technology that analyzes traffic flow and congestion conditions to determine routes that allow vehicles to travel efficiently and traffic light control timings.

[1217] "Means for sending instructions to traffic lights and digital signs" refers to means for transmitting calculated optimal traffic control information to traffic lights and digital signs and for carrying out actual traffic control.

[1218] "Means for continuously monitoring and optimizing traffic conditions" refers to technology that constantly monitors collected traffic data and performs optimization in real time as necessary.

[1219] "Means for notifying users of traffic information" refers to technology for informing users of calculated optimal routes and traffic information.

[1220] "Autonomous vehicle cameras and sensors" means devices installed on autonomous vehicles that sense surrounding traffic conditions and collect data.

[1221] The "emotion engine" is a technology that analyzes the driver's facial expressions, voice, pulse rate, etc. to recognize their current emotional state.

[1222] The "means for suggesting optimal transportation routes according to emotional state" is a technology for suggesting optimal routes to reduce driver stress based on the recognized emotional state.

[1223] This system analyzes traffic data in real time, generates traffic simulations using generative AI models, and calculates optimal traffic routes and traffic signal timings. In addition, by incorporating an emotion engine that recognizes the user's emotions, it is possible to suggest optimal routes that reduce the user's stress.

[1224] System configuration

[1225] The system includes the following components:

[1226] 1. Cameras and Sensors

[1227] 2. Data collection and analysis server

[1228] 3. Servers that use generative AI models

[1229] 4. Server that runs the optimization algorithm

[1230] 5. Traffic lights and digital signs that receive instructions

[1231] 6. User Device with Emotion Engine

[1232] 7. Terminals that notify users of traffic information

[1233] System Operation

[1234] Data collection from cameras and sensors

[1235] Cameras and sensors will be installed at intersections, major roads, and pedestrian areas throughout the city to collect real-time data. For example, cameras on major roads will capture the number and speed of vehicles, while sensors will measure weather and road conditions.

[1236] Data analysis

[1237] The server immediately preprocesses the collected data to remove noise, fill in missing values, and standardize the data. This preprocessed data is then input into a generative AI model. For example, if there is a sudden increase in traffic volume at a particular intersection, the noise-removed data is used to evaluate the risk of congestion at that point.

[1238] Traffic simulation generation

[1239] The server generates traffic simulations using generative AI models. The simulations use a 3D model of the city to visualize vehicle and pedestrian movements in real time. For example, a simulation can be run at a specific intersection to visually confirm which route will avoid congestion.

[1240] Calculating optimal traffic routes and traffic light timings

[1241] The server uses optimization algorithms to calculate optimal traffic routes and traffic light timings based on analysis and simulation results. For example, a genetic algorithm can be used to readjust the timing of traffic lights at a given intersection to improve overall traffic flow.

[1242] Sending and executing instructions

[1243] Based on the calculated results, the server sends instructions to traffic lights and digital signs, which change the traffic light cycle and digital sign display in real time, for example, extending the green light time at a particular intersection or allowing priority traffic in certain lanes.

[1244] Recognizing user emotions with an emotion engine

[1245] An emotion engine built into the user device recognizes emotions in real time from the user's facial expressions, voice, pulse rate, etc. For example, if the user is feeling impatient or angry, the emotion engine will detect that emotional state.

[1246] Traffic information notification to users

[1247] The server adjusts and notifies traffic information and optimal routes based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, it will suggest a less congested route and select a relaxing route.

[1248] Specific examples

[1249] For example, if you enter a prompt such as "Analyze the current traffic situation, and if the driver is feeling stressed, choose the optimal route for the self-driving vehicle to relax," the system will collect and analyze traffic data, recognize the user's emotional state using its emotion engine, and suggest the optimal route.

[1250] In this way, the traffic management system of the present invention enables optimization of traffic conditions in real time, rapid response, and provision of customized traffic information according to the user's emotions, thereby maintaining smooth and comfortable traffic flow.

[1251] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1252] Step 1:

[1253] Traffic data is collected from cameras and sensors. The server collects data in real time from cameras and sensors installed at each intersection, major road, and pedestrian area in the city. The input includes the number of vehicles, speed, weather, and road surface conditions at each location. This data is collected and temporarily stored. The output is the collected raw data.

[1254] Step 2:

[1255] Preprocess the collected data. The server denoises the collected data, imputes missing values, and standardizes the data. This preprocessing generates clean data suitable for analysis. The input includes the raw data collected in step 1. The output is the preprocessed data.

[1256] Step 3:

[1257] The preprocessed data is used to input data into a generative AI model. The server inputs the preprocessed data into the generative AI model to generate a traffic simulation. Specifically, the model predicts future traffic patterns based on past data and current conditions. The input includes the preprocessed data. The output is the traffic simulation results.

[1258] Step 4:

[1259] Calculates optimal traffic routes and traffic light timings. The server runs an optimization algorithm based on traffic simulations to calculate optimal routes and traffic light timings. Specifically, it uses a genetic algorithm to optimize traffic flow. Simulation results are included as inputs. Optimal traffic routes and traffic light timings are obtained as outputs.

[1260] Step 5:

[1261] Based on the calculation results, the server sends instructions to traffic lights and digital signs. The server transmits the calculation results to the traffic lights and digital signs, which then perform the actual traffic control. The inputs include optimal traffic routes and traffic light timings. The output is control information for the traffic lights and digital signs.

[1262] Step 6:

[1263] The emotion engine recognizes the user's emotions. The user device uses the emotion engine to analyze the driver's facial expressions, voice, pulse rate, etc. to recognize their current emotional state. Input includes the user's biometric information. The recognized emotional state is obtained as output.

[1264] Step 7:

[1265] The optimal transportation route is proposed based on the user's emotional state. The server adjusts the traffic information based on the recognized emotional state and notifies the user of the optimal route. The input includes the emotional state recognized in step 6. The output is optimal route information adapted to the emotional state.

[1266] These steps enable optimal traffic information and route suggestions based on the user's emotional state. For example, when the prompt "Analyze the current traffic situation and select the optimal route for the autonomous vehicle to relax if the driver is feeling stressed" is entered, the system will suggest the optimal route through the above steps.

[1267] 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.

[1268] 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.

[1269] 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.

[1270] [Fourth embodiment]

[1271] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1272] 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.

[1273] 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).

[1274] 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.

[1275] 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.

[1276] 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).

[1277] 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.

[1278] 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.

[1279] 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.

[1280] 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.

[1281] 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.

[1282] 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.

[1283] 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."

[1284] The present invention is a system for optimizing urban traffic management in real time. This system uses multiple cameras and sensors, generative AI models, and optimization algorithms to reduce traffic congestion and accidents. Specific embodiments of the system are described below.

[1285] System configuration

[1286] The system includes the following components:

[1287] 1. Cameras and Sensors

[1288] 2. Data collection and analysis server

[1289] 3. Server that runs the optimization algorithm

[1290] 4. Traffic lights and digital signs that receive instructions

[1291] 5. Terminal that notifies users of traffic information

[1292] System Operation

[1293] Data collection from cameras and sensors

[1294] The devices collect data in real time from cameras and sensors installed at intersections, major roads, and pedestrian areas throughout the city.

[1295] For example, cameras installed on major roads capture the number and speed of vehicles and detect pedestrian movement, while sensors measure weather and road conditions.

[1296] Data analysis by server

[1297] The server immediately preprocesses the collected data to remove noise, impute missing values, and standardize the data, then inputs this preprocessed data into the generative AI model.

[1298] As a specific example, if there is a sudden increase in traffic volume at a particular intersection, the noise-removed data can be used to assess the risk of congestion at that point.

[1299] Traffic simulation generation

[1300] The server uses a generative AI model to generate traffic simulations, which use a 3D model of the city to visualize vehicle and pedestrian movements in real time.

[1301] As a concrete example, a simulation is performed at a specific intersection to visually confirm which route can avoid the congestion.

[1302] Calculating optimal traffic routes and traffic light timings

[1303] The server uses an optimization algorithm to calculate optimal traffic routes and traffic light timings based on analysis results and simulations.

[1304] As a specific example, a genetic algorithm is used to re-adjust the timing of traffic lights at an intersection to improve overall traffic flow.

[1305] Sending and executing instructions

[1306] The server sends instructions to traffic lights and digital signs based on the calculated results, which changes the traffic light cycle and the display content of digital signs in real time.

[1307] As a specific example, the green light time at a particular intersection may be extended to allow priority traffic to certain lanes.

[1308] Real-time monitoring and feedback

[1309] The server continuously monitors traffic conditions, reanalyzing and optimizing the system based on new data collected, and taking immediate action if traffic conditions change.

[1310] As a concrete example, if a sudden accident occurs, the information will be analyzed immediately and instructions for a new optimal route will be sent to traffic lights and digital signs.

[1311] Traffic information notification to users

[1312] Users receive traffic information via a dedicated app or information board, including current traffic conditions, optimal detour routes, and predicted congestion information.

[1313] As a specific example, the system will notify the user's smartphone in real time of the "optimal route from the current location to the destination" and the "congestion status at specific intersections."

[1314] In this way, the traffic management system according to the present invention enables real-time optimization of traffic conditions and rapid response, thereby keeping traffic flow smooth.

[1315] The processing flow will be explained below.

[1316] Step 1:

[1317] The devices collect data in real time from cameras and sensors installed at intersections and major roads across the city.

[1318] Specifically, cameras capture the number and speed of vehicles, and sensors measure weather and road conditions.

[1319] Step 2:

[1320] The terminals transmit the collected traffic data to a central server.

[1321] Specifically, the terminal packetizes data at set intervals and sends it to the server using a secure communication protocol.

[1322] Step 3:

[1323] Preprocessing data received by the server.

[1324] Specific operations include noise removal, data integration, missing value completion, and data standardization.

[1325] Step 4:

[1326] The server inputs the preprocessed data into the generative AI model.

[1327] Specifically, the formatted data is converted into the input format for the AI ​​model and supplied to the model.

[1328] Step 5:

[1329] The server generates a traffic simulation using a generative AI model.

[1330] Specifically, it works by reflecting real-time data in a 3D model of the city and virtually recreating traffic movements.

[1331] Step 6:

[1332] The server calculates the optimal traffic route and traffic light timing based on the simulation results.

[1333] Specifically, the system uses genetic algorithms and linear programming to search for and calculate optimal solutions.

[1334] Step 7:

[1335] The server sends the calculated optimal traffic light timing instructions to the traffic lights and digital signs.

[1336] Specifically, the traffic light communicates with the server via an API to change the traffic light cycle.

[1337] Step 8:

[1338] The server continuously monitors traffic conditions.

[1339] Specifically, new data is collected and analyzed to be reflected in the system.

[1340] Step 9:

[1341] The server runs the feedback loop as needed.

[1342] Specifically, the data is analyzed and optimized again, and the instructions on traffic lights and digital signs are updated.

[1343] Step 10:

[1344] Users receive traffic information through a dedicated app or information board.

[1345] Specifically, it notifies smartphone apps and web portals of real-time traffic information and optimal routes.

[1346] Example 1

[1347] 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."

[1348] In urban traffic management, traffic congestion and accidents occur frequently, and smoother traffic flow is required. To address this, a more efficient system that realizes real-time traffic optimization is needed, as conventional traffic management systems have limitations in real-time adaptability and accuracy.

[1349] 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.

[1350] In this invention, the server includes means for pre-processing traffic data collected from cameras and sensors in real time, means for using the pre-processed data to input into a generative AI model to generate a traffic simulation, and means for calculating optimal traffic routes and traffic light timings using an optimization algorithm, thereby enabling analysis and optimization of traffic conditions in real time.

[1351] "Cameras and sensors" are devices that collect visual and non-visual data and are installed to monitor and measure traffic conditions.

[1352] "Traffic data" is a general term for information collected to describe traffic conditions, such as the number, speed, and location of vehicles, pedestrian movement, weather conditions, and road conditions.

[1353] "Preprocessing" refers to the process of converting collected raw data into a format that is easy to analyze by performing processes such as noise removal, missing value completion, and standardization.

[1354] A "generative AI model" is a mathematical model that uses machine learning technology to generate traffic simulations and predict future traffic conditions.

[1355] "Traffic simulation" is a virtual traffic flow model based on real traffic conditions, and is a means of visualizing the movement of vehicles and pedestrians, the effects of traffic lights, etc.

[1356] An "optimization algorithm" is a computational method used to calculate optimal traffic routes and traffic light timings under given conditions.

[1357] "Traffic lights and digital signs" means signal control devices and information display electronic billboards installed for traffic management purposes.

[1358] "Traffic information" refers to information used to keep traffic flowing smoothly, such as real-time traffic conditions, congestion forecasts, and optimal detour routes.

[1359] "User" means any person or entity that uses the system to receive traffic information.

[1360] This invention is a system for optimizing urban traffic management in real time. This system uses multiple cameras and sensors, generative AI models, and optimization algorithms to reduce traffic congestion and accidents. It also automatically manages traffic signals and digital signs, and notifies users of traffic information. Specific aspects of the system are described below.

[1361] System configuration

[1362] The system consists of the following components:

[1363] 1. Cameras and Sensors

[1364] 2. Data collection and analysis server

[1365] 3. Server that runs the optimization algorithm

[1366] 4. Traffic lights and digital signs that receive instructions

[1367] 5. Terminal that notifies users of traffic information

[1368] Data collection from cameras and sensors

[1369] The devices collect real-time data from cameras and sensors installed at intersections, major roads, and pedestrian areas throughout the city. Specifically, cameras installed on major roads capture the number of vehicles, their speeds, and pedestrian movements, while weather sensors collect data such as outside temperature, precipitation, and wind speed.

[1370] Data Preprocessing

[1371] The server preprocesses the collected data, applying algorithms for noise removal, missing value imputation, and standardization to convert it into a format that is easier to analyze. For example, it removes outliers and estimates missing data to improve the accuracy of the data.

[1372] Generative AI model inputs

[1373] The server then inputs the pre-processed data into a generative AI model, which is used to predict future traffic conditions based on vehicle counts, speeds, weather information, pedestrian data, and more.

[1374] Traffic simulation generation

[1375] The server generates traffic simulations using generative AI models. The simulations run on a 3D model of the city and graphically visualize the movements of vehicles and pedestrians. For example, the simulation can predict whether a particular intersection will experience congestion.

[1376] Calculating optimal traffic routes and traffic light timings

[1377] The server uses optimization algorithms to calculate optimal traffic routes and traffic light timings based on analysis results and simulations. For example, it uses genetic algorithms to adjust traffic light cycles and perform calculations to maximize traffic flow.

[1378] Sending and executing instructions

[1379] Based on the results of the calculations, the server sends instructions to traffic lights and digital signs, which can change the traffic light cycle and sign display in real time, for example extending the green light time at an intersection or giving priority to certain lanes.

[1380] Real-time monitoring and feedback

[1381] The server continuously monitors traffic conditions, reanalyzing and optimizing them based on new data. If traffic conditions change, it immediately formulates countermeasures and sends instructions to traffic lights and digital signs again.

[1382] Traffic information notification to users

[1383] Users receive the latest traffic information through a dedicated app or information board. Notifications include current traffic conditions, optimal detour routes, and predicted congestion information. For example, users' smartphone apps can receive real-time notifications of the "optimal route from their current location to their destination" and "congestion status at specific intersections."

[1384] This system will enable the optimization of urban traffic conditions in real time, ensuring smooth traffic flow.

[1385] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1386] Step 1:

[1387] The device collects data from cameras and sensors installed at intersections, major roads, and pedestrian areas within the city. It takes in the number of vehicles, their speed, weather information, and pedestrian movements as input, and provides raw data as output. Specifically, cameras installed on major roads capture the number and speed of vehicles once per second, while sensors measure temperature and precipitation.

[1388] Step 2:

[1389] The server preprocesses the raw data. As input, it receives raw data from cameras and sensors, performs noise removal, missing value imputation, and data standardization. As output, it generates preprocessed clean data. Specifically, it filters out outliers and imputes missing data using a mean imputation algorithm.

[1390] Step 3:

[1391] The server inputs the preprocessed data into the generative AI model. The clean data is provided as input to the AI ​​model, and a traffic simulation is generated as output. Specifically, the number of vehicles, speed, and weather information are input into the AI ​​model to predict future traffic conditions.

[1392] Step 4:

[1393] The server runs an optimization algorithm based on the generated traffic simulation. It takes in traffic simulation data as input and applies the optimization algorithm to calculate the optimal traffic route and traffic light timing. The optimal route and traffic light timing are obtained as output. Specifically, it uses a genetic algorithm to perform calculations to adjust the traffic light cycle at intersections.

[1394] Step 5:

[1395] The server sends instructions to traffic lights and digital signs based on the calculation results. As input, it receives optimal traffic light timing and route information, and sends execution instructions to traffic lights and digital signs. As output, the status of the traffic lights and signs that received the instructions is changed in real time. Specific actions include issuing an instruction to the traffic lights to extend the green light and setting priority for certain lanes.

[1396] Step 6:

[1397] The server continuously monitors traffic conditions and reanalyzes and optimizes based on newly collected data. It performs necessary reanalysis based on continuously acquired traffic data as input. It generates new optimization results as output. Specifically, it captures information about accidents and congestion and retransmits detour route instructions to traffic lights and digital signs.

[1398] Step 7:

[1399] Users receive the latest traffic information through a dedicated app or information board. The system receives traffic information from the server as input and displays real-time traffic conditions, detour routes, congestion forecasts, etc. as output. Specific operations include notifying the user's smartphone of the "optimal route from the current location to the destination" and the "congestion status of specific intersections."

[1400] (Application example 1)

[1401] 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."

[1402] Urban traffic management is becoming increasingly complex, and efficient traffic management is difficult, especially in areas where traffic congestion and accidents are frequent. In particular, the introduction of autonomous vehicles has increased the need for traffic management systems that enable these vehicles to select optimal routes in real time and safely reach their destinations. In order to solve this problem, the present invention aims to provide an optimal traffic management system for autonomous vehicles to operate efficiently and safely.

[1403] 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.

[1404] In this invention, the server includes means for analyzing traffic data collected from cameras and sensors in real time, means for generating traffic simulations using a generative AI model, means for calculating optimal traffic routes and traffic light timings, means for sending instructions to traffic lights and digital signs based on the calculation results, means for continuously monitoring and optimizing traffic conditions, means for notifying users of traffic information, means for collecting data from cameras and sensors equipped in autonomous vehicles and analyzing the data in real time, means for calculating optimal routes based on the analysis results and sending instructions to the autonomous vehicles, and means for continuously monitoring traffic conditions while the autonomous vehicles are moving and updating the routes as necessary. This enables autonomous vehicles to monitor urban traffic conditions in real time, receive instructions on optimal routes and traffic light timings, and operate safely and efficiently.

[1405] A "camera" is a device for taking images and recording them as data.

[1406] A "sensor" is a device that detects a physical quantity and converts it into an electrical signal.

[1407] "Real time" refers to the instantaneous capture and processing of ongoing events.

[1408] "Analysis" is the process of investigating and examining collected data to extract meaning.

[1409] A "generative AI model" is an artificial intelligence model that can generate new samples from data.

[1410] "Traffic simulation" is the process of simulating traffic movements on a computer.

[1411] An "optimal route" is the route to a destination that is the most efficient in terms of time, distance, etc.

[1412] A "traffic light" is a device that emits a light signal to control traffic flow.

[1413] A "digital sign" is a sign that displays information using a digital display.

[1414] "Monitoring" refers to the continuous observation and recording of specific data or conditions.

[1415] "Optimization" refers to adjusting processes and systems to make the most efficient use of resources.

[1416] A "user" is a person or device that uses a system or service.

[1417] An "autonomous vehicle" is a vehicle that uses artificial intelligence and sensors to drive and move automatically.

[1418] "Analysis results" refers to the specific information and conclusions obtained through the analysis.

[1419] "Sending instructions" refers to conveying orders for a particular action or behavior.

[1420] This invention is a system that enables autonomous vehicles to monitor urban traffic conditions in real time and optimize operation. This system is composed of a combination of cameras, sensors, generative AI models, traffic simulation, optimization algorithms, monitoring functions, and information notification functions.

[1421] System configuration

[1422] The system includes the following components:

[1423] 1. Cameras and sensors: These are equipped on autonomous vehicles and collect real-time information on surrounding traffic conditions, vehicle counts, pedestrian movement, weather conditions, and more.

[1424] 2. Server: Analyzes collected data in real time and generates traffic simulations using generative AI models.

[1425] 3. Optimization algorithm: Based on the simulation results, the optimal traffic routes and traffic light timings are calculated.

[1426] 4. Instruction sending device: Sends instructions to traffic lights and digital signs based on the calculation results.

[1427] 5. Monitoring device: Continuously monitors traffic conditions and optimizes the entire system as needed.

[1428] 6. User device: Drivers and autonomous vehicles receive information on optimal traffic routes and predicted congestion.

[1429] System processing procedure

[1430] 1. Data Collection:

[1431] Cameras and sensors capture the surrounding traffic conditions and send the data to a server in real time.

[1432] 2. Data Analysis:

[1433] The server preprocesses the collected data, removing noise and standardizing the data.

[1434] 3. Simulation generation:

[1435] The pre-processed data is fed into a generative AI model to generate a city-wide traffic simulation.

[1436] 4. Run the optimization algorithm:

[1437] Based on the simulation results, the optimal route and traffic light timing are calculated.

[1438] 5. Send instructions:

[1439] Optimal routes and traffic light timings are transmitted in real time to autonomous vehicles and digital signs.

[1440] 6. Real-time monitoring:

[1441] Continuously monitor traffic conditions and update routes and traffic light timings as needed.

[1442] 7. Notice to Users:

[1443] The user or vehicle system receives optimal operational information through a dedicated app.

[1444] Hardware and software used

[1445] Cameras and sensors: devices that capture images and convert physical quantities into electrical signals (e.g., LIDAR, radar)

[1446] Server: Data analysis, AI model execution, optimization algorithm execution (e.g. TensorFlow model)

[1447] Generative AI models: AI that generates new examples (e.g., GANs, generative models)

[1448] Cloud platform: Integrated management of data transmission, analysis, and optimization processes (e.g., AWS, Google Cloud)

[1449] Specific examples

[1450] For example, if there is a sudden increase in traffic volume at an intersection, cameras and sensors collect that information, which is then instantly analyzed by a server. A generative AI model then runs a simulation based on that data, and an optimization algorithm calculates the optimal route and traffic light timing. This information is then sent in real time to the autonomous vehicle, which then selects the optimal route and drives accordingly.

[1451] Prompt Sentence Examples

[1452] "What is the best route for travel based on current vehicle count, weather, and road conditions? Analyze the last 10 minutes of data at major intersections to find a route that avoids traffic jams."

[1453] The present invention enables autonomous vehicles to monitor traffic conditions in real time and operate safely and efficiently.

[1454] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1455] Step 1:

[1456] Data collection

[1457] The device collects data from cameras and sensors installed in the autonomous vehicle, specifically using LIDAR and radar to capture the surrounding traffic conditions, and transmits the data in real time.

[1458] Input: Real-time traffic data (number of vehicles, location, speed, weather, etc.)

[1459] Output: Raw data collected

[1460] Step 2:

[1461] Data Preprocessing

[1462] The server preprocesses the collected raw data, removing noise, filling in missing data, and standardizing the data. Specifically, it uses a scaler to standardize the data and applies a filter to remove noise.

[1463] Input: Raw data collected

[1464] Output: Preprocessed data (denoised and in standardized format)

[1465] Step 3:

[1466] Simulation Generation

[1467] The server inputs the preprocessed data into a generative AI model to generate a traffic simulation. The model uses the data to predict traffic trends across the city and visualizes them as a 3D model. Specifically, the traffic simulation is generated using frameworks such as TensorFlow.

[1468] Input: Preprocessed data

[1469] Output: Generated traffic simulation (traffic trends throughout the city)

[1470] Step 4:

[1471] Calculating optimal routes and traffic light timings

[1472] The server calculates optimal traffic routes and traffic light timings based on the simulation results obtained from the generative AI model. A specific example is solving optimization problems using genetic algorithms.

[1473] Input: Traffic simulation results

[1474] Output: Optimal traffic routes and traffic light timings

[1475] Step 5:

[1476] Sending instructions

[1477] The server calculates optimal routes and traffic light timings and sends them as instructions to autonomous vehicles and digital signs, allowing them to optimally operate in real time.

[1478] Input: Optimal traffic routes and traffic light timings

[1479] Output: Instructions received by autonomous vehicles and digital signs

[1480] Step 6:

[1481] Real-time monitoring

[1482] The device continuously monitors traffic conditions, and the server analyzes new data and updates the entire system as needed. Specifically, if a new situation arises, the data is re-collected and the process described above is repeated.

[1483] Input: Real-time new traffic data

[1484] Output: Updated optimal route and traffic light timings

[1485] Step 7:

[1486] User Notifications

[1487] Users receive optimal travel information through a dedicated app, which displays real-time traffic conditions, optimal routes, predicted congestion information, and more.

[1488] Input: Optimal route and traffic information from the server

[1489] Output: Traffic information received by the user through the app

[1490] For example, the prompt is:

[1491] "What is the best route for travel based on current vehicle count, weather, and road conditions? Analyze the last 10 minutes of data at major intersections to find a route that avoids traffic jams."

[1492] 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.

[1493] The present invention combines a system that analyzes traffic data collected from cameras and sensors in real time, generates traffic simulations using generative AI models, and calculates optimal traffic routes and traffic light timings with an emotion engine that recognizes user emotions. Specific embodiments of the system are described below.

[1494] System configuration

[1495] The system includes the following components:

[1496] 1. Cameras and Sensors

[1497] 2. Data collection and analysis server

[1498] 3. Servers that use generative AI models

[1499] 4. Server that runs the optimization algorithm

[1500] 5. Traffic lights and digital signs that receive instructions

[1501] 6. User Device with Emotion Engine

[1502] 7. Terminals that notify users of traffic information

[1503] System Operation

[1504] Data collection from cameras and sensors

[1505] The devices collect real-time data from cameras and sensors installed at intersections, major roads, and pedestrian areas throughout the city. For example, cameras installed on major roads capture the number and speed of vehicles, while sensors measure weather and road conditions.

[1506] Data analysis by server

[1507] The server immediately preprocesses the collected data to remove noise, fill in missing values, and standardize the data. This preprocessed data is then input into a generative AI model. For example, if there is a sudden increase in traffic volume at a particular intersection, the noise-removed data can be used to evaluate the risk of congestion at that point.

[1508] Traffic simulation generation

[1509] The server generates traffic simulations using generative AI models. The simulations use a 3D model of the city to visualize vehicle and pedestrian movements in real time. For example, a simulation of a specific intersection can be performed to visually confirm which route will avoid congestion.

[1510] Calculating optimal traffic routes and traffic light timings

[1511] The server uses optimization algorithms to calculate optimal traffic routes and traffic light timings based on analysis results and simulations. For example, a genetic algorithm is used to readjust the timing of traffic lights at a certain intersection to improve overall traffic flow.

[1512] Sending and executing instructions

[1513] The server sends instructions to traffic lights and digital signs based on the calculated results, which changes the traffic light cycle and digital sign display in real time. For example, it extends the green light time at certain intersections and allows priority traffic to certain lanes.

[1514] Real-time monitoring and feedback

[1515] The server continuously monitors traffic conditions, reanalyzing and optimizing them based on new data collected. If there are changes in traffic conditions, countermeasures are taken immediately. For example, if a sudden accident occurs, the information is analyzed immediately and new optimal route instructions are sent to traffic lights and digital signs.

[1516] Recognizing user emotions with an emotion engine

[1517] An emotion engine built into the user's device recognizes emotions in real time from the user's facial expressions, voice, pulse rate, etc. For example, if the user is feeling impatient or angry, the emotion engine will detect that emotional state.

[1518] Traffic information notification to users

[1519] The server adjusts and notifies traffic information and optimal routes based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, the server will suggest a less congested route and select a route that will allow them to relax.

[1520] In this way, the traffic management system of the present invention enables optimization of traffic conditions in real time, rapid response, and provision of customized traffic information according to the user's emotions, thereby maintaining smooth and comfortable traffic flow.

[1521] The processing flow will be explained below.

[1522] Step 1:

[1523] The devices collect data in real time from cameras and sensors installed at intersections and major roads across the city.

[1524] Specifically, cameras capture the number and speed of vehicles, and sensors measure weather and road conditions.

[1525] Step 2:

[1526] The terminals transmit the collected traffic data to a central server.

[1527] Specifically, the terminal packetizes data at set intervals and sends it to the server using a secure communication protocol.

[1528] Step 3:

[1529] Preprocessing data received by the server.

[1530] Specific operations include noise removal, data integration, missing value completion, and data standardization.

[1531] Step 4:

[1532] The server inputs the preprocessed data into the generative AI model.

[1533] Specifically, the formatted data is converted into the input format for the AI ​​model and supplied to the model.

[1534] Step 5:

[1535] The server generates a traffic simulation using a generative AI model.

[1536] Specifically, it works by reflecting real-time data in a 3D model of the city and virtually recreating traffic movements.

[1537] Step 6:

[1538] The server calculates the optimal traffic route and traffic light timing based on the simulation results.

[1539] Specifically, the system uses genetic algorithms and linear programming to search for and calculate optimal solutions.

[1540] Step 7:

[1541] The server sends the calculated optimal traffic light timing instructions to the traffic lights and digital signs.

[1542] Specifically, the traffic light communicates with the server via an API to change the traffic light cycle.

[1543] Step 8:

[1544] The server continuously monitors traffic conditions.

[1545] Specifically, new data is collected and analyzed to be reflected in the system.

[1546] Step 9:

[1547] The server runs the feedback loop as needed.

[1548] Specifically, the data is analyzed and optimized again, and the instructions on traffic lights and digital signs are updated.

[1549] Step 10:

[1550] The emotion engine built into the user's device recognizes emotions in real time from the user's facial expressions, voice, pulse rate, etc.

[1551] Specifically, it uses facial recognition technology and biometric sensors to determine whether the user is feeling anxiety or anger.

[1552] Step 11:

[1553] The server adjusts and notifies traffic information and optimal routes based on the user's emotions recognized by the emotion engine.

[1554] Specifically, if the user is feeling stressed, the system suggests a less congested route and selects a route that allows the user to relax.

[1555] Step 12:

[1556] Users receive traffic information through a dedicated app or information board.

[1557] Specifically, it notifies smartphone apps and web portals of real-time traffic information and optimal routes.

[1558] Example 2

[1559] 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."

[1560] Current urban traffic management systems lack the ability to analyze traffic data in real time and provide optimal routes, increasing the risk of traffic congestion and accidents. Furthermore, they lack the means to provide traffic information that takes into account the user's emotional state, and there is a lack of means to improve the user experience. This makes it difficult to maintain smooth traffic flow while reducing user stress.

[1561] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for analyzing traffic data collected from cameras and sensors in real time, a means for generating a traffic simulation using a generative AI model, and a means for calculating optimal traffic routes and traffic light timings. This enables real-time traffic data analysis and the presentation of optimal traffic routes.

[1562] Furthermore, the server includes means for continuously monitoring and optimizing statistical data, means for notifying users of traffic information, means for recognizing users' emotions in real time, and means for adjusting traffic information based on the users' emotions, thereby enabling the provision of customized traffic information according to the users' emotional state while maintaining smooth traffic flow.

[1563] "Traffic data" refers to information such as the number of vehicles, their speed, weather, and road conditions collected from cameras and sensors.

[1564] "Real-time analysis" refers to the immediate processing of traffic data collected from cameras and sensors to analyze current traffic conditions.

[1565] A "generative AI model" is a type of artificial intelligence used to simulate future traffic conditions based on collected data.

[1566] "Traffic simulation" is the process of virtually recreating traffic conditions across an entire city using generative AI models.

[1567] An "optimization algorithm" is a mathematical method for calculating optimal traffic routes and traffic light timings based on given data and conditions.

[1568] A "traffic light" is a lighting device installed to control traffic at intersections and major roads.

[1569] "Information display device" means a digital sign or other display device used to display traffic conditions or instructions.

[1570] "Monitoring" is the process of continuously collecting and analyzing traffic data.

[1571] "Feedback" refers to adjusting system settings and traffic light timing based on data obtained through monitoring.

[1572] The "emotion engine" is a technology that recognizes emotions in real time from a user's facial expressions, voice, pulse rate, etc.

[1573] "User" refers to a vehicle driver or pedestrian using the system.

[1574] "Customized traffic information" is information that provides optimal traffic routes and notification content according to the user's emotional state.

[1575] The present invention combines a system that analyzes traffic data collected from cameras and sensors in real time, generates traffic simulations using generative AI models, and calculates optimal traffic routes and traffic light timings with an emotion engine that recognizes user emotions. Specific embodiments of the system are described below.

[1576] System configuration

[1577] The system includes the following components:

[1578] 1. Cameras and Sensors

[1579] 2. Data collection and analysis server

[1580] 3. Servers that use generative AI models

[1581] 4. Server that runs the optimization algorithm

[1582] 5. Signals and information display devices that receive instructions

[1583] 6. User Device with Emotion Engine

[1584] 7. Terminals that notify users of traffic information

[1585] System Operation

[1586] Data collection from cameras and sensors

[1587] The devices collect real-time data from cameras and sensors installed at intersections, major roads, and pedestrian areas throughout the city. For example, cameras installed on major roads capture the number and speed of vehicles, while sensors measure weather and road conditions.

[1588] Data analysis by server

[1589] The server immediately preprocesses the collected data, removing noise, filling in missing values, and standardizing the data. For example, if there is a sudden increase in traffic volume at a particular intersection, the noise-removed data can be used to evaluate the risk of congestion at that point.

[1590] Traffic simulation generation

[1591] The server generates traffic simulations using generative AI models. The simulations use a 3D model of the city to visualize vehicle and pedestrian movements in real time. For example, a simulation of a specific intersection can be performed to visually confirm which route will avoid congestion.

[1592] Calculating optimal traffic routes and traffic light timings

[1593] The server uses optimization algorithms to calculate optimal traffic routes and traffic light timings based on analysis results and simulations. For example, a genetic algorithm is used to readjust the timing of traffic lights at a certain intersection to improve overall traffic flow.

[1594] Sending and executing instructions

[1595] The server sends instructions to traffic lights and information display devices based on the calculated results. This allows the traffic light cycle and the information displayed on the information display devices to be changed in real time. For example, the green light time at a specific intersection can be extended, and priority traffic can be given to certain lanes.

[1596] Real-time monitoring and feedback

[1597] The server continuously monitors traffic conditions, reanalyzing and optimizing them based on new data collected. If there are changes in traffic conditions, countermeasures are taken immediately. For example, if a sudden accident occurs, the information is analyzed immediately and new optimal route instructions are sent to traffic lights and information display devices.

[1598] Recognizing user emotions with an emotion engine

[1599] An emotion engine built into the user's device recognizes emotions in real time from the user's facial expressions, voice, pulse rate, etc. For example, if the user is feeling impatient or angry, the emotion engine will detect that emotional state.

[1600] Traffic information notification to users

[1601] The server adjusts and notifies traffic information and optimal routes based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, the server will suggest a less congested route and select a route that will allow them to relax.

[1602] Prompt Sentence Examples

[1603] "If the user is stressed, suggest the best route to avoid the congestion. For example, imagine a scenario where it's Friday evening and you're stuck in traffic going through a particular intersection."

[1604] In this way, the traffic management system of the present invention enables optimization of traffic conditions in real time, rapid response, and provision of customized traffic information according to the user's emotions, thereby maintaining smooth and comfortable traffic flow.

[1605] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1606] Step 1:

[1607] The device collects real-time data from cameras and sensors installed at each intersection, major road, and pedestrian area within the city. This data includes the number and speed of vehicles, weather, road conditions, etc. For example, cameras installed on major roads capture the number and speed of vehicles every second, while sensors continuously measure changes in weather and road conditions. This data collection is stored directly in a database and can be accessed for subsequent processing steps.

[1608] Step 2:

[1609] The server receives the data collected in step 1 and performs noise removal, missing value imputation, and data standardization. This preprocessing is essential to improve the accuracy of data analysis. The input data is the raw data collected in step 1, and the output is clean data with noise removed and missing values ​​imputed. For example, outliers are removed and missing weather data is imputed from past data.

[1610] Step 3:

[1611] The server inputs the preprocessed data into a generative AI model to generate a traffic simulation. The input data is the clean data from Step 2, and the output is a 3D traffic model of the entire city. Specifically, it can simulate vehicle and pedestrian movements at specific intersections in real time and predict future traffic congestion.

[1612] Step 4:

[1613] The server uses an optimization algorithm to calculate traffic routes and traffic light timings based on the simulation results obtained from the generative AI model. The input data are the simulation results from Step 3, and the output is the optimal traffic route and traffic light timing settings. Specifically, it evaluates multiple possible traffic light timing settings and finds the part that maximizes overall traffic efficiency.

[1614] Step 5:

[1615] The server sends instructions to traffic lights and information display devices based on the optimal traffic route and traffic light timing settings. The input data is the calculation results from step 4, and the output is the traffic light timing settings and updates to the information display devices. Specifically, the server controls the traffic lights to extend the green light time for specific lanes and allow priority traffic.

[1616] Step 6:

[1617] The server continuously monitors traffic conditions and reanalyzes and optimizes the system based on newly collected data. The input data is traffic data updated in real time, and the output is the latest optimization results. For example, if a traffic accident occurs, the information is analyzed immediately and new optimal route instructions are sent to traffic lights and information display devices.

[1618] Step 7:

[1619] The emotion engine built into the user's device recognizes emotions in real time from the user's facial expressions, voice, pulse rate, etc. The input data is the user's emotional indicators, and the output is the detected emotional state. Specifically, it detects when the user is feeling impatient or anger while in the car.

[1620] Step 8:

[1621] The server recognizes the user's emotions and adjusts and notifies traffic information and optimal routes based on those emotions. The input data is the user's emotional state detected in step 7 and real-time traffic data, and the output is adjusted traffic information notified to the user. For example, if the user is feeling stressed, a notification is sent suggesting an optimal route that avoids traffic jams.

[1622] This enables the system to optimize traffic conditions in real time, respond quickly, and provide customized traffic information according to the user's emotions.

[1623] (Application example 2)

[1624] 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."

[1625] In modern transportation systems, traffic congestion and accidents occur frequently, often hindering efficient travel. In particular, increased traffic at specific intersections or during certain times of the day causes congestion, necessitating the optimization of traffic flow. Furthermore, because the driver's emotional state affects driving safety and efficiency, it is necessary to reduce driver stress and provide a comfortable driving environment. However, conventional traffic management systems have difficulty analyzing data in real time and proposing optimal routes, and they do not propose routes that take driver emotions into account.

[1626] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing traffic data collected from cameras and sensors in real time, means for generating a traffic simulation using a generative AI model, and means for calculating optimal traffic routes and traffic light timings. This makes it possible to analyze traffic conditions in real time and calculate optimal routes and traffic light timings. In addition, the server includes means for analyzing data collected from cameras and sensors of an autonomous vehicle in real time and updating traffic information, means equipped with an emotion engine that recognizes the driver's emotions, and means for proposing optimal traffic routes according to the driver's emotional state, making it possible to propose routes that adapt to the driver's emotional state and reduce stress.

[1627] "Cameras and sensors" are devices used to collect information on traffic conditions, vehicles, pedestrians, etc. in real time.

[1628] "Real-time analysis means" refers to technology for processing and analyzing collected data immediately.

[1629] A "generative AI model" is an artificial intelligence model used to generate traffic simulations and predict future traffic patterns based on past data and current conditions.

[1630] The "means for generating traffic simulations" refers to the use of collected traffic data to virtually reproduce the movement of the entire traffic system and calculate optimal routes and traffic light timings.

[1631] "Means for calculating optimal traffic routes and traffic light timings" refers to technology that analyzes traffic flow and congestion conditions to determine routes that allow vehicles to travel efficiently and traffic light control timings.

[1632] "Means for sending instructions to traffic lights and digital signs" refers to means for transmitting calculated optimal traffic control information to traffic lights and digital signs and for carrying out actual traffic control.

[1633] "Means for continuously monitoring and optimizing traffic conditions" refers to technology that constantly monitors collected traffic data and performs optimization in real time as necessary.

[1634] "Means for notifying users of traffic information" refers to technology for informing users of calculated optimal routes and traffic information.

[1635] "Autonomous vehicle cameras and sensors" means devices installed on autonomous vehicles that sense surrounding traffic conditions and collect data.

[1636] The "emotion engine" is a technology that analyzes the driver's facial expressions, voice, pulse rate, etc. to recognize their current emotional state.

[1637] The "means for suggesting optimal transportation routes according to emotional state" is a technology for suggesting optimal routes to reduce driver stress based on the recognized emotional state.

[1638] This system analyzes traffic data in real time, generates traffic simulations using generative AI models, and calculates optimal traffic routes and traffic signal timings. In addition, by incorporating an emotion engine that recognizes the user's emotions, it is possible to suggest optimal routes that reduce the user's stress.

[1639] System configuration

[1640] The system includes the following components:

[1641] 1. Cameras and Sensors

[1642] 2. Data collection and analysis server

[1643] 3. Servers that use generative AI models

[1644] 4. Server that runs the optimization algorithm

[1645] 5. Traffic lights and digital signs that receive instructions

[1646] 6. User Device with Emotion Engine

[1647] 7. Terminals that notify users of traffic information

[1648] System Operation

[1649] Data collection from cameras and sensors

[1650] Cameras and sensors will be installed at intersections, major roads, and pedestrian areas throughout the city to collect real-time data. For example, cameras on major roads will capture the number and speed of vehicles, while sensors will measure weather and road conditions.

[1651] Data analysis

[1652] The server immediately preprocesses the collected data to remove noise, fill in missing values, and standardize the data. This preprocessed data is then input into a generative AI model. For example, if there is a sudden increase in traffic volume at a particular intersection, the noise-removed data is used to evaluate the risk of congestion at that point.

[1653] Traffic simulation generation

[1654] The server generates traffic simulations using generative AI models. The simulations use a 3D model of the city to visualize vehicle and pedestrian movements in real time. For example, a simulation can be run at a specific intersection to visually confirm which route will avoid congestion.

[1655] Calculating optimal traffic routes and traffic light timings

[1656] The server uses optimization algorithms to calculate optimal traffic routes and traffic light timings based on analysis and simulation results. For example, a genetic algorithm can be used to readjust the timing of traffic lights at a given intersection to improve overall traffic flow.

[1657] Sending and executing instructions

[1658] Based on the calculated results, the server sends instructions to traffic lights and digital signs, which change the traffic light cycle and digital sign display in real time, for example, extending the green light time at a particular intersection or allowing priority traffic in certain lanes.

[1659] Recognizing user emotions with an emotion engine

[1660] An emotion engine built into the user device recognizes emotions in real time from the user's facial expressions, voice, pulse rate, etc. For example, if the user is feeling impatient or angry, the emotion engine will detect that emotional state.

[1661] Traffic information notification to users

[1662] The server adjusts and notifies traffic information and optimal routes based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, it will suggest a less congested route and select a relaxing route.

[1663] Specific examples

[1664] For example, if you enter a prompt such as "Analyze the current traffic situation, and if the driver is feeling stressed, choose the optimal route for the self-driving vehicle to relax," the system will collect and analyze traffic data, recognize the user's emotional state using its emotion engine, and suggest the optimal route.

[1665] In this way, the traffic management system of the present invention enables optimization of traffic conditions in real time, rapid response, and provision of customized traffic information according to the user's emotions, thereby maintaining smooth and comfortable traffic flow.

[1666] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1667] Step 1:

[1668] Traffic data is collected from cameras and sensors. The server collects data in real time from cameras and sensors installed at each intersection, major road, and pedestrian area in the city. The input includes the number of vehicles, speed, weather, and road surface conditions at each location. This data is collected and temporarily stored. The output is the collected raw data.

[1669] Step 2:

[1670] Preprocess the collected data. The server denoises the collected data, imputes missing values, and standardizes the data. This preprocessing generates clean data suitable for analysis. The input includes the raw data collected in step 1. The output is the preprocessed data.

[1671] Step 3:

[1672] The preprocessed data is used to input data into a generative AI model. The server inputs the preprocessed data into the generative AI model to generate a traffic simulation. Specifically, the model predicts future traffic patterns based on past data and current conditions. The input includes the preprocessed data. The output is the traffic simulation results.

[1673] Step 4:

[1674] Calculates optimal traffic routes and traffic light timings. The server runs an optimization algorithm based on traffic simulations to calculate optimal routes and traffic light timings. Specifically, it uses a genetic algorithm to optimize traffic flow. Simulation results are included as inputs. Optimal traffic routes and traffic light timings are obtained as outputs.

[1675] Step 5:

[1676] Based on the calculation results, the server sends instructions to traffic lights and digital signs. The server transmits the calculation results to the traffic lights and digital signs, which then perform the actual traffic control. The inputs include optimal traffic routes and traffic light timings. The output is control information for the traffic lights and digital signs.

[1677] Step 6:

[1678] The emotion engine recognizes the user's emotions. The user device uses the emotion engine to analyze the driver's facial expressions, voice, pulse rate, etc. to recognize their current emotional state. Input includes the user's biometric information. The recognized emotional state is obtained as output.

[1679] Step 7:

[1680] The optimal transportation route is proposed based on the user's emotional state. The server adjusts the traffic information based on the recognized emotional state and notifies the user of the optimal route. The input includes the emotional state recognized in step 6. The output is optimal route information adapted to the emotional state.

[1681] These steps enable optimal traffic information and route suggestions based on the user's emotional state. For example, when the prompt "Analyze the current traffic situation and select the optimal route for the autonomous vehicle to relax if the driver is feeling stressed" is entered, the system will suggest the optimal route through the above steps.

[1682] 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.

[1683] 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.

[1684] 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.

[1685] 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.

[1686] 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.

[1687] 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.

[1688] 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).

[1689] 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.

[1690] 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."

[1691] 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.

[1692] 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).

[1693] 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.

[1694] 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.

[1695] 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.

[1696] 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.

[1697] 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.

[1698] 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.

[1699] 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.

[1700] 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.

[1701] 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.

[1702] 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.

[1703] The following is further disclosed regarding the above embodiment.

[1704] (Claim 1)

[1705] A means of analyzing traffic data collected from cameras and sensors in real time;

[1706] a means for generating a traffic simulation using a generative AI model;

[1707] a means for calculating optimal traffic routes and traffic light timings;

[1708] means for sending instructions to traffic lights and digital signs based on the results of the calculations;

[1709] A means of continuously monitoring and optimizing traffic conditions;

[1710] means for notifying a user of traffic information;

[1711] A system including:

[1712] (Claim 2)

[1713] means for preprocessing data collected from the cameras and sensors;

[1714] A means of inputting the pre-processed data into a generative AI model; and

[1715] means for calculating optimal traffic routes and traffic light timings using an optimization algorithm;

[1716] 10. The system of claim 1, comprising:

[1717] (Claim 3)

[1718] means including ancillary digital signature management;

[1719] A means of analyzing and improving traffic patterns at specific times and locations;

[1720] 10. The system of claim 1, comprising:

[1721] "Example 1"

[1722] (Claim 1)

[1723] a means of pre-processing traffic data collected from cameras and sensors in real time;

[1724] a means for using the preprocessed data to input a generative AI model to generate a traffic simulation; and

[1725] means for calculating optimal traffic routes and traffic light timings using an optimization algorithm;

[1726] means for sending instructions to traffic lights and digital signs based on the results of the calculations;

[1727] A means of continuously monitoring traffic conditions and reanalyzing and optimizing based on new data;

[1728] means for notifying a user of traffic information;

[1729] A system including:

[1730] (Claim 2)

[1731] a means of standardizing data collected from cameras and sensors;

[1732] a means for noise removal and missing value imputation;

[1733] A means of inputting the pre-processed data into a generative AI model; and

[1734] means for calculating optimal traffic routes and traffic light timings using an optimization algorithm;

[1735] 10. The system of claim 1, comprising:

[1736] (Claim 3)

[1737] means including ancillary digital signature management;

[1738] A means of analyzing and improving traffic patterns at specific times and locations;

[1739] 10. The system of claim 1, comprising:

[1740] "Application Example 1"

[1741] (Claim 1)

[1742] A means of analyzing traffic data collected from cameras and sensors in real time;

[1743] a means for generating a traffic simulation using a generative AI model;

[1744] a means for calculating optimal traffic routes and traffic light timings;

[1745] means for sending instructions to traffic lights and digital signs based on the results of the calculations;

[1746] A means of continuously monitoring and optimizing traffic conditions;

[1747] means for notifying a user of traffic information;

[1748] a means for collecting data from cameras and sensors mounted on the autonomous vehicle and analyzing this data in real time;

[1749] A means of calculating the optimal route based on the analysis results and sending instructions to the autonomous vehicle;

[1750] a means for continuously monitoring traffic conditions while the autonomous vehicle is in motion and updating the route as needed;

[1751] A system including:

[1752] (Claim 2)

[1753] means for preprocessing data collected from the cameras and sensors;

[1754] A means of inputting the pre-processed data into a generative AI model; and

[1755] means for calculating optimal traffic routes and traffic light timings using an optimization algorithm;

[1756] means for transmitting the analysis results to the autonomous vehicle and causing the vehicle to operate based on the instructions;

[1757] 10. The system of claim 1, comprising:

[1758] (Claim 3)

[1759] means including ancillary digital signature management;

[1760] A means of analyzing and improving traffic patterns at specific times and locations;

[1761] A means to input prompt statements into the generative AI model and calculate the optimal route based on the generated traffic simulation, and

[1762] 10. The system of claim 1, comprising:

[1763] "Example 2: Combining Emotion Engines"

[1764] (Claim 1)

[1765] A means of analyzing traffic data collected from cameras and sensors in real time;

[1766] a means for generating a traffic simulation using a generative AI model;

[1767] a means for calculating optimal traffic routes and traffic light timings;

[1768] means for sending instructions to traffic lights and information display devices based on the calculation results;

[1769] A means of continuously monitoring and optimizing statistical data;

[1770] means for notifying a user of traffic information;

[1771] means for recognizing user emotions in real time;

[1772] means for adjusting traffic information based on user emotions;

[1773] A system including:

[1774] (Claim 2)

[1775] means for preprocessing data collected from the cameras and sensors;

[1776] A means of inputting the pre-processed data into a generative AI model; and

[1777] means for calculating optimal traffic routes and traffic light timings using an optimization algorithm;

[1778] 10. The system of claim 1, comprising:

[1779] (Claim 3)

[1780] means for managing ancillary information display devices;

[1781] A means of analyzing and improving traffic patterns at specific times and locations;

[1782] 10. The system of claim 1, comprising:

[1783] "Application example 2 when combining emotion engines"

[1784] (Claim 1)

[1785] A means of analyzing traffic data collected from cameras and sensors in real time;

[1786] a means for generating a traffic simulation using a generative AI model;

[1787] a means for calculating optimal traffic routes and traffic light timings;

[1788] means for sending instructions to traffic lights and digital signs based on the results of the calculations;

[1789] A means of continuously monitoring and optimizing traffic conditions;

[1790] means for notifying a user of traffic information;

[1791] A means of analyzing data collected from cameras and sensors on autonomous vehicles in real time and updating traffic information;

[1792] A means equipped with an emotion engine that recognizes the driver's emotions;

[1793] A means of suggesting optimal transportation routes depending on emotional state;

[1794] A system including:

[1795] (Claim 2)

[1796] means for preprocessing data collected from the cameras and sensors;

[1797] A means of inputting the pre-processed data into a generative AI model; and

[1798] means for calculating optimal traffic routes and traffic light timings using an optimization algorithm;

[1799] 10. The system of claim 1, comprising:

[1800] (Claim 3)

[1801] means including ancillary digital signature management;

[1802] A means of analyzing and improving traffic patterns at specific times and locations;

[1803] 10. The system of claim 1, comprising: [Explanation of symbols]

[1804] 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 means of analyzing traffic data collected from cameras and sensors in real time; a means for generating a traffic simulation using a generative AI model; means for calculating optimal traffic routes and traffic light timings; means for sending instructions to traffic lights and digital signs based on the results of the calculations; A means of continuously monitoring and optimizing traffic conditions; means for notifying a user of traffic information; A system including:

2. means for preprocessing data collected from the cameras and sensors; A means of inputting the pre-processed data into a generative AI model; and means for calculating optimal traffic routes and traffic light timings using an optimization algorithm; The system of claim 1 , comprising:

3. means including ancillary digital signature management; A means of analyzing and improving traffic patterns at specific times and locations; The system of claim 1 , comprising:

Images