System

Abstract

A system according to an embodiment aims to optimize the control of traffic signals based on traffic volume and the number of pedestrians.SOLUTION: A system according to an embodiment includes a sensor, a data collection unit, a proposal unit, an implementation unit, a real-time data aggregation unit, and a signal switching unit. A sensor is installed for each signal. The data collection unit measures the traffic volume and the number of pedestrians based on the data collected by the sensor. The proposal unit proposes an optimal lighting time of each signal based on the data collected by the data collection unit. The implementation unit implements the lighting time proposed by the proposal unit. The real-time data aggregation unit aggregates data in real time. The signal switching unit optimizes signal switching on the basis of the data aggregated by the real-time data aggregation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has fixed control of traffic signals and is unable to respond to fluctuations in traffic volume or pedestrian numbers, leaving room for improvement in terms of optimizing traffic flow.

[0005] The system according to the embodiment aims to optimize the control of traffic signals based on traffic volume and pedestrian numbers. [Means for solving the problem]

[0006] The system according to the embodiment includes a sensor, a data collection unit, a proposal unit, an implementation unit, a real-time data aggregation unit, and a signal switching unit. The sensor is installed at each signal. The data collection unit measures traffic volume and the number of pedestrians based on data collected by the sensor. The proposal unit proposes optimal lighting times for each signal based on data collected by the data collection unit. The implementation unit implements the lighting times proposed by the proposal unit. The real-time data aggregation unit aggregates data in real time. The signal switching unit optimizes signal switching based on data aggregated by the real-time data aggregation unit. [Effects of the Invention]

[0007] An embodiment of the system can optimize traffic signal control based on traffic volume and pedestrian count. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A traffic signal control system according to an embodiment of the present invention utilizes AI technology to optimize traffic signal control. This system measures traffic volume and the number of pedestrians using sensors installed at each signal and other data collection means, and based on this data, AI proposes and implements optimal signal lighting times and traffic light placement. Data is also aggregated in real time, and AI optimizes signal switching. This allows the traffic signal control system to efficiently and safely control traffic signals.

[0029] A traffic signal control system according to an embodiment includes a sensor, a data collection unit, a proposal unit, an implementation unit, a real-time data aggregation unit, and a signal switching unit. The sensor is installed at each signal and measures the volume of automobile traffic, the volume of bicycles, and the number of pedestrians over a certain period of time. For example, the sensor counts the passing of automobiles and collects the data. The sensor also measures the number of bicycles and pedestrians. The data collection unit measures the traffic volume and the number of pedestrians based on the data collected by the sensor. For example, the data collection unit analyzes the data from the sensor and calculates the traffic volume and the number of pedestrians. The proposal unit proposes optimal lighting times for each signal based on the data collected by the data collection unit. For example, the proposal unit extends the green light time during times of heavy traffic and the red light time during times of light traffic. Furthermore, when there are many pedestrians, the pedestrian signal is prioritized. The implementation unit implements the lighting times proposed by the proposal unit. For example, the implementation unit sets the proposed lighting times for the traffic lights and performs actual signal control. The real-time data aggregation unit aggregates data in real time. For example, the system collects information from surveillance cameras installed on roads, location information from car navigation systems and dashcams, and location information from smartphones carried by passengers and pedestrians. The signal switching unit optimizes signal switching based on the data collected by the real-time data aggregation unit. For example, the signal switching unit extends the green light time when traffic volume increases sharply and extends the red light time when traffic volume decreases. In addition, when an emergency vehicle approaches, the signal switching unit adjusts the signal to allow the emergency vehicle to pass more easily. This allows the traffic signal control system to control traffic signals efficiently and safely.

[0030] In addition to sensor data, the data collection unit can use a drone to collect traffic conditions from the air in real time and have the AI ​​analyze the data. For example, the data collection unit uses a drone to capture traffic conditions from the air in real time and have the AI ​​analyze the video data. For example, the drone periodically patrols specific intersections and captures traffic volume and congestion conditions. The data collection unit also monitors traffic conditions over a wide area in real time using a camera mounted on the drone and sends the data to the AI. For example, the drone uses a high-resolution camera to simultaneously capture traffic conditions at multiple intersections. The data collection unit also uses a drone to record traffic conditions in detail during specific time periods and have the AI ​​analyze the data. For example, the drone captures traffic conditions during rush hour or when an event is held, and the AI ​​analyzes the data. In this way, more detailed traffic information can be obtained by collecting and analyzing traffic conditions from the air in real time.

[0031] The data collection unit collects data from pedestrians' smartwatches and fitness trackers, enabling detailed understanding of pedestrians' movements and speeds. The data collection unit collects walking data from, for example, pedestrians' smartwatches and fitness trackers and analyzes it using AI. For example, it acquires pedestrians' step counts and movement speeds in real time. The data collection unit also analyzes pedestrians' movement patterns based on the data from the smartwatches and fitness trackers, and reflects this information in traffic light control. For example, it identifies routes with high pedestrian traffic during specific times of the day. The data collection unit also collects data such as heart rate and stress levels from pedestrians' smartwatches and fitness trackers, and analyzes this data using AI. For example, if a pedestrian's stress level is high, the lighting time of the traffic light can be adjusted. This allows for detailed understanding of pedestrian movements and speeds, which can be reflected in traffic light control.

[0032] The real-time data aggregation unit can collect weather data and event information in addition to traffic volume data and have the AI ​​analyze it. For example, the real-time data aggregation unit can collect weather data in real time in addition to traffic volume data and have the AI ​​analyze it. For example, it can analyze fluctuations in traffic volume on rainy or snowy days and reflect this in signal control. The real-time data aggregation unit also collects event information (concerts, sporting events, etc.), and the AI ​​predicts traffic volume based on that information. For example, it can predict an increase in traffic volume when a large-scale event is held and adjust signal control. The real-time data aggregation unit can also combine weather data and event information to build a system that predicts fluctuations in traffic volume. For example, it can predict traffic volume when a large-scale event is held on a day with bad weather and optimize signal control. In this way, by collecting and analyzing weather data and event information, it is possible to predict traffic conditions in more detail.

[0033] The real-time data aggregator can collect automobile engine sounds and horn sounds using a sensor and determine the degree of traffic congestion and urgency. For example, the real-time data aggregator collects automobile engine sounds using a sensor and analyzes their volume and frequency to determine the degree of traffic congestion. For example, it determines that a location where a lot of engine sounds are heard has heavy traffic. The real-time data aggregator also collects horn sounds using a sensor and analyzes their frequency and volume to determine the degree of traffic urgency. For example, it determines that a location where horn sounds are frequently heard has traffic congestion or an emergency. The real-time data aggregator also analyzes engine sounds and horn sounds in combination to build a system that comprehensively determines the degree of traffic congestion and urgency. For example, it determines that a location where a lot of engine sounds and horn sounds are frequently heard is particularly congested. In this way, it is possible to collect engine sounds and horn sounds and determine the degree of traffic congestion and urgency, and reflect this in traffic signal control.

[0034] The suggestion unit can reflect past traffic accident data in the lighting times suggested by the AI, giving priority to safety. For example, the suggestion unit has the AI ​​learn from past traffic accident data, and gives top priority to safety when suggesting traffic light lighting times. For example, at intersections where accidents frequently occur, the green light time is shortened and the red light time is extended. The suggestion unit also adjusts traffic light lighting times for specific times of the day or day of the week based on traffic accident data. For example, if accidents frequently occur at night or on weekends, the traffic light lighting times for those times are adjusted. The suggestion unit also analyzes traffic accident data and optimizes traffic light lighting times for specific intersections or roads. For example, at intersections where accidents frequently occur, pedestrian signals are given priority. In this way, by reflecting past traffic accident data, traffic light control that gives top priority to safety is possible.

[0035] The suggestion unit can reflect public transportation schedules in its suggestion of traffic light times, thereby minimizing bus and train delays. For example, the suggestion unit collects public transportation schedules as data and takes them into consideration when the AI ​​suggests traffic light times. For example, the green light time is extended to match the bus arrival time. The suggestion unit also optimizes traffic light times to minimize bus and train delays based on the public transportation schedule. For example, the traffic light time is adjusted to match the train departure time. The suggestion unit also has the AI ​​learn public transportation schedule data and adjust traffic light times during specific time periods. For example, the traffic light time is optimized to match the bus route. In this way, bus and train delays can be minimized by reflecting public transportation schedules.

[0036] The suggestion unit can reflect the availability of parking spaces in the suggestion of lighting times, thereby facilitating smooth access to parking spaces. For example, the suggestion unit collects data on the availability of parking spaces in the surrounding area and takes this into consideration when the AI ​​suggests traffic light lighting times. For example, if the parking space is full, the green light time is shortened. The suggestion unit also optimizes the traffic light lighting time to facilitate smooth access to parking spaces based on the availability of parking spaces. For example, if the parking space is vacant, the green light time is extended. The suggestion unit also has the AI ​​learn data on parking space availability and adjust the traffic light lighting time during specific time periods. For example, if the parking space is congested, the red light time is extended. In this way, smooth access to parking spaces can be achieved by reflecting the availability of parking spaces.

[0037] The proposal unit can reflect past traffic accident data when deciding where to install pedestrian-vehicle separated traffic lights, giving priority to safety. For example, the proposal unit trains AI on past traffic accident data and gives top priority to safety when proposing locations for installing pedestrian-vehicle separated traffic lights. For example, pedestrian-vehicle separated traffic lights are installed at intersections where accidents frequently occur. The proposal unit also adjusts the installation locations of pedestrian-vehicle separated traffic lights for specific time periods and days of the week based on traffic accident data. For example, if accidents frequently occur at night or on weekends, pedestrian-vehicle separated traffic lights are installed during those times. The proposal unit also analyzes traffic accident data and optimizes the installation locations of pedestrian-vehicle separated traffic lights at specific intersections and roads. For example, pedestrian signals are given priority at intersections where accidents frequently occur. In this way, by reflecting past traffic accident data, it becomes possible to install pedestrian-vehicle separated traffic lights with top priority given to safety.

[0038] The proposal unit can predict peak-hour traffic volume by taking into account the opening and closing times of commercial facilities and schools when proposing pedestrian-vehicle separated traffic signals. For example, the proposal unit collects data on the opening and closing times of surrounding commercial facilities and schools, and takes this data into consideration when the AI ​​proposes locations for installing pedestrian-vehicle separated traffic signals. For example, pedestrian-vehicle separated traffic signals are installed to coincide with school arrival and departure times. The proposal unit also predicts peak-hour traffic volume based on the opening and closing times of commercial facilities and schools, and optimizes the installation locations of pedestrian-vehicle separated traffic signals. For example, pedestrian-vehicle separated traffic signals are installed to coincide with the opening times of shopping malls. The proposal unit also trains the AI ​​to learn data on the opening and closing times of commercial facilities and schools, and adjusts the installation locations of pedestrian-vehicle separated traffic signals for specific time periods. For example, pedestrian signals are prioritized during school arrival and departure times. In this way, by taking into account the opening and closing times of commercial facilities and schools, traffic volume during peak hours can be predicted and reflected in the installation of pedestrian-vehicle separated traffic signals.

[0039] The proposal unit can reflect public transportation schedules in its proposals for pedestrian-vehicle separated traffic lights, thereby minimizing bus and train delays. The proposal unit, for example, collects public transportation schedules as data and takes them into consideration when the AI ​​proposes locations for installing pedestrian-vehicle separated traffic lights. For example, pedestrian-vehicle separated traffic lights are installed to coincide with bus arrival times. The proposal unit also optimizes the installation locations of pedestrian-vehicle separated traffic lights to minimize bus and train delays based on the public transportation schedules. For example, pedestrian-vehicle separated traffic lights are installed to coincide with train departure times. The proposal unit also has the AI ​​learn public transportation schedule data and adjust the installation locations of pedestrian-vehicle separated traffic lights for specific time periods. For example, pedestrian-vehicle separated traffic lights are installed to coincide with bus routes. In this way, bus and train delays can be minimized by reflecting public transportation schedules.

[0040] The suggestion unit can reflect the availability of parking spaces in the proposal for pedestrian-vehicle separated traffic lights, thereby facilitating access to parking spaces. For example, the suggestion unit collects data on the availability of parking spaces in the surrounding area and takes this into consideration when the AI ​​proposes locations for installing pedestrian-vehicle separated traffic lights. For example, if the parking space is full, a pedestrian-vehicle separated traffic light is installed. The suggestion unit also optimizes the location for installing pedestrian-vehicle separated traffic lights to facilitate access to parking spaces based on the availability of parking spaces. For example, if the parking space is empty, a pedestrian-vehicle separated traffic light is installed. The suggestion unit also has the AI ​​learn data on the availability of parking spaces and adjust the location for installing pedestrian-vehicle separated traffic lights during specific time periods. For example, if the parking space is congested, a pedestrian-vehicle separated traffic light is installed. In this way, by reflecting the availability of parking spaces, access to parking spaces can be made smoother.

[0041] The real-time data aggregation unit adds aerial footage from a drone to real-time data, enabling a more detailed understanding of traffic conditions. For example, the real-time data aggregation unit uses a drone to capture traffic conditions from the sky in real time and has the AI ​​analyze the video data. For example, a drone periodically patrols specific intersections and captures traffic volume and congestion conditions. The real-time data aggregation unit also monitors wide-area traffic conditions in real time using a camera mounted on the drone and sends the data to the AI. For example, a drone uses a high-resolution camera to simultaneously capture traffic conditions at multiple intersections. The real-time data aggregation unit also uses a drone to record traffic conditions in detail during specific time periods and has the AI ​​analyze the data. For example, a drone captures traffic conditions during rush hour or when an event is held, and the AI ​​analyzes the data. By adding aerial footage from a drone, traffic conditions can be grasped in more detail.

[0042] The real-time data aggregator adds data from pedestrians' smartwatches and fitness trackers to the real-time data, enabling a detailed understanding of pedestrian movement and speed. The real-time data aggregator collects walking data, for example, from pedestrians' smartwatches and fitness trackers, and analyzes it using AI. For example, it acquires pedestrians' step counts and movement speeds in real time. The real-time data aggregator also analyzes pedestrian movement patterns based on the data from the smartwatches and fitness trackers, and reflects this information in traffic light control. For example, it identifies routes with high pedestrian traffic during specific periods of time. The real-time data aggregator also collects data such as heart rate and stress level from pedestrians' smartwatches and fitness trackers, and analyzes this data using AI. For example, if a pedestrian's stress level is high, the lighting time of the traffic light can be adjusted. By adding data from pedestrians' smartwatches and fitness trackers, a detailed understanding of pedestrian movement and speed can be achieved.

[0043] The real-time data aggregator adds weather data and event information to real-time data to predict traffic conditions. For example, the real-time data aggregator collects weather data in real time in addition to traffic volume data and has AI analyze it. For example, it analyzes fluctuations in traffic volume on rainy or snowy days and reflects this in traffic signal control. The real-time data aggregator also collects event information (such as concerts and sporting events), and the AI ​​predicts traffic volume based on that information. For example, it predicts an increase in traffic volume when a large-scale event is held and adjusts traffic signal control. The real-time data aggregator also combines weather data and event information to build a system that predicts fluctuations in traffic volume. For example, it predicts traffic volume when a large-scale event is held on a day with bad weather and optimizes traffic signal control. In this way, adding weather data and event information makes it possible to more accurately predict traffic conditions.

[0044] The real-time data aggregator can add automobile engine sounds and horn sounds to real-time data to determine the degree of traffic congestion and urgency. The real-time data aggregator, for example, collects automobile engine sounds using a sensor and analyzes their volume and frequency to determine the degree of traffic congestion. For example, it determines that a location where a lot of engine sounds are heard has heavy traffic. The real-time data aggregator also collects horn sounds using a sensor and analyzes their frequency and volume to determine the degree of traffic urgency. For example, it determines that a location where horn sounds are frequently heard has traffic congestion or an emergency. The real-time data aggregator also analyzes engine sounds and horn sounds in combination to build a system that comprehensively determines the degree of traffic congestion and urgency. For example, it determines that a location where a lot of engine sounds and horn sounds are frequently heard is particularly congested. In this way, by adding automobile engine sounds and horn sounds, it is possible to more accurately determine the degree of traffic congestion and urgency.

[0045] The signal switching unit can reflect past traffic accident data and prioritize safety when optimizing signal switching in real time. The signal switching unit, for example, has AI learn from past traffic accident data and prioritizes safety when optimizing signal switching. For example, at intersections where accidents frequently occur, the green light time is shortened and the red light time is extended. The signal switching unit also adjusts signal switching for specific times of the day or day of the week based on traffic accident data. For example, if accidents frequently occur at night or on weekends, the signal switching unit adjusts signal switching for those times. The signal switching unit also analyzes traffic accident data and optimizes signal switching at specific intersections or roads. For example, at intersections where accidents frequently occur, pedestrian signals are prioritized. In this way, by reflecting past traffic accident data, signal switching that prioritizes safety becomes possible.

[0046] The signal switching unit can predict peak-hour traffic volume by taking into account the opening and closing times of commercial facilities and schools when optimizing real-time signal switching. For example, the signal switching unit collects data on the opening and closing times of surrounding commercial facilities and schools and takes this into account when the AI ​​optimizes signal switching. For example, it adjusts signal switching to coincide with school arrival and departure times. The signal switching unit also predicts peak-hour traffic volume based on the opening and closing times of commercial facilities and schools and optimizes signal switching. For example, it extends the green light time to coincide with the opening time of a shopping mall. The signal switching unit also trains the AI ​​to learn data on the opening and closing times of commercial facilities and schools and adjusts signal switching during specific time periods. For example, it prioritizes pedestrian signals during school arrival and departure times. This makes it possible to predict peak-hour traffic volume and reflect it in signal switching by taking into account the opening and closing times of commercial facilities and schools.

[0047] The signal switching unit reflects parking availability in optimizing real-time signal switching, thereby facilitating smooth access to parking lots. The signal switching unit, for example, collects data on the availability of surrounding parking lots and takes this into consideration when the AI ​​optimizes signal switching. For example, if a parking lot is full, the green light time is shortened. The signal switching unit also optimizes signal switching to facilitate smooth access to parking lots based on parking availability. For example, if a parking lot is vacant, the green light time is extended. The signal switching unit also has the AI ​​learn parking lot availability data and adjust signal switching during specific time periods. For example, if the parking lot is congested, the red light time is extended. In this way, by reflecting parking availability, access to parking lots can be smoothed.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The suggestion unit can reflect the opening and closing times of nearby commercial facilities and schools in the lighting times suggested by the AI. For example, it can extend the green light time to coincide with school arrival and departure times, and adjust the lighting time of the traffic light to coincide with the opening time of the commercial facility. The suggestion unit can also predict fluctuations in traffic volume during specific time periods and optimize the lighting time of the traffic light. For example, it can predict that traffic volume around commercial facilities will increase on weekends and holidays, and adjust the lighting time of the traffic light. This makes it possible to smooth traffic flow by taking into account the opening and closing times of commercial facilities and schools.

[0050] The data collection unit collects information on the availability of nearby parking lots in real time and has the AI ​​analyze the information. For example, if the parking lot is full, the green light time is shortened, and if the parking lot is empty, the green light time is extended. The data collection unit also adjusts the lighting time of traffic lights during specific times based on the availability of parking lots. For example, if the parking lot is congested, the red light time is extended. This makes it possible to smooth access to parking lots by reflecting the availability of parking lots.

[0051] The real-time data aggregation unit can collect weather data and event information in addition to traffic volume data, and have the AI ​​analyze this. For example, it can analyze fluctuations in traffic volume on rainy or snowy days and reflect this in signal control. It also collects event information (concerts, sporting events, etc.), and the AI ​​predicts traffic volume based on that information. For example, it can predict an increase in traffic volume when a large-scale event is held and adjust signal control accordingly. In this way, by collecting and analyzing weather data and event information, it is possible to predict traffic conditions in more detail.

[0052] The real-time data aggregation unit can collect automobile engine sounds and horn sounds using sensors and determine the degree of traffic congestion and urgency. For example, automobile engine sounds are collected using sensors and their volume and frequency are analyzed to determine the degree of traffic congestion. For example, a location where a lot of engine sounds can be heard is determined to have heavy traffic. Also, horn sounds are collected using sensors and their frequency and volume are analyzed to determine the degree of traffic urgency. For example, a location where horn sounds can be heard frequently is determined to have traffic congestion or an emergency. In this way, by collecting engine sounds and horn sounds and determining the degree of traffic congestion and urgency, it is possible to reflect this in traffic light control.

[0053] The proposal unit can reflect past traffic accident data in the lighting times proposed by the AI, prioritizing safety. For example, at intersections where accidents frequently occur, the green light time can be shortened and the red light time extended. In addition, based on traffic accident data, the lighting times for traffic lights during specific times of the day or on specific days of the week can be adjusted. For example, if accidents frequently occur at night or on weekends, the lighting times for traffic lights during those times can be adjusted. In this way, by reflecting past traffic accident data, it becomes possible to control traffic lights with safety as the top priority.

[0054] The proposal unit can reflect public transportation schedules in its proposals for lighting times, thereby minimizing bus and train delays. For example, public transportation schedules are collected as data and taken into consideration when the AI ​​proposes traffic light lighting times. For example, the green light time can be extended to match the bus arrival time. In addition, based on the public transportation schedule, the signal lighting time can be optimized to minimize bus and train delays. For example, the signal lighting time can be adjusted to match the train departure time. In this way, bus and train delays can be minimized by reflecting public transportation schedules.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: Sensors are installed at each traffic light and measure the volume of automobile traffic, bicycle traffic, and pedestrian traffic over a certain period of time. For example, the sensors count the number of passing automobiles and collect that data. They also measure the number of bicycles and pedestrians. Step 2: The data collection unit measures traffic volume and the number of pedestrians based on the data collected by the sensors. For example, the data collection unit analyzes the data from the sensors and calculates traffic volume and the number of pedestrians. Step 3: The proposal unit proposes the optimal lighting time for each traffic light based on the data collected by the data collection unit. For example, the proposal unit may extend the green light time during times of heavy traffic and the red light time during times of light traffic. In addition, the proposal unit may prioritize pedestrian signals when there are many pedestrians. Step 4: The implementation unit implements the lighting time proposed by the proposal unit. For example, the implementation unit sets the proposed lighting time in a traffic light and performs actual traffic light control. Step 5: The real-time data aggregation unit aggregates data in real time, such as image capture information from surveillance cameras installed on roads, location information from car navigation systems and dashcams, and location information from smartphones held by passengers and pedestrians. Step 6: The signal switching unit optimizes signal switching based on the data collected by the real-time data aggregation unit. For example, the signal switching unit extends the green light time when traffic volume increases sharply, and extends the red light time when traffic volume decreases. Also, when an emergency vehicle approaches, the signal switching unit adjusts the signal to allow the emergency vehicle to pass more easily.

[0057] (Example 2) A traffic signal control system according to an embodiment of the present invention utilizes AI technology to optimize traffic signal control. This system measures traffic volume and the number of pedestrians using sensors installed at each signal and other data collection means, and based on this data, AI proposes and implements optimal signal lighting times and traffic light placement. Data is also aggregated in real time, and AI optimizes signal switching. This allows the traffic signal control system to efficiently and safely control traffic signals.

[0058] A traffic signal control system according to an embodiment includes a sensor, a data collection unit, a proposal unit, an implementation unit, a real-time data aggregation unit, and a signal switching unit. The sensor is installed at each signal and measures the volume of automobile traffic, the volume of bicycles, and the number of pedestrians over a certain period of time. For example, the sensor counts the passing of automobiles and collects the data. The sensor also measures the number of bicycles and pedestrians. The data collection unit measures the traffic volume and the number of pedestrians based on the data collected by the sensor. For example, the data collection unit analyzes the data from the sensor and calculates the traffic volume and the number of pedestrians. The proposal unit proposes optimal lighting times for each signal based on the data collected by the data collection unit. For example, the proposal unit extends the green light time during times of heavy traffic and the red light time during times of light traffic. Furthermore, when there are many pedestrians, the pedestrian signal is prioritized. The implementation unit implements the lighting times proposed by the proposal unit. For example, the implementation unit sets the proposed lighting times for the traffic lights and performs actual signal control. The real-time data aggregation unit aggregates data in real time. For example, the system collects information from surveillance cameras installed on roads, location information from car navigation systems and dashcams, and location information from smartphones carried by passengers and pedestrians. The signal switching unit optimizes signal switching based on the data collected by the real-time data aggregation unit. For example, the signal switching unit extends the green light time when traffic volume increases sharply and extends the red light time when traffic volume decreases. In addition, when an emergency vehicle approaches, the signal switching unit adjusts the signal to allow the emergency vehicle to pass more easily. This allows the traffic signal control system to control traffic signals efficiently and safely.

[0059] In addition to sensor data, the data collection unit can use a drone to collect traffic conditions from the air in real time and have the AI ​​analyze the data. For example, the data collection unit uses a drone to capture traffic conditions from the air in real time and have the AI ​​analyze the video data. For example, the drone periodically patrols specific intersections and captures traffic volume and congestion conditions. The data collection unit also monitors traffic conditions over a wide area in real time using a camera mounted on the drone and sends the data to the AI. For example, the drone uses a high-resolution camera to simultaneously capture traffic conditions at multiple intersections. The data collection unit also uses a drone to record traffic conditions in detail during specific time periods and have the AI ​​analyze the data. For example, the drone captures traffic conditions during rush hour or when an event is held, and the AI ​​analyzes the data. In this way, more detailed traffic information can be obtained by collecting and analyzing traffic conditions from the air in real time.

[0060] The data collection unit collects data from pedestrians' smartwatches and fitness trackers, enabling detailed understanding of pedestrians' movements and speeds. The data collection unit collects walking data from, for example, pedestrians' smartwatches and fitness trackers and analyzes it using AI. For example, it acquires pedestrians' step counts and movement speeds in real time. The data collection unit also analyzes pedestrians' movement patterns based on the data from the smartwatches and fitness trackers, and reflects this information in traffic light control. For example, it identifies routes with high pedestrian traffic during specific times of the day. The data collection unit also collects data such as heart rate and stress levels from pedestrians' smartwatches and fitness trackers, and analyzes this data using AI. For example, if a pedestrian's stress level is high, the lighting time of the traffic light can be adjusted. This allows for detailed understanding of pedestrian movements and speeds, which can be reflected in traffic light control.

[0061] The data collection unit uses the emotion estimation function to collect the emotional states of pedestrians and drivers and reflect the levels of stress and impatience in the data. The data collection unit, for example, captures the facial expressions of pedestrians and drivers with a camera and analyzes their emotional states using the emotion estimation function. For example, the camera analyzes pedestrians' facial expressions in real time to measure the levels of stress and impatience. The data collection unit also analyzes the driver's voice and uses the emotion estimation function to grasp their emotional states. For example, it analyzes the driver's tone of voice and speaking style to measure the levels of stress and impatience. The data collection unit also collects biometric data (such as heart rate and galvanic skin response) of pedestrians and drivers and analyzes their emotional states using the emotion estimation function. For example, it measures stress levels based on fluctuations in heart rate. In this way, emotional states can be collected and reflected in traffic light control, thereby reducing stress and impatience.

[0062] The real-time data aggregation unit can collect weather data and event information in addition to traffic volume data and have the AI ​​analyze it. For example, the real-time data aggregation unit can collect weather data in real time in addition to traffic volume data and have the AI ​​analyze it. For example, it can analyze fluctuations in traffic volume on rainy or snowy days and reflect this in signal control. The real-time data aggregation unit also collects event information (concerts, sporting events, etc.), and the AI ​​predicts traffic volume based on that information. For example, it can predict an increase in traffic volume when a large-scale event is held and adjust signal control. The real-time data aggregation unit can also combine weather data and event information to build a system that predicts fluctuations in traffic volume. For example, it can predict traffic volume when a large-scale event is held on a day with bad weather and optimize signal control. In this way, by collecting and analyzing weather data and event information, it is possible to predict traffic conditions in more detail.

[0063] The real-time data aggregator can collect automobile engine sounds and horn sounds using a sensor and determine the degree of traffic congestion and urgency. For example, the real-time data aggregator collects automobile engine sounds using a sensor and analyzes their volume and frequency to determine the degree of traffic congestion. For example, it determines that a location where a lot of engine sounds are heard has heavy traffic. The real-time data aggregator also collects horn sounds using a sensor and analyzes their frequency and volume to determine the degree of traffic urgency. For example, it determines that a location where horn sounds are frequently heard has traffic congestion or an emergency. The real-time data aggregator also analyzes engine sounds and horn sounds in combination to build a system that comprehensively determines the degree of traffic congestion and urgency. For example, it determines that a location where a lot of engine sounds and horn sounds are frequently heard is particularly congested. In this way, it is possible to collect engine sounds and horn sounds and determine the degree of traffic congestion and urgency, and reflect this in traffic signal control.

[0064] The real-time data aggregator uses the emotion estimation function to collect the driver's emotional state during times of high traffic volume in real time and reflect it in traffic signal control. The real-time data aggregator, for example, uses a camera to capture the driver's facial expressions during times of high traffic volume and analyzes the emotional state using the emotion estimation function. For example, the camera analyzes the driver's facial expressions in real time to measure the level of stress and impatience. The real-time data aggregator also analyzes the driver's voice and uses the emotion estimation function to grasp the emotional state. For example, it analyzes the driver's tone of voice and speaking style to measure the level of stress and impatience. The real-time data aggregator also collects the driver's biometric data (such as heart rate and galvanic skin response) and analyzes the emotional state using the emotion estimation function. For example, it measures the stress level based on fluctuations in heart rate. In this way, the driver's emotional state can be collected in real time and reflected in traffic signal control, thereby reducing stress and impatience.

[0065] The suggestion unit can reflect past traffic accident data in the lighting times suggested by the AI, giving priority to safety. For example, the suggestion unit has the AI ​​learn from past traffic accident data, and gives top priority to safety when suggesting traffic light lighting times. For example, at intersections where accidents frequently occur, the green light time is shortened and the red light time is extended. The suggestion unit also adjusts traffic light lighting times for specific times of the day or day of the week based on traffic accident data. For example, if accidents frequently occur at night or on weekends, the traffic light lighting times for those times are adjusted. The suggestion unit also analyzes traffic accident data and optimizes traffic light lighting times for specific intersections or roads. For example, at intersections where accidents frequently occur, pedestrian signals are given priority. In this way, by reflecting past traffic accident data, traffic light control that gives top priority to safety is possible.

[0066] The suggestion unit can use the emotion estimation function to consider the emotional states of pedestrians and drivers and propose lighting times to reduce stress. The suggestion unit, for example, uses the emotion estimation function to analyze the emotional states of pedestrians and drivers in real time and adjust the lighting times of traffic lights. For example, if stress is high, the green light time is extended. The suggestion unit also proposes lighting times for traffic lights to reduce stress based on emotional data of pedestrians and drivers. For example, if there is high impatience, the red light time is shortened. The suggestion unit also uses the emotion estimation function to analyze the emotional state during a specific time period and optimize the lighting times for traffic lights based on the data. For example, if stress is high during rush hour, the green light time is extended. In this way, lighting times to reduce stress can be proposed by considering the emotional states.

[0067] The suggestion unit can reflect public transportation schedules in its suggestion of traffic light times, thereby minimizing bus and train delays. For example, the suggestion unit collects public transportation schedules as data and takes them into consideration when the AI ​​suggests traffic light times. For example, the green light time is extended to match the bus arrival time. The suggestion unit also optimizes traffic light times to minimize bus and train delays based on the public transportation schedule. For example, the traffic light time is adjusted to match the train departure time. The suggestion unit also has the AI ​​learn public transportation schedule data and adjust traffic light times during specific time periods. For example, the traffic light time is optimized to match the bus route. In this way, bus and train delays can be minimized by reflecting public transportation schedules.

[0068] The suggestion unit can reflect the availability of parking spaces in the suggestion of lighting times, thereby facilitating smooth access to parking spaces. For example, the suggestion unit collects data on the availability of parking spaces in the surrounding area and takes this into consideration when the AI ​​suggests traffic light lighting times. For example, if the parking space is full, the green light time is shortened. The suggestion unit also optimizes the traffic light lighting time to facilitate smooth access to parking spaces based on the availability of parking spaces. For example, if the parking space is vacant, the green light time is extended. The suggestion unit also has the AI ​​learn data on parking space availability and adjust the traffic light lighting time during specific time periods. For example, if the parking space is congested, the red light time is extended. In this way, smooth access to parking spaces can be achieved by reflecting the availability of parking spaces.

[0069] The suggestion unit can use the emotion estimation function to analyze the emotional state of the driver during a specified time period and suggest a lighting time that causes less stress. The suggestion unit, for example, uses the emotion estimation function to analyze the emotional state of the driver during a specified time period in real time and adjust the lighting time of the traffic light. For example, if stress is high, the green light time is extended. The suggestion unit also suggests a lighting time of the traffic light to reduce stress based on the driver's emotional data. For example, if the driver is very impatient, the red light time is shortened. The suggestion unit also uses the emotion estimation function to analyze the emotional state during a specified time period and optimize the lighting time of the traffic light based on the data. For example, if stress is high during rush hour, the green light time is extended. In this way, by analyzing the emotional state of the driver during a specified time period and suggesting a lighting time that causes less stress, traffic flow can be smoothed.

[0070] The proposal unit can reflect past traffic accident data when deciding where to install pedestrian-vehicle separated traffic lights, giving priority to safety. For example, the proposal unit trains AI on past traffic accident data and gives top priority to safety when proposing locations for installing pedestrian-vehicle separated traffic lights. For example, pedestrian-vehicle separated traffic lights are installed at intersections where accidents frequently occur. The proposal unit also adjusts the installation locations of pedestrian-vehicle separated traffic lights for specific time periods and days of the week based on traffic accident data. For example, if accidents frequently occur at night or on weekends, pedestrian-vehicle separated traffic lights are installed during those times. The proposal unit also analyzes traffic accident data and optimizes the installation locations of pedestrian-vehicle separated traffic lights at specific intersections and roads. For example, pedestrian signals are given priority at intersections where accidents frequently occur. In this way, by reflecting past traffic accident data, it becomes possible to install pedestrian-vehicle separated traffic lights with top priority given to safety.

[0071] The proposal unit can predict peak-hour traffic volume by taking into account the opening and closing times of commercial facilities and schools when proposing pedestrian-vehicle separated traffic signals. For example, the proposal unit collects data on the opening and closing times of surrounding commercial facilities and schools, and takes this data into consideration when the AI ​​proposes locations for installing pedestrian-vehicle separated traffic signals. For example, pedestrian-vehicle separated traffic signals are installed to coincide with school arrival and departure times. The proposal unit also predicts peak-hour traffic volume based on the opening and closing times of commercial facilities and schools, and optimizes the installation locations of pedestrian-vehicle separated traffic signals. For example, pedestrian-vehicle separated traffic signals are installed to coincide with the opening times of shopping malls. The proposal unit also trains the AI ​​to learn data on the opening and closing times of commercial facilities and schools, and adjusts the installation locations of pedestrian-vehicle separated traffic signals for specific time periods. For example, pedestrian signals are prioritized during school arrival and departure times. In this way, by taking into account the opening and closing times of commercial facilities and schools, traffic volume during peak hours can be predicted and reflected in the installation of pedestrian-vehicle separated traffic signals.

[0072] The suggestion unit can use the emotion estimation function to consider the emotional states of pedestrians and drivers and propose locations for installing pedestrian-vehicle separated traffic lights to reduce stress. The suggestion unit, for example, uses the emotion estimation function to analyze the emotional states of pedestrians and drivers in real time and adjust the installation locations of pedestrian-vehicle separated traffic lights. For example, pedestrian-vehicle separated traffic lights are installed in locations where stress is high. The suggestion unit also proposes locations for installing pedestrian-vehicle separated traffic lights to reduce stress based on emotional data of pedestrians and drivers. For example, pedestrian-vehicle separated traffic lights are installed in locations where impatience is high. The suggestion unit also uses the emotion estimation function to analyze emotional states during specific time periods and optimize the installation locations of pedestrian-vehicle separated traffic lights based on the data. For example, pedestrian-vehicle separated traffic lights are installed in locations where stress is high during rush hour. In this way, it is possible to propose locations for installing pedestrian-vehicle separated traffic lights to reduce stress by considering the emotional states.

[0073] The proposal unit can reflect public transportation schedules in its proposals for pedestrian-vehicle separated traffic lights, thereby minimizing bus and train delays. The proposal unit, for example, collects public transportation schedules as data and takes them into consideration when the AI ​​proposes locations for installing pedestrian-vehicle separated traffic lights. For example, pedestrian-vehicle separated traffic lights are installed to coincide with bus arrival times. The proposal unit also optimizes the installation locations of pedestrian-vehicle separated traffic lights to minimize bus and train delays based on the public transportation schedules. For example, pedestrian-vehicle separated traffic lights are installed to coincide with train departure times. The proposal unit also has the AI ​​learn public transportation schedule data and adjust the installation locations of pedestrian-vehicle separated traffic lights for specific time periods. For example, pedestrian-vehicle separated traffic lights are installed to coincide with bus routes. In this way, bus and train delays can be minimized by reflecting public transportation schedules.

[0074] The suggestion unit can reflect the availability of parking spaces in the proposal for pedestrian-vehicle separated traffic lights, thereby facilitating access to parking spaces. For example, the suggestion unit collects data on the availability of parking spaces in the surrounding area and takes this into consideration when the AI ​​proposes locations for installing pedestrian-vehicle separated traffic lights. For example, if the parking space is full, a pedestrian-vehicle separated traffic light is installed. The suggestion unit also optimizes the location for installing pedestrian-vehicle separated traffic lights to facilitate access to parking spaces based on the availability of parking spaces. For example, if the parking space is empty, a pedestrian-vehicle separated traffic light is installed. The suggestion unit also has the AI ​​learn data on the availability of parking spaces and adjust the location for installing pedestrian-vehicle separated traffic lights during specific time periods. For example, if the parking space is congested, a pedestrian-vehicle separated traffic light is installed. In this way, by reflecting the availability of parking spaces, access to parking spaces can be made smoother.

[0075] The real-time data aggregation unit adds aerial footage from a drone to real-time data, enabling a more detailed understanding of traffic conditions. For example, the real-time data aggregation unit uses a drone to capture traffic conditions from the sky in real time and has the AI ​​analyze the video data. For example, a drone periodically patrols specific intersections and captures traffic volume and congestion conditions. The real-time data aggregation unit also monitors wide-area traffic conditions in real time using a camera mounted on the drone and sends the data to the AI. For example, a drone uses a high-resolution camera to simultaneously capture traffic conditions at multiple intersections. The real-time data aggregation unit also uses a drone to record traffic conditions in detail during specific time periods and has the AI ​​analyze the data. For example, a drone captures traffic conditions during rush hour or when an event is held, and the AI ​​analyzes the data. By adding aerial footage from a drone, traffic conditions can be grasped in more detail.

[0076] The real-time data aggregator adds data from pedestrians' smartwatches and fitness trackers to the real-time data, enabling a detailed understanding of pedestrian movement and speed. The real-time data aggregator collects walking data, for example, from pedestrians' smartwatches and fitness trackers, and analyzes it using AI. For example, it acquires pedestrians' step counts and movement speeds in real time. The real-time data aggregator also analyzes pedestrian movement patterns based on the data from the smartwatches and fitness trackers, and reflects this information in traffic light control. For example, it identifies routes with high pedestrian traffic during specific periods of time. The real-time data aggregator also collects data such as heart rate and stress level from pedestrians' smartwatches and fitness trackers, and analyzes this data using AI. For example, if a pedestrian's stress level is high, the lighting time of the traffic light can be adjusted. By adding data from pedestrians' smartwatches and fitness trackers, a detailed understanding of pedestrian movement and speed can be achieved.

[0077] The real-time data aggregator uses the emotion estimation function to collect the emotional states of pedestrians and drivers in real time and reflect them in traffic signal control. The real-time data aggregator, for example, captures the facial expressions of pedestrians and drivers with a camera and analyzes their emotional states using the emotion estimation function. For example, the camera analyzes pedestrians' facial expressions in real time to measure the level of stress and impatience. The real-time data aggregator also analyzes the driver's voice and uses the emotion estimation function to grasp their emotional state. For example, it analyzes the driver's tone of voice and speaking style to measure the level of stress and impatience. The real-time data aggregator also collects biometric data (such as heart rate and galvanic skin response) of pedestrians and drivers and analyzes their emotional states using the emotion estimation function. For example, it measures stress levels based on fluctuations in heart rate. In this way, the emotion estimation function can be used to collect emotional states in real time and reflect them in traffic signal control, thereby reducing stress and impatience.

[0078] The real-time data aggregator adds weather data and event information to real-time data to predict traffic conditions. For example, the real-time data aggregator collects weather data in real time in addition to traffic volume data and has AI analyze it. For example, it analyzes fluctuations in traffic volume on rainy or snowy days and reflects this in traffic signal control. The real-time data aggregator also collects event information (such as concerts and sporting events), and the AI ​​predicts traffic volume based on that information. For example, it predicts an increase in traffic volume when a large-scale event is held and adjusts traffic signal control. The real-time data aggregator also combines weather data and event information to build a system that predicts fluctuations in traffic volume. For example, it predicts traffic volume when a large-scale event is held on a day with bad weather and optimizes traffic signal control. In this way, adding weather data and event information makes it possible to more accurately predict traffic conditions.

[0079] The real-time data aggregator can add automobile engine sounds and horn sounds to real-time data to determine the degree of traffic congestion and urgency. The real-time data aggregator, for example, collects automobile engine sounds using a sensor and analyzes their volume and frequency to determine the degree of traffic congestion. For example, it determines that a location where a lot of engine sounds are heard has heavy traffic. The real-time data aggregator also collects horn sounds using a sensor and analyzes their frequency and volume to determine the degree of traffic urgency. For example, it determines that a location where horn sounds are frequently heard has traffic congestion or an emergency. The real-time data aggregator also analyzes engine sounds and horn sounds in combination to build a system that comprehensively determines the degree of traffic congestion and urgency. For example, it determines that a location where a lot of engine sounds and horn sounds are frequently heard is particularly congested. In this way, by adding automobile engine sounds and horn sounds, it is possible to more accurately determine the degree of traffic congestion and urgency.

[0080] The real-time data aggregator uses the emotion estimation function to collect the driver's emotional state during times of high traffic volume in real time and reflect it in traffic signal control. The real-time data aggregator, for example, uses a camera to capture the driver's facial expressions during times of high traffic volume and analyzes the emotional state using the emotion estimation function. For example, the camera analyzes the driver's facial expressions in real time to measure the level of stress and impatience. The real-time data aggregator also analyzes the driver's voice and uses the emotion estimation function to grasp the emotional state. For example, it analyzes the driver's tone of voice and speaking style to measure the level of stress and impatience. The real-time data aggregator also collects the driver's biometric data (such as heart rate and galvanic skin response) and analyzes the emotional state using the emotion estimation function. For example, it measures the stress level based on fluctuations in heart rate. In this way, the driver's emotional state can be collected in real time and reflected in traffic signal control, thereby reducing stress and impatience.

[0081] The signal switching unit can reflect past traffic accident data and prioritize safety when optimizing signal switching in real time. The signal switching unit, for example, has AI learn from past traffic accident data and prioritizes safety when optimizing signal switching. For example, at intersections where accidents frequently occur, the green light time is shortened and the red light time is extended. The signal switching unit also adjusts signal switching for specific times of the day or day of the week based on traffic accident data. For example, if accidents frequently occur at night or on weekends, the signal switching unit adjusts signal switching for those times. The signal switching unit also analyzes traffic accident data and optimizes signal switching at specific intersections or roads. For example, at intersections where accidents frequently occur, pedestrian signals are prioritized. In this way, by reflecting past traffic accident data, signal switching that prioritizes safety becomes possible.

[0082] The signal switching unit can predict peak-hour traffic volume by taking into account the opening and closing times of commercial facilities and schools when optimizing real-time signal switching. For example, the signal switching unit collects data on the opening and closing times of surrounding commercial facilities and schools and takes this into account when the AI ​​optimizes signal switching. For example, it adjusts signal switching to coincide with school arrival and departure times. The signal switching unit also predicts peak-hour traffic volume based on the opening and closing times of commercial facilities and schools and optimizes signal switching. For example, it extends the green light time to coincide with the opening time of a shopping mall. The signal switching unit also trains the AI ​​to learn data on the opening and closing times of commercial facilities and schools and adjusts signal switching during specific time periods. For example, it prioritizes pedestrian signals during school arrival and departure times. This makes it possible to predict peak-hour traffic volume and reflect it in signal switching by taking into account the opening and closing times of commercial facilities and schools.

[0083] The signal switching unit can use the emotion estimation function to consider the emotional states of pedestrians and drivers and propose signal switching to reduce stress. The signal switching unit, for example, uses the emotion estimation function to analyze the emotional states of pedestrians and drivers in real time and adjust signal switching. For example, if stress is high, the green light time is extended. The signal switching unit also proposes signal switching to reduce stress based on emotional data of pedestrians and drivers. For example, if there is high impatience, the red light time is shortened. The signal switching unit also uses the emotion estimation function to analyze the emotional state during a specific time period and optimize signal switching based on the data. For example, if stress is high during rush hour, the green light time is extended. In this way, signal switching to reduce stress can be proposed by considering the emotional state.

[0084] The signal switching unit reflects parking availability in optimizing real-time signal switching, thereby facilitating smooth access to parking lots. The signal switching unit, for example, collects data on the availability of surrounding parking lots and takes this into consideration when the AI ​​optimizes signal switching. For example, if a parking lot is full, the green light time is shortened. The signal switching unit also optimizes signal switching to facilitate smooth access to parking lots based on parking availability. For example, if a parking lot is vacant, the green light time is extended. The signal switching unit also has the AI ​​learn parking lot availability data and adjust signal switching during specific time periods. For example, if the parking lot is congested, the red light time is extended. In this way, by reflecting parking availability, access to parking lots can be smoothed.

[0085] The signal switching unit can use the emotion estimation function to analyze the emotional state of the driver during a specified time period and suggest a signal switching that will reduce stress. The signal switching unit, for example, uses the emotion estimation function to analyze the emotional state of the driver during a specified time period in real time and adjust the signal switching. For example, if stress is high, the green light time is extended. The signal switching unit also suggests signal switching to reduce stress based on the driver's emotion data. For example, if the driver is feeling very anxious, the red light time is shortened. The signal switching unit also uses the emotion estimation function to analyze the emotional state during a specified time period and optimize the signal switching based on the data. For example, if stress is high during rush hour, the green light time is extended. In this way, by analyzing the emotional state of the driver during a specified time period and suggesting a signal switching that will reduce stress, traffic flow can be smoothed.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The suggestion unit can reflect the opening and closing times of nearby commercial facilities and schools in the lighting times suggested by the AI. For example, it can extend the green light time to coincide with school arrival and departure times, and adjust the lighting time of the traffic light to coincide with the opening time of the commercial facility. The suggestion unit can also predict fluctuations in traffic volume during specific time periods and optimize the lighting time of the traffic light. For example, it can predict that traffic volume around commercial facilities will increase on weekends and holidays, and adjust the lighting time of the traffic light. This makes it possible to smooth traffic flow by taking into account the opening and closing times of commercial facilities and schools.

[0088] The data collection unit collects information on the availability of nearby parking lots in real time and has the AI ​​analyze the information. For example, if the parking lot is full, the green light time is shortened, and if the parking lot is empty, the green light time is extended. The data collection unit also adjusts the lighting time of traffic lights during specific times based on the availability of parking lots. For example, if the parking lot is congested, the red light time is extended. This makes it possible to smooth access to parking lots by reflecting the availability of parking lots.

[0089] The data collection unit can use the emotion estimation function to collect the emotional states of pedestrians and drivers and reflect them in traffic light control. For example, the facial expressions of pedestrians and drivers are captured with a camera, and the emotion estimation function is used to analyze their emotional states. For example, the camera analyzes pedestrians' facial expressions in real time to measure their levels of stress and impatience. The data collection unit can also analyze the driver's voice and use the emotion estimation function to understand their emotional states. For example, the driver's tone of voice and speaking style can be analyzed to measure their levels of stress and impatience. In this way, emotional states can be collected and reflected in traffic light control, thereby reducing stress and impatience.

[0090] The real-time data aggregation unit can collect weather data and event information in addition to traffic volume data, and have the AI ​​analyze this. For example, it can analyze fluctuations in traffic volume on rainy or snowy days and reflect this in signal control. It also collects event information (concerts, sporting events, etc.), and the AI ​​predicts traffic volume based on that information. For example, it can predict an increase in traffic volume when a large-scale event is held and adjust signal control accordingly. In this way, by collecting and analyzing weather data and event information, it is possible to predict traffic conditions in more detail.

[0091] The real-time data aggregation unit can collect automobile engine sounds and horn sounds using sensors and determine the degree of traffic congestion and urgency. For example, automobile engine sounds are collected using sensors and their volume and frequency are analyzed to determine the degree of traffic congestion. For example, a location where a lot of engine sounds can be heard is determined to have heavy traffic. Also, horn sounds are collected using sensors and their frequency and volume are analyzed to determine the degree of traffic urgency. For example, a location where horn sounds can be heard frequently is determined to have traffic congestion or an emergency. In this way, by collecting engine sounds and horn sounds and determining the degree of traffic congestion and urgency, it is possible to reflect this in traffic light control.

[0092] The real-time data aggregation unit can use the emotion estimation function to collect the driver's emotional state during times of high traffic volume in real time and reflect it in traffic signal control. For example, a camera can capture the driver's facial expressions during times of high traffic volume, and the emotion estimation function can analyze the driver's emotional state. For example, the camera can analyze the driver's facial expressions in real time to measure the level of stress and impatience. The real-time data aggregation unit can also analyze the driver's voice and use the emotion estimation function to understand the driver's emotional state. For example, the driver's tone of voice and speaking style can be analyzed to measure the level of stress and impatience. In this way, the driver's emotional state can be collected in real time and reflected in traffic signal control, thereby reducing stress and impatience.

[0093] The proposal unit can reflect past traffic accident data in the lighting times proposed by the AI, prioritizing safety. For example, at intersections where accidents frequently occur, the green light time can be shortened and the red light time extended. In addition, based on traffic accident data, the lighting times for traffic lights during specific times of the day or on specific days of the week can be adjusted. For example, if accidents frequently occur at night or on weekends, the lighting times for traffic lights during those times can be adjusted. In this way, by reflecting past traffic accident data, it becomes possible to control traffic lights with safety as the top priority.

[0094] The suggestion unit can use the emotion estimation function to consider the emotional states of pedestrians and drivers and propose lighting times to reduce stress. For example, the emotion estimation function can be used to analyze the emotional states of pedestrians and drivers in real time and adjust the lighting times of traffic lights. For example, if stress is high, the green light time can be extended. In addition, based on the emotional data of pedestrians and drivers, lighting times to reduce stress can be proposed. For example, if there is high impatience, the red light time can be shortened. In this way, lighting times to reduce stress can be proposed by considering the emotional states.

[0095] The proposal unit can reflect public transportation schedules in its proposals for lighting times, thereby minimizing bus and train delays. For example, public transportation schedules are collected as data and taken into consideration when the AI ​​proposes traffic light lighting times. For example, the green light time can be extended to match the bus arrival time. In addition, based on the public transportation schedule, the signal lighting time can be optimized to minimize bus and train delays. For example, the signal lighting time can be adjusted to match the train departure time. In this way, bus and train delays can be minimized by reflecting public transportation schedules.

[0096] The suggestion unit can use the emotion estimation function to analyze the emotional state of the driver during a specified time period and suggest lighting times that will cause less stress. For example, the emotion estimation function can be used to analyze the emotional state of the driver during a specified time period in real time and adjust the lighting times for traffic lights. For example, if stress is high, the green light time can be extended. In addition, based on the driver's emotional data, the suggestion unit can suggest lighting times for traffic lights to reduce stress. For example, if the driver is feeling very anxious, the red light time can be shortened. In this way, by analyzing the emotional state of the driver during a specified time period and suggesting lighting times that will cause less stress, traffic flow can be smoothed.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: Sensors are installed at each traffic light and measure the volume of automobile traffic, bicycle traffic, and pedestrian traffic over a certain period of time. For example, the sensors count the number of passing automobiles and collect that data. They also measure the number of bicycles and pedestrians. Step 2: The data collection unit measures traffic volume and the number of pedestrians based on the data collected by the sensors. For example, the data collection unit analyzes the data from the sensors and calculates traffic volume and the number of pedestrians. Step 3: The proposal unit proposes the optimal lighting time for each traffic light based on the data collected by the data collection unit. For example, the proposal unit may extend the green light time during times of heavy traffic and the red light time during times of light traffic. In addition, the proposal unit may prioritize pedestrian signals when there are many pedestrians. Step 4: The implementation unit implements the lighting time proposed by the proposal unit. For example, the implementation unit sets the proposed lighting time in a traffic light and performs actual traffic light control. Step 5: The real-time data aggregation unit aggregates data in real time, such as image capture information from surveillance cameras installed on roads, location information from car navigation systems and dashcams, and location information from smartphones held by passengers and pedestrians. Step 6: The signal switching unit optimizes signal switching based on the data collected by the real-time data aggregation unit. For example, the signal switching unit extends the green light time when traffic volume increases sharply, and extends the red light time when traffic volume decreases. Also, when an emergency vehicle approaches, the signal switching unit adjusts the signal to allow the emergency vehicle to pass more easily.

[0099] 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.

[0100] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 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.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0105] 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.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0120] 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.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0135] 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.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] 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.

[0139] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0140] 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.

[0141] 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.

[0142] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0143] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] 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.

[0149] FIG. 9 illustrates 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0150] 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.

[0151] 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).

[0152] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] 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."

[0154] 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.

[0155] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, 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. A processor also includes 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.

[0160] The hardware resource that executes the specific process 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 process may be a single processor.

[0161] 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.

[0162] 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.

[0163] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0164] 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.

[0165] 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. [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. 1. A system for optimizing traffic signal control, comprising: Sensors installed at each signal, a data collection unit that measures traffic volume and the number of pedestrians based on data collected by the sensors; a suggestion unit that suggests an optimal lighting time for each traffic light based on the data collected by the data collection unit; an implementation unit that implements the lighting time suggested by the suggestion unit; a real-time data aggregation unit that aggregates data in real time; a signal switching unit that optimizes signal switching based on the data aggregated by the real-time data aggregation unit; A system characterized by:

2. The data collection unit In addition to the data from the sensors, drones will be used to collect real-time traffic conditions from the air and the data will be analyzed by the AI.

2. The system of claim 1.

3. The real-time data aggregation unit In addition to traffic data, weather data and event information will be collected and analyzed by the AI.

2. The system of claim 1.

4. The proposal unit The lighting times proposed by the AI ​​are based on past traffic accident data, prioritizing safety.

2. The system of claim 1.

5. The signal switching unit When optimizing signal switching in real time, past traffic accident data is reflected and safety is given priority.

2. The system of claim 1.

6. The data collection unit Collecting the emotional state of pedestrians and drivers, and reflecting their stress and impatience levels in the data 2. The system of claim 1.

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