system

Abstract

We provide the system. [Solution] A device that analyzes traffic condition data acquired from traffic information devices, A device that optimizes the switching sequence of signal devices based on analyzed data, A device that communicates optimized signal operation to the signaling equipment, A device that predicts the color of the traffic signal when a vehicle passes through an intersection and transmits the prediction information, A device that notifies the driver via the in-vehicle information system, A device that automatically adjusts the vehicle's speed based on predicted 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 method for controlling a persona chatbot, which is 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 the chatbot's 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] Stress, fatigue of drivers caused by traffic congestion and waiting for signals, as well as deterioration of fuel efficiency are major problems in modern urban traffic. In particular, the inability of drivers to accurately predict signal changes and repeated unnecessary acceleration and deceleration exacerbate these problems. Furthermore, inappropriate control of traffic signals also leads to an increase in carbon dioxide emissions. The purpose of this invention is to solve these problems, smooth the traffic flow, and reduce the burden on drivers.

Means for Solving the Problems

[0005] This invention provides a means for analyzing traffic data acquired from traffic sensors and optimizing the timing of traffic light changes based on that data. Based on the analyzed traffic data, the optimized signal pattern is transmitted to the traffic lights, and information is also transmitted that predicts the color of the signal when the driver passes through an intersection. Furthermore, by notifying the driver of this predictive information through an in-vehicle terminal, unnecessary acceleration and deceleration are reduced. This contributes to alleviating traffic congestion, improving fuel efficiency, and reducing carbon dioxide emissions.

[0006] A "traffic sensor" is a device installed to collect data on traffic flow and vehicle movement.

[0007] "Traffic data" refers to information collected from traffic sensors, and includes data such as vehicle speed, traffic volume, distance between vehicles, and road surface conditions.

[0008] "Analysis" is the process of processing collected traffic data to derive meaningful information.

[0009] A "traffic light" is a device that emits light signals to control the passage of vehicles and pedestrians at intersections and on roads.

[0010] "Switching timing" refers to the time setting for when the color of a traffic light changes.

[0011] "Optimization" is the process of adjusting the operation methods and timing of traffic signals in order to pursue the most efficient and effective state depending on the situation.

[0012] The term "driver" refers to a person who operates a vehicle and participates in the flow of traffic.

[0013] An "intersection" is a point where roads intersect or merge, and where traffic is controlled by traffic lights.

[0014] The "in-vehicle terminal" is a device installed inside a vehicle and is capable of receiving and displaying information through its communication function.

[0015] "Notification" refers to the act of presenting information or warnings from the system to the driver.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the labeled 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.

[0020] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the labeled 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, etc.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention relates to a system that optimally controls traffic signals using data from traffic sensors and provides drivers with predictive signal information. This system works in conjunction with a server and an in-vehicle terminal via communication to improve traffic efficiency and reduce the burden on drivers.

[0038] The server first acquires real-time data from traffic sensors installed at each intersection. This allows for real-time monitoring of traffic flow and congestion. The server then inputs this data into an AI model for analysis. This model uses machine learning to analyze past and present traffic patterns and optimize the timing of traffic signal changes.

[0039] Once the traffic light control is optimized, the server remotely transmits the results to each traffic light. This ensures that the signal color changes at the appropriate time, reducing unnecessary waiting times and congestion.

[0040] Next, the server predicts when the driver's vehicle will reach the intersection and calculates the traffic light color at that time. This prediction information is then transmitted to the in-vehicle terminal.

[0041] The in-vehicle terminal receives signal prediction information from the server and uses this information to provide specific guidance to the driver. For example, the terminal may display "The next signal will turn red in 20 seconds" on its screen or issue a warning to the driver through voice guidance.

[0042] Users (drivers) can use this predictive information to adjust the vehicle's speed and route. Specifically, by knowing the remaining time until the next traffic light turns red, they can avoid sudden acceleration and braking, resulting in smoother driving.

[0043] In this way, the present invention contributes to smoother traffic flow and improved driving comfort through efficient control of traffic signals and accurate information provision to drivers.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server acquires data in real time from traffic sensors and cameras installed at intersections. This includes information such as traffic volume, vehicle speed, and following distance. The server stores the acquired data in a database.

[0047] Step 2:

[0048] The server inputs accumulated traffic data into an AI model for analysis. The AI ​​model uses machine learning algorithms to predict traffic flow based on past data and current conditions. Based on the analysis results, the server calculates the optimal timing for switching traffic signals.

[0049] Step 3:

[0050] The server transmits the optimal signal control pattern obtained from the analysis to the traffic lights. The traffic lights switch signal colors according to the received pattern, controlling traffic flow to ensure smooth operation.

[0051] Step 4:

[0052] The server predicts when the vehicle will arrive at the intersection and calculates the color of the traffic light when the driver passes through the intersection. This predicted information is then transmitted to the in-vehicle terminal.

[0053] Step 5:

[0054] The terminal notifies the driver via display and voice based on signal prediction information received from the server. For example, it provides information such as, "The next traffic light will turn red in 15 seconds," helping the driver adjust their speed appropriately.

[0055] Step 6:

[0056] The user (driver) adjusts their driving style based on notifications from the device. By following predictive information, the user can avoid unnecessary acceleration and deceleration, allowing them to pass through intersections smoothly and efficiently.

[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] Conventional traffic signal control methods are unable to keep up with real-time changes in traffic conditions, resulting in inefficient signal timing that causes traffic congestion and unnecessary waiting times. Furthermore, it is difficult to provide drivers with appropriate traffic signal information, potentially compromising driving smoothness and safety. Therefore, there is a need for a system that can streamline traffic flow and provide drivers with accurate and timely signal information.

[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 means for analyzing traffic flow data acquired from traffic measurement equipment, means for optimizing the switching timing of traffic signal devices based on the analyzed data, and means for transmitting the optimized signal timing to the traffic signal devices. This enables efficient management of traffic flow and accurate provision of signal information to drivers.

[0062] A "traffic measurement device" is a device that detects traffic flow, speed, and the presence of vehicles in real time and outputs that information as a digital signal.

[0063] "Traffic flow data" refers to statistical information obtained from traffic measurement equipment, including the number and speed of vehicles passing through a specific point within a given time period.

[0064] "Means of analysis" refers to algorithms and programs used to process acquired data and identify traffic patterns and trends.

[0065] A "traffic signal system" is a device that displays signals to ensure safety at intersections and pedestrian crossings, using red, yellow, and blue lights to direct traffic.

[0066] "Switching timing" refers to the time interval between when a traffic signal system displays each light signal.

[0067] "Wireless control" is a method of operating traffic signal equipment from a remote location using a communication network, and is a control method that does not require a physical connection.

[0068] A "driver" refers to a person who is currently operating a vehicle and is the entity that receives traffic information.

[0069] An "in-vehicle electronic device" is a digital device installed in a vehicle that displays or provides information to the driver via voice.

[0070] A "display device" is a device used to visually display information, and includes liquid crystal screens and LED displays.

[0071] An "audio output device" is a device that generates audio information based on stored digital data and provides it as audio through a speaker.

[0072] This invention is a system that analyzes and optimizes real-time traffic information using a server, in-vehicle electronic devices, and traffic measurement equipment to improve the efficiency of traffic signal control. The server receives data acquired from traffic measurement equipment and uses a generative AI model to analyze it. Specifically, it inputs data into a machine learning model built using software frameworks such as TENSORFLOW® or PyTorch to analyze traffic patterns.

[0073] The server performs calculations to optimize the timing of signal switching based on the analyzed data. The analysis results are transmitted wirelessly to the traffic signal equipment, and the operation schedule of the traffic signals is adjusted as needed. This makes it possible to reduce unnecessary delays and congestion, and to smooth the flow of traffic.

[0074] The in-vehicle electronic device receives predictive information transmitted from a server. This device can provide information to the driver in several ways. It can display information such as "The next traffic light will turn red in 30 seconds" on the display, or it can issue a warning to the driver using an audio output device. This allows the driver to properly guide the vehicle's speed and path, reducing the risk of sudden acceleration or braking.

[0075] As a concrete example, consider a family's vehicle heading to a tourist destination on a Saturday afternoon. A server uses an AI model to analyze road congestion in real time and optimizes traffic light switching based on the results. An in-vehicle electronic device informs the driver of the status of the next traffic light, enabling a smoother drive. An example of a prompt message would be, "Please tell me how to calculate the optimal traffic light switching timing based on data from traffic sensors and transmit it to the traffic lights and the in-vehicle terminal." In this way, the system achieves efficient control of traffic signals and accurate information provision to drivers.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The server acquires data in real time from traffic measurement equipment. This data includes the number of vehicles on the road, their speeds, and the status of traffic signals. The server receives this data and stores it in a database to prepare for later analysis. The input is data from traffic measurement equipment, and the output is the data record stored by the server.

[0079] Step 2:

[0080] The server inputs stored traffic data into a generating AI model. This AI model uses machine learning algorithms to analyze past and present traffic patterns. Data input is obtained from the server's database, and the AI ​​model performs predictive analysis based on pattern recognition. The output is an optimization proposal for signal switching timing, which is used in subsequent processing.

[0081] Step 3:

[0082] The server generates commands to optimize the switching timing of traffic signal devices using the analysis results from the AI ​​model. These commands are used to adjust the operating schedule of each traffic signal. The server transmits these commands to the traffic signals via a wireless communication network. The input is the analysis results from the AI ​​model, and the output is the specific command to the traffic signal.

[0083] Step 4:

[0084] The server predicts the traffic light color the driver will encounter at an intersection, sending this prediction to the vehicle's electronic device. This prediction is calculated by an AI model based on the car's current position and speed. The server then transmits this information to the electronic device. The inputs are the car's position and speed, and the AI ​​model's prediction data; the output is the predicted traffic light information.

[0085] Step 5:

[0086] The terminal receives signal prediction information from the server and notifies the driver. Specifically, the terminal displays the prediction information on its screen and warns the driver through voice guidance. The input is signal prediction information received from the server, and the output is visual and auditory information provided to the driver.

[0087] Step 6:

[0088] The user adjusts the vehicle's speed and driving operations based on the information provided by the device. For example, if the user knows how much time is left until the traffic light turns red, they can decelerate smoothly. The input is the information from the device, and the output is the user's driving actions.

[0089] (Application Example 1)

[0090] 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."

[0091] In recent years, with the advancement of autonomous driving technology, there has been a growing demand for smoother traffic flow and improved safety. However, conventional traffic signal control systems rely on fixed patterns, making them unable to flexibly respond to traffic conditions and resulting in congestion and unnecessary stops. Furthermore, the limited information available to drivers regarding predicting traffic signals makes efficient driving difficult. This invention aims to solve these problems.

[0092] 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.

[0093] In this invention, the server includes means for analyzing traffic condition data acquired from a traffic information device, means for optimizing the switching sequence of signal devices based on the analyzed data, and means for communicating the optimized signal operation to the signal devices. This makes it possible to optimize traffic flow and automatically adjust vehicle speeds based on predictive information.

[0094] A "traffic information device" is a device that acquires traffic condition data in real time and has the function of providing information necessary for signal control to a server.

[0095] "Analysis" is the process of analyzing traffic patterns based on acquired traffic data and deriving information useful for signal control.

[0096] A "traffic signal device" is a traffic light installed in an intersection area, and its role is to control the color of the signal to facilitate smooth traffic flow.

[0097] "Signal operation" refers to a pattern that indicates the sequence and timing of signal switching, which is adjusted to optimize traffic flow.

[0098] An "in-vehicle information system" is a device installed in a vehicle that has the function of notifying the driver of information transmitted from a server.

[0099] A "driver" is a person involved in operating a vehicle and is a user who utilizes signal prediction information to drive efficiently.

[0100] "Predictive information" refers to information that estimates future traffic light colors and timing of changes based on traffic condition data and AI analysis.

[0101] "Travel speed" refers to the speed at which a vehicle is moving, and it is adjusted according to traffic conditions and signal information.

[0102] The system implementing this invention optimizes traffic signal control based on traffic information to support autonomous vehicle driving. The server collects real-time traffic condition data from traffic information devices. This data is analyzed by a generative AI model using Python and TensorFlow. Based on the acquired data, the model optimizes the switching order of traffic signal devices and adjusts signal operation at the appropriate timing.

[0103] Optimized signal operation information is communicated to the signaling equipment, and the predicted timing of signal changes is transmitted to the on-board information system. The on-board information system notifies the driver of this predicted information and also has a function to automatically adjust the driving speed. This enables smooth passage through intersections.

[0104] As a concrete example, if the system predicts that a traffic light will turn green in an urban area in 3 seconds, the in-vehicle information system will adjust the vehicle's speed to allow it to pass through the intersection. Through this process, traffic flow will be smoother and the operational efficiency of autonomous vehicles will be improved.

[0105] An example of a prompt using a generative AI model is: "Based on the current traffic sensor data, calculate the estimated time until the traffic light at the next intersection turns red. Please output in seconds." This prompt allows the server to generate traffic light prediction information quickly and accurately.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The server receives real-time traffic data from traffic information devices. This data includes the number and speed of vehicles at each intersection, as well as the degree of road congestion. The server stores the received data in a database in preparation for subsequent analysis.

[0109] Step 2:

[0110] The server inputs accumulated traffic data into a generating AI model. This model, implemented using Python and TensorFlow, analyzes past and present traffic patterns. The server analyzes the data and calculates the optimal switching sequence for traffic signals. From this analysis, it can predict future signal change timings.

[0111] Step 3:

[0112] The server communicates optimized signal operation information, derived from the analysis results, to the signaling equipment. Network protocols are used for communication, instructing the signaling equipment to switch signals in the appropriate sequence. This streamlines traffic flow.

[0113] Step 4:

[0114] Along with signal operation information, the server transmits predicted signal information for when the vehicle approaches the intersection to the onboard information system. The transmitted information includes the timing of the next signal change and recommended vehicle speed.

[0115] Step 5:

[0116] The in-vehicle infotainment system receives predictive signal information transmitted from the server and generates instructions to automatically adjust the vehicle's speed. This allows the vehicle to avoid unnecessary stops and pass through intersections smoothly. The infotainment system also notifies the driver of this information via display and audio.

[0117] 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.

[0118] This invention combines an emotional engine with a system that optimizes traffic flow and reduces driver stress. The system includes a server, an in-vehicle terminal, and an emotional engine, which work together to assist the driver.

[0119] The server collects and analyzes traffic data acquired from traffic sensors and cameras in real time. By analyzing the traffic data using an AI model, the timing of traffic light changes is optimized. The optimal signal control pattern is transmitted to the traffic lights remotely, controlling them to ensure smooth traffic flow.

[0120] Furthermore, the server predicts the time it will take the vehicle to reach the intersection and calculates the traffic light color the driver will see when passing through the intersection. This information is transmitted to the in-vehicle terminal to help the driver accelerate and decelerate efficiently. The system also incorporates an emotion engine that evaluates the driver's emotions in real time.

[0121] The in-vehicle terminal receives predictive signal information transmitted from the server and emotion evaluation data from the emotion engine. Based on this, the terminal provides the driver with appropriate notifications tailored to the situation. For example, if the driver is experiencing stress, the notification content can be made gentler and the advice reduced, demonstrating a response that takes the driver's psychological state into consideration.

[0122] Drivers can adjust their driving based on notifications from the in-car terminal. The emotion engine evaluates the driver's emotional state, providing support tailored to their individual driving style and psychological state. This allows drivers to continue driving more safely and comfortably.

[0123] This invention not only improves the efficiency of traffic flow but also reduces the psychological burden on drivers, thereby enhancing the overall driving experience. For example, if a driver is frustrated in congested traffic, the emotional engine can recognize this state and provide calming voice guidance, thereby promoting safe driving.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The server acquires real-time traffic data from traffic sensors and cameras installed on the roads. This data includes the speed and volume of passing vehicles, traffic light waiting times, etc., and is aggregated into a database.

[0127] Step 2:

[0128] The server inputs aggregated traffic data into an AI model, which analyzes traffic conditions in real time. This model incorporates an algorithm to optimize traffic signal switching timing based on historical data. The analysis results are transmitted to the traffic signals, and signal switching is performed remotely.

[0129] Step 3:

[0130] The server performs calculations to predict when the driver's vehicle will reach the intersection. Based on this prediction, it determines the color of the traffic light when the driver passes through the intersection and transmits this information to the in-vehicle terminal.

[0131] Step 4:

[0132] The server inputs not only traffic data but also data on the driver's facial expressions and voice, obtained from cameras and sensors inside the vehicle, into the emotion engine. The emotion engine analyzes the driver's emotional state and provides the results to the server.

[0133] Step 5:

[0134] The terminal creates notifications for the driver based on predictive signal information received from the server and emotional data from the emotion engine. These notifications are delivered via display and voice guidance, designed to minimize driver stress.

[0135] Step 6:

[0136] The user (driver) adjusts their driving style according to notifications from their device. Based on traffic light color prediction information, they can optimize acceleration and deceleration, and also take into account advice that considers their own emotional state, enabling safe and comfortable driving.

[0137] (Example 2)

[0138] 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".

[0139] In today's traffic environment, traffic congestion and poorly timed traffic signals disrupt efficient traffic flow, causing significant stress to drivers. In particular, prolonged periods of these conditions can increase drivers' psychological burden and potentially hinder safe driving. There is a need to address these challenges, optimize traffic flow, and reduce drivers' psychological burden.

[0140] 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.

[0141] In this invention, the server includes means for analyzing mobile data acquired from a traffic information device, means for optimizing the timing of signal device switching based on the analyzed information, and means for determining notification content according to the driver's emotional state and guiding the driver accordingly. This makes traffic flow smoother and enables safe and comfortable driving support that takes the driver's emotions into consideration.

[0142] A "traffic information device" is a device, such as a sensor or camera, installed to monitor traffic conditions on roads and is responsible for collecting real-time data on moving objects.

[0143] "Mobile vehicle data" refers to information such as the location, speed, and number of vehicles collected by traffic information devices, and is used for analyzing traffic conditions.

[0144] A "traffic signal device" is a traffic signal installed at intersections and on roads, and is a device that displays signals to vehicles and pedestrians in order to control the flow of traffic.

[0145] "Emotional processing means" refers to systems and algorithms for evaluating a driver's emotions in real time, enabling responses based on the driver's psychological state.

[0146] A "mobile terminal" is an electronic device installed in a vehicle that transmits notifications and information from a server to the driver.

[0147] A "driver" refers to the driver of a vehicle moving on a road, and is an individual positioned as a user of the transportation system.

[0148] "Optimization" refers to adjusting traffic signal switching and traffic management methods to achieve the best possible state in order to make traffic flow more efficient and smoother.

[0149] "Notification content" refers to information and instructions transmitted to the driver via a mobile terminal, including signal prediction information and driving advice.

[0150] This invention is a system aimed at improving traffic flow efficiency and reducing the psychological burden on drivers. This system provides driver assistance by coordinating a server, an in-vehicle terminal, and an emotion processing mechanism.

[0151] The server collects real-time mobile data from traffic information devices. This data is used to analyze traffic flow and calculate the optimal timing for switching traffic signals. The server then transmits the optimized signal control pattern to the traffic signals, thereby optimizing traffic flow at intersections.

[0152] The in-vehicle terminal receives information from the server and provides appropriate notifications to the driver. This includes predictive information about the color of traffic lights at intersections, allowing the driver to adjust their driving accordingly. Furthermore, the terminal evaluates the driver's emotional state in real time through an emotion processing system and generates notifications based on that information.

[0153] Drivers can use notifications from in-vehicle terminals to adjust their driving style and reduce stress while driving. For example, if a driver is experiencing high stress in congested traffic, an emotional processing system will detect this state and send a notification to encourage relaxation. This notification is provided through voice guidance and screen displays, allowing the driver to maintain safe and comfortable driving.

[0154] An example of a prompt might be, "Provide advice to help a driver in traffic congestion feel more relaxed." This prompt allows the system to generate appropriate feedback tailored to the driver's emotional state, supporting safer driving.

[0155] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0156] Step 1:

[0157] The server collects real-time mobile data from traffic information devices.

[0158] It receives data such as vehicle location, speed, and number from traffic information devices as input.

[0159] As part of data processing, this raw data is used to quantify traffic flow and analyze its trends.

[0160] The output will be an analysis showing the current traffic situation.

[0161] Step 2:

[0162] The server uses a generated AI model to optimize the switching timing of signal devices based on the analysis results.

[0163] The results of the traffic situation analysis obtained in Step 1 will be used as input.

[0164] As a data calculation, an algorithm is executed to calculate the optimal signal timing for smooth traffic flow.

[0165] The output generates an optimized signal switching pattern.

[0166] Step 3:

[0167] The server sends the optimized signal switching pattern to the signaling device.

[0168] The signal pattern generated in step 2 is used as the input.

[0169] In terms of specific operation, data is transmitted to a signaling device via the network, and the signal is switched based on that data.

[0170] As an output, the timing of traffic signals at intersections is appropriately adjusted.

[0171] Step 4:

[0172] The server uses predictive calculations based on an AI model to calculate the time it will take the driver to reach the intersection and predict the color of the traffic light.

[0173] The system receives real-time updated data on vehicle movements as input.

[0174] As part of the data processing, the signal status at the time of passing through the intersection is simulated, taking into account the driver's predicted arrival time.

[0175] The output generates information about the predicted signal color.

[0176] Step 5:

[0177] The terminal receives signal prediction information and emotional state evaluation results from the server.

[0178] The inputs received are the signal prediction information generated in step 4 and data from the emotion processing device.

[0179] Specifically, this involves preparing to provide appropriate notifications to the driver.

[0180] The output determines the content of the notification to the driver.

[0181] Step 6:

[0182] The user (driver) adjusts driving operations based on notifications from the device.

[0183] The input includes signal prediction information and emotion-based advice presented by the device.

[0184] Specifically, the system checks notifications and, if necessary, accelerates, decelerates, or changes lanes.

[0185] As an output, safer and more efficient operation will be achieved.

[0186] (Application Example 2)

[0187] 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 device 14 will be referred to as the "terminal."

[0188] Optimizing traffic flow and reducing driver stress are crucial challenges, but conventional traffic control systems struggle to optimize traffic flow while considering the driver's psychological state. Furthermore, timely signal changes based on traffic information and appropriate notifications to drivers remain challenges. Additionally, when driving is done under stress, psychological support for drivers is often insufficient.

[0189] 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.

[0190] In this invention, the server includes means for analyzing traffic data acquired from a traffic information collection device, means for optimizing the switching timing of an information display device based on the analyzed data, and means for evaluating the driver's emotional state and presenting information that takes the driver's psychological state into consideration based on the evaluation. This makes it possible to optimize the flow of traffic while simultaneously reducing the psychological burden on the driver and providing a safe and comfortable driving environment.

[0191] A "traffic information collection device" is a device used to collect data on traffic conditions, detecting things like the flow of vehicles and traffic density on roads using sensors and cameras.

[0192] "Means of analysis" refers to devices or software used to perform the process of analyzing collected data and extracting information according to a specific purpose.

[0193] An "information display device" is a device, such as a traffic light or a display, that provides information visually or audibly based on analyzed data.

[0194] A "driver's terminal" is a computer device installed inside a vehicle to provide information to the driver, and includes navigation and notification functions.

[0195] "Assessing emotional state" is a process of analyzing the driver's psychological and emotional state to determine appropriate responses, and often involves using an emotional engine.

[0196] "Psychologically sensitive information" refers to customized information provided in consideration of the driver's current psychological state, including relaxation techniques and advice.

[0197] An "automatic suggestion device" is a device that automatically provides selected relaxation methods and support information according to the driver's emotional state.

[0198] Modes for carrying out the invention

[0199] The system that implements this application consists of a traffic information collection device, a server, a driver terminal, and an automatic display device. The server aggregates and analyzes traffic data acquired from the traffic information collection device in real time. This analysis uses AI analysis modules such as TensorFlow and scikit-learn. Based on the analyzed data, the system optimizes the signal switching timing of the information display device and generates signal control information.

[0200] The driver terminal receives information from the server and notifies the driver. At the same time, it uses an emotion analysis library (e.g., affectiva) to evaluate the driver's emotional state and adjusts the notification content according to the driver's psychological state. Furthermore, based on the emotion evaluation, an automated presentation device provides relaxation-enhancing music, aromatherapy, visuals, etc.

[0201] For example, if a driver feels stressed while driving on a congested road, the server detects this and notifies the driver's device with the message, "Please enjoy some relaxing music." Simultaneously, the in-car audio system automatically plays relaxing music, and an aroma diffuser is activated to promote relaxation. In this way, it is possible to provide multifaceted support to reduce the driver's psychological burden.

[0202] An example of a prompt message might be: "Please come up with a new feature that analyzes the driver's emotional state in real time and suggests the optimal relaxation method."

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The server receives real-time traffic data from traffic information collection devices. This data includes vehicle position, speed, and traffic density information obtained from sensors and cameras. The server uses this data as input and analyzes it using an AI analysis module to output information necessary for optimizing signal switching. Specifically, it uses TensorFlow to apply a traffic flow model and predict the timing of the next signal operation.

[0206] Step 2:

[0207] Based on the analysis results, the server generates control data to optimize the signal switching timing of the information display device. Using a generated AI model, it calculates the predicted optimal signal pattern and sends the result to the traffic lights. This adjusts the signals to ensure smooth traffic flow at intersections.

[0208] Step 3:

[0209] The server transmits signal status prediction information to the terminal of a driver approaching the intersection. The driver's terminal receives this information and notifies the driver visually or audibly. Inputs include current and next signal status information, and outputs include messages displayed on the screen and voice guidance. The notifications are designed to help drivers react smoothly to the next signal.

[0210] Step 4:

[0211] The driver terminal analyzes the driver's voice and facial expression data using an emotion analysis library (e.g., affectiva). It uses the driver's facial expression data as input to evaluate their emotional state. Based on this evaluation, the terminal outputs optimal relaxation information for the driver. Specifically, if the status is determined to be high stress, the driver terminal will display a message such as, "Take a deep breath and refresh yourself."

[0212] Step 5:

[0213] The user receives instructions based on their emotional assessment results and accepts relaxation measures to adjust the in-car environment. An automated display system offers options such as playing relaxation music or activating an aroma diffuser. This allows the user to follow notifications on the driver's terminal and take specific actions to improve the in-car environment.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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".

[0230] This invention relates to a system that optimally controls traffic signals using data from traffic sensors and provides drivers with predictive signal information. This system works in conjunction with a server and an in-vehicle terminal via communication to improve traffic efficiency and reduce the burden on drivers.

[0231] The server first acquires real-time data from traffic sensors installed at each intersection. This allows for real-time monitoring of traffic flow and congestion. The server then inputs this data into an AI model for analysis. This model uses machine learning to analyze past and present traffic patterns and optimize the timing of traffic signal changes.

[0232] Once the traffic light control is optimized, the server remotely transmits the results to each traffic light. This ensures that the signal color changes at the appropriate time, reducing unnecessary waiting times and congestion.

[0233] Next, the server predicts when the driver's vehicle will reach the intersection and calculates the traffic light color at that time. This prediction information is then transmitted to the in-vehicle terminal.

[0234] The in-vehicle terminal receives signal prediction information from the server and uses this information to provide specific guidance to the driver. For example, the terminal may display "The next signal will turn red in 20 seconds" on its screen or issue a warning to the driver through voice guidance.

[0235] Users (drivers) can use this predictive information to adjust the vehicle's speed and route. Specifically, by knowing the remaining time until the next traffic light turns red, they can avoid sudden acceleration and braking, resulting in smoother driving.

[0236] In this way, the present invention contributes to smoother traffic flow and improved driving comfort through efficient control of traffic signals and accurate information provision to drivers.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The server acquires data in real time from traffic sensors and cameras installed at intersections. This includes information such as traffic volume, vehicle speed, and following distance. The server stores the acquired data in a database.

[0240] Step 2:

[0241] The server inputs accumulated traffic data into an AI model for analysis. The AI ​​model uses machine learning algorithms to predict traffic flow based on past data and current conditions. Based on the analysis results, the server calculates the optimal timing for switching traffic signals.

[0242] Step 3:

[0243] The server transmits the optimal signal control pattern obtained from the analysis to the traffic lights. The traffic lights switch signal colors according to the received pattern, controlling traffic flow to ensure smooth operation.

[0244] Step 4:

[0245] The server predicts when the vehicle will arrive at the intersection and calculates the color of the traffic light when the driver passes through the intersection. This predicted information is then transmitted to the in-vehicle terminal.

[0246] Step 5:

[0247] The terminal notifies the driver via display and voice based on signal prediction information received from the server. For example, it provides information such as, "The next traffic light will turn red in 15 seconds," helping the driver adjust their speed appropriately.

[0248] Step 6:

[0249] The user (driver) adjusts their driving style based on notifications from the device. By following predictive information, the user can avoid unnecessary acceleration and deceleration, allowing them to pass through intersections smoothly and efficiently.

[0250] (Example 1)

[0251] 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."

[0252] Conventional traffic signal control methods are unable to keep up with real-time changes in traffic conditions, resulting in inefficient signal timing that causes traffic congestion and unnecessary waiting times. Furthermore, it is difficult to provide drivers with appropriate traffic signal information, potentially compromising driving smoothness and safety. Therefore, there is a need for a system that can streamline traffic flow and provide drivers with accurate and timely signal information.

[0253] 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.

[0254] In this invention, the server includes means for analyzing traffic flow data acquired from traffic measurement equipment, means for optimizing the switching timing of traffic signal devices based on the analyzed data, and means for transmitting the optimized signal timing to the traffic signal devices. This enables efficient management of traffic flow and accurate provision of signal information to drivers.

[0255] A "traffic measurement device" is a device that detects traffic flow, speed, and the presence of vehicles in real time and outputs that information as a digital signal.

[0256] "Traffic flow data" refers to statistical information obtained from traffic measurement equipment, including the number and speed of vehicles passing through a specific point within a given time period.

[0257] "Means of analysis" refers to algorithms and programs used to process acquired data and identify traffic patterns and trends.

[0258] A "traffic signal system" is a device that displays signals to ensure safety at intersections and pedestrian crossings, using red, yellow, and blue lights to direct traffic.

[0259] "Switching timing" refers to the time interval between when a traffic signal system displays each light signal.

[0260] "Wireless control" is a method of operating traffic signal equipment from a remote location using a communication network, and is a control method that does not require a physical connection.

[0261] A "driver" refers to a person who is currently operating a vehicle and is the entity that receives traffic information.

[0262] An "in-vehicle electronic device" is a digital device installed in a vehicle that displays or provides information to the driver via voice.

[0263] A "display device" is a device used to visually display information, and includes liquid crystal screens and LED displays.

[0264] An "audio output device" is a device that generates audio information based on stored digital data and provides it as audio through a speaker.

[0265] This invention is a system that analyzes and optimizes real-time traffic information using a server, in-vehicle electronic devices, and traffic measurement equipment to improve the efficiency of traffic signal control. The server receives data acquired from traffic measurement equipment and uses a generative AI model to analyze it. Specifically, it inputs data into a machine learning model built using software frameworks such as TensorFlow or PyTorch to analyze traffic patterns.

[0266] The server performs calculations to optimize the timing of signal switching based on the analyzed data. The analysis results are transmitted wirelessly to the traffic signal equipment, and the operation schedule of the traffic signals is adjusted as needed. This makes it possible to reduce unnecessary delays and congestion, and to smooth the flow of traffic.

[0267] The in-vehicle electronic device receives predictive information transmitted from a server. This device can provide information to the driver in several ways. It can display information such as "The next traffic light will turn red in 30 seconds" on the display, or it can issue a warning to the driver using an audio output device. This allows the driver to properly guide the vehicle's speed and path, reducing the risk of sudden acceleration or braking.

[0268] As a concrete example, consider a family's vehicle heading to a tourist destination on a Saturday afternoon. A server uses an AI model to analyze road congestion in real time and optimizes traffic light switching based on the results. An in-vehicle electronic device informs the driver of the status of the next traffic light, enabling a smoother drive. An example of a prompt message would be, "Please tell me how to calculate the optimal traffic light switching timing based on data from traffic sensors and transmit it to the traffic lights and the in-vehicle terminal." In this way, the system achieves efficient control of traffic signals and accurate information provision to drivers.

[0269] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0270] Step 1:

[0271] The server acquires data in real time from traffic measurement equipment. This data includes the number of vehicles on the road, their speeds, and the status of traffic signals. The server receives this data and stores it in a database to prepare for later analysis. The input is data from traffic measurement equipment, and the output is the data record stored by the server.

[0272] Step 2:

[0273] The server inputs stored traffic data into a generating AI model. This AI model uses machine learning algorithms to analyze past and present traffic patterns. Data input is obtained from the server's database, and the AI ​​model performs predictive analysis based on pattern recognition. The output is an optimization proposal for signal switching timing, which is used in subsequent processing.

[0274] Step 3:

[0275] The server generates commands to optimize the switching timing of traffic signal devices using the analysis results from the AI ​​model. These commands are used to adjust the operating schedule of each traffic signal. The server transmits these commands to the traffic signals via a wireless communication network. The input is the analysis results from the AI ​​model, and the output is the specific command to the traffic signal.

[0276] Step 4:

[0277] The server predicts the traffic light color the driver will encounter at an intersection, sending this prediction to the vehicle's electronic device. This prediction is calculated by an AI model based on the car's current position and speed. The server then transmits this information to the electronic device. The inputs are the car's position and speed, and the AI ​​model's prediction data; the output is the predicted traffic light information.

[0278] Step 5:

[0279] The terminal receives signal prediction information from the server and notifies the driver. Specifically, the terminal displays the prediction information on its screen and warns the driver through voice guidance. The input is signal prediction information received from the server, and the output is visual and auditory information provided to the driver.

[0280] Step 6:

[0281] Based on the information provided by the terminal, the user adjusts the speed and driving operations of the vehicle. For example, if the user knows the time until the signal turns red, the user can perform smooth deceleration. The input is the information from the terminal, and the output is the user's driving behavior.

[0282] (Application Example 1)

[0283] Next, Application Example 1 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".

[0284] In recent years, with the evolution of autonomous driving technology, smoother traffic and improved safety have been demanded. However, since conventional signal control systems rely on fixed patterns, they cannot flexibly respond to traffic situations, and there are problems such as traffic jams and unnecessary stops. In addition, there is also a problem that the signal prediction information for drivers is limited and efficient driving is difficult. The purpose of the present invention is to solve these problems.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0286] In this invention, the server includes means for analyzing traffic situation data acquired from a traffic information device, means for optimizing the switching order of signal devices based on the analyzed data, and means for communicating the optimized signal operation to the signal device. As a result, it becomes possible to optimize the traffic flow and automatically adjust the traveling speed of the vehicle based on the prediction information.

[0287] The "traffic information device" is a device that acquires traffic situation data in real time and has a function of providing the server with information necessary for signal control. ​​​​​​A "traffic signal device" is a traffic light installed in an intersection area, and its role is to control the color of the signal to facilitate smooth traffic flow.

[0290] "Signal operation" refers to a pattern that indicates the sequence and timing of signal switching, which is adjusted to optimize traffic flow.

[0291] An "in-vehicle information system" is a device installed in a vehicle that has the function of notifying the driver of information transmitted from a server.

[0292] A "driver" is a person involved in operating a vehicle and is a user who utilizes signal prediction information to drive efficiently.

[0293] "Predictive information" refers to information that estimates future traffic light colors and timing of changes based on traffic condition data and AI analysis.

[0294] "Travel speed" refers to the speed at which a vehicle is moving, and it is adjusted according to traffic conditions and signal information.

[0295] The system implementing this invention optimizes traffic signal control based on traffic information to support autonomous vehicle driving. The server collects real-time traffic condition data from traffic information devices. This data is analyzed by a generative AI model using Python and TensorFlow. Based on the acquired data, the model optimizes the switching order of traffic signal devices and adjusts signal operation at the appropriate timing.

[0296] Optimized signal operation information is communicated to the signaling equipment, and the predicted timing of signal changes is transmitted to the on-board information system. The on-board information system notifies the driver of this predicted information and also has a function to automatically adjust the driving speed. This enables smooth passage through intersections.

[0297] As a concrete example, if the system predicts that a traffic light will turn green in an urban area in 3 seconds, the in-vehicle information system will adjust the vehicle's speed to allow it to pass through the intersection. Through this process, traffic flow will be smoother and the operational efficiency of autonomous vehicles will be improved.

[0298] An example of a prompt using a generative AI model is: "Based on the current traffic sensor data, calculate the estimated time until the traffic light at the next intersection turns red. Please output in seconds." This prompt allows the server to generate traffic light prediction information quickly and accurately.

[0299] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0300] Step 1:

[0301] The server receives real-time traffic data from traffic information devices. This data includes the number and speed of vehicles at each intersection, as well as the degree of road congestion. The server stores the received data in a database in preparation for subsequent analysis.

[0302] Step 2:

[0303] The server inputs accumulated traffic data into a generating AI model. This model, implemented using Python and TensorFlow, analyzes past and present traffic patterns. The server analyzes the data and calculates the optimal switching sequence for traffic signals. From this analysis, it can predict future signal change timings.

[0304] Step 3:

[0305] The server communicates optimized signal operation information, derived from the analysis results, to the signaling equipment. Network protocols are used for communication, instructing the signaling equipment to switch signals in the appropriate sequence. This streamlines traffic flow.

[0306] Step 4:

[0307] Along with the signal operation information, the server transmits the predicted signal information when the vehicle reaches the intersection area to the in-vehicle information device. The transmitted information includes the next signal switching timing and the recommended value of the vehicle traveling speed.

[0308] Step 5:

[0309] The in-vehicle information device receives the predicted signal information transmitted from the server and generates an instruction to automatically adjust the traveling speed. As a result, the vehicle can avoid unnecessary stops and smoothly pass through the intersection area. The in-vehicle information device also notifies the driver of this information via display or voice.

[0310] 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 identification model 59 and perform specific processing using the user's emotion.

[0311] The present invention combines an emotion engine with a system that optimizes traffic flow and reduces driver stress. The system includes a server, an in-vehicle terminal, and an emotion engine, and they cooperate to provide driver support.

[0312] The server aggregates and analyzes in real time the traffic data obtained from traffic sensors and cameras. By analyzing the traffic data using an AI model, the switching timing of traffic lights is optimized. The optimal signal control pattern is transmitted to the traffic lights by remote operation, and the traffic flow is controlled to proceed smoothly.

[0313] Furthermore, the server predicts the time when the vehicle reaches the intersection and calculates the color of the signal when the driver passes through the intersection. By transmitting this information to the in-vehicle terminal, it supports the driver to perform acceleration and deceleration without waste. In addition, an emotion engine is incorporated into this system to evaluate the driver's emotion in real time.

[0314] The in-vehicle terminal receives predictive signal information transmitted from the server and emotion evaluation data from the emotion engine. Based on this, the terminal provides the driver with appropriate notifications tailored to the situation. For example, if the driver is experiencing stress, the notification content can be made gentler and the advice reduced, demonstrating a response that takes the driver's psychological state into consideration.

[0315] Drivers can adjust their driving based on notifications from the in-car terminal. The emotion engine evaluates the driver's emotional state, providing support tailored to their individual driving style and psychological state. This allows drivers to continue driving more safely and comfortably.

[0316] This invention not only improves the efficiency of traffic flow but also reduces the psychological burden on drivers, thereby enhancing the overall driving experience. For example, if a driver is frustrated in congested traffic, the emotional engine can recognize this state and provide calming voice guidance, thereby promoting safe driving.

[0317] The following describes the processing flow.

[0318] Step 1:

[0319] The server acquires real-time traffic data from traffic sensors and cameras installed on the roads. This data includes the speed and volume of passing vehicles, traffic light waiting times, etc., and is aggregated into a database.

[0320] Step 2:

[0321] The server inputs aggregated traffic data into an AI model, which analyzes traffic conditions in real time. This model incorporates an algorithm to optimize traffic signal switching timing based on historical data. The analysis results are transmitted to the traffic signals, and signal switching is performed remotely.

[0322] Step 3:

[0323] The server performs calculations to predict when the driver's vehicle will reach the intersection. Based on this prediction, it determines the color of the traffic light when the driver passes through the intersection and transmits this information to the in-vehicle terminal.

[0324] Step 4:

[0325] The server inputs not only traffic data but also data on the driver's facial expressions and voice, obtained from cameras and sensors inside the vehicle, into the emotion engine. The emotion engine analyzes the driver's emotional state and provides the results to the server.

[0326] Step 5:

[0327] The terminal creates notifications for the driver based on predictive signal information received from the server and emotional data from the emotion engine. These notifications are delivered via display and voice guidance, designed to minimize driver stress.

[0328] Step 6:

[0329] The user (driver) adjusts their driving style according to notifications from their device. Based on traffic light color prediction information, they can optimize acceleration and deceleration, and also take into account advice that considers their own emotional state, enabling safe and comfortable driving.

[0330] (Example 2)

[0331] 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 glasses 214 will be referred to as the "terminal".

[0332] In today's traffic environment, traffic congestion and poorly timed traffic signals disrupt efficient traffic flow, causing significant stress to drivers. In particular, prolonged periods of these conditions can increase drivers' psychological burden and potentially hinder safe driving. There is a need to address these challenges, optimize traffic flow, and reduce drivers' psychological burden.

[0333] 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.

[0334] In this invention, the server includes means for analyzing mobile data acquired from a traffic information device, means for optimizing the timing of signal device switching based on the analyzed information, and means for determining notification content according to the driver's emotional state and guiding the driver accordingly. This makes traffic flow smoother and enables safe and comfortable driving support that takes the driver's emotions into consideration.

[0335] A "traffic information device" is a device, such as a sensor or camera, installed to monitor traffic conditions on roads and is responsible for collecting real-time data on moving objects.

[0336] "Mobile vehicle data" refers to information such as the location, speed, and number of vehicles collected by traffic information devices, and is used for analyzing traffic conditions.

[0337] A "traffic signal device" is a traffic signal installed at intersections and on roads, and is a device that displays signals to vehicles and pedestrians in order to control the flow of traffic.

[0338] "Emotional processing means" refers to systems and algorithms for evaluating a driver's emotions in real time, enabling responses based on the driver's psychological state.

[0339] A "mobile terminal" is an electronic device installed in a vehicle that transmits notifications and information from a server to the driver.

[0340] A "driver" refers to the driver of a vehicle moving on a road, and is an individual positioned as a user of the transportation system.

[0341] "Optimization" refers to adjusting traffic signal switching and traffic management methods to achieve the best possible state in order to make traffic flow more efficient and smoother.

[0342] "Notification content" refers to information and instructions transmitted to the driver via a mobile terminal, including signal prediction information and driving advice.

[0343] This invention is a system aimed at improving traffic flow efficiency and reducing the psychological burden on drivers. This system provides driver assistance by coordinating a server, an in-vehicle terminal, and an emotion processing mechanism.

[0344] The server collects real-time mobile data from traffic information devices. This data is used to analyze traffic flow and calculate the optimal timing for switching traffic signals. The server then transmits the optimized signal control pattern to the traffic signals, thereby optimizing traffic flow at intersections.

[0345] The in-vehicle terminal receives information from the server and provides appropriate notifications to the driver. This includes predictive information about the color of traffic lights at intersections, allowing the driver to adjust their driving accordingly. Furthermore, the terminal evaluates the driver's emotional state in real time through an emotion processing system and generates notifications based on that information.

[0346] Drivers can use notifications from in-vehicle terminals to adjust their driving style and reduce stress while driving. For example, if a driver is experiencing high stress in congested traffic, an emotional processing system will detect this state and send a notification to encourage relaxation. This notification is provided through voice guidance and screen displays, allowing the driver to maintain safe and comfortable driving.

[0347] An example of a prompt might be, "Provide advice to help a driver in traffic congestion feel more relaxed." This prompt allows the system to generate appropriate feedback tailored to the driver's emotional state, supporting safer driving.

[0348] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0349] Step 1:

[0350] The server collects real-time mobile data from traffic information devices.

[0351] It receives data such as vehicle location, speed, and number from traffic information devices as input.

[0352] As part of data processing, this raw data is used to quantify traffic flow and analyze its trends.

[0353] The output will be an analysis showing the current traffic situation.

[0354] Step 2:

[0355] The server uses a generated AI model to optimize the switching timing of signal devices based on the analysis results.

[0356] The results of the traffic situation analysis obtained in Step 1 will be used as input.

[0357] As a data calculation, an algorithm is executed to calculate the optimal signal timing for smooth traffic flow.

[0358] The output generates an optimized signal switching pattern.

[0359] Step 3:

[0360] The server sends the optimized signal switching pattern to the signaling device.

[0361] The signal pattern generated in step 2 is used as the input.

[0362] In terms of specific operation, data is transmitted to a signaling device via the network, and the signal is switched based on that data.

[0363] As an output, the timing of traffic signals at intersections is appropriately adjusted.

[0364] Step 4:

[0365] The server uses predictive calculations based on an AI model to calculate the time it will take the driver to reach the intersection and predict the color of the traffic light.

[0366] The system receives real-time updated data on vehicle movements as input.

[0367] As part of the data processing, the signal status at the time of passing through the intersection is simulated, taking into account the driver's predicted arrival time.

[0368] The output generates information about the predicted signal color.

[0369] Step 5:

[0370] The terminal receives signal prediction information and emotional state evaluation results from the server.

[0371] The inputs received are the signal prediction information generated in step 4 and data from the emotion processing device.

[0372] Specifically, this involves preparing to provide appropriate notifications to the driver.

[0373] The output determines the content of the notification to the driver.

[0374] Step 6:

[0375] The user (driver) adjusts driving operations based on notifications from the device.

[0376] The input includes signal prediction information and emotion-based advice presented by the device.

[0377] Specifically, the system checks notifications and, if necessary, accelerates, decelerates, or changes lanes.

[0378] As an output, safer and more efficient operation will be achieved.

[0379] (Application Example 2)

[0380] 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."

[0381] Optimizing traffic flow and reducing driver stress are crucial challenges, but conventional traffic control systems struggle to optimize traffic flow while considering the driver's psychological state. Furthermore, timely signal changes based on traffic information and appropriate notifications to drivers remain challenges. Additionally, when driving is done under stress, psychological support for drivers is often insufficient.

[0382] 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.

[0383] In this invention, the server includes means for analyzing traffic data acquired from a traffic information collection device, means for optimizing the switching timing of an information display device based on the analyzed data, and means for evaluating the driver's emotional state and presenting information that takes the driver's psychological state into consideration based on the evaluation. This makes it possible to optimize the flow of traffic while simultaneously reducing the psychological burden on the driver and providing a safe and comfortable driving environment.

[0384] A "traffic information collection device" is a device used to collect data on traffic conditions, detecting things like the flow of vehicles and traffic density on roads using sensors and cameras.

[0385] "Means of analysis" refers to devices or software used to perform the process of analyzing collected data and extracting information according to a specific purpose.

[0386] An "information display device" is a device, such as a traffic light or a display, that provides information visually or audibly based on analyzed data.

[0387] A "driver's terminal" is a computer device installed inside a vehicle to provide information to the driver, and includes navigation and notification functions.

[0388] "Assessing emotional state" is a process of analyzing the driver's psychological and emotional state to determine appropriate responses, and often involves using an emotional engine.

[0389] "Psychologically sensitive information" refers to customized information provided in consideration of the driver's current psychological state, including relaxation techniques and advice.

[0390] An "automatic suggestion device" is a device that automatically provides selected relaxation methods and support information according to the driver's emotional state.

[0391] Modes for carrying out the invention

[0392] The system that implements this application consists of a traffic information collection device, a server, a driver terminal, and an automatic display device. The server aggregates and analyzes traffic data acquired from the traffic information collection device in real time. This analysis uses AI analysis modules such as TensorFlow and scikit-learn. Based on the analyzed data, the system optimizes the signal switching timing of the information display device and generates signal control information.

[0393] The driver terminal receives information from the server and notifies the driver. At the same time, it uses an emotion analysis library (e.g., affectiva) to evaluate the driver's emotional state and adjusts the notification content according to the driver's psychological state. Furthermore, based on the emotion evaluation, an automated presentation device provides relaxation-enhancing music, aromatherapy, visuals, etc.

[0394] For example, if a driver feels stressed while driving on a congested road, the server detects this and notifies the driver's device with the message, "Please enjoy some relaxing music." Simultaneously, the in-car audio system automatically plays relaxing music, and an aroma diffuser is activated to promote relaxation. In this way, it is possible to provide multifaceted support to reduce the driver's psychological burden.

[0395] An example of a prompt message might be: "Please come up with a new feature that analyzes the driver's emotional state in real time and suggests the optimal relaxation method."

[0396] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0397] Step 1:

[0398] The server receives real-time traffic data from traffic information collection devices. This data includes vehicle position, speed, and traffic density information obtained from sensors and cameras. The server uses this data as input and analyzes it using an AI analysis module to output information necessary for optimizing signal switching. Specifically, it uses TensorFlow to apply a traffic flow model and predict the timing of the next signal operation.

[0399] Step 2:

[0400] Based on the analysis results, the server generates control data to optimize the signal switching timing of the information display device. Using a generated AI model, it calculates the predicted optimal signal pattern and sends the result to the traffic lights. This adjusts the signals to ensure smooth traffic flow at intersections.

[0401] Step 3:

[0402] The server transmits signal status prediction information to the terminal of a driver approaching the intersection. The driver's terminal receives this information and notifies the driver visually or audibly. Inputs include current and next signal status information, and outputs include messages displayed on the screen and voice guidance. The notifications are designed to help drivers react smoothly to the next signal.

[0403] Step 4:

[0404] The driver terminal analyzes the driver's voice and facial expression data using an emotion analysis library (e.g., affectiva). It uses the driver's facial expression data as input to evaluate their emotional state. Based on this evaluation, the terminal outputs optimal relaxation information for the driver. Specifically, if the status is determined to be high stress, the driver terminal will display a message such as, "Take a deep breath and refresh yourself."

[0405] Step 5:

[0406] The user receives instructions based on their emotional assessment results and accepts relaxation measures to adjust the in-car environment. An automated display system offers options such as playing relaxation music or activating an aroma diffuser. This allows the user to follow notifications on the driver's terminal and take specific actions to improve the in-car environment.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] [Third Embodiment]

[0411] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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).

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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".

[0423] This invention relates to a system that optimally controls traffic signals using data from traffic sensors and provides drivers with predictive signal information. This system works in conjunction with a server and an in-vehicle terminal via communication to improve traffic efficiency and reduce the burden on drivers.

[0424] The server first acquires real-time data from traffic sensors installed at each intersection. This allows for real-time monitoring of traffic flow and congestion. The server then inputs this data into an AI model for analysis. This model uses machine learning to analyze past and present traffic patterns and optimize the timing of traffic signal changes.

[0425] Once the traffic light control is optimized, the server remotely transmits the results to each traffic light. This ensures that the signal color changes at the appropriate time, reducing unnecessary waiting times and congestion.

[0426] Next, the server predicts when the driver's vehicle will reach the intersection and calculates the traffic light color at that time. This prediction information is then transmitted to the in-vehicle terminal.

[0427] The in-vehicle terminal receives signal prediction information from the server and uses this information to provide specific guidance to the driver. For example, the terminal may display "The next signal will turn red in 20 seconds" on its screen or issue a warning to the driver through voice guidance.

[0428] Users (drivers) can use this predictive information to adjust the vehicle's speed and route. Specifically, by knowing the remaining time until the next traffic light turns red, they can avoid sudden acceleration and braking, resulting in smoother driving.

[0429] In this way, the present invention contributes to smoother traffic flow and improved driving comfort through efficient control of traffic signals and accurate information provision to drivers.

[0430] The following describes the processing flow.

[0431] Step 1:

[0432] The server acquires data in real time from traffic sensors and cameras installed at intersections. This includes information such as traffic volume, vehicle speed, and following distance. The server stores the acquired data in a database.

[0433] Step 2:

[0434] The server inputs accumulated traffic data into an AI model for analysis. The AI ​​model uses machine learning algorithms to predict traffic flow based on past data and current conditions. Based on the analysis results, the server calculates the optimal timing for switching traffic signals.

[0435] Step 3:

[0436] The server transmits the optimal signal control pattern obtained from the analysis to the traffic lights. The traffic lights switch signal colors according to the received pattern, controlling traffic flow to ensure smooth operation.

[0437] Step 4:

[0438] The server predicts when the vehicle will arrive at the intersection and calculates the color of the traffic light when the driver passes through the intersection. This predicted information is then transmitted to the in-vehicle terminal.

[0439] Step 5:

[0440] The terminal notifies the driver via display and voice based on signal prediction information received from the server. For example, it provides information such as, "The next traffic light will turn red in 15 seconds," helping the driver adjust their speed appropriately.

[0441] Step 6:

[0442] The user (driver) adjusts their driving style based on notifications from the device. By following predictive information, the user can avoid unnecessary acceleration and deceleration, allowing them to pass through intersections smoothly and efficiently.

[0443] (Example 1)

[0444] 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."

[0445] Conventional traffic signal control methods are unable to keep up with real-time changes in traffic conditions, resulting in inefficient signal timing that causes traffic congestion and unnecessary waiting times. Furthermore, it is difficult to provide drivers with appropriate traffic signal information, potentially compromising driving smoothness and safety. Therefore, there is a need for a system that can streamline traffic flow and provide drivers with accurate and timely signal information.

[0446] 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.

[0447] In this invention, the server includes means for analyzing traffic flow data acquired from traffic measurement equipment, means for optimizing the switching timing of traffic signal devices based on the analyzed data, and means for transmitting the optimized signal timing to the traffic signal devices. This enables efficient management of traffic flow and accurate provision of signal information to drivers.

[0448] A "traffic measurement device" is a device that detects traffic flow, speed, and the presence of vehicles in real time and outputs that information as a digital signal.

[0449] "Traffic flow data" refers to statistical information obtained from traffic measurement equipment, including the number and speed of vehicles passing through a specific point within a given time period.

[0450] "Means of analysis" refers to algorithms and programs used to process acquired data and identify traffic patterns and trends.

[0451] A "traffic signal system" is a device that displays signals to ensure safety at intersections and pedestrian crossings, using red, yellow, and blue lights to direct traffic.

[0452] "Switching timing" refers to the time interval between when a traffic signal system displays each light signal.

[0453] "Wireless control" is a method of operating traffic signal equipment from a remote location using a communication network, and is a control method that does not require a physical connection.

[0454] A "driver" refers to a person who is currently operating a vehicle and is the entity that receives traffic information.

[0455] An "in-vehicle electronic device" is a digital device installed in a vehicle that displays or provides information to the driver via voice.

[0456] A "display device" is a device used to visually display information, and includes liquid crystal screens and LED displays.

[0457] An "audio output device" is a device that generates audio information based on stored digital data and provides it as audio through a speaker.

[0458] This invention is a system that analyzes and optimizes real-time traffic information using a server, in-vehicle electronic devices, and traffic measurement equipment to improve the efficiency of traffic signal control. The server receives data acquired from traffic measurement equipment and uses a generative AI model to analyze it. Specifically, it inputs data into a machine learning model built using software frameworks such as TensorFlow or PyTorch to analyze traffic patterns.

[0459] The server performs calculations to optimize the timing of signal switching based on the analyzed data. The analysis results are transmitted wirelessly to the traffic signal equipment, and the operation schedule of the traffic signals is adjusted as needed. This makes it possible to reduce unnecessary delays and congestion, and to smooth the flow of traffic.

[0460] The in-vehicle electronic device receives predictive information transmitted from a server. This device can provide information to the driver in several ways. It can display information such as "The next traffic light will turn red in 30 seconds" on the display, or it can issue a warning to the driver using an audio output device. This allows the driver to properly guide the vehicle's speed and path, reducing the risk of sudden acceleration or braking.

[0461] As a concrete example, consider a family's vehicle heading to a tourist destination on a Saturday afternoon. A server uses an AI model to analyze road congestion in real time and optimizes traffic light switching based on the results. An in-vehicle electronic device informs the driver of the status of the next traffic light, enabling a smoother drive. An example of a prompt message would be, "Please tell me how to calculate the optimal traffic light switching timing based on data from traffic sensors and transmit it to the traffic lights and the in-vehicle terminal." In this way, the system achieves efficient control of traffic signals and accurate information provision to drivers.

[0462] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0463] Step 1:

[0464] The server acquires data in real time from traffic measurement equipment. This data includes the number of vehicles on the road, their speeds, and the status of traffic signals. The server receives this data and stores it in a database to prepare for later analysis. The input is data from traffic measurement equipment, and the output is the data record stored by the server.

[0465] Step 2:

[0466] The server inputs stored traffic data into a generating AI model. This AI model uses machine learning algorithms to analyze past and present traffic patterns. Data input is obtained from the server's database, and the AI ​​model performs predictive analysis based on pattern recognition. The output is an optimization proposal for signal switching timing, which is used in subsequent processing.

[0467] Step 3:

[0468] The server generates commands to optimize the switching timing of traffic signal devices using the analysis results from the AI ​​model. These commands are used to adjust the operating schedule of each traffic signal. The server transmits these commands to the traffic signals via a wireless communication network. The input is the analysis results from the AI ​​model, and the output is the specific command to the traffic signal.

[0469] Step 4:

[0470] The server predicts the traffic light color the driver will encounter at an intersection, sending this prediction to the vehicle's electronic device. This prediction is calculated by an AI model based on the car's current position and speed. The server then transmits this information to the electronic device. The inputs are the car's position and speed, and the AI ​​model's prediction data; the output is the predicted traffic light information.

[0471] Step 5:

[0472] The terminal receives signal prediction information from the server and notifies the driver. Specifically, the terminal displays the prediction information on its screen and warns the driver through voice guidance. The input is signal prediction information received from the server, and the output is visual and auditory information provided to the driver.

[0473] Step 6:

[0474] The user adjusts the vehicle's speed and driving operations based on the information provided by the device. For example, if the user knows how much time is left until the traffic light turns red, they can decelerate smoothly. The input is the information from the device, and the output is the user's driving actions.

[0475] (Application Example 1)

[0476] 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."

[0477] In recent years, with the advancement of autonomous driving technology, there has been a growing demand for smoother traffic flow and improved safety. However, conventional traffic signal control systems rely on fixed patterns, making them unable to flexibly respond to traffic conditions and resulting in congestion and unnecessary stops. Furthermore, the limited information available to drivers regarding predicting traffic signals makes efficient driving difficult. This invention aims to solve these problems.

[0478] 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.

[0479] In this invention, the server includes means for analyzing traffic condition data acquired from a traffic information device, means for optimizing the switching sequence of signal devices based on the analyzed data, and means for communicating the optimized signal operation to the signal devices. This makes it possible to optimize traffic flow and automatically adjust vehicle speeds based on predictive information.

[0480] A "traffic information device" is a device that acquires traffic condition data in real time and has the function of providing information necessary for signal control to a server.

[0481] "Analysis" is the process of analyzing traffic patterns based on acquired traffic data and deriving information useful for signal control.

[0482] A "traffic signal device" is a traffic light installed in an intersection area, and its role is to control the color of the signal to facilitate smooth traffic flow.

[0483] "Signal operation" refers to a pattern that indicates the sequence and timing of signal switching, which is adjusted to optimize traffic flow.

[0484] An "in-vehicle information system" is a device installed in a vehicle that has the function of notifying the driver of information transmitted from a server.

[0485] A "driver" is a person involved in operating a vehicle and is a user who utilizes signal prediction information to drive efficiently.

[0486] "Predictive information" refers to information that estimates future traffic light colors and timing of changes based on traffic condition data and AI analysis.

[0487] "Travel speed" refers to the speed at which a vehicle is moving, and it is adjusted according to traffic conditions and signal information.

[0488] The system implementing this invention optimizes traffic signal control based on traffic information to support autonomous vehicle driving. The server collects real-time traffic condition data from traffic information devices. This data is analyzed by a generative AI model using Python and TensorFlow. Based on the acquired data, the model optimizes the switching order of traffic signal devices and adjusts signal operation at the appropriate timing.

[0489] Optimized signal operation information is communicated to the signaling equipment, and the predicted timing of signal changes is transmitted to the on-board information system. The on-board information system notifies the driver of this predicted information and also has a function to automatically adjust the driving speed. This enables smooth passage through intersections.

[0490] As a concrete example, if the system predicts that a traffic light will turn green in an urban area in 3 seconds, the in-vehicle information system will adjust the vehicle's speed to allow it to pass through the intersection. Through this process, traffic flow will be smoother and the operational efficiency of autonomous vehicles will be improved.

[0491] An example of a prompt using a generative AI model is: "Based on the current traffic sensor data, calculate the estimated time until the traffic light at the next intersection turns red. Please output in seconds." This prompt allows the server to generate traffic light prediction information quickly and accurately.

[0492] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0493] Step 1:

[0494] The server receives real-time traffic data from traffic information devices. This data includes the number and speed of vehicles at each intersection, as well as the degree of road congestion. The server stores the received data in a database in preparation for subsequent analysis.

[0495] Step 2:

[0496] The server inputs accumulated traffic data into a generating AI model. This model, implemented using Python and TensorFlow, analyzes past and present traffic patterns. The server analyzes the data and calculates the optimal switching sequence for traffic signals. From this analysis, it can predict future signal change timings.

[0497] Step 3:

[0498] The server communicates optimized signal operation information, derived from the analysis results, to the signaling equipment. Network protocols are used for communication, instructing the signaling equipment to switch signals in the appropriate sequence. This streamlines traffic flow.

[0499] Step 4:

[0500] Along with signal operation information, the server transmits predicted signal information for when the vehicle approaches the intersection to the onboard information system. The transmitted information includes the timing of the next signal change and recommended vehicle speed.

[0501] Step 5:

[0502] The in-vehicle infotainment system receives predictive signal information transmitted from the server and generates instructions to automatically adjust the vehicle's speed. This allows the vehicle to avoid unnecessary stops and pass through intersections smoothly. The infotainment system also notifies the driver of this information via display and audio.

[0503] 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.

[0504] This invention combines an emotional engine with a system that optimizes traffic flow and reduces driver stress. The system includes a server, an in-vehicle terminal, and an emotional engine, which work together to assist the driver.

[0505] The server collects and analyzes traffic data acquired from traffic sensors and cameras in real time. By analyzing the traffic data using an AI model, the timing of traffic light changes is optimized. The optimal signal control pattern is transmitted to the traffic lights remotely, controlling them to ensure smooth traffic flow.

[0506] Furthermore, the server predicts the time it will take the vehicle to reach the intersection and calculates the traffic light color the driver will see when passing through the intersection. This information is transmitted to the in-vehicle terminal to help the driver accelerate and decelerate efficiently. The system also incorporates an emotion engine that evaluates the driver's emotions in real time.

[0507] The in-vehicle terminal receives predictive signal information transmitted from the server and emotion evaluation data from the emotion engine. Based on this, the terminal provides the driver with appropriate notifications tailored to the situation. For example, if the driver is experiencing stress, the notification content can be made gentler and the advice reduced, demonstrating a response that takes the driver's psychological state into consideration.

[0508] Drivers can adjust their driving based on notifications from the in-car terminal. The emotion engine evaluates the driver's emotional state, providing support tailored to their individual driving style and psychological state. This allows drivers to continue driving more safely and comfortably.

[0509] This invention not only improves the efficiency of traffic flow but also reduces the psychological burden on drivers, thereby enhancing the overall driving experience. For example, if a driver is frustrated in congested traffic, the emotional engine can recognize this state and provide calming voice guidance, thereby promoting safe driving.

[0510] The following describes the processing flow.

[0511] Step 1:

[0512] The server acquires real-time traffic data from traffic sensors and cameras installed on the roads. This data includes the speed and volume of passing vehicles, traffic light waiting times, etc., and is aggregated into a database.

[0513] Step 2:

[0514] The server inputs aggregated traffic data into an AI model, which analyzes traffic conditions in real time. This model incorporates an algorithm to optimize traffic signal switching timing based on historical data. The analysis results are transmitted to the traffic signals, and signal switching is performed remotely.

[0515] Step 3:

[0516] The server performs calculations to predict when the driver's vehicle will reach the intersection. Based on this prediction, it determines the color of the traffic light when the driver passes through the intersection and transmits this information to the in-vehicle terminal.

[0517] Step 4:

[0518] The server inputs not only traffic data but also data on the driver's facial expressions and voice, obtained from cameras and sensors inside the vehicle, into the emotion engine. The emotion engine analyzes the driver's emotional state and provides the results to the server.

[0519] Step 5:

[0520] The terminal creates notifications for the driver based on predictive signal information received from the server and emotional data from the emotion engine. These notifications are delivered via display and voice guidance, designed to minimize driver stress.

[0521] Step 6:

[0522] The user (driver) adjusts their driving style according to notifications from their device. Based on traffic light color prediction information, they can optimize acceleration and deceleration, and also take into account advice that considers their own emotional state, enabling safe and comfortable driving.

[0523] (Example 2)

[0524] 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."

[0525] In today's traffic environment, traffic congestion and poorly timed traffic signals disrupt efficient traffic flow, causing significant stress to drivers. In particular, prolonged periods of these conditions can increase drivers' psychological burden and potentially hinder safe driving. There is a need to address these challenges, optimize traffic flow, and reduce drivers' psychological burden.

[0526] 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.

[0527] In this invention, the server includes means for analyzing mobile data acquired from a traffic information device, means for optimizing the timing of signal device switching based on the analyzed information, and means for determining notification content according to the driver's emotional state and guiding the driver accordingly. This makes traffic flow smoother and enables safe and comfortable driving support that takes the driver's emotions into consideration.

[0528] A "traffic information device" is a device, such as a sensor or camera, installed to monitor traffic conditions on roads and is responsible for collecting real-time data on moving objects.

[0529] "Mobile vehicle data" refers to information such as the location, speed, and number of vehicles collected by traffic information devices, and is used for analyzing traffic conditions.

[0530] A "traffic signal device" is a traffic signal installed at intersections and on roads, and is a device that displays signals to vehicles and pedestrians in order to control the flow of traffic.

[0531] "Emotional processing means" refers to systems and algorithms for evaluating a driver's emotions in real time, enabling responses based on the driver's psychological state.

[0532] A "mobile terminal" is an electronic device installed in a vehicle that transmits notifications and information from a server to the driver.

[0533] A "driver" refers to the driver of a vehicle moving on a road, and is an individual positioned as a user of the transportation system.

[0534] "Optimization" refers to adjusting traffic signal switching and traffic management methods to achieve the best possible state in order to make traffic flow more efficient and smoother.

[0535] "Notification content" refers to information and instructions transmitted to the driver via a mobile terminal, including signal prediction information and driving advice.

[0536] This invention is a system aimed at improving traffic flow efficiency and reducing the psychological burden on drivers. This system provides driver assistance by coordinating a server, an in-vehicle terminal, and an emotion processing mechanism.

[0537] The server collects real-time mobile data from traffic information devices. This data is used to analyze traffic flow and calculate the optimal timing for switching traffic signals. The server then transmits the optimized signal control pattern to the traffic signals, thereby optimizing traffic flow at intersections.

[0538] The in-vehicle terminal receives information from the server and provides appropriate notifications to the driver. This includes predictive information about the color of traffic lights at intersections, allowing the driver to adjust their driving accordingly. Furthermore, the terminal evaluates the driver's emotional state in real time through an emotion processing system and generates notifications based on that information.

[0539] Drivers can use notifications from in-vehicle terminals to adjust their driving style and reduce stress while driving. For example, if a driver is experiencing high stress in congested traffic, an emotional processing system will detect this state and send a notification to encourage relaxation. This notification is provided through voice guidance and screen displays, allowing the driver to maintain safe and comfortable driving.

[0540] An example of a prompt might be, "Provide advice to help a driver in traffic congestion feel more relaxed." This prompt allows the system to generate appropriate feedback tailored to the driver's emotional state, supporting safer driving.

[0541] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0542] Step 1:

[0543] The server collects real-time mobile data from traffic information devices.

[0544] It receives data such as vehicle location, speed, and number from traffic information devices as input.

[0545] As part of data processing, this raw data is used to quantify traffic flow and analyze its trends.

[0546] The output will be an analysis showing the current traffic situation.

[0547] Step 2:

[0548] The server uses a generated AI model to optimize the switching timing of signal devices based on the analysis results.

[0549] The results of the traffic situation analysis obtained in Step 1 will be used as input.

[0550] As a data calculation, an algorithm is executed to calculate the optimal signal timing for smooth traffic flow.

[0551] The output generates an optimized signal switching pattern.

[0552] Step 3:

[0553] The server sends the optimized signal switching pattern to the signaling device.

[0554] The signal pattern generated in step 2 is used as the input.

[0555] In terms of specific operation, data is transmitted to a signaling device via the network, and the signal is switched based on that data.

[0556] As an output, the timing of traffic signals at intersections is appropriately adjusted.

[0557] Step 4:

[0558] The server uses predictive calculations based on an AI model to calculate the time it will take the driver to reach the intersection and predict the color of the traffic light.

[0559] The system receives real-time updated data on vehicle movements as input.

[0560] As part of the data processing, the signal status at the time of passing through the intersection is simulated, taking into account the driver's predicted arrival time.

[0561] The output generates information about the predicted signal color.

[0562] Step 5:

[0563] The terminal receives signal prediction information and emotional state evaluation results from the server.

[0564] The inputs received are the signal prediction information generated in step 4 and data from the emotion processing device.

[0565] Specifically, this involves preparing to provide appropriate notifications to the driver.

[0566] The output determines the content of the notification to the driver.

[0567] Step 6:

[0568] The user (driver) adjusts driving operations based on notifications from the device.

[0569] The input includes signal prediction information and emotion-based advice presented by the device.

[0570] Specifically, the system checks notifications and, if necessary, accelerates, decelerates, or changes lanes.

[0571] As an output, safer and more efficient operation will be achieved.

[0572] (Application Example 2)

[0573] 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."

[0574] Optimizing traffic flow and reducing driver stress are crucial challenges, but conventional traffic control systems struggle to optimize traffic flow while considering the driver's psychological state. Furthermore, timely signal changes based on traffic information and appropriate notifications to drivers remain challenges. Additionally, when driving is done under stress, psychological support for drivers is often insufficient.

[0575] 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.

[0576] In this invention, the server includes means for analyzing traffic data acquired from a traffic information collection device, means for optimizing the switching timing of an information display device based on the analyzed data, and means for evaluating the driver's emotional state and presenting information that takes the driver's psychological state into consideration based on the evaluation. This makes it possible to optimize the flow of traffic while simultaneously reducing the psychological burden on the driver and providing a safe and comfortable driving environment.

[0577] A "traffic information collection device" is a device used to collect data on traffic conditions, detecting things like the flow of vehicles and traffic density on roads using sensors and cameras.

[0578] "Means of analysis" refers to devices or software used to perform the process of analyzing collected data and extracting information according to a specific purpose.

[0579] An "information display device" is a device, such as a traffic light or a display, that provides information visually or audibly based on analyzed data.

[0580] A "driver's terminal" is a computer device installed inside a vehicle to provide information to the driver, and includes navigation and notification functions.

[0581] "Assessing emotional state" is a process of analyzing the driver's psychological and emotional state to determine appropriate responses, and often involves using an emotional engine.

[0582] "Psychologically sensitive information" refers to customized information provided in consideration of the driver's current psychological state, including relaxation techniques and advice.

[0583] An "automatic suggestion device" is a device that automatically provides selected relaxation methods and support information according to the driver's emotional state.

[0584] Modes for carrying out the invention

[0585] The system that implements this application consists of a traffic information collection device, a server, a driver terminal, and an automatic display device. The server aggregates and analyzes traffic data acquired from the traffic information collection device in real time. This analysis uses AI analysis modules such as TensorFlow and scikit-learn. Based on the analyzed data, the system optimizes the signal switching timing of the information display device and generates signal control information.

[0586] The driver terminal receives information from the server and notifies the driver. At the same time, it uses an emotion analysis library (e.g., affectiva) to evaluate the driver's emotional state and adjusts the notification content according to the driver's psychological state. Furthermore, based on the emotion evaluation, an automated presentation device provides relaxation-enhancing music, aromatherapy, visuals, etc.

[0587] For example, if a driver feels stressed while driving on a congested road, the server detects this and notifies the driver's device with the message, "Please enjoy some relaxing music." Simultaneously, the in-car audio system automatically plays relaxing music, and an aroma diffuser is activated to promote relaxation. In this way, it is possible to provide multifaceted support to reduce the driver's psychological burden.

[0588] An example of a prompt message might be: "Please come up with a new feature that analyzes the driver's emotional state in real time and suggests the optimal relaxation method."

[0589] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0590] Step 1:

[0591] The server receives real-time traffic data from traffic information collection devices. This data includes vehicle position, speed, and traffic density information obtained from sensors and cameras. The server uses this data as input and analyzes it using an AI analysis module to output information necessary for optimizing signal switching. Specifically, it uses TensorFlow to apply a traffic flow model and predict the timing of the next signal operation.

[0592] Step 2:

[0593] Based on the analysis results, the server generates control data to optimize the signal switching timing of the information display device. Using a generated AI model, it calculates the predicted optimal signal pattern and sends the result to the traffic lights. This adjusts the signals to ensure smooth traffic flow at intersections.

[0594] Step 3:

[0595] The server transmits signal status prediction information to the terminal of a driver approaching the intersection. The driver's terminal receives this information and notifies the driver visually or audibly. Inputs include current and next signal status information, and outputs include messages displayed on the screen and voice guidance. The notifications are designed to help drivers react smoothly to the next signal.

[0596] Step 4:

[0597] The driver terminal analyzes the driver's voice and facial expression data using an emotion analysis library (e.g., affectiva). It uses the driver's facial expression data as input to evaluate their emotional state. Based on this evaluation, the terminal outputs optimal relaxation information for the driver. Specifically, if the status is determined to be high stress, the driver terminal will display a message such as, "Take a deep breath and refresh yourself."

[0598] Step 5:

[0599] The user receives instructions based on their emotional assessment results and accepts relaxation measures to adjust the in-car environment. An automated display system offers options such as playing relaxation music or activating an aroma diffuser. This allows the user to follow notifications on the driver's terminal and take specific actions to improve the in-car environment.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] [Fourth Embodiment]

[0604] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0605] 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.

[0606] 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).

[0607] 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.

[0608] 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.

[0609] 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).

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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".

[0617] This invention relates to a system that optimally controls traffic signals using data from traffic sensors and provides drivers with predictive signal information. This system works in conjunction with a server and an in-vehicle terminal via communication to improve traffic efficiency and reduce the burden on drivers.

[0618] The server first acquires real-time data from traffic sensors installed at each intersection. This allows for real-time monitoring of traffic flow and congestion. The server then inputs this data into an AI model for analysis. This model uses machine learning to analyze past and present traffic patterns and optimize the timing of traffic signal changes.

[0619] Once the traffic light control is optimized, the server remotely transmits the results to each traffic light. This ensures that the signal color changes at the appropriate time, reducing unnecessary waiting times and congestion.

[0620] Next, the server predicts when the driver's vehicle will reach the intersection and calculates the traffic light color at that time. This prediction information is then transmitted to the in-vehicle terminal.

[0621] The in-vehicle terminal receives signal prediction information from the server and uses this information to provide specific guidance to the driver. For example, the terminal may display "The next signal will turn red in 20 seconds" on its screen or issue a warning to the driver through voice guidance.

[0622] Users (drivers) can use this predictive information to adjust the vehicle's speed and route. Specifically, by knowing the remaining time until the next traffic light turns red, they can avoid sudden acceleration and braking, resulting in smoother driving.

[0623] In this way, the present invention contributes to smoother traffic flow and improved driving comfort through efficient control of traffic signals and accurate information provision to drivers.

[0624] The following describes the processing flow.

[0625] Step 1:

[0626] The server acquires data in real time from traffic sensors and cameras installed at intersections. This includes information such as traffic volume, vehicle speed, and following distance. The server stores the acquired data in a database.

[0627] Step 2:

[0628] The server inputs accumulated traffic data into an AI model for analysis. The AI ​​model uses machine learning algorithms to predict traffic flow based on past data and current conditions. Based on the analysis results, the server calculates the optimal timing for switching traffic signals.

[0629] Step 3:

[0630] The server transmits the optimal signal control pattern obtained from the analysis to the traffic lights. The traffic lights switch signal colors according to the received pattern, controlling traffic flow to ensure smooth operation.

[0631] Step 4:

[0632] The server predicts when the vehicle will arrive at the intersection and calculates the color of the traffic light when the driver passes through the intersection. This predicted information is then transmitted to the in-vehicle terminal.

[0633] Step 5:

[0634] The terminal notifies the driver via display and voice based on signal prediction information received from the server. For example, it provides information such as, "The next traffic light will turn red in 15 seconds," helping the driver adjust their speed appropriately.

[0635] Step 6:

[0636] The user (driver) adjusts their driving style based on notifications from the device. By following predictive information, the user can avoid unnecessary acceleration and deceleration, allowing them to pass through intersections smoothly and efficiently.

[0637] (Example 1)

[0638] 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".

[0639] Conventional traffic signal control methods are unable to keep up with real-time changes in traffic conditions, resulting in inefficient signal timing that causes traffic congestion and unnecessary waiting times. Furthermore, it is difficult to provide drivers with appropriate traffic signal information, potentially compromising driving smoothness and safety. Therefore, there is a need for a system that can streamline traffic flow and provide drivers with accurate and timely signal information.

[0640] 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.

[0641] In this invention, the server includes means for analyzing traffic flow data acquired from traffic measurement equipment, means for optimizing the switching timing of traffic signal devices based on the analyzed data, and means for transmitting the optimized signal timing to the traffic signal devices. This enables efficient management of traffic flow and accurate provision of signal information to drivers.

[0642] A "traffic measurement device" is a device that detects traffic flow, speed, and the presence of vehicles in real time and outputs that information as a digital signal.

[0643] "Traffic flow data" refers to statistical information obtained from traffic measurement equipment, including the number and speed of vehicles passing through a specific point within a given time period.

[0644] "Means of analysis" refers to algorithms and programs used to process acquired data and identify traffic patterns and trends.

[0645] A "traffic signal system" is a device that displays signals to ensure safety at intersections and pedestrian crossings, using red, yellow, and blue lights to direct traffic.

[0646] "Switching timing" refers to the time interval between when a traffic signal system displays each light signal.

[0647] "Wireless control" is a method of operating traffic signal equipment from a remote location using a communication network, and is a control method that does not require a physical connection.

[0648] A "driver" refers to a person who is currently operating a vehicle and is the entity that receives traffic information.

[0649] An "in-vehicle electronic device" is a digital device installed in a vehicle that displays or provides information to the driver via voice.

[0650] A "display device" is a device used to visually display information, and includes liquid crystal screens and LED displays.

[0651] An "audio output device" is a device that generates audio information based on stored digital data and provides it as audio through a speaker.

[0652] This invention is a system that analyzes and optimizes real-time traffic information using a server, in-vehicle electronic devices, and traffic measurement equipment to improve the efficiency of traffic signal control. The server receives data acquired from traffic measurement equipment and uses a generative AI model to analyze it. Specifically, it inputs data into a machine learning model built using software frameworks such as TensorFlow or PyTorch to analyze traffic patterns.

[0653] The server performs calculations to optimize the timing of signal switching based on the analyzed data. The analysis results are transmitted wirelessly to the traffic signal equipment, and the operation schedule of the traffic signals is adjusted as needed. This makes it possible to reduce unnecessary delays and congestion, and to smooth the flow of traffic.

[0654] The in-vehicle electronic device receives predictive information transmitted from a server. This device can provide information to the driver in several ways. It can display information such as "The next traffic light will turn red in 30 seconds" on the display, or it can issue a warning to the driver using an audio output device. This allows the driver to properly guide the vehicle's speed and path, reducing the risk of sudden acceleration or braking.

[0655] As a concrete example, consider a family's vehicle heading to a tourist destination on a Saturday afternoon. A server uses an AI model to analyze road congestion in real time and optimizes traffic light switching based on the results. An in-vehicle electronic device informs the driver of the status of the next traffic light, enabling a smoother drive. An example of a prompt message would be, "Please tell me how to calculate the optimal traffic light switching timing based on data from traffic sensors and transmit it to the traffic lights and the in-vehicle terminal." In this way, the system achieves efficient control of traffic signals and accurate information provision to drivers.

[0656] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0657] Step 1:

[0658] The server acquires data in real time from traffic measurement equipment. This data includes the number of vehicles on the road, their speeds, and the status of traffic signals. The server receives this data and stores it in a database to prepare for later analysis. The input is data from traffic measurement equipment, and the output is the data record stored by the server.

[0659] Step 2:

[0660] The server inputs stored traffic data into a generating AI model. This AI model uses machine learning algorithms to analyze past and present traffic patterns. Data input is obtained from the server's database, and the AI ​​model performs predictive analysis based on pattern recognition. The output is an optimization proposal for signal switching timing, which is used in subsequent processing.

[0661] Step 3:

[0662] The server generates commands to optimize the switching timing of traffic signal devices using the analysis results from the AI ​​model. These commands are used to adjust the operating schedule of each traffic signal. The server transmits these commands to the traffic signals via a wireless communication network. The input is the analysis results from the AI ​​model, and the output is the specific command to the traffic signal.

[0663] Step 4:

[0664] The server predicts the traffic light color the driver will encounter at an intersection, sending this prediction to the vehicle's electronic device. This prediction is calculated by an AI model based on the car's current position and speed. The server then transmits this information to the electronic device. The inputs are the car's position and speed, and the AI ​​model's prediction data; the output is the predicted traffic light information.

[0665] Step 5:

[0666] The terminal receives signal prediction information from the server and notifies the driver. Specifically, the terminal displays the prediction information on its screen and warns the driver through voice guidance. The input is signal prediction information received from the server, and the output is visual and auditory information provided to the driver.

[0667] Step 6:

[0668] The user adjusts the vehicle's speed and driving operations based on the information provided by the device. For example, if the user knows how much time is left until the traffic light turns red, they can decelerate smoothly. The input is the information from the device, and the output is the user's driving actions.

[0669] (Application Example 1)

[0670] 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".

[0671] In recent years, with the advancement of autonomous driving technology, there has been a growing demand for smoother traffic flow and improved safety. However, conventional traffic signal control systems rely on fixed patterns, making them unable to flexibly respond to traffic conditions and resulting in congestion and unnecessary stops. Furthermore, the limited information available to drivers regarding predicting traffic signals makes efficient driving difficult. This invention aims to solve these problems.

[0672] 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.

[0673] In this invention, the server includes means for analyzing traffic condition data acquired from a traffic information device, means for optimizing the switching sequence of signal devices based on the analyzed data, and means for communicating the optimized signal operation to the signal devices. This makes it possible to optimize traffic flow and automatically adjust vehicle speeds based on predictive information.

[0674] A "traffic information device" is a device that acquires traffic condition data in real time and has the function of providing information necessary for signal control to a server.

[0675] "Analysis" is the process of analyzing traffic patterns based on acquired traffic data and deriving information useful for signal control.

[0676] A "traffic signal device" is a traffic light installed in an intersection area, and its role is to control the color of the signal to facilitate smooth traffic flow.

[0677] "Signal operation" refers to a pattern that indicates the sequence and timing of signal switching, which is adjusted to optimize traffic flow.

[0678] An "in-vehicle information system" is a device installed in a vehicle that has the function of notifying the driver of information transmitted from a server.

[0679] A "driver" is a person involved in operating a vehicle and is a user who utilizes signal prediction information to drive efficiently.

[0680] "Predictive information" refers to information that estimates future traffic light colors and timing of changes based on traffic condition data and AI analysis.

[0681] "Travel speed" refers to the speed at which a vehicle is moving, and it is adjusted according to traffic conditions and signal information.

[0682] The system implementing this invention optimizes traffic signal control based on traffic information to support autonomous vehicle driving. The server collects real-time traffic condition data from traffic information devices. This data is analyzed by a generative AI model using Python and TensorFlow. Based on the acquired data, the model optimizes the switching order of traffic signal devices and adjusts signal operation at the appropriate timing.

[0683] Optimized signal operation information is communicated to the signaling equipment, and the predicted timing of signal changes is transmitted to the on-board information system. The on-board information system notifies the driver of this predicted information and also has a function to automatically adjust the driving speed. This enables smooth passage through intersections.

[0684] As a concrete example, if the system predicts that a traffic light will turn green in an urban area in 3 seconds, the in-vehicle information system will adjust the vehicle's speed to allow it to pass through the intersection. Through this process, traffic flow will be smoother and the operational efficiency of autonomous vehicles will be improved.

[0685] An example of a prompt using a generative AI model is: "Based on the current traffic sensor data, calculate the estimated time until the traffic light at the next intersection turns red. Please output in seconds." This prompt allows the server to generate traffic light prediction information quickly and accurately.

[0686] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0687] Step 1:

[0688] The server receives real-time traffic data from traffic information devices. This data includes the number and speed of vehicles at each intersection, as well as the degree of road congestion. The server stores the received data in a database in preparation for subsequent analysis.

[0689] Step 2:

[0690] The server inputs accumulated traffic data into a generating AI model. This model, implemented using Python and TensorFlow, analyzes past and present traffic patterns. The server analyzes the data and calculates the optimal switching sequence for traffic signals. From this analysis, it can predict future signal change timings.

[0691] Step 3:

[0692] The server communicates optimized signal operation information, derived from the analysis results, to the signaling equipment. Network protocols are used for communication, instructing the signaling equipment to switch signals in the appropriate sequence. This streamlines traffic flow.

[0693] Step 4:

[0694] Along with signal operation information, the server transmits predicted signal information for when the vehicle approaches the intersection to the onboard information system. The transmitted information includes the timing of the next signal change and recommended vehicle speed.

[0695] Step 5:

[0696] The in-vehicle infotainment system receives predictive signal information transmitted from the server and generates instructions to automatically adjust the vehicle's speed. This allows the vehicle to avoid unnecessary stops and pass through intersections smoothly. The infotainment system also notifies the driver of this information via display and audio.

[0697] 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.

[0698] This invention combines an emotional engine with a system that optimizes traffic flow and reduces driver stress. The system includes a server, an in-vehicle terminal, and an emotional engine, which work together to assist the driver.

[0699] The server collects and analyzes traffic data acquired from traffic sensors and cameras in real time. By analyzing the traffic data using an AI model, the timing of traffic light changes is optimized. The optimal signal control pattern is transmitted to the traffic lights remotely, controlling them to ensure smooth traffic flow.

[0700] Furthermore, the server predicts the time it will take the vehicle to reach the intersection and calculates the traffic light color the driver will see when passing through the intersection. This information is transmitted to the in-vehicle terminal to help the driver accelerate and decelerate efficiently. The system also incorporates an emotion engine that evaluates the driver's emotions in real time.

[0701] The in-vehicle terminal receives predictive signal information transmitted from the server and emotion evaluation data from the emotion engine. Based on this, the terminal provides the driver with appropriate notifications tailored to the situation. For example, if the driver is experiencing stress, the notification content can be made gentler and the advice reduced, demonstrating a response that takes the driver's psychological state into consideration.

[0702] Drivers can adjust their driving based on notifications from the in-car terminal. The emotion engine evaluates the driver's emotional state, providing support tailored to their individual driving style and psychological state. This allows drivers to continue driving more safely and comfortably.

[0703] This invention not only improves the efficiency of traffic flow but also reduces the psychological burden on drivers, thereby enhancing the overall driving experience. For example, if a driver is frustrated in congested traffic, the emotional engine can recognize this state and provide calming voice guidance, thereby promoting safe driving.

[0704] The following describes the processing flow.

[0705] Step 1:

[0706] The server acquires real-time traffic data from traffic sensors and cameras installed on the roads. This data includes the speed and volume of passing vehicles, traffic light waiting times, etc., and is aggregated into a database.

[0707] Step 2:

[0708] The server inputs aggregated traffic data into an AI model, which analyzes traffic conditions in real time. This model incorporates an algorithm to optimize traffic signal switching timing based on historical data. The analysis results are transmitted to the traffic signals, and signal switching is performed remotely.

[0709] Step 3:

[0710] The server performs calculations to predict when the driver's vehicle will reach the intersection. Based on this prediction, it determines the color of the traffic light when the driver passes through the intersection and transmits this information to the in-vehicle terminal.

[0711] Step 4:

[0712] The server inputs not only traffic data but also data on the driver's facial expressions and voice, obtained from cameras and sensors inside the vehicle, into the emotion engine. The emotion engine analyzes the driver's emotional state and provides the results to the server.

[0713] Step 5:

[0714] The terminal creates notifications for the driver based on predictive signal information received from the server and emotional data from the emotion engine. These notifications are delivered via display and voice guidance, designed to minimize driver stress.

[0715] Step 6:

[0716] The user (driver) adjusts their driving style according to notifications from their device. Based on traffic light color prediction information, they can optimize acceleration and deceleration, and also take into account advice that considers their own emotional state, enabling safe and comfortable driving.

[0717] (Example 2)

[0718] 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".

[0719] In today's traffic environment, traffic congestion and poorly timed traffic signals disrupt efficient traffic flow, causing significant stress to drivers. In particular, prolonged periods of these conditions can increase drivers' psychological burden and potentially hinder safe driving. There is a need to address these challenges, optimize traffic flow, and reduce drivers' psychological burden.

[0720] 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.

[0721] In this invention, the server includes means for analyzing mobile data acquired from a traffic information device, means for optimizing the timing of signal device switching based on the analyzed information, and means for determining notification content according to the driver's emotional state and guiding the driver accordingly. This makes traffic flow smoother and enables safe and comfortable driving support that takes the driver's emotions into consideration.

[0722] A "traffic information device" is a device, such as a sensor or camera, installed to monitor traffic conditions on roads and is responsible for collecting real-time data on moving objects.

[0723] "Mobile vehicle data" refers to information such as the location, speed, and number of vehicles collected by traffic information devices, and is used for analyzing traffic conditions.

[0724] A "traffic signal device" is a traffic signal installed at intersections and on roads, and is a device that displays signals to vehicles and pedestrians in order to control the flow of traffic.

[0725] "Emotional processing means" refers to systems and algorithms for evaluating a driver's emotions in real time, enabling responses based on the driver's psychological state.

[0726] A "mobile terminal" is an electronic device installed in a vehicle that transmits notifications and information from a server to the driver.

[0727] A "driver" refers to the driver of a vehicle moving on a road, and is an individual positioned as a user of the transportation system.

[0728] "Optimization" refers to adjusting traffic signal switching and traffic management methods to achieve the best possible state in order to make traffic flow more efficient and smoother.

[0729] "Notification content" refers to information and instructions transmitted to the driver via a mobile terminal, including signal prediction information and driving advice.

[0730] This invention is a system aimed at improving traffic flow efficiency and reducing the psychological burden on drivers. This system provides driver assistance by coordinating a server, an in-vehicle terminal, and an emotion processing mechanism.

[0731] The server collects real-time mobile data from traffic information devices. This data is used to analyze traffic flow and calculate the optimal timing for switching traffic signals. The server then transmits the optimized signal control pattern to the traffic signals, thereby optimizing traffic flow at intersections.

[0732] The in-vehicle terminal receives information from the server and provides appropriate notifications to the driver. This includes predictive information about the color of traffic lights at intersections, allowing the driver to adjust their driving accordingly. Furthermore, the terminal evaluates the driver's emotional state in real time through an emotion processing system and generates notifications based on that information.

[0733] Drivers can use notifications from in-vehicle terminals to adjust their driving style and reduce stress while driving. For example, if a driver is experiencing high stress in congested traffic, an emotional processing system will detect this state and send a notification to encourage relaxation. This notification is provided through voice guidance and screen displays, allowing the driver to maintain safe and comfortable driving.

[0734] An example of a prompt might be, "Provide advice to help a driver in traffic congestion feel more relaxed." This prompt allows the system to generate appropriate feedback tailored to the driver's emotional state, supporting safer driving.

[0735] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0736] Step 1:

[0737] The server collects real-time mobile data from traffic information devices.

[0738] It receives data such as vehicle location, speed, and number from traffic information devices as input.

[0739] As part of data processing, this raw data is used to quantify traffic flow and analyze its trends.

[0740] The output will be an analysis showing the current traffic situation.

[0741] Step 2:

[0742] The server uses a generated AI model to optimize the switching timing of signal devices based on the analysis results.

[0743] The results of the traffic situation analysis obtained in Step 1 will be used as input.

[0744] As a data calculation, an algorithm is executed to calculate the optimal signal timing for smooth traffic flow.

[0745] The output generates an optimized signal switching pattern.

[0746] Step 3:

[0747] The server sends the optimized signal switching pattern to the signaling device.

[0748] The signal pattern generated in step 2 is used as the input.

[0749] In terms of specific operation, data is transmitted to a signaling device via the network, and the signal is switched based on that data.

[0750] As an output, the timing of traffic signals at intersections is appropriately adjusted.

[0751] Step 4:

[0752] The server uses predictive calculations based on an AI model to calculate the time it will take the driver to reach the intersection and predict the color of the traffic light.

[0753] The system receives real-time updated data on vehicle movements as input.

[0754] As part of the data processing, the signal status at the time of passing through the intersection is simulated, taking into account the driver's predicted arrival time.

[0755] The output generates information about the predicted signal color.

[0756] Step 5:

[0757] The terminal receives signal prediction information and emotional state evaluation results from the server.

[0758] The inputs received are the signal prediction information generated in step 4 and data from the emotion processing device.

[0759] Specifically, this involves preparing to provide appropriate notifications to the driver.

[0760] The output determines the content of the notification to the driver.

[0761] Step 6:

[0762] The user (driver) adjusts driving operations based on notifications from the device.

[0763] The input includes signal prediction information and emotion-based advice presented by the device.

[0764] Specifically, the system checks notifications and, if necessary, accelerates, decelerates, or changes lanes.

[0765] As an output, safer and more efficient operation will be achieved.

[0766] (Application Example 2)

[0767] 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".

[0768] Optimizing traffic flow and reducing driver stress are crucial challenges, but conventional traffic control systems struggle to optimize traffic flow while considering the driver's psychological state. Furthermore, timely signal changes based on traffic information and appropriate notifications to drivers remain challenges. Additionally, when driving is done under stress, psychological support for drivers is often insufficient.

[0769] 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.

[0770] In this invention, the server includes means for analyzing traffic data acquired from a traffic information collection device, means for optimizing the switching timing of an information display device based on the analyzed data, and means for evaluating the driver's emotional state and presenting information that takes the driver's psychological state into consideration based on the evaluation. This makes it possible to optimize the flow of traffic while simultaneously reducing the psychological burden on the driver and providing a safe and comfortable driving environment.

[0771] A "traffic information collection device" is a device used to collect data on traffic conditions, detecting things like the flow of vehicles and traffic density on roads using sensors and cameras.

[0772] "Means of analysis" refers to devices or software used to perform the process of analyzing collected data and extracting information according to a specific purpose.

[0773] An "information display device" is a device, such as a traffic light or a display, that provides information visually or audibly based on analyzed data.

[0774] A "driver's terminal" is a computer device installed inside a vehicle to provide information to the driver, and includes navigation and notification functions.

[0775] "Assessing emotional state" is a process of analyzing the driver's psychological and emotional state to determine appropriate responses, and often involves using an emotional engine.

[0776] "Psychologically sensitive information" refers to customized information provided in consideration of the driver's current psychological state, including relaxation techniques and advice.

[0777] An "automatic suggestion device" is a device that automatically provides selected relaxation methods and support information according to the driver's emotional state.

[0778] Modes for carrying out the invention

[0779] The system that implements this application consists of a traffic information collection device, a server, a driver terminal, and an automatic display device. The server aggregates and analyzes traffic data acquired from the traffic information collection device in real time. This analysis uses AI analysis modules such as TensorFlow and scikit-learn. Based on the analyzed data, the system optimizes the signal switching timing of the information display device and generates signal control information.

[0780] The driver terminal receives information from the server and notifies the driver. At the same time, it uses an emotion analysis library (e.g., affectiva) to evaluate the driver's emotional state and adjusts the notification content according to the driver's psychological state. Furthermore, based on the emotion evaluation, an automated presentation device provides relaxation-enhancing music, aromatherapy, visuals, etc.

[0781] For example, if a driver feels stressed while driving on a congested road, the server detects this and notifies the driver's device with the message, "Please enjoy some relaxing music." Simultaneously, the in-car audio system automatically plays relaxing music, and an aroma diffuser is activated to promote relaxation. In this way, it is possible to provide multifaceted support to reduce the driver's psychological burden.

[0782] An example of a prompt message might be: "Please come up with a new feature that analyzes the driver's emotional state in real time and suggests the optimal relaxation method."

[0783] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0784] Step 1:

[0785] The server receives real-time traffic data from traffic information collection devices. This data includes vehicle position, speed, and traffic density information obtained from sensors and cameras. The server uses this data as input and analyzes it using an AI analysis module to output information necessary for optimizing signal switching. Specifically, it uses TensorFlow to apply a traffic flow model and predict the timing of the next signal operation.

[0786] Step 2:

[0787] Based on the analysis results, the server generates control data to optimize the signal switching timing of the information display device. Using a generated AI model, it calculates the predicted optimal signal pattern and sends the result to the traffic lights. This adjusts the signals to ensure smooth traffic flow at intersections.

[0788] Step 3:

[0789] The server transmits signal status prediction information to the terminal of a driver approaching the intersection. The driver's terminal receives this information and notifies the driver visually or audibly. Inputs include current and next signal status information, and outputs include messages displayed on the screen and voice guidance. The notifications are designed to help drivers react smoothly to the next signal.

[0790] Step 4:

[0791] The driver terminal analyzes the driver's voice and facial expression data using an emotion analysis library (e.g., affectiva). It uses the driver's facial expression data as input to evaluate their emotional state. Based on this evaluation, the terminal outputs optimal relaxation information for the driver. Specifically, if the status is determined to be high stress, the driver terminal will display a message such as, "Take a deep breath and refresh yourself."

[0792] Step 5:

[0793] The user receives instructions based on their emotional assessment results and accepts relaxation measures to adjust the in-car environment. An automated display system offers options such as playing relaxation music or activating an aroma diffuser. This allows the user to follow notifications on the driver's terminal and take specific actions to improve the in-car environment.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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."

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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 as being incorporated by reference.

[0815] The following is further disclosed regarding the embodiments described above.

[0816] (Claim 1)

[0817] A means of analyzing traffic data acquired from traffic sensors,

[0818] A means for optimizing the timing of traffic signal switching based on the analyzed data,

[0819] A means for transmitting an optimized signal pattern to a traffic light,

[0820] A means for predicting the color of the traffic light when the driver passes through an intersection and transmitting the prediction information,

[0821] A means of notifying the driver via an in-vehicle terminal,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, which remotely controls the timing of signal switching.

[0825] (Claim 3)

[0826] The system according to claim 1, wherein the driver is notified of a signal color prediction information via a display or voice guidance.

[0827] "Example 1"

[0828] (Claim 1)

[0829] A means for analyzing traffic flow data acquired from traffic measurement equipment,

[0830] A means for optimizing the switching timing of traffic signal devices based on analyzed data,

[0831] A means for transmitting optimized signal timing to a traffic signal device,

[0832] A means for predicting the state of traffic signals when a driver passes through an intersection and transmitting the predicted information,

[0833] A means of notifying the driver via an in-vehicle electronic device,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1 for wirelessly controlling the switching timing of a traffic signal device.

[0837] (Claim 3)

[0838] The system according to claim 1, wherein signal status prediction information to be notified to the driver is provided by a display device or an audio output device.

[0839] "Application Example 1"

[0840] (Claim 1)

[0841] A device that analyzes traffic condition data acquired from traffic information devices,

[0842] A device that optimizes the switching sequence of signal devices based on analyzed data,

[0843] A device that communicates optimized signal operation to the signaling equipment,

[0844] A device that predicts the color of the traffic signal when a vehicle passes through an intersection and transmits the prediction information,

[0845] A device that notifies the driver via the in-vehicle information system,

[0846] A device that automatically adjusts the vehicle's speed based on predicted signal information,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1 for remotely controlling the switching order of signal devices.

[0850] (Claim 3)

[0851] The system according to claim 1, wherein the color prediction information of the signal to be notified to the driver is provided by a display device or an audio output device.

[0852] "Example 2 of combining an emotion engine"

[0853] (Claim 1)

[0854] A means for analyzing mobile data acquired from traffic information devices,

[0855] A means for optimizing the switching timing of signal devices based on the analyzed information,

[0856] Means for transmitting optimized signal control to a signaling device,

[0857] A means for predicting the signal display when a driver passes through an intersection and transmitting the prediction information,

[0858] A means of notifying the driver via a mobile terminal,

[0859] An emotional processing method for evaluating the emotional state of the driver,

[0860] A means of determining notification content based on the driver's emotional state and guiding them accordingly,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, which remotely controls the timing of signal switching.

[0864] (Claim 3)

[0865] The system according to claim 1, wherein the display prediction information of the signal to be notified to the driver is provided by a display device or voice guidance.

[0866] "Application example 2 when combining with an emotional engine"

[0867] (Claim 1)

[0868] A means for analyzing traffic data acquired from a traffic information collection device,

[0869] A means for optimizing the switching timing of the information display device based on the analyzed data,

[0870] Means for transmitting an optimized signal pattern to an information display device,

[0871] A means for predicting the display state when the driver passes through an intersection and transmitting the predicted information,

[0872] A means of notifying the driver via the driver's terminal,

[0873] A means for evaluating the driver's emotional state and presenting information that takes the driver's psychological state into consideration based on the evaluation,

[0874] A means of providing relaxation methods through an automated presentation device,

[0875] A system that includes this.

[0876] (Claim 2)

[0877] The system according to claim 1, which remotely controls the switching timing of an information display device.

[0878] (Claim 3)

[0879] The system according to claim 1, wherein predictive information on the display state to be notified to the driver is provided by a visual display device or an audio guidance device, and further provides guidance on relaxation measures according to the driver's emotional state. [Explanation of Symbols]

[0880] 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. A device that analyzes traffic condition data acquired from traffic information devices, A device that optimizes the switching sequence of signal devices based on analyzed data, A device that communicates optimized signal operation to the signaling equipment, A device that predicts the color of the traffic signal when a vehicle passes through an intersection and transmits the prediction information, A device that notifies the driver via the in-vehicle information system, A device that automatically adjusts the vehicle's speed based on predicted signal information, A system that includes this.

2. The system according to claim 1, which remotely controls the switching order of signal devices.

3. The system according to claim 1, wherein the color prediction information of the signal to be notified to the driver is provided by a display device or an audio output device.

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