System

Abstract

A system is provided.SOLUTION: A system comprising: means for installing a sensor in each traffic light and measuring a traffic volume of automobiles, a traffic volume of bicycles, and the number of pedestrians for a certain period of time; means for proposing an optimal switching timing of each signal by artificial intelligence based on the collected data and setting a basic signal switching time based on the proposal; means for aggregating monitoring cameras, devices providing location information, and other traffic data in real time and accumulating them in a central server; and means for optimizing and reflecting a switching time of each traffic light by artificial intelligence based on the traffic volume data accumulated in real time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Current traffic light systems have preset timings for switching between green and red lights at intersections, preventing flexible response to traffic volume. This can lead to problems such as long waiting lines in the right-turn lane during certain times of the day, and vehicles traveling straight through having to wait at red lights for long periods of time even when traffic volume is low. Furthermore, when large numbers of pedestrians cross the street, traffic can be impeded, resulting in chronic congestion. Furthermore, this can lead to social problems such as increased air pollution and driver stress. As such, the current fixed signal switching system poses many problems from the perspectives of traffic efficiency and environmental protection, and solutions are needed. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system that includes: a means for installing sensors at each traffic light to measure the volume of automobile traffic, bicycle traffic, and the number of pedestrians over a certain period of time; a means for using artificial intelligence to propose optimal switching timings for each traffic light based on the collected data and setting basic signal switching times based on the proposals; a means for aggregating surveillance camera, location information providing devices, and other traffic data in real time and storing it in a central server; and a means for optimizing and reflecting the switching times of each traffic light based on the traffic volume data collected in real time using artificial intelligence. In particular, by adding a means for analyzing the need for pedestrian-vehicle separated traffic lights using artificial intelligence and changing the traffic lights to pedestrian-vehicle separated types based on the analysis results, and a means for instantly changing the green and red light times of traffic lights based on real-time traffic volume data, signal switching can be optimized in real time, thereby achieving smooth traffic flow and environmental protection.

[0006] "Sensors" are devices used to measure automobile traffic, bicycle traffic, and pedestrian numbers.

[0007] "Traffic volume data" is information that indicates the number of automobiles, bicycles, and pedestrians passing through a certain period of time.

[0008] "Artificial intelligence" refers to programs and algorithms that analyze collected data and suggest optimal signal switching timings.

[0009] "Signal switching timing" refers to a time setting that determines the duration for which the green and red lights of a traffic light at an intersection are on.

[0010] A "surveillance camera" is a device that captures traffic conditions on roads and provides the video data in real time.

[0011] A "device that provides location information" is a device that acquires and transmits location information indicating its presence in a specific location, such as a car navigation system or smartphone.

[0012] The "central server" is a central computer system for centrally managing and analyzing collected traffic volume data.

[0013] A "pedestrian-vehicle separated traffic signal" is a traffic signal that separates and controls pedestrian and vehicle traffic.

[0014] "Real-time optimization" is the process of instantly changing traffic light settings based on current traffic conditions to facilitate smooth traffic flow. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] An embodiment of a real-time traffic light optimization system according to the present invention will be described. In this system, sensors are installed at each traffic light to measure the volume of automobile traffic, bicycle traffic, and the number of pedestrians over a certain period of time. The collected data is sent to a server and analyzed using artificial intelligence. Based on the analysis results, optimal switching timings for each traffic light are proposed and reflected in the traffic lights.

[0037] 1. Sensor installation and data collection

[0038] The server issues instructions to install sensors at each traffic light at an intersection. The terminals (sensor installation team) follow the instructions and install the sensors on the traffic lights. The sensors measure the traffic volume of cars, bicycles, and pedestrians over a certain period of time and send the data to the server. For example, suppose sensors X, Y, and Z are installed at intersection A and data is collected for one week.

[0039] 2. Data analysis and signal switching timing proposal

[0040] The collected data is analyzed by the server using artificial intelligence. The server then organizes the acquired traffic volume data and inputs it into the artificial intelligence. The artificial intelligence then analyzes the data and calculates the optimal switching timing for each traffic light. The server receives the analysis results and sets new switching timings for each traffic light.

[0041] 3. Proposal for a Pedestrian-Vehicle Separation Traffic Light

[0042] The server analyzes the data to identify intersections that require pedestrian-vehicle separated traffic lights. Based on the collected data, the AI ​​determines whether a particular intersection requires a pedestrian-vehicle separated traffic light. For example, if the number of pedestrians at intersection B is extremely high, the AI ​​will suggest the need for a pedestrian-vehicle separated traffic light. Based on this suggestion, the server installs a pedestrian-vehicle separated traffic light at the relevant intersection.

[0043] 4. Aggregating real-time data and optimizing signal switching

[0044] The data collection targets will be expanded to include surveillance cameras and devices that provide location information (such as car navigation systems and smartphones). Terminals (surveillance cameras and smart devices) will send traffic data to a server in real time. The server will then aggregate this data in real time and upload it to a central server. Based on the real-time data, artificial intelligence will calculate the optimal switching timing for each traffic light and immediately update the traffic lights.

[0045] (Example)

[0046] For example, at a certain intersection C, real-time location information from surveillance cameras and smartphone apps is sent to a server. The server analyzes this information and detects a sudden increase in traffic volume. Based on this information, the AI ​​determines that the green light at traffic light D should be extended by 10 seconds, and the server immediately adjusts traffic light D based on that judgment. This allows traffic at intersection C to flow smoothly.

[0047] The processing flow will be explained below.

[0048] Step 1:

[0049] The server issues instructions to install sensors at each intersection. Based on these instructions, the terminals (sensor installation teams) install sensors on the traffic lights at the intersections. These sensors are used to measure the volume of automobile, bicycle, and pedestrian traffic.

[0050] Step 2:

[0051] The device (sensor) measures traffic volume data for a certain period of time. For example, the sensor measures the number of passing cars, bicycles, and pedestrians every hour, and sends this data to a server.

[0052] Step 3:

[0053] The server receives the collected traffic data, formats it, removes noise, and then passes it to an artificial intelligence to prepare it for analysis.

[0054] Step 4:

[0055] The AI ​​analyzes the provided data and calculates the optimal switching timing for each traffic light. The AI's analysis results suggest the optimal duration of the green and red lights for each traffic light.

[0056] Step 5:

[0057] The server receives the signal switching timings suggested by the AI ​​and reflects the settings for each traffic light, so that the traffic lights at each intersection are set to switch at the optimal timing.

[0058] Step 6:

[0059] The server then provides the collected data to the AI ​​to analyze the need for pedestrian-separated traffic lights. The AI ​​then considers the volume of automobile and pedestrian traffic and determines whether pedestrian-separated traffic lights are necessary.

[0060] Step 7:

[0061] Based on the results of the AI ​​analysis, the server issues instructions to install or change the settings of traffic lights at intersections where pedestrian-vehicle separated traffic lights are deemed necessary. The terminal (traffic light installation team) receives these instructions and installs pedestrian-vehicle separated traffic lights at the relevant intersections.

[0062] Step 8:

[0063] The server issues instructions to collect traffic data in real time from surveillance cameras, car navigation systems, smartphones, etc. The terminals (these devices) collect data in real time and send it to the server.

[0064] Step 9:

[0065] The server aggregates the real-time data it receives and uploads it to a central server, including traffic and location information.

[0066] Step 10:

[0067] The server passes real-time data to the AI, which then recalculates the optimal switching timing for each traffic light on the fly. Based on the AI's calculations, the server instantly adjusts the settings for each traffic light.

[0068] Specific examples

[0069] For example, at intersection C, real-time location information from surveillance cameras and smartphone apps is sent to a server. The server analyzes this information and detects an increase in traffic volume. Based on this real-time data, the artificial intelligence determines that the green light at traffic light D should be extended by 10 seconds. Based on this judgment, the server immediately changes the settings of traffic light D to extend the green light time. This allows traffic at intersection C to flow more smoothly.

[0070] Example 1

[0071] 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."

[0072] Traffic signal control is based on fixed patterns and cannot adequately respond to sudden fluctuations in traffic volume, which can disrupt the smooth flow of traffic. It is also difficult to properly evaluate the need for pedestrian-vehicle separated traffic signals and install them appropriately. Therefore, a system is needed to alleviate traffic congestion, prevent traffic accidents, and improve convenience for road users.

[0073] 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.

[0074] In this invention, the server includes a means for installing sensors at each traffic light and measuring the volume of automobile traffic, bicycle traffic, and pedestrians for a certain period of time, a means for proposing the optimal switching timing for each traffic light using artificial intelligence based on the collected data and setting the basic signal switching time based on the proposal, and a means for aggregating monitoring devices, terminals that provide location information, and other traffic data in real time and accumulating it in a central control device, thereby making it possible to respond quickly and appropriately to fluctuations in traffic volume and optimize traffic flow.

[0075] "Sensors" are devices used to measure the volume of automobile, bicycle, and pedestrian traffic.

[0076] "Artificial intelligence" is a technology that analyzes collected data and calculates the optimal switching timing for each traffic light.

[0077] A "traffic light" is a device installed to control traffic, and emits signals to vehicles and pedestrians indicating whether to proceed or stop.

[0078] "Switching timing" refers to the time when a traffic light switches from green to red to yellow.

[0079] "Monitoring devices" are devices installed to monitor traffic conditions in real time, including cameras and sensors.

[0080] A "terminal that provides location information" is a device such as a car navigation system or a smartphone that detects the current location and transmits it to a server.

[0081] "Central control device" refers to a server that controls traffic lights based on aggregated traffic data.

[0082] A "pedestrian-vehicle separated traffic signal" is a traffic signal that separates and controls pedestrian and vehicle traffic, allowing pedestrians to cross intersections safely.

[0083] "Real-time aggregation" means that collected traffic data is immediately fed into a central control device.

[0084] An embodiment of a real-time optimization system for traffic lights according to the present invention will be described. In this system, sensors are installed at each traffic light to measure the volume of automobile traffic, bicycle traffic, and the number of pedestrians over a certain period of time. The collected data is sent to a server and analyzed by artificial intelligence. Based on the analysis results, optimal switching timings for each traffic light are proposed and reflected in the traffic lights.

[0085] First, the server issues instructions to install sensors on the traffic lights at each intersection. The terminals (sensor installation team) follow these instructions and install the sensors on the road. The sensors measure the traffic volume of cars, bicycles, and pedestrians over a certain period of time and send the data to the server. For example, let's consider the case where sensors X, Y, and Z are installed at intersection A and data is collected for one week. The sensors operate 24 hours a day, continuously measuring the flow rate of each traffic element.

[0086] The collected data is sent to a server, where it is organized and preprocessed. The server then inputs this organized data into a generative AI model (using, for example, a machine learning framework such as TensorFlow or PyTorch) to analyze traffic patterns and trends. The AI ​​calculates the optimal traffic light switching timing and returns the results to the server. The server then sets the new switching timing for the traffic lights and reflects it at each traffic light. For example, specific adjustments may be made, such as "extending the green light for traffic light A by 10 seconds between 5:00 PM and 7:00 PM on weekdays."

[0087] The server also analyzes the collected data to determine whether a particular intersection needs a pedestrian-vehicle-separated traffic light. Based on pedestrian data, the AI ​​determines whether a particular intersection (e.g., intersection B) needs a pedestrian-vehicle-separated traffic light. For example, it uses a specific algorithm, such as "If there are an extremely high number of pedestrians at intersection B, suggest installing a pedestrian-vehicle-separated traffic light."

[0088] Furthermore, terminals (surveillance cameras and smart devices) collect traffic data in real time and send it to a server. The server then collects this real-time data in a central database and analyzes it using artificial intelligence. Based on the real-time data, the optimal switching timing for each traffic light is calculated and the results are immediately reflected in the traffic lights. For example, specific adjustments are made in real time, such as "due to a sudden increase in traffic volume at intersection C, immediately extend the green light at traffic light D by 10 seconds."

[0089] For actual operation, detailed instructions can be obtained by entering the following prompt sentence into the generative AI model:

[0090] Prompt: "Describe how you can detect sudden increases in traffic volume at an intersection based on real-time location information from surveillance cameras and a smartphone app, and then optimize traffic light timing based on the results."

[0091] In this way, the system of the present invention integrates various technologies to highly optimize traffic signal control and achieve smooth traffic flow.

[0092] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0093] Step 1:

[0094] The server issues instructions to install sensors at traffic lights at intersections, including the specific intersection location and the type of sensor to install.

[0095] (Input) Location information and sensor type

[0096] (Output) Sensor installation instructions

[0097] Specifically, the server generates instructions such as "Install sensors X, Y, and Z at intersection A" and sends them to the terminal.

[0098] Step 2:

[0099] The terminal (sensor installation team) follows the instructions of the server and installs the sensor at the designated traffic light.

[0100] (Input) Sensor installation instructions

[0101] (Output) Data from installed sensors

[0102] Specifically, the sensor installation team physically installs designated sensors X, Y, and Z on the traffic lights at intersection A.

[0103] Step 3:

[0104] Sensors attached to the device measure the volume of cars, bicycles, and pedestrians over a period of time and send the data to a server.

[0105] (Input) Traffic volume data measurement instructions

[0106] (Output) Traffic volume data

[0107] Specifically, the sensor operates 24 hours a day and sends the measured data to the server every day.

[0108] Step 4:

[0109] The server receives the collected data, organizes it, and pre-processes it for input into artificial intelligence.

[0110] (Input) Traffic volume data

[0111] (Output) Preprocessed data

[0112] Specifically, the server classifies the traffic volume data by time period and removes outliers.

[0113] Step 5:

[0114] The server then feeds the pre-processed data into an artificial intelligence system (e.g., TensorFlow or PyTorch) to analyze traffic patterns and trends.

[0115] (Input) Preprocessed traffic data

[0116] (Output) Analysis results (optimal switching timing)

[0117] Specifically, the artificial intelligence uses machine learning algorithms to analyze traffic volume and calculate the optimal timing for switching signals.

[0118] Step 6:

[0119] The server receives the analysis results from the artificial intelligence and sends instructions to each traffic light to set new switching timings.

[0120] (Input) Analysis results (optimal switching timing)

[0121] (Output) Setting instructions to traffic lights

[0122] As a specific operation, the server sends a specific adjustment to the traffic light, such as "Extend the green light of traffic light A by 10 seconds between 5:00 PM and 7:00 PM."

[0123] Step 7:

[0124] The terminal (traffic light) immediately sets and executes the new switching timing based on instructions from the server.

[0125] (Input) Setting instructions to traffic lights

[0126] (Output) Signal control based on new signal switching timing

[0127] Specifically, traffic lights switch between green and red based on set timing.

[0128] Step 8:

[0129] The server receives traffic data collected in real time from the monitoring devices and location information providing terminals and accumulates it in the central control device.

[0130] (Input) Real-time data from monitoring devices and location information providing devices

[0131] (Output) Aggregated real-time data

[0132] Specifically, the server periodically updates and aggregates smartphone location information and surveillance camera video data.

[0133] Step 9:

[0134] The server uses artificial intelligence to analyze the aggregated real-time data and issues instructions to instantly reflect the optimal switching timing for each traffic light.

[0135] (Input) Accumulated real-time data

[0136] (Output) Real-time analysis results (optimal switching timing)

[0137] Specifically, the artificial intelligence analyzes real-time data and calculates adjustment results such as, "Due to a sudden increase in traffic volume at intersection C, immediately extend the green light at traffic light D by 10 seconds."

[0138] Step 10:

[0139] The terminal (traffic light) sets new switching timing in real time based on instructions from the server, optimizing traffic flow.

[0140] (Input) Real-time setting instructions

[0141] (Output) Real-time adjusted signal control

[0142] Specifically, the traffic lights instantly adjust their signal timing based on the analysis results, ensuring smooth traffic flow.

[0143] (Application example 1)

[0144] 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."

[0145] Conventional traffic light systems have difficulty responding to fluctuations in traffic volume in real time, resulting in problems with traffic congestion and pedestrian safety. Furthermore, with the increasing number of autonomous vehicles, there has been insufficient coordination between real-time traffic signals and route optimization for autonomous vehicles. This has resulted in insufficient traffic optimization and the inability to ensure smooth overall traffic flow.

[0146] 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.

[0147] In this invention, the server includes: means for installing sensors at each traffic light and measuring the volume of automobile traffic, bicycle traffic, and pedestrians for a certain period of time; means for proposing optimal switching timings for each traffic light using artificial intelligence based on the collected data and setting basic signal switching times based on the proposals; means for aggregating monitoring equipment, location information providing devices, and other traffic data in real time and accumulating it in a central processing unit; means for optimizing and reflecting the switching times of each traffic light using artificial intelligence based on the traffic volume data accumulated in real time; and means for proposing optimal routes for autonomous vehicles in real time. This not only optimizes traffic lights in real time, making traffic flow smoother, but also makes it possible to provide optimal routes to autonomous vehicles in real time.

[0148] A "sensor" is a device that detects traffic volume, vehicle and pedestrian movements, and measures the data.

[0149] "Automobile traffic volume" refers to the volume of automobile traffic over a certain period of time.

[0150] "Bicycle traffic volume" refers to the number of bicycles traveling over a certain period of time.

[0151] "Number of pedestrians" refers to the number of pedestrians in a given period of time.

[0152] "Artificial intelligence" is a technology that analyzes measured traffic data and proposes optimal timing for switching traffic lights.

[0153] "Optimal traffic light switching timing" refers to the signal switching time that ensures smooth traffic flow and reduces traffic congestion.

[0154] "Monitoring equipment" means devices that monitor traffic conditions and collect data.

[0155] A "device that provides location information" is a device that provides location information in real time, such as a car navigation system or a smart device.

[0156] "Other traffic data" includes any additional information related to traffic volume.

[0157] "Central processing unit" refers to a device that aggregates and processes traffic data collected in real time.

[0158] "Autonomous vehicle" refers to a vehicle that is driven automatically by a system.

[0159] "Optimal route" refers to the shortest or most efficient route to a destination, taking into account traffic conditions.

[0160] The real-time optimization system for traffic lights according to the present invention is configured as follows: In this system, sensors are installed at each traffic light to measure the volume of automobile traffic, bicycle traffic, and the number of pedestrians over a certain period of time. The collected data is then sent to a central processing unit and analyzed using artificial intelligence. Based on the analyzed data, optimal switching timing for each traffic light is proposed, and the traffic light switching times are set based on these proposals. In addition, traffic data is aggregated in real time using monitoring equipment and devices that provide location information, allowing for flexible response to fluctuations in traffic volume.

[0161] The server issues instructions to install sensors at each traffic light. The sensors measure traffic volume at intersections and send the data to a central processing unit. The central processing unit inputs the collected data into artificial intelligence and calculates the optimal switching timing for each traffic light. Based on the results of this analysis, the traffic light switching times are set. In addition, real-time data from monitoring equipment and devices that provide location information is also collected by the central processing unit and analyzed by artificial intelligence. This allows the switching timing of each traffic light to be optimized in real time.

[0162] As a specific example of its use, at a certain intersection, traffic volume data obtained by sensors and monitoring equipment is sent to a central processing unit. The central processing unit analyzes the data using artificial intelligence and detects a sudden increase in traffic volume. Based on this, it issues a command to extend the green light at the traffic light by 10 seconds. This command is immediately reflected in the traffic light, maintaining smooth traffic flow.

[0163] The system also supports autonomous vehicles, which receive traffic data from the server and receive real-time recommendations for optimal routes, enabling autonomous vehicles to drive smoothly and improving overall traffic efficiency.

[0164] The hardware used includes sensors, monitoring equipment, ECUs (electronic control units) of autonomous vehicles, etc. The software used includes central processing unit programs (analysis modules implemented in Python and API servers using Flask) and artificial intelligence models (using TensorFlow and PyTorch).

[0165] To illustrate, here are some example prompts:

[0166] "Generate analysis results to optimize the current traffic light switching timing based on traffic volume data. The intersection ID is 1234, and the current traffic volume data is as follows: number of vehicles 100, number of bicycles 20, number of pedestrians 50. Please propose the signal switching timing based on this data."

[0167] In this way, traffic flow can be optimized and traffic congestion and safety problems can be solved.

[0168] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0169] Step 1:

[0170] Sensors are installed at each traffic light. The terminal (sensor installation team) installs the sensors on the traffic lights according to instructions. The sensors measure the volume of automobile traffic, bicycle traffic, and the number of pedestrians. This data is necessary to understand traffic flow.

[0171] Input: Sensor installation location, sensor type (for cars, bicycles, pedestrians)

[0172] Output: Installed sensors

[0173] Step 2:

[0174] The traffic volume data acquired by the sensor is sent to a server. The terminal (sensor) measures traffic volume data over a certain period of time and sends it to the server via the Internet. The collected data is necessary to understand fluctuations in traffic volume in real time.

[0175] Input: Traffic data

[0176] Output: Traffic data sent to the server

[0177] Step 3:

[0178] The server inputs the collected traffic volume data into an artificial intelligence (AI) model for analysis. The server preprocesses the data using a data analysis algorithm implemented in Python, and then inputs the data into an AI model built using TensorFlow or PyTorch. The analysis results in the calculation of the optimal switching timing for each traffic light.

[0179] Input: Traffic data (cars, bicycles, pedestrians)

[0180] Output: Optimal signal switching timing

[0181] Step 4:

[0182] The server then sets the traffic light switching times based on the analysis results. The server then sends commands to the traffic light controller to reflect the optimal switching timing, thereby efficiently managing traffic flow.

[0183] Input: Optimal signal switching timing

[0184] Output: Set signal switching time

[0185] Step 5:

[0186] Real-time data is collected from the monitoring equipment and location information providing devices. The terminals (monitoring equipment and location information providing devices) measure changes in traffic conditions in real time and send the data to the server. This allows real-time traffic conditions to be understood.

[0187] Input: Data from monitoring and location devices

[0188] Output: Real-time data aggregated on the server

[0189] Step 6:

[0190] The server inputs the collected data into the AI ​​model in real time and re-optimizes the switching timing of each traffic light. The server pre-processes the collected data using Python and inputs it into the AI ​​model. This allows the appropriate signal switching timing to be recalculated according to traffic flow.

[0191] Input: Real-time data

[0192] Output: Re-optimized signal switching timing

[0193] Step 7:

[0194] The server then sends the re-optimized results to the traffic light control device, which immediately adjusts the traffic light switching times. Because traffic light switching is adjusted in real time, traffic flow can be managed smoothly and without interruption.

[0195] Input: Re-optimized signal switching timing

[0196] Output: Adjusted signal switching time

[0197] Step 8:

[0198] The server proposes optimal routes to autonomous vehicles in real time and transmits optimal driving route information to each autonomous vehicle, allowing the autonomous vehicle to drive the optimal route according to traffic conditions.

[0199] Input: Reoptimized traffic information

[0200] Output: Optimal route information for the autonomous vehicle

[0201] Through these steps, the server implements a system that collects and analyzes traffic data and controls traffic lights, optimizing traffic conditions in real time.

[0202] 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.

[0203] The real-time optimization system for traffic lights according to the present invention begins by installing sensors at each traffic light and measuring the volume of automobile traffic, bicycle traffic, and pedestrians over a certain period of time. Furthermore, an emotion engine that recognizes user emotions is incorporated, and emotional data of users around each traffic light is also collected. This makes it possible to propose optimal signal switching timings based on traffic volume and emotional data, and adjust them in real time.

[0204] 1. Sensor installation and data collection

[0205] The server issues instructions to install sensors at each traffic light at an intersection. The terminals (sensor installation team) follow the instructions and install the sensors on the traffic lights. The sensors measure the traffic volume of cars, bicycles, and pedestrians for a certain period of time and send the data to the server. The terminals (emotion engines) also collect emotion data from surrounding users and send it to the server. For example, let's assume that sensors X, Y, and Z and an emotion engine are installed at intersection A and one week's worth of traffic volume data and emotion data is collected.

[0206] 2. Data analysis and signal switching timing proposal

[0207] The collected traffic volume data and emotion data are analyzed by the server using artificial intelligence. The server then organizes the acquired data and inputs it into the artificial intelligence. The artificial intelligence then integrates and analyzes both sets of data to calculate the optimal switching timing for each traffic light. The server receives the analysis results and sets new switching timings for each traffic light.

[0208] 3. Proposal for a Pedestrian-Vehicle Separation Traffic Light

[0209] The server analyzes data to identify intersections where pedestrian-vehicle separated traffic lights are needed. The AI ​​considers vehicle and pedestrian traffic volume and emotional data to determine whether pedestrian-vehicle separated traffic lights are needed. For example, if there is an extremely large number of pedestrians at intersection B and the stress levels of users in the area are high, the AI ​​will suggest the need for pedestrian-vehicle separated traffic lights. Based on this suggestion, the server will install pedestrian-vehicle separated traffic lights at the relevant intersections.

[0210] 4. Aggregating real-time data and optimizing signal switching

[0211] The data collection targets will be expanded to include surveillance cameras, devices that provide location information (such as car navigation systems and smartphones), and emotion engines. Terminals (surveillance cameras, smart devices, and emotion engines) will send traffic data and emotion data to a server in real time. The server will then aggregate this data in real time and upload it to a central server. Based on the real-time data, artificial intelligence will recalculate the optimal switching timing for each traffic light and immediately reflect this in the traffic lights.

[0212] (Example)

[0213] For example, at a certain intersection C, real-time location information and emotion data are sent to a server from surveillance cameras, a smartphone app, and an emotion engine. The server analyzes this data and detects a sudden increase in traffic volume and high user stress levels. Based on this information, the AI ​​determines that extending the green light at traffic light D by 10 seconds will reduce stress in the short term. The server immediately adjusts traffic light D based on this judgment. This allows traffic at intersection C to flow smoothly and reduces user stress.

[0214] The processing flow will be explained below.

[0215] Step 1:

[0216] The server issues instructions to install sensors and emotion engines at each intersection. Based on these instructions, the terminals (sensor installation teams) install sensors and emotion engines on the traffic lights at the intersections. These sensors measure the traffic volume of cars, bicycles, and pedestrians, and the emotion engines collect emotion data from surrounding users.

[0217] Step 2:

[0218] The device (sensor) measures traffic volume data over a certain period of time. For example, the sensor measures the number of passing cars, bicycles, and pedestrians every hour and sends this data to the server. The device (emotion engine) collects user emotion data and sends it to the server.

[0219] Step 3:

[0220] The server receives the collected traffic and emotion data, formats the data, removes noise, and then passes the formatted data to the AI ​​to prepare it for analysis.

[0221] Step 4:

[0222] The AI ​​analyzes the provided data and calculates the optimal switching timing for each traffic light. The AI's analysis results suggest the optimal duration of the green and red lights for each traffic light. Furthermore, it also takes into account the user's emotional data and changes the timing to reduce stress.

[0223] Step 5:

[0224] The server receives the signal switching timings suggested by the AI ​​and reflects the settings for each traffic light, so that the traffic lights at each intersection are set to switch at the optimal timing.

[0225] Step 6:

[0226] The server then provides the collected traffic volume data and emotion data to the AI ​​to analyze the necessity of pedestrian-vehicle separated traffic lights. The AI ​​then considers the traffic volume of cars and pedestrians and the user's emotion data to determine whether pedestrian-vehicle separated traffic lights are necessary.

[0227] Step 7:

[0228] Based on the results of the AI ​​analysis, the server issues instructions to install or change the settings of traffic lights at intersections where pedestrian-vehicle separated traffic lights are deemed necessary. The terminal (traffic light installation team) receives these instructions and installs pedestrian-vehicle separated traffic lights at the relevant intersections.

[0229] Step 8:

[0230] The server issues instructions to collect traffic data and emotion data in real time from surveillance cameras, car navigation systems, smartphones, etc. The terminals (these devices) collect data in real time and send it to the server.

[0231] Step 9:

[0232] The server aggregates the received real-time data and uploads it to a central server, including traffic volume information, location information, and user sentiment information.

[0233] Step 10:

[0234] The server passes real-time data to the AI, which then recalculates the optimal switching timing for each traffic light on the fly. Based on the results of the AI ​​calculations, the server instantly adjusts the settings of each traffic light.

[0235] Specific examples

[0236] For example, at intersection C, real-time location information and emotion data from surveillance cameras, a smartphone app, and an emotion engine are sent to a server. The server analyzes this data and detects that traffic volume has suddenly increased, causing users' stress levels to rise. Based on this data, the artificial intelligence determines that the green light at traffic light D should be extended by 10 seconds. Based on this judgment, the server immediately changes the settings of traffic light D to extend the green light time. As a result, traffic at intersection C flows more smoothly and users' stress levels are reduced.

[0237] Example 2

[0238] 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."

[0239] Conventional traffic signal systems face challenges in effectively managing the different traffic volumes of automobiles, bicycles, and pedestrians, while achieving optimal switching timing for each signal. Furthermore, they lack the ability to properly determine the need for pedestrian-vehicle separated signals and utilize real-time data to optimize traffic flow. Furthermore, there is a need to reduce user stress and provide a more comfortable traffic environment by taking into account not only traffic data but also user emotional data.

[0240] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0241] In this invention, the server includes: means for installing sensors at each traffic light and measuring the volume of automobile traffic, bicycle traffic, and pedestrians for a certain period of time; means for proposing optimal switching timing for each traffic light using artificial intelligence based on the collected data and setting basic signal switching times based on the proposals; means for aggregating traffic data from surveillance cameras, devices that provide location information, and other sources in real time and accumulating it in a central server; means for collecting emotional data of surrounding users and adjusting the timing of switching lights based on that data; and means for optimizing and reflecting the switching times of each traffic light using artificial intelligence based on the traffic volume data and emotional data collected in real time. This makes it possible to propose and adjust optimal switching timing for traffic lights based on traffic volume and emotional data, reducing user stress and achieving smooth traffic flow.

[0242] A "sensor" is a device installed at each traffic light to measure traffic volume, measuring the number of cars, bicycles, and pedestrians over a certain period of time.

[0243] "Traffic signal timing" refers to the time it takes for a traffic light to switch between green, yellow, and red, and is optimized based on traffic volume and other data.

[0244] "Artificial intelligence" is a technology that analyzes collected traffic volume data and emotion data to suggest optimal traffic light switching timing.

[0245] A "surveillance camera" is a device that monitors traffic conditions in real time and transmits the video data to a server.

[0246] A "device that provides location information" is a device that provides user location data to a server, such as a car navigation system or smartphone.

[0247] The "central server" is a server that aggregates and analyzes data sent from each traffic light, sensor, surveillance camera, etc.

[0248] "Emotion data" is data that indicates the user's emotional state and is collected by the emotion engine. It is obtained by analyzing the facial expressions, tone of voice, etc. of surrounding users.

[0249] A "pedestrian-vehicle separated traffic light" is a traffic light that separates the crossing timing of pedestrians and vehicles, and is used to improve traffic flow and safety.

[0250] "Real-time aggregated traffic volume data" refers to traffic volume data transmitted in real time from sensors and devices and aggregated on a central server.

[0251] "Traffic green and red duration" refers to the length of time a traffic light displays a green or red light, and is adjusted based on traffic conditions and emotion data.

[0252] "Traffic data" refers to data that indicates traffic conditions, including the number and location information of automobiles, bicycles, and pedestrians.

[0253] MODE FOR CARRYING OUT THE INVENTION

[0254] The real-time optimization system for traffic lights according to the present invention utilizes various devices and artificial intelligence to optimize the timing of traffic light switching based on traffic volume data and emotion data. Specific embodiments for implementing this system are described below.

[0255] First, the server issues instructions to install sensors at each traffic light at an intersection. These sensors measure the volume of automobile, bicycle, and pedestrian traffic over a set period of time. The devices (sensor installation team) follow the instructions and install the sensors on the traffic lights. They also install an emotion engine to collect emotion data from surrounding users. The data obtained from the sensors and emotion engine is periodically sent to the server.

[0256] For example, consider installing sensors X, Y, Z and an emotion engine at intersection A and collecting traffic volume data and emotion data for one week.

[0257] The collected traffic volume data and emotion data are analyzed by the server using artificial intelligence. The server then organizes and inputs the acquired data into a format that the AI ​​can use. The AI ​​then integrates and analyzes this data to calculate the optimal switching timing for each traffic light. For example, if the data shows that the number of pedestrians increases during a certain time period, which increases the user's stress level during that time period, the AI ​​will suggest extending the green light on the traffic light during that time period.

[0258] The server then sets new switching timings for each traffic light based on the analysis results obtained from the AI, and these settings are made in real time and are adjusted as traffic conditions and the user's emotional state change.

[0259] Furthermore, the server aggregates real-time data from surveillance cameras and devices that provide location information (such as car navigation systems and smartphones) and inputs it into the central server. Terminals (surveillance cameras, smart devices, and emotion engines) send traffic data and emotion data to the server in real time. The server aggregates this data in real time, and artificial intelligence recalculates the optimal switching timing for each traffic light and immediately updates the traffic lights.

[0260] For example, at a certain intersection C, real-time location information and emotion data are sent to a server from surveillance cameras, a smartphone app, and an emotion engine. The server analyzes this data and detects a sudden increase in traffic volume and high user stress levels. Based on this information, the AI ​​determines that extending the green light at traffic light D by 10 seconds will reduce stress in the short term. The server immediately adjusts traffic light D based on this judgment. This allows traffic at intersection C to flow smoothly and reduces user stress.

[0261] An example prompt might be, "Based on traffic volume and emotion data at intersection A, please suggest the optimal timing for switching the traffic lights. The current situation is as follows: vehicle traffic volume is at its peak and pedestrian stress levels are high."

[0262] This system makes it possible to propose and adjust optimal traffic light switching timing based on traffic volume and emotion data, reducing user stress and ensuring smooth traffic flow.

[0263] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0264] Step 1:

[0265] The server issues instructions to install sensors at each intersection. These instructions include the type of sensor (traffic volume sensor or emotion engine) and details of the installation location. The inputs include the intersection location information and the sensor information to be installed. The output generates specific instructions for the installation team. In concrete terms, the server sends the instructions over the network to the terminals of the sensor installation team.

[0266] Step 2:

[0267] The terminals (sensor installation team) follow instructions from the server and install sensors and emotion engines at designated locations at each intersection. The input is the instructions from the server. The output is a report to the server indicating that the sensors and emotion engines have been installed. In concrete terms, the installation team secures the sensors at the designated locations and installs the emotion engines.

[0268] Step 3:

[0269] The sensors measure the traffic volume of cars, bicycles, and pedestrians over a certain period of time and send the data to the server. At the same time, the emotion engine collects user emotion data and sends it to the server. The input is registered data from passing vehicles and pedestrians. The output is the measured data sent to the server. The specific operation is to detect traffic volume in real time and analyze the emotional state.

[0270] Step 4:

[0271] The server organizes the data received from the sensors and emotion engine. The inputs include traffic volume data and emotion data. The output is data formatted to be input into the artificial intelligence. Specific operations include storing the data in a database, converting the format, and removing noise.

[0272] Step 5:

[0273] The AI ​​integrates and analyzes traffic volume data and emotion data to calculate the optimal switching timing for each traffic light. The inputs are organized traffic volume data and emotion data. The output is a proposal for the optimal signal switching timing. The specific operation is to use a machine learning algorithm to perform optimization calculations.

[0274] Step 6:

[0275] The server sets new switching timings for each traffic light based on the analysis results obtained from the AI. The input is the optimization proposal made by the AI. The output is a setting instruction for the traffic light. Specifically, the new setting is sent to the traffic light's control device, and the traffic light then corrects its operation.

[0276] Step 7:

[0277] The server aggregates real-time data from surveillance cameras and devices that provide location information, and inputs it into the central server. The input is real-time data from each device. The output is aggregated data. Specifically, the system sends video data from surveillance cameras and location information from smartphone apps to the central server.

[0278] Step 8:

[0279] The artificial intelligence recalculates the optimal signal switching timing based on the latest data. The inputs are traffic volume data and emotion data collected in real time. The output is the recalculated optimal signal switching timing. Specifically, the AI ​​analyzes the real-time data and immediately produces results.

[0280] Step 9:

[0281] The server immediately reflects the recalculated signal switching timings in the traffic lights. The input is the optimization results from the artificial intelligence. The output is an update instruction for each traffic light. Specifically, the server sends the new settings to the traffic light controller, and the traffic light adjusts its operation accordingly.

[0282] (Application example 2)

[0283] 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."

[0284] Conventional traffic light control systems have difficulty adapting to traffic volume in real time, which often disrupts the smooth flow of traffic. Furthermore, signals are not optimized to accommodate pedestrian, bicycle, and automobile traffic volumes, which can result in traffic accidents and congestion. Furthermore, with the spread of autonomous vehicles, real-time coordination with traffic lights is required, but current systems are unable to meet this demand.

[0285] The specific processing by the specific 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 installing sensors at each traffic light and measuring the volume of automobile traffic, bicycle traffic, and pedestrians for a certain period of time; means for using artificial intelligence to propose optimal switching timing for each traffic light based on the collected data and for setting basic signal switching times based on the proposals; means for aggregating traffic data from surveillance cameras, devices that provide location information, and other sources in real time and accumulating it in a central server; means for optimizing and reflecting the switching times of each traffic light based on the traffic volume data accumulated in real time using artificial intelligence; and means for providing optimal traffic signal timing information to automobiles in real time and adjusting the speed of automobiles. This enables smooth traffic flow and improved safety by optimizing the switching timing of traffic lights in response to traffic volume in real time and appropriately adjusting the speed of autonomous vehicles.

[0286] A "sensor" is a device used to measure traffic volume, number of pedestrians, number of bicycles, etc. over a certain period of time.

[0287] "Traffic data" is a collection of information about vehicle, bicycle, and pedestrian traffic.

[0288] "Emotional data" is a collection of information that measures the emotions and stress levels of transportation users.

[0289] "Artificial intelligence" is a technology that analyzes collected traffic volume data and emotion data to calculate the optimal timing for switching traffic lights.

[0290] "Signal switching timing" refers to the time it takes for a traffic light to switch from red to green, or vice versa.

[0291] A "traffic light" is a device installed at intersections and roads to control the flow of traffic.

[0292] A "surveillance camera" is a device that observes and records traffic conditions and user behavior in real time.

[0293] A "location information device" is a device, such as a car navigation system or smartphone, that acquires information about transportation users and their locations in real time.

[0294] The "central server" is a computer system that centrally manages and analyzes collected traffic volume data and emotion data.

[0295] "Optimization" refers to the adjustment or result of adjustment to make traffic flow most efficient.

[0296] A "pedestrian-vehicle separated traffic light" is a system that switches signals so that pedestrians and vehicles can move safely without crossing paths.

[0297] An "autonomous vehicle" is a vehicle that is designed to navigate roads autonomously without the need for driver intervention.

[0298] "Speed ​​adjustment" refers to changing the speed of an autonomous vehicle so that it travels at an appropriate speed in accordance with the timing of traffic lights.

[0299] The system of the present invention realizes real-time optimization of traffic signals and consists of the following main components:

[0300] Sensor installation and data collection

[0301] The server issues instructions to install sensors at each traffic light. The sensors measure the traffic volume of cars, pedestrians, and bicycles on the road for a certain period of time and send the data to the server. Each sensor also has a built-in emotion engine that collects emotional data from road users. This allows traffic volume and emotional data for each intersection to be obtained and aggregated on the server.

[0302] Data analysis and signal switching timing proposals

[0303] The collected data is input into artificial intelligence by the server and analyzed. The artificial intelligence combines traffic volume data and emotion data to calculate the optimal switching timing for each traffic light. The server receives the analysis results and sets new switching timings for the traffic lights. It also proposes the installation of pedestrian-vehicle separated traffic lights when separation between pedestrians and vehicles is necessary.

[0304] Aggregating real-time data and optimizing signal switching

[0305] Data collected in real time from surveillance cameras, location-based devices (e.g., smartphones and car navigation systems), and emotion engines is sent to a server. The server aggregates and analyzes this data. AI uses the real-time data to recalculate the optimal switching timing for each traffic light and reflects this in the traffic lights.

[0306] Collaboration with autonomous vehicles

[0307] The system provides automated vehicles with optimal traffic signal timing information and adjusts their speed in real time, allowing them to maintain an optimal speed and drive efficiently in line with traffic light switching timings.

[0308] Hardware and software used

[0309] The hardware used in this invention includes sensors installed at intersections, surveillance cameras, location information providing devices, etc. The software is a program using Python and various AI model APIs, and is managed by a server.

[0310] Example of program execution

[0311] As a concrete example, at a certain intersection, real-time location information and emotion data from surveillance cameras, smartphone apps, and emotion engines are sent to a server. The server analyzes this data and detects a sudden increase in traffic volume and high user stress levels. Based on this information, the AI ​​extends the green light at traffic lights, aiming to reduce stress in the short term. The server immediately adjusts the traffic lights accordingly.

[0312] Prompt Sentence Examples

[0313] "Create a program that predicts the optimal timing for switching traffic lights at an intersection and adjusts the speed of an autonomous vehicle in real time."

[0314] As a result, the system of the present invention enables optimization of traffic signals according to traffic volume and real-time speed adjustment of autonomous vehicles, thereby improving traffic flow and safety.

[0315] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0316] Step 1:

[0317] The server collects traffic volume data and emotion data from various sensors, surveillance cameras, and location information providing devices installed at intersections. These data include vehicle traffic volume, bicycle traffic volume, pedestrian count, and user location and emotion information. The input of the data collection is the sensor information from these devices, and the output is raw data transferred to the server.

[0318] Step 2:

[0319] The server aggregates the collected data in real time to a central server. At this stage, data sent from multiple devices is consolidated and stored in a database. The input is traffic volume data and sentiment data obtained from the devices, and the output is organized database entries. Specifically, data points from each device are consolidated in one place.

[0320] Step 3:

[0321] The server inputs the aggregated data into an artificial intelligence system for analysis. The AI ​​uses traffic volume data and emotion data to perform calculations to propose optimal switching timings for each traffic light. The input is the aggregated data, and the output is a proposal for optimal switching timings for the traffic lights.

[0322] Step 4:

[0323] The server sets the basic signal switching times for each traffic light based on the results of the AI ​​analysis. These settings are reflected in the traffic lights and used for actual traffic control. The input is the AI ​​analysis results, and the output is the new switching timing set for the traffic light.

[0324] Step 5:

[0325] The server provides optimal traffic signal timing information to the autonomous vehicle in real time. This information includes the current status of the traffic lights and the next switching timing, and the vehicle adjusts its speed based on this information. The input is the traffic light timing information, and the output is the speed adjustment instruction sent to the autonomous vehicle.

[0326] Step 6:

[0327] The terminal uses the traffic signal information acquired by the autonomous vehicle to adjust the vehicle's speed in real time. Specifically, it calculates the optimal speed based on the current timing information of the traffic lights and changes the vehicle's speed. The input is the traffic signal information, and the output is the adjusted vehicle speed.

[0328] Step 7:

[0329] The server monitors the autonomous vehicle's behavior based on new speed information and provides real-time feedback as needed. Feedback is provided based on speed adjustment accuracy and traffic fluctuations, allowing for safer driving with greater precision. The input is autonomous vehicle status data, and the output is updated speed instructions and other control commands.

[0330] 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.

[0331] 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.

[0332] 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.

[0333] [Second embodiment]

[0334] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0335] 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.

[0336] 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).

[0337] 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.

[0338] 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.

[0339] 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).

[0340] 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.

[0341] 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.

[0342] 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.

[0343] 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.

[0344] 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.

[0345] 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."

[0346] An embodiment of a real-time traffic light optimization system according to the present invention will be described. In this system, sensors are installed at each traffic light to measure the volume of automobile traffic, bicycle traffic, and the number of pedestrians over a certain period of time. The collected data is sent to a server and analyzed using artificial intelligence. Based on the analysis results, optimal switching timings for each traffic light are proposed and reflected in the traffic lights.

[0347] 1. Sensor installation and data collection

[0348] The server issues instructions to install sensors at each traffic light at an intersection. The terminals (sensor installation team) follow the instructions and install the sensors on the traffic lights. The sensors measure the traffic volume of cars, bicycles, and pedestrians over a certain period of time and send the data to the server. For example, suppose sensors X, Y, and Z are installed at intersection A and data is collected for one week.

[0349] 2. Data analysis and signal switching timing proposal

[0350] The collected data is analyzed by the server using artificial intelligence. The server then organizes the acquired traffic volume data and inputs it into the artificial intelligence. The artificial intelligence then analyzes the data and calculates the optimal switching timing for each traffic light. The server receives the analysis results and sets new switching timings for each traffic light.

[0351] 3. Proposal for a Pedestrian-Vehicle Separation Traffic Light

[0352] The server analyzes the data to identify intersections that require pedestrian-vehicle separated traffic lights. Based on the collected data, the AI ​​determines whether a particular intersection requires a pedestrian-vehicle separated traffic light. For example, if the number of pedestrians at intersection B is extremely high, the AI ​​will suggest the need for a pedestrian-vehicle separated traffic light. Based on this suggestion, the server installs a pedestrian-vehicle separated traffic light at the relevant intersection.

[0353] 4. Aggregating real-time data and optimizing signal switching

[0354] The data collection targets will be expanded to include surveillance cameras and devices that provide location information (such as car navigation systems and smartphones). Terminals (surveillance cameras and smart devices) will send traffic data to a server in real time. The server will then aggregate this data in real time and upload it to a central server. Based on the real-time data, artificial intelligence will calculate the optimal switching timing for each traffic light and immediately update the traffic lights.

[0355] (Example)

[0356] For example, at a certain intersection C, real-time location information from surveillance cameras and smartphone apps is sent to a server. The server analyzes this information and detects a sudden increase in traffic volume. Based on this information, the AI ​​determines that the green light at traffic light D should be extended by 10 seconds, and the server immediately adjusts traffic light D based on that judgment. This allows traffic at intersection C to flow smoothly.

[0357] The processing flow will be explained below.

[0358] Step 1:

[0359] The server issues instructions to install sensors at each intersection. Based on these instructions, the terminals (sensor installation teams) install sensors on the traffic lights at the intersections. These sensors are used to measure the volume of automobile, bicycle, and pedestrian traffic.

[0360] Step 2:

[0361] The device (sensor) measures traffic volume data for a certain period of time. For example, the sensor measures the number of passing cars, bicycles, and pedestrians every hour, and sends this data to a server.

[0362] Step 3:

[0363] The server receives the collected traffic data, formats it, removes noise, and then passes it to an artificial intelligence to prepare it for analysis.

[0364] Step 4:

[0365] The AI ​​analyzes the provided data and calculates the optimal switching timing for each traffic light. The AI's analysis results suggest the optimal duration of the green and red lights for each traffic light.

[0366] Step 5:

[0367] The server receives the signal switching timings suggested by the AI ​​and reflects the settings for each traffic light, so that the traffic lights at each intersection are set to switch at the optimal timing.

[0368] Step 6:

[0369] The server then provides the collected data to the AI ​​to analyze the need for pedestrian-separated traffic lights. The AI ​​then considers the volume of automobile and pedestrian traffic and determines whether pedestrian-separated traffic lights are necessary.

[0370] Step 7:

[0371] Based on the results of the AI ​​analysis, the server issues instructions to install or change the settings of traffic lights at intersections where pedestrian-vehicle separated traffic lights are deemed necessary. The terminal (traffic light installation team) receives these instructions and installs pedestrian-vehicle separated traffic lights at the relevant intersections.

[0372] Step 8:

[0373] The server issues instructions to collect traffic data in real time from surveillance cameras, car navigation systems, smartphones, etc. The terminals (these devices) collect data in real time and send it to the server.

[0374] Step 9:

[0375] The server aggregates the real-time data it receives and uploads it to a central server, including traffic and location information.

[0376] Step 10:

[0377] The server passes real-time data to the AI, which then recalculates the optimal switching timing for each traffic light on the fly. Based on the AI's calculations, the server instantly adjusts the settings for each traffic light.

[0378] Specific examples

[0379] For example, at intersection C, real-time location information from surveillance cameras and smartphone apps is sent to a server. The server analyzes this information and detects an increase in traffic volume. Based on this real-time data, the artificial intelligence determines that the green light at traffic light D should be extended by 10 seconds. Based on this judgment, the server immediately changes the settings of traffic light D to extend the green light time. This allows traffic at intersection C to flow more smoothly.

[0380] Example 1

[0381] 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."

[0382] Traffic signal control is based on fixed patterns and cannot adequately respond to sudden fluctuations in traffic volume, which can disrupt the smooth flow of traffic. It is also difficult to properly evaluate the need for pedestrian-vehicle separated traffic signals and install them appropriately. Therefore, a system is needed to alleviate traffic congestion, prevent traffic accidents, and improve convenience for road users.

[0383] 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.

[0384] In this invention, the server includes a means for installing sensors at each traffic light and measuring the volume of automobile traffic, bicycle traffic, and pedestrians for a certain period of time, a means for proposing the optimal switching timing for each traffic light using artificial intelligence based on the collected data and setting the basic signal switching time based on the proposal, and a means for aggregating monitoring devices, terminals that provide location information, and other traffic data in real time and accumulating it in a central control device, thereby making it possible to respond quickly and appropriately to fluctuations in traffic volume and optimize traffic flow.

[0385] "Sensors" are devices used to measure the volume of automobile, bicycle, and pedestrian traffic.

[0386] "Artificial intelligence" is a technology that analyzes collected data and calculates the optimal switching timing for each traffic light.

[0387] A "traffic light" is a device installed to control traffic, and emits signals to vehicles and pedestrians indicating whether to proceed or stop.

[0388] "Switching timing" refers to the time when a traffic light switches from green to red to yellow.

[0389] "Monitoring devices" are devices installed to monitor traffic conditions in real time, including cameras and sensors.

[0390] A "terminal that provides location information" is a device such as a car navigation system or a smartphone that detects the current location and transmits it to a server.

[0391] "Central control device" refers to a server that controls traffic lights based on aggregated traffic data.

[0392] A "pedestrian-vehicle separated traffic signal" is a traffic signal that separates and controls pedestrian and vehicle traffic, allowing pedestrians to cross intersections safely.

[0393] "Real-time aggregation" means that collected traffic data is immediately fed into a central control device.

[0394] An embodiment of a real-time optimization system for traffic lights according to the present invention will be described. In this system, sensors are installed at each traffic light to measure the volume of automobile traffic, bicycle traffic, and the number of pedestrians over a certain period of time. The collected data is sent to a server and analyzed by artificial intelligence. Based on the analysis results, optimal switching timings for each traffic light are proposed and reflected in the traffic lights.

[0395] First, the server issues instructions to install sensors on the traffic lights at each intersection. The terminals (sensor installation team) follow these instructions and install the sensors on the road. The sensors measure the traffic volume of cars, bicycles, and pedestrians over a certain period of time and send the data to the server. For example, let's consider the case where sensors X, Y, and Z are installed at intersection A and data is collected for one week. The sensors operate 24 hours a day, continuously measuring the flow rate of each traffic element.

[0396] The collected data is sent to a server, where it is organized and preprocessed. The server then inputs this organized data into a generative AI model (using, for example, a machine learning framework such as TensorFlow or PyTorch) to analyze traffic patterns and trends. The AI ​​calculates the optimal traffic light switching timing and returns the results to the server. The server then sets the new switching timing for the traffic lights and reflects it at each traffic light. For example, specific adjustments may be made, such as "extending the green light for traffic light A by 10 seconds between 5:00 PM and 7:00 PM on weekdays."

[0397] The server also analyzes the collected data to determine whether a particular intersection needs a pedestrian-vehicle-separated traffic light. Based on pedestrian data, the AI ​​determines whether a particular intersection (e.g., intersection B) needs a pedestrian-vehicle-separated traffic light. For example, it uses a specific algorithm, such as "If there are an extremely high number of pedestrians at intersection B, suggest installing a pedestrian-vehicle-separated traffic light."

[0398] Furthermore, terminals (surveillance cameras and smart devices) collect traffic data in real time and send it to a server. The server then collects this real-time data in a central database and analyzes it using artificial intelligence. Based on the real-time data, the optimal switching timing for each traffic light is calculated and the results are immediately reflected in the traffic lights. For example, specific adjustments are made in real time, such as "due to a sudden increase in traffic volume at intersection C, immediately extend the green light at traffic light D by 10 seconds."

[0399] For actual operation, detailed instructions can be obtained by entering the following prompt sentence into the generative AI model:

[0400] Prompt: "Describe how you can detect sudden increases in traffic volume at an intersection based on real-time location information from surveillance cameras and a smartphone app, and then optimize traffic light timing based on the results."

[0401] In this way, the system of the present invention integrates various technologies to highly optimize traffic signal control and achieve smooth traffic flow.

[0402] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0403] Step 1:

[0404] The server issues instructions to install sensors at traffic lights at intersections, including the specific intersection location and the type of sensor to install.

[0405] (Input) Location information and sensor type

[0406] (Output) Sensor installation instructions

[0407] Specifically, the server generates instructions such as "Install sensors X, Y, and Z at intersection A" and sends them to the terminal.

[0408] Step 2:

[0409] The terminal (sensor installation team) follows the instructions of the server and installs the sensor at the designated traffic light.

[0410] (Input) Sensor installation instructions

[0411] (Output) Data from installed sensors

[0412] Specifically, the sensor installation team physically installs designated sensors X, Y, and Z on the traffic lights at intersection A.

[0413] Step 3:

[0414] Sensors attached to the device measure the volume of cars, bicycles, and pedestrians over a period of time and send the data to a server.

[0415] (Input) Traffic volume data measurement instructions

[0416] (Output) Traffic volume data

[0417] Specifically, the sensor operates 24 hours a day and sends the measured data to the server every day.

[0418] Step 4:

[0419] The server receives the collected data, organizes it, and pre-processes it for input into artificial intelligence.

[0420] (Input) Traffic volume data

[0421] (Output) Preprocessed data

[0422] Specifically, the server classifies the traffic volume data by time period and removes outliers.

[0423] Step 5:

[0424] The server then feeds the pre-processed data into an artificial intelligence system (e.g., TensorFlow or PyTorch) to analyze traffic patterns and trends.

[0425] (Input) Preprocessed traffic data

[0426] (Output) Analysis results (optimal switching timing)

[0427] Specifically, the artificial intelligence uses machine learning algorithms to analyze traffic volume and calculate the optimal timing for switching signals.

[0428] Step 6:

[0429] The server receives the analysis results from the artificial intelligence and sends instructions to each traffic light to set new switching timings.

[0430] (Input) Analysis results (optimal switching timing)

[0431] (Output) Setting instructions to traffic lights

[0432] As a specific operation, the server sends a specific adjustment to the traffic light, such as "Extend the green light of traffic light A by 10 seconds between 5:00 PM and 7:00 PM."

[0433] Step 7:

[0434] The terminal (traffic light) immediately sets and executes the new switching timing based on instructions from the server.

[0435] (Input) Setting instructions to traffic lights

[0436] (Output) Signal control based on new signal switching timing

[0437] Specifically, traffic lights switch between green and red based on set timing.

[0438] Step 8:

[0439] The server receives traffic data collected in real time from the monitoring devices and location information providing terminals and accumulates it in the central control device.

[0440] (Input) Real-time data from monitoring devices and location information providing devices

[0441] (Output) Aggregated real-time data

[0442] Specifically, the server periodically updates and aggregates smartphone location information and surveillance camera video data.

[0443] Step 9:

[0444] The server uses artificial intelligence to analyze the aggregated real-time data and issues instructions to instantly reflect the optimal switching timing for each traffic light.

[0445] (Input) Accumulated real-time data

[0446] (Output) Real-time analysis results (optimal switching timing)

[0447] Specifically, the artificial intelligence analyzes real-time data and calculates adjustment results such as, "Due to a sudden increase in traffic volume at intersection C, immediately extend the green light at traffic light D by 10 seconds."

[0448] Step 10:

[0449] The terminal (traffic light) sets new switching timing in real time based on instructions from the server, optimizing traffic flow.

[0450] (Input) Real-time setting instructions

[0451] (Output) Real-time adjusted signal control

[0452] Specifically, the traffic lights instantly adjust their signal timing based on the analysis results, ensuring smooth traffic flow.

[0453] (Application example 1)

[0454] 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."

[0455] Conventional traffic light systems have difficulty responding to fluctuations in traffic volume in real time, resulting in problems with traffic congestion and pedestrian safety. Furthermore, with the increasing number of autonomous vehicles, there has been insufficient coordination between real-time traffic signals and route optimization for autonomous vehicles. This has resulted in insufficient traffic optimization and the inability to ensure smooth overall traffic flow.

[0456] 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.

[0457] In this invention, the server includes: means for installing sensors at each traffic light and measuring the volume of automobile traffic, bicycle traffic, and pedestrians for a certain period of time; means for proposing optimal switching timings for each traffic light using artificial intelligence based on the collected data and setting basic signal switching times based on the proposals; means for aggregating monitoring equipment, location information providing devices, and other traffic data in real time and accumulating it in a central processing unit; means for optimizing and reflecting the switching times of each traffic light using artificial intelligence based on the traffic volume data accumulated in real time; and means for proposing optimal routes for autonomous vehicles in real time. This not only optimizes traffic lights in real time, making traffic flow smoother, but also makes it possible to provide optimal routes to autonomous vehicles in real time.

[0458] A "sensor" is a device that detects traffic volume, vehicle and pedestrian movements, and measures the data.

[0459] "Automobile traffic volume" refers to the volume of automobile traffic over a certain period of time.

[0460] "Bicycle traffic volume" refers to the number of bicycles traveling over a certain period of time.

[0461] "Number of pedestrians" refers to the number of pedestrians in a given period of time.

[0462] "Artificial intelligence" is a technology that analyzes measured traffic data and proposes optimal timing for switching traffic lights.

[0463] "Optimal traffic light switching timing" refers to the signal switching time that ensures smooth traffic flow and reduces traffic congestion.

[0464] "Monitoring equipment" means devices that monitor traffic conditions and collect data.

[0465] A "device that provides location information" is a device that provides location information in real time, such as a car navigation system or a smart device.

[0466] "Other traffic data" includes any additional information related to traffic volume.

[0467] "Central processing unit" refers to a device that aggregates and processes traffic data collected in real time.

[0468] "Autonomous vehicle" refers to a vehicle that is driven automatically by a system.

[0469] "Optimal route" refers to the shortest or most efficient route to a destination, taking into account traffic conditions.

[0470] The real-time optimization system for traffic lights according to the present invention is configured as follows: In this system, sensors are installed at each traffic light to measure the volume of automobile traffic, bicycle traffic, and the number of pedestrians over a certain period of time. The collected data is then sent to a central processing unit and analyzed using artificial intelligence. Based on the analyzed data, optimal switching timing for each traffic light is proposed, and the traffic light switching times are set based on these proposals. In addition, traffic data is aggregated in real time using monitoring equipment and devices that provide location information, allowing for flexible response to fluctuations in traffic volume.

[0471] The server issues instructions to install sensors at each traffic light. The sensors measure traffic volume at intersections and send the data to a central processing unit. The central processing unit inputs the collected data into artificial intelligence and calculates the optimal switching timing for each traffic light. Based on the results of this analysis, the traffic light switching times are set. In addition, real-time data from monitoring equipment and devices that provide location information is also collected by the central processing unit and analyzed by artificial intelligence. This allows the switching timing of each traffic light to be optimized in real time.

[0472] As a specific example of its use, at a certain intersection, traffic volume data obtained by sensors and monitoring equipment is sent to a central processing unit. The central processing unit analyzes the data using artificial intelligence and detects a sudden increase in traffic volume. Based on this, it issues a command to extend the green light at the traffic light by 10 seconds. This command is immediately reflected in the traffic light, maintaining smooth traffic flow.

[0473] The system also supports autonomous vehicles, which receive traffic data from the server and receive real-time recommendations for optimal routes, enabling autonomous vehicles to drive smoothly and improving overall traffic efficiency.

[0474] The hardware used includes sensors, monitoring equipment, ECUs (electronic control units) of autonomous vehicles, etc. The software used includes central processing unit programs (analysis modules implemented in Python and API servers using Flask) and artificial intelligence models (using TensorFlow and PyTorch).

[0475] To illustrate, here are some example prompts:

[0476] "Generate analysis results to optimize the current traffic light switching timing based on traffic volume data. The intersection ID is 1234, and the current traffic volume data is as follows: number of vehicles 100, number of bicycles 20, number of pedestrians 50. Please propose the signal switching timing based on this data."

[0477] In this way, traffic flow can be optimized and traffic congestion and safety problems can be solved.

[0478] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0479] Step 1:

[0480] Sensors are installed at each traffic light. The terminal (sensor installation team) installs the sensors on the traffic lights according to instructions. The sensors measure the volume of automobile traffic, bicycle traffic, and the number of pedestrians. This data is necessary to understand traffic flow.

[0481] Input: Sensor installation location, sensor type (for cars, bicycles, pedestrians)

[0482] Output: Installed sensors

[0483] Step 2:

[0484] The traffic volume data acquired by the sensor is sent to a server. The terminal (sensor) measures traffic volume data over a certain period of time and sends it to the server via the Internet. The collected data is necessary to understand fluctuations in traffic volume in real time.

[0485] Input: Traffic data

[0486] Output: Traffic data sent to the server

[0487] Step 3:

[0488] The server inputs the collected traffic volume data into an artificial intelligence (AI) model for analysis. The server preprocesses the data using a data analysis algorithm implemented in Python, and then inputs the data into an AI model built using TensorFlow or PyTorch. The analysis results in the calculation of the optimal switching timing for each traffic light.

[0489] Input: Traffic data (cars, bicycles, pedestrians)

[0490] Output: Optimal signal switching timing

[0491] Step 4:

[0492] The server then sets the traffic light switching times based on the analysis results. The server then sends commands to the traffic light controller to reflect the optimal switching timing, thereby efficiently managing traffic flow.

[0493] Input: Optimal signal switching timing

[0494] Output: Set signal switching time

[0495] Step 5:

[0496] Real-time data is collected from the monitoring equipment and location information providing devices. The terminals (monitoring equipment and location information providing devices) measure changes in traffic conditions in real time and send the data to the server. This allows real-time traffic conditions to be understood.

[0497] Input: Data from monitoring and location devices

[0498] Output: Real-time data aggregated on the server

[0499] Step 6:

[0500] The server inputs the collected data into the AI ​​model in real time and re-optimizes the switching timing of each traffic light. The server pre-processes the collected data using Python and inputs it into the AI ​​model. This allows the appropriate signal switching timing to be recalculated according to traffic flow.

[0501] Input: Real-time data

[0502] Output: Re-optimized signal switching timing

[0503] Step 7:

[0504] The server then sends the re-optimized results to the traffic light control device, which immediately adjusts the traffic light switching times. Because traffic light switching is adjusted in real time, traffic flow can be managed smoothly and without interruption.

[0505] Input: Re-optimized signal switching timing

[0506] Output: Adjusted signal switching time

[0507] Step 8:

[0508] The server proposes optimal routes to autonomous vehicles in real time and transmits optimal driving route information to each autonomous vehicle, allowing the autonomous vehicle to drive the optimal route according to traffic conditions.

[0509] Input: Reoptimized traffic information

[0510] Output: Optimal route information for the autonomous vehicle

[0511] Through these steps, the server implements a system that collects and analyzes traffic data and controls traffic lights, optimizing traffic conditions in real time.

[0512] 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.

[0513] The real-time optimization system for traffic lights according to the present invention begins by installing sensors at each traffic light and measuring the volume of automobile traffic, bicycle traffic, and pedestrians over a certain period of time. Furthermore, an emotion engine that recognizes user emotions is incorporated, and emotional data of users around each traffic light is also collected. This makes it possible to propose optimal signal switching timings based on traffic volume and emotional data, and adjust them in real time.

[0514] 1. Sensor installation and data collection

[0515] The server issues instructions to install sensors at each traffic light at an intersection. The terminals (sensor installation team) follow the instructions and install the sensors on the traffic lights. The sensors measure the traffic volume of cars, bicycles, and pedestrians for a certain period of time and send the data to the server. The terminals (emotion engines) also collect emotion data from surrounding users and send it to the server. For example, let's assume that sensors X, Y, and Z and an emotion engine are installed at intersection A and one week's worth of traffic volume data and emotion data is collected.

[0516] 2. Data analysis and signal switching timing proposal

[0517] The collected traffic volume data and emotion data are analyzed by the server using artificial intelligence. The server then organizes the acquired data and inputs it into the artificial intelligence. The artificial intelligence then integrates and analyzes both sets of data to calculate the optimal switching timing for each traffic light. The server receives the analysis results and sets new switching timings for each traffic light.

[0518] 3. Proposal for a Pedestrian-Vehicle Separation Traffic Light

[0519] The server analyzes data to identify intersections where pedestrian-vehicle separated traffic lights are needed. The AI ​​considers vehicle and pedestrian traffic volume and emotional data to determine whether pedestrian-vehicle separated traffic lights are needed. For example, if there is an extremely large number of pedestrians at intersection B and the stress levels of users in the area are high, the AI ​​will suggest the need for pedestrian-vehicle separated traffic lights. Based on this suggestion, the server will install pedestrian-vehicle separated traffic lights at the relevant intersections.

[0520] 4. Aggregating real-time data and optimizing signal switching

[0521] The data collection targets will be expanded to include surveillance cameras, devices that provide location information (such as car navigation systems and smartphones), and emotion engines. Terminals (surveillance cameras, smart devices, and emotion engines) will send traffic data and emotion data to a server in real time. The server will then aggregate this data in real time and upload it to a central server. Based on the real-time data, artificial intelligence will recalculate the optimal switching timing for each traffic light and immediately reflect this in the traffic lights.

[0522] (Example)

[0523] For example, at a certain intersection C, real-time location information and emotion data are sent to a server from surveillance cameras, a smartphone app, and an emotion engine. The server analyzes this data and detects a sudden increase in traffic volume and high user stress levels. Based on this information, the AI ​​determines that extending the green light at traffic light D by 10 seconds will reduce stress in the short term. The server immediately adjusts traffic light D based on this judgment. This allows traffic at intersection C to flow smoothly and reduces user stress.

[0524] The processing flow will be explained below.

[0525] Step 1:

[0526] The server issues instructions to install sensors and emotion engines at each intersection. Based on these instructions, the terminals (sensor installation teams) install sensors and emotion engines on the traffic lights at the intersections. These sensors measure the traffic volume of cars, bicycles, and pedestrians, and the emotion engines collect emotion data from surrounding users.

[0527] Step 2:

[0528] The device (sensor) measures traffic volume data over a certain period of time. For example, the sensor measures the number of passing cars, bicycles, and pedestrians every hour and sends this data to the server. The device (emotion engine) collects user emotion data and sends it to the server.

[0529] Step 3:

[0530] The server receives the collected traffic and emotion data, formats the data, removes noise, and then passes the formatted data to the AI ​​to prepare it for analysis.

[0531] Step 4:

[0532] The AI ​​analyzes the provided data and calculates the optimal switching timing for each traffic light. The AI's analysis results suggest the optimal duration of the green and red lights for each traffic light. Furthermore, it also takes into account the user's emotional data and changes the timing to reduce stress.

[0533] Step 5:

[0534] The server receives the signal switching timings suggested by the AI ​​and reflects the settings for each traffic light, so that the traffic lights at each intersection are set to switch at the optimal timing.

[0535] Step 6:

[0536] The server then provides the collected traffic volume data and emotion data to the AI ​​to analyze the necessity of pedestrian-vehicle separated traffic lights. The AI ​​then considers the traffic volume of cars and pedestrians and the user's emotion data to determine whether pedestrian-vehicle separated traffic lights are necessary.

[0537] Step 7:

[0538] Based on the results of the AI ​​analysis, the server issues instructions to install or change the settings of traffic lights at intersections where pedestrian-vehicle separated traffic lights are deemed necessary. The terminal (traffic light installation team) receives these instructions and installs pedestrian-vehicle separated traffic lights at the relevant intersections.

[0539] Step 8:

[0540] The server issues instructions to collect traffic data and emotion data in real time from surveillance cameras, car navigation systems, smartphones, etc. The terminals (these devices) collect data in real time and send it to the server.

[0541] Step 9:

[0542] The server aggregates the received real-time data and uploads it to a central server, including traffic volume information, location information, and user sentiment information.

[0543] Step 10:

[0544] The server passes real-time data to the AI, which then recalculates the optimal switching timing for each traffic light on the fly. Based on the results of the AI ​​calculations, the server instantly adjusts the settings of each traffic light.

[0545] Specific examples

[0546] For example, at intersection C, real-time location information and emotion data from surveillance cameras, a smartphone app, and an emotion engine are sent to a server. The server analyzes this data and detects that traffic volume has suddenly increased, causing users' stress levels to rise. Based on this data, the artificial intelligence determines that the green light at traffic light D should be extended by 10 seconds. Based on this judgment, the server immediately changes the settings of traffic light D to extend the green light time. As a result, traffic at intersection C flows more smoothly and users' stress levels are reduced.

[0547] Example 2

[0548] 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."

[0549] Conventional traffic signal systems face challenges in effectively managing the different traffic volumes of automobiles, bicycles, and pedestrians, while achieving optimal switching timing for each signal. Furthermore, they lack the ability to properly determine the need for pedestrian-vehicle separated signals and utilize real-time data to optimize traffic flow. Furthermore, there is a need to reduce user stress and provide a more comfortable traffic environment by taking into account not only traffic data but also user emotional data.

[0550] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0551] In this invention, the server includes: means for installing sensors at each traffic light and measuring the volume of automobile traffic, bicycle traffic, and pedestrians for a certain period of time; means for proposing optimal switching timing for each traffic light using artificial intelligence based on the collected data and setting basic signal switching times based on the proposals; means for aggregating traffic data from surveillance cameras, devices that provide location information, and other sources in real time and accumulating it in a central server; means for collecting emotional data of surrounding users and adjusting the timing of switching lights based on that data; and means for optimizing and reflecting the switching times of each traffic light using artificial intelligence based on the traffic volume data and emotional data collected in real time. This makes it possible to propose and adjust optimal switching timing for traffic lights based on traffic volume and emotional data, reducing user stress and achieving smooth traffic flow.

[0552] A "sensor" is a device installed at each traffic light to measure traffic volume, measuring the number of cars, bicycles, and pedestrians over a certain period of time.

[0553] "Traffic signal timing" refers to the time it takes for a traffic light to switch between green, yellow, and red, and is optimized based on traffic volume and other data.

[0554] "Artificial intelligence" is a technology that analyzes collected traffic volume data and emotion data to suggest optimal traffic light switching timing.

[0555] A "surveillance camera" is a device that monitors traffic conditions in real time and transmits the video data to a server.

[0556] A "device that provides location information" is a device that provides user location data to a server, such as a car navigation system or smartphone.

[0557] The "central server" is a server that aggregates and analyzes data sent from each traffic light, sensor, surveillance camera, etc.

[0558] "Emotion data" is data that indicates the user's emotional state and is collected by the emotion engine. It is obtained by analyzing the facial expressions, tone of voice, etc. of surrounding users.

[0559] A "pedestrian-vehicle separated traffic light" is a traffic light that separates the crossing timing of pedestrians and vehicles, and is used to improve traffic flow and safety.

[0560] "Real-time aggregated traffic volume data" refers to traffic volume data transmitted in real time from sensors and devices and aggregated on a central server.

[0561] "Traffic green and red duration" refers to the length of time a traffic light displays a green or red light, and is adjusted based on traffic conditions and emotion data.

[0562] "Traffic data" refers to data that indicates traffic conditions, including the number and location information of automobiles, bicycles, and pedestrians.

[0563] MODE FOR CARRYING OUT THE INVENTION

[0564] The real-time optimization system for traffic lights according to the present invention utilizes various devices and artificial intelligence to optimize the timing of traffic light switching based on traffic volume data and emotion data. Specific embodiments for implementing this system are described below.

[0565] First, the server issues instructions to install sensors at each traffic light at an intersection. These sensors measure the volume of automobile, bicycle, and pedestrian traffic over a set period of time. The devices (sensor installation team) follow the instructions and install the sensors on the traffic lights. They also install an emotion engine to collect emotion data from surrounding users. The data obtained from the sensors and emotion engine is periodically sent to the server.

[0566] For example, consider installing sensors X, Y, Z and an emotion engine at intersection A and collecting traffic volume data and emotion data for one week.

[0567] The collected traffic volume data and emotion data are analyzed by the server using artificial intelligence. The server then organizes and inputs the acquired data into a format that the AI ​​can use. The AI ​​then integrates and analyzes this data to calculate the optimal switching timing for each traffic light. For example, if the data shows that the number of pedestrians increases during a certain time period, which increases the user's stress level during that time period, the AI ​​will suggest extending the green light on the traffic light during that time period.

[0568] The server then sets new switching timings for each traffic light based on the analysis results obtained from the AI, and these settings are made in real time and are adjusted as traffic conditions and the user's emotional state change.

[0569] Furthermore, the server aggregates real-time data from surveillance cameras and devices that provide location information (such as car navigation systems and smartphones) and inputs it into the central server. Terminals (surveillance cameras, smart devices, and emotion engines) send traffic data and emotion data to the server in real time. The server aggregates this data in real time, and artificial intelligence recalculates the optimal switching timing for each traffic light and immediately updates the traffic lights.

[0570] For example, at a certain intersection C, real-time location information and emotion data are sent to a server from surveillance cameras, a smartphone app, and an emotion engine. The server analyzes this data and detects a sudden increase in traffic volume and high user stress levels. Based on this information, the AI ​​determines that extending the green light at traffic light D by 10 seconds will reduce stress in the short term. The server immediately adjusts traffic light D based on this judgment. This allows traffic at intersection C to flow smoothly and reduces user stress.

[0571] An example prompt might be, "Based on traffic volume and emotion data at intersection A, please suggest the optimal timing for switching the traffic lights. The current situation is as follows: vehicle traffic volume is at its peak and pedestrian stress levels are high."

[0572] This system makes it possible to propose and adjust optimal traffic light switching timing based on traffic volume and emotion data, reducing user stress and ensuring smooth traffic flow.

[0573] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0574] Step 1:

[0575] The server issues instructions to install sensors at each intersection. These instructions include the type of sensor (traffic volume sensor or emotion engine) and details of the installation location. The inputs include the intersection location information and the sensor information to be installed. The output generates specific instructions for the installation team. In concrete terms, the server sends the instructions over the network to the terminals of the sensor installation team.

[0576] Step 2:

[0577] The terminals (sensor installation team) follow instructions from the server and install sensors and emotion engines at designated locations at each intersection. The input is the instructions from the server. The output is a report to the server indicating that the sensors and emotion engines have been installed. In concrete terms, the installation team secures the sensors at the designated locations and installs the emotion engines.

[0578] Step 3:

[0579] The sensors measure the traffic volume of cars, bicycles, and pedestrians over a certain period of time and send the data to the server. At the same time, the emotion engine collects user emotion data and sends it to the server. The input is registered data from passing vehicles and pedestrians. The output is the measured data sent to the server. The specific operation is to detect traffic volume in real time and analyze the emotional state.

[0580] Step 4:

[0581] The server organizes the data received from the sensors and emotion engine. The inputs include traffic volume data and emotion data. The output is data formatted to be input into the artificial intelligence. Specific operations include storing the data in a database, converting the format, and removing noise.

[0582] Step 5:

[0583] The AI ​​integrates and analyzes traffic volume data and emotion data to calculate the optimal switching timing for each traffic light. The inputs are organized traffic volume data and emotion data. The output is a proposal for the optimal signal switching timing. The specific operation is to use a machine learning algorithm to perform optimization calculations.

[0584] Step 6:

[0585] The server sets new switching timings for each traffic light based on the analysis results obtained from the AI. The input is the optimization proposal made by the AI. The output is a setting instruction for the traffic light. Specifically, the new setting is sent to the traffic light's control device, and the traffic light then corrects its operation.

[0586] Step 7:

[0587] The server aggregates real-time data from surveillance cameras and devices that provide location information, and inputs it into the central server. The input is real-time data from each device. The output is aggregated data. Specifically, the system sends video data from surveillance cameras and location information from smartphone apps to the central server.

[0588] Step 8:

[0589] The artificial intelligence recalculates the optimal signal switching timing based on the latest data. The inputs are traffic volume data and emotion data collected in real time. The output is the recalculated optimal signal switching timing. Specifically, the AI ​​analyzes the real-time data and immediately produces results.

[0590] Step 9:

[0591] The server immediately reflects the recalculated signal switching timings in the traffic lights. The input is the optimization results from the artificial intelligence. The output is an update instruction for each traffic light. Specifically, the server sends the new settings to the traffic light controller, and the traffic light adjusts its operation accordingly.

[0592] (Application example 2)

[0593] 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."

[0594] Conventional traffic light control systems have difficulty adapting to traffic volume in real time, which often disrupts the smooth flow of traffic. Furthermore, signals are not optimized to accommodate pedestrian, bicycle, and automobile traffic volumes, which can result in traffic accidents and congestion. Furthermore, with the spread of autonomous vehicles, real-time coordination with traffic lights is required, but current systems are unable to meet this demand.

[0595] The specific processing by the specific 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 installing sensors at each traffic light and measuring the volume of automobile traffic, bicycle traffic, and pedestrians for a certain period of time; means for using artificial intelligence to propose optimal switching timing for each traffic light based on the collected data and for setting basic signal switching times based on the proposals; means for aggregating traffic data from surveillance cameras, devices that provide location information, and other sources in real time and accumulating it in a central server; means for optimizing and reflecting the switching times of each traffic light based on the traffic volume data accumulated in real time using artificial intelligence; and means for providing optimal traffic signal timing information to automobiles in real time and adjusting the speed of automobiles. This enables smooth traffic flow and improved safety by optimizing the switching timing of traffic lights in response to traffic volume in real time and appropriately adjusting the speed of autonomous vehicles.

[0596] A "sensor" is a device used to measure traffic volume, number of pedestrians, number of bicycles, etc. over a certain period of time.

[0597] "Traffic data" is a collection of information about vehicle, bicycle, and pedestrian traffic.

[0598] "Emotional data" is a collection of information that measures the emotions and stress levels of transportation users.

[0599] "Artificial intelligence" is a technology that analyzes collected traffic volume data and emotion data to calculate the optimal timing for switching traffic lights.

[0600] "Signal switching timing" refers to the time it takes for a traffic light to switch from red to green, or vice versa.

[0601] A "traffic light" is a device installed at intersections and roads to control the flow of traffic.

[0602] A "surveillance camera" is a device that observes and records traffic conditions and user behavior in real time.

[0603] A "location information device" is a device, such as a car navigation system or smartphone, that acquires information about transportation users and their locations in real time.

[0604] The "central server" is a computer system that centrally manages and analyzes collected traffic volume data and emotion data.

[0605] "Optimization" refers to the adjustment or result of adjustment to make traffic flow most efficient.

[0606] A "pedestrian-vehicle separated traffic light" is a system that switches signals so that pedestrians and vehicles can move safely without crossing paths.

[0607] An "autonomous vehicle" is a vehicle that is designed to navigate roads autonomously without the need for driver intervention.

[0608] "Speed ​​adjustment" refers to changing the speed of an autonomous vehicle so that it travels at an appropriate speed in accordance with the timing of traffic lights.

[0609] The system of the present invention realizes real-time optimization of traffic signals and consists of the following main components:

[0610] Sensor installation and data collection

[0611] The server issues instructions to install sensors at each traffic light. The sensors measure the traffic volume of cars, pedestrians, and bicycles on the road for a certain period of time and send the data to the server. Each sensor also has a built-in emotion engine that collects emotional data from road users. This allows traffic volume and emotional data for each intersection to be obtained and aggregated on the server.

[0612] Data analysis and signal switching timing proposals

[0613] The collected data is input into artificial intelligence by the server and analyzed. The artificial intelligence combines traffic volume data and emotion data to calculate the optimal switching timing for each traffic light. The server receives the analysis results and sets new switching timings for the traffic lights. It also proposes the installation of pedestrian-vehicle separated traffic lights when separation between pedestrians and vehicles is necessary.

[0614] Aggregating real-time data and optimizing signal switching

[0615] Data collected in real time from surveillance cameras, location-based devices (e.g., smartphones and car navigation systems), and emotion engines is sent to a server. The server aggregates and analyzes this data. AI uses the real-time data to recalculate the optimal switching timing for each traffic light and reflects this in the traffic lights.

[0616] Collaboration with autonomous vehicles

[0617] The system provides automated vehicles with optimal traffic signal timing information and adjusts their speed in real time, allowing them to maintain an optimal speed and drive efficiently in line with traffic light switching timings.

[0618] Hardware and software used

[0619] The hardware used in this invention includes sensors installed at intersections, surveillance cameras, location information providing devices, etc. The software is a program using Python and various AI model APIs, and is managed by a server.

[0620] Example of program execution

[0621] As a concrete example, at a certain intersection, real-time location information and emotion data from surveillance cameras, smartphone apps, and emotion engines are sent to a server. The server analyzes this data and detects a sudden increase in traffic volume and high user stress levels. Based on this information, the AI ​​extends the green light at traffic lights, aiming to reduce stress in the short term. The server immediately adjusts the traffic lights accordingly.

[0622] Prompt Sentence Examples

[0623] "Create a program that predicts the optimal timing for switching traffic lights at an intersection and adjusts the speed of an autonomous vehicle in real time."

[0624] As a result, the system of the present invention enables optimization of traffic signals according to traffic volume and real-time speed adjustment of autonomous vehicles, thereby improving traffic flow and safety.

[0625] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0626] Step 1:

[0627] The server collects traffic volume data and emotion data from various sensors, surveillance cameras, and location information providing devices installed at intersections. These data include vehicle traffic volume, bicycle traffic volume, pedestrian count, and user location and emotion information. The input of the data collection is the sensor information from these devices, and the output is raw data transferred to the server.

[0628] Step 2:

[0629] The server aggregates the collected data in real time to a central server. At this stage, data sent from multiple devices is consolidated and stored in a database. The input is traffic volume data and sentiment data obtained from the devices, and the output is organized database entries. Specifically, data points from each device are consolidated in one place.

[0630] Step 3:

[0631] The server inputs the aggregated data into an artificial intelligence system for analysis. The AI ​​uses traffic volume data and emotion data to perform calculations to propose optimal switching timings for each traffic light. The input is the aggregated data, and the output is a proposal for optimal switching timings for the traffic lights.

[0632] Step 4:

[0633] The server sets the basic signal switching times for each traffic light based on the results of the AI ​​analysis. These settings are reflected in the traffic lights and used for actual traffic control. The input is the AI ​​analysis results, and the output is the new switching timing set for the traffic light.

[0634] Step 5:

[0635] The server provides optimal traffic signal timing information to the autonomous vehicle in real time. This information includes the current status of the traffic lights and the next switching timing, and the vehicle adjusts its speed based on this information. The input is the traffic light timing information, and the output is the speed adjustment instruction sent to the autonomous vehicle.

[0636] Step 6:

[0637] The terminal uses the traffic signal information acquired by the autonomous vehicle to adjust the vehicle's speed in real time. Specifically, it calculates the optimal speed based on the current timing information of the traffic lights and changes the vehicle's speed. The input is the traffic signal information, and the output is the adjusted vehicle speed.

[0638] Step 7:

[0639] The server monitors the autonomous vehicle's behavior based on new speed information and provides real-time feedback as needed. Feedback is provided based on speed adjustment accuracy and traffic fluctuations, allowing for safer driving with greater precision. The input is autonomous vehicle status data, and the output is updated speed instructions and other control commands.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] [Third embodiment]

[0644] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0645] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0646] 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).

[0647] 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.

[0648] 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.

[0649] 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).

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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."

[0656] An embodiment of a real-time traffic light optimization system according to the present invention will be described. In this system, sensors are installed at each traffic light to measure the volume of automobile traffic, bicycle traffic, and the number of pedestrians over a certain period of time. The collected data is sent to a server and analyzed using artificial intelligence. Based on the analysis results, optimal switching timings for each traffic light are proposed and reflected in the traffic lights.

[0657] 1. Sensor installation and data collection

[0658] The server issues instructions to install sensors at each traffic light at an intersection. The terminals (sensor installation team) follow the instructions and install the sensors on the traffic lights. The sensors measure the traffic volume of cars, bicycles, and pedestrians over a certain period of time and send the data to the server. For example, suppose sensors X, Y, and Z are installed at intersection A and data is collected for one week.

[0659] 2. Data analysis and signal switching timing proposal

[0660] The collected data is analyzed by the server using artificial intelligence. The server then organizes the acquired traffic volume data and inputs it into the artificial intelligence. The artificial intelligence then analyzes the data and calculates the optimal switching timing for each traffic light. The server receives the analysis results and sets new switching timings for each traffic light.

[0661] 3. Proposal for a Pedestrian-Vehicle Separation Traffic Light

[0662] The server analyzes the data to identify intersections that require pedestrian-vehicle separated traffic lights. Based on the collected data, the AI ​​determines whether a particular intersection requires a pedestrian-vehicle separated traffic light. For example, if the number of pedestrians at intersection B is extremely high, the AI ​​will suggest the need for a pedestrian-vehicle separated traffic light. Based on this suggestion, the server installs a pedestrian-vehicle separated traffic light at the relevant intersection.

[0663] 4. Aggregating real-time data and optimizing signal switching

[0664] The data collection targets will be expanded to include surveillance cameras and devices that provide location information (such as car navigation systems and smartphones). Terminals (surveillance cameras and smart devices) will send traffic data to a server in real time. The server will then aggregate this data in real time and upload it to a central server. Based on the real-time data, artificial intelligence will calculate the optimal switching timing for each traffic light and immediately update the traffic lights.

[0665] (Example)

[0666] For example, at a certain intersection C, real-time location information from surveillance cameras and smartphone apps is sent to a server. The server analyzes this information and detects a sudden increase in traffic volume. Based on this information, the AI ​​determines that the green light at traffic light D should be extended by 10 seconds, and the server immediately adjusts traffic light D based on that judgment. This allows traffic at intersection C to flow smoothly.

[0667] The processing flow will be explained below.

[0668] Step 1:

[0669] The server issues instructions to install sensors at each intersection. Based on these instructions, the terminals (sensor installation teams) install sensors on the traffic lights at the intersections. These sensors are used to measure the volume of automobile, bicycle, and pedestrian traffic.

[0670] Step 2:

[0671] The device (sensor) measures traffic volume data for a certain period of time. For example, the sensor measures the number of passing cars, bicycles, and pedestrians every hour, and sends this data to a server.

[0672] Step 3:

[0673] The server receives the collected traffic data, formats it, removes noise, and then passes it to an artificial intelligence to prepare it for analysis.

[0674] Step 4:

[0675] The AI ​​analyzes the provided data and calculates the optimal switching timing for each traffic light. The AI's analysis results suggest the optimal duration of the green and red lights for each traffic light.

[0676] Step 5:

[0677] The server receives the signal switching timings suggested by the AI ​​and reflects the settings for each traffic light, so that the traffic lights at each intersection are set to switch at the optimal timing.

[0678] Step 6:

[0679] The server then provides the collected data to the AI ​​to analyze the need for pedestrian-separated traffic lights. The AI ​​then considers the volume of automobile and pedestrian traffic and determines whether pedestrian-separated traffic lights are necessary.

[0680] Step 7:

[0681] Based on the results of the AI ​​analysis, the server issues instructions to install or change the settings of traffic lights at intersections where pedestrian-vehicle separated traffic lights are deemed necessary. The terminal (traffic light installation team) receives these instructions and installs pedestrian-vehicle separated traffic lights at the relevant intersections.

[0682] Step 8:

[0683] The server issues instructions to collect traffic data in real time from surveillance cameras, car navigation systems, smartphones, etc. The terminals (these devices) collect data in real time and send it to the server.

[0684] Step 9:

[0685] The server aggregates the real-time data it receives and uploads it to a central server, including traffic and location information.

[0686] Step 10:

[0687] The server passes real-time data to the AI, which then recalculates the optimal switching timing for each traffic light on the fly. Based on the AI's calculations, the server instantly adjusts the settings for each traffic light.

[0688] Specific examples

[0689] For example, at intersection C, real-time location information from surveillance cameras and smartphone apps is sent to a server. The server analyzes this information and detects an increase in traffic volume. Based on this real-time data, the artificial intelligence determines that the green light at traffic light D should be extended by 10 seconds. Based on this judgment, the server immediately changes the settings of traffic light D to extend the green light time. This allows traffic at intersection C to flow more smoothly.

[0690] Example 1

[0691] 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."

[0692] Traffic signal control is based on fixed patterns and cannot adequately respond to sudden fluctuations in traffic volume, which can disrupt the smooth flow of traffic. It is also difficult to properly evaluate the need for pedestrian-vehicle separated traffic signals and install them appropriately. Therefore, a system is needed to alleviate traffic congestion, prevent traffic accidents, and improve convenience for road users.

[0693] 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.

[0694] In this invention, the server includes a means for installing sensors at each traffic light and measuring the volume of automobile traffic, bicycle traffic, and pedestrians for a certain period of time, a means for proposing the optimal switching timing for each traffic light using artificial intelligence based on the collected data and setting the basic signal switching time based on the proposal, and a means for aggregating monitoring devices, terminals that provide location information, and other traffic data in real time and accumulating it in a central control device, thereby making it possible to respond quickly and appropriately to fluctuations in traffic volume and optimize traffic flow.

[0695] "Sensors" are devices used to measure the volume of automobile, bicycle, and pedestrian traffic.

[0696] "Artificial intelligence" is a technology that analyzes collected data and calculates the optimal switching timing for each traffic light.

[0697] A "traffic light" is a device installed to control traffic, and emits signals to vehicles and pedestrians indicating whether to proceed or stop.

[0698] "Switching timing" refers to the time when a traffic light switches from green to red to yellow.

[0699] "Monitoring devices" are devices installed to monitor traffic conditions in real time, including cameras and sensors.

[0700] A "terminal that provides location information" is a device such as a car navigation system or a smartphone that detects the current location and transmits it to a server.

[0701] "Central control device" refers to a server that controls traffic lights based on aggregated traffic data.

[0702] A "pedestrian-vehicle separated traffic signal" is a traffic signal that separates and controls pedestrian and vehicle traffic, allowing pedestrians to cross intersections safely.

[0703] "Real-time aggregation" means that collected traffic data is immediately fed into a central control device.

[0704] An embodiment of a real-time optimization system for traffic lights according to the present invention will be described. In this system, sensors are installed at each traffic light to measure the volume of automobile traffic, bicycle traffic, and the number of pedestrians over a certain period of time. The collected data is sent to a server and analyzed by artificial intelligence. Based on the analysis results, optimal switching timings for each traffic light are proposed and reflected in the traffic lights.

[0705] First, the server issues instructions to install sensors on the traffic lights at each intersection. The terminals (sensor installation team) follow these instructions and install the sensors on the road. The sensors measure the traffic volume of cars, bicycles, and pedestrians over a certain period of time and send the data to the server. For example, let's consider the case where sensors X, Y, and Z are installed at intersection A and data is collected for one week. The sensors operate 24 hours a day, continuously measuring the flow rate of each traffic element.

[0706] The collected data is sent to a server, where it is organized and preprocessed. The server then inputs this organized data into a generative AI model (using, for example, a machine learning framework such as TensorFlow or PyTorch) to analyze traffic patterns and trends. The AI ​​calculates the optimal traffic light switching timing and returns the results to the server. The server then sets the new switching timing for the traffic lights and reflects it at each traffic light. For example, specific adjustments may be made, such as "extending the green light for traffic light A by 10 seconds between 5:00 PM and 7:00 PM on weekdays."

[0707] The server also analyzes the collected data to determine whether a particular intersection needs a pedestrian-vehicle-separated traffic light. Based on pedestrian data, the AI ​​determines whether a particular intersection (e.g., intersection B) needs a pedestrian-vehicle-separated traffic light. For example, it uses a specific algorithm, such as "If there are an extremely high number of pedestrians at intersection B, suggest installing a pedestrian-vehicle-separated traffic light."

[0708] Furthermore, terminals (surveillance cameras and smart devices) collect traffic data in real time and send it to a server. The server then collects this real-time data in a central database and analyzes it using artificial intelligence. Based on the real-time data, the optimal switching timing for each traffic light is calculated and the results are immediately reflected in the traffic lights. For example, specific adjustments are made in real time, such as "due to a sudden increase in traffic volume at intersection C, immediately extend the green light at traffic light D by 10 seconds."

[0709] For actual operation, detailed instructions can be obtained by entering the following prompt sentence into the generative AI model:

[0710] Prompt: "Describe how you can detect sudden increases in traffic volume at an intersection based on real-time location information from surveillance cameras and a smartphone app, and then optimize traffic light timing based on the results."

[0711] In this way, the system of the present invention integrates various technologies to highly optimize traffic signal control and achieve smooth traffic flow.

[0712] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0713] Step 1:

[0714] The server issues instructions to install sensors at traffic lights at intersections, including the specific intersection location and the type of sensor to install.

[0715] (Input) Location information and sensor type

[0716] (Output) Sensor installation instructions

[0717] Specifically, the server generates instructions such as "Install sensors X, Y, and Z at intersection A" and sends them to the terminal.

[0718] Step 2:

[0719] The terminal (sensor installation team) follows the instructions of the server and installs the sensor at the designated traffic light.

[0720] (Input) Sensor installation instructions

[0721] (Output) Data from installed sensors

[0722] Specifically, the sensor installation team physically installs designated sensors X, Y, and Z on the traffic lights at intersection A.

[0723] Step 3:

[0724] Sensors attached to the device measure the volume of cars, bicycles, and pedestrians over a period of time and send the data to a server.

[0725] (Input) Traffic volume data measurement instructions

[0726] (Output) Traffic volume data

[0727] Specifically, the sensor operates 24 hours a day and sends the measured data to the server every day.

[0728] Step 4:

[0729] The server receives the collected data, organizes it, and pre-processes it for input into artificial intelligence.

[0730] (Input) Traffic volume data

[0731] (Output) Preprocessed data

[0732] Specifically, the server classifies the traffic volume data by time period and removes outliers.

[0733] Step 5:

[0734] The server then feeds the pre-processed data into an artificial intelligence system (e.g., TensorFlow or PyTorch) to analyze traffic patterns and trends.

[0735] (Input) Preprocessed traffic data

[0736] (Output) Analysis results (optimal switching timing)

[0737] Specifically, the artificial intelligence uses machine learning algorithms to analyze traffic volume and calculate the optimal timing for switching signals.

[0738] Step 6:

[0739] The server receives the analysis results from the artificial intelligence and sends instructions to each traffic light to set new switching timings.

[0740] (Input) Analysis results (optimal switching timing)

[0741] (Output) Setting instructions to traffic lights

[0742] As a specific operation, the server sends a specific adjustment to the traffic light, such as "Extend the green light of traffic light A by 10 seconds between 5:00 PM and 7:00 PM."

[0743] Step 7:

[0744] The terminal (traffic light) immediately sets and executes the new switching timing based on instructions from the server.

[0745] (Input) Setting instructions to traffic lights

[0746] (Output) Signal control based on new signal switching timing

[0747] Specifically, traffic lights switch between green and red based on set timing.

[0748] Step 8:

[0749] The server receives traffic data collected in real time from the monitoring devices and location information providing terminals and accumulates it in the central control device.

[0750] (Input) Real-time data from monitoring devices and location information providing devices

[0751] (Output) Aggregated real-time data

[0752] Specifically, the server periodically updates and aggregates smartphone location information and surveillance camera video data.

[0753] Step 9:

[0754] The server uses artificial intelligence to analyze the aggregated real-time data and issues instructions to instantly reflect the optimal switching timing for each traffic light.

[0755] (Input) Accumulated real-time data

[0756] (Output) Real-time analysis results (optimal switching timing)

[0757] Specifically, the artificial intelligence analyzes real-time data and calculates adjustment results such as, "Due to a sudden increase in traffic volume at intersection C, immediately extend the green light at traffic light D by 10 seconds."

[0758] Step 10:

[0759] The terminal (traffic light) sets new switching timing in real time based on instructions from the server, optimizing traffic flow.

[0760] (Input) Real-time setting instructions

[0761] (Output) Real-time adjusted signal control

[0762] Specifically, the traffic lights instantly adjust their signal timing based on the analysis results, ensuring smooth traffic flow.

[0763] (Application example 1)

[0764] 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."

[0765] Conventional traffic light systems have difficulty responding to fluctuations in traffic volume in real time, resulting in problems with traffic congestion and pedestrian safety. Furthermore, with the increasing number of autonomous vehicles, there has been insufficient coordination between real-time traffic signals and route optimization for autonomous vehicles. This has resulted in insufficient traffic optimization and the inability to ensure smooth overall traffic flow.

[0766] 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.

[0767] In this invention, the server includes: means for installing sensors at each traffic light and measuring the volume of automobile traffic, bicycle traffic, and pedestrians for a certain period of time; means for proposing optimal switching timings for each traffic light using artificial intelligence based on the collected data and setting basic signal switching times based on the proposals; means for aggregating monitoring equipment, location information providing devices, and other traffic data in real time and accumulating it in a central processing unit; means for optimizing and reflecting the switching times of each traffic light using artificial intelligence based on the traffic volume data accumulated in real time; and means for proposing optimal routes for autonomous vehicles in real time. This not only optimizes traffic lights in real time, making traffic flow smoother, but also makes it possible to provide optimal routes to autonomous vehicles in real time.

[0768] A "sensor" is a device that detects traffic volume, vehicle and pedestrian movements, and measures the data.

[0769] "Automobile traffic volume" refers to the volume of automobile traffic over a certain period of time.

[0770] "Bicycle traffic volume" refers to the number of bicycles traveling over a certain period of time.

[0771] "Number of pedestrians" refers to the number of pedestrians in a given period of time.

[0772] "Artificial intelligence" is a technology that analyzes measured traffic data and proposes optimal timing for switching traffic lights.

[0773] "Optimal traffic light switching timing" refers to the signal switching time that ensures smooth traffic flow and reduces traffic congestion.

[0774] "Monitoring equipment" means devices that monitor traffic conditions and collect data.

[0775] A "device that provides location information" is a device that provides location information in real time, such as a car navigation system or a smart device.

[0776] "Other traffic data" includes any additional information related to traffic volume.

[0777] "Central processing unit" refers to a device that aggregates and processes traffic data collected in real time.

[0778] "Autonomous vehicle" refers to a vehicle that is driven automatically by a system.

[0779] "Optimal route" refers to the shortest or most efficient route to a destination, taking into account traffic conditions.

[0780] The real-time optimization system for traffic lights according to the present invention is configured as follows: In this system, sensors are installed at each traffic light to measure the volume of automobile traffic, bicycle traffic, and the number of pedestrians over a certain period of time. The collected data is then sent to a central processing unit and analyzed using artificial intelligence. Based on the analyzed data, optimal switching timing for each traffic light is proposed, and the traffic light switching times are set based on these proposals. In addition, traffic data is aggregated in real time using monitoring equipment and devices that provide location information, allowing for flexible response to fluctuations in traffic volume.

[0781] The server issues instructions to install sensors at each traffic light. The sensors measure traffic volume at intersections and send the data to a central processing unit. The central processing unit inputs the collected data into artificial intelligence and calculates the optimal switching timing for each traffic light. Based on the results of this analysis, the traffic light switching times are set. In addition, real-time data from monitoring equipment and devices that provide location information is also collected by the central processing unit and analyzed by artificial intelligence. This allows the switching timing of each traffic light to be optimized in real time.

[0782] As a specific example of its use, at a certain intersection, traffic volume data obtained by sensors and monitoring equipment is sent to a central processing unit. The central processing unit analyzes the data using artificial intelligence and detects a sudden increase in traffic volume. Based on this, it issues a command to extend the green light at the traffic light by 10 seconds. This command is immediately reflected in the traffic light, maintaining smooth traffic flow.

[0783] The system also supports autonomous vehicles, which receive traffic data from the server and receive real-time recommendations for optimal routes, enabling autonomous vehicles to drive smoothly and improving overall traffic efficiency.

[0784] The hardware used includes sensors, monitoring equipment, ECUs (electronic control units) of autonomous vehicles, etc. The software used includes central processing unit programs (analysis modules implemented in Python and API servers using Flask) and artificial intelligence models (using TensorFlow and PyTorch).

[0785] To illustrate, here are some example prompts:

[0786] "Generate analysis results to optimize the current traffic light switching timing based on traffic volume data. The intersection ID is 1234, and the current traffic volume data is as follows: number of vehicles 100, number of bicycles 20, number of pedestrians 50. Please propose the signal switching timing based on this data."

[0787] In this way, traffic flow can be optimized and traffic congestion and safety problems can be solved.

[0788] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0789] Step 1:

[0790] Sensors are installed at each traffic light. The terminal (sensor installation team) installs the sensors on the traffic lights according to instructions. The sensors measure the volume of automobile traffic, bicycle traffic, and the number of pedestrians. This data is necessary to understand traffic flow.

[0791] Input: Sensor installation location, sensor type (for cars, bicycles, pedestrians)

[0792] Output: Installed sensors

[0793] Step 2:

[0794] The traffic volume data acquired by the sensor is sent to a server. The terminal (sensor) measures traffic volume data over a certain period of time and sends it to the server via the Internet. The collected data is necessary to understand fluctuations in traffic volume in real time.

[0795] Input: Traffic data

[0796] Output: Traffic data sent to the server

[0797] Step 3:

[0798] The server inputs the collected traffic volume data into an artificial intelligence (AI) model for analysis. The server preprocesses the data using a data analysis algorithm implemented in Python, and then inputs the data into an AI model built using TensorFlow or PyTorch. The analysis results in the calculation of the optimal switching timing for each traffic light.

[0799] Input: Traffic data (cars, bicycles, pedestrians)

[0800] Output: Optimal signal switching timing

[0801] Step 4:

[0802] The server then sets the traffic light switching times based on the analysis results. The server then sends commands to the traffic light controller to reflect the optimal switching timing, thereby efficiently managing traffic flow.

[0803] Input: Optimal signal switching timing

[0804] Output: Set signal switching time

[0805] Step 5:

[0806] Real-time data is collected from the monitoring equipment and location information providing devices. The terminals (monitoring equipment and location information providing devices) measure changes in traffic conditions in real time and send the data to the server. This allows real-time traffic conditions to be understood.

[0807] Input: Data from monitoring and location devices

[0808] Output: Real-time data aggregated on the server

[0809] Step 6:

[0810] The server inputs the collected data into the AI ​​model in real time and re-optimizes the switching timing of each traffic light. The server pre-processes the collected data using Python and inputs it into the AI ​​model. This allows the appropriate signal switching timing to be recalculated according to traffic flow.

[0811] Input: Real-time data

[0812] Output: Re-optimized signal switching timing

[0813] Step 7:

[0814] The server then sends the re-optimized results to the traffic light control device, which immediately adjusts the traffic light switching times. Because traffic light switching is adjusted in real time, traffic flow can be managed smoothly and without interruption.

[0815] Input: Re-optimized signal switching timing

[0816] Output: Adjusted signal switching time

[0817] Step 8:

[0818] The server proposes optimal routes to autonomous vehicles in real time and transmits optimal driving route information to each autonomous vehicle, allowing the autonomous vehicle to drive the optimal route according to traffic conditions.

[0819] Input: Reoptimized traffic information

[0820] Output: Optimal route information for the autonomous vehicle

[0821] Through these steps, the server implements a system that collects and analyzes traffic data and controls traffic lights, optimizing traffic conditions in real time.

[0822] 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.

[0823] The real-time optimization system for traffic lights according to the present invention begins by installing sensors at each traffic light and measuring the volume of automobile traffic, bicycle traffic, and pedestrians over a certain period of time. Furthermore, an emotion engine that recognizes user emotions is incorporated, and emotional data of users around each traffic light is also collected. This makes it possible to propose optimal signal switching timings based on traffic volume and emotional data, and adjust them in real time.

[0824] 1. Sensor installation and data collection

[0825] The server issues instructions to install sensors at each traffic light at an intersection. The terminals (sensor installation team) follow the instructions and install the sensors on the traffic lights. The sensors measure the traffic volume of cars, bicycles, and pedestrians for a certain period of time and send the data to the server. The terminals (emotion engines) also collect emotion data from surrounding users and send it to the server. For example, let's assume that sensors X, Y, and Z and an emotion engine are installed at intersection A and one week's worth of traffic volume data and emotion data is collected.

[0826] 2. Data analysis and signal switching timing proposal

[0827] The collected traffic volume data and emotion data are analyzed by the server using artificial intelligence. The server then organizes the acquired data and inputs it into the artificial intelligence. The artificial intelligence then integrates and analyzes both sets of data to calculate the optimal switching timing for each traffic light. The server receives the analysis results and sets new switching timings for each traffic light.

[0828] 3. Proposal for a Pedestrian-Vehicle Separation Traffic Light

[0829] The server analyzes data to identify intersections where pedestrian-vehicle separated traffic lights are needed. The AI ​​considers vehicle and pedestrian traffic volume and emotional data to determine whether pedestrian-vehicle separated traffic lights are needed. For example, if there is an extremely large number of pedestrians at intersection B and the stress levels of users in the area are high, the AI ​​will suggest the need for pedestrian-vehicle separated traffic lights. Based on this suggestion, the server will install pedestrian-vehicle separated traffic lights at the relevant intersections.

[0830] 4. Aggregating real-time data and optimizing signal switching

[0831] The data collection targets will be expanded to include surveillance cameras, devices that provide location information (such as car navigation systems and smartphones), and emotion engines. Terminals (surveillance cameras, smart devices, and emotion engines) will send traffic data and emotion data to a server in real time. The server will then aggregate this data in real time and upload it to a central server. Based on the real-time data, artificial intelligence will recalculate the optimal switching timing for each traffic light and immediately reflect this in the traffic lights.

[0832] (Example)

[0833] For example, at a certain intersection C, real-time location information and emotion data are sent to a server from surveillance cameras, a smartphone app, and an emotion engine. The server analyzes this data and detects a sudden increase in traffic volume and high user stress levels. Based on this information, the AI ​​determines that extending the green light at traffic light D by 10 seconds will reduce stress in the short term. The server immediately adjusts traffic light D based on this judgment. This allows traffic at intersection C to flow smoothly and reduces user stress.

[0834] The processing flow will be explained below.

[0835] Step 1:

[0836] The server issues instructions to install sensors and emotion engines at each intersection. Based on these instructions, the terminals (sensor installation teams) install sensors and emotion engines on the traffic lights at the intersections. These sensors measure the traffic volume of cars, bicycles, and pedestrians, and the emotion engines collect emotion data from surrounding users.

[0837] Step 2:

[0838] The device (sensor) measures traffic volume data over a certain period of time. For example, the sensor measures the number of passing cars, bicycles, and pedestrians every hour and sends this data to the server. The device (emotion engine) collects user emotion data and sends it to the server.

[0839] Step 3:

[0840] The server receives the collected traffic and emotion data, formats the data, removes noise, and then passes the formatted data to the AI ​​to prepare it for analysis.

[0841] Step 4:

[0842] The AI ​​analyzes the provided data and calculates the optimal switching timing for each traffic light. The AI's analysis results suggest the optimal duration of the green and red lights for each traffic light. Furthermore, it also takes into account the user's emotional data and changes the timing to reduce stress.

[0843] Step 5:

[0844] The server receives the signal switching timings suggested by the AI ​​and reflects the settings for each traffic light, so that the traffic lights at each intersection are set to switch at the optimal timing.

[0845] Step 6:

[0846] The server then provides the collected traffic volume data and emotion data to the AI ​​to analyze the necessity of pedestrian-vehicle separated traffic lights. The AI ​​then considers the traffic volume of cars and pedestrians and the user's emotion data to determine whether pedestrian-vehicle separated traffic lights are necessary.

[0847] Step 7:

[0848] Based on the results of the AI ​​analysis, the server issues instructions to install or change the settings of traffic lights at intersections where pedestrian-vehicle separated traffic lights are deemed necessary. The terminal (traffic light installation team) receives these instructions and installs pedestrian-vehicle separated traffic lights at the relevant intersections.

[0849] Step 8:

[0850] The server issues instructions to collect traffic data and emotion data in real time from surveillance cameras, car navigation systems, smartphones, etc. The terminals (these devices) collect data in real time and send it to the server.

[0851] Step 9:

[0852] The server aggregates the received real-time data and uploads it to a central server, including traffic volume information, location information, and user sentiment information.

[0853] Step 10:

[0854] The server passes real-time data to the AI, which then recalculates the optimal switching timing for each traffic light on the fly. Based on the results of the AI ​​calculations, the server instantly adjusts the settings of each traffic light.

[0855] Specific examples

[0856] For example, at intersection C, real-time location information and emotion data from surveillance cameras, a smartphone app, and an emotion engine are sent to a server. The server analyzes this data and detects that traffic volume has suddenly increased, causing users' stress levels to rise. Based on this data, the artificial intelligence determines that the green light at traffic light D should be extended by 10 seconds. Based on this judgment, the server immediately changes the settings of traffic light D to extend the green light time. As a result, traffic at intersection C flows more smoothly and users' stress levels are reduced.

[0857] Example 2

[0858] 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."

[0859] Conventional traffic signal systems face challenges in effectively managing the different traffic volumes of automobiles, bicycles, and pedestrians, while achieving optimal switching timing for each signal. Furthermore, they lack the ability to properly determine the need for pedestrian-vehicle separated signals and utilize real-time data to optimize traffic flow. Furthermore, there is a need to reduce user stress and provide a more comfortable traffic environment by taking into account not only traffic data but also user emotional data.

[0860] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0861] In this invention, the server includes: means for installing sensors at each traffic light and measuring the volume of automobile traffic, bicycle traffic, and pedestrians for a certain period of time; means for proposing optimal switching timing for each traffic light using artificial intelligence based on the collected data and setting basic signal switching times based on the proposals; means for aggregating traffic data from surveillance cameras, devices that provide location information, and other sources in real time and accumulating it in a central server; means for collecting emotional data of surrounding users and adjusting the timing of switching lights based on that data; and means for optimizing and reflecting the switching times of each traffic light using artificial intelligence based on the traffic volume data and emotional data collected in real time. This makes it possible to propose and adjust optimal switching timing for traffic lights based on traffic volume and emotional data, reducing user stress and achieving smooth traffic flow.

[0862] A "sensor" is a device installed at each traffic light to measure traffic volume, measuring the number of cars, bicycles, and pedestrians over a certain period of time.

[0863] "Traffic signal timing" refers to the time it takes for a traffic light to switch between green, yellow, and red, and is optimized based on traffic volume and other data.

[0864] "Artificial intelligence" is a technology that analyzes collected traffic volume data and emotion data to suggest optimal traffic light switching timing.

[0865] A "surveillance camera" is a device that monitors traffic conditions in real time and transmits the video data to a server.

[0866] A "device that provides location information" is a device that provides user location data to a server, such as a car navigation system or smartphone.

[0867] The "central server" is a server that aggregates and analyzes data sent from each traffic light, sensor, surveillance camera, etc.

[0868] "Emotion data" is data that indicates the user's emotional state and is collected by the emotion engine. It is obtained by analyzing the facial expressions, tone of voice, etc. of surrounding users.

[0869] A "pedestrian-vehicle separated traffic light" is a traffic light that separates the crossing timing of pedestrians and vehicles, and is used to improve traffic flow and safety.

[0870] "Real-time aggregated traffic volume data" refers to traffic volume data transmitted in real time from sensors and devices and aggregated on a central server.

[0871] "Traffic green and red duration" refers to the length of time a traffic light displays a green or red light, and is adjusted based on traffic conditions and emotion data.

[0872] "Traffic data" refers to data that indicates traffic conditions, including the number and location information of automobiles, bicycles, and pedestrians.

[0873] MODE FOR CARRYING OUT THE INVENTION

[0874] The real-time optimization system for traffic lights according to the present invention utilizes various devices and artificial intelligence to optimize the timing of traffic light switching based on traffic volume data and emotion data. Specific embodiments for implementing this system are described below.

[0875] First, the server issues instructions to install sensors at each traffic light at an intersection. These sensors measure the volume of automobile, bicycle, and pedestrian traffic over a set period of time. The devices (sensor installation team) follow the instructions and install the sensors on the traffic lights. They also install an emotion engine to collect emotion data from surrounding users. The data obtained from the sensors and emotion engine is periodically sent to the server.

[0876] For example, consider installing sensors X, Y, Z and an emotion engine at intersection A and collecting traffic volume data and emotion data for one week.

[0877] The collected traffic volume data and emotion data are analyzed by the server using artificial intelligence. The server then organizes and inputs the acquired data into a format that the AI ​​can use. The AI ​​then integrates and analyzes this data to calculate the optimal switching timing for each traffic light. For example, if the data shows that the number of pedestrians increases during a certain time period, which increases the user's stress level during that time period, the AI ​​will suggest extending the green light on the traffic light during that time period.

[0878] The server then sets new switching timings for each traffic light based on the analysis results obtained from the AI, and these settings are made in real time and are adjusted as traffic conditions and the user's emotional state change.

[0879] Furthermore, the server aggregates real-time data from surveillance cameras and devices that provide location information (such as car navigation systems and smartphones) and inputs it into the central server. Terminals (surveillance cameras, smart devices, and emotion engines) send traffic data and emotion data to the server in real time. The server aggregates this data in real time, and artificial intelligence recalculates the optimal switching timing for each traffic light and immediately updates the traffic lights.

[0880] For example, at a certain intersection C, real-time location information and emotion data are sent to a server from surveillance cameras, a smartphone app, and an emotion engine. The server analyzes this data and detects a sudden increase in traffic volume and high user stress levels. Based on this information, the AI ​​determines that extending the green light at traffic light D by 10 seconds will reduce stress in the short term. The server immediately adjusts traffic light D based on this judgment. This allows traffic at intersection C to flow smoothly and reduces user stress.

[0881] An example prompt might be, "Based on traffic volume and emotion data at intersection A, please suggest the optimal timing for switching the traffic lights. The current situation is as follows: vehicle traffic volume is at its peak and pedestrian stress levels are high."

[0882] This system makes it possible to propose and adjust optimal traffic light switching timing based on traffic volume and emotion data, reducing user stress and ensuring smooth traffic flow.

[0883] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0884] Step 1:

[0885] The server issues instructions to install sensors at each intersection. These instructions include the type of sensor (traffic volume sensor or emotion engine) and details of the installation location. The inputs include the intersection location information and the sensor information to be installed. The output generates specific instructions for the installation team. In concrete terms, the server sends the instructions over the network to the terminals of the sensor installation team.

[0886] Step 2:

[0887] The terminals (sensor installation team) follow instructions from the server and install sensors and emotion engines at designated locations at each intersection. The input is the instructions from the server. The output is a report to the server indicating that the sensors and emotion engines have been installed. In concrete terms, the installation team secures the sensors at the designated locations and installs the emotion engines.

[0888] Step 3:

[0889] The sensors measure the traffic volume of cars, bicycles, and pedestrians over a certain period of time and send the data to the server. At the same time, the emotion engine collects user emotion data and sends it to the server. The input is registered data from passing vehicles and pedestrians. The output is the measured data sent to the server. The specific operation is to detect traffic volume in real time and analyze the emotional state.

[0890] Step 4:

[0891] The server organizes the data received from the sensors and emotion engine. The inputs include traffic volume data and emotion data. The output is data formatted to be input into the artificial intelligence. Specific operations include storing the data in a database, converting the format, and removing noise.

[0892] Step 5:

[0893] The AI ​​integrates and analyzes traffic volume data and emotion data to calculate the optimal switching timing for each traffic light. The inputs are organized traffic volume data and emotion data. The output is a proposal for the optimal signal switching timing. The specific operation is to use a machine learning algorithm to perform optimization calculations.

[0894] Step 6:

[0895] The server sets new switching timings for each traffic light based on the analysis results obtained from the AI. The input is the optimization proposal made by the AI. The output is a setting instruction for the traffic light. Specifically, the new setting is sent to the traffic light's control device, and the traffic light then corrects its operation.

[0896] Step 7:

[0897] The server aggregates real-time data from surveillance cameras and devices that provide location information, and inputs it into the central server. The input is real-time data from each device. The output is aggregated data. Specifically, the system sends video data from surveillance cameras and location information from smartphone apps to the central server.

[0898] Step 8:

[0899] The artificial intelligence recalculates the optimal signal switching timing based on the latest data. The inputs are traffic volume data and emotion data collected in real time. The output is the recalculated optimal signal switching timing. Specifically, the AI ​​analyzes the real-time data and immediately produces results.

[0900] Step 9:

[0901] The server immediately reflects the recalculated signal switching timings in the traffic lights. The input is the optimization results from the artificial intelligence. The output is an update instruction for each traffic light. Specifically, the server sends the new settings to the traffic light controller, and the traffic light adjusts its operation accordingly.

[0902] (Application example 2)

[0903] 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."

[0904] Conventional traffic light control systems have difficulty adapting to traffic volume in real time, which often disrupts the smooth flow of traffic. Furthermore, signals are not optimized to accommodate pedestrian, bicycle, and automobile traffic volumes, which can result in traffic accidents and congestion. Furthermore, with the spread of autonomous vehicles, real-time coordination with traffic lights is required, but current systems are unable to meet this demand.

[0905] The specific processing by the specific 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 installing sensors at each traffic light and measuring the volume of automobile traffic, bicycle traffic, and pedestrians for a certain period of time; means for using artificial intelligence to propose optimal switching timing for each traffic light based on the collected data and for setting basic signal switching times based on the proposals; means for aggregating traffic data from surveillance cameras, devices that provide location information, and other sources in real time and accumulating it in a central server; means for optimizing and reflecting the switching times of each traffic light based on the traffic volume data accumulated in real time using artificial intelligence; and means for providing optimal traffic signal timing information to automobiles in real time and adjusting the speed of automobiles. This enables smooth traffic flow and improved safety by optimizing the switching timing of traffic lights in response to traffic volume in real time and appropriately adjusting the speed of autonomous vehicles.

[0906] A "sensor" is a device used to measure traffic volume, number of pedestrians, number of bicycles, etc. over a certain period of time.

[0907] "Traffic data" is a collection of information about vehicle, bicycle, and pedestrian traffic.

[0908] "Emotional data" is a collection of information that measures the emotions and stress levels of transportation users.

[0909] "Artificial intelligence" is a technology that analyzes collected traffic volume data and emotion data to calculate the optimal timing for switching traffic lights.

[0910] "Signal switching timing" refers to the time it takes for a traffic light to switch from red to green, or vice versa.

[0911] A "traffic light" is a device installed at intersections and roads to control the flow of traffic.

[0912] A "surveillance camera" is a device that observes and records traffic conditions and user behavior in real time.

[0913] A "location information device" is a device, such as a car navigation system or smartphone, that acquires information about transportation users and their locations in real time.

[0914] The "central server" is a computer system that centrally manages and analyzes collected traffic volume data and emotion data.

[0915] "Optimization" refers to the adjustment or result of adjustment to make traffic flow most efficient.

[0916] A "pedestrian-vehicle separated traffic light" is a system that switches signals so that pedestrians and vehicles can move safely without crossing paths.

[0917] An "autonomous vehicle" is a vehicle that is designed to navigate roads autonomously without the need for driver intervention.

[0918] "Speed ​​adjustment" refers to changing the speed of an autonomous vehicle so that it travels at an appropriate speed in accordance with the timing of traffic lights.

[0919] The system of the present invention realizes real-time optimization of traffic signals and consists of the following main components:

[0920] Sensor installation and data collection

[0921] The server issues instructions to install sensors at each traffic light. The sensors measure the traffic volume of cars, pedestrians, and bicycles on the road for a certain period of time and send the data to the server. Each sensor also has a built-in emotion engine that collects emotional data from road users. This allows traffic volume and emotional data for each intersection to be obtained and aggregated on the server.

[0922] Data analysis and signal switching timing proposals

[0923] The collected data is input into artificial intelligence by the server and analyzed. The artificial intelligence combines traffic volume data and emotion data to calculate the optimal switching timing for each traffic light. The server receives the analysis results and sets new switching timings for the traffic lights. It also proposes the installation of pedestrian-vehicle separated traffic lights when separation between pedestrians and vehicles is necessary.

[0924] Aggregating real-time data and optimizing signal switching

[0925] Data collected in real time from surveillance cameras, location-based devices (e.g., smartphones and car navigation systems), and emotion engines is sent to a server. The server aggregates and analyzes this data. AI uses the real-time data to recalculate the optimal switching timing for each traffic light and reflects this in the traffic lights.

[0926] Collaboration with autonomous vehicles

[0927] The system provides automated vehicles with optimal traffic signal timing information and adjusts their speed in real time, allowing them to maintain an optimal speed and drive efficiently in line with traffic light switching timings.

[0928] Hardware and software used

[0929] The hardware used in this invention includes sensors installed at intersections, surveillance cameras, location information providing devices, etc. The software is a program using Python and various AI model APIs, and is managed by a server.

[0930] Example of program execution

[0931] As a concrete example, at a certain intersection, real-time location information and emotion data from surveillance cameras, smartphone apps, and emotion engines are sent to a server. The server analyzes this data and detects a sudden increase in traffic volume and high user stress levels. Based on this information, the AI ​​extends the green light at traffic lights, aiming to reduce stress in the short term. The server immediately adjusts the traffic lights accordingly.

[0932] Prompt Sentence Examples

[0933] "Create a program that predicts the optimal timing for switching traffic lights at an intersection and adjusts the speed of an autonomous vehicle in real time."

[0934] As a result, the system of the present invention enables optimization of traffic signals according to traffic volume and real-time speed adjustment of autonomous vehicles, thereby improving traffic flow and safety.

[0935] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0936] Step 1:

[0937] The server collects traffic volume data and emotion data from various sensors, surveillance cameras, and location information providing devices installed at intersections. These data include vehicle traffic volume, bicycle traffic volume, pedestrian count, and user location and emotion information. The input of the data collection is the sensor information from these devices, and the output is raw data transferred to the server.

[0938] Step 2:

[0939] The server aggregates the collected data in real time to a central server. At this stage, data sent from multiple devices is consolidated and stored in a database. The input is traffic volume data and sentiment data obtained from the devices, and the output is organized database entries. Specifically, data points from each device are consolidated in one place.

[0940] Step 3:

[0941] The server inputs the aggregated data into an artificial intelligence system for analysis. The AI ​​uses traffic volume data and emotion data to perform calculations to propose optimal switching timings for each traffic light. The input is the aggregated data, and the output is a proposal for optimal switching timings for the traffic lights.

[0942] Step 4:

[0943] The server sets the basic signal switching times for each traffic light based on the results of the AI ​​analysis. These settings are reflected in the traffic lights and used for actual traffic control. The input is the AI ​​analysis results, and the output is the new switching timing set for the traffic light.

[0944] Step 5:

[0945] The server provides optimal traffic signal timing information to the autonomous vehicle in real time. This information includes the current status of the traffic lights and the next switching timing, and the vehicle adjusts its speed based on this information. The input is the traffic light timing information, and the output is the speed adjustment instruction sent to the autonomous vehicle.

[0946] Step 6:

[0947] The terminal uses the traffic signal information acquired by the autonomous vehicle to adjust the vehicle's speed in real time. Specifically, it calculates the optimal speed based on the current timing information of the traffic lights and changes the vehicle's speed. The input is the traffic signal information, and the output is the adjusted vehicle speed.

[0948] Step 7:

[0949] The server monitors the autonomous vehicle's behavior based on new speed information and provides real-time feedback as needed. Feedback is provided based on speed adjustment accuracy and traffic fluctuations, allowing for safer driving with greater precision. The input is autonomous vehicle status data, and the output is updated speed instructions and other control commands.

[0950] 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.

[0951] 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.

[0952] 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.

[0953] [Fourth embodiment]

[0954] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0955] 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.

[0956] 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).

[0957] 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.

[0958] 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.

[0959] 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).

[0960] 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.

[0961] 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.

[0962] 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.

[0963] 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.

[0964] 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.

[0965] 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.

[0966] 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."

[0967] An embodiment of a real-time traffic light optimization system according to the present invention will be described. In this system, sensors are installed at each traffic light to measure the volume of automobile traffic, bicycle traffic, and the number of pedestrians over a certain period of time. The collected data is sent to a server and analyzed using artificial intelligence. Based on the analysis results, optimal switching timings for each traffic light are proposed and reflected in the traffic lights.

[0968] 1. Sensor installation and data collection

[0969] The server issues instructions to install sensors at each traffic light at an intersection. The terminals (sensor installation team) follow the instructions and install the sensors on the traffic lights. The sensors measure the traffic volume of cars, bicycles, and pedestrians over a certain period of time and send the data to the server. For example, suppose sensors X, Y, and Z are installed at intersection A and data is collected for one week.

[0970] 2. Data analysis and signal switching timing proposal

[0971] The collected data is analyzed by the server using artificial intelligence. The server then organizes the acquired traffic volume data and inputs it into the artificial intelligence. The artificial intelligence then analyzes the data and calculates the optimal switching timing for each traffic light. The server receives the analysis results and sets new switching timings for each traffic light.

[0972] 3. Proposal for a Pedestrian-Vehicle Separation Traffic Light

[0973] The server analyzes the data to identify intersections that require pedestrian-vehicle separated traffic lights. Based on the collected data, the AI ​​determines whether a particular intersection requires a pedestrian-vehicle separated traffic light. For example, if the number of pedestrians at intersection B is extremely high, the AI ​​will suggest the need for a pedestrian-vehicle separated traffic light. Based on this suggestion, the server installs a pedestrian-vehicle separated traffic light at the relevant intersection.

[0974] 4. Aggregating real-time data and optimizing signal switching

[0975] The data collection targets will be expanded to include surveillance cameras and devices that provide location information (such as car navigation systems and smartphones). Terminals (surveillance cameras and smart devices) will send traffic data to a server in real time. The server will then aggregate this data in real time and upload it to a central server. Based on the real-time data, artificial intelligence will calculate the optimal switching timing for each traffic light and immediately update the traffic lights.

[0976] (Example)

[0977] For example, at a certain intersection C, real-time location information from surveillance cameras and smartphone apps is sent to a server. The server analyzes this information and detects a sudden increase in traffic volume. Based on this information, the AI ​​determines that the green light at traffic light D should be extended by 10 seconds, and the server immediately adjusts traffic light D based on that judgment. This allows traffic at intersection C to flow smoothly.

[0978] The processing flow will be explained below.

[0979] Step 1:

[0980] The server issues instructions to install sensors at each intersection. Based on these instructions, the terminals (sensor installation teams) install sensors on the traffic lights at the intersections. These sensors are used to measure the volume of automobile, bicycle, and pedestrian traffic.

[0981] Step 2:

[0982] The device (sensor) measures traffic volume data for a certain period of time. For example, the sensor measures the number of passing cars, bicycles, and pedestrians every hour, and sends this data to a server.

[0983] Step 3:

[0984] The server receives the collected traffic data, formats it, removes noise, and then passes it to an artificial intelligence to prepare it for analysis.

[0985] Step 4:

[0986] The AI ​​analyzes the provided data and calculates the optimal switching timing for each traffic light. The AI's analysis results suggest the optimal duration of the green and red lights for each traffic light.

[0987] Step 5:

[0988] The server receives the signal switching timings suggested by the AI ​​and reflects the settings for each traffic light, so that the traffic lights at each intersection are set to switch at the optimal timing.

[0989] Step 6:

[0990] The server then provides the collected data to the AI ​​to analyze the need for pedestrian-separated traffic lights. The AI ​​then considers the volume of automobile and pedestrian traffic and determines whether pedestrian-separated traffic lights are necessary.

[0991] Step 7:

[0992] Based on the results of the AI ​​analysis, the server issues instructions to install or change the settings of traffic lights at intersections where pedestrian-vehicle separated traffic lights are deemed necessary. The terminal (traffic light installation team) receives these instructions and installs pedestrian-vehicle separated traffic lights at the relevant intersections.

[0993] Step 8:

[0994] The server issues instructions to collect traffic data in real time from surveillance cameras, car navigation systems, smartphones, etc. The terminals (these devices) collect data in real time and send it to the server.

[0995] Step 9:

[0996] The server aggregates the real-time data it receives and uploads it to a central server, including traffic and location information.

[0997] Step 10:

[0998] The server passes real-time data to the AI, which then recalculates the optimal switching timing for each traffic light on the fly. Based on the AI's calculations, the server instantly adjusts the settings for each traffic light.

[0999] Specific examples

[1000] For example, at intersection C, real-time location information from surveillance cameras and smartphone apps is sent to a server. The server analyzes this information and detects an increase in traffic volume. Based on this real-time data, the artificial intelligence determines that the green light at traffic light D should be extended by 10 seconds. Based on this judgment, the server immediately changes the settings of traffic light D to extend the green light time. This allows traffic at intersection C to flow more smoothly.

[1001] Example 1

[1002] 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."

[1003] Traffic signal control is based on fixed patterns and cannot adequately respond to sudden fluctuations in traffic volume, which can disrupt the smooth flow of traffic. It is also difficult to properly evaluate the need for pedestrian-vehicle separated traffic signals and install them appropriately. Therefore, a system is needed to alleviate traffic congestion, prevent traffic accidents, and improve convenience for road users.

[1004] 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.

[1005] In this invention, the server includes a means for installing sensors at each traffic light and measuring the volume of automobile traffic, bicycle traffic, and pedestrians for a certain period of time, a means for proposing the optimal switching timing for each traffic light using artificial intelligence based on the collected data and setting the basic signal switching time based on the proposal, and a means for aggregating monitoring devices, terminals that provide location information, and other traffic data in real time and accumulating it in a central control device, thereby making it possible to respond quickly and appropriately to fluctuations in traffic volume and optimize traffic flow.

[1006] "Sensors" are devices used to measure the volume of automobile, bicycle, and pedestrian traffic.

[1007] "Artificial intelligence" is a technology that analyzes collected data and calculates the optimal switching timing for each traffic light.

[1008] A "traffic light" is a device installed to control traffic, and emits signals to vehicles and pedestrians indicating whether to proceed or stop.

[1009] "Switching timing" refers to the time when a traffic light switches from green to red to yellow.

[1010] "Monitoring devices" are devices installed to monitor traffic conditions in real time, including cameras and sensors.

[1011] A "terminal that provides location information" is a device such as a car navigation system or a smartphone that detects the current location and transmits it to a server.

[1012] "Central control device" refers to a server that controls traffic lights based on aggregated traffic data.

[1013] A "pedestrian-vehicle separated traffic signal" is a traffic signal that separates and controls pedestrian and vehicle traffic, allowing pedestrians to cross intersections safely.

[1014] "Real-time aggregation" means that collected traffic data is immediately fed into a central control device.

[1015] An embodiment of a real-time optimization system for traffic lights according to the present invention will be described. In this system, sensors are installed at each traffic light to measure the volume of automobile traffic, bicycle traffic, and the number of pedestrians over a certain period of time. The collected data is sent to a server and analyzed by artificial intelligence. Based on the analysis results, optimal switching timings for each traffic light are proposed and reflected in the traffic lights.

[1016] First, the server issues instructions to install sensors on the traffic lights at each intersection. The terminals (sensor installation team) follow these instructions and install the sensors on the road. The sensors measure the traffic volume of cars, bicycles, and pedestrians over a certain period of time and send the data to the server. For example, let's consider the case where sensors X, Y, and Z are installed at intersection A and data is collected for one week. The sensors operate 24 hours a day, continuously measuring the flow rate of each traffic element.

[1017] The collected data is sent to a server, where it is organized and preprocessed. The server then inputs this organized data into a generative AI model (using, for example, a machine learning framework such as TensorFlow or PyTorch) to analyze traffic patterns and trends. The AI ​​calculates the optimal traffic light switching timing and returns the results to the server. The server then sets the new switching timing for the traffic lights and reflects it at each traffic light. For example, specific adjustments may be made, such as "extending the green light for traffic light A by 10 seconds between 5:00 PM and 7:00 PM on weekdays."

[1018] The server also analyzes the collected data to determine whether a particular intersection needs a pedestrian-vehicle-separated traffic light. Based on pedestrian data, the AI ​​determines whether a particular intersection (e.g., intersection B) needs a pedestrian-vehicle-separated traffic light. For example, it uses a specific algorithm, such as "If there are an extremely high number of pedestrians at intersection B, suggest installing a pedestrian-vehicle-separated traffic light."

[1019] Furthermore, terminals (surveillance cameras and smart devices) collect traffic data in real time and send it to a server. The server then collects this real-time data in a central database and analyzes it using artificial intelligence. Based on the real-time data, the optimal switching timing for each traffic light is calculated and the results are immediately reflected in the traffic lights. For example, specific adjustments are made in real time, such as "due to a sudden increase in traffic volume at intersection C, immediately extend the green light at traffic light D by 10 seconds."

[1020] For actual operation, detailed instructions can be obtained by entering the following prompt sentence into the generative AI model:

[1021] Prompt: "Describe how you can detect sudden increases in traffic volume at an intersection based on real-time location information from surveillance cameras and a smartphone app, and then optimize traffic light timing based on the results."

[1022] In this way, the system of the present invention integrates various technologies to highly optimize traffic signal control and achieve smooth traffic flow.

[1023] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1024] Step 1:

[1025] The server issues instructions to install sensors at traffic lights at intersections, including the specific intersection location and the type of sensor to install.

[1026] (Input) Location information and sensor type

[1027] (Output) Sensor installation instructions

[1028] Specifically, the server generates instructions such as "Install sensors X, Y, and Z at intersection A" and sends them to the terminal.

[1029] Step 2:

[1030] The terminal (sensor installation team) follows the instructions of the server and installs the sensor at the designated traffic light.

[1031] (Input) Sensor installation instructions

[1032] (Output) Data from installed sensors

[1033] Specifically, the sensor installation team physically installs designated sensors X, Y, and Z on the traffic lights at intersection A.

[1034] Step 3:

[1035] Sensors attached to the device measure the volume of cars, bicycles, and pedestrians over a period of time and send the data to a server.

[1036] (Input) Traffic volume data measurement instructions

[1037] (Output) Traffic volume data

[1038] Specifically, the sensor operates 24 hours a day and sends the measured data to the server every day.

[1039] Step 4:

[1040] The server receives the collected data, organizes it, and pre-processes it for input into artificial intelligence.

[1041] (Input) Traffic volume data

[1042] (Output) Preprocessed data

[1043] Specifically, the server classifies the traffic volume data by time period and removes outliers.

[1044] Step 5:

[1045] The server then feeds the pre-processed data into an artificial intelligence system (e.g., TensorFlow or PyTorch) to analyze traffic patterns and trends.

[1046] (Input) Preprocessed traffic data

[1047] (Output) Analysis results (optimal switching timing)

[1048] Specifically, the artificial intelligence uses machine learning algorithms to analyze traffic volume and calculate the optimal timing for switching signals.

[1049] Step 6:

[1050] The server receives the analysis results from the artificial intelligence and sends instructions to each traffic light to set new switching timings.

[1051] (Input) Analysis results (optimal switching timing)

[1052] (Output) Setting instructions to traffic lights

[1053] As a specific operation, the server sends a specific adjustment to the traffic light, such as "Extend the green light of traffic light A by 10 seconds between 5:00 PM and 7:00 PM."

[1054] Step 7:

[1055] The terminal (traffic light) immediately sets and executes the new switching timing based on instructions from the server.

[1056] (Input) Setting instructions to traffic lights

[1057] (Output) Signal control based on new signal switching timing

[1058] Specifically, traffic lights switch between green and red based on set timing.

[1059] Step 8:

[1060] The server receives traffic data collected in real time from the monitoring devices and location information providing terminals and accumulates it in the central control device.

[1061] (Input) Real-time data from monitoring devices and location information providing devices

[1062] (Output) Aggregated real-time data

[1063] Specifically, the server periodically updates and aggregates smartphone location information and surveillance camera video data.

[1064] Step 9:

[1065] The server uses artificial intelligence to analyze the aggregated real-time data and issues instructions to instantly reflect the optimal switching timing for each traffic light.

[1066] (Input) Accumulated real-time data

[1067] (Output) Real-time analysis results (optimal switching timing)

[1068] Specifically, the artificial intelligence analyzes real-time data and calculates adjustment results such as, "Due to a sudden increase in traffic volume at intersection C, immediately extend the green light at traffic light D by 10 seconds."

[1069] Step 10:

[1070] The terminal (traffic light) sets new switching timing in real time based on instructions from the server, optimizing traffic flow.

[1071] (Input) Real-time setting instructions

[1072] (Output) Real-time adjusted signal control

[1073] Specifically, the traffic lights instantly adjust their signal timing based on the analysis results, ensuring smooth traffic flow.

[1074] (Application example 1)

[1075] 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."

[1076] Conventional traffic light systems have difficulty responding to fluctuations in traffic volume in real time, resulting in problems with traffic congestion and pedestrian safety. Furthermore, with the increasing number of autonomous vehicles, there has been insufficient coordination between real-time traffic signals and route optimization for autonomous vehicles. This has resulted in insufficient traffic optimization and the inability to ensure smooth overall traffic flow.

[1077] 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.

[1078] In this invention, the server includes: means for installing sensors at each traffic light and measuring the volume of automobile traffic, bicycle traffic, and pedestrians for a certain period of time; means for proposing optimal switching timings for each traffic light using artificial intelligence based on the collected data and setting basic signal switching times based on the proposals; means for aggregating monitoring equipment, location information providing devices, and other traffic data in real time and accumulating it in a central processing unit; means for optimizing and reflecting the switching times of each traffic light using artificial intelligence based on the traffic volume data accumulated in real time; and means for proposing optimal routes for autonomous vehicles in real time. This not only optimizes traffic lights in real time, making traffic flow smoother, but also makes it possible to provide optimal routes to autonomous vehicles in real time.

[1079] A "sensor" is a device that detects traffic volume, vehicle and pedestrian movements, and measures the data.

[1080] "Automobile traffic volume" refers to the volume of automobile traffic over a certain period of time.

[1081] "Bicycle traffic volume" refers to the number of bicycles traveling over a certain period of time.

[1082] "Number of pedestrians" refers to the number of pedestrians in a given period of time.

[1083] "Artificial intelligence" is a technology that analyzes measured traffic data and proposes optimal timing for switching traffic lights.

[1084] "Optimal traffic light switching timing" refers to the signal switching time that ensures smooth traffic flow and reduces traffic congestion.

[1085] "Monitoring equipment" means devices that monitor traffic conditions and collect data.

[1086] A "device that provides location information" is a device that provides location information in real time, such as a car navigation system or a smart device.

[1087] "Other traffic data" includes any additional information related to traffic volume.

[1088] "Central processing unit" refers to a device that aggregates and processes traffic data collected in real time.

[1089] "Autonomous vehicle" refers to a vehicle that is driven automatically by a system.

[1090] "Optimal route" refers to the shortest or most efficient route to a destination, taking into account traffic conditions.

[1091] The real-time optimization system for traffic lights according to the present invention is configured as follows: In this system, sensors are installed at each traffic light to measure the volume of automobile traffic, bicycle traffic, and the number of pedestrians over a certain period of time. The collected data is then sent to a central processing unit and analyzed using artificial intelligence. Based on the analyzed data, optimal switching timing for each traffic light is proposed, and the traffic light switching times are set based on these proposals. In addition, traffic data is aggregated in real time using monitoring equipment and devices that provide location information, allowing for flexible response to fluctuations in traffic volume.

[1092] The server issues instructions to install sensors at each traffic light. The sensors measure traffic volume at intersections and send the data to a central processing unit. The central processing unit inputs the collected data into artificial intelligence and calculates the optimal switching timing for each traffic light. Based on the results of this analysis, the traffic light switching times are set. In addition, real-time data from monitoring equipment and devices that provide location information is also collected by the central processing unit and analyzed by artificial intelligence. This allows the switching timing of each traffic light to be optimized in real time.

[1093] As a specific example of its use, at a certain intersection, traffic volume data obtained by sensors and monitoring equipment is sent to a central processing unit. The central processing unit analyzes the data using artificial intelligence and detects a sudden increase in traffic volume. Based on this, it issues a command to extend the green light at the traffic light by 10 seconds. This command is immediately reflected in the traffic light, maintaining smooth traffic flow.

[1094] The system also supports autonomous vehicles, which receive traffic data from the server and receive real-time recommendations for optimal routes, enabling autonomous vehicles to drive smoothly and improving overall traffic efficiency.

[1095] The hardware used includes sensors, monitoring equipment, ECUs (electronic control units) of autonomous vehicles, etc. The software used includes central processing unit programs (analysis modules implemented in Python and API servers using Flask) and artificial intelligence models (using TensorFlow and PyTorch).

[1096] To illustrate, here are some example prompts:

[1097] "Generate analysis results to optimize the current traffic light switching timing based on traffic volume data. The intersection ID is 1234, and the current traffic volume data is as follows: number of vehicles 100, number of bicycles 20, number of pedestrians 50. Please propose the signal switching timing based on this data."

[1098] In this way, traffic flow can be optimized and traffic congestion and safety problems can be solved.

[1099] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1100] Step 1:

[1101] Sensors are installed at each traffic light. The terminal (sensor installation team) installs the sensors on the traffic lights according to instructions. The sensors measure the volume of automobile traffic, bicycle traffic, and the number of pedestrians. This data is necessary to understand traffic flow.

[1102] Input: Sensor installation location, sensor type (for cars, bicycles, pedestrians)

[1103] Output: Installed sensors

[1104] Step 2:

[1105] The traffic volume data acquired by the sensor is sent to a server. The terminal (sensor) measures traffic volume data over a certain period of time and sends it to the server via the Internet. The collected data is necessary to understand fluctuations in traffic volume in real time.

[1106] Input: Traffic data

[1107] Output: Traffic data sent to the server

[1108] Step 3:

[1109] The server inputs the collected traffic volume data into an artificial intelligence (AI) model for analysis. The server preprocesses the data using a data analysis algorithm implemented in Python, and then inputs the data into an AI model built using TensorFlow or PyTorch. The analysis results in the calculation of the optimal switching timing for each traffic light.

[1110] Input: Traffic data (cars, bicycles, pedestrians)

[1111] Output: Optimal signal switching timing

[1112] Step 4:

[1113] The server then sets the traffic light switching times based on the analysis results. The server then sends commands to the traffic light controller to reflect the optimal switching timing, thereby efficiently managing traffic flow.

[1114] Input: Optimal signal switching timing

[1115] Output: Set signal switching time

[1116] Step 5:

[1117] Real-time data is collected from the monitoring equipment and location information providing devices. The terminals (monitoring equipment and location information providing devices) measure changes in traffic conditions in real time and send the data to the server. This allows real-time traffic conditions to be understood.

[1118] Input: Data from monitoring and location devices

[1119] Output: Real-time data aggregated on the server

[1120] Step 6:

[1121] The server inputs the collected data into the AI ​​model in real time and re-optimizes the switching timing of each traffic light. The server pre-processes the collected data using Python and inputs it into the AI ​​model. This allows the appropriate signal switching timing to be recalculated according to traffic flow.

[1122] Input: Real-time data

[1123] Output: Re-optimized signal switching timing

[1124] Step 7:

[1125] The server then sends the re-optimized results to the traffic light control device, which immediately adjusts the traffic light switching times. Because traffic light switching is adjusted in real time, traffic flow can be managed smoothly and without interruption.

[1126] Input: Re-optimized signal switching timing

[1127] Output: Adjusted signal switching time

[1128] Step 8:

[1129] The server proposes optimal routes to autonomous vehicles in real time and transmits optimal driving route information to each autonomous vehicle, allowing the autonomous vehicle to drive the optimal route according to traffic conditions.

[1130] Input: Reoptimized traffic information

[1131] Output: Optimal route information for the autonomous vehicle

[1132] Through these steps, the server implements a system that collects and analyzes traffic data and controls traffic lights, optimizing traffic conditions in real time.

[1133] 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.

[1134] The real-time optimization system for traffic lights according to the present invention begins by installing sensors at each traffic light and measuring the volume of automobile traffic, bicycle traffic, and pedestrians over a certain period of time. Furthermore, an emotion engine that recognizes user emotions is incorporated, and emotional data of users around each traffic light is also collected. This makes it possible to propose optimal signal switching timings based on traffic volume and emotional data, and adjust them in real time.

[1135] 1. Sensor installation and data collection

[1136] The server issues instructions to install sensors at each traffic light at an intersection. The terminals (sensor installation team) follow the instructions and install the sensors on the traffic lights. The sensors measure the traffic volume of cars, bicycles, and pedestrians for a certain period of time and send the data to the server. The terminals (emotion engines) also collect emotion data from surrounding users and send it to the server. For example, let's assume that sensors X, Y, and Z and an emotion engine are installed at intersection A and one week's worth of traffic volume data and emotion data is collected.

[1137] 2. Data analysis and signal switching timing proposal

[1138] The collected traffic volume data and emotion data are analyzed by the server using artificial intelligence. The server then organizes the acquired data and inputs it into the artificial intelligence. The artificial intelligence then integrates and analyzes both sets of data to calculate the optimal switching timing for each traffic light. The server receives the analysis results and sets new switching timings for each traffic light.

[1139] 3. Proposal for a Pedestrian-Vehicle Separation Traffic Light

[1140] The server analyzes data to identify intersections where pedestrian-vehicle separated traffic lights are needed. The AI ​​considers vehicle and pedestrian traffic volume and emotional data to determine whether pedestrian-vehicle separated traffic lights are needed. For example, if there is an extremely large number of pedestrians at intersection B and the stress levels of users in the area are high, the AI ​​will suggest the need for pedestrian-vehicle separated traffic lights. Based on this suggestion, the server will install pedestrian-vehicle separated traffic lights at the relevant intersections.

[1141] 4. Aggregating real-time data and optimizing signal switching

[1142] The data collection targets will be expanded to include surveillance cameras, devices that provide location information (such as car navigation systems and smartphones), and emotion engines. Terminals (surveillance cameras, smart devices, and emotion engines) will send traffic data and emotion data to a server in real time. The server will then aggregate this data in real time and upload it to a central server. Based on the real-time data, artificial intelligence will recalculate the optimal switching timing for each traffic light and immediately reflect this in the traffic lights.

[1143] (Example)

[1144] For example, at a certain intersection C, real-time location information and emotion data are sent to a server from surveillance cameras, a smartphone app, and an emotion engine. The server analyzes this data and detects a sudden increase in traffic volume and high user stress levels. Based on this information, the AI ​​determines that extending the green light at traffic light D by 10 seconds will reduce stress in the short term. The server immediately adjusts traffic light D based on this judgment. This allows traffic at intersection C to flow smoothly and reduces user stress.

[1145] The processing flow will be explained below.

[1146] Step 1:

[1147] The server issues instructions to install sensors and emotion engines at each intersection. Based on these instructions, the terminals (sensor installation teams) install sensors and emotion engines on the traffic lights at the intersections. These sensors measure the traffic volume of cars, bicycles, and pedestrians, and the emotion engines collect emotion data from surrounding users.

[1148] Step 2:

[1149] The device (sensor) measures traffic volume data over a certain period of time. For example, the sensor measures the number of passing cars, bicycles, and pedestrians every hour and sends this data to the server. The device (emotion engine) collects user emotion data and sends it to the server.

[1150] Step 3:

[1151] The server receives the collected traffic and emotion data, formats the data, removes noise, and then passes the formatted data to the AI ​​to prepare it for analysis.

[1152] Step 4:

[1153] The AI ​​analyzes the provided data and calculates the optimal switching timing for each traffic light. The AI's analysis results suggest the optimal duration of the green and red lights for each traffic light. Furthermore, it also takes into account the user's emotional data and changes the timing to reduce stress.

[1154] Step 5:

[1155] The server receives the signal switching timings suggested by the AI ​​and reflects the settings for each traffic light, so that the traffic lights at each intersection are set to switch at the optimal timing.

[1156] Step 6:

[1157] The server then provides the collected traffic volume data and emotion data to the AI ​​to analyze the necessity of pedestrian-vehicle separated traffic lights. The AI ​​then considers the traffic volume of cars and pedestrians and the user's emotion data to determine whether pedestrian-vehicle separated traffic lights are necessary.

[1158] Step 7:

[1159] Based on the results of the AI ​​analysis, the server issues instructions to install or change the settings of traffic lights at intersections where pedestrian-vehicle separated traffic lights are deemed necessary. The terminal (traffic light installation team) receives these instructions and installs pedestrian-vehicle separated traffic lights at the relevant intersections.

[1160] Step 8:

[1161] The server issues instructions to collect traffic data and emotion data in real time from surveillance cameras, car navigation systems, smartphones, etc. The terminals (these devices) collect data in real time and send it to the server.

[1162] Step 9:

[1163] The server aggregates the received real-time data and uploads it to a central server, including traffic volume information, location information, and user sentiment information.

[1164] Step 10:

[1165] The server passes real-time data to the AI, which then recalculates the optimal switching timing for each traffic light on the fly. Based on the results of the AI ​​calculations, the server instantly adjusts the settings of each traffic light.

[1166] Specific examples

[1167] For example, at intersection C, real-time location information and emotion data from surveillance cameras, a smartphone app, and an emotion engine are sent to a server. The server analyzes this data and detects that traffic volume has suddenly increased, causing users' stress levels to rise. Based on this data, the artificial intelligence determines that the green light at traffic light D should be extended by 10 seconds. Based on this judgment, the server immediately changes the settings of traffic light D to extend the green light time. As a result, traffic at intersection C flows more smoothly and users' stress levels are reduced.

[1168] Example 2

[1169] 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."

[1170] Conventional traffic signal systems face challenges in effectively managing the different traffic volumes of automobiles, bicycles, and pedestrians, while achieving optimal switching timing for each signal. Furthermore, they lack the ability to properly determine the need for pedestrian-vehicle separated signals and utilize real-time data to optimize traffic flow. Furthermore, there is a need to reduce user stress and provide a more comfortable traffic environment by taking into account not only traffic data but also user emotional data.

[1171] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1172] In this invention, the server includes: means for installing sensors at each traffic light and measuring the volume of automobile traffic, bicycle traffic, and pedestrians for a certain period of time; means for proposing optimal switching timing for each traffic light using artificial intelligence based on the collected data and setting basic signal switching times based on the proposals; means for aggregating traffic data from surveillance cameras, devices that provide location information, and other sources in real time and accumulating it in a central server; means for collecting emotional data of surrounding users and adjusting the timing of switching lights based on that data; and means for optimizing and reflecting the switching times of each traffic light using artificial intelligence based on the traffic volume data and emotional data collected in real time. This makes it possible to propose and adjust optimal switching timing for traffic lights based on traffic volume and emotional data, reducing user stress and achieving smooth traffic flow.

[1173] A "sensor" is a device installed at each traffic light to measure traffic volume, measuring the number of cars, bicycles, and pedestrians over a certain period of time.

[1174] "Traffic signal timing" refers to the time it takes for a traffic light to switch between green, yellow, and red, and is optimized based on traffic volume and other data.

[1175] "Artificial intelligence" is a technology that analyzes collected traffic volume data and emotion data to suggest optimal traffic light switching timing.

[1176] A "surveillance camera" is a device that monitors traffic conditions in real time and transmits the video data to a server.

[1177] A "device that provides location information" is a device that provides user location data to a server, such as a car navigation system or smartphone.

[1178] The "central server" is a server that aggregates and analyzes data sent from each traffic light, sensor, surveillance camera, etc.

[1179] "Emotion data" is data that indicates the user's emotional state and is collected by the emotion engine. It is obtained by analyzing the facial expressions, tone of voice, etc. of surrounding users.

[1180] A "pedestrian-vehicle separated traffic light" is a traffic light that separates the crossing timing of pedestrians and vehicles, and is used to improve traffic flow and safety.

[1181] "Real-time aggregated traffic volume data" refers to traffic volume data transmitted in real time from sensors and devices and aggregated on a central server.

[1182] "Traffic green and red duration" refers to the length of time a traffic light displays a green or red light, and is adjusted based on traffic conditions and emotion data.

[1183] "Traffic data" refers to data that indicates traffic conditions, including the number and location information of automobiles, bicycles, and pedestrians.

[1184] MODE FOR CARRYING OUT THE INVENTION

[1185] The real-time optimization system for traffic lights according to the present invention utilizes various devices and artificial intelligence to optimize the timing of traffic light switching based on traffic volume data and emotion data. Specific embodiments for implementing this system are described below.

[1186] First, the server issues instructions to install sensors at each traffic light at an intersection. These sensors measure the volume of automobile, bicycle, and pedestrian traffic over a set period of time. The devices (sensor installation team) follow the instructions and install the sensors on the traffic lights. They also install an emotion engine to collect emotion data from surrounding users. The data obtained from the sensors and emotion engine is periodically sent to the server.

[1187] For example, consider installing sensors X, Y, Z and an emotion engine at intersection A and collecting traffic volume data and emotion data for one week.

[1188] The collected traffic volume data and emotion data are analyzed by the server using artificial intelligence. The server then organizes and inputs the acquired data into a format that the AI ​​can use. The AI ​​then integrates and analyzes this data to calculate the optimal switching timing for each traffic light. For example, if the data shows that the number of pedestrians increases during a certain time period, which increases the user's stress level during that time period, the AI ​​will suggest extending the green light on the traffic light during that time period.

[1189] The server then sets new switching timings for each traffic light based on the analysis results obtained from the AI, and these settings are made in real time and are adjusted as traffic conditions and the user's emotional state change.

[1190] Furthermore, the server aggregates real-time data from surveillance cameras and devices that provide location information (such as car navigation systems and smartphones) and inputs it into the central server. Terminals (surveillance cameras, smart devices, and emotion engines) send traffic data and emotion data to the server in real time. The server aggregates this data in real time, and artificial intelligence recalculates the optimal switching timing for each traffic light and immediately updates the traffic lights.

[1191] For example, at a certain intersection C, real-time location information and emotion data are sent to a server from surveillance cameras, a smartphone app, and an emotion engine. The server analyzes this data and detects a sudden increase in traffic volume and high user stress levels. Based on this information, the AI ​​determines that extending the green light at traffic light D by 10 seconds will reduce stress in the short term. The server immediately adjusts traffic light D based on this judgment. This allows traffic at intersection C to flow smoothly and reduces user stress.

[1192] An example prompt might be, "Based on traffic volume and emotion data at intersection A, please suggest the optimal timing for switching the traffic lights. The current situation is as follows: vehicle traffic volume is at its peak and pedestrian stress levels are high."

[1193] This system makes it possible to propose and adjust optimal traffic light switching timing based on traffic volume and emotion data, reducing user stress and ensuring smooth traffic flow.

[1194] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1195] Step 1:

[1196] The server issues instructions to install sensors at each intersection. These instructions include the type of sensor (traffic volume sensor or emotion engine) and details of the installation location. The inputs include the intersection location information and the sensor information to be installed. The output generates specific instructions for the installation team. In concrete terms, the server sends the instructions over the network to the terminals of the sensor installation team.

[1197] Step 2:

[1198] The terminals (sensor installation team) follow instructions from the server and install sensors and emotion engines at designated locations at each intersection. The input is the instructions from the server. The output is a report to the server indicating that the sensors and emotion engines have been installed. In concrete terms, the installation team secures the sensors at the designated locations and installs the emotion engines.

[1199] Step 3:

[1200] The sensors measure the traffic volume of cars, bicycles, and pedestrians over a certain period of time and send the data to the server. At the same time, the emotion engine collects user emotion data and sends it to the server. The input is registered data from passing vehicles and pedestrians. The output is the measured data sent to the server. The specific operation is to detect traffic volume in real time and analyze the emotional state.

[1201] Step 4:

[1202] The server organizes the data received from the sensors and emotion engine. The inputs include traffic volume data and emotion data. The output is data formatted to be input into the artificial intelligence. Specific operations include storing the data in a database, converting the format, and removing noise.

[1203] Step 5:

[1204] The AI ​​integrates and analyzes traffic volume data and emotion data to calculate the optimal switching timing for each traffic light. The inputs are organized traffic volume data and emotion data. The output is a proposal for the optimal signal switching timing. The specific operation is to use a machine learning algorithm to perform optimization calculations.

[1205] Step 6:

[1206] The server sets new switching timings for each traffic light based on the analysis results obtained from the AI. The input is the optimization proposal made by the AI. The output is a setting instruction for the traffic light. Specifically, the new setting is sent to the traffic light's control device, and the traffic light then corrects its operation.

[1207] Step 7:

[1208] The server aggregates real-time data from surveillance cameras and devices that provide location information, and inputs it into the central server. The input is real-time data from each device. The output is aggregated data. Specifically, the system sends video data from surveillance cameras and location information from smartphone apps to the central server.

[1209] Step 8:

[1210] The artificial intelligence recalculates the optimal signal switching timing based on the latest data. The inputs are traffic volume data and emotion data collected in real time. The output is the recalculated optimal signal switching timing. Specifically, the AI ​​analyzes the real-time data and immediately produces results.

[1211] Step 9:

[1212] The server immediately reflects the recalculated signal switching timings in the traffic lights. The input is the optimization results from the artificial intelligence. The output is an update instruction for each traffic light. Specifically, the server sends the new settings to the traffic light controller, and the traffic light adjusts its operation accordingly.

[1213] (Application example 2)

[1214] 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."

[1215] Conventional traffic light control systems have difficulty adapting to traffic volume in real time, which often disrupts the smooth flow of traffic. Furthermore, signals are not optimized to accommodate pedestrian, bicycle, and automobile traffic volumes, which can result in traffic accidents and congestion. Furthermore, with the spread of autonomous vehicles, real-time coordination with traffic lights is required, but current systems are unable to meet this demand.

[1216] The specific processing by the specific 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 installing sensors at each traffic light and measuring the volume of automobile traffic, bicycle traffic, and pedestrians for a certain period of time; means for using artificial intelligence to propose optimal switching timing for each traffic light based on the collected data and for setting basic signal switching times based on the proposals; means for aggregating traffic data from surveillance cameras, devices that provide location information, and other sources in real time and accumulating it in a central server; means for optimizing and reflecting the switching times of each traffic light based on the traffic volume data accumulated in real time using artificial intelligence; and means for providing optimal traffic signal timing information to automobiles in real time and adjusting the speed of automobiles. This enables smooth traffic flow and improved safety by optimizing the switching timing of traffic lights in response to traffic volume in real time and appropriately adjusting the speed of autonomous vehicles.

[1217] A "sensor" is a device used to measure traffic volume, number of pedestrians, number of bicycles, etc. over a certain period of time.

[1218] "Traffic data" is a collection of information about vehicle, bicycle, and pedestrian traffic.

[1219] "Emotional data" is a collection of information that measures the emotions and stress levels of transportation users.

[1220] "Artificial intelligence" is a technology that analyzes collected traffic volume data and emotion data to calculate the optimal timing for switching traffic lights.

[1221] "Signal switching timing" refers to the time it takes for a traffic light to switch from red to green, or vice versa.

[1222] A "traffic light" is a device installed at intersections and roads to control the flow of traffic.

[1223] A "surveillance camera" is a device that observes and records traffic conditions and user behavior in real time.

[1224] A "location information device" is a device, such as a car navigation system or smartphone, that acquires information about transportation users and their locations in real time.

[1225] The "central server" is a computer system that centrally manages and analyzes collected traffic volume data and emotion data.

[1226] "Optimization" refers to the adjustment or result of adjustment to make traffic flow most efficient.

[1227] A "pedestrian-vehicle separated traffic light" is a system that switches signals so that pedestrians and vehicles can move safely without crossing paths.

[1228] An "autonomous vehicle" is a vehicle that is designed to navigate roads autonomously without the need for driver intervention.

[1229] "Speed ​​adjustment" refers to changing the speed of an autonomous vehicle so that it travels at an appropriate speed in accordance with the timing of traffic lights.

[1230] The system of the present invention realizes real-time optimization of traffic signals and consists of the following main components:

[1231] Sensor installation and data collection

[1232] The server issues instructions to install sensors at each traffic light. The sensors measure the traffic volume of cars, pedestrians, and bicycles on the road for a certain period of time and send the data to the server. Each sensor also has a built-in emotion engine that collects emotional data from road users. This allows traffic volume and emotional data for each intersection to be obtained and aggregated on the server.

[1233] Data analysis and signal switching timing proposals

[1234] The collected data is input into artificial intelligence by the server and analyzed. The artificial intelligence combines traffic volume data and emotion data to calculate the optimal switching timing for each traffic light. The server receives the analysis results and sets new switching timings for the traffic lights. It also proposes the installation of pedestrian-vehicle separated traffic lights when separation between pedestrians and vehicles is necessary.

[1235] Aggregating real-time data and optimizing signal switching

[1236] Data collected in real time from surveillance cameras, location-based devices (e.g., smartphones and car navigation systems), and emotion engines is sent to a server. The server aggregates and analyzes this data. AI uses the real-time data to recalculate the optimal switching timing for each traffic light and reflects this in the traffic lights.

[1237] Collaboration with autonomous vehicles

[1238] The system provides automated vehicles with optimal traffic signal timing information and adjusts their speed in real time, allowing them to maintain an optimal speed and drive efficiently in line with traffic light switching timings.

[1239] Hardware and software used

[1240] The hardware used in this invention includes sensors installed at intersections, surveillance cameras, location information providing devices, etc. The software is a program using Python and various AI model APIs, and is managed by a server.

[1241] Example of program execution

[1242] As a concrete example, at a certain intersection, real-time location information and emotion data from surveillance cameras, smartphone apps, and emotion engines are sent to a server. The server analyzes this data and detects a sudden increase in traffic volume and high user stress levels. Based on this information, the AI ​​extends the green light at traffic lights, aiming to reduce stress in the short term. The server immediately adjusts the traffic lights accordingly.

[1243] Prompt Sentence Examples

[1244] "Create a program that predicts the optimal timing for switching traffic lights at an intersection and adjusts the speed of an autonomous vehicle in real time."

[1245] As a result, the system of the present invention enables optimization of traffic signals according to traffic volume and real-time speed adjustment of autonomous vehicles, thereby improving traffic flow and safety.

[1246] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1247] Step 1:

[1248] The server collects traffic volume data and emotion data from various sensors, surveillance cameras, and location information providing devices installed at intersections. These data include vehicle traffic volume, bicycle traffic volume, pedestrian count, and user location and emotion information. The input of the data collection is the sensor information from these devices, and the output is raw data transferred to the server.

[1249] Step 2:

[1250] The server aggregates the collected data in real time to a central server. At this stage, data sent from multiple devices is consolidated and stored in a database. The input is traffic volume data and sentiment data obtained from the devices, and the output is organized database entries. Specifically, data points from each device are consolidated in one place.

[1251] Step 3:

[1252] The server inputs the aggregated data into an artificial intelligence system for analysis. The AI ​​uses traffic volume data and emotion data to perform calculations to propose optimal switching timings for each traffic light. The input is the aggregated data, and the output is a proposal for optimal switching timings for the traffic lights.

[1253] Step 4:

[1254] The server sets the basic signal switching times for each traffic light based on the results of the AI ​​analysis. These settings are reflected in the traffic lights and used for actual traffic control. The input is the AI ​​analysis results, and the output is the new switching timing set for the traffic light.

[1255] Step 5:

[1256] The server provides optimal traffic signal timing information to the autonomous vehicle in real time. This information includes the current status of the traffic lights and the next switching timing, and the vehicle adjusts its speed based on this information. The input is the traffic light timing information, and the output is the speed adjustment instruction sent to the autonomous vehicle.

[1257] Step 6:

[1258] The terminal uses the traffic signal information acquired by the autonomous vehicle to adjust the vehicle's speed in real time. Specifically, it calculates the optimal speed based on the current timing information of the traffic lights and changes the vehicle's speed. The input is the traffic signal information, and the output is the adjusted vehicle speed.

[1259] Step 7:

[1260] The server monitors the autonomous vehicle's behavior based on new speed information and provides real-time feedback as needed. Feedback is provided based on speed adjustment accuracy and traffic fluctuations, allowing for safer driving with greater precision. The input is autonomous vehicle status data, and the output is updated speed instructions and other control commands.

[1261] 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.

[1262] 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.

[1263] 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.

[1264] 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.

[1265] 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.

[1266] 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.

[1267] 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).

[1268] 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.

[1269] 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."

[1270] 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.

[1271] 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).

[1272] 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.

[1273] 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.

[1274] 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.

[1275] 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.

[1276] 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.

[1277] 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.

[1278] 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.

[1279] 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.

[1280] 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.

[1281] 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.

[1282] The following is further disclosed regarding the above embodiment.

[1283] (Claim 1)

[1284] A method of installing sensors at each traffic light to measure the volume of automobile traffic, bicycle traffic, and pedestrians for a certain period of time,

[1285] A means for proposing optimal switching timings for each signal using artificial intelligence based on the collected data, and setting basic signal switching times based on the proposals;

[1286] a means for aggregating surveillance camera, location device, and other traffic data in real time and storing it on a central server;

[1287] A means for optimizing and reflecting the switching times of each traffic light using artificial intelligence based on the traffic volume data collected in real time;

[1288] A system including:

[1289] (Claim 2)

[1290] 2. The system of claim 1, further comprising means for analyzing the need for a pedestrian-vehicle separated traffic light using artificial intelligence and changing the traffic light to a pedestrian-vehicle separated traffic light based on the analysis results.

[1291] (Claim 3)

[1292] 2. The system of claim 1, further comprising means for instantly changing the green and red signal times of a traffic light based on said real-time traffic data.

[1293] "Example 1"

[1294] (Claim 1)

[1295] A method of installing sensors at each traffic light to measure the volume of automobile traffic, bicycle traffic, and pedestrians for a certain period of time,

[1296] A means for proposing optimal switching timings for each signal using artificial intelligence based on the collected data, and setting basic signal switching times based on the proposals;

[1297] a means for aggregating and integrating in a central control unit monitoring devices, terminals providing location information, and other traffic data in real time;

[1298] A means for optimizing and reflecting the switching times of each traffic light using artificial intelligence based on the traffic volume data collected in real time;

[1299] A system including:

[1300] (Claim 2)

[1301] 2. The system of claim 1, further comprising means for analyzing the need for a pedestrian-vehicle separated traffic light using artificial intelligence and changing the traffic light to a pedestrian-vehicle separated traffic light based on the analysis results.

[1302] (Claim 3)

[1303] 2. The system of claim 1, further comprising means for instantly changing the green and red signal times of a traffic light based on said real-time traffic data.

[1304] "Application Example 1"

[1305] (Claim 1)

[1306] A method of installing sensors at each traffic light to measure the volume of automobile traffic, bicycle traffic, and pedestrians for a certain period of time,

[1307] A means for proposing optimal switching timings for each signal using artificial intelligence based on the collected data, and setting basic signal switching times based on the proposals;

[1308] a means for aggregating and integrating monitoring equipment, location information providing devices, and other traffic data in real time into a central processing unit;

[1309] A means for optimizing and reflecting the switching times of each traffic light using artificial intelligence based on the traffic volume data collected in real time;

[1310] A means of proposing optimal routes to autonomous vehicles in real time;

[1311] A system including:

[1312] (Claim 2)

[1313] 2. The system of claim 1, further comprising means for analyzing the need for a pedestrian-vehicle separated traffic light using artificial intelligence and changing the traffic light to a pedestrian-vehicle separated traffic light based on the analysis results.

[1314] (Claim 3)

[1315] 2. The system of claim 1, further comprising means for instantly changing the green and red signal times of a traffic light based on said real-time traffic data.

[1316] "Example 2: Combining Emotion Engines"

[1317] (Claim 1)

[1318] A method of installing sensors at each traffic light to measure the volume of automobile traffic, bicycle traffic, and pedestrians for a certain period of time,

[1319] A means for proposing optimal switching timings for each signal using artificial intelligence based on the collected data, and setting basic signal switching times based on the proposals;

[1320] a means for aggregating surveillance camera, location device, and other traffic data in real time and storing it on a central server;

[1321] A means for collecting emotion data of surrounding users and adjusting the timing of switching traffic lights based on the data;

[1322] A means for optimizing and reflecting the switching times of each traffic light using artificial intelligence based on the traffic volume data and emotion data collected in real time;

[1323] A system including:

[1324] (Claim 2)

[1325] 2. The system of claim 1, further comprising means for analyzing the need for a pedestrian-vehicle separated traffic light using artificial intelligence and changing the traffic light to a pedestrian-vehicle separated traffic light based on the analysis results.

[1326] (Claim 3)

[1327] 10. The system of claim 1, further comprising means for instantly changing the green and red signal times of a traffic light based on the real-time traffic volume data and sentiment data.

[1328] "Application example 2 when combining emotion engines"

[1329] (Claim 1)

[1330] A method of installing sensors at each traffic light to measure the volume of automobile traffic, bicycle traffic, and pedestrians for a certain period of time,

[1331] A means for proposing optimal switching timings for each signal using artificial intelligence based on the collected data, and setting basic signal switching times based on the proposals;

[1332] a means for aggregating surveillance camera, location device, and other traffic data in real time and storing it on a central server;

[1333] A means for optimizing and reflecting the switching times of each traffic light using artificial intelligence based on the traffic volume data collected in real time;

[1334] means for providing optimal traffic signal timing information to a vehicle in real time to adjust the speed of the vehicle;

[1335] A system including:

[1336] (Claim 2)

[1337] 2. The system of claim 1, further comprising means for analyzing the need for a pedestrian-vehicle separated traffic light using artificial intelligence and changing the traffic light to a pedestrian-vehicle separated traffic light based on the analysis results.

[1338] (Claim 3)

[1339] 2. The system of claim 1, further comprising means for instantly changing the green and red signal times of a traffic light based on said real-time traffic data. [Explanation of symbols]

[1340] 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 method of installing sensors at each traffic light to measure the volume of automobile traffic, bicycle traffic, and pedestrians for a certain period of time, A means for proposing optimal switching timings for each signal using artificial intelligence based on the collected data, and setting basic signal switching times based on the proposals; a means for aggregating surveillance camera, location device, and other traffic data in real time and storing it on a central server; A means for optimizing and reflecting the switching times of each traffic light using artificial intelligence based on the traffic volume data collected in real time; A system including:

2. 2. The system according to claim 1, further comprising means for analyzing the necessity of a pedestrian-vehicle separated traffic signal by artificial intelligence and changing the traffic signal to a pedestrian-vehicle separated traffic signal based on the analysis result.

3. 2. The system according to claim 1, further comprising means for instantly changing the green and red signal times of a traffic light based on said real-time traffic data.

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