system

Abstract

The system according to this embodiment aims to analyze traffic infrastructure data and optimally adjust traffic signals and lanes. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an adjustment unit, and an execution unit. The collection unit collects traffic infrastructure data. The analysis unit analyzes the data collected by the collection unit. The adjustment unit adjusts traffic signals and lanes based on the analysis results obtained by the analysis unit. The execution unit executes the results adjusted by the adjustment unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, traffic infrastructure data has not been fully utilized effectively to reduce traffic congestion and traffic accidents, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze traffic infrastructure data and optimally adjust traffic signals and lanes.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an adjustment unit, and an execution unit. The data collection unit collects traffic infrastructure data. The analysis unit analyzes the data collected by the data collection unit. The adjustment unit adjusts traffic signals and lanes based on the analysis results obtained by the analysis unit. The execution unit executes the results adjusted by the adjustment unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze traffic infrastructure data and optimally adjust traffic signals and lanes. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The traffic management system according to an embodiment of the present invention aims to alleviate traffic congestion, reduce traffic accidents, manage infrastructure deterioration, improve energy efficiency and environmental protection, and support policy decision-making through the cooperation of autonomous driving technology and the infrastructure that supports it. This traffic management system uses generative AI to analyze traffic infrastructure data and grasp traffic flow and bottleneck locations in real time. Next, an AI agent automatically executes appropriate measures such as adjusting traffic signals and changing lanes to alleviate traffic congestion. The AI ​​agent also adjusts the timing of road signs and signals and implements traffic accident prevention measures through the activation of a warning system. Furthermore, the AI ​​agent constantly monitors the infrastructure coordination status, detects abnormalities due to deterioration early, and supports timely maintenance. In addition, the AI ​​agent optimizes the traffic infrastructure, reduces unnecessary energy consumption, and mitigates the environmental impact. Finally, the results of traffic infrastructure data analysis by generative AI are provided to policymakers to promote data-driven public policy. For example, the traffic management system uses generative AI to analyze data collected from sensors and cameras to predict traffic flow. This makes it possible to prevent traffic congestion from occurring. Next, the traffic management system uses AI agents to automatically implement appropriate measures such as adjusting traffic signals and changing lanes to alleviate traffic congestion. For example, during peak traffic hours, it adjusts signal timing and increases the number of lanes to smooth traffic flow. The traffic management system also uses AI agents to adjust road signs and signal timing and implement accident prevention measures through the activation of warning systems. For example, at dangerous intersections, it adjusts signal timing and sends warnings to vehicles to prevent accidents. Furthermore, the traffic management system's AI agents constantly monitor infrastructure connectivity, detecting abnormalities due to aging early and supporting timely maintenance. For example, sensors installed on roads and bridges monitor the condition of structures in real time, and maintenance is performed if abnormalities are detected. In addition, the traffic management system's AI agents optimize traffic infrastructure, reducing unnecessary energy consumption and mitigating environmental impact. For example, it suggests optimal acceleration and deceleration patterns to improve fuel efficiency.Furthermore, by coordinating multiple traffic signals and minimizing the number of vehicle stops, emissions are reduced. Finally, the traffic management system provides policymakers with the results of traffic infrastructure data analysis generated by AI, promoting data-driven public policy. For example, it integrates and analyzes traffic flow, accidents, infrastructure conditions, and environmental data, and simulates the effects of proposed policies to suggest optimal measures. In this way, the traffic management system can achieve traffic congestion reduction, decrease traffic accidents, manage aging infrastructure, improve energy efficiency and environmental protection, and support policymaking.

[0029] The traffic management system according to this embodiment comprises a collection unit, an analysis unit, an adjustment unit, and an execution unit. The collection unit collects traffic infrastructure data. The collection unit collects traffic infrastructure data from, for example, sensors and cameras. The collection unit can collect vehicle detection data and environmental data on roads using sensors. The collection unit can monitor traffic conditions and collect image data using cameras. The collection unit can also collect vehicle location information using GPS data. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data using a generation AI to grasp traffic flow and bottleneck locations in real time. The analysis unit predicts traffic flow by having the generation AI analyze data collected from sensors and cameras. The analysis unit can have the generation AI grasp changes in traffic flow in real time and identify bottleneck locations. The analysis unit can also have the generation AI make predictions to prevent traffic congestion from occurring. The adjustment unit adjusts traffic signals and lanes based on the analysis results obtained by the analysis unit. The adjustment unit adjusts the timing of traffic signals and changes lanes, for example. During periods of heavy traffic, the adjustment unit can adjust the timing of signals and increase the number of lanes to smooth the flow of traffic. The adjustment unit can also change the time distribution of green and red signals to optimize the signal cycle. The adjustment unit can increase or decrease lanes and change directions to optimize traffic flow. The execution unit executes the results adjusted by the adjustment unit. The execution unit executes the adjusted signal timing and lane change instructions, for example. The execution unit can operate the signal control system and change the timing of signals. The execution unit can issue lane change instructions and guide the flow of vehicles. The execution unit can execute the adjusted results and alleviate traffic congestion. As a result, the traffic management system according to the embodiment can achieve traffic congestion reduction, a decrease in traffic accidents, infrastructure aging management, energy efficiency and environmental protection, and support for policy decision-making.

[0030] The data collection unit collects traffic infrastructure data. For example, it collects traffic infrastructure data from sensors and cameras. Specifically, sensors installed on roads detect the passage of vehicles and collect detailed data such as their speed, direction, and vehicle type. This allows for real-time understanding of road congestion and traffic flow. Environmental data includes temperature, humidity, rainfall, and wind speed, and these are important factors influencing traffic conditions. Cameras are installed at intersections and major roads to monitor traffic conditions. Image data obtained from cameras records vehicle movement and pedestrian flow in detail and is used to visually understand traffic flow. Furthermore, GPS data can be used to collect vehicle location information. This makes it possible to track the movement path and current location of specific vehicles and analyze traffic flow in detail. The data collection unit centrally manages this diverse data and transmits it to a central database in real time. Thus, the data collection unit efficiently and accurately collects traffic infrastructure data and plays a supporting role in the information infrastructure of the entire system. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, during periods of rapid traffic volume, increasing the frequency of data collection and obtaining detailed information enables a quicker response. This allows the data collection unit, as the foundation of the traffic management system, to efficiently and effectively collect data and improve the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit. Using generative AI, the analysis unit analyzes the collected data to understand traffic flow and bottleneck locations in real time. Specifically, the generative AI analyzes data collected from sensors and cameras to predict traffic flow. For example, the generative AI uses image recognition technology to analyze camera footage and understand vehicle movement and traffic flow in detail. This allows for real-time monitoring of traffic flow and identification of bottleneck locations. Furthermore, the generative AI utilizes historical data and statistical information to make predictions to prevent traffic congestion. For example, based on historical traffic data, it can predict fluctuations in traffic volume during specific times of day or on specific days of the week, and predict the occurrence of congestion. Based on this data, the generative AI can understand changes in traffic flow in real time and identify bottleneck locations. Additionally, the generative AI uses anomaly detection algorithms to detect unusual patterns and abnormal data, and issue early warnings. This allows the analysis unit to quickly and accurately analyze the collected data and understand the surrounding risk situation in real time. Furthermore, the analysis unit can also perform long-term risk assessments and trend analyses. For example, based on historical data, it is possible to predict fluctuations in risk in specific regions or time periods and formulate future countermeasures. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The adjustment unit adjusts traffic signals and lanes based on the analysis results obtained by the analysis unit. Specifically, it adjusts the timing of traffic signals and changes lanes. For example, during peak traffic hours, adjusting the timing of signals and increasing the number of lanes can smooth the flow of traffic. The adjustment unit can also optimize the signal cycle by changing the time distribution of green and red lights. This optimizes traffic flow at specific intersections and roads, preventing congestion. Furthermore, the adjustment unit can optimize traffic flow by increasing or decreasing lanes and changing their direction. For example, during specific times, increasing lanes in the direction of heavy traffic and decreasing lanes in the opposite direction can smooth the flow of traffic. The adjustment unit performs these adjustments in real time, allowing it to respond quickly to changes in traffic conditions. In addition, the adjustment unit can record the results of traffic signal and lane adjustments and use this information for future improvements. For example, it can evaluate how effective a particular adjustment was and reflect this in subsequent adjustments. This allows the adjustment unit to efficiently and effectively adjust traffic signals and lanes, optimizing traffic flow. Furthermore, the coordination unit can collaborate with other systems and departments to achieve comprehensive traffic management. For example, it can prioritize signal adjustments by considering the operating status of public transport and emergency vehicles. As a result, the coordination unit, as the core of the traffic management system, can optimize traffic flow and improve the overall performance of the system.

[0033] The execution unit implements the results adjusted by the adjustment unit. Specifically, it executes the adjusted signal timing and lane change instructions. The execution unit can operate the signal control system and change the timing of signals. For example, at a specific intersection, it can change the time distribution of green and red lights to smooth the flow of traffic. Furthermore, the execution unit can issue lane change instructions and guide the flow of vehicles. For example, at a specific time of day, it can smooth the flow of traffic by increasing the number of lanes in the direction of heavy traffic and decreasing the number of lanes in the opposite direction. The execution unit can execute these instructions quickly and accurately and respond to changes in traffic conditions. Furthermore, the execution unit can record the adjusted results and use them to improve future operations. For example, it can evaluate how effective a particular execution was and reflect that in the next execution. This allows the execution unit to efficiently and effectively implement the results adjusted by the adjustment unit and optimize the flow of traffic. In addition, the execution unit can collaborate with other systems and departments to achieve comprehensive traffic management. For example, it can prioritize signal adjustments considering the operation status of public transport and emergency vehicles. This allows the execution unit, as the core of the traffic management system, to optimize traffic flow and improve the overall system performance.

[0034] The data collection unit can collect traffic infrastructure data from sensors and cameras. For example, the data collection unit can use sensors to collect vehicle detection data and environmental data on roads. The data collection unit can also use cameras to monitor traffic conditions and collect image data. The data collection unit can also collect vehicle location information using GPS data. This allows for an accurate understanding of the traffic infrastructure situation through data collection from sensors and cameras. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from sensors and cameras into a generating AI and have the generating AI perform data analysis.

[0035] The analysis unit can analyze the collected data and grasp traffic flow and bottleneck locations in real time. The analysis unit can, for example, use a generative AI to analyze the collected data and predict traffic flow. The analysis unit can use the generative AI to grasp changes in traffic flow in real time and identify bottleneck locations. The analysis unit can also use the generative AI to make predictions to prevent traffic congestion from occurring. In this way, by grasping traffic flow and bottleneck locations in real time, traffic congestion can be prevented from occurring. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the analysis of traffic flow and bottleneck locations.

[0036] The adjustment unit can adjust the timing of traffic signals and lane changes. For example, the adjustment unit can adjust the timing of traffic signals and make lane changes. During periods of heavy traffic, the adjustment unit can adjust the timing of signals and increase the number of lanes to smooth the flow of traffic. The adjustment unit can also optimize the signal cycle by changing the time distribution of green and red lights. The adjustment unit can optimize traffic flow by increasing or decreasing lanes and changing their direction. As a result, traffic congestion can be alleviated by adjusting the timing of traffic signals and lane changes. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can have a generating AI perform the adjustment of traffic signal timing and lane changes.

[0037] The execution unit can implement the adjusted results and alleviate traffic congestion. For example, the execution unit can implement adjusted signal timings and lane change instructions. The execution unit can operate the signal control system and change the timing of signals. The execution unit can issue lane change instructions and guide the flow of vehicles. In this way, traffic congestion can be alleviated by implementing the adjusted results. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can have a generating AI perform the implementation of the adjusted results.

[0038] The execution unit can adjust the timing of road signs and signals and activate warning systems. For example, the execution unit can change the content displayed on road signs and adjust the timing of signals. The execution unit can activate warning systems and send warnings to vehicles. At dangerous intersections, the execution unit can prevent accidents by adjusting the timing of signals and sending warnings to vehicles. In this way, accident prevention measures can be implemented by adjusting the timing of road signs and signals and activating warning systems. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can have a generating AI perform the adjustment of the timing of road signs and signals and the activation of warning systems.

[0039] The data collection unit can collect infrastructure condition data from sensors installed on roads and bridges. For example, the data collection unit collects infrastructure condition data using sensors installed on roads and bridges. The data collection unit can collect information on road cracks and bridge deterioration using sensors. The data collection unit can monitor the condition of structures in real time and perform maintenance if an abnormality is detected. This allows for early detection of abnormalities due to aging by collecting infrastructure condition data from sensors installed on roads and bridges. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input infrastructure condition data acquired from sensors into a generating AI and have the generating AI perform data analysis.

[0040] The analysis unit can analyze the collected infrastructure condition data and detect abnormalities due to aging at an early stage. For example, the analysis unit can use a generative AI to analyze the collected infrastructure condition data and identify abnormalities due to aging. The analysis unit can use the generative AI to analyze the infrastructure condition data and evaluate the size of cracks and the degree of deterioration. The analysis unit can also use the generative AI to detect abnormalities due to aging at an early stage and determine the need for maintenance. In this way, abnormalities due to aging can be detected at an early stage by analyzing the collected infrastructure condition data. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without using a generative AI. For example, the analysis unit can input the collected infrastructure condition data into a generative AI and have the generative AI perform the analysis of abnormalities due to aging.

[0041] The adjustment unit can propose an optimal acceleration / deceleration pattern and improve fuel efficiency. The adjustment unit proposes an optimal acceleration / deceleration pattern, for example, using a generative AI. The adjustment unit can use the generative AI to analyze changes in the vehicle's speed and propose an acceleration / deceleration pattern to improve fuel efficiency. The adjustment unit can also use the generative AI to propose an optimal acceleration / deceleration pattern based on the vehicle's driving data and improve fuel efficiency. In this way, fuel efficiency can be improved by proposing an optimal acceleration / deceleration pattern. Some or all of the above processing in the adjustment unit may be performed using a generative AI, for example, or without using a generative AI. For example, the adjustment unit can input vehicle driving data into the generative AI and have the generative AI propose an optimal acceleration / deceleration pattern.

[0042] The adjustment unit can coordinate multiple signals to minimize the number of times vehicles stop. The adjustment unit can coordinate multiple signals, for example, using a generation AI. The adjustment unit can minimize the number of times vehicles stop by having the generation AI synchronize the signals. The adjustment unit can also optimize the timing of signals by having the generation AI communicate with the signals. As a result, by coordinating multiple signals, the number of times vehicles stop can be minimized and traffic flow can be made smoother. Some or all of the above processing in the adjustment unit may be performed using a generation AI, for example, or without a generation AI. For example, the adjustment unit can have the generation AI perform signal synchronization and communication between signals.

[0043] The analysis unit can integrate and analyze traffic flow, accidents, infrastructure status, and environmental data and provide it to policymakers. For example, the analysis unit can use generative AI to integrate and analyze traffic flow, accidents, infrastructure status, and environmental data. The analysis unit can use generative AI to integrate data and evaluate traffic infrastructure. The analysis unit can also use generative AI to provide the results of the integrated analysis to policymakers, thereby promoting data-driven public policy. This allows for the promotion of data-driven public policy through integrated analysis of traffic flow, accidents, infrastructure status, and environmental data. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can have generative AI perform integrated analysis of traffic flow, accidents, infrastructure status, and environmental data.

[0044] The data collection unit can change the frequency of data collection based on specific time periods or events. For example, the data collection unit can use generative AI to adjust the frequency of data collection based on specific time periods or events. During rush hour, the data collection unit can increase the frequency of traffic infrastructure data collection to gain a detailed understanding of changes in traffic flow. During large-scale events, the data collection unit can focus on collecting surrounding traffic infrastructure data to monitor congestion in real time. At night and on holidays, the data collection unit can reduce the collection frequency to collect only the minimum necessary data. This allows for data collection tailored to traffic conditions by changing the frequency of data collection based on specific time periods or events. Some or all of the above processing in the data collection unit may be performed using generative AI, for example, or without generative AI. For example, the data collection unit can have generative AI perform the adjustment of the data collection frequency based on specific time periods or events.

[0045] The data collection unit can collect not only traffic volume but also environmental data such as weather and road conditions. For example, the data collection unit can use a generative AI to collect weather data and road condition data in addition to traffic volume data. The data collection unit can collect weather data in addition to traffic volume data to understand changes in traffic flow during rainy weather. The data collection unit can collect road condition data to monitor road surface conditions and construction information in real time. The data collection unit can collect air quality and noise levels as environmental data to comprehensively evaluate the impact on traffic infrastructure. This makes it possible to comprehensively evaluate traffic infrastructure by collecting not only traffic volume but also environmental data such as weather and road conditions. Some or all of the above processing in the data collection unit may be performed using a generative AI, for example, or without using a generative AI. For example, the data collection unit can have a generative AI perform the collection of traffic volume data, weather data, and road condition data.

[0046] The data collection unit can prioritize the collection of data from specific regions by considering geographical location information. For example, the data collection unit can use a generative AI to prioritize the collection of data from specific regions by considering geographical location information when collecting traffic infrastructure data. The data collection unit can prioritize the collection of data from urban areas with high traffic volume, which can be used to alleviate traffic congestion. The data collection unit can focus on collecting data from areas with a high incidence of traffic accidents, which can strengthen accident prevention measures. The data collection unit can prioritize the collection of data from areas where infrastructure is aging, which can be reflected in maintenance plans. As a result, by prioritizing the collection of data from specific regions by considering geographical location information, it becomes possible to collect data that is tailored to the traffic conditions of each region. Some or all of the above processing in the data collection unit may be performed using a generative AI, for example, or without using a generative AI. For example, the data collection unit can have a generative AI perform data collection that takes geographical location information into consideration.

[0047] The data collection unit can integrate and collect real-time information from social media. For example, the data collection unit can use generative AI to integrate and collect real-time information from social media when collecting traffic infrastructure data. The data collection unit can analyze posts on social media and collect traffic congestion and accident information in real time. The data collection unit can collect user feedback and use it to improve traffic infrastructure. The data collection unit can collect event information from social media and reflect it in adjusting traffic infrastructure. As a result, by integrating and collecting real-time information from social media, it is possible to respond quickly to changes in traffic conditions. Some or all of the above processing in the data collection unit may be performed using generative AI, for example, or without generative AI. For example, the data collection unit can have generative AI perform the collection of real-time information from social media.

[0048] The analysis unit can predict current traffic flow and bottleneck locations by referring to past traffic data. For example, the analysis unit uses a generating AI to refer to past traffic data and predict current traffic flow and bottleneck locations. The analysis unit can predict current traffic flow based on past traffic data. The analysis unit can identify bottleneck locations by referring to past traffic data. The analysis unit can also analyze past traffic data and predict the occurrence of traffic congestion. This allows for the prediction of current traffic flow and bottleneck locations by referring to past traffic data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input past traffic data into a generating AI and have the generating AI perform predictions of current traffic flow and bottleneck locations.

[0049] The analysis unit can apply an anomaly detection algorithm to detect abnormal traffic patterns. For example, the analysis unit can use a generative AI to apply an anomaly detection algorithm to detect abnormal traffic patterns. The analysis unit can apply the anomaly detection algorithm to identify the cause of traffic congestion. The analysis unit can use the anomaly detection algorithm to predict the occurrence of traffic accidents. The analysis unit can also apply the anomaly detection algorithm to detect anomalies in traffic flow. This makes it possible to identify the cause of traffic congestion and accidents by detecting abnormal traffic patterns. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can have a generative AI perform the detection of abnormal traffic patterns.

[0050] The analysis unit can integrate and analyze not only traffic data, but also environmental and socioeconomic data. For example, the analysis unit uses generative AI to integrate and analyze not only traffic data, but also environmental and socioeconomic data. The analysis unit can integrate traffic data and environmental data to comprehensively analyze the causes of traffic congestion. The analysis unit can integrate traffic data and socioeconomic data to propose improvements to traffic infrastructure. The analysis unit can integrate traffic data, environmental data, and socioeconomic data and provide this information to policymakers. This enables a comprehensive evaluation of traffic infrastructure by integrating and analyzing not only traffic data, but also environmental and socioeconomic data. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can have generative AI perform integrated analysis of traffic data, environmental data, and socioeconomic data.

[0051] The analysis unit can improve the accuracy of its analysis by combining different analysis algorithms. For example, the analysis unit can improve the accuracy of its analysis by combining different analysis algorithms using a generative AI. The analysis unit can improve the accuracy of its traffic flow predictions by combining different analysis algorithms. The analysis unit can improve the accuracy of its traffic accident predictions by applying different analysis algorithms. The analysis unit can detect the deterioration of traffic infrastructure at an early stage by combining different analysis algorithms. This allows for improved analysis accuracy by combining different analysis algorithms. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can have a generative AI execute combinations of different analysis algorithms.

[0052] The adjustment unit can dynamically adjust signal timing and lane changes in accordance with real-time traffic conditions. For example, the adjustment unit can use a generation AI to dynamically adjust signal timing and lane changes in accordance with real-time traffic conditions. The adjustment unit can monitor real-time traffic conditions and dynamically adjust signal timing. The adjustment unit can dynamically change lanes based on real-time traffic data. The adjustment unit can coordinate signal timing and lane changes in accordance with real-time traffic conditions. This allows for smoother traffic flow by dynamically adjusting signal timing and lane changes in accordance with real-time traffic conditions. Some or all of the above-described processes in the adjustment unit may be performed using a generation AI, for example, or without a generation AI. For example, the adjustment unit can have a generation AI perform adjustments to signal timing and lane changes in accordance with real-time traffic conditions.

[0053] The adjustment unit can apply special adjustment algorithms to respond to specific events or emergencies. For example, the adjustment unit can use generative AI to apply special adjustment algorithms to respond to specific events or emergencies. The adjustment unit can specially adjust the timing of traffic signals when large-scale events are held. The adjustment unit can specially change lanes when emergencies occur. The adjustment unit can optimize traffic flow by applying special adjustment algorithms to respond to specific events or emergencies. This allows for the optimization of traffic flow by applying special adjustment algorithms to respond to specific events or emergencies. Some or all of the above processing in the adjustment unit may be performed using, for example, generative AI, or without generative AI. For example, the adjustment unit can have generative AI perform the application of special adjustment algorithms.

[0054] The adjustment unit can adjust not only traffic signals and lanes, but also parking lot usage and public transport operation status. For example, the adjustment unit uses a generation AI to adjust not only traffic signals and lanes, but also parking lot usage and public transport operation status. The adjustment unit can adjust the timing of traffic signals to optimize parking lot usage. The adjustment unit can change lanes and adjust public transport operation status. The adjustment unit can comprehensively adjust traffic signals, lanes, parking lots, and public transport operation status. This enables comprehensive traffic management by adjusting not only traffic signals and lanes, but also parking lot usage and public transport operation status. Some or all of the above processing in the adjustment unit may be performed using a generation AI, for example, or without a generation AI. For example, the adjustment unit can have a generation AI perform adjustments to parking lot usage and public transport operation status.

[0055] The adjustment unit can maximize the effectiveness of adjustments by coordinating with different traffic management systems. The adjustment unit can maximize the effectiveness of adjustments by coordinating with different traffic management systems, for example, using generative AI. The adjustment unit can optimize signal timing by coordinating with different traffic management systems. The adjustment unit can optimize lane changes by coordinating with different traffic management systems. The adjustment unit can maximize the effectiveness of traffic flow adjustments by coordinating with different traffic management systems. In this way, the effectiveness of adjustments can be maximized by coordinating with different traffic management systems. Some or all of the above-described processes in the adjustment unit may be performed using, for example, generative AI, or without generative AI. For example, the adjustment unit can have generative AI perform the coordination with different traffic management systems.

[0056] The execution unit can provide real-time feedback on the adjusted results and make readjustments as needed. For example, the execution unit can use a generative AI to provide real-time feedback on the adjusted results and make readjustments as needed. The execution unit can provide real-time feedback on the adjusted results and readjust the timing of traffic signals. Based on the adjusted results, the execution unit can readjust lane changes in real time. The execution unit can provide feedback on the adjusted results and adjust traffic infrastructure as needed. This allows for real-time feedback on the adjusted results and enables readjustments as needed. Some or all of the above-described processes in the execution unit may be performed using a generative AI, for example, or without a generative AI. For example, the execution unit can have a generative AI perform the feedback of adjusted results and readjustments.

[0057] The execution unit can apply different execution algorithms depending on specific traffic conditions. For example, the execution unit can use a generative AI to apply different execution algorithms depending on specific traffic conditions. When there is traffic congestion, the execution unit can apply an execution algorithm for congestion mitigation. When a traffic accident occurs, the execution unit can apply an execution algorithm for accident response. When an emergency occurs, the execution unit can apply an execution algorithm for emergency response. In this way, traffic flow can be optimized by applying different execution algorithms depending on specific traffic conditions. Some or all of the above processing in the execution unit may be performed using a generative AI, for example, or without using a generative AI. For example, the execution unit can have a generative AI perform the application of an execution algorithm according to specific traffic conditions.

[0058] The execution unit can include not only the adjustment of traffic signals and lanes, but also the activation of road signs and warning systems. For example, the execution unit can use generative AI to include the activation of road signs and warning systems, in addition to the adjustment of traffic signals and lanes. In addition to adjusting traffic signals, the execution unit can change the display content of road signs. In addition to changing lanes, the execution unit can activate warning systems. The execution unit can comprehensively adjust traffic signals, lanes, road signs, and warning systems. This enables comprehensive traffic management by including the activation of road signs and warning systems, in addition to the adjustment of traffic signals and lanes. Some or all of the above-described processes in the execution unit may be performed using, for example, generative AI, or without generative AI. For example, the execution unit can have generative AI perform the activation of road signs and warning systems.

[0059] The execution unit can maximize the effectiveness of its execution by coordinating with different traffic management systems. For example, the execution unit can maximize the effectiveness of its execution by coordinating with different traffic management systems using generative AI. The execution unit can optimize signal timing by coordinating with different traffic management systems. The execution unit can optimize lane changes by coordinating with different traffic management systems. The execution unit can maximize the effectiveness of traffic flow adjustment by coordinating with different traffic management systems. Thus, the effectiveness of the execution can be maximized by coordinating with different traffic management systems. Some or all of the above-described processes in the execution unit may be performed using, for example, generative AI, or without generative AI. For example, the execution unit can have generative AI perform the coordination with different traffic management systems.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The adjustment unit can adjust not only traffic signals and lanes, but also the operation of public transportation. For example, it can adjust the timing of traffic signals to optimize bus and train schedules. It can also change lanes and create dedicated lanes for public transportation to ensure smoother operation. Furthermore, it can monitor the operation of public transportation in real time and adjust schedules as needed. This allows for comprehensive traffic management by adjusting not only traffic signals and lanes, but also the operation of public transportation.

[0062] The data collection unit can collect not only traffic infrastructure data but also data on surrounding commercial facilities and tourist attractions. For example, it can collect information on the operating hours and congestion levels of commercial facilities to predict changes in traffic flow. It can also collect event information at tourist attractions to understand traffic conditions during events. Furthermore, by collecting data on commercial facilities and tourist attractions, it can be used to optimize traffic infrastructure. In this way, by collecting data on surrounding commercial facilities and tourist attractions in addition to traffic infrastructure data, comprehensive traffic management becomes possible.

[0063] The adjustment unit can adjust not only traffic signals and lanes, but also parking lot usage. For example, it can adjust the timing of traffic signals to optimize parking lot usage. It can also change lanes to facilitate access to parking lots. Furthermore, it can monitor parking lot usage in real time and adjust parking lot usage as needed. This enables comprehensive traffic management by adjusting parking lot usage in addition to traffic signals and lanes.

[0064] The data collection unit can collect not only traffic infrastructure data but also data on surrounding commercial facilities and tourist attractions. For example, it can collect information on the operating hours and congestion levels of commercial facilities to predict changes in traffic flow. It can also collect event information at tourist attractions to understand traffic conditions during events. Furthermore, by collecting data on commercial facilities and tourist attractions, it can be used to optimize traffic infrastructure. In this way, by collecting data on surrounding commercial facilities and tourist attractions in addition to traffic infrastructure data, comprehensive traffic management becomes possible.

[0065] The adjustment unit can adjust not only traffic signals and lanes, but also parking lot usage. For example, it can adjust the timing of traffic signals to optimize parking lot usage. It can also change lanes to facilitate access to parking lots. Furthermore, it can monitor parking lot usage in real time and adjust parking lot usage as needed. This enables comprehensive traffic management by adjusting parking lot usage in addition to traffic signals and lanes.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The collection unit collects traffic infrastructure data. The collection unit can collect traffic infrastructure data from sensors and cameras, and can collect vehicle detection data on roads, environmental data, traffic condition image data, and GPS data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses a generation AI to grasp traffic flow and bottleneck locations in real time and predict traffic flow. Step 3: The adjustment unit adjusts traffic signals and lanes based on the analysis results obtained by the analysis unit. The adjustment unit adjusts the timing of traffic signals and changes lanes. Step 4: The execution unit executes the results adjusted by the adjustment unit. The execution unit executes the adjusted signal timing and lane change instructions, operates the signal control system to change the signal timing, and guides the flow of vehicles.

[0068] (Example of form 2) The traffic management system according to an embodiment of the present invention aims to alleviate traffic congestion, reduce traffic accidents, manage infrastructure deterioration, improve energy efficiency and environmental protection, and support policy decision-making through the cooperation of autonomous driving technology and the infrastructure that supports it. This traffic management system uses generative AI to analyze traffic infrastructure data and grasp traffic flow and bottleneck locations in real time. Next, an AI agent automatically executes appropriate measures such as adjusting traffic signals and changing lanes to alleviate traffic congestion. The AI ​​agent also adjusts the timing of road signs and signals and implements traffic accident prevention measures through the activation of a warning system. Furthermore, the AI ​​agent constantly monitors the infrastructure coordination status, detects abnormalities due to deterioration early, and supports timely maintenance. In addition, the AI ​​agent optimizes the traffic infrastructure, reduces unnecessary energy consumption, and mitigates the environmental impact. Finally, the results of traffic infrastructure data analysis by generative AI are provided to policymakers to promote data-driven public policy. For example, the traffic management system uses generative AI to analyze data collected from sensors and cameras to predict traffic flow. This makes it possible to prevent traffic congestion from occurring. Next, the traffic management system uses AI agents to automatically implement appropriate measures such as adjusting traffic signals and changing lanes to alleviate traffic congestion. For example, during peak traffic hours, it adjusts signal timing and increases the number of lanes to smooth traffic flow. The traffic management system also uses AI agents to adjust road signs and signal timing and implement accident prevention measures through the activation of warning systems. For example, at dangerous intersections, it adjusts signal timing and sends warnings to vehicles to prevent accidents. Furthermore, the traffic management system's AI agents constantly monitor infrastructure connectivity, detecting abnormalities due to aging early and supporting timely maintenance. For example, sensors installed on roads and bridges monitor the condition of structures in real time, and maintenance is performed if abnormalities are detected. In addition, the traffic management system's AI agents optimize traffic infrastructure, reducing unnecessary energy consumption and mitigating environmental impact. For example, it suggests optimal acceleration and deceleration patterns to improve fuel efficiency.Furthermore, by coordinating multiple traffic signals and minimizing the number of vehicle stops, emissions are reduced. Finally, the traffic management system provides policymakers with the results of traffic infrastructure data analysis generated by AI, promoting data-driven public policy. For example, it integrates and analyzes traffic flow, accidents, infrastructure conditions, and environmental data, and simulates the effects of proposed policies to suggest optimal measures. In this way, the traffic management system can achieve traffic congestion reduction, decrease traffic accidents, manage aging infrastructure, improve energy efficiency and environmental protection, and support policymaking.

[0069] The traffic management system according to this embodiment comprises a collection unit, an analysis unit, an adjustment unit, and an execution unit. The collection unit collects traffic infrastructure data. The collection unit collects traffic infrastructure data from, for example, sensors and cameras. The collection unit can collect vehicle detection data and environmental data on roads using sensors. The collection unit can monitor traffic conditions and collect image data using cameras. The collection unit can also collect vehicle location information using GPS data. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data using a generation AI to grasp traffic flow and bottleneck locations in real time. The analysis unit predicts traffic flow by having the generation AI analyze data collected from sensors and cameras. The analysis unit can have the generation AI grasp changes in traffic flow in real time and identify bottleneck locations. The analysis unit can also have the generation AI make predictions to prevent traffic congestion from occurring. The adjustment unit adjusts traffic signals and lanes based on the analysis results obtained by the analysis unit. The adjustment unit adjusts the timing of traffic signals and changes lanes, for example. During periods of heavy traffic, the adjustment unit can adjust the timing of signals and increase the number of lanes to smooth the flow of traffic. The adjustment unit can also change the time distribution of green and red signals to optimize the signal cycle. The adjustment unit can increase or decrease lanes and change directions to optimize traffic flow. The execution unit executes the results adjusted by the adjustment unit. The execution unit executes the adjusted signal timing and lane change instructions, for example. The execution unit can operate the signal control system and change the timing of signals. The execution unit can issue lane change instructions and guide the flow of vehicles. The execution unit can execute the adjusted results and alleviate traffic congestion. As a result, the traffic management system according to the embodiment can achieve traffic congestion reduction, a decrease in traffic accidents, infrastructure aging management, energy efficiency and environmental protection, and support for policy decision-making.

[0070] The data collection unit collects traffic infrastructure data. For example, it collects traffic infrastructure data from sensors and cameras. Specifically, sensors installed on roads detect the passage of vehicles and collect detailed data such as their speed, direction, and vehicle type. This allows for real-time understanding of road congestion and traffic flow. Environmental data includes temperature, humidity, rainfall, and wind speed, and these are important factors influencing traffic conditions. Cameras are installed at intersections and major roads to monitor traffic conditions. Image data obtained from cameras records vehicle movement and pedestrian flow in detail and is used to visually understand traffic flow. Furthermore, GPS data can be used to collect vehicle location information. This makes it possible to track the movement path and current location of specific vehicles and analyze traffic flow in detail. The data collection unit centrally manages this diverse data and transmits it to a central database in real time. Thus, the data collection unit efficiently and accurately collects traffic infrastructure data and plays a supporting role in the information infrastructure of the entire system. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, during periods of rapid traffic volume, increasing the frequency of data collection and obtaining detailed information enables a quicker response. This allows the data collection unit, as the foundation of the traffic management system, to efficiently and effectively collect data and improve the overall system performance.

[0071] The analysis unit analyzes the data collected by the collection unit. Using generative AI, the analysis unit analyzes the collected data to understand traffic flow and bottleneck locations in real time. Specifically, the generative AI analyzes data collected from sensors and cameras to predict traffic flow. For example, the generative AI uses image recognition technology to analyze camera footage and understand vehicle movement and traffic flow in detail. This allows for real-time monitoring of traffic flow and identification of bottleneck locations. Furthermore, the generative AI utilizes historical data and statistical information to make predictions to prevent traffic congestion. For example, based on historical traffic data, it can predict fluctuations in traffic volume during specific times of day or on specific days of the week, and predict the occurrence of congestion. Based on this data, the generative AI can understand changes in traffic flow in real time and identify bottleneck locations. Additionally, the generative AI uses anomaly detection algorithms to detect unusual patterns and abnormal data, and issue early warnings. This allows the analysis unit to quickly and accurately analyze the collected data and understand the surrounding risk situation in real time. Furthermore, the analysis unit can also perform long-term risk assessments and trend analyses. For example, based on historical data, it is possible to predict fluctuations in risk in specific regions or time periods and formulate future countermeasures. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0072] The adjustment unit adjusts traffic signals and lanes based on the analysis results obtained by the analysis unit. Specifically, it adjusts the timing of traffic signals and changes lanes. For example, during peak traffic hours, adjusting the timing of signals and increasing the number of lanes can smooth the flow of traffic. The adjustment unit can also optimize the signal cycle by changing the time distribution of green and red lights. This optimizes traffic flow at specific intersections and roads, preventing congestion. Furthermore, the adjustment unit can optimize traffic flow by increasing or decreasing lanes and changing their direction. For example, during specific times, increasing lanes in the direction of heavy traffic and decreasing lanes in the opposite direction can smooth the flow of traffic. The adjustment unit performs these adjustments in real time, allowing it to respond quickly to changes in traffic conditions. In addition, the adjustment unit can record the results of traffic signal and lane adjustments and use this information for future improvements. For example, it can evaluate how effective a particular adjustment was and reflect this in subsequent adjustments. This allows the adjustment unit to efficiently and effectively adjust traffic signals and lanes, optimizing traffic flow. Furthermore, the coordination unit can collaborate with other systems and departments to achieve comprehensive traffic management. For example, it can prioritize signal adjustments by considering the operating status of public transport and emergency vehicles. As a result, the coordination unit, as the core of the traffic management system, can optimize traffic flow and improve the overall performance of the system.

[0073] The execution unit implements the results adjusted by the adjustment unit. Specifically, it executes the adjusted signal timing and lane change instructions. The execution unit can operate the signal control system and change the timing of signals. For example, at a specific intersection, it can change the time distribution of green and red lights to smooth the flow of traffic. Furthermore, the execution unit can issue lane change instructions and guide the flow of vehicles. For example, at a specific time of day, it can smooth the flow of traffic by increasing the number of lanes in the direction of heavy traffic and decreasing the number of lanes in the opposite direction. The execution unit can execute these instructions quickly and accurately and respond to changes in traffic conditions. Furthermore, the execution unit can record the adjusted results and use them to improve future operations. For example, it can evaluate how effective a particular execution was and reflect that in the next execution. This allows the execution unit to efficiently and effectively implement the results adjusted by the adjustment unit and optimize the flow of traffic. In addition, the execution unit can collaborate with other systems and departments to achieve comprehensive traffic management. For example, it can prioritize signal adjustments considering the operation status of public transport and emergency vehicles. This allows the execution unit, as the core of the traffic management system, to optimize traffic flow and improve the overall system performance.

[0074] The data collection unit can collect traffic infrastructure data from sensors and cameras. For example, the data collection unit can use sensors to collect vehicle detection data and environmental data on roads. The data collection unit can also use cameras to monitor traffic conditions and collect image data. The data collection unit can also collect vehicle location information using GPS data. This allows for an accurate understanding of the traffic infrastructure situation through data collection from sensors and cameras. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from sensors and cameras into a generating AI and have the generating AI perform data analysis.

[0075] The analysis unit can analyze the collected data and grasp traffic flow and bottleneck locations in real time. The analysis unit can, for example, use a generative AI to analyze the collected data and predict traffic flow. The analysis unit can use the generative AI to grasp changes in traffic flow in real time and identify bottleneck locations. The analysis unit can also use the generative AI to make predictions to prevent traffic congestion from occurring. In this way, by grasping traffic flow and bottleneck locations in real time, traffic congestion can be prevented from occurring. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the analysis of traffic flow and bottleneck locations.

[0076] The adjustment unit can adjust the timing of traffic signals and lane changes. For example, the adjustment unit can adjust the timing of traffic signals and make lane changes. During periods of heavy traffic, the adjustment unit can adjust the timing of signals and increase the number of lanes to smooth the flow of traffic. The adjustment unit can also optimize the signal cycle by changing the time distribution of green and red lights. The adjustment unit can optimize traffic flow by increasing or decreasing lanes and changing their direction. As a result, traffic congestion can be alleviated by adjusting the timing of traffic signals and lane changes. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can have a generating AI perform the adjustment of traffic signal timing and lane changes.

[0077] The execution unit can implement the adjusted results and alleviate traffic congestion. For example, the execution unit can implement adjusted signal timings and lane change instructions. The execution unit can operate the signal control system and change the timing of signals. The execution unit can issue lane change instructions and guide the flow of vehicles. In this way, traffic congestion can be alleviated by implementing the adjusted results. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can have a generating AI perform the implementation of the adjusted results.

[0078] The execution unit can adjust the timing of road signs and signals and activate warning systems. For example, the execution unit can change the content displayed on road signs and adjust the timing of signals. The execution unit can activate warning systems and send warnings to vehicles. At dangerous intersections, the execution unit can prevent accidents by adjusting the timing of signals and sending warnings to vehicles. In this way, accident prevention measures can be implemented by adjusting the timing of road signs and signals and activating warning systems. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can have a generating AI perform the adjustment of the timing of road signs and signals and the activation of warning systems.

[0079] The data collection unit can collect infrastructure condition data from sensors installed on roads and bridges. For example, the data collection unit collects infrastructure condition data using sensors installed on roads and bridges. The data collection unit can collect information on road cracks and bridge deterioration using sensors. The data collection unit can monitor the condition of structures in real time and perform maintenance if an abnormality is detected. This allows for early detection of abnormalities due to aging by collecting infrastructure condition data from sensors installed on roads and bridges. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input infrastructure condition data acquired from sensors into a generating AI and have the generating AI perform data analysis.

[0080] The analysis unit can analyze the collected infrastructure condition data and detect abnormalities due to aging at an early stage. For example, the analysis unit can use a generative AI to analyze the collected infrastructure condition data and identify abnormalities due to aging. The analysis unit can use the generative AI to analyze the infrastructure condition data and evaluate the size of cracks and the degree of deterioration. The analysis unit can also use the generative AI to detect abnormalities due to aging at an early stage and determine the need for maintenance. In this way, abnormalities due to aging can be detected at an early stage by analyzing the collected infrastructure condition data. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without using a generative AI. For example, the analysis unit can input the collected infrastructure condition data into a generative AI and have the generative AI perform the analysis of abnormalities due to aging.

[0081] The adjustment unit can propose an optimal acceleration / deceleration pattern and improve fuel efficiency. The adjustment unit proposes an optimal acceleration / deceleration pattern, for example, using a generative AI. The adjustment unit can use the generative AI to analyze changes in the vehicle's speed and propose an acceleration / deceleration pattern to improve fuel efficiency. The adjustment unit can also use the generative AI to propose an optimal acceleration / deceleration pattern based on the vehicle's driving data and improve fuel efficiency. In this way, fuel efficiency can be improved by proposing an optimal acceleration / deceleration pattern. Some or all of the above processing in the adjustment unit may be performed using a generative AI, for example, or without using a generative AI. For example, the adjustment unit can input vehicle driving data into the generative AI and have the generative AI propose an optimal acceleration / deceleration pattern.

[0082] The adjustment unit can coordinate multiple signals to minimize the number of times vehicles stop. The adjustment unit can coordinate multiple signals, for example, using a generation AI. The adjustment unit can minimize the number of times vehicles stop by having the generation AI synchronize the signals. The adjustment unit can also optimize the timing of signals by having the generation AI communicate with the signals. As a result, by coordinating multiple signals, the number of times vehicles stop can be minimized and traffic flow can be made smoother. Some or all of the above processing in the adjustment unit may be performed using a generation AI, for example, or without a generation AI. For example, the adjustment unit can have the generation AI perform signal synchronization and communication between signals.

[0083] The analysis unit can integrate and analyze traffic flow, accidents, infrastructure status, and environmental data and provide it to policymakers. For example, the analysis unit can use generative AI to integrate and analyze traffic flow, accidents, infrastructure status, and environmental data. The analysis unit can use generative AI to integrate data and evaluate traffic infrastructure. The analysis unit can also use generative AI to provide the results of the integrated analysis to policymakers, thereby promoting data-driven public policy. This allows for the promotion of data-driven public policy through integrated analysis of traffic flow, accidents, infrastructure status, and environmental data. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can have generative AI perform integrated analysis of traffic flow, accidents, infrastructure status, and environmental data.

[0084] The data collection unit can estimate the user's emotions and adjust the timing of traffic infrastructure data collection based on the estimated user emotions. The data collection unit estimates the user's emotions, for example, using an emotion engine or generative AI. If the user is stressed, the data collection unit can increase the frequency of traffic infrastructure data collection to enhance real-time situational awareness. If the user is relaxed, the data collection unit can decrease the collection frequency and collect only the necessary data. If the user is in a hurry, the data collection unit can prioritize the collection of important data to enable a quick response. This allows for more appropriate data collection by adjusting the timing of traffic infrastructure data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The data collection unit can change the frequency of data collection based on specific time periods or events. For example, the data collection unit can use generative AI to adjust the frequency of data collection based on specific time periods or events. During rush hour, the data collection unit can increase the frequency of traffic infrastructure data collection to gain a detailed understanding of changes in traffic flow. During large-scale events, the data collection unit can focus on collecting surrounding traffic infrastructure data to monitor congestion in real time. At night and on holidays, the data collection unit can reduce the collection frequency to collect only the minimum necessary data. This allows for data collection tailored to traffic conditions by changing the frequency of data collection based on specific time periods or events. Some or all of the above processing in the data collection unit may be performed using generative AI, for example, or without generative AI. For example, the data collection unit can have generative AI perform the adjustment of the data collection frequency based on specific time periods or events.

[0086] The data collection unit can collect not only traffic volume but also environmental data such as weather and road conditions. For example, the data collection unit can use a generative AI to collect weather data and road condition data in addition to traffic volume data. The data collection unit can collect weather data in addition to traffic volume data to understand changes in traffic flow during rainy weather. The data collection unit can collect road condition data to monitor road surface conditions and construction information in real time. The data collection unit can collect air quality and noise levels as environmental data to comprehensively evaluate the impact on traffic infrastructure. This makes it possible to comprehensively evaluate traffic infrastructure by collecting not only traffic volume but also environmental data such as weather and road conditions. Some or all of the above processing in the data collection unit may be performed using a generative AI, for example, or without using a generative AI. For example, the data collection unit can have a generative AI perform the collection of traffic volume data, weather data, and road condition data.

[0087] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. The data collection unit estimates the user's emotions using, for example, an emotion engine or generative AI. If the user is stressed, the data collection unit can prioritize collecting data related to traffic congestion. If the user is relaxed, the data collection unit can prioritize collecting environmental data to support a comfortable journey. If the user is in a hurry, the data collection unit can prioritize collecting data related to traffic accidents and obstacles. This allows for the priority collection of more important data by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The data collection unit can prioritize the collection of data from specific regions by considering geographical location information. For example, the data collection unit can use a generative AI to prioritize the collection of data from specific regions by considering geographical location information when collecting traffic infrastructure data. The data collection unit can prioritize the collection of data from urban areas with high traffic volume, which can be used to alleviate traffic congestion. The data collection unit can focus on collecting data from areas with a high incidence of traffic accidents, which can strengthen accident prevention measures. The data collection unit can prioritize the collection of data from areas where infrastructure is aging, which can be reflected in maintenance plans. As a result, by prioritizing the collection of data from specific regions by considering geographical location information, it becomes possible to collect data that is tailored to the traffic conditions of each region. Some or all of the above processing in the data collection unit may be performed using a generative AI, for example, or without using a generative AI. For example, the data collection unit can have a generative AI perform data collection that takes geographical location information into consideration.

[0089] The data collection unit can integrate and collect real-time information from social media. For example, the data collection unit can use generative AI to integrate and collect real-time information from social media when collecting traffic infrastructure data. The data collection unit can analyze posts on social media and collect traffic congestion and accident information in real time. The data collection unit can collect user feedback and use it to improve traffic infrastructure. The data collection unit can collect event information from social media and reflect it in adjusting traffic infrastructure. As a result, by integrating and collecting real-time information from social media, it is possible to respond quickly to changes in traffic conditions. Some or all of the above processing in the data collection unit may be performed using generative AI, for example, or without generative AI. For example, the data collection unit can have generative AI perform the collection of real-time information from social media.

[0090] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit estimates the user's emotions using, for example, an emotion engine or a generative AI. If the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, it becomes possible to provide the user with the most optimal information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The analysis unit can predict current traffic flow and bottleneck locations by referring to past traffic data. For example, the analysis unit uses a generating AI to refer to past traffic data and predict current traffic flow and bottleneck locations. The analysis unit can predict current traffic flow based on past traffic data. The analysis unit can identify bottleneck locations by referring to past traffic data. The analysis unit can also analyze past traffic data and predict the occurrence of traffic congestion. This allows for the prediction of current traffic flow and bottleneck locations by referring to past traffic data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input past traffic data into a generating AI and have the generating AI perform predictions of current traffic flow and bottleneck locations.

[0092] The analysis unit can apply an anomaly detection algorithm to detect abnormal traffic patterns. For example, the analysis unit can use a generative AI to apply an anomaly detection algorithm to detect abnormal traffic patterns. The analysis unit can apply the anomaly detection algorithm to identify the cause of traffic congestion. The analysis unit can use the anomaly detection algorithm to predict the occurrence of traffic accidents. The analysis unit can also apply the anomaly detection algorithm to detect anomalies in traffic flow. This makes it possible to identify the cause of traffic congestion and accidents by detecting abnormal traffic patterns. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can have a generative AI perform the detection of abnormal traffic patterns.

[0093] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated user emotions. The analysis unit estimates the user's emotions using, for example, an emotion engine or generative AI. If the user is feeling stressed, the analysis unit can prioritize displaying analysis results related to traffic congestion. If the user is relaxed, the analysis unit can prioritize displaying analysis results related to environmental data. If the user is in a hurry, the analysis unit can prioritize displaying analysis results related to traffic accidents. In this way, by prioritizing analysis results based on the user's emotions, information important to the user can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The analysis unit can integrate and analyze not only traffic data, but also environmental and socioeconomic data. For example, the analysis unit uses generative AI to integrate and analyze not only traffic data, but also environmental and socioeconomic data. The analysis unit can integrate traffic data and environmental data to comprehensively analyze the causes of traffic congestion. The analysis unit can integrate traffic data and socioeconomic data to propose improvements to traffic infrastructure. The analysis unit can integrate traffic data, environmental data, and socioeconomic data and provide this information to policymakers. This enables a comprehensive evaluation of traffic infrastructure by integrating and analyzing not only traffic data, but also environmental and socioeconomic data. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can have generative AI perform integrated analysis of traffic data, environmental data, and socioeconomic data.

[0095] The analysis unit can improve the accuracy of its analysis by combining different analysis algorithms. For example, the analysis unit can improve the accuracy of its analysis by combining different analysis algorithms using a generative AI. The analysis unit can improve the accuracy of its traffic flow predictions by combining different analysis algorithms. The analysis unit can improve the accuracy of its traffic accident predictions by applying different analysis algorithms. The analysis unit can detect the deterioration of traffic infrastructure at an early stage by combining different analysis algorithms. This allows for improved analysis accuracy by combining different analysis algorithms. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can have a generative AI execute combinations of different analysis algorithms.

[0096] The adjustment unit can estimate the user's emotions and modify the traffic signal and lane adjustment methods based on the estimated user emotions. The adjustment unit estimates the user's emotions, for example, using an emotion engine or generative AI. If the user is stressed, the adjustment unit can adjust the timing of traffic signals to ensure smooth traffic flow. If the user is relaxed, the adjustment unit can change lanes to support comfortable travel. If the user is in a hurry, the adjustment unit can optimize the timing of traffic signals to enable rapid travel. This allows for more appropriate traffic adjustment by modifying the traffic signal and lane adjustment methods based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The adjustment unit can dynamically adjust signal timing and lane changes in accordance with real-time traffic conditions. For example, the adjustment unit can use a generation AI to dynamically adjust signal timing and lane changes in accordance with real-time traffic conditions. The adjustment unit can monitor real-time traffic conditions and dynamically adjust signal timing. The adjustment unit can dynamically change lanes based on real-time traffic data. The adjustment unit can coordinate signal timing and lane changes in accordance with real-time traffic conditions. This allows for smoother traffic flow by dynamically adjusting signal timing and lane changes in accordance with real-time traffic conditions. Some or all of the above-described processes in the adjustment unit may be performed using a generation AI, for example, or without a generation AI. For example, the adjustment unit can have a generation AI perform adjustments to signal timing and lane changes in accordance with real-time traffic conditions.

[0098] The adjustment unit can apply special adjustment algorithms to respond to specific events or emergencies. For example, the adjustment unit can use generative AI to apply special adjustment algorithms to respond to specific events or emergencies. The adjustment unit can specially adjust the timing of traffic signals when large-scale events are held. The adjustment unit can specially change lanes when emergencies occur. The adjustment unit can optimize traffic flow by applying special adjustment algorithms to respond to specific events or emergencies. This allows for the optimization of traffic flow by applying special adjustment algorithms to respond to specific events or emergencies. Some or all of the above processing in the adjustment unit may be performed using, for example, generative AI, or without generative AI. For example, the adjustment unit can have generative AI perform the application of special adjustment algorithms.

[0099] The adjustment unit can estimate the user's emotions and determine the priority of adjustments based on the estimated user emotions. The adjustment unit estimates the user's emotions, for example, using an emotion engine or generative AI. If the user is stressed, the adjustment unit can prioritize alleviating traffic congestion. If the user is relaxed, the adjustment unit can prioritize adjusting environmental data. If the user is in a hurry, the adjustment unit can prioritize preventing traffic accidents. In this way, by determining the priority of adjustments based on the user's emotions, more important adjustments can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The adjustment unit can adjust not only traffic signals and lanes, but also parking lot usage and public transport operation status. For example, the adjustment unit uses a generation AI to adjust not only traffic signals and lanes, but also parking lot usage and public transport operation status. The adjustment unit can adjust the timing of traffic signals to optimize parking lot usage. The adjustment unit can change lanes and adjust public transport operation status. The adjustment unit can comprehensively adjust traffic signals, lanes, parking lots, and public transport operation status. This enables comprehensive traffic management by adjusting not only traffic signals and lanes, but also parking lot usage and public transport operation status. Some or all of the above processing in the adjustment unit may be performed using a generation AI, for example, or without a generation AI. For example, the adjustment unit can have a generation AI perform adjustments to parking lot usage and public transport operation status.

[0101] The adjustment unit can maximize the effectiveness of adjustments by coordinating with different traffic management systems. The adjustment unit can maximize the effectiveness of adjustments by coordinating with different traffic management systems, for example, using generative AI. The adjustment unit can optimize signal timing by coordinating with different traffic management systems. The adjustment unit can optimize lane changes by coordinating with different traffic management systems. The adjustment unit can maximize the effectiveness of traffic flow adjustments by coordinating with different traffic management systems. In this way, the effectiveness of adjustments can be maximized by coordinating with different traffic management systems. Some or all of the above-described processes in the adjustment unit may be performed using, for example, generative AI, or without generative AI. For example, the adjustment unit can have generative AI perform the coordination with different traffic management systems.

[0102] The execution unit can estimate the user's emotions and adjust the display method of the execution results based on the estimated user emotions. The execution unit estimates the user's emotions, for example, using an emotion engine or generative AI. If the user is tense, the execution unit can provide a simple and highly visible display method. If the user is relaxed, the execution unit can provide a display method that includes detailed information. If the user is in a hurry, the execution unit can provide a display method that gets straight to the point. By adjusting the display method of the execution results based on the user's emotions, it becomes possible to provide the user with the most optimal information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The execution unit can provide real-time feedback on the adjusted results and make readjustments as needed. For example, the execution unit can use a generative AI to provide real-time feedback on the adjusted results and make readjustments as needed. The execution unit can provide real-time feedback on the adjusted results and readjust the timing of traffic signals. Based on the adjusted results, the execution unit can readjust lane changes in real time. The execution unit can provide feedback on the adjusted results and adjust traffic infrastructure as needed. This allows for real-time feedback on the adjusted results and enables readjustments as needed. Some or all of the above-described processes in the execution unit may be performed using a generative AI, for example, or without a generative AI. For example, the execution unit can have a generative AI perform the feedback of adjusted results and readjustments.

[0104] The execution unit can apply different execution algorithms depending on specific traffic conditions. For example, the execution unit can use a generative AI to apply different execution algorithms depending on specific traffic conditions. When there is traffic congestion, the execution unit can apply an execution algorithm for congestion mitigation. When a traffic accident occurs, the execution unit can apply an execution algorithm for accident response. When an emergency occurs, the execution unit can apply an execution algorithm for emergency response. In this way, traffic flow can be optimized by applying different execution algorithms depending on specific traffic conditions. Some or all of the above processing in the execution unit may be performed using a generative AI, for example, or without using a generative AI. For example, the execution unit can have a generative AI perform the application of an execution algorithm according to specific traffic conditions.

[0105] The execution unit can estimate the user's emotions and determine the priority of executions based on the estimated user emotions. The execution unit estimates the user's emotions, for example, using an emotion engine or generative AI. If the user is stressed, the execution unit can prioritize traffic congestion mitigation. If the user is relaxed, the execution unit can prioritize environmental data adjustment. If the user is in a hurry, the execution unit can prioritize accident prevention. This allows for prioritizing more important executions by determining the priority of executions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The execution unit can include not only the adjustment of traffic signals and lanes, but also the activation of road signs and warning systems. For example, the execution unit can use generative AI to include the activation of road signs and warning systems, in addition to the adjustment of traffic signals and lanes. In addition to adjusting traffic signals, the execution unit can change the display content of road signs. In addition to changing lanes, the execution unit can activate warning systems. The execution unit can comprehensively adjust traffic signals, lanes, road signs, and warning systems. This enables comprehensive traffic management by including the activation of road signs and warning systems, in addition to the adjustment of traffic signals and lanes. Some or all of the above-described processes in the execution unit may be performed using, for example, generative AI, or without generative AI. For example, the execution unit can have generative AI perform the activation of road signs and warning systems.

[0107] The execution unit can maximize the effectiveness of its execution by coordinating with different traffic management systems. For example, the execution unit can maximize the effectiveness of its execution by coordinating with different traffic management systems using generative AI. The execution unit can optimize signal timing by coordinating with different traffic management systems. The execution unit can optimize lane changes by coordinating with different traffic management systems. The execution unit can maximize the effectiveness of traffic flow adjustment by coordinating with different traffic management systems. Thus, the effectiveness of the execution can be maximized by coordinating with different traffic management systems. Some or all of the above-described processes in the execution unit may be performed using, for example, generative AI, or without generative AI. For example, the execution unit can have generative AI perform the coordination with different traffic management systems.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] In addition to analyzing traffic data, the analysis unit can also analyze user emotion data to understand user reactions to traffic conditions. For example, if a user is feeling stressed, the analysis unit can identify the cause of traffic congestion and suggest measures to reduce stress. If a user is relaxed, it can provide information to support a comfortable journey. Furthermore, if a user is in a hurry, it can suggest the optimal route to enable quick travel. In this way, by analyzing traffic conditions based on user emotions and suggesting appropriate measures, user satisfaction can be improved.

[0110] The adjustment unit can adjust not only traffic signals and lanes, but also the operation of public transportation. For example, it can adjust the timing of traffic signals to optimize bus and train schedules. It can also change lanes and create dedicated lanes for public transportation to ensure smoother operation. Furthermore, it can monitor the operation of public transportation in real time and adjust schedules as needed. This allows for comprehensive traffic management by adjusting not only traffic signals and lanes, but also the operation of public transportation.

[0111] The execution unit can adjust its execution method based on the user's emotions when executing the adjusted results. For example, if the user is stressed, the execution unit can adjust the timing of traffic signals to ensure smooth traffic flow. If the user is relaxed, it can change lanes to support a comfortable journey. Furthermore, if the user is in a hurry, it can optimize the timing of traffic signals to enable faster travel. This allows for more appropriate traffic management by adjusting the execution method based on the user's emotions.

[0112] The data collection unit can collect not only traffic infrastructure data but also data on surrounding commercial facilities and tourist attractions. For example, it can collect information on the operating hours and congestion levels of commercial facilities to predict changes in traffic flow. It can also collect event information at tourist attractions to understand traffic conditions during events. Furthermore, by collecting data on commercial facilities and tourist attractions, it can be used to optimize traffic infrastructure. In this way, by collecting data on surrounding commercial facilities and tourist attractions in addition to traffic infrastructure data, comprehensive traffic management becomes possible.

[0113] In addition to analyzing traffic data, the analysis unit can also analyze user emotion data to understand user reactions to traffic conditions. For example, if a user is feeling stressed, the analysis unit can identify the cause of traffic congestion and suggest measures to reduce stress. If a user is relaxed, it can provide information to support a comfortable journey. Furthermore, if a user is in a hurry, it can suggest the optimal route to enable quick travel. In this way, by analyzing traffic conditions based on user emotions and suggesting appropriate measures, user satisfaction can be improved.

[0114] The adjustment unit can adjust not only traffic signals and lanes, but also parking lot usage. For example, it can adjust the timing of traffic signals to optimize parking lot usage. It can also change lanes to facilitate access to parking lots. Furthermore, it can monitor parking lot usage in real time and adjust parking lot usage as needed. This enables comprehensive traffic management by adjusting parking lot usage in addition to traffic signals and lanes.

[0115] The execution unit can adjust its execution method based on the user's emotions when executing the adjusted results. For example, if the user is stressed, the execution unit can adjust the timing of traffic signals to ensure smooth traffic flow. If the user is relaxed, it can change lanes to support a comfortable journey. Furthermore, if the user is in a hurry, it can optimize the timing of traffic signals to enable faster travel. This allows for more appropriate traffic management by adjusting the execution method based on the user's emotions.

[0116] The data collection unit can collect not only traffic infrastructure data but also data on surrounding commercial facilities and tourist attractions. For example, it can collect information on the operating hours and congestion levels of commercial facilities to predict changes in traffic flow. It can also collect event information at tourist attractions to understand traffic conditions during events. Furthermore, by collecting data on commercial facilities and tourist attractions, it can be used to optimize traffic infrastructure. In this way, by collecting data on surrounding commercial facilities and tourist attractions in addition to traffic infrastructure data, comprehensive traffic management becomes possible.

[0117] In addition to analyzing traffic data, the analysis unit can also analyze user emotion data to understand user reactions to traffic conditions. For example, if a user is feeling stressed, the analysis unit can identify the cause of traffic congestion and suggest measures to reduce stress. If a user is relaxed, it can provide information to support a comfortable journey. Furthermore, if a user is in a hurry, it can suggest the optimal route to enable quick travel. In this way, by analyzing traffic conditions based on user emotions and suggesting appropriate measures, user satisfaction can be improved.

[0118] The adjustment unit can adjust not only traffic signals and lanes, but also parking lot usage. For example, it can adjust the timing of traffic signals to optimize parking lot usage. It can also change lanes to facilitate access to parking lots. Furthermore, it can monitor parking lot usage in real time and adjust parking lot usage as needed. This enables comprehensive traffic management by adjusting parking lot usage in addition to traffic signals and lanes.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The collection unit collects traffic infrastructure data. The collection unit can collect traffic infrastructure data from sensors and cameras, and can collect vehicle detection data on roads, environmental data, traffic condition image data, and GPS data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses a generation AI to grasp traffic flow and bottleneck locations in real time and predict traffic flow. Step 3: The adjustment unit adjusts traffic signals and lanes based on the analysis results obtained by the analysis unit. The adjustment unit adjusts the timing of traffic signals and changes lanes. Step 4: The execution unit executes the results adjusted by the adjustment unit. The execution unit executes the adjusted signal timing and lane change instructions, operates the signal control system to change the signal timing, and guides the flow of vehicles.

[0121] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0124] Each of the multiple elements described above, including the collection unit, analysis unit, adjustment unit, and execution unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects traffic infrastructure data using the camera 42 and sensors of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data using generated AI to grasp traffic flow and bottleneck locations in real time. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12, which adjusts traffic signals and lanes based on the analysis results. The execution unit is implemented by the control unit 46A of the smart device 14, which executes the adjusted signal timing and lane change instructions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the data collection unit, analysis unit, adjustment unit, and execution unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects traffic infrastructure data using the camera 42 and sensors of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data using generated AI to grasp traffic flow and bottleneck locations in real time. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12, which adjusts traffic signals and lanes based on the analysis results. The execution unit is implemented by the control unit 46A of the smart glasses 214, which executes the adjusted signal timing and lane change instructions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the collection unit, analysis unit, adjustment unit, and execution unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects traffic infrastructure data using the camera 42 and sensors of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data using generated AI to grasp traffic flow and bottleneck locations in real time. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12, which adjusts traffic signals and lanes based on the analysis results. The execution unit is implemented by the control unit 46A of the headset terminal 314, which executes the adjusted signal timing and lane change instructions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the 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.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the collection unit, analysis unit, adjustment unit, and execution unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects traffic infrastructure data using the camera 42 and sensors of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data using generated AI to grasp traffic flow and bottleneck locations in real time. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12, which adjusts traffic signals and lanes based on the analysis results. The execution unit is implemented by the control unit 46A of the robot 414, which executes the adjusted signal timing and lane change instructions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0174] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) The data collection department collects transportation infrastructure data, An analysis unit analyzes the data collected by the aforementioned collection unit, An adjustment unit that adjusts traffic signals and lanes based on the analysis results obtained by the aforementioned analysis unit, The system comprises an execution unit that executes the results adjusted by the adjustment unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect traffic infrastructure data from sensors and cameras. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to understand traffic flow and bottleneck locations in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The adjustment unit is, Adjusting the timing of traffic signals and lane changes The system described in Appendix 1, characterized by the features described herein. (Note 5) The execution unit is, Implement the adjusted results to alleviate traffic congestion. The system described in Appendix 1, characterized by the features described herein. (Note 6) The execution unit is, Adjust the timing of road signs and traffic lights to activate warning systems. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Collect infrastructure condition data from sensors installed on roads and bridges. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The collected infrastructure status data is analyzed to detect abnormalities caused by aging at an early stage. The system described in Appendix 1, characterized by the features described herein. (Note 9) The adjustment unit is, It proposes the optimal acceleration and deceleration pattern to improve fuel efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 10) The adjustment unit is, By coordinating multiple signals, the number of times vehicles need to stop is minimized. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, We integrate and analyze traffic flow, accidents, infrastructure conditions, and environmental data, and provide it to policymakers. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of traffic infrastructure data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is The frequency of data collection can be changed based on specific time periods or events. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is In addition to traffic volume, environmental data such as weather and road conditions will also be collected. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is Prioritize the collection of data from specific regions, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is Integrate and collect real-time information from social media. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, By referring to past traffic data, we can predict current traffic flow and the location of bottlenecks. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, Apply an anomaly detection algorithm to detect abnormal traffic patterns. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, We will integrate and analyze not only traffic data, but also environmental and socioeconomic data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, Combining different analysis algorithms improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 24) The adjustment unit is, The system estimates user emotions and modifies traffic signals and lane adjustments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The adjustment unit is, Dynamically adjusts traffic signal timing and lane changes based on real-time traffic conditions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The adjustment unit is, Apply special coordination algorithms to respond to specific events or emergencies. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, It estimates the user's emotions and determines the priority of adjustments based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The adjustment unit is, It adjusts not only traffic signals and lanes, but also parking lot usage and public transportation operations. The system described in Appendix 1, characterized by the features described herein. (Note 29) The adjustment unit is, Maximize the effectiveness of coordination by coordinating with different traffic management systems. The system described in Appendix 1, characterized by the features described herein. (Note 30) The execution unit is, It estimates the user's emotions and adjusts how the results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The execution unit is, The adjusted results are fed back in real time, and readjustments are made as needed. The system described in Appendix 1, characterized by the features described herein. (Note 32) The execution unit is, Apply different execution algorithms depending on specific traffic conditions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The execution unit is, It estimates the user's emotions and determines the priority of actions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The execution unit is, This includes not only traffic signals and lane adjustments, but also the operation of road signs and warning systems. The system described in Appendix 1, characterized by the features described herein. (Note 35) The execution unit is, Maximize the effectiveness of implementation by coordinating with different traffic management systems. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects transportation infrastructure data, An analysis unit analyzes the data collected by the aforementioned collection unit, An adjustment unit that adjusts traffic signals and lanes based on the analysis results obtained by the aforementioned analysis unit, The system comprises an execution unit that executes the results adjusted by the adjustment unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect traffic infrastructure data from sensors and cameras. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to understand traffic flow and bottleneck locations in real time. The system according to feature 1.

4. The adjustment unit is, Adjusting the timing of traffic signals and lane changes The system according to feature 1.

5. The execution unit is, Implement the adjusted results to alleviate traffic congestion. The system according to feature 1.

6. The execution unit is, Adjust the timing of road signs and traffic lights to activate warning systems. The system according to feature 1.

7. The aforementioned collection unit is Collect infrastructure condition data from sensors installed on roads and bridges. The system according to feature 1.

8. The aforementioned analysis unit, The collected infrastructure status data is analyzed to detect abnormalities caused by aging at an early stage. The system according to feature 1.

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