system

Abstract

We provide the system. [Solution] Means of collecting traffic data, A means of analyzing collected data and predicting the control timing of traffic signals, Means for optimizing the state of a traffic signal based on predicted control timing, A means of notifying users of optimized traffic signal information, A system that includes this.

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 in 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] The current control system of traffic signal lights cannot fully consider traffic flow and driver convenience, resulting in traffic congestion, unnecessary acceleration and deceleration, deterioration of fuel consumption, and driver fatigue. This problem also increases the environmental load. There is a need to develop a system that supports drivers in predicting signal changes and optimizes traffic flow.

Means for Solving the Problems

[0005] The present invention relates to a system comprising means for collecting traffic data, means for analyzing the collected data and predicting the control timing of traffic signals, means for optimizing the state of traffic signals based on the predicted control timing, and means for notifying the user of the optimized traffic signal information. In addition, the system includes means for monitoring traffic flow and collecting feedback on the user's driving behavior, and by updating the analysis and prediction means using the collected feedback data to improve accuracy, it is possible to alleviate traffic congestion and improve fuel efficiency.

[0006] "Traffic data" refers to information used to understand traffic conditions, such as the flow of traffic, the number and speed of vehicles, and the current status of traffic signals.

[0007] "Analysis" is the process of using collected traffic data to evaluate current traffic conditions and predict future changes to traffic signals.

[0008] "Traffic light control timing" refers to control parameters related to the timing and sequence in which traffic lights change from green to yellow to red.

[0009] "Optimization" refers to adjusting the operation of traffic signals based on collected and analyzed data in order to maximize traffic flow and alleviate traffic congestion.

[0010] "Users" refers to drivers and other stakeholders on a traffic route who receive predictive information provided by the system and adjust their driving behavior according to traffic conditions.

[0011] "Monitoring" refers to the process of monitoring traffic conditions and changes in user driving behavior in real time and collecting that data.

[0012] "Feedback" refers to data that records user reactions and changes to the information and optimization measures provided by the system, and is used to improve the system in the future.

[0013] "Improving accuracy" means enhancing the system's predictive and optimization capabilities to achieve more precise signal control and traffic flow optimization. [Brief explanation of the drawing]

[0014] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] The 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.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] The present invention involves using an AI agent to collect and analyze traffic data in real time in order to optimize traffic signal management. The system includes a server, a user terminal, and a signal control device.

[0036] The server acquires information from traffic data collection sensors and cameras. This allows the server to collect data such as the number of vehicles, their speeds, and traffic flow at specific intersections. The server then passes this data to an AI agent, which analyzes the traffic situation and predicts when the traffic signals should change next.

[0037] The AI ​​agent integrates and analyzes historical and real-time data. For example, based on traffic flow patterns at an intersection during the morning rush hour, the server might determine that keeping the traffic light green longer than usual would reduce congestion. Based on this prediction, the server sends appropriate control commands to the traffic signal control system to adjust the traffic light display.

[0038] Users receive predictive information from an AI agent through their device. The device uses voice and screen to communicate information to the driver, such as, "The next traffic light will turn green in 20 seconds." This allows drivers to avoid excessive acceleration or sudden braking and to drive smoothly. For example, when a user approaches an intersection, the device notifies them, "Slowing down will allow you to time your drive to coincide with the traffic light turning green," thereby contributing to smoother traffic flow.

[0039] Furthermore, the server monitors the user's driving behavior and collects feedback information. This data is used to improve the overall accuracy of the system and is reflected in subsequent predictive models. In this way, the system is continuously improved, contributing to the reduction of traffic congestion and improved fuel efficiency.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server collects traffic data. The server acquires data in real time from sensors and cameras installed at intersections and records the number of vehicles, their speeds, the current status of traffic lights, and other information in a database.

[0043] Step 2:

[0044] The server preprocesses the data. The server ensures data quality by removing outliers from the collected data and converting it into a format suitable for analysis.

[0045] Step 3:

[0046] The server uses an AI agent to analyze traffic conditions. The server provides pre-processed data to the AI ​​agent, which analyzes traffic flow and past signal patterns to predict signal changes.

[0047] Step 4:

[0048] The server optimizes the signals. Based on the analysis results of the AI ​​agent, the server adjusts the timing of the traffic light changes and optimizes the display state of the remotely controllable traffic lights.

[0049] Step 5:

[0050] The terminal notifies the user of information. The terminal receives optimization information from the server and provides the user with information such as "The next signal will turn red in 10 seconds" through voice or screen display.

[0051] Step 6:

[0052] The user adjusts their driving behavior. Based on notifications from their device, the user adjusts their driving pace to avoid sudden acceleration and deceleration and to ensure smooth driving.

[0053] Step 7:

[0054] The server collects feedback. The server monitors the user's driving patterns and changes in traffic conditions, and collects feedback data based on that.

[0055] Step 8:

[0056] The server updates the AI ​​model. The server uses the collected feedback data to retrain the AI ​​agent, improving the prediction accuracy of signal control.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] It is necessary to improve the efficiency and accuracy of traffic signal management to alleviate traffic congestion while ensuring smooth traffic flow. Existing systems are insufficient in real-time data analysis and signal control optimization, and the provision of appropriate information to drivers is limited, so further improvements are needed.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes a device for collecting traffic information, a device for analyzing the collected information and predicting the operating timing of the control device, and a device for optimizing the state of the control device based on the predicted operating timing. This enables highly accurate signal control in real time, realizing effective information provision to drivers and smooth management of traffic flow.

[0062] A "device for collecting traffic information" is a device that has the function of acquiring traffic-related data using sensors and cameras and storing it in digital format.

[0063] A "device that analyzes and predicts the timing of control device operation" is a device that analyzes collected traffic data and executes an algorithm to predict the timing of signal system operation.

[0064] A "device for optimizing the state of a control device" is a device that generates commands to adjust the operation of a signaling system based on predicted operating timings, thereby achieving optimal traffic conditions.

[0065] A "device for monitoring and collecting data on the user's driving behavior" is a device that detects the user's driving patterns and records driving data and driving behavior.

[0066] A "device that updates analysis and prediction equipment and improves accuracy using data" is a device that utilizes collected feedback data to dynamically improve analysis and prediction algorithms, thereby increasing the overall system accuracy.

[0067] This invention provides a system for collecting and analyzing traffic information in real time in order to optimize traffic signal management. The system mainly consists of a server, terminals, and signal control devices.

[0068] The server collects traffic information using sensors and cameras. This includes various sensors installed at intersections that measure the number of vehicles, their speeds, and traffic flow in real time. The collected data is sent to the server and then passed on to an AI agent. The AI ​​agent uses a generative AI model to analyze past traffic patterns and real-time information to predict the optimal timing for traffic signal operation. For example, it can decide to extend the green light duration to alleviate congestion during rush hour.

[0069] The terminal's role is to notify the user of signal timing prediction information from the server. For example, when a driver approaches an intersection, the terminal notifies the driver that "maintaining your speed will allow you to time your arrival with the green light," enabling smoother driving. This information is provided through the terminal's display and voice assistant. The terminal also monitors the user's driving behavior and collects feedback data to achieve even more accurate traffic management.

[0070] Furthermore, the server uses the collected feedback data to update its analysis model to improve the overall accuracy of the system. This continuous improvement process leads to optimization of signal control, which is expected to result in reduced traffic congestion and improved fuel efficiency.

[0071] An example of a prompt might be: "At 7:30 AM, predict the optimal signal timing at the XX intersection based on real-time traffic flow data. Also, generate advice to help users navigate the intersection smoothly." This prompt allows the AI ​​model to provide data for optimal signal control based on specific traffic conditions.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The server uses a device that collects real-time traffic information from sensors and cameras installed at intersections. Input data includes the number of vehicles, their speed, and traffic flow. This data is recorded in a log file and prepared for the next analysis step.

[0075] Step 2:

[0076] The server passes the collected traffic data to the AI ​​agent. The AI ​​agent uses a generative AI model to analyze the data and predicts the timing of traffic light operation based on the results. The input consists of collected real-time data and historical traffic pattern data. The analysis results in the output of optimized control timing, such as increasing the green light duration.

[0077] Step 3:

[0078] The server sends a command to the signal control device based on the analysis results. Specifically, it instructs the control device to extend the green light duration by 30 seconds. The input in this step is the optimized data output from the AI ​​agent, which adjusts the state of the traffic lights.

[0079] Step 4:

[0080] The terminal retrieves signal timing prediction information from the server and notifies the user. The terminal's input is the prediction information from the server, and its output is voice guidance and screen display for the driver. Specifically, the terminal notifies the driver, "If you maintain your speed, you will be able to time your arrival to coincide with the green light," allowing the driver to pass through the intersection smoothly.

[0081] Step 5:

[0082] The server monitors the user's driving behavior and collects feedback data. Inputs include information such as the user's driving speed and frequency of stopping at traffic lights. This data is used to update the analysis model, improving the system's accuracy and influencing future predictions. The output is presented as an improved traffic control algorithm.

[0083] (Application Example 1)

[0084] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0085] Managing traffic signals in cities is a critical issue from the perspective of traffic congestion and fuel efficiency. Conventional traffic signal systems often rely on historical data and static timing, making them incapable of responding to real-time traffic conditions. Furthermore, drivers lack the information necessary to efficiently manage waiting times and acceleration / deceleration, which further exacerbates congestion. To improve this situation, a system is needed that dynamically optimizes signals based on real-time traffic data and provides drivers with effective information.

[0086] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0087] In this invention, the server includes means for collecting traffic data, means for analyzing the collected data and predicting the control timing of the signal control device, means for optimizing the state of the signal control device based on the predicted control timing, and means for notifying the user of the timing of changes in traffic signals by voice and advising on the optimal driving speed. This enables efficient management of traffic flow in real time, and drivers can take appropriate driving actions to alleviate congestion and improve fuel efficiency.

[0088] "Traffic data" refers to information about moving objects such as the number of vehicles at intersections and on roads, their speed, and traffic flow.

[0089] A "traffic signal control device" is a device that controls the display of traffic signals and manages traffic flow at intersections and on roads.

[0090] "Predictive means" refers to functions or methods for estimating future states or events based on predetermined data.

[0091] "Voice notification means" refers to functions or devices that transmit information to users using voice.

[0092] "Driving advice" refers to instructions and suggestions provided to drivers to ensure safe and efficient travel, including instructions regarding appropriate speed and behavior.

[0093] The system for carrying out this invention includes a server, a terminal, and a signal control device. The server collects traffic data using sensors and cameras installed at intersections and on roads. This includes the number of vehicles, speed, and traffic flow. The system also utilizes Python and data analysis libraries such as Tensorflow® for data collection.

[0094] The server analyzes collected traffic data and uses a generative AI model to predict the optimal control timing for signal control devices in real time. This AI model integrates and analyzes historical and real-time data to achieve efficient signal control.

[0095] The terminal provides voice notifications to the user (driver) based on information from the server. Specifically, it provides voice notifications to the driver regarding the optimal speed and timing of traffic light changes, assisting with driving. This voice notification function allows drivers to optimize acceleration and deceleration, which can help alleviate traffic congestion and improve fuel efficiency.

[0096] As a concrete example, the terminal might advise the driver via voice, "There are 23 seconds until the next traffic light. If you maintain a speed of 55 km / h, you can pass through without stopping." In this way, drivers can anticipate traffic flow and contribute to smoother traffic flow.

[0097] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0098] Step 1:

[0099] The server collects traffic data from sensors and cameras installed along the roads. This data includes vehicle count, speed, and traffic flow. The input is real-time sensor and camera data, and the output is the collected raw traffic data.

[0100] Step 2:

[0101] The server analyzes the collected traffic data. It cleanses the data and removes noise using tools such as Python or TensorFlow. The input for this step is the raw traffic data collected in step 1, and the output is data that has been formatted for analysis.

[0102] Step 3:

[0103] The server uses a generative AI model to predict the optimal control timing for the signal control device. Analyzed data is input into the model to calculate the timing for optimizing the signal state. The input here is formatted traffic data, and the output is an instruction for the control timing to the signal control device.

[0104] Step 4:

[0105] The terminal provides voice notifications to the user based on control timing information received from the server. Specifically, it provides real-time advice to the driver using prompts such as, "23 seconds until the next traffic light. If you maintain a speed of 55 km / h, you can pass through the traffic light without stopping." The input here is control timing information from the server, and the output is a voice notification to the user.

[0106] Step 5:

[0107] The user receives notifications from the device and takes the recommended speed and driving actions. This helps avoid excessive acceleration and braking, improving fuel efficiency. The input is voice advice from the device, and the output is the actual driving actions.

[0108] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0109] This invention combines a system for optimizing the control of traffic signals with an emotion engine that recognizes user emotions. The system includes a server, a user terminal, a signal control device, and an emotion engine.

[0110] The server collects traffic data in real time from sensors and cameras installed at intersections. The server passes the data to an AI agent, which analyzes traffic flow and predicts the timing of traffic signal changes. This information is sent to the traffic signal control system and used to implement optimized traffic signals.

[0111] Furthermore, the user terminal is equipped with an emotion engine that detects emotions by analyzing the driver's facial expressions and tone of voice through the camera and microphone. For example, if the user is feeling stressed, the emotion engine detects this state and provides appropriate feedback to the system.

[0112] The server takes feedback from the emotion engine into account and adjusts traffic signals and user notifications accordingly. For example, if it determines that a driver is irritated, a smoother signal pattern will be suggested. The user terminal also provides driving advice that takes the user's emotional state into account. For example, it might play a voice message such as, "Take a deep breath and relax while you wait."

[0113] Furthermore, the server continuously monitors data on the user's emotional state and driving conditions, and collects feedback. This data is used to improve the AI ​​agent, contributing to increased accuracy in traffic signal control. In this way, the system not only improves traffic efficiency but also provides comfortable driving support for the user.

[0114] The following describes the processing flow.

[0115] Step 1:

[0116] The server collects traffic data. Using sensors and cameras installed at intersections, the server acquires real-time data on the number of vehicles, their speeds, and traffic flow, and stores it in a database.

[0117] Step 2:

[0118] The server preprocesses the traffic data it acquires. The server removes noise and outliers from the acquired data and processes it into a format suitable for analysis.

[0119] Step 3:

[0120] The server uses an AI agent to analyze traffic conditions. Based on pre-processed data, the server analyzes traffic flow and predicts when the next traffic light will change.

[0121] Step 4:

[0122] The device recognizes the user's emotions using an emotion engine. The device uses an in-car camera and microphone to capture the user's facial expressions and voice, and the emotion engine analyzes emotions such as stress, frustration, and relaxation.

[0123] Step 5:

[0124] The server optimizes signals and adjusts notifications. The server considers the AI ​​agent's analysis results and emotional data from the terminal to adjust the timing of traffic light switching and send instructions to the signal control unit. It also customizes the content of information provided to the user.

[0125] Step 6:

[0126] The device notifies the user of information tailored to their emotional state. The device provides messages appropriate to the user's emotional state and sends notifications that include specific advice, such as, "You have 8 seconds until the next traffic light. Please drive calmly."

[0127] Step 7:

[0128] The user adjusts their driving behavior. Based on information from their device, the user strives to maintain a reasonable driving pace, resulting in safe and smooth driving.

[0129] Step 8:

[0130] The server collects feedback. The server records changes in the user's driving behavior and emotional state, and uses this data to retrain the AI ​​model, contributing to improved system performance.

[0131] (Example 2)

[0132] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0133] In modern transportation systems, controlling traffic signals is crucial for smooth traffic flow. However, traditional methods have struggled to optimize signals while considering traffic conditions and the emotional state of individual drivers. As a result, congestion and driver stress have not always been mitigated. Furthermore, the lack of mechanisms to utilize real-time feedback on changing traffic conditions and driver emotions has created a need to improve both traffic efficiency and safety.

[0134] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0135] In this invention, the server includes means for acquiring traffic information, means for analyzing the acquired information and predicting the control timing of the signal device, and means for detecting the user's emotional state. This makes it possible to optimize signal control by simultaneously considering traffic conditions and the driver's emotional state, thereby improving traffic efficiency and driver comfort.

[0136] "Traffic information" refers to data that shows the state of traffic flow, including the number of vehicles, their speed, and the length of queues waiting at traffic lights.

[0137] "Traffic signaling equipment" refers to traffic lights used to control the flow of traffic at intersections and other locations.

[0138] "Control timing" refers to the setting of the timing for when traffic signals change, and is optimized to ensure a smooth flow of traffic.

[0139] "User emotional state" refers to information that indicates the user's psychological state and reactions, and is used to evaluate things like stress levels and calmness.

[0140] "Feedback" refers to response information regarding the user's driving behavior and emotional state, which is useful for improving the system and increasing the accuracy of signal control.

[0141] This invention aims to achieve optimal signal control in a traffic signal control system by combining traffic flow and driver emotions. Specifically, it is configured as a system including a server, terminals, and control devices such as traffic signals.

[0142] The server first collects traffic information through various sensors and cameras installed at intersections. This information includes data such as vehicle flow, speed, and the length of queues of cars waiting at traffic lights. Sensor technology and high-resolution cameras are used to collect this data.

[0143] The collected data is analyzed by an AI agent on the server. The AI ​​agent uses machine learning algorithms to model traffic conditions and predict the timing of the next traffic signal. The software required to determine the timing of traffic signal control includes statistical analysis tools and a machine learning environment.

[0144] Meanwhile, the user terminal is equipped with an emotion engine that analyzes the driver's facial expressions and voice tone through the camera and microphone. This analysis detects the user's emotional state. This technology is realized using computer vision and voice analysis algorithms.

[0145] The server takes into account the emotional state obtained from the user's terminal and adjusts traffic signals and provides appropriate notifications to the user. For example, if congestion is expected or the user's emotional state indicates stress, a signal pattern is suggested to allow for smoother traffic flow. The user terminal also provides voice advice such as, "Take a deep breath and relax while you wait."

[0146] Furthermore, the system continuously collects feedback and uses it to improve the accuracy of signal control by enhancing the AI ​​agent. This method simultaneously achieves traffic efficiency and a comfortable driving experience for users.

[0147] An example of a prompt would be, "How should this system use real-time driver sentiment analysis and traffic data to adjust traffic light timing and optimize traffic flow?"

[0148] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0149] Step 1:

[0150] The server collects traffic information from sensors and cameras installed at intersections. Inputs include the number of vehicles, their speed, and the length of queues of cars waiting at traffic lights. By collecting this data, the current traffic situation can be understood. Specifically, sensors detect vehicle movement, and cameras record the footage.

[0151] Step 2:

[0152] The server passes the collected traffic data to the AI ​​agent, which analyzes the traffic flow. The input is the data obtained in step 1, and the output of the analysis is a prediction of the next signal timing. The AI ​​agent applies a machine learning algorithm to model the traffic flow. Specifically, the AI ​​analyzes the data patterns during peak times and derives the appropriate timing for signal control.

[0153] Step 3:

[0154] The server transmits the analysis results to the signal control unit, which then adjusts the timing of the traffic lights. The predicted signal timing, which is the output of step 2, is used as input, and the optimized signal control is performed as output. Specifically, the signal control unit adjusts the timing of the red and green lights to ensure smooth traffic flow.

[0155] Step 4:

[0156] The user terminal uses an emotion engine to detect the driver's emotions. Inputs include facial expression data and voice tone from the terminal's camera and microphone, and output is the user's emotional state. Specifically, the camera captures the driver's face, and analysis software determines the emotion.

[0157] Step 5:

[0158] The user terminal sends the detected emotional state to the server, which uses it as feedback information. The input is the emotional state obtained in step 4, and the output is a suggested additional adjustment for signal control. Specifically, the server receives the emotional state and regenerates the suggested adjustment for signal control.

[0159] Step 6:

[0160] The server provides notifications and adjustments to the user and signals based on their emotional state. The input is the feedback information from step 5, and the output is updated signal patterns and user notifications. Specifically, a message encouraging relaxation is sent to the user through the device.

[0161] Step 7:

[0162] The server continuously monitors traffic conditions and user feedback, accumulating this data to improve the system's accuracy. Inputs include real-time traffic data and feedback, while output contributes to improving the AI ​​agent. Specifically, new data is incorporated into the AI ​​model, optimizing the control algorithm.

[0163] (Application Example 2)

[0164] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0165] Conventional traffic signal control systems optimize signals using only traffic data, failing to consider the impact of drivers' emotional states on traffic flow. As a result, emotions such as stress and frustration could negatively affect driving behavior. Therefore, there was a need to develop a traffic signal control system that takes drivers' emotions into account.

[0166] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0167] In this invention, the server includes means for collecting traffic data, means for recognizing the driver's emotions, and means for notifying the user of optimized traffic signal information and providing driving advice. This enables real-time traffic signal control that takes into account the driver's emotional state.

[0168] "Means of collecting traffic data" refers to devices and methods that use sensors and cameras installed at intersections and on roads to collect information about traffic conditions and vehicle flow in real time.

[0169] "Means for predicting the timing of traffic signal control" refers to devices or methods for analyzing collected traffic data and calculating the optimal timing for signal changes at each intersection.

[0170] "Means for optimizing the state of traffic signals" refers to devices or methods for controlling and setting traffic signals to maximize traffic efficiency based on predicted control timing.

[0171] "Means of recognizing a driver's emotions" refers to devices and methods that analyze a driver's facial expressions and voice via cameras and microphones to infer and judge their emotional state.

[0172] "Means for analyzing recognized emotional information and further optimizing signal control" refers to devices or methods that adjust the control patterns of traffic signals based on driver emotional data to make traffic flow smoother.

[0173] "Means of notifying users and providing driving advice" refers to devices and methods that inform drivers of optimized traffic signal information and safe and comfortable driving advice through voice or visual means.

[0174] A system implementing this invention includes a server equipped with the necessary functions to collect and analyze traffic data and optimize traffic signal control, and a user terminal for recognizing drivers' emotions and utilizing that information.

[0175] The server collects traffic data using sensors and cameras installed at intersections. This data is analyzed in real time and processed by an AI agent to evaluate traffic flow. Based on the analysis results, control patterns are generated to optimize the timing of traffic light control. Machine learning frameworks such as Python and TensorFlow are used for processing by the AI ​​agent.

[0176] Meanwhile, on the user's device, an emotion engine operates. This engine collects the driver's facial expressions and voice tone via cameras and microphones built into the smartphone or in-car system, and recognizes their emotions. OpenCV can be used for image processing, and machine learning frameworks such as TensorFlow are used for voice analysis.

[0177] This allows the server to provide users with advice based on their emotional state, in addition to controlling traffic signals. For example, if the system detects that a driver is stressed, it can adjust the signal control pattern to facilitate smoother traffic flow. An actual voice message might be something like, "Take a deep breath and relax."

[0178] An example of a prompt for a generative AI model would be, "Generate the optimal traffic signal pattern when the driver's stress level is high."

[0179] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0180] Step 1:

[0181] The server collects real-time traffic data from sensors and cameras installed at intersections. Inputs include video and numerical data from sensors and cameras, which are converted into digital signals. Outputs are recorded as traffic flow and vehicle location information.

[0182] Step 2:

[0183] The server analyzes collected traffic data using an AI agent. It receives digitized traffic information as input and analyzes traffic patterns using an AI model (such as TensorFlow). The output is predictive data regarding traffic signal control timing. Specific operations include detecting traffic volume peaks and congestion levels.

[0184] Step 3:

[0185] The server optimizes the signal change pattern based on predicted control timing. It uses the analysis results of an AI agent as input to perform appropriate timing calculations. The output is an optimized signal schedule. Its operation involves generating a smooth signal change sequence.

[0186] Step 4:

[0187] The device collects facial expressions and voice data from the driver's smartphone or in-vehicle system via cameras and microphones. The input is real-time video and audio data, which is sent to the emotion engine. The output is data indicating the driver's emotional state. Specific operations include emotion estimation using a facial recognition algorithm.

[0188] Step 5:

[0189] The terminal transmits the driver's emotional state to a server, which is used as feedback for further optimization of signal control. The input is emotional data sent from the terminal, and the output is an adaptive signal pattern corresponding to the emotion. In this step, a method is employed to analyze the emotional data and incorporate it into the traffic signal control.

[0190] Step 6:

[0191] The user terminal provides drivers with optimized signal information and emotion-based advice. Inputs include an optimized signal schedule and driver emotion information, while output is visual or audio advice. Specific operations include a process of generating messages using speech synthesis functionality.

[0192] Step 7:

[0193] The server updates the AI ​​agent using the obtained driver emotional state and traffic data to improve accuracy. The input is feedback data, which is used as training data for the generating AI model. The output is the improved predictive model. The process involves retraining the AI ​​model and tuning its parameters.

[0194] 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.

[0195] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0196] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0197] [Second Embodiment]

[0198] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0199] 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.

[0200] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0201] 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.

[0202] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0203] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0204] 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.

[0205] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0206] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0207] The 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.

[0208] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0209] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0210] The present invention involves using an AI agent to collect and analyze traffic data in real time in order to optimize traffic signal management. The system includes a server, a user terminal, and a signal control device.

[0211] The server acquires information from traffic data collection sensors and cameras. This allows the server to collect data such as the number of vehicles, their speeds, and traffic flow at specific intersections. The server then passes this data to an AI agent, which analyzes the traffic situation and predicts when the traffic signals should change next.

[0212] The AI ​​agent integrates and analyzes historical and real-time data. For example, based on traffic flow patterns at an intersection during the morning rush hour, the server might determine that keeping the traffic light green longer than usual would reduce congestion. Based on this prediction, the server sends appropriate control commands to the traffic signal control system to adjust the traffic light display.

[0213] Users receive predictive information from an AI agent through their device. The device uses voice and screen to communicate information to the driver, such as, "The next traffic light will turn green in 20 seconds." This allows drivers to avoid excessive acceleration or sudden braking and to drive smoothly. For example, when a user approaches an intersection, the device notifies them, "Slowing down will allow you to time your drive to coincide with the traffic light turning green," thereby contributing to smoother traffic flow.

[0214] Furthermore, the server monitors the user's driving behavior and collects feedback information. This data is used to improve the overall accuracy of the system and is reflected in subsequent predictive models. In this way, the system is continuously improved, contributing to the reduction of traffic congestion and improved fuel efficiency.

[0215] The following describes the processing flow.

[0216] Step 1:

[0217] The server collects traffic data. The server acquires data in real time from sensors and cameras installed at intersections and records the number of vehicles, their speeds, the current status of traffic lights, and other information in a database.

[0218] Step 2:

[0219] The server preprocesses the data. The server ensures data quality by removing outliers from the collected data and converting it into a format suitable for analysis.

[0220] Step 3:

[0221] The server uses an AI agent to analyze traffic conditions. The server provides pre-processed data to the AI ​​agent, which analyzes traffic flow and past signal patterns to predict signal changes.

[0222] Step 4:

[0223] The server optimizes the signals. Based on the analysis results of the AI ​​agent, the server adjusts the timing of the traffic light changes and optimizes the display state of the remotely controllable traffic lights.

[0224] Step 5:

[0225] The terminal notifies the user of information. The terminal receives optimization information from the server and provides the user with information such as "The next signal will turn red in 10 seconds" through voice or screen display.

[0226] Step 6:

[0227] The user adjusts their driving behavior. Based on notifications from their device, the user adjusts their driving pace to avoid sudden acceleration and deceleration and to ensure smooth driving.

[0228] Step 7:

[0229] The server collects feedback. The server monitors the user's driving patterns and changes in traffic conditions, and collects feedback data based on that.

[0230] Step 8:

[0231] The server updates the AI ​​model. The server uses the collected feedback data to retrain the AI ​​agent, improving the prediction accuracy of signal control.

[0232] (Example 1)

[0233] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0234] It is necessary to improve the efficiency and accuracy of traffic signal management to alleviate traffic congestion while ensuring smooth traffic flow. Existing systems are insufficient in real-time data analysis and signal control optimization, and the provision of appropriate information to drivers is limited, so further improvements are needed.

[0235] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0236] In this invention, the server includes a device for collecting traffic information, a device for analyzing the collected information and predicting the operating timing of the control device, and a device for optimizing the state of the control device based on the predicted operating timing. This enables highly accurate signal control in real time, realizing effective information provision to drivers and smooth management of traffic flow.

[0237] A "device for collecting traffic information" is a device that has the function of acquiring traffic-related data using sensors and cameras and storing it in digital format.

[0238] A "device that analyzes and predicts the timing of control device operation" is a device that analyzes collected traffic data and executes an algorithm to predict the timing of signal system operation.

[0239] A "device for optimizing the state of a control device" is a device that generates commands to adjust the operation of a signaling system based on predicted operating timings, thereby achieving optimal traffic conditions.

[0240] A "device for monitoring and collecting data on the user's driving behavior" is a device that detects the user's driving patterns and records driving data and driving behavior.

[0241] A "device that updates analysis and prediction equipment and improves accuracy using data" is a device that utilizes collected feedback data to dynamically improve analysis and prediction algorithms, thereby increasing the overall system accuracy.

[0242] This invention provides a system for collecting and analyzing traffic information in real time in order to optimize traffic signal management. The system mainly consists of a server, terminals, and signal control devices.

[0243] The server collects traffic information using sensors and cameras. This includes various sensors installed at intersections that measure the number of vehicles, their speeds, and traffic flow in real time. The collected data is sent to the server and then passed on to an AI agent. The AI ​​agent uses a generative AI model to analyze past traffic patterns and real-time information to predict the optimal timing for traffic signal operation. For example, it can decide to extend the green light duration to alleviate congestion during rush hour.

[0244] The terminal's role is to notify the user of signal timing prediction information from the server. For example, when a driver approaches an intersection, the terminal notifies the driver that "maintaining your speed will allow you to time your arrival with the green light," enabling smoother driving. This information is provided through the terminal's display and voice assistant. The terminal also monitors the user's driving behavior and collects feedback data to achieve even more accurate traffic management.

[0245] Furthermore, the server uses the collected feedback data to update its analysis model to improve the overall accuracy of the system. This continuous improvement process leads to optimization of signal control, which is expected to result in reduced traffic congestion and improved fuel efficiency.

[0246] An example of a prompt might be: "At 7:30 AM, predict the optimal signal timing at the XX intersection based on real-time traffic flow data. Also, generate advice to help users navigate the intersection smoothly." This prompt allows the AI ​​model to provide data for optimal signal control based on specific traffic conditions.

[0247] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0248] Step 1:

[0249] The server uses a device that collects real-time traffic information from sensors and cameras installed at intersections. Input data includes the number of vehicles, their speed, and traffic flow. This data is recorded in a log file and prepared for the next analysis step.

[0250] Step 2:

[0251] The server passes the collected traffic data to the AI ​​agent. The AI ​​agent uses a generative AI model to analyze the data and predicts the timing of traffic light operation based on the results. The input consists of collected real-time data and historical traffic pattern data. The analysis results in the output of optimized control timing, such as increasing the green light duration.

[0252] Step 3:

[0253] The server sends a command to the signal control device based on the analysis results. Specifically, it instructs the control device to extend the green light duration by 30 seconds. The input in this step is the optimized data output from the AI ​​agent, which adjusts the state of the traffic lights.

[0254] Step 4:

[0255] The terminal retrieves signal timing prediction information from the server and notifies the user. The terminal's input is the prediction information from the server, and its output is voice guidance and screen display for the driver. Specifically, the terminal notifies the driver, "If you maintain your speed, you will be able to time your arrival to coincide with the green light," allowing the driver to pass through the intersection smoothly.

[0256] Step 5:

[0257] The server monitors the user's driving behavior and collects feedback data. Inputs include information such as the user's driving speed and frequency of stopping at traffic lights. This data is used to update the analysis model, improving the system's accuracy and influencing future predictions. The output is presented as an improved traffic control algorithm.

[0258] (Application Example 1)

[0259] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0260] Managing traffic signals in cities is a critical issue from the perspective of traffic congestion and fuel efficiency. Conventional traffic signal systems often rely on historical data and static timing, making them incapable of responding to real-time traffic conditions. Furthermore, drivers lack the information necessary to efficiently manage waiting times and acceleration / deceleration, which further exacerbates congestion. To improve this situation, a system is needed that dynamically optimizes signals based on real-time traffic data and provides drivers with effective information.

[0261] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0262] In this invention, the server includes means for collecting traffic data, means for analyzing the collected data and predicting the control timing of the signal control device, means for optimizing the state of the signal control device based on the predicted control timing, and means for notifying the user of the timing of changes in traffic signals by voice and advising on the optimal driving speed. This enables efficient management of traffic flow in real time, and drivers can take appropriate driving actions to alleviate congestion and improve fuel efficiency.

[0263] "Traffic data" refers to information about moving objects such as the number of vehicles at intersections and on roads, their speed, and traffic flow.

[0264] A "traffic signal control device" is a device that controls the display of traffic signals and manages traffic flow at intersections and on roads.

[0265] "Predictive means" refers to functions or methods for estimating future states or events based on predetermined data.

[0266] "Voice notification means" refers to functions or devices that transmit information to users using voice.

[0267] "Driving advice" refers to instructions and suggestions provided to drivers to ensure safe and efficient travel, including instructions regarding appropriate speed and behavior.

[0268] The system for carrying out this invention includes a server, a terminal, and a signal control device. The server collects traffic data using sensors and cameras installed at intersections and on roads. This includes the number of vehicles, speed, and traffic flow. Data collection also utilizes data analysis libraries such as Python and TensorFlow.

[0269] The server analyzes collected traffic data and uses a generative AI model to predict the optimal control timing for signal control devices in real time. This AI model integrates and analyzes historical and real-time data to achieve efficient signal control.

[0270] The terminal provides voice notifications to the user (driver) based on information from the server. Specifically, it provides voice notifications to the driver regarding the optimal speed and timing of traffic light changes, assisting with driving. This voice notification function allows drivers to optimize acceleration and deceleration, which can help alleviate traffic congestion and improve fuel efficiency.

[0271] As a concrete example, the terminal might advise the driver via voice, "There are 23 seconds until the next traffic light. If you maintain a speed of 55 km / h, you can pass through without stopping." In this way, drivers can anticipate traffic flow and contribute to smoother traffic flow.

[0272] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0273] Step 1:

[0274] The server collects traffic data from sensors and cameras installed along the roads. This data includes vehicle count, speed, and traffic flow. The input is real-time sensor and camera data, and the output is the collected raw traffic data.

[0275] Step 2:

[0276] The server analyzes the collected traffic data. It cleanses the data and removes noise using tools such as Python or TensorFlow. The input for this step is the raw traffic data collected in step 1, and the output is data that has been formatted for analysis.

[0277] Step 3:

[0278] The server uses a generative AI model to predict the optimal control timing for the signal control device. Analyzed data is input into the model to calculate the timing for optimizing the signal state. The input here is formatted traffic data, and the output is an instruction for the control timing to the signal control device.

[0279] Step 4:

[0280] Based on the control timing information received from the server, the terminal gives a voice notification to the user. Specifically, in real time, it provides advice to the driver using a prompt sentence such as "It will take 23 seconds until the next signal. If you keep your speed at 55 km / h, you can pass through the signal without stopping." Here, the input is the control timing information from the server, and the output is the voice notification to the user.

[0281] Step 5:

[0282] The user receives the notification from the terminal and takes the recommended speed and driving actions. This can avoid unreasonable acceleration and braking and improve fuel efficiency. The input is the voice advice from the terminal, and the output is the actual driving actions.

[0283] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0284] The present invention combines an emotion engine for recognizing the user's emotion with a system for optimizing the control of traffic signals. The system includes a server, a user terminal, a signal control device, and an emotion engine.

[0285] The server collects traffic data in real time from sensors and cameras installed at intersections. The server passes the data to an AI agent, analyzes the traffic flow, and predicts the change timing of the traffic signals. This information is sent to the signal control device and used to execute optimized traffic signals.

[0286] Furthermore, an emotion engine is installed in the user terminal, and it detects emotions by analyzing the driver's facial expressions and voice tones through a camera and a microphone. For example, when the user is feeling stressed, the emotion engine detects that state and provides appropriate feedback to the system.

[0287] The server takes into account the feedback from the emotion engine and appropriately changes the traffic signal adjustment and the content of the notification to the user. For example, when the driver is judged to be irritated, a smoother signal pattern is proposed. Also, the user terminal provides the user with driving advice considering the emotional state. As an example of this, an audio message such as "Let's wait while taking a deep breath and relaxing" may be played.

[0288] Also, the server continuously monitors the data of the user's emotional state and driving situation and collects feedback. This data is utilized for the improvement of the AI agent and contributes to the improvement of the accuracy of traffic signal control. In this way, the system realizes not only the improvement of traffic efficiency but also the comfortable driving support for the user.

[0289] The following describes the processing flow.

[0290] Step 1:

[0291] The server collects traffic data. The server uses sensors and cameras installed at intersections to obtain the number of vehicles, speed, and traffic flow in real time and stores them in a database.

[0292] Step 2:

[0293] The server preprocesses the traffic data it has obtained. The server removes noise and outliers from the obtained data and processes it into a form suitable for analysis.

[0294] Step 3:

[0295] The server analyzes the traffic situation using an AI agent. The server analyzes the traffic flow based on the preprocessed data and then predicts the timing when the signal will change.

[0296] Step 4:

[0297] The terminal recognizes the user's emotions with an emotion engine. The terminal uses in-vehicle cameras and microphones to acquire the user's expressions and voices, and the emotion engine analyzes emotions such as stress, irritation, and relaxation.

[0298] Step 5:

[0299] The server optimizes the signals and adjusts the notifications. The server considers the analysis results of the AI agent and the emotion data from the terminal, adjusts the switching timing of the traffic lights, and sends instructions to the signal control device. Also, it customizes the content of the information provided to the user.

[0300] Step 6:

[0301] The terminal notifies the user of the adjusted information. The terminal provides a message suitable for the emotional state and sends a notification to the user that includes specific advice such as "There are 8 seconds until the next signal. Please drive calmly."

[0302] Step 7:

[0303] The user adjusts their driving behavior. Based on the information from the terminal, the user aims for a reasonable driving pace to achieve safe and smooth driving.

[0304] Step 8:

[0305] The server collects feedback. The server records the changes in the user's driving operations and the monitoring results of the emotional state. This is utilized as data for retraining the AI model, contributing to the improvement of the system's performance.

[0306] (Example 2)

[0307] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0308] In modern transportation systems, controlling traffic signals is crucial for smooth traffic flow. However, traditional methods have struggled to optimize signals while considering traffic conditions and the emotional state of individual drivers. As a result, congestion and driver stress have not always been mitigated. Furthermore, the lack of mechanisms to utilize real-time feedback on changing traffic conditions and driver emotions has created a need to improve both traffic efficiency and safety.

[0309] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0310] In this invention, the server includes means for acquiring traffic information, means for analyzing the acquired information and predicting the control timing of the signal device, and means for detecting the user's emotional state. This makes it possible to optimize signal control by simultaneously considering traffic conditions and the driver's emotional state, thereby improving traffic efficiency and driver comfort.

[0311] "Traffic information" refers to data that shows the state of traffic flow, including the number of vehicles, their speed, and the length of queues waiting at traffic lights.

[0312] "Traffic signaling equipment" refers to traffic lights used to control the flow of traffic at intersections and other locations.

[0313] "Control timing" refers to the setting of the timing for when traffic signals change, and is optimized to ensure a smooth flow of traffic.

[0314] "User emotional state" refers to information that indicates the user's psychological state and reactions, and is used to evaluate things like stress levels and calmness.

[0315] "Feedback" refers to response information regarding the user's driving behavior and emotional state, which is useful for improving the system and increasing the accuracy of signal control.

[0316] This invention aims to achieve optimal signal control in a traffic signal control system by combining traffic flow and driver emotions. Specifically, it is configured as a system including a server, terminals, and control devices such as traffic signals.

[0317] The server first collects traffic information through various sensors and cameras installed at intersections. This information includes data such as vehicle flow, speed, and the length of queues of cars waiting at traffic lights. Sensor technology and high-resolution cameras are used to collect this data.

[0318] The collected data is analyzed by an AI agent on the server. The AI ​​agent uses machine learning algorithms to model traffic conditions and predict the timing of the next traffic signal. The software required to determine the timing of traffic signal control includes statistical analysis tools and a machine learning environment.

[0319] Meanwhile, the user terminal is equipped with an emotion engine that analyzes the driver's facial expressions and voice tone through the camera and microphone. This analysis detects the user's emotional state. This technology is realized using computer vision and voice analysis algorithms.

[0320] The server takes into account the emotional state obtained from the user's terminal and adjusts traffic signals and provides appropriate notifications to the user. For example, if congestion is expected or the user's emotional state indicates stress, a signal pattern is suggested to allow for smoother traffic flow. The user terminal also provides voice advice such as, "Take a deep breath and relax while you wait."

[0321] Furthermore, the system continuously collects feedback and uses it to improve the accuracy of signal control by enhancing the AI ​​agent. This method simultaneously achieves traffic efficiency and a comfortable driving experience for users.

[0322] An example of a prompt would be, "How should this system use real-time driver sentiment analysis and traffic data to adjust traffic light timing and optimize traffic flow?"

[0323] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0324] Step 1:

[0325] The server collects traffic information from sensors and cameras installed at intersections. Inputs include the number of vehicles, their speed, and the length of queues of cars waiting at traffic lights. By collecting this data, the current traffic situation can be understood. Specifically, sensors detect vehicle movement, and cameras record the footage.

[0326] Step 2:

[0327] The server passes the collected traffic data to the AI ​​agent, which analyzes the traffic flow. The input is the data obtained in step 1, and the output of the analysis is a prediction of the next signal timing. The AI ​​agent applies a machine learning algorithm to model the traffic flow. Specifically, the AI ​​analyzes the data patterns during peak times and derives the appropriate timing for signal control.

[0328] Step 3:

[0329] The server transmits the analysis results to the signal control unit, which then adjusts the timing of the traffic lights. The predicted signal timing, which is the output of step 2, is used as input, and the optimized signal control is performed as output. Specifically, the signal control unit adjusts the timing of the red and green lights to ensure smooth traffic flow.

[0330] Step 4:

[0331] The user terminal uses an emotion engine to detect the driver's emotions. Inputs include facial expression data and voice tone from the terminal's camera and microphone, and output is the user's emotional state. Specifically, the camera captures the driver's face, and analysis software determines the emotion.

[0332] Step 5:

[0333] The user terminal sends the detected emotional state to the server, which uses it as feedback information. The input is the emotional state obtained in step 4, and the output is a suggested additional adjustment for signal control. Specifically, the server receives the emotional state and regenerates the suggested adjustment for signal control.

[0334] Step 6:

[0335] The server provides notifications and adjustments to the user and signals based on their emotional state. The input is the feedback information from step 5, and the output is updated signal patterns and user notifications. Specifically, a message encouraging relaxation is sent to the user through the device.

[0336] Step 7:

[0337] The server continuously monitors traffic conditions and user feedback, accumulating this data to improve the system's accuracy. Inputs include real-time traffic data and feedback, while output contributes to improving the AI ​​agent. Specifically, new data is incorporated into the AI ​​model, optimizing the control algorithm.

[0338] (Application Example 2)

[0339] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0340] Conventional traffic signal control systems optimize signals using only traffic data, failing to consider the impact of drivers' emotional states on traffic flow. As a result, emotions such as stress and frustration could negatively affect driving behavior. Therefore, there was a need to develop a traffic signal control system that takes drivers' emotions into account.

[0341] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0342] In this invention, the server includes means for collecting traffic data, means for recognizing the driver's emotions, and means for notifying the user of optimized traffic signal information and providing driving advice. This enables real-time traffic signal control that takes into account the driver's emotional state.

[0343] "Means of collecting traffic data" refers to devices and methods that use sensors and cameras installed at intersections and on roads to collect information about traffic conditions and vehicle flow in real time.

[0344] "Means for predicting the timing of traffic signal control" refers to devices or methods for analyzing collected traffic data and calculating the optimal timing for signal changes at each intersection.

[0345] "Means for optimizing the state of traffic signals" refers to devices or methods for controlling and setting traffic signals to maximize traffic efficiency based on predicted control timing.

[0346] "Means of recognizing a driver's emotions" refers to devices and methods that analyze a driver's facial expressions and voice via cameras and microphones to infer and judge their emotional state.

[0347] "Means for analyzing recognized emotional information and further optimizing signal control" refers to devices or methods that adjust the control patterns of traffic signals based on driver emotional data to make traffic flow smoother.

[0348] "Means of notifying users and providing driving advice" refers to devices and methods that inform drivers of optimized traffic signal information and safe and comfortable driving advice through voice or visual means.

[0349] A system implementing this invention includes a server equipped with the necessary functions to collect and analyze traffic data and optimize traffic signal control, and a user terminal for recognizing drivers' emotions and utilizing that information.

[0350] The server collects traffic data using sensors and cameras installed at intersections. This data is analyzed in real time and processed by an AI agent to evaluate traffic flow. Based on the analysis results, control patterns are generated to optimize the timing of traffic light control. Machine learning frameworks such as Python and TensorFlow are used for processing by the AI ​​agent.

[0351] Meanwhile, on the user's device, an emotion engine operates. This engine collects the driver's facial expressions and voice tone via cameras and microphones built into the smartphone or in-car system, and recognizes their emotions. OpenCV can be used for image processing, and machine learning frameworks such as TensorFlow are used for voice analysis.

[0352] This allows the server to provide users with advice based on their emotional state, in addition to controlling traffic signals. For example, if the system detects that a driver is stressed, it can adjust the signal control pattern to facilitate smoother traffic flow. An actual voice message might be something like, "Take a deep breath and relax."

[0353] An example of a prompt for a generative AI model would be, "Generate the optimal traffic signal pattern when the driver's stress level is high."

[0354] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0355] Step 1:

[0356] The server collects real-time traffic data from sensors and cameras installed at intersections. Inputs include video and numerical data from sensors and cameras, which are converted into digital signals. Outputs are recorded as traffic flow and vehicle location information.

[0357] Step 2:

[0358] The server analyzes collected traffic data using an AI agent. It receives digitized traffic information as input and analyzes traffic patterns using an AI model (such as TensorFlow). The output is predictive data regarding traffic signal control timing. Specific operations include detecting traffic volume peaks and congestion levels.

[0359] Step 3:

[0360] The server optimizes the signal change pattern based on predicted control timing. It uses the analysis results of an AI agent as input to perform appropriate timing calculations. The output is an optimized signal schedule. Its operation involves generating a smooth signal change sequence.

[0361] Step 4:

[0362] The device collects facial expressions and voice data from the driver's smartphone or in-vehicle system via cameras and microphones. The input is real-time video and audio data, which is sent to the emotion engine. The output is data indicating the driver's emotional state. Specific operations include emotion estimation using a facial recognition algorithm.

[0363] Step 5:

[0364] The terminal transmits the driver's emotional state to a server, which is used as feedback for further optimization of signal control. The input is emotional data sent from the terminal, and the output is an adaptive signal pattern corresponding to the emotion. In this step, a method is employed to analyze the emotional data and incorporate it into the traffic signal control.

[0365] Step 6:

[0366] The user terminal provides drivers with optimized signal information and emotion-based advice. Inputs include an optimized signal schedule and driver emotion information, while output is visual or audio advice. Specific operations include a process of generating messages using speech synthesis functionality.

[0367] Step 7:

[0368] The server updates the AI ​​agent using the obtained driver emotional state and traffic data to improve accuracy. The input is feedback data, which is used as training data for the generating AI model. The output is the improved predictive model. The process involves retraining the AI ​​model and tuning its parameters.

[0369] 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.

[0370] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0371] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0372] [Third Embodiment]

[0373] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0374] 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.

[0375] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0376] 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.

[0377] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0378] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0379] 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.

[0380] 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.

[0381] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0382] The 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.

[0383] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0384] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0385] The present invention involves using an AI agent to collect and analyze traffic data in real time in order to optimize traffic signal management. The system includes a server, a user terminal, and a signal control device.

[0386] The server acquires information from traffic data collection sensors and cameras. This allows the server to collect data such as the number of vehicles, their speeds, and traffic flow at specific intersections. The server then passes this data to an AI agent, which analyzes the traffic situation and predicts when the traffic signals should change next.

[0387] The AI ​​agent integrates and analyzes historical and real-time data. For example, based on traffic flow patterns at an intersection during the morning rush hour, the server might determine that keeping the traffic light green longer than usual would reduce congestion. Based on this prediction, the server sends appropriate control commands to the traffic signal control system to adjust the traffic light display.

[0388] Users receive predictive information from an AI agent through their device. The device uses voice and screen to communicate information to the driver, such as, "The next traffic light will turn green in 20 seconds." This allows drivers to avoid excessive acceleration or sudden braking and to drive smoothly. For example, when a user approaches an intersection, the device notifies them, "Slowing down will allow you to time your drive to coincide with the traffic light turning green," thereby contributing to smoother traffic flow.

[0389] Furthermore, the server monitors the user's driving behavior and collects feedback information. This data is used to improve the overall accuracy of the system and is reflected in subsequent predictive models. In this way, the system is continuously improved, contributing to the reduction of traffic congestion and improved fuel efficiency.

[0390] The following describes the processing flow.

[0391] Step 1:

[0392] The server collects traffic data. The server acquires data in real time from sensors and cameras installed at intersections and records the number of vehicles, their speeds, the current status of traffic lights, and other information in a database.

[0393] Step 2:

[0394] The server preprocesses the data. The server ensures data quality by removing outliers from the collected data and converting it into a format suitable for analysis.

[0395] Step 3:

[0396] The server uses an AI agent to analyze traffic conditions. The server provides pre-processed data to the AI ​​agent, which analyzes traffic flow and past signal patterns to predict signal changes.

[0397] Step 4:

[0398] The server optimizes the signals. Based on the analysis results of the AI ​​agent, the server adjusts the timing of the traffic light changes and optimizes the display state of the remotely controllable traffic lights.

[0399] Step 5:

[0400] The terminal notifies the user of information. The terminal receives optimization information from the server and provides the user with information such as "The next signal will turn red in 10 seconds" through voice or screen display.

[0401] Step 6:

[0402] The user adjusts their driving behavior. Based on notifications from their device, the user adjusts their driving pace to avoid sudden acceleration and deceleration and to ensure smooth driving.

[0403] Step 7:

[0404] The server collects feedback. The server monitors the user's driving patterns and changes in traffic conditions, and collects feedback data based on that.

[0405] Step 8:

[0406] The server updates the AI ​​model. The server uses the collected feedback data to retrain the AI ​​agent, improving the prediction accuracy of signal control.

[0407] (Example 1)

[0408] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0409] It is necessary to improve the efficiency and accuracy of traffic signal management to alleviate traffic congestion while ensuring smooth traffic flow. Existing systems are insufficient in real-time data analysis and signal control optimization, and the provision of appropriate information to drivers is limited, so further improvements are needed.

[0410] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0411] In this invention, the server includes a device for collecting traffic information, a device for analyzing the collected information and predicting the operating timing of the control device, and a device for optimizing the state of the control device based on the predicted operating timing. This enables highly accurate signal control in real time, realizing effective information provision to drivers and smooth management of traffic flow.

[0412] A "device for collecting traffic information" is a device that has the function of acquiring traffic-related data using sensors and cameras and storing it in digital format.

[0413] A "device that analyzes and predicts the timing of control device operation" is a device that analyzes collected traffic data and executes an algorithm to predict the timing of signal system operation.

[0414] A "device for optimizing the state of a control device" is a device that generates commands to adjust the operation of a signaling system based on predicted operating timings, thereby achieving optimal traffic conditions.

[0415] A "device for monitoring and collecting data on the user's driving behavior" is a device that detects the user's driving patterns and records driving data and driving behavior.

[0416] A "device that updates analysis and prediction equipment and improves accuracy using data" is a device that utilizes collected feedback data to dynamically improve analysis and prediction algorithms, thereby increasing the overall system accuracy.

[0417] This invention provides a system for collecting and analyzing traffic information in real time in order to optimize traffic signal management. The system mainly consists of a server, terminals, and signal control devices.

[0418] The server collects traffic information using sensors and cameras. This includes various sensors installed at intersections that measure the number of vehicles, their speeds, and traffic flow in real time. The collected data is sent to the server and then passed on to an AI agent. The AI ​​agent uses a generative AI model to analyze past traffic patterns and real-time information to predict the optimal timing for traffic signal operation. For example, it can decide to extend the green light duration to alleviate congestion during rush hour.

[0419] The terminal's role is to notify the user of signal timing prediction information from the server. For example, when a driver approaches an intersection, the terminal notifies the driver that "maintaining your speed will allow you to time your arrival with the green light," enabling smoother driving. This information is provided through the terminal's display and voice assistant. The terminal also monitors the user's driving behavior and collects feedback data to achieve even more accurate traffic management.

[0420] Furthermore, the server uses the collected feedback data to update its analysis model to improve the overall accuracy of the system. This continuous improvement process leads to optimization of signal control, which is expected to result in reduced traffic congestion and improved fuel efficiency.

[0421] An example of a prompt might be: "At 7:30 AM, predict the optimal signal timing at the XX intersection based on real-time traffic flow data. Also, generate advice to help users navigate the intersection smoothly." This prompt allows the AI ​​model to provide data for optimal signal control based on specific traffic conditions.

[0422] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0423] Step 1:

[0424] The server uses a device that collects real-time traffic information from sensors and cameras installed at intersections. Input data includes the number of vehicles, their speed, and traffic flow. This data is recorded in a log file and prepared for the next analysis step.

[0425] Step 2:

[0426] The server passes the collected traffic data to the AI ​​agent. The AI ​​agent uses a generative AI model to analyze the data and predicts the timing of traffic light operation based on the results. The input consists of collected real-time data and historical traffic pattern data. The analysis results in the output of optimized control timing, such as increasing the green light duration.

[0427] Step 3:

[0428] The server sends a command to the signal control device based on the analysis results. Specifically, it instructs the control device to extend the green light duration by 30 seconds. The input in this step is the optimized data output from the AI ​​agent, which adjusts the state of the traffic lights.

[0429] Step 4:

[0430] The terminal retrieves signal timing prediction information from the server and notifies the user. The terminal's input is the prediction information from the server, and its output is voice guidance and screen display for the driver. Specifically, the terminal notifies the driver, "If you maintain your speed, you will be able to time your arrival to coincide with the green light," allowing the driver to pass through the intersection smoothly.

[0431] Step 5:

[0432] The server monitors the user's driving behavior and collects feedback data. Inputs include information such as the user's driving speed and frequency of stopping at traffic lights. This data is used to update the analysis model, improving the system's accuracy and influencing future predictions. The output is presented as an improved traffic control algorithm.

[0433] (Application Example 1)

[0434] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0435] Managing traffic signals in cities is a critical issue from the perspective of traffic congestion and fuel efficiency. Conventional traffic signal systems often rely on historical data and static timing, making them incapable of responding to real-time traffic conditions. Furthermore, drivers lack the information necessary to efficiently manage waiting times and acceleration / deceleration, which further exacerbates congestion. To improve this situation, a system is needed that dynamically optimizes signals based on real-time traffic data and provides drivers with effective information.

[0436] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0437] In this invention, the server includes means for collecting traffic data, means for analyzing the collected data and predicting the control timing of the signal control device, means for optimizing the state of the signal control device based on the predicted control timing, and means for notifying the user of the timing of changes in traffic signals by voice and advising on the optimal driving speed. This enables efficient management of traffic flow in real time, and drivers can take appropriate driving actions to alleviate congestion and improve fuel efficiency.

[0438] "Traffic data" refers to information about moving objects such as the number of vehicles at intersections and on roads, their speed, and traffic flow.

[0439] A "traffic signal control device" is a device that controls the display of traffic signals and manages traffic flow at intersections and on roads.

[0440] "Predictive means" refers to functions or methods for estimating future states or events based on predetermined data.

[0441] "Voice notification means" refers to functions or devices that transmit information to users using voice.

[0442] "Driving advice" refers to instructions and suggestions provided to drivers to ensure safe and efficient travel, including instructions regarding appropriate speed and behavior.

[0443] The system for carrying out this invention includes a server, a terminal, and a signal control device. The server collects traffic data using sensors and cameras installed at intersections and on roads. This includes the number of vehicles, speed, and traffic flow. Data collection also utilizes data analysis libraries such as Python and TensorFlow.

[0444] The server analyzes collected traffic data and uses a generative AI model to predict the optimal control timing for signal control devices in real time. This AI model integrates and analyzes historical and real-time data to achieve efficient signal control.

[0445] The terminal provides voice notifications to the user (driver) based on information from the server. Specifically, it provides voice notifications to the driver regarding the optimal speed and timing of traffic light changes, assisting with driving. This voice notification function allows drivers to optimize acceleration and deceleration, which can help alleviate traffic congestion and improve fuel efficiency.

[0446] As a concrete example, the terminal might advise the driver via voice, "There are 23 seconds until the next traffic light. If you maintain a speed of 55 km / h, you can pass through without stopping." In this way, drivers can anticipate traffic flow and contribute to smoother traffic flow.

[0447] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0448] Step 1:

[0449] The server collects traffic data from sensors and cameras installed along the roads. This data includes vehicle count, speed, and traffic flow. The input is real-time sensor and camera data, and the output is the collected raw traffic data.

[0450] Step 2:

[0451] The server analyzes the collected traffic data. It cleanses the data and removes noise using tools such as Python or TensorFlow. The input for this step is the raw traffic data collected in step 1, and the output is data that has been formatted for analysis.

[0452] Step 3:

[0453] The server uses a generative AI model to predict the optimal control timing for the signal control device. Analyzed data is input into the model to calculate the timing for optimizing the signal state. The input here is formatted traffic data, and the output is an instruction for the control timing to the signal control device.

[0454] Step 4:

[0455] The terminal provides voice notifications to the user based on control timing information received from the server. Specifically, it provides real-time advice to the driver using prompts such as, "23 seconds until the next traffic light. If you maintain a speed of 55 km / h, you can pass through the traffic light without stopping." The input here is control timing information from the server, and the output is a voice notification to the user.

[0456] Step 5:

[0457] The user receives notifications from the device and takes the recommended speed and driving actions. This helps avoid excessive acceleration and braking, improving fuel efficiency. The input is voice advice from the device, and the output is the actual driving actions.

[0458] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0459] This invention combines a system for optimizing the control of traffic signals with an emotion engine that recognizes user emotions. The system includes a server, a user terminal, a signal control device, and an emotion engine.

[0460] The server collects traffic data in real time from sensors and cameras installed at intersections. The server passes the data to an AI agent, which analyzes traffic flow and predicts the timing of traffic signal changes. This information is sent to the traffic signal control system and used to implement optimized traffic signals.

[0461] Furthermore, the user terminal is equipped with an emotion engine that detects emotions by analyzing the driver's facial expressions and tone of voice through the camera and microphone. For example, if the user is feeling stressed, the emotion engine detects this state and provides appropriate feedback to the system.

[0462] The server takes feedback from the emotion engine into account and adjusts traffic signals and user notifications accordingly. For example, if it determines that a driver is irritated, a smoother signal pattern will be suggested. The user terminal also provides driving advice that takes the user's emotional state into account. For example, it might play a voice message such as, "Take a deep breath and relax while you wait."

[0463] Furthermore, the server continuously monitors data on the user's emotional state and driving conditions, and collects feedback. This data is used to improve the AI ​​agent, contributing to increased accuracy in traffic signal control. In this way, the system not only improves traffic efficiency but also provides comfortable driving support for the user.

[0464] The following describes the processing flow.

[0465] Step 1:

[0466] The server collects traffic data. Using sensors and cameras installed at intersections, the server acquires real-time data on the number of vehicles, their speeds, and traffic flow, and stores it in a database.

[0467] Step 2:

[0468] The server preprocesses the traffic data it acquires. The server removes noise and outliers from the acquired data and processes it into a format suitable for analysis.

[0469] Step 3:

[0470] The server uses an AI agent to analyze traffic conditions. Based on pre-processed data, the server analyzes traffic flow and predicts when the next traffic light will change.

[0471] Step 4:

[0472] The device recognizes the user's emotions using an emotion engine. The device uses an in-car camera and microphone to capture the user's facial expressions and voice, and the emotion engine analyzes emotions such as stress, frustration, and relaxation.

[0473] Step 5:

[0474] The server optimizes signals and adjusts notifications. The server considers the AI ​​agent's analysis results and emotional data from the terminal to adjust the timing of traffic light switching and send instructions to the signal control unit. It also customizes the content of information provided to the user.

[0475] Step 6:

[0476] The device notifies the user of information tailored to their emotional state. The device provides messages appropriate to the user's emotional state and sends notifications that include specific advice, such as, "You have 8 seconds until the next traffic light. Please drive calmly."

[0477] Step 7:

[0478] The user adjusts their driving behavior. Based on information from their device, the user strives to maintain a reasonable driving pace, resulting in safe and smooth driving.

[0479] Step 8:

[0480] The server collects feedback. The server records changes in the user's driving behavior and emotional state, and uses this data to retrain the AI ​​model, contributing to improved system performance.

[0481] (Example 2)

[0482] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0483] In modern transportation systems, controlling traffic signals is crucial for smooth traffic flow. However, traditional methods have struggled to optimize signals while considering traffic conditions and the emotional state of individual drivers. As a result, congestion and driver stress have not always been mitigated. Furthermore, the lack of mechanisms to utilize real-time feedback on changing traffic conditions and driver emotions has created a need to improve both traffic efficiency and safety.

[0484] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0485] In this invention, the server includes means for acquiring traffic information, means for analyzing the acquired information and predicting the control timing of the signal device, and means for detecting the user's emotional state. This makes it possible to optimize signal control by simultaneously considering traffic conditions and the driver's emotional state, thereby improving traffic efficiency and driver comfort.

[0486] "Traffic information" refers to data that shows the state of traffic flow, including the number of vehicles, their speed, and the length of queues waiting at traffic lights.

[0487] "Traffic signaling equipment" refers to traffic lights used to control the flow of traffic at intersections and other locations.

[0488] "Control timing" refers to the setting of the timing for when traffic signals change, and is optimized to ensure a smooth flow of traffic.

[0489] "User emotional state" refers to information that indicates the user's psychological state and reactions, and is used to evaluate things like stress levels and calmness.

[0490] "Feedback" refers to response information regarding the user's driving behavior and emotional state, which is useful for improving the system and increasing the accuracy of signal control.

[0491] This invention aims to achieve optimal signal control in a traffic signal control system by combining traffic flow and driver emotions. Specifically, it is configured as a system including a server, terminals, and control devices such as traffic signals.

[0492] The server first collects traffic information through various sensors and cameras installed at intersections. This information includes data such as vehicle flow, speed, and the length of queues of cars waiting at traffic lights. Sensor technology and high-resolution cameras are used to collect this data.

[0493] The collected data is analyzed by an AI agent on the server. The AI ​​agent uses machine learning algorithms to model traffic conditions and predict the timing of the next traffic signal. The software required to determine the timing of traffic signal control includes statistical analysis tools and a machine learning environment.

[0494] Meanwhile, the user terminal is equipped with an emotion engine that analyzes the driver's facial expressions and voice tone through the camera and microphone. This analysis detects the user's emotional state. This technology is realized using computer vision and voice analysis algorithms.

[0495] The server takes into account the emotional state obtained from the user's terminal and adjusts traffic signals and provides appropriate notifications to the user. For example, if congestion is expected or the user's emotional state indicates stress, a signal pattern is suggested to allow for smoother traffic flow. The user terminal also provides voice advice such as, "Take a deep breath and relax while you wait."

[0496] Furthermore, the system continuously collects feedback and uses it to improve the accuracy of signal control by enhancing the AI ​​agent. This method simultaneously achieves traffic efficiency and a comfortable driving experience for users.

[0497] An example of a prompt would be, "How should this system use real-time driver sentiment analysis and traffic data to adjust traffic light timing and optimize traffic flow?"

[0498] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0499] Step 1:

[0500] The server collects traffic information from sensors and cameras installed at intersections. Inputs include the number of vehicles, their speed, and the length of queues of cars waiting at traffic lights. By collecting this data, the current traffic situation can be understood. Specifically, sensors detect vehicle movement, and cameras record the footage.

[0501] Step 2:

[0502] The server passes the collected traffic data to the AI ​​agent, which analyzes the traffic flow. The input is the data obtained in step 1, and the output of the analysis is a prediction of the next signal timing. The AI ​​agent applies a machine learning algorithm to model the traffic flow. Specifically, the AI ​​analyzes the data patterns during peak times and derives the appropriate timing for signal control.

[0503] Step 3:

[0504] The server transmits the analysis results to the signal control unit, which then adjusts the timing of the traffic lights. The predicted signal timing, which is the output of step 2, is used as input, and the optimized signal control is performed as output. Specifically, the signal control unit adjusts the timing of the red and green lights to ensure smooth traffic flow.

[0505] Step 4:

[0506] The user terminal uses an emotion engine to detect the driver's emotions. Inputs include facial expression data and voice tone from the terminal's camera and microphone, and output is the user's emotional state. Specifically, the camera captures the driver's face, and analysis software determines the emotion.

[0507] Step 5:

[0508] The user terminal sends the detected emotional state to the server, which uses it as feedback information. The input is the emotional state obtained in step 4, and the output is a suggested additional adjustment for signal control. Specifically, the server receives the emotional state and regenerates the suggested adjustment for signal control.

[0509] Step 6:

[0510] The server provides notifications and adjustments to the user and signals based on their emotional state. The input is the feedback information from step 5, and the output is updated signal patterns and user notifications. Specifically, a message encouraging relaxation is sent to the user through the device.

[0511] Step 7:

[0512] The server continuously monitors traffic conditions and user feedback, accumulating this data to improve the system's accuracy. Inputs include real-time traffic data and feedback, while output contributes to improving the AI ​​agent. Specifically, new data is incorporated into the AI ​​model, optimizing the control algorithm.

[0513] (Application Example 2)

[0514] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0515] Conventional traffic signal control systems optimize signals using only traffic data, failing to consider the impact of drivers' emotional states on traffic flow. As a result, emotions such as stress and frustration could negatively affect driving behavior. Therefore, there was a need to develop a traffic signal control system that takes drivers' emotions into account.

[0516] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0517] In this invention, the server includes means for collecting traffic data, means for recognizing the driver's emotions, and means for notifying the user of optimized traffic signal information and providing driving advice. This enables real-time traffic signal control that takes into account the driver's emotional state.

[0518] "Means of collecting traffic data" refers to devices and methods that use sensors and cameras installed at intersections and on roads to collect information about traffic conditions and vehicle flow in real time.

[0519] "Means for predicting the timing of traffic signal control" refers to devices or methods for analyzing collected traffic data and calculating the optimal timing for signal changes at each intersection.

[0520] "Means for optimizing the state of traffic signals" refers to devices or methods for controlling and setting traffic signals to maximize traffic efficiency based on predicted control timing.

[0521] "Means of recognizing a driver's emotions" refers to devices and methods that analyze a driver's facial expressions and voice via cameras and microphones to infer and judge their emotional state.

[0522] "Means for analyzing recognized emotional information and further optimizing signal control" refers to devices or methods that adjust the control patterns of traffic signals based on driver emotional data to make traffic flow smoother.

[0523] "Means of notifying users and providing driving advice" refers to devices and methods that inform drivers of optimized traffic signal information and safe and comfortable driving advice through voice or visual means.

[0524] A system implementing this invention includes a server equipped with the necessary functions to collect and analyze traffic data and optimize traffic signal control, and a user terminal for recognizing drivers' emotions and utilizing that information.

[0525] The server collects traffic data using sensors and cameras installed at intersections. This data is analyzed in real time and processed by an AI agent to evaluate traffic flow. Based on the analysis results, control patterns are generated to optimize the timing of traffic light control. Machine learning frameworks such as Python and TensorFlow are used for processing by the AI ​​agent.

[0526] Meanwhile, on the user's device, an emotion engine operates. This engine collects the driver's facial expressions and voice tone via cameras and microphones built into the smartphone or in-car system, and recognizes their emotions. OpenCV can be used for image processing, and machine learning frameworks such as TensorFlow are used for voice analysis.

[0527] This allows the server to provide users with advice based on their emotional state, in addition to controlling traffic signals. For example, if the system detects that a driver is stressed, it can adjust the signal control pattern to facilitate smoother traffic flow. An actual voice message might be something like, "Take a deep breath and relax."

[0528] An example of a prompt for a generative AI model would be, "Generate the optimal traffic signal pattern when the driver's stress level is high."

[0529] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0530] Step 1:

[0531] The server collects real-time traffic data from sensors and cameras installed at intersections. Inputs include video and numerical data from sensors and cameras, which are converted into digital signals. Outputs are recorded as traffic flow and vehicle location information.

[0532] Step 2:

[0533] The server analyzes collected traffic data using an AI agent. It receives digitized traffic information as input and analyzes traffic patterns using an AI model (such as TensorFlow). The output is predictive data regarding traffic signal control timing. Specific operations include detecting traffic volume peaks and congestion levels.

[0534] Step 3:

[0535] The server optimizes the signal change pattern based on predicted control timing. It uses the analysis results of an AI agent as input to perform appropriate timing calculations. The output is an optimized signal schedule. Its operation involves generating a smooth signal change sequence.

[0536] Step 4:

[0537] The device collects facial expressions and voice data from the driver's smartphone or in-vehicle system via cameras and microphones. The input is real-time video and audio data, which is sent to the emotion engine. The output is data indicating the driver's emotional state. Specific operations include emotion estimation using a facial recognition algorithm.

[0538] Step 5:

[0539] The terminal transmits the driver's emotional state to a server, which is used as feedback for further optimization of signal control. The input is emotional data sent from the terminal, and the output is an adaptive signal pattern corresponding to the emotion. In this step, a method is employed to analyze the emotional data and incorporate it into the traffic signal control.

[0540] Step 6:

[0541] The user terminal provides drivers with optimized signal information and emotion-based advice. Inputs include an optimized signal schedule and driver emotion information, while output is visual or audio advice. Specific operations include a process of generating messages using speech synthesis functionality.

[0542] Step 7:

[0543] The server updates the AI ​​agent using the obtained driver emotional state and traffic data to improve accuracy. The input is feedback data, which is used as training data for the generating AI model. The output is the improved predictive model. The process involves retraining the AI ​​model and tuning its parameters.

[0544] 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.

[0545] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0546] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0547] [Fourth Embodiment]

[0548] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0549] 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.

[0550] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0551] 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.

[0552] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0553] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0554] 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.

[0555] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0556] 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.

[0557] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0558] The 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.

[0559] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0560] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0561] The present invention involves using an AI agent to collect and analyze traffic data in real time in order to optimize traffic signal management. The system includes a server, a user terminal, and a signal control device.

[0562] The server acquires information from traffic data collection sensors and cameras. This allows the server to collect data such as the number of vehicles, their speeds, and traffic flow at specific intersections. The server then passes this data to an AI agent, which analyzes the traffic situation and predicts when the traffic signals should change next.

[0563] The AI ​​agent integrates and analyzes historical and real-time data. For example, based on traffic flow patterns at an intersection during the morning rush hour, the server might determine that keeping the traffic light green longer than usual would reduce congestion. Based on this prediction, the server sends appropriate control commands to the traffic signal control system to adjust the traffic light display.

[0564] Users receive predictive information from an AI agent through their device. The device uses voice and screen to communicate information to the driver, such as, "The next traffic light will turn green in 20 seconds." This allows drivers to avoid excessive acceleration or sudden braking and to drive smoothly. For example, when a user approaches an intersection, the device notifies them, "Slowing down will allow you to time your drive to coincide with the traffic light turning green," thereby contributing to smoother traffic flow.

[0565] Furthermore, the server monitors the user's driving behavior and collects feedback information. This data is used to improve the overall accuracy of the system and is reflected in subsequent predictive models. In this way, the system is continuously improved, contributing to the reduction of traffic congestion and improved fuel efficiency.

[0566] The following describes the processing flow.

[0567] Step 1:

[0568] The server collects traffic data. The server acquires data in real time from sensors and cameras installed at intersections and records the number of vehicles, their speeds, the current status of traffic lights, and other information in a database.

[0569] Step 2:

[0570] The server preprocesses the data. The server ensures data quality by removing outliers from the collected data and converting it into a format suitable for analysis.

[0571] Step 3:

[0572] The server uses an AI agent to analyze traffic conditions. The server provides pre-processed data to the AI ​​agent, which analyzes traffic flow and past signal patterns to predict signal changes.

[0573] Step 4:

[0574] The server optimizes the signals. Based on the analysis results of the AI ​​agent, the server adjusts the timing of the traffic light changes and optimizes the display state of the remotely controllable traffic lights.

[0575] Step 5:

[0576] The terminal notifies the user of information. The terminal receives optimization information from the server and provides the user with information such as "The next signal will turn red in 10 seconds" through voice or screen display.

[0577] Step 6:

[0578] The user adjusts their driving behavior. Based on notifications from their device, the user adjusts their driving pace to avoid sudden acceleration and deceleration and to ensure smooth driving.

[0579] Step 7:

[0580] The server collects feedback. The server monitors the user's driving patterns and changes in traffic conditions, and collects feedback data based on that.

[0581] Step 8:

[0582] The server updates the AI ​​model. The server uses the collected feedback data to retrain the AI ​​agent, improving the prediction accuracy of signal control.

[0583] (Example 1)

[0584] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0585] It is necessary to improve the efficiency and accuracy of traffic signal management to alleviate traffic congestion while ensuring smooth traffic flow. Existing systems are insufficient in real-time data analysis and signal control optimization, and the provision of appropriate information to drivers is limited, so further improvements are needed.

[0586] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0587] In this invention, the server includes a device for collecting traffic information, a device for analyzing the collected information and predicting the operating timing of the control device, and a device for optimizing the state of the control device based on the predicted operating timing. This enables highly accurate signal control in real time, realizing effective information provision to drivers and smooth management of traffic flow.

[0588] A "device for collecting traffic information" is a device that has the function of acquiring traffic-related data using sensors and cameras and storing it in digital format.

[0589] A "device that analyzes and predicts the timing of control device operation" is a device that analyzes collected traffic data and executes an algorithm to predict the timing of signal system operation.

[0590] A "device for optimizing the state of a control device" is a device that generates commands to adjust the operation of a signaling system based on predicted operating timings, thereby achieving optimal traffic conditions.

[0591] A "device for monitoring and collecting data on the user's driving behavior" is a device that detects the user's driving patterns and records driving data and driving behavior.

[0592] A "device that updates analysis and prediction equipment and improves accuracy using data" is a device that utilizes collected feedback data to dynamically improve analysis and prediction algorithms, thereby increasing the overall system accuracy.

[0593] This invention provides a system for collecting and analyzing traffic information in real time in order to optimize traffic signal management. The system mainly consists of a server, terminals, and signal control devices.

[0594] The server collects traffic information using sensors and cameras. This includes various sensors installed at intersections that measure the number of vehicles, their speeds, and traffic flow in real time. The collected data is sent to the server and then passed on to an AI agent. The AI ​​agent uses a generative AI model to analyze past traffic patterns and real-time information to predict the optimal timing for traffic signal operation. For example, it can decide to extend the green light duration to alleviate congestion during rush hour.

[0595] The terminal's role is to notify the user of signal timing prediction information from the server. For example, when a driver approaches an intersection, the terminal notifies the driver that "maintaining your speed will allow you to time your arrival with the green light," enabling smoother driving. This information is provided through the terminal's display and voice assistant. The terminal also monitors the user's driving behavior and collects feedback data to achieve even more accurate traffic management.

[0596] Furthermore, the server uses the collected feedback data to update its analysis model to improve the overall accuracy of the system. This continuous improvement process leads to optimization of signal control, which is expected to result in reduced traffic congestion and improved fuel efficiency.

[0597] An example of a prompt might be: "At 7:30 AM, predict the optimal signal timing at the XX intersection based on real-time traffic flow data. Also, generate advice to help users navigate the intersection smoothly." This prompt allows the AI ​​model to provide data for optimal signal control based on specific traffic conditions.

[0598] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0599] Step 1:

[0600] The server uses a device that collects real-time traffic information from sensors and cameras installed at intersections. Input data includes the number of vehicles, their speed, and traffic flow. This data is recorded in a log file and prepared for the next analysis step.

[0601] Step 2:

[0602] The server passes the collected traffic data to the AI ​​agent. The AI ​​agent uses a generative AI model to analyze the data and predicts the timing of traffic light operation based on the results. The input consists of collected real-time data and historical traffic pattern data. The analysis results in the output of optimized control timing, such as increasing the green light duration.

[0603] Step 3:

[0604] The server sends a command to the signal control device based on the analysis results. Specifically, it instructs the control device to extend the green light duration by 30 seconds. The input in this step is the optimized data output from the AI ​​agent, which adjusts the state of the traffic lights.

[0605] Step 4:

[0606] The terminal retrieves signal timing prediction information from the server and notifies the user. The terminal's input is the prediction information from the server, and its output is voice guidance and screen display for the driver. Specifically, the terminal notifies the driver, "If you maintain your speed, you will be able to time your arrival to coincide with the green light," allowing the driver to pass through the intersection smoothly.

[0607] Step 5:

[0608] The server monitors the user's driving behavior and collects feedback data. Inputs include information such as the user's driving speed and frequency of stopping at traffic lights. This data is used to update the analysis model, improving the system's accuracy and influencing future predictions. The output is presented as an improved traffic control algorithm.

[0609] (Application Example 1)

[0610] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0611] Managing traffic signals in cities is a critical issue from the perspective of traffic congestion and fuel efficiency. Conventional traffic signal systems often rely on historical data and static timing, making them incapable of responding to real-time traffic conditions. Furthermore, drivers lack the information necessary to efficiently manage waiting times and acceleration / deceleration, which further exacerbates congestion. To improve this situation, a system is needed that dynamically optimizes signals based on real-time traffic data and provides drivers with effective information.

[0612] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0613] In this invention, the server includes means for collecting traffic data, means for analyzing the collected data and predicting the control timing of the signal control device, means for optimizing the state of the signal control device based on the predicted control timing, and means for notifying the user of the timing of changes in traffic signals by voice and advising on the optimal driving speed. This enables efficient management of traffic flow in real time, and drivers can take appropriate driving actions to alleviate congestion and improve fuel efficiency.

[0614] "Traffic data" refers to information about moving objects such as the number of vehicles at intersections and on roads, their speed, and traffic flow.

[0615] A "traffic signal control device" is a device that controls the display of traffic signals and manages traffic flow at intersections and on roads.

[0616] "Predictive means" refers to functions or methods for estimating future states or events based on predetermined data.

[0617] "Voice notification means" refers to functions or devices that transmit information to users using voice.

[0618] "Driving advice" refers to instructions and suggestions provided to drivers to ensure safe and efficient travel, including instructions regarding appropriate speed and behavior.

[0619] The system for carrying out this invention includes a server, a terminal, and a signal control device. The server collects traffic data using sensors and cameras installed at intersections and on roads. This includes the number of vehicles, speed, and traffic flow. Data collection also utilizes data analysis libraries such as Python and TensorFlow.

[0620] The server analyzes collected traffic data and uses a generative AI model to predict the optimal control timing for signal control devices in real time. This AI model integrates and analyzes historical and real-time data to achieve efficient signal control.

[0621] The terminal provides voice notifications to the user (driver) based on information from the server. Specifically, it provides voice notifications to the driver regarding the optimal speed and timing of traffic light changes, assisting with driving. This voice notification function allows drivers to optimize acceleration and deceleration, which can help alleviate traffic congestion and improve fuel efficiency.

[0622] As a concrete example, the terminal might advise the driver via voice, "There are 23 seconds until the next traffic light. If you maintain a speed of 55 km / h, you can pass through without stopping." In this way, drivers can anticipate traffic flow and contribute to smoother traffic flow.

[0623] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0624] Step 1:

[0625] The server collects traffic data from sensors and cameras installed along the roads. This data includes vehicle count, speed, and traffic flow. The input is real-time sensor and camera data, and the output is the collected raw traffic data.

[0626] Step 2:

[0627] The server analyzes the collected traffic data. It cleanses the data and removes noise using tools such as Python or TensorFlow. The input for this step is the raw traffic data collected in step 1, and the output is data that has been formatted for analysis.

[0628] Step 3:

[0629] The server uses a generative AI model to predict the optimal control timing for the signal control device. Analyzed data is input into the model to calculate the timing for optimizing the signal state. The input here is formatted traffic data, and the output is an instruction for the control timing to the signal control device.

[0630] Step 4:

[0631] The terminal provides voice notifications to the user based on control timing information received from the server. Specifically, it provides real-time advice to the driver using prompts such as, "23 seconds until the next traffic light. If you maintain a speed of 55 km / h, you can pass through the traffic light without stopping." The input here is control timing information from the server, and the output is a voice notification to the user.

[0632] Step 5:

[0633] The user receives notifications from the device and takes the recommended speed and driving actions. This helps avoid excessive acceleration and braking, improving fuel efficiency. The input is voice advice from the device, and the output is the actual driving actions.

[0634] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0635] This invention combines a system for optimizing the control of traffic signals with an emotion engine that recognizes user emotions. The system includes a server, a user terminal, a signal control device, and an emotion engine.

[0636] The server collects traffic data in real time from sensors and cameras installed at intersections. The server passes the data to an AI agent, which analyzes traffic flow and predicts the timing of traffic signal changes. This information is sent to the traffic signal control system and used to implement optimized traffic signals.

[0637] Furthermore, the user terminal is equipped with an emotion engine that detects emotions by analyzing the driver's facial expressions and tone of voice through the camera and microphone. For example, if the user is feeling stressed, the emotion engine detects this state and provides appropriate feedback to the system.

[0638] The server takes feedback from the emotion engine into account and adjusts traffic signals and user notifications accordingly. For example, if it determines that a driver is irritated, a smoother signal pattern will be suggested. The user terminal also provides driving advice that takes the user's emotional state into account. For example, it might play a voice message such as, "Take a deep breath and relax while you wait."

[0639] Furthermore, the server continuously monitors data on the user's emotional state and driving conditions, and collects feedback. This data is used to improve the AI ​​agent, contributing to increased accuracy in traffic signal control. In this way, the system not only improves traffic efficiency but also provides comfortable driving support for the user.

[0640] The following describes the processing flow.

[0641] Step 1:

[0642] The server collects traffic data. Using sensors and cameras installed at intersections, the server acquires real-time data on the number of vehicles, their speeds, and traffic flow, and stores it in a database.

[0643] Step 2:

[0644] The server preprocesses the traffic data it acquires. The server removes noise and outliers from the acquired data and processes it into a format suitable for analysis.

[0645] Step 3:

[0646] The server uses an AI agent to analyze traffic conditions. Based on pre-processed data, the server analyzes traffic flow and predicts when the next traffic light will change.

[0647] Step 4:

[0648] The device recognizes the user's emotions using an emotion engine. The device uses an in-car camera and microphone to capture the user's facial expressions and voice, and the emotion engine analyzes emotions such as stress, frustration, and relaxation.

[0649] Step 5:

[0650] The server optimizes signals and adjusts notifications. The server considers the AI ​​agent's analysis results and emotional data from the terminal to adjust the timing of traffic light switching and send instructions to the signal control unit. It also customizes the content of information provided to the user.

[0651] Step 6:

[0652] The device notifies the user of information tailored to their emotional state. The device provides messages appropriate to the user's emotional state and sends notifications that include specific advice, such as, "You have 8 seconds until the next traffic light. Please drive calmly."

[0653] Step 7:

[0654] The user adjusts their driving behavior. Based on information from their device, the user strives to maintain a reasonable driving pace, resulting in safe and smooth driving.

[0655] Step 8:

[0656] The server collects feedback. The server records changes in the user's driving behavior and emotional state, and uses this data to retrain the AI ​​model, contributing to improved system performance.

[0657] (Example 2)

[0658] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0659] In modern transportation systems, controlling traffic signals is crucial for smooth traffic flow. However, traditional methods have struggled to optimize signals while considering traffic conditions and the emotional state of individual drivers. As a result, congestion and driver stress have not always been mitigated. Furthermore, the lack of mechanisms to utilize real-time feedback on changing traffic conditions and driver emotions has created a need to improve both traffic efficiency and safety.

[0660] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0661] In this invention, the server includes means for acquiring traffic information, means for analyzing the acquired information and predicting the control timing of the signal device, and means for detecting the user's emotional state. This makes it possible to optimize signal control by simultaneously considering traffic conditions and the driver's emotional state, thereby improving traffic efficiency and driver comfort.

[0662] "Traffic information" refers to data that shows the state of traffic flow, including the number of vehicles, their speed, and the length of queues waiting at traffic lights.

[0663] "Traffic signaling equipment" refers to traffic lights used to control the flow of traffic at intersections and other locations.

[0664] "Control timing" refers to the setting of the timing for when traffic signals change, and is optimized to ensure a smooth flow of traffic.

[0665] "User emotional state" refers to information that indicates the user's psychological state and reactions, and is used to evaluate things like stress levels and calmness.

[0666] "Feedback" refers to response information regarding the user's driving behavior and emotional state, which is useful for improving the system and increasing the accuracy of signal control.

[0667] This invention aims to achieve optimal signal control in a traffic signal control system by combining traffic flow and driver emotions. Specifically, it is configured as a system including a server, terminals, and control devices such as traffic signals.

[0668] The server first collects traffic information through various sensors and cameras installed at intersections. This information includes data such as vehicle flow, speed, and the length of queues of cars waiting at traffic lights. Sensor technology and high-resolution cameras are used to collect this data.

[0669] The collected data is analyzed by an AI agent on the server. The AI ​​agent uses machine learning algorithms to model traffic conditions and predict the timing of the next traffic signal. The software required to determine the timing of traffic signal control includes statistical analysis tools and a machine learning environment.

[0670] Meanwhile, the user terminal is equipped with an emotion engine that analyzes the driver's facial expressions and voice tone through the camera and microphone. This analysis detects the user's emotional state. This technology is realized using computer vision and voice analysis algorithms.

[0671] The server takes into account the emotional state obtained from the user's terminal and adjusts traffic signals and provides appropriate notifications to the user. For example, if congestion is expected or the user's emotional state indicates stress, a signal pattern is suggested to allow for smoother traffic flow. The user terminal also provides voice advice such as, "Take a deep breath and relax while you wait."

[0672] Furthermore, the system continuously collects feedback and uses it to improve the accuracy of signal control by enhancing the AI ​​agent. This method simultaneously achieves traffic efficiency and a comfortable driving experience for users.

[0673] An example of a prompt would be, "How should this system use real-time driver sentiment analysis and traffic data to adjust traffic light timing and optimize traffic flow?"

[0674] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0675] Step 1:

[0676] The server collects traffic information from sensors and cameras installed at intersections. Inputs include the number of vehicles, their speed, and the length of queues of cars waiting at traffic lights. By collecting this data, the current traffic situation can be understood. Specifically, sensors detect vehicle movement, and cameras record the footage.

[0677] Step 2:

[0678] The server passes the collected traffic data to the AI ​​agent, which analyzes the traffic flow. The input is the data obtained in step 1, and the output of the analysis is a prediction of the next signal timing. The AI ​​agent applies a machine learning algorithm to model the traffic flow. Specifically, the AI ​​analyzes the data patterns during peak times and derives the appropriate timing for signal control.

[0679] Step 3:

[0680] The server transmits the analysis results to the signal control unit, which then adjusts the timing of the traffic lights. The predicted signal timing, which is the output of step 2, is used as input, and the optimized signal control is performed as output. Specifically, the signal control unit adjusts the timing of the red and green lights to ensure smooth traffic flow.

[0681] Step 4:

[0682] The user terminal uses an emotion engine to detect the driver's emotions. Inputs include facial expression data and voice tone from the terminal's camera and microphone, and output is the user's emotional state. Specifically, the camera captures the driver's face, and analysis software determines the emotion.

[0683] Step 5:

[0684] The user terminal sends the detected emotional state to the server, which uses it as feedback information. The input is the emotional state obtained in step 4, and the output is a suggested additional adjustment for signal control. Specifically, the server receives the emotional state and regenerates the suggested adjustment for signal control.

[0685] Step 6:

[0686] The server provides notifications and adjustments to the user and signals based on their emotional state. The input is the feedback information from step 5, and the output is updated signal patterns and user notifications. Specifically, a message encouraging relaxation is sent to the user through the device.

[0687] Step 7:

[0688] The server continuously monitors traffic conditions and user feedback, accumulating this data to improve the system's accuracy. Inputs include real-time traffic data and feedback, while output contributes to improving the AI ​​agent. Specifically, new data is incorporated into the AI ​​model, optimizing the control algorithm.

[0689] (Application Example 2)

[0690] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0691] Conventional traffic signal control systems optimize signals using only traffic data, failing to consider the impact of drivers' emotional states on traffic flow. As a result, emotions such as stress and frustration could negatively affect driving behavior. Therefore, there was a need to develop a traffic signal control system that takes drivers' emotions into account.

[0692] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0693] In this invention, the server includes means for collecting traffic data, means for recognizing the driver's emotions, and means for notifying the user of optimized traffic signal information and providing driving advice. This enables real-time traffic signal control that takes into account the driver's emotional state.

[0694] "Means of collecting traffic data" refers to devices and methods that use sensors and cameras installed at intersections and on roads to collect information about traffic conditions and vehicle flow in real time.

[0695] "Means for predicting the timing of traffic signal control" refers to devices or methods for analyzing collected traffic data and calculating the optimal timing for signal changes at each intersection.

[0696] "Means for optimizing the state of traffic signals" refers to devices or methods for controlling and setting traffic signals to maximize traffic efficiency based on predicted control timing.

[0697] "Means of recognizing a driver's emotions" refers to devices and methods that analyze a driver's facial expressions and voice via cameras and microphones to infer and judge their emotional state.

[0698] "Means for analyzing recognized emotional information and further optimizing signal control" refers to devices or methods that adjust the control patterns of traffic signals based on driver emotional data to make traffic flow smoother.

[0699] "Means of notifying users and providing driving advice" refers to devices and methods that inform drivers of optimized traffic signal information and safe and comfortable driving advice through voice or visual means.

[0700] A system implementing this invention includes a server equipped with the necessary functions to collect and analyze traffic data and optimize traffic signal control, and a user terminal for recognizing drivers' emotions and utilizing that information.

[0701] The server collects traffic data using sensors and cameras installed at intersections. This data is analyzed in real time and processed by an AI agent to evaluate traffic flow. Based on the analysis results, control patterns are generated to optimize the timing of traffic light control. Machine learning frameworks such as Python and TensorFlow are used for processing by the AI ​​agent.

[0702] Meanwhile, on the user's device, an emotion engine operates. This engine collects the driver's facial expressions and voice tone via cameras and microphones built into the smartphone or in-car system, and recognizes their emotions. OpenCV can be used for image processing, and machine learning frameworks such as TensorFlow are used for voice analysis.

[0703] This allows the server to provide users with advice based on their emotional state, in addition to controlling traffic signals. For example, if the system detects that a driver is stressed, it can adjust the signal control pattern to facilitate smoother traffic flow. An actual voice message might be something like, "Take a deep breath and relax."

[0704] An example of a prompt for a generative AI model would be, "Generate the optimal traffic signal pattern when the driver's stress level is high."

[0705] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0706] Step 1:

[0707] The server collects real-time traffic data from sensors and cameras installed at intersections. Inputs include video and numerical data from sensors and cameras, which are converted into digital signals. Outputs are recorded as traffic flow and vehicle location information.

[0708] Step 2:

[0709] The server analyzes collected traffic data using an AI agent. It receives digitized traffic information as input and analyzes traffic patterns using an AI model (such as TensorFlow). The output is predictive data regarding traffic signal control timing. Specific operations include detecting traffic volume peaks and congestion levels.

[0710] Step 3:

[0711] The server optimizes the signal change pattern based on predicted control timing. It uses the analysis results of an AI agent as input to perform appropriate timing calculations. The output is an optimized signal schedule. Its operation involves generating a smooth signal change sequence.

[0712] Step 4:

[0713] The device collects facial expressions and voice data from the driver's smartphone or in-vehicle system via cameras and microphones. The input is real-time video and audio data, which is sent to the emotion engine. The output is data indicating the driver's emotional state. Specific operations include emotion estimation using a facial recognition algorithm.

[0714] Step 5:

[0715] The terminal transmits the driver's emotional state to a server, which is used as feedback for further optimization of signal control. The input is emotional data sent from the terminal, and the output is an adaptive signal pattern corresponding to the emotion. In this step, a method is employed to analyze the emotional data and incorporate it into the traffic signal control.

[0716] Step 6:

[0717] The user terminal provides drivers with optimized signal information and emotion-based advice. Inputs include an optimized signal schedule and driver emotion information, while output is visual or audio advice. Specific operations include a process of generating messages using speech synthesis functionality.

[0718] Step 7:

[0719] The server updates the AI ​​agent using the obtained driver emotional state and traffic data to improve accuracy. The input is feedback data, which is used as training data for the generating AI model. The output is the improved predictive model. The process involves retraining the AI ​​model and tuning its parameters.

[0720] 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.

[0721] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0722] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0723] 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.

[0724] Figure 9 shows an 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.

[0725] 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.

[0726] 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.

[0727] 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, motorcycles, etc., 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, for example, based 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.

[0728] 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."

[0729] 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.

[0730] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0731] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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 the like 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.

[0740] 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.

[0741] The following is further disclosed regarding the embodiments described above.

[0742] (Claim 1)

[0743] Means of collecting traffic data,

[0744] A means of analyzing collected data and predicting the control timing of traffic signals,

[0745] Means for optimizing the state of a traffic signal based on predicted control timing,

[0746] A means of notifying users of optimized traffic signal information,

[0747] A system that includes this.

[0748] (Claim 2)

[0749] The system according to claim 1, further comprising means for monitoring traffic flow and collecting feedback on user driving behavior.

[0750] (Claim 3)

[0751] The system according to claim 1, further comprising means for updating analysis and prediction means and improving accuracy using collected feedback data.

[0752] "Example 1"

[0753] (Claim 1)

[0754] A device for collecting traffic information,

[0755] A device that analyzes collected information and predicts the operating timing of the control device,

[0756] A device that optimizes the state of the control device based on predicted operating timing,

[0757] A device that notifies the user of information about the optimized control device,

[0758] A system that includes this.

[0759] (Claim 2)

[0760] The system according to claim 1, further comprising a device for monitoring traffic flow and collecting data on the driving behavior of users.

[0761] (Claim 3)

[0762] The system according to claim 1, further comprising a device that updates and improves the accuracy of analysis and prediction devices using collected data.

[0763] "Application Example 1"

[0764] (Claim 1)

[0765] Means of collecting traffic data,

[0766] A means for analyzing collected data and predicting the control timing of a signal control device,

[0767] Means for optimizing the state of the signal control device based on predicted control timing,

[0768] A means of notifying the user of information about the optimized signal control device,

[0769] A means of notifying users by voice about the timing of changes in traffic signals and advising them on the optimal driving speed,

[0770] A system that includes this.

[0771] (Claim 2)

[0772] The system according to claim 1, further comprising means for monitoring traffic flow and collecting feedback on users' travel behavior.

[0773] (Claim 3)

[0774] The system according to claim 1, further comprising means for updating analysis and prediction means to improve accuracy using collected feedback data and optimizing driving advice based on the timing of changes in traffic signals.

[0775] "Example 2 of combining an emotion engine"

[0776] (Claim 1)

[0777] Means of obtaining traffic information,

[0778] A means for analyzing acquired information and predicting the control timing of a signal device,

[0779] Means for optimizing the state of a signaling device based on predicted control timing,

[0780] A means of detecting the user's emotional state,

[0781] Means for adjusting information to the signaling device and the user based on the detected emotional state,

[0782] A system that includes this.

[0783] (Claim 2)

[0784] The system according to claim 1, further comprising means for monitoring traffic flow and collecting feedback on users' driving behavior and emotions.

[0785] (Claim 3)

[0786] The system according to claim 1, further comprising means for updating analysis and prediction means and improving accuracy using collected feedback data.

[0787] "Application example 2 when combining with an emotional engine"

[0788] (Claim 1)

[0789] Means of collecting traffic data,

[0790] A means of analyzing collected data and predicting the control timing of traffic signals,

[0791] Means for optimizing the state of a traffic signal based on predicted control timing,

[0792] Means of recognizing the driver's emotions,

[0793] A means to analyze recognized emotional information and further optimize signal control,

[0794] A means of notifying users of optimized traffic signal information and providing driving advice,

[0795] A system that includes this.

[0796] (Claim 2)

[0797] The system according to claim 1, further comprising means for monitoring traffic flow and collecting feedback on the user's driving behavior and emotional state.

[0798] (Claim 3)

[0799] The system according to claim 1, further comprising means for updating and improving the accuracy of analysis and prediction means using collected feedback data and sentiment information. [Explanation of Symbols]

[0800] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of collecting traffic data, A means of analyzing collected data and predicting the control timing of traffic signals, Means for optimizing the state of a traffic signal based on predicted control timing, A means of notifying users of optimized traffic signal information, A system that includes this.

2. The system according to claim 1, further comprising means for monitoring traffic flow and collecting feedback on user driving behavior.

3. The system according to claim 1, further comprising means for updating analysis and prediction means and improving accuracy using collected feedback data.

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