system

Abstract

Provide a system. The system includes: a means for collecting location data from vehicles, pedestrians, bicycles, and motorcycles; means for controlling traffic lights at an intersection; a means for calculating an optimal route to a destination; means for predicting the risk of road accidents and providing warnings; means for providing fuel-efficient driving instructions; A means of calculating and providing evacuation routes in emergencies Including system.

Description

[Technical Field]

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

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

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

[0004] Traffic congestion in urban areas, along with the resulting traffic accidents, increased energy consumption, and delayed emergency response, are becoming serious problems. At intersections where vehicles, pedestrians, bicycles, and motorbikes all intersect, optimizing traffic light timing and vehicle routes is particularly difficult, increasing the risk of accidents. Meanwhile, energy waste is also a serious problem, creating a need for driver assistance systems to improve fuel efficiency. A system that can solve these problems and achieve safe and efficient traffic management is needed. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for collecting location data from vehicles, pedestrians, bicycles, and motorcycles, a means for controlling traffic lights at intersections, a means for calculating the optimal route to a destination, a means for predicting the risk of traffic accidents and providing warnings, a means for providing driving instructions to improve fuel efficiency, and a means for calculating and providing evacuation routes in emergencies. This system enables real-time monitoring of traffic conditions and optimal signal control, reducing the risk of accidents and improving energy efficiency. It also makes it possible to quickly provide optimal evacuation routes in emergencies.

[0006] "Location Data" means information that indicates the current geographic location of a vehicle, pedestrian, bicycle, or motorcycle.

[0007] A "traffic light" is a device installed to control the flow of traffic, and emits light signals such as red and green lights.

[0008] An "intersection" is a place where multiple roads intersect and where traffic lights and other equipment are installed to manage traffic flow.

[0009] An "optimal route" is the most efficient and safe route calculated taking into account factors such as travel time to the destination, distance, and traffic conditions.

[0010] A "traffic accident" is an incident involving a vehicle, pedestrian, bicycle, or motorcycle resulting in damage to life or property due to a collision or contact.

[0011] "Fuel efficiency" is an index that indicates the amount of fuel consumed by a vehicle to travel a certain distance, and is used to evaluate efficient driving.

[0012] "Driving instructions" refers to information that includes specific instructions and advice to encourage the driver to drive appropriately.

[0013] "Emergency" refers to an abnormal situation, such as a natural disaster or accident, that disrupts normal traffic conditions and requires a rapid response.

[0014] An "escape route" is a route chosen for safe evacuation in an emergency.

[0015] A "system" is a collection of mechanisms or devices in which multiple components function in conjunction with one another. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

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

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

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

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

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention integrates various systems and methods related to automated driving and traffic management to improve traffic efficiency and safety in urban areas. The system collects location data from vehicles, pedestrians, bicycles, and motorcycles, and uses this data to control intersection signals, calculate optimal routes to destinations, predict the risk of traffic accidents, provide driving instructions to improve fuel efficiency, and calculate and provide evacuation routes in emergencies.

[0038] Data Collection and Management

[0039] The server collects location data from vehicles and mobile devices. Vehicles and mobile devices are equipped with GPS and sensors to detect speed and direction of travel. This data is sent to the server and stored in a database in real time.

[0040] Examples:

[0041] The server retrieves the intersection data and determines that there are currently 15 cars, 10 pedestrians, and 5 cyclists. The location data for each is updated every few seconds.

[0042] Signal Control

[0043] The server calculates the optimal signal timing based on all the location data sent to the intersection. The traffic light control takes into account the congestion situation at the intersection and the direction of each participant. The calculation results are sent to the traffic light terminal at the intersection, and the traffic light changes its signal accordingly.

[0044] Examples:

[0045] If an intersection is congested, the server will extend the red light time to allow pedestrians to cross the road safely.

[0046] Optimal route calculation

[0047] The server calculates the optimal route based on each vehicle's current location and destination information. This process includes traffic volume data, road construction information, and accident information. The calculated results are sent to each vehicle's terminal, and the driver follows the route displayed on the terminal.

[0048] Examples:

[0049] If the user's usual route to a destination is congested, the server will take this into account and suggest a route that avoids the traffic, thereby improving fuel economy and saving time.

[0050] Traffic accident prediction and avoidance

[0051] The server analyzes the location and speed data collected in real time to predict the risk of traffic accidents. If the risk is determined to be high, the server sends a warning to the vehicle involved. The device receives the warning and urges the user to slow down or stop.

[0052] Examples:

[0053] As the user approaches an intersection, the server analyzes the location and speed of other vehicles to detect potential collision risks, and the device issues a "brake" warning, allowing the user to slow down accordingly.

[0054] Improved fuel efficiency

[0055] The server analyzes each vehicle's fuel economy data and driving patterns and provides advice on maximizing energy efficiency, while the device displays specific driving instructions for improving fuel efficiency in real time.

[0056] Examples:

[0057] While driving, the device will notify the driver to avoid sudden acceleration and deceleration, helping to reduce fuel consumption.

[0058] Emergency response

[0059] The server analyzes emergency data in real time and calculates the optimal evacuation route. The calculation results are sent to the relevant terminals, and the evacuation route is displayed to the user in real time.

[0060] Examples:

[0061] When an earthquake occurs in the city, the server calculates safe evacuation routes and provides accurate information to users, who can then evacuate safely.

[0062] These program processes and their interaction significantly improve traffic optimization and safety. Each step is executed in real time, enabling dynamic management of traffic conditions across the city, resulting in an efficient and safe urban environment.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] Terminals (vehicles and mobile devices) periodically collect location and speed data from GPS modules and speed sensors, which are then uploaded to the cloud and received by a server in real time.

[0066] Step 2:

[0067] The server records the received location and speed data in a database to understand the latest traffic conditions. This data is cross-referenced to identify different types of mobility, such as vehicles, pedestrians, bicycles, and motorcycles.

[0068] Step 3:

[0069] The server runs an algorithm to calculate intersection signal timings based on current traffic conditions, using each participant's heading, speed, location, and historical traffic data for analysis.

[0070] Step 4:

[0071] The server transmits the calculated signal timing to the signal terminal, which changes the color and timing of the signal according to the received information.

[0072] Step 5:

[0073] The server receives destination information from each vehicle and calculates the optimal route to that point, incorporating the latest traffic data, road construction information, and accident information, allowing the vehicle to select the best route.

[0074] Step 6:

[0075] The server then sends the calculated optimal route information to the terminals in each vehicle. The terminals receive the information and display the direction and route to the destination on their screens. The driver can follow this information to reach their destination safely and efficiently.

[0076] Step 7:

[0077] The server performs real-time analysis to predict the risk of traffic accidents by analyzing the intersection points, speed, and direction of travel of each vehicle, and if it determines that the risk is high, it sends a warning signal to the terminal of the vehicle in question.

[0078] Step 8:

[0079] The terminal (vehicle) receives the warning signal sent from the server and displays specific instructions to the driver, such as "apply the brakes" or "change direction," thereby preventing potential accidents.

[0080] Step 9:

[0081] The server collects fuel economy data from each vehicle and analyzes driving patterns. It generates advice for improving fuel economy and sends that information to each vehicle's terminal. The advice includes instructions to avoid sudden acceleration and deceleration.

[0082] Step 10:

[0083] The device (vehicle) displays fuel efficiency improvement advice to the driver in real time, and the driver can improve fuel efficiency by adjusting their driving behavior according to the advice.

[0084] Step 11:

[0085] The server quickly calculates evacuation routes in the event of an emergency. In the event of a real emergency (e.g., a natural disaster), the server assesses the situation and determines the safest route based on the latest traffic conditions and geographical information.

[0086] Step 12:

[0087] The server sends emergency evacuation route information to all related devices, which then display the received information to drivers and users to help them evacuate safely.

[0088] By combining these processing steps, the system can optimize traffic, improve safety, and enable efficient emergency response, improving traffic management efficiency across the city.

[0089] Example 1

[0090] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0091] In modern urban transportation systems, multiple traffic participants, including vehicles, pedestrians, bicycles, and motorcycles, use the roads at the same time, resulting in congestion on highways and intersections, which reduces traffic efficiency and compromises safety. In particular, it is difficult to respond appropriately to traffic congestion, accidents, and emergencies. Furthermore, optimizing fuel efficiency is also an important issue.

[0092] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0093] In this invention, the server includes means for collecting location data from vehicles, pedestrians, bicycles, and motorcycles, means for saving the collected location data in a database in real time, means for controlling intersection traffic lights in real time, means for calculating the optimal route to a destination based on traffic volume data, accident information, and road construction information, means for predicting the risk of traffic accidents based on location data and speed data and providing warnings to relevant vehicles, means for analyzing vehicle fuel efficiency data and operation patterns and providing driving instructions to improve fuel efficiency in real time, and means for calculating evacuation routes in emergencies and providing them to relevant vehicles.This enables efficient management of urban traffic, improved safety, and reduced fuel consumption.

[0094] "Vehicle" refers to automobiles, motorcycles, etc. used as a means of transportation in urban transportation systems.

[0095] "Pedestrian" refers to people who travel on foot in urban transportation systems.

[0096] "Bicycle" refers to a two-wheeled, human-powered vehicle in urban transportation systems.

[0097] "Motorcycle" refers to a two-wheeled engine-powered vehicle in urban transportation systems.

[0098] "Location data" refers to the current location information of vehicles, pedestrians, bicycles, and motorcycles obtained from positioning systems such as GPS.

[0099] "Real-time" refers to data collection, analysis, processing, and communication occurring without delay.

[0100] "Database" refers to an information collection system for efficiently storing and managing location data and traffic information.

[0101] A "traffic light" refers to a traffic signal device used to control traffic flow at intersections, etc.

[0102] "Optimal route" refers to the most efficient driving route to a destination, calculated taking into account current traffic conditions.

[0103] "Traffic data" refers to information about the number and flow of vehicles, pedestrians, bicycles, and motorbikes on specific roads or areas.

[0104] "Accident information" refers to information regarding the circumstances, location, scale, and scope of impact of a traffic accident.

[0105] "Road construction information" refers to information such as the status, location, and duration of road repairs and construction.

[0106] "Risk of traffic accident" refers to the possibility of a traffic accident predicted by analyzing location data and speed data.

[0107] "Fuel Economy Data" means information relating to a vehicle's fuel consumption.

[0108] An "operation pattern" refers to the series of movements of a vehicle, including how it actually moves and how it accelerates and decelerates.

[0109] An "emergency" refers to a situation in which normal traffic conditions are significantly disrupted due to a disaster, accident, or other incident.

[0110] An "evacuation route" refers to a route calculated for safe evacuation in an emergency.

[0111] This invention integrates various systems and methods related to automated driving and traffic management to improve traffic efficiency and safety in urban areas. The system collects location data from vehicles, pedestrians, bicycles, and motorcycles, and uses this data to control intersection signals, calculate optimal routes to destinations, predict the risk of traffic accidents, provide driving instructions to improve fuel efficiency, and calculate and provide evacuation routes in emergencies.

[0112] Data Collection and Management

[0113] The server manages location data collected from each vehicle and mobile device in real time. Specifically, vehicles are equipped with GPS, speed sensors, and direction sensors, and the data obtained from these is sent to the server. The server stores this data in a database and grasps the traffic situation throughout the city in real time. For example, the server obtains data on an intersection and confirms that there are currently 15 cars, 10 pedestrians, and 5 cyclists. This information is updated every few seconds.

[0114] Signal Control

[0115] The server calculates the optimal traffic light timing based on all the location data sent to the intersection. The server takes into account the congestion at the intersection and the direction of each participant, and sends the calculation results to the traffic light terminal at the intersection. The traffic light then changes its signal accordingly. For example, if the intersection is congested, the server will extend the red light time to allow pedestrians to cross the road safely.

[0116] Optimal route calculation

[0117] The server calculates the optimal route based on each vehicle's current location and destination information. Traffic volume data, accident information, and road construction information are included in the calculation, and the calculation results are sent to each vehicle's terminal. The driver then follows the route suggested by the terminal. For example, if the user's usual route to their destination is congested, the server will take this into account and suggest a route that avoids the traffic jam. This improves fuel efficiency and saves time.

[0118] Traffic accident prediction and avoidance

[0119] The server analyzes the location and speed data collected in real time to predict the risk of traffic accidents. If the risk is determined to be high, the server sends a warning to the vehicles involved. The device receives the warning and urges the user to slow down or stop. For example, when a user approaches an intersection, the server analyzes the location and speed of other vehicles and detects a potential risk of collision. The device issues a "brake" warning, and the user slows down accordingly.

[0120] Improved fuel efficiency

[0121] The server analyzes each vehicle's fuel economy data and driving patterns and provides advice on maximizing energy efficiency. The device displays specific driving instructions in real time to improve fuel efficiency. For example, the device may notify the driver to avoid sudden acceleration and deceleration while driving, thereby reducing fuel consumption.

[0122] Emergency response

[0123] The server instantly analyzes emergency data and calculates the optimal evacuation route. The calculation results are sent to the relevant devices, which then present the evacuation route to the user in real time. For example, when a disaster occurs in a city, the server calculates the safest evacuation route and provides reliable information to the user. The user can evacuate safely according to this information.

[0124] Prompt Sentence Examples

[0125] "Please create a prompt for an AI model that calculates the optimal route based on location data collected from each vehicle."

[0126] "Generate prompt statements to optimize traffic light control at an intersection."

[0127] "Please provide a prompt to calculate the best evacuation route in an emergency."

[0128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0129] Step 1:

[0130] Data collection

[0131] The server collects location data from each vehicle and mobile device. As input, it receives location data, speed data, and heading data obtained from the GPS, speed sensor, and heading direction sensor of the vehicle and mobile device. As output, this data is sent to the server. Specifically, the vehicle's GPS periodically updates the location data, and the sensors detect the current speed and heading direction. The data is sent to the server every three seconds.

[0132] Step 2:

[0133] Data storage

[0134] The server stores the collected location data in a database in real time. The inputs are the location data, speed data, and heading data collected in step 1. The output is the location data stored in the database. Specifically, the server creates an entry in the database and stores the latest location and speed information for each vehicle. This allows the latest location information of all vehicles at the intersection to be known.

[0135] Step 3:

[0136] Analysis of intersection congestion

[0137] The server analyzes the collected location data and determines the congestion status of the intersection. The input is the location data stored in the database in step 2. The output is traffic volume information for the intersection. Specifically, it counts the number of vehicles, pedestrians, and bicycles at the intersection and tally the traffic volume heading in each direction. For example, it identifies that 10 cars are traveling from the north.

[0138] Step 4:

[0139] Signal Timing Calculations

[0140] The server calculates the optimal timing for switching traffic lights based on the analysis results. The input is the traffic volume information obtained in step 3. The output is generated signal control data. Specifically, the server determines the optimal signal timing, such as extending the red light to relieve congestion. For example, to ensure pedestrian safety, the crosswalk signal may be extended by 30 seconds.

[0141] Step 5:

[0142] Transmission of signal control data

[0143] The server sends the calculated signal control data to the traffic light terminals at the intersection. The input is the signal control data generated in step 4. The output is instructions sent to each traffic light terminal. In concrete terms, the traffic light terminals control the signals according to the instructions from the server.

[0144] Step 6:

[0145] Receiving route information

[0146] The user enters destination information via the device and sends that information to the server. The input is the destination information entered by the user. The output is the transmission of the destination information to the server. The specific operation is when the user enters the destination into the navigation app and taps the "Calculate route" button.

[0147] Step 7:

[0148] Calculating the best route

[0149] The server analyzes traffic volume data, accident information, and road construction information to calculate the optimal route. The inputs include received route information data and real-time traffic volume data, accident information, and road construction information. The output is optimal route data. Specifically, the server calculates the shortest route in a few seconds based on the current traffic conditions and construction information. For example, it recommends back roads and avoids major roads to avoid traffic jams.

[0150] Step 8:

[0151] Sending route data

[0152] The server sends the calculated optimal route data to the terminal. The input is the optimal route data generated in step 7. The output is the route data sent to the navigation terminal of each vehicle. Specifically, the terminal displays the route shown in the navigation app on a map and guides the user.

[0153] Step 9:

[0154] Traffic accident risk prediction

[0155] The server analyzes location and speed data collected in real time to predict the risk of traffic accidents. The input is real-time location and speed data. The output is risk warning data. Specifically, the server analyzes the speed and direction of vehicles and detects abnormal patterns near intersections. For example, it determines that two cars are approaching at high speed.

[0156] Step 10:

[0157] Sending risk warnings

[0158] If the server determines that the risk is high, it sends a warning to the vehicles involved. The input is the risk warning data generated in step 9. The output is a warning message sent to the terminal of each vehicle. Specifically, the terminal displays an alert saying, "There is a risk of collision ahead. Please slow down."

[0159] Step 11:

[0160] Analysis of fuel consumption data

[0161] The server analyzes each vehicle's fuel efficiency data and driving patterns and generates advice to maximize energy efficiency. The inputs are fuel efficiency data and driving pattern data for each vehicle. The output is advice to improve fuel efficiency. Specifically, the server detects patterns of sudden acceleration and deceleration and generates driving instructions to avoid them.

[0162] Step 12:

[0163] Notification of fuel economy improvement advice

[0164] The terminal displays driving instructions for improving fuel economy in real time. The input is the fuel economy improvement advice generated in step 11. The output is the displayed driving instruction message. Specifically, the terminal displays specific driving instructions in real time, such as "maintain current speed" or "slow down for the next traffic light."

[0165] Step 13:

[0166] Emergency Data Collection

[0167] The server instantly collects and analyzes emergency data. The inputs include disaster information and emergency notification data. The output is damage situation data. Specifically, the server receives emergency information such as earthquakes and fires and plots the damage situation on a map.

[0168] Step 14:

[0169] Evacuation route calculation

[0170] The server calculates the optimal evacuation route based on the collected emergency information. The input is the damage situation data generated in step 13. The output is evacuation route data. Specifically, the server calculates a safe evacuation route based on traffic signal data and traffic information. For example, it selects a route that avoids areas where fires are occurring.

[0171] Step 15:

[0172] Evacuation route notification

[0173] The terminal notifies the user of the optimal evacuation route. The input is the evacuation route data generated in step 14. The output is evacuation route instructions displayed on the user terminal. Specifically, the terminal displays specific instructions such as "Please evacuate through this road" and guides the user to a safe route on a map.

[0174] (Application example 1)

[0175] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0176] Conventional traffic management systems have had difficulty properly integrating location information from vehicles, pedestrians, bicycles, motorbikes, and other moving objects to control traffic signals, calculate optimal routes, and predict traffic accidents in real time. Furthermore, they have not adequately provided appropriate evacuation routes in emergencies or given driving instructions to improve fuel efficiency. For these reasons, there is a need to improve traffic efficiency and safety in urban areas.

[0177] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0178] In this invention, the server includes means for collecting location data from vehicles, mobile objects, motorcycles, and motorbikes, means for controlling traffic signals at intersections, means for calculating the optimal route to a destination, means for predicting the risk of traffic accidents and providing warnings, means for providing driving instructions to improve fuel efficiency, means for calculating and providing evacuation routes in emergencies, communication means for transmitting and receiving data in real time, and high-speed computing means for processing and analyzing the collected data, thereby enabling improved traffic efficiency and safety in urban areas.

[0179] A "vehicle" is a land-based moving object that is powered by a motor or engine.

[0180] "Mobile object" refers to any object that can be moved to transport people or cargo.

[0181] A "two-wheeled vehicle" is a vehicle that typically has two wheels and is steered by a handlebar.

[0182] "Motorcycle" generally refers to a two-wheeled vehicle that can carry one or two people.

[0183] "Location data" refers to coordinate information such as latitude and longitude for a specific location obtained from a positioning system such as a GPS.

[0184] A traffic signal is a device installed at an appropriate location at an intersection or road to control traffic flow using red, yellow, and green lights.

[0185] An "optimal route" is the most efficient route that saves time and fuel when a vehicle or person travels to a destination.

[0186] "Risk of traffic accident" refers to the possibility of a traffic accident occurring, estimated based on traffic conditions and the behavior of other vehicles and pedestrians.

[0187] A "warning" is a notice or guidance to warn of danger.

[0188] "Fuel efficiency" is a measure of the efficient use of fuel consumed during driving.

[0189] "Driving instructions" refer to advice or instructions regarding specific driving actions provided to the driver.

[0190] An "evacuation route" is a route used to safely evacuate in an emergency.

[0191] "Communication means" is a general term for technologies and devices for sending and receiving data.

[0192] "High-speed computing means" refers to computer systems and algorithms for rapidly processing and analyzing large amounts of data in real time.

[0193] "Real-time" refers to the ability to process and react instantly to information about ongoing events.

[0194] To realize this invention, the following elements are required:

[0195] 1. Hardware:

[0196] Vehicles and moving objects are fitted with a GPS module, a communication module, and sensors for detecting speed and direction of travel.

[0197] A traffic light control device is installed at the intersection and manages the traffic lights in cooperation with a server.

[0198] The server is constructed with a computer having high-performance computing power and includes a system for processing large amounts of data in real time.

[0199] 2. Software:

[0200] Server-side software includes Python Flask or Django, MySQL® as the database, and a RESTful API for communication.

[0201] On the vehicle terminal side, a smartphone app (such as React Native) or a dedicated in-vehicle terminal is used.

[0202] 3. Data processing and calculation:

[0203] Real-time location data collection and transmission:

[0204] The GPS module collects location information (latitude, longitude, speed, and direction of travel) of vehicles and other moving objects and periodically transmits it to a server.

[0205] Signal Control:

[0206] The server uses the collected location data to assess the congestion situation at intersections and run algorithms to optimize traffic light timing.

[0207] Optimal route calculation:

[0208] The server calculates the optimal route based on each vehicle's current location and destination information, taking into account traffic conditions and road construction and accident information.

[0209] Traffic Accident Prediction and Warning:

[0210] The server analyzes real-time location and speed data collected from mobile devices, predicts the risk of traffic accidents, and sends warnings to relevant devices.

[0211] Improved fuel efficiency:

[0212] The server analyzes each vehicle's driving patterns and fuel consumption data and provides real-time driving instructions to maximize energy efficiency.

[0213] Emergency evacuation route provided:

[0214] The server instantly analyzes the necessary data in the event of an emergency, calculates the optimal evacuation route, and provides it to the user.

[0215] As a concrete example, consider the case of an autonomous vehicle heading towards Shibuya Crossing in Tokyo. The vehicle sends real-time location data to a server, which then calculates traffic signal control and the optimal route based on the congestion situation at the intersection and the positions of other vehicles and pedestrians. The user can reach their destination safely and efficiently by following the information provided by the server.

[0216] Also, as an example of a prompt sentence when using a generative AI model, you can enter the following:

[0217] Example prompt sentence:

[0218] Please explain the overview of a traffic management system for autonomous vehicles. This system collects real-time location information of vehicles, pedestrians, and cyclists, and provides functions such as controlling intersection signals, suggesting optimal routes, and predicting the risk of traffic accidents.

[0219] This will significantly improve traffic efficiency and safety in urban areas.

[0220] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0221] Step 1:

[0222] The server collects location data from vehicles, mobile objects, motorcycles, and single-wheeled vehicles. This location data includes latitude, longitude, speed, and direction of travel collected using a GPS module. The input is real-time location data sent from each mobile object, and the output is location data stored on the server.

[0223] Step 2:

[0224] The server stores the collected location data in a database in real time. Specifically, the server immediately writes the location data it receives into the database. The input is the location data collected in step 1, and the output is the stored location data.

[0225] Step 3:

[0226] The server controls the traffic lights at intersections based on the collected location data. Specifically, the server runs an algorithm that evaluates the congestion situation at each intersection and calculates the optimal signal timing. The input is the location data saved in step 2, and the output is the signal control instructions.

[0227] Step 4:

[0228] The server calculates the optimal route based on each vehicle's current location and destination information. This process includes traffic volume data, road construction information, and accident information. The input is the destination information and the location data saved in step 2, and the output is the optimal route guidance.

[0229] Step 5:

[0230] The server analyzes the collected real-time location and speed data to predict the risk of traffic accidents. If the risk is determined to be high, the server sends a warning to the relevant device. The input is the vehicle's location and speed data, and the output is a warning message.

[0231] Step 6:

[0232] The server analyzes each vehicle's driving patterns and fuel consumption data and provides driving instructions to improve fuel efficiency. These instructions are displayed on the in-vehicle display in real time. The input is driving patterns and fuel consumption data, and the output is driving instructions.

[0233] Step 7:

[0234] The server analyzes emergency data and calculates the optimal evacuation route. The calculation results are sent to the relevant devices, and the evacuation route is presented to the user in real time. The input is emergency incident occurrence data and location data, and the output is evacuation route guidance.

[0235] These steps will improve traffic efficiency and safety in urban areas.

[0236] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0237] This invention combines an emotion engine with an automated driving and traffic management system to recognize human emotions and further improve traffic efficiency and safety. This system collects location data from vehicles, pedestrians, bicycles, and motorcycles, controls intersection signals, calculates optimal routes to destinations, predicts the risk of traffic accidents, provides driving instructions to improve fuel efficiency, and calculates and provides evacuation routes in emergencies. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, more advanced driving assistance can be achieved.

[0238] Data Collection and Management

[0239] The server collects location data from vehicles and mobile devices. Vehicles and mobile devices are equipped with GPS and sensors to detect speed and direction of travel. This data is sent to the server and stored in a database in real time.

[0240] Examples:

[0241] The server retrieves the intersection data and determines that there are currently 15 cars, 10 pedestrians, and 5 cyclists. The location data for each is updated every few seconds.

[0242] Signal Control

[0243] The server calculates the optimal signal timing based on all the location data sent to the intersection. The traffic light control takes into account the congestion situation at the intersection and the direction of each participant. The calculation results are sent to the traffic light terminal at the intersection, and the traffic light changes its signal accordingly.

[0244] Examples:

[0245] If an intersection is congested, the server will extend the red light time to allow pedestrians to cross the road safely.

[0246] Optimal route calculation

[0247] The server calculates the optimal route based on each vehicle's current location and destination information. This process includes traffic volume data, road construction information, and accident information. The calculated results are sent to each vehicle's terminal, and the driver follows the route displayed on the terminal.

[0248] Examples:

[0249] If the user's usual route to a destination is congested, the server will take this into account and suggest a route that avoids the traffic, thereby improving fuel economy and saving time.

[0250] Traffic accident prediction and avoidance

[0251] The server analyzes the location and speed data collected in real time to predict the risk of traffic accidents. If the risk is determined to be high, the server sends a warning to the vehicle involved. The device receives the warning and urges the user to slow down or stop.

[0252] Examples:

[0253] As the user approaches an intersection, the server analyzes the location and speed of other vehicles to detect potential collision risks, and the device issues a "brake" warning, allowing the user to slow down accordingly.

[0254] Improved fuel efficiency

[0255] The server analyzes each vehicle's fuel economy data and driving patterns and provides advice on maximizing energy efficiency, while the device displays specific driving instructions for improving fuel efficiency in real time.

[0256] Examples:

[0257] While driving, the device will notify the driver to avoid sudden acceleration and deceleration, helping to reduce fuel consumption.

[0258] Emergency response

[0259] The server analyzes emergency data in real time and calculates the optimal evacuation route. The calculation results are sent to the relevant terminals, and the evacuation route is displayed to the user in real time.

[0260] Examples:

[0261] When an earthquake occurs in the city, the server calculates safe evacuation routes and provides accurate information to users, who can then evacuate safely.

[0262] Applying the Emotion Engine

[0263] The server collects user emotion data from the vehicle's cameras and microphones, which is then analyzed by the emotion engine, which evaluates the user's stress level and emotional state and provides driving assistance information as needed.

[0264] Example 1:

[0265] If the user exhibits high stress levels while driving, the emotion engine detects this and the server sends instructions to the terminal to play calming music to provide a more relaxing driving environment.

[0266] Example 2:

[0267] If the user is feeling anxious or impatient, the emotion engine will detect this and the server will issue an alert to prevent unsafe driving behavior. For example, if there is a high possibility of distraction, the device will display a message urging the user to focus on driving.

[0268] These steps will enable the system to not only perform traditional traffic management functions but also realize advanced driving assistance that takes into account the user's emotional state, improving traffic efficiency and safety, optimizing fuel efficiency, and improving the accuracy of emergency response.

[0269] The processing flow will be explained below.

[0270] Step 1:

[0271] Terminals (vehicles and mobile devices) periodically collect location and speed data from GPS modules and speed sensors, which are then uploaded to the cloud and received by a server in real time.

[0272] Step 2:

[0273] The server records the received location and speed data in a database to understand the latest traffic conditions. This data is cross-referenced to identify different types of mobility, such as vehicles, pedestrians, bicycles, and motorcycles.

[0274] Step 3:

[0275] The server runs an algorithm to calculate intersection signal timings based on current traffic conditions, using each participant's heading, speed, location, and historical traffic data for analysis.

[0276] Step 4:

[0277] The server transmits the calculated signal timing to the signal terminal, which changes the color and timing of the signal according to the received information.

[0278] Step 5:

[0279] The server receives destination information from each vehicle and calculates the optimal route to that point, incorporating the latest traffic data, road construction information, and accident information, allowing the vehicle to select the best route.

[0280] Step 6:

[0281] The server then sends the calculated optimal route information to the terminals in each vehicle. The terminals receive the information and display the direction and route to the destination on their screens. The driver can follow this information to reach their destination safely and efficiently.

[0282] Step 7:

[0283] The server performs real-time analysis to predict the risk of traffic accidents by analyzing the intersection points, speed, and direction of travel of each vehicle, and if it determines that the risk is high, it sends a warning signal to the terminal of the vehicle in question.

[0284] Step 8:

[0285] The terminal (vehicle) receives the warning signal sent from the server and displays specific instructions to the driver, such as "apply the brakes" or "change direction," thereby preventing potential accidents.

[0286] Step 9:

[0287] The server collects fuel economy data from each vehicle and analyzes driving patterns. It generates advice for improving fuel economy and sends that information to each vehicle's terminal. The advice includes instructions to avoid sudden acceleration and deceleration.

[0288] Step 10:

[0289] The device (vehicle) displays fuel efficiency improvement advice to the driver in real time, and the driver can improve fuel efficiency by adjusting their driving behavior according to the advice.

[0290] Step 11:

[0291] The server quickly calculates evacuation routes in the event of an emergency. In the event of a real emergency (e.g., a natural disaster), the server assesses the situation and determines the safest route based on the latest traffic conditions and geographical information.

[0292] Step 12:

[0293] The server sends emergency evacuation route information to all related devices, which then display the received information to drivers and users to help them evacuate safely.

[0294] Step 13:

[0295] The terminal (vehicle interface) uses the camera and microphone in the vehicle to collect the user's emotional data (e.g., facial expressions and tone of voice), which is then sent to the emotion engine.

[0296] Step 14:

[0297] The emotion engine analyzes the received emotion data and evaluates the user's emotional state, recognizing whether the user is in a high-stress state or relaxed.

[0298] Step 15:

[0299] The server receives the analysis results from the emotion engine and adjusts the driving assistance information based on the user's emotional state, for example, by instructing the playing of calming music if the user is in a high-stress state.

[0300] Step 16:

[0301] The terminal (vehicle) adjusts driving assistance information and entertainment settings based on instructions from the server, providing a relaxing environment for the user.

[0302] Step 17:

[0303] The server adjusts the optimal route and traffic signals according to the user's emotional state. For example, if the user is feeling anxious, the server will provide a route that prioritizes safety.

[0304] By combining these processing steps, the system can optimize traffic, improve safety, respond efficiently to emergencies, and even provide advanced driving assistance that takes into account the user's emotional state.

[0305] Example 2

[0306] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0307] Conventional traffic management systems collect vehicle and pedestrian position data to control traffic signals, suggest optimal routes, predict traffic accidents, improve fuel efficiency, and respond to emergencies. However, they do not provide driving assistance that takes the user's emotional state into consideration. As a result, traffic safety risks caused by users' stress levels and emotional changes are not adequately addressed. To solve this problem, a system that collects and analyzes user emotional data in real time is needed.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0309] In this invention, the server includes means for collecting position data from vehicles, pedestrians, bicycles, and motorcycles, means for controlling traffic lights at intersections, means for calculating the optimal route to a destination, means for predicting the risk of traffic accidents and providing warnings, means for providing driving instructions to improve fuel efficiency, means for calculating and providing evacuation routes in emergencies, means for collecting and analyzing user emotion data in real time, and means for providing driving assistance information based on the user's emotion state, thereby enabling advanced driving assistance that takes the user's emotion state into consideration.

[0310] "Vehicle" means a means of transportation for travel over roads using an engine, motor, or other driving device.

[0311] A "pedestrian" is a person who moves on foot on a road or sidewalk.

[0312] A "bicycle" is a two-wheeled vehicle that is human-powered by pedaling.

[0313] A "motorcycle" is a two- or three-wheeled vehicle powered by an engine and used primarily as personal transportation.

[0314] "Location data" is data that indicates the geographic location of an object, typically expressed as latitude and longitude.

[0315] An "intersection traffic light" is a signal device installed at a road intersection to control the passage of vehicles and pedestrians.

[0316] A "destination" is a place that is set as the final purpose of a trip.

[0317] An "optimal route" is a route that is judged to be the most efficient from a departure point to a destination, taking into consideration time, distance, traffic conditions, and the like.

[0318] "Risk of traffic accidents" refers to the danger of collisions between vehicles or contact between vehicles and pedestrians that may occur on the road.

[0319] A "Warning" is a notice or alert issued to inform of a potential hazard or risk.

[0320] "Improving fuel efficiency" refers to the act of reducing energy consumption and mitigating the burden on the environment by improving the efficiency of fuel consumed when a vehicle travels.

[0321] "Driving instructions" are specific instructions for operations and actions provided to the driver to improve fuel efficiency and ensure safe driving.

[0322] An "emergency evacuation route" is a route that is set up for safe evacuation in the event of an emergency such as a disaster or accident.

[0323] "User" refers to an individual who uses the system, particularly a driver or passenger of a vehicle.

[0324] "Emotional data" is data that indicates the user's emotions and psychological state, and is collected through facial recognition, voice analysis, and the like.

[0325] "Analysis" is the process of examining and analyzing collected data in detail to extract meaningful information.

[0326] "Driving assistance information" refers to information and advice provided to drivers to help them drive safely and comfortably.

[0327] "Real-time" means that the time from data collection to the provision of analysis results is very short, almost instantaneous.

[0328] This invention is a traffic management and driving assistance system that collects location data from vehicles, pedestrians, bicycles, and motorcycles. The system also has the function of analyzing users' emotional data in real time and providing appropriate driving assistance information based on their emotional state.

[0329] System Configuration

[0330] This system consists of the following main components:

[0331] Server: Responsible for collecting and analyzing location data and emotion data. The server is installed with database management systems (e.g., MySQL, PostgreSQL), real-time data analysis software (e.g., Apache Kafka, Apache Flink), emotion recognition algorithms (e.g., OpenCV, Google Cloud Speech-to-Text), etc.

[0332] Terminal: Installed in a vehicle or mobile device, it transmits location data and displays driving assistance information.

[0333] User: Drives the vehicle and uses the assistance information provided by the system.

[0334] Data collection

[0335] The server collects location data from vehicles and mobile devices using GPS and various sensors (e.g., accelerometers and gyroscopes). This data is stored in a database in real time.

[0336] Examples:

[0337] For example, the server receives location data such as "Latitude: 35.6895, Longitude: 139.6917" from the vehicle every five seconds and stores it in a database.

[0338] Signal Control

[0339] The server analyzes the location data to calculate optimal signal timings to control traffic lights at intersections, taking into account traffic volume and congestion, and sends instructions to the traffic lights.

[0340] Examples:

[0341] At intersections with congestion, the server extends the green time of the traffic lights to smooth traffic flow.

[0342] Optimal route calculation

[0343] The server calculates the optimal route based on each vehicle's current location and destination information, taking into account traffic volume, construction information, accident information, etc., and sends the proposed route to the terminal.

[0344] Examples:

[0345] If the user's usual route to their destination is congested, the server calculates an alternative route and guides them to a route that avoids the traffic jam.

[0346] Traffic accident prediction and avoidance

[0347] The server analyzes location and speed data to predict the risk of traffic accidents, and if the risk is high, sends a warning to the device to prompt the user to take appropriate measures.

[0348] Examples:

[0349] When approaching an intersection, the server analyzes data from other vehicles, and if there is a risk of collision, the device displays a "brake" warning.

[0350] Improved fuel efficiency

[0351] The server analyzes fuel efficiency data for each vehicle and provides driving instructions to maximize energy efficiency.

[0352] Examples:

[0353] The device will notify the user to avoid sudden acceleration and deceleration, encouraging them to drive calmly.

[0354] Emergency response

[0355] The server analyzes emergency data and calculates the optimal evacuation route, sending that information to the device and encouraging the user to evacuate safely.

[0356] Examples:

[0357] In the event of an earthquake, the server will calculate safe evacuation routes and provide appropriate information to users.

[0358] Emotion Recognition and Driver Assistance

[0359] The server collects and analyzes the user's emotional data from the vehicle's cameras and microphones, evaluates their emotional state, and provides driving assistance information as needed.

[0360] Examples:

[0361] If the user indicates high stress levels, the device will play calming music to help improve the driving environment.

[0362] Prompt Sentence Examples

[0363] Here are some example prompts to input to the generative AI model:

[0364] "Explain how the system collects data, controls traffic lights, calculates optimal routes, predicts traffic accidents, improves fuel efficiency, responds to emergencies, and recognizes emotions."

[0365] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0366] The flow of this system's program processing

[0367] Step 1: Data collection

[0368] Input: Location data from vehicles and mobile devices

[0369] The server collects location data from the GPS and various sensors installed in vehicles and mobile devices every five seconds.

[0370] Output: Collected location data is stored in a database

[0371] Specific operation: The server receives location data such as "latitude: 35.6895, longitude: 139.6917" from each device and stores it in a database in real time.

[0372] Step 2: Signal Control

[0373] Input: Position data of vehicles, pedestrians, and bicycles near the intersection

[0374] The server analyzes location data near intersections and calculates optimal traffic light timings.

[0375] Output: Instructions to the traffic light terminal based on the calculation results

[0376] Specific operation: When congestion occurs, the server sends an instruction to the traffic light terminal to extend the green light time.

[0377] Step 3: Calculate the optimal route

[0378] Input: Current location data and destination information for each vehicle

[0379] The server calculates the optimal route based on traffic volume data, construction information, and accident information.

[0380] Output: The calculated optimal route is sent to the vehicle's terminal.

[0381] Specific operation: When the normal route is congested, the server calculates an alternative route and displays it on the vehicle terminal. The route displayed on the terminal is specific information such as "Please take X street."

[0382] Step 4: Traffic accident prediction and prevention

[0383] Input: Position and velocity data

[0384] The server analyzes location and speed data to predict the risk of traffic accidents.

[0385] Output: Warning message for high-risk vehicles

[0386] Specific operation: When approaching an intersection, the server analyzes data from other vehicles and displays a warning message on the device such as "Please reduce speed as there is a high risk of collision with the vehicle ahead."

[0387] Step 5: Improve fuel economy

[0388] Input: Vehicle fuel consumption data and driving patterns

[0389] The server analyzes the collected fuel economy data and generates driving instructions.

[0390] Output: Driving instructions aimed at improving fuel efficiency are displayed on the vehicle's terminal.

[0391] Specific behavior: The device will display a message such as "Gentle acceleration and deceleration is recommended" as an instruction to reduce sudden acceleration and deceleration.

[0392] Step 6: Emergency response

[0393] Input: Emergency data (disaster information, etc.)

[0394] The server analyzes the emergency data and calculates evacuation routes.

[0395] Output: The calculated evacuation route is sent to the vehicle's terminal.

[0396] Specific operation: In the event of an earthquake, the device will calculate a safe evacuation route and display specific instructions such as "This is the route to the nearest evacuation site."

[0397] Step 7: Emotion Recognition and Driver Assistance

[0398] Input: Emotion data from cameras and microphones in the vehicle

[0399] The server analyzes the collected emotional data and evaluates the user's emotional state.

[0400] Output: Driving assistance information based on the user's emotional state is displayed on the vehicle's terminal.

[0401] Specific behavior: If the user indicates a high stress level, the device will display the message "Your stress level is high, so we will play relaxing music" and actually play music.

[0402] The above is the specific processing flow of this system.

[0403] (Application example 2)

[0404] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0405] Conventional automated driving and traffic management systems aim to improve traffic efficiency and safety, but they are unable to consider the emotional state of drivers and pedestrians. This can result in the inability to provide appropriate support to users who are stressed or anxious, potentially increasing the risk of traffic accidents and reducing driving efficiency. Furthermore, it is difficult to adapt fuel efficiency optimization and emergency response to the emotional state of individual users. Therefore, there is a need for a system that provides advanced driving assistance based on the user's emotional state, further improving traffic efficiency and safety.

[0406] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0407] In this invention, the server includes means for collecting position data from vehicles, pedestrians, bicycles, and motorcycles, means for controlling traffic lights at intersections, means for calculating the optimal route to a destination, means for predicting the risk of traffic accidents and providing warnings, means for providing driving instructions to improve fuel efficiency, means for calculating and providing evacuation routes in emergencies, means for collecting and analyzing emotional data, and means for providing driving support information based on the emotional state of the user. This allows for the provision of driving support based on the user's emotional state, reducing the risk of traffic accidents, improving driving efficiency, optimizing fuel efficiency, and responding quickly in emergencies.

[0408] "Vehicle" means a means of transportation equipped with an engine or motor for traveling on roads, including automobiles, buses, trucks, etc.

[0409] "Pedestrian" refers to a person walking on a road.

[0410] A "bicycle" is a two-wheeled vehicle that is propelled by pedaling.

[0411] A "bike" is a vehicle equipped with an engine and running on two or three wheels, also known as a motorcycle.

[0412] "Location Data" means information about the geographic location of vehicles, pedestrians, bicycles, motorbikes, etc., obtained by GPS or other means.

[0413] A "traffic light" is a device that emits optical signals to control intersections and vehicle traffic.

[0414] An "optimal route" is the most efficient route to a destination, taking into account factors such as traffic conditions, distance, and time.

[0415] "Risk of traffic accidents" refers to the potential possibility of an accident analyzed from traffic conditions and driving behavior.

[0416] "Warning" is a notification that warns you in advance of the risk of a traffic accident or other high-risk situation.

[0417] "Fuel efficiency advice" is advice to minimize energy consumption and encourage efficient driving.

[0418] An "evacuation route" is a recommended route for safe evacuation in an emergency.

[0419] "Emotion data" is data on the user's emotional state based on information such as facial expressions, tone of voice, and heart rate collected using a camera or microphone.

[0420] "Driving assistance information" refers to various types of information provided based on traffic conditions and the user's emotional state to support driving.

[0421] This invention is a system that combines a traffic management system and an emotion recognition engine to improve traffic efficiency and safety and provide driving assistance based on the user's emotions. The main components of this system will be described below.

[0422] First, vehicles, pedestrians, bicycles, and motorbikes each have GPS functionality, and their location data is sent to a server. The server collects this location data and stores it in a database in real time. Specifically, sensors are used to detect the location, speed, and direction of travel of vehicles, pedestrians, bicycles, and motorbikes.

[0423] The server has a means of controlling the traffic lights at the intersection, and calculates the optimal signal timing taking into account the congestion situation at the intersection and the direction of travel of each participant. The calculation results are sent to the traffic light terminal, and the traffic light changes its signal according to the instructions.

[0424] The server then calculates the optimal route based on the vehicle's current location and destination information. This process includes traffic data, road construction information, and accident information. The calculated results are sent to each vehicle's terminal, and the driver follows the proposed route.

[0425] The server also analyzes the location and speed data collected in real time to predict the risk of traffic accidents. If a high risk is detected, the server sends a warning to the vehicle involved, and the device receives the warning, urging the user to slow down or stop.

[0426] To improve fuel efficiency, the server analyzes each vehicle's fuel consumption data and driving patterns and provides advice on maximizing energy efficiency, which then displays specific driving instructions on the device in real time to improve fuel efficiency.

[0427] In an emergency, the server instantly analyzes the emergency data and calculates the optimal evacuation route, which is then sent to the relevant terminals and displayed to the user in real time.

[0428] The emotion engine collects the user's emotional data from the vehicle's cameras and microphones and evaluates their emotional state. The server analyzes the user's stress level and emotional state and provides driving assistance information based on the analysis. For example, if the user shows a high stress level, the emotion engine detects this and the server sends an instruction to the device to play calming music to provide a more relaxing driving environment.

[0429] Specific examples include extending the red light time at traffic jams to ensure pedestrian safety, or prompting the user to play relaxing music if they are showing high stress levels on their way home.

[0430] Prompt Sentence Examples

[0431] Invention details: A system that incorporates an emotion engine into autonomous driving and traffic management systems to recognize human emotions and improve traffic efficiency and safety based on those emotions. It collects location data from vehicles, pedestrians, bicycles, and motorcycles, controls traffic signals, calculates optimal routes, predicts traffic accidents, improves fuel efficiency, and responds to emergencies. It also incorporates an emotion engine that recognizes the user's emotions to provide driving assistance.

[0432] Application: Autonomous vehicles

[0433] Implemented application: Smart driver assistance assistant

[0434] Application features:

[0435] Real-time traffic situation and user emotional state analysis

[0436] Route guidance for maximizing traffic efficiency

[0437] Signal control, emergency response

[0438] Helps reduce stress and anxiety

[0439] Example: If the user indicates high stress levels, the server sends an instruction to play relaxing music.

[0440] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0441] Step 1:

[0442] Collect location data for vehicles, pedestrians, bicycles, and motorcycles.

[0443] The server obtains location data, speed, and direction of travel from the GPS and sensors installed on each vehicle. This is done in real time and stored in a database. The input is the location and speed data sent from each vehicle, and the output is a database that is updated in real time.

[0444] Step 2:

[0445] Controlling traffic lights at intersections.

[0446] The server analyzes the collected location data and calculates the optimal signal timing taking into account the congestion situation at the intersection and the direction of travel of each moving object. The input is the location data and direction of travel of moving objects near the intersection, and the output is control instructions to the traffic light terminal.

[0447] Step 3:

[0448] Calculate the optimal route.

[0449] The server calculates the optimal route based on the vehicle's current location and destination information, taking into account traffic volume data, road construction information, and accident information. The calculation results are sent to each vehicle's terminal. The input is the vehicle's current location data, destination information, and traffic information, and the output is optimal route information.

[0450] Step 4:

[0451] Predicts the risk of traffic accidents and provides warnings.

[0452] The server analyzes the location and speed data collected in real time to predict the risk of potential traffic accidents. If the risk is determined to be high, it sends a warning to the vehicle involved. The terminal receives the warning and urges the user to slow down or stop. The input is the location and speed data of the moving vehicle, and the output is a risk warning.

[0453] Step 5:

[0454] Provides driving instructions to improve fuel efficiency.

[0455] The server analyzes each vehicle's fuel efficiency data and driving patterns and provides advice on maximizing energy efficiency. The terminal displays specific driving instructions for improving fuel efficiency in real time. The input is the vehicle's fuel efficiency data and driving patterns, and the output is driving instructions.

[0456] Step 6:

[0457] Calculates and provides evacuation routes in case of an emergency.

[0458] The server instantly analyzes emergency data and calculates the optimal evacuation route. The calculation results are sent to the relevant terminals, which then present the evacuation route to the user in real time. The input is the location data of each mobile device in the emergency and evacuation destination information, and the output is evacuation route information.

[0459] Step 7:

[0460] Collect and analyze emotional data.

[0461] The emotion engine collects user emotion data from cameras and microphones in the vehicle and analyzes it. The input is emotion data acquired from the cameras and microphones, and the output is an evaluation of the user's emotional state.

[0462] Step 8:

[0463] Providing driving assistance information based on emotional state.

[0464] After the emotion engine analyzes the user's emotional state, the server provides driving assistance information as needed. For example, if the user indicates a high stress level, the server sends an instruction to the terminal to play relaxing music. The input is the user's emotional state evaluated by the emotion engine, and the output is driving assistance information.

[0465] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0466] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0467] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0468] [Second embodiment]

[0469] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0470] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0471] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0472] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0473] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0474] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0475] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0476] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0477] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0478] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0479] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0480] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0481] This invention integrates various systems and methods related to automated driving and traffic management to improve traffic efficiency and safety in urban areas. The system collects location data from vehicles, pedestrians, bicycles, and motorcycles, and uses this data to control intersection signals, calculate optimal routes to destinations, predict the risk of traffic accidents, provide driving instructions to improve fuel efficiency, and calculate and provide evacuation routes in emergencies.

[0482] Data Collection and Management

[0483] The server collects location data from vehicles and mobile devices. Vehicles and mobile devices are equipped with GPS and sensors to detect speed and direction of travel. This data is sent to the server and stored in a database in real time.

[0484] Examples:

[0485] The server retrieves the intersection data and determines that there are currently 15 cars, 10 pedestrians, and 5 cyclists. The location data for each is updated every few seconds.

[0486] Signal Control

[0487] The server calculates the optimal signal timing based on all the location data sent to the intersection. The traffic light control takes into account the congestion situation at the intersection and the direction of each participant. The calculation results are sent to the traffic light terminal at the intersection, and the traffic light changes its signal accordingly.

[0488] Examples:

[0489] If an intersection is congested, the server will extend the red light time to allow pedestrians to cross the road safely.

[0490] Optimal route calculation

[0491] The server calculates the optimal route based on each vehicle's current location and destination information. This process includes traffic volume data, road construction information, and accident information. The calculated results are sent to each vehicle's terminal, and the driver follows the route displayed on the terminal.

[0492] Examples:

[0493] If the user's usual route to a destination is congested, the server will take this into account and suggest a route that avoids the traffic, thereby improving fuel economy and saving time.

[0494] Traffic accident prediction and avoidance

[0495] The server analyzes the location and speed data collected in real time to predict the risk of traffic accidents. If the risk is determined to be high, the server sends a warning to the vehicle involved. The device receives the warning and urges the user to slow down or stop.

[0496] Examples:

[0497] As the user approaches an intersection, the server analyzes the location and speed of other vehicles to detect potential collision risks, and the device issues a "brake" warning, allowing the user to slow down accordingly.

[0498] Improved fuel efficiency

[0499] The server analyzes each vehicle's fuel economy data and driving patterns and provides advice on maximizing energy efficiency, while the device displays specific driving instructions for improving fuel efficiency in real time.

[0500] Examples:

[0501] While driving, the device will notify the driver to avoid sudden acceleration and deceleration, helping to reduce fuel consumption.

[0502] Emergency response

[0503] The server analyzes emergency data in real time and calculates the optimal evacuation route. The calculation results are sent to the relevant terminals, and the evacuation route is displayed to the user in real time.

[0504] Examples:

[0505] When an earthquake occurs in the city, the server calculates safe evacuation routes and provides accurate information to users, who can then evacuate safely.

[0506] These program processes and their interaction significantly improve traffic optimization and safety. Each step is executed in real time, enabling dynamic management of traffic conditions across the city, resulting in an efficient and safe urban environment.

[0507] The processing flow will be explained below.

[0508] Step 1:

[0509] Terminals (vehicles and mobile devices) periodically collect location and speed data from GPS modules and speed sensors, which are then uploaded to the cloud and received by a server in real time.

[0510] Step 2:

[0511] The server records the received location and speed data in a database to understand the latest traffic conditions. This data is cross-referenced to identify different types of mobility, such as vehicles, pedestrians, bicycles, and motorcycles.

[0512] Step 3:

[0513] The server runs an algorithm to calculate intersection signal timings based on current traffic conditions, using each participant's heading, speed, location, and historical traffic data for analysis.

[0514] Step 4:

[0515] The server transmits the calculated signal timing to the signal terminal, which changes the color and timing of the signal according to the received information.

[0516] Step 5:

[0517] The server receives destination information from each vehicle and calculates the optimal route to that point, incorporating the latest traffic data, road construction information, and accident information, allowing the vehicle to select the best route.

[0518] Step 6:

[0519] The server then sends the calculated optimal route information to the terminals in each vehicle. The terminals receive the information and display the direction and route to the destination on their screens. The driver can follow this information to reach their destination safely and efficiently.

[0520] Step 7:

[0521] The server performs real-time analysis to predict the risk of traffic accidents by analyzing the intersection points, speed, and direction of travel of each vehicle, and if it determines that the risk is high, it sends a warning signal to the terminal of the vehicle in question.

[0522] Step 8:

[0523] The terminal (vehicle) receives the warning signal sent from the server and displays specific instructions to the driver, such as "apply the brakes" or "change direction," thereby preventing potential accidents.

[0524] Step 9:

[0525] The server collects fuel economy data from each vehicle and analyzes driving patterns. It generates advice for improving fuel economy and sends that information to each vehicle's terminal. The advice includes instructions to avoid sudden acceleration and deceleration.

[0526] Step 10:

[0527] The device (vehicle) displays fuel efficiency improvement advice to the driver in real time, and the driver can improve fuel efficiency by adjusting their driving behavior according to the advice.

[0528] Step 11:

[0529] The server quickly calculates evacuation routes in the event of an emergency. In the event of a real emergency (e.g., a natural disaster), the server assesses the situation and determines the safest route based on the latest traffic conditions and geographical information.

[0530] Step 12:

[0531] The server sends emergency evacuation route information to all related devices, which then display the received information to drivers and users to help them evacuate safely.

[0532] By combining these processing steps, the system can optimize traffic, improve safety, and enable efficient emergency response, improving traffic management efficiency across the city.

[0533] Example 1

[0534] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0535] In modern urban transportation systems, multiple traffic participants, including vehicles, pedestrians, bicycles, and motorcycles, use the roads at the same time, resulting in congestion on highways and intersections, which reduces traffic efficiency and compromises safety. In particular, it is difficult to respond appropriately to traffic congestion, accidents, and emergencies. Furthermore, optimizing fuel efficiency is also an important issue.

[0536] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0537] In this invention, the server includes means for collecting location data from vehicles, pedestrians, bicycles, and motorcycles, means for saving the collected location data in a database in real time, means for controlling intersection traffic lights in real time, means for calculating the optimal route to a destination based on traffic volume data, accident information, and road construction information, means for predicting the risk of traffic accidents based on location data and speed data and providing warnings to relevant vehicles, means for analyzing vehicle fuel efficiency data and operation patterns and providing driving instructions to improve fuel efficiency in real time, and means for calculating evacuation routes in emergencies and providing them to relevant vehicles.This enables efficient management of urban traffic, improved safety, and reduced fuel consumption.

[0538] "Vehicle" refers to automobiles, motorcycles, etc. used as a means of transportation in urban transportation systems.

[0539] "Pedestrian" refers to people who travel on foot in urban transportation systems.

[0540] "Bicycle" refers to a two-wheeled, human-powered vehicle in urban transportation systems.

[0541] "Motorcycle" refers to a two-wheeled engine-powered vehicle in urban transportation systems.

[0542] "Location data" refers to the current location information of vehicles, pedestrians, bicycles, and motorcycles obtained from positioning systems such as GPS.

[0543] "Real-time" refers to data collection, analysis, processing, and communication occurring without delay.

[0544] "Database" refers to an information collection system for efficiently storing and managing location data and traffic information.

[0545] A "traffic light" refers to a traffic signal device used to control traffic flow at intersections, etc.

[0546] "Optimal route" refers to the most efficient driving route to a destination, calculated taking into account current traffic conditions.

[0547] "Traffic data" refers to information about the number and flow of vehicles, pedestrians, bicycles, and motorbikes on specific roads or areas.

[0548] "Accident information" refers to information regarding the circumstances, location, scale, and scope of impact of a traffic accident.

[0549] "Road construction information" refers to information such as the status, location, and duration of road repairs and construction.

[0550] "Risk of traffic accident" refers to the possibility of a traffic accident predicted by analyzing location data and speed data.

[0551] "Fuel Economy Data" means information relating to a vehicle's fuel consumption.

[0552] An "operation pattern" refers to the series of movements of a vehicle, including how it actually moves and how it accelerates and decelerates.

[0553] An "emergency" refers to a situation in which normal traffic conditions are significantly disrupted due to a disaster, accident, or other incident.

[0554] An "evacuation route" refers to a route calculated for safe evacuation in an emergency.

[0555] This invention integrates various systems and methods related to automated driving and traffic management to improve traffic efficiency and safety in urban areas. The system collects location data from vehicles, pedestrians, bicycles, and motorcycles, and uses this data to control intersection signals, calculate optimal routes to destinations, predict the risk of traffic accidents, provide driving instructions to improve fuel efficiency, and calculate and provide evacuation routes in emergencies.

[0556] Data Collection and Management

[0557] The server manages location data collected from each vehicle and mobile device in real time. Specifically, vehicles are equipped with GPS, speed sensors, and direction sensors, and the data obtained from these is sent to the server. The server stores this data in a database and grasps the traffic situation throughout the city in real time. For example, the server obtains data on an intersection and confirms that there are currently 15 cars, 10 pedestrians, and 5 cyclists. This information is updated every few seconds.

[0558] Signal Control

[0559] The server calculates the optimal traffic light timing based on all the location data sent to the intersection. The server takes into account the congestion at the intersection and the direction of each participant, and sends the calculation results to the traffic light terminal at the intersection. The traffic light then changes its signal accordingly. For example, if the intersection is congested, the server will extend the red light time to allow pedestrians to cross the road safely.

[0560] Optimal route calculation

[0561] The server calculates the optimal route based on each vehicle's current location and destination information. Traffic volume data, accident information, and road construction information are included in the calculation, and the calculation results are sent to each vehicle's terminal. The driver then follows the route suggested by the terminal. For example, if the user's usual route to their destination is congested, the server will take this into account and suggest a route that avoids the traffic jam. This improves fuel efficiency and saves time.

[0562] Traffic accident prediction and avoidance

[0563] The server analyzes the location and speed data collected in real time to predict the risk of traffic accidents. If the risk is determined to be high, the server sends a warning to the vehicles involved. The device receives the warning and urges the user to slow down or stop. For example, when a user approaches an intersection, the server analyzes the location and speed of other vehicles and detects a potential risk of collision. The device issues a "brake" warning, and the user slows down accordingly.

[0564] Improved fuel efficiency

[0565] The server analyzes each vehicle's fuel economy data and driving patterns and provides advice on maximizing energy efficiency. The device displays specific driving instructions in real time to improve fuel efficiency. For example, the device may notify the driver to avoid sudden acceleration and deceleration while driving, thereby reducing fuel consumption.

[0566] Emergency response

[0567] The server instantly analyzes emergency data and calculates the optimal evacuation route. The calculation results are sent to the relevant devices, which then present the evacuation route to the user in real time. For example, when a disaster occurs in a city, the server calculates the safest evacuation route and provides reliable information to the user. The user can evacuate safely according to this information.

[0568] Prompt Sentence Examples

[0569] "Please create a prompt for an AI model that calculates the optimal route based on location data collected from each vehicle."

[0570] "Generate prompt statements to optimize traffic light control at an intersection."

[0571] "Please provide a prompt to calculate the best evacuation route in an emergency."

[0572] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0573] Step 1:

[0574] Data collection

[0575] The server collects location data from each vehicle and mobile device. As input, it receives location data, speed data, and heading data obtained from the GPS, speed sensor, and heading direction sensor of the vehicle and mobile device. As output, this data is sent to the server. Specifically, the vehicle's GPS periodically updates the location data, and the sensors detect the current speed and heading direction. The data is sent to the server every three seconds.

[0576] Step 2:

[0577] Data storage

[0578] The server stores the collected location data in a database in real time. The inputs are the location data, speed data, and heading data collected in step 1. The output is the location data stored in the database. Specifically, the server creates an entry in the database and stores the latest location and speed information for each vehicle. This allows the latest location information of all vehicles at the intersection to be known.

[0579] Step 3:

[0580] Analysis of intersection congestion

[0581] The server analyzes the collected location data and determines the congestion status of the intersection. The input is the location data stored in the database in step 2. The output is traffic volume information for the intersection. Specifically, it counts the number of vehicles, pedestrians, and bicycles at the intersection and tally the traffic volume heading in each direction. For example, it identifies that 10 cars are traveling from the north.

[0582] Step 4:

[0583] Signal Timing Calculations

[0584] The server calculates the optimal timing for switching traffic lights based on the analysis results. The input is the traffic volume information obtained in step 3. The output is generated signal control data. Specifically, the server determines the optimal signal timing, such as extending the red light to relieve congestion. For example, to ensure pedestrian safety, the crosswalk signal may be extended by 30 seconds.

[0585] Step 5:

[0586] Transmission of signal control data

[0587] The server sends the calculated signal control data to the traffic light terminals at the intersection. The input is the signal control data generated in step 4. The output is instructions sent to each traffic light terminal. In concrete terms, the traffic light terminals control the signals according to the instructions from the server.

[0588] Step 6:

[0589] Receiving route information

[0590] The user enters destination information via the device and sends that information to the server. The input is the destination information entered by the user. The output is the transmission of the destination information to the server. The specific operation is when the user enters the destination into the navigation app and taps the "Calculate route" button.

[0591] Step 7:

[0592] Calculating the best route

[0593] The server analyzes traffic volume data, accident information, and road construction information to calculate the optimal route. The inputs include received route information data and real-time traffic volume data, accident information, and road construction information. The output is optimal route data. Specifically, the server calculates the shortest route in a few seconds based on the current traffic conditions and construction information. For example, it recommends back roads and avoids major roads to avoid traffic jams.

[0594] Step 8:

[0595] Sending route data

[0596] The server sends the calculated optimal route data to the terminal. The input is the optimal route data generated in step 7. The output is the route data sent to the navigation terminal of each vehicle. Specifically, the terminal displays the route shown in the navigation app on a map and guides the user.

[0597] Step 9:

[0598] Traffic accident risk prediction

[0599] The server analyzes location and speed data collected in real time to predict the risk of traffic accidents. The input is real-time location and speed data. The output is risk warning data. Specifically, the server analyzes the speed and direction of vehicles and detects abnormal patterns near intersections. For example, it determines that two cars are approaching at high speed.

[0600] Step 10:

[0601] Sending risk warnings

[0602] If the server determines that the risk is high, it sends a warning to the vehicles involved. The input is the risk warning data generated in step 9. The output is a warning message sent to the terminal of each vehicle. Specifically, the terminal displays an alert saying, "There is a risk of collision ahead. Please slow down."

[0603] Step 11:

[0604] Analysis of fuel consumption data

[0605] The server analyzes each vehicle's fuel efficiency data and driving patterns and generates advice to maximize energy efficiency. The inputs are fuel efficiency data and driving pattern data for each vehicle. The output is advice to improve fuel efficiency. Specifically, the server detects patterns of sudden acceleration and deceleration and generates driving instructions to avoid them.

[0606] Step 12:

[0607] Notification of fuel economy improvement advice

[0608] The terminal displays driving instructions for improving fuel economy in real time. The input is the fuel economy improvement advice generated in step 11. The output is the displayed driving instruction message. Specifically, the terminal displays specific driving instructions in real time, such as "maintain current speed" or "slow down for the next traffic light."

[0609] Step 13:

[0610] Emergency Data Collection

[0611] The server instantly collects and analyzes emergency data. The inputs include disaster information and emergency notification data. The output is damage situation data. Specifically, the server receives emergency information such as earthquakes and fires and plots the damage situation on a map.

[0612] Step 14:

[0613] Evacuation route calculation

[0614] The server calculates the optimal evacuation route based on the collected emergency information. The input is the damage situation data generated in step 13. The output is evacuation route data. Specifically, the server calculates a safe evacuation route based on traffic signal data and traffic information. For example, it selects a route that avoids areas where fires are occurring.

[0615] Step 15:

[0616] Evacuation route notification

[0617] The terminal notifies the user of the optimal evacuation route. The input is the evacuation route data generated in step 14. The output is evacuation route instructions displayed on the user terminal. Specifically, the terminal displays specific instructions such as "Please evacuate through this road" and guides the user to a safe route on a map.

[0618] (Application example 1)

[0619] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0620] Conventional traffic management systems have had difficulty properly integrating location information from vehicles, pedestrians, bicycles, motorbikes, and other moving objects to control traffic signals, calculate optimal routes, and predict traffic accidents in real time. Furthermore, they have not adequately provided appropriate evacuation routes in emergencies or given driving instructions to improve fuel efficiency. For these reasons, there is a need to improve traffic efficiency and safety in urban areas.

[0621] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0622] In this invention, the server includes means for collecting location data from vehicles, mobile objects, motorcycles, and motorbikes, means for controlling traffic signals at intersections, means for calculating the optimal route to a destination, means for predicting the risk of traffic accidents and providing warnings, means for providing driving instructions to improve fuel efficiency, means for calculating and providing evacuation routes in emergencies, communication means for transmitting and receiving data in real time, and high-speed computing means for processing and analyzing the collected data, thereby enabling improved traffic efficiency and safety in urban areas.

[0623] A "vehicle" is a land-based moving object that is powered by a motor or engine.

[0624] "Mobile object" refers to any object that can be moved to transport people or cargo.

[0625] A "two-wheeled vehicle" is a vehicle that typically has two wheels and is steered by a handlebar.

[0626] "Motorcycle" generally refers to a two-wheeled vehicle that can carry one or two people.

[0627] "Location data" refers to coordinate information such as latitude and longitude for a specific location obtained from a positioning system such as a GPS.

[0628] A traffic signal is a device installed at an appropriate location at an intersection or road to control traffic flow using red, yellow, and green lights.

[0629] An "optimal route" is the most efficient route that saves time and fuel when a vehicle or person travels to a destination.

[0630] "Risk of traffic accident" refers to the possibility of a traffic accident occurring, estimated based on traffic conditions and the behavior of other vehicles and pedestrians.

[0631] A "warning" is a notice or guidance to warn of danger.

[0632] "Fuel efficiency" is a measure of the efficient use of fuel consumed during driving.

[0633] "Driving instructions" refer to advice or instructions regarding specific driving actions provided to the driver.

[0634] An "evacuation route" is a route used to safely evacuate in an emergency.

[0635] "Communication means" is a general term for technologies and devices for sending and receiving data.

[0636] "High-speed computing means" refers to computer systems and algorithms for rapidly processing and analyzing large amounts of data in real time.

[0637] "Real-time" refers to the ability to process and react instantly to information about ongoing events.

[0638] To realize this invention, the following elements are required:

[0639] 1. Hardware:

[0640] Vehicles and moving objects are fitted with a GPS module, a communication module, and sensors for detecting speed and direction of travel.

[0641] A traffic light control device is installed at the intersection and manages the traffic lights in cooperation with a server.

[0642] The server is constructed with a computer having high-performance computing power and includes a system for processing large amounts of data in real time.

[0643] 2. Software:

[0644] Server-side software includes Python Flask or Django, MySQL as the database, and a RESTful API for communication.

[0645] On the vehicle terminal side, a smartphone app (such as React Native) or a dedicated in-vehicle terminal is used.

[0646] 3. Data processing and calculation:

[0647] Real-time location data collection and transmission:

[0648] The GPS module collects location information (latitude, longitude, speed, and direction of travel) of vehicles and other moving objects and periodically transmits it to a server.

[0649] Signal Control:

[0650] The server uses the collected location data to assess the congestion situation at intersections and run algorithms to optimize traffic light timing.

[0651] Optimal route calculation:

[0652] The server calculates the optimal route based on each vehicle's current location and destination information, taking into account traffic conditions and road construction and accident information.

[0653] Traffic Accident Prediction and Warning:

[0654] The server analyzes real-time location and speed data collected from mobile devices, predicts the risk of traffic accidents, and sends warnings to relevant devices.

[0655] Improved fuel efficiency:

[0656] The server analyzes each vehicle's driving patterns and fuel consumption data and provides real-time driving instructions to maximize energy efficiency.

[0657] Emergency evacuation route provided:

[0658] The server instantly analyzes the necessary data in the event of an emergency, calculates the optimal evacuation route, and provides it to the user.

[0659] As a concrete example, consider the case of an autonomous vehicle heading towards Shibuya Crossing in Tokyo. The vehicle sends real-time location data to a server, which then calculates traffic signal control and the optimal route based on the congestion situation at the intersection and the positions of other vehicles and pedestrians. The user can reach their destination safely and efficiently by following the information provided by the server.

[0660] Also, as an example of a prompt sentence when using a generative AI model, you can enter the following:

[0661] Example prompt sentence:

[0662] Please explain the overview of a traffic management system for autonomous vehicles. This system collects real-time location information of vehicles, pedestrians, and cyclists, and provides functions such as controlling intersection signals, suggesting optimal routes, and predicting the risk of traffic accidents.

[0663] This will significantly improve traffic efficiency and safety in urban areas.

[0664] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0665] Step 1:

[0666] The server collects location data from vehicles, mobile objects, motorcycles, and single-wheeled vehicles. This location data includes latitude, longitude, speed, and direction of travel collected using a GPS module. The input is real-time location data sent from each mobile object, and the output is location data stored on the server.

[0667] Step 2:

[0668] The server stores the collected location data in a database in real time. Specifically, the server immediately writes the location data it receives into the database. The input is the location data collected in step 1, and the output is the stored location data.

[0669] Step 3:

[0670] The server controls the traffic lights at intersections based on the collected location data. Specifically, the server runs an algorithm that evaluates the congestion situation at each intersection and calculates the optimal signal timing. The input is the location data saved in step 2, and the output is the signal control instructions.

[0671] Step 4:

[0672] The server calculates the optimal route based on each vehicle's current location and destination information. This process includes traffic volume data, road construction information, and accident information. The input is the destination information and the location data saved in step 2, and the output is the optimal route guidance.

[0673] Step 5:

[0674] The server analyzes the collected real-time location and speed data to predict the risk of traffic accidents. If the risk is determined to be high, the server sends a warning to the relevant device. The input is the vehicle's location and speed data, and the output is a warning message.

[0675] Step 6:

[0676] The server analyzes each vehicle's driving patterns and fuel consumption data and provides driving instructions to improve fuel efficiency. These instructions are displayed on the in-vehicle display in real time. The input is driving patterns and fuel consumption data, and the output is driving instructions.

[0677] Step 7:

[0678] The server analyzes emergency data and calculates the optimal evacuation route. The calculation results are sent to the relevant devices, and the evacuation route is presented to the user in real time. The input is emergency incident occurrence data and location data, and the output is evacuation route guidance.

[0679] These steps will improve traffic efficiency and safety in urban areas.

[0680] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0681] This invention combines an emotion engine with an automated driving and traffic management system to recognize human emotions and further improve traffic efficiency and safety. This system collects location data from vehicles, pedestrians, bicycles, and motorcycles, controls intersection signals, calculates optimal routes to destinations, predicts the risk of traffic accidents, provides driving instructions to improve fuel efficiency, and calculates and provides evacuation routes in emergencies. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, more advanced driving assistance can be achieved.

[0682] Data Collection and Management

[0683] The server collects location data from vehicles and mobile devices. Vehicles and mobile devices are equipped with GPS and sensors to detect speed and direction of travel. This data is sent to the server and stored in a database in real time.

[0684] Examples:

[0685] The server retrieves the intersection data and determines that there are currently 15 cars, 10 pedestrians, and 5 cyclists. The location data for each is updated every few seconds.

[0686] Signal Control

[0687] The server calculates the optimal signal timing based on all the location data sent to the intersection. The traffic light control takes into account the congestion situation at the intersection and the direction of each participant. The calculation results are sent to the traffic light terminal at the intersection, and the traffic light changes its signal accordingly.

[0688] Examples:

[0689] If an intersection is congested, the server will extend the red light time to allow pedestrians to cross the road safely.

[0690] Optimal route calculation

[0691] The server calculates the optimal route based on each vehicle's current location and destination information. This process includes traffic volume data, road construction information, and accident information. The calculated results are sent to each vehicle's terminal, and the driver follows the route displayed on the terminal.

[0692] Examples:

[0693] If the user's usual route to a destination is congested, the server will take this into account and suggest a route that avoids the traffic, thereby improving fuel economy and saving time.

[0694] Traffic accident prediction and avoidance

[0695] The server analyzes the location and speed data collected in real time to predict the risk of traffic accidents. If the risk is determined to be high, the server sends a warning to the vehicle involved. The device receives the warning and urges the user to slow down or stop.

[0696] Examples:

[0697] As the user approaches an intersection, the server analyzes the location and speed of other vehicles to detect potential collision risks, and the device issues a "brake" warning, allowing the user to slow down accordingly.

[0698] Improved fuel efficiency

[0699] The server analyzes each vehicle's fuel economy data and driving patterns and provides advice on maximizing energy efficiency, while the device displays specific driving instructions for improving fuel efficiency in real time.

[0700] Examples:

[0701] While driving, the device will notify the driver to avoid sudden acceleration and deceleration, helping to reduce fuel consumption.

[0702] Emergency response

[0703] The server analyzes emergency data in real time and calculates the optimal evacuation route. The calculation results are sent to the relevant terminals, and the evacuation route is displayed to the user in real time.

[0704] Examples:

[0705] When an earthquake occurs in the city, the server calculates safe evacuation routes and provides accurate information to users, who can then evacuate safely.

[0706] Applying the Emotion Engine

[0707] The server collects user emotion data from the vehicle's cameras and microphones, which is then analyzed by the emotion engine, which evaluates the user's stress level and emotional state and provides driving assistance information as needed.

[0708] Example 1:

[0709] If the user exhibits high stress levels while driving, the emotion engine detects this and the server sends instructions to the terminal to play calming music to provide a more relaxing driving environment.

[0710] Example 2:

[0711] If the user is feeling anxious or impatient, the emotion engine will detect this and the server will issue an alert to prevent unsafe driving behavior. For example, if there is a high possibility of distraction, the device will display a message urging the user to focus on driving.

[0712] These steps will enable the system to not only perform traditional traffic management functions but also realize advanced driving assistance that takes into account the user's emotional state, improving traffic efficiency and safety, optimizing fuel efficiency, and improving the accuracy of emergency response.

[0713] The processing flow will be explained below.

[0714] Step 1:

[0715] Terminals (vehicles and mobile devices) periodically collect location and speed data from GPS modules and speed sensors, which are then uploaded to the cloud and received by a server in real time.

[0716] Step 2:

[0717] The server records the received location and speed data in a database to understand the latest traffic conditions. This data is cross-referenced to identify different types of mobility, such as vehicles, pedestrians, bicycles, and motorcycles.

[0718] Step 3:

[0719] The server runs an algorithm to calculate intersection signal timings based on current traffic conditions, using each participant's heading, speed, location, and historical traffic data for analysis.

[0720] Step 4:

[0721] The server transmits the calculated signal timing to the signal terminal, which changes the color and timing of the signal according to the received information.

[0722] Step 5:

[0723] The server receives destination information from each vehicle and calculates the optimal route to that point, incorporating the latest traffic data, road construction information, and accident information, allowing the vehicle to select the best route.

[0724] Step 6:

[0725] The server then sends the calculated optimal route information to the terminals in each vehicle. The terminals receive the information and display the direction and route to the destination on their screens. The driver can follow this information to reach their destination safely and efficiently.

[0726] Step 7:

[0727] The server performs real-time analysis to predict the risk of traffic accidents by analyzing the intersection points, speed, and direction of travel of each vehicle, and if it determines that the risk is high, it sends a warning signal to the terminal of the vehicle in question.

[0728] Step 8:

[0729] The terminal (vehicle) receives the warning signal sent from the server and displays specific instructions to the driver, such as "apply the brakes" or "change direction," thereby preventing potential accidents.

[0730] Step 9:

[0731] The server collects fuel economy data from each vehicle and analyzes driving patterns. It generates advice for improving fuel economy and sends that information to each vehicle's terminal. The advice includes instructions to avoid sudden acceleration and deceleration.

[0732] Step 10:

[0733] The device (vehicle) displays fuel efficiency improvement advice to the driver in real time, and the driver can improve fuel efficiency by adjusting their driving behavior according to the advice.

[0734] Step 11:

[0735] The server quickly calculates evacuation routes in the event of an emergency. In the event of a real emergency (e.g., a natural disaster), the server assesses the situation and determines the safest route based on the latest traffic conditions and geographical information.

[0736] Step 12:

[0737] The server sends emergency evacuation route information to all related devices, which then display the received information to drivers and users to help them evacuate safely.

[0738] Step 13:

[0739] The terminal (vehicle interface) uses the camera and microphone in the vehicle to collect the user's emotional data (e.g., facial expressions and tone of voice), which is then sent to the emotion engine.

[0740] Step 14:

[0741] The emotion engine analyzes the received emotion data and evaluates the user's emotional state, recognizing whether the user is in a high-stress state or relaxed.

[0742] Step 15:

[0743] The server receives the analysis results from the emotion engine and adjusts the driving assistance information based on the user's emotional state, for example, by instructing the playing of calming music if the user is in a high-stress state.

[0744] Step 16:

[0745] The terminal (vehicle) adjusts driving assistance information and entertainment settings based on instructions from the server, providing a relaxing environment for the user.

[0746] Step 17:

[0747] The server adjusts the optimal route and traffic signals according to the user's emotional state. For example, if the user is feeling anxious, the server will provide a route that prioritizes safety.

[0748] By combining these processing steps, the system can optimize traffic, improve safety, respond efficiently to emergencies, and even provide advanced driving assistance that takes into account the user's emotional state.

[0749] Example 2

[0750] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0751] Conventional traffic management systems collect vehicle and pedestrian position data to control traffic signals, suggest optimal routes, predict traffic accidents, improve fuel efficiency, and respond to emergencies. However, they do not provide driving assistance that takes the user's emotional state into consideration. As a result, traffic safety risks caused by users' stress levels and emotional changes are not adequately addressed. To solve this problem, a system that collects and analyzes user emotional data in real time is needed.

[0752] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0753] In this invention, the server includes means for collecting position data from vehicles, pedestrians, bicycles, and motorcycles, means for controlling traffic lights at intersections, means for calculating the optimal route to a destination, means for predicting the risk of traffic accidents and providing warnings, means for providing driving instructions to improve fuel efficiency, means for calculating and providing evacuation routes in emergencies, means for collecting and analyzing user emotion data in real time, and means for providing driving assistance information based on the user's emotion state, thereby enabling advanced driving assistance that takes the user's emotion state into consideration.

[0754] "Vehicle" means a means of transportation for travel over roads using an engine, motor, or other driving device.

[0755] A "pedestrian" is a person who moves on foot on a road or sidewalk.

[0756] A "bicycle" is a two-wheeled vehicle that is human-powered by pedaling.

[0757] A "motorcycle" is a two- or three-wheeled vehicle powered by an engine and used primarily as personal transportation.

[0758] "Location data" is data that indicates the geographic location of an object, typically expressed as latitude and longitude.

[0759] An "intersection traffic light" is a signal device installed at a road intersection to control the passage of vehicles and pedestrians.

[0760] A "destination" is a place that is set as the final purpose of a trip.

[0761] An "optimal route" is a route that is judged to be the most efficient from a departure point to a destination, taking into consideration time, distance, traffic conditions, and the like.

[0762] "Risk of traffic accidents" refers to the danger of collisions between vehicles or contact between vehicles and pedestrians that may occur on the road.

[0763] A "Warning" is a notice or alert issued to inform of a potential hazard or risk.

[0764] "Improving fuel efficiency" refers to the act of reducing energy consumption and mitigating the burden on the environment by improving the efficiency of fuel consumed when a vehicle travels.

[0765] "Driving instructions" are specific instructions for operations and actions provided to the driver to improve fuel efficiency and ensure safe driving.

[0766] An "emergency evacuation route" is a route that is set up for safe evacuation in the event of an emergency such as a disaster or accident.

[0767] "User" refers to an individual who uses the system, particularly a driver or passenger of a vehicle.

[0768] "Emotional data" is data that indicates the user's emotions and psychological state, and is collected through facial recognition, voice analysis, and the like.

[0769] "Analysis" is the process of examining and analyzing collected data in detail to extract meaningful information.

[0770] "Driving assistance information" refers to information and advice provided to drivers to help them drive safely and comfortably.

[0771] "Real-time" means that the time from data collection to the provision of analysis results is very short, almost instantaneous.

[0772] This invention is a traffic management and driving assistance system that collects location data from vehicles, pedestrians, bicycles, and motorcycles. The system also has the function of analyzing users' emotional data in real time and providing appropriate driving assistance information based on their emotional state.

[0773] System Configuration

[0774] This system consists of the following main components:

[0775] Server: Responsible for collecting and analyzing location data and emotion data. The server is installed with database management systems (e.g., MySQL, PostgreSQL), real-time data analysis software (e.g., Apache Kafka, Apache Flink), emotion recognition algorithms (e.g., OpenCV, Google Cloud Speech-to-Text), etc.

[0776] Terminal: Installed in a vehicle or mobile device, it transmits location data and displays driving assistance information.

[0777] User: Drives the vehicle and uses the assistance information provided by the system.

[0778] Data collection

[0779] The server collects location data from vehicles and mobile devices using GPS and various sensors (e.g., accelerometers and gyroscopes). This data is stored in a database in real time.

[0780] Examples:

[0781] For example, the server receives location data such as "Latitude: 35.6895, Longitude: 139.6917" from the vehicle every five seconds and stores it in a database.

[0782] Signal Control

[0783] The server analyzes the location data to calculate optimal signal timings to control traffic lights at intersections, taking into account traffic volume and congestion, and sends instructions to the traffic lights.

[0784] Examples:

[0785] At intersections with congestion, the server extends the green time of the traffic lights to smooth traffic flow.

[0786] Optimal route calculation

[0787] The server calculates the optimal route based on each vehicle's current location and destination information, taking into account traffic volume, construction information, accident information, etc., and sends the proposed route to the terminal.

[0788] Examples:

[0789] If the user's usual route to their destination is congested, the server calculates an alternative route and guides them to a route that avoids the traffic jam.

[0790] Traffic accident prediction and avoidance

[0791] The server analyzes location and speed data to predict the risk of traffic accidents, and if the risk is high, sends a warning to the device to prompt the user to take appropriate measures.

[0792] Examples:

[0793] When approaching an intersection, the server analyzes data from other vehicles, and if there is a risk of collision, the device displays a "brake" warning.

[0794] Improved fuel efficiency

[0795] The server analyzes fuel efficiency data for each vehicle and provides driving instructions to maximize energy efficiency.

[0796] Examples:

[0797] The device will notify the user to avoid sudden acceleration and deceleration, encouraging them to drive calmly.

[0798] Emergency response

[0799] The server analyzes emergency data and calculates the optimal evacuation route, sending that information to the device and encouraging the user to evacuate safely.

[0800] Examples:

[0801] In the event of an earthquake, the server will calculate safe evacuation routes and provide appropriate information to users.

[0802] Emotion Recognition and Driver Assistance

[0803] The server collects and analyzes the user's emotional data from the vehicle's cameras and microphones, evaluates their emotional state, and provides driving assistance information as needed.

[0804] Examples:

[0805] If the user indicates high stress levels, the device will play calming music to help improve the driving environment.

[0806] Prompt Sentence Examples

[0807] Here are some example prompts to input to the generative AI model:

[0808] "Explain how the system collects data, controls traffic lights, calculates optimal routes, predicts traffic accidents, improves fuel efficiency, responds to emergencies, and recognizes emotions."

[0809] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0810] The flow of this system's program processing

[0811] Step 1: Data collection

[0812] Input: Location data from vehicles and mobile devices

[0813] The server collects location data from the GPS and various sensors installed in vehicles and mobile devices every five seconds.

[0814] Output: Collected location data is stored in a database

[0815] Specific operation: The server receives location data such as "latitude: 35.6895, longitude: 139.6917" from each device and stores it in a database in real time.

[0816] Step 2: Signal Control

[0817] Input: Position data of vehicles, pedestrians, and bicycles near the intersection

[0818] The server analyzes location data near intersections and calculates optimal traffic light timings.

[0819] Output: Instructions to the traffic light terminal based on the calculation results

[0820] Specific operation: When congestion occurs, the server sends an instruction to the traffic light terminal to extend the green light time.

[0821] Step 3: Calculate the optimal route

[0822] Input: Current location data and destination information for each vehicle

[0823] The server calculates the optimal route based on traffic volume data, construction information, and accident information.

[0824] Output: The calculated optimal route is sent to the vehicle's terminal.

[0825] Specific operation: When the normal route is congested, the server calculates an alternative route and displays it on the vehicle terminal. The route displayed on the terminal is specific information such as "Please take X street."

[0826] Step 4: Traffic accident prediction and prevention

[0827] Input: Position and velocity data

[0828] The server analyzes location and speed data to predict the risk of traffic accidents.

[0829] Output: Warning message for high-risk vehicles

[0830] Specific operation: When approaching an intersection, the server analyzes data from other vehicles and displays a warning message on the device such as "Please reduce speed as there is a high risk of collision with the vehicle ahead."

[0831] Step 5: Improve fuel economy

[0832] Input: Vehicle fuel consumption data and driving patterns

[0833] The server analyzes the collected fuel economy data and generates driving instructions.

[0834] Output: Driving instructions aimed at improving fuel efficiency are displayed on the vehicle's terminal.

[0835] Specific behavior: The device will display a message such as "Gentle acceleration and deceleration is recommended" as an instruction to reduce sudden acceleration and deceleration.

[0836] Step 6: Emergency response

[0837] Input: Emergency data (disaster information, etc.)

[0838] The server analyzes the emergency data and calculates evacuation routes.

[0839] Output: The calculated evacuation route is sent to the vehicle's terminal.

[0840] Specific operation: In the event of an earthquake, the device will calculate a safe evacuation route and display specific instructions such as "This is the route to the nearest evacuation site."

[0841] Step 7: Emotion Recognition and Driver Assistance

[0842] Input: Emotion data from cameras and microphones in the vehicle

[0843] The server analyzes the collected emotional data and evaluates the user's emotional state.

[0844] Output: Driving assistance information based on the user's emotional state is displayed on the vehicle's terminal.

[0845] Specific behavior: If the user indicates a high stress level, the device will display the message "Your stress level is high, so we will play relaxing music" and actually play music.

[0846] The above is the specific processing flow of this system.

[0847] (Application example 2)

[0848] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0849] Conventional automated driving and traffic management systems aim to improve traffic efficiency and safety, but they are unable to consider the emotional state of drivers and pedestrians. This can result in the inability to provide appropriate support to users who are stressed or anxious, potentially increasing the risk of traffic accidents and reducing driving efficiency. Furthermore, it is difficult to adapt fuel efficiency optimization and emergency response to the emotional state of individual users. Therefore, there is a need for a system that provides advanced driving assistance based on the user's emotional state, further improving traffic efficiency and safety.

[0850] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0851] In this invention, the server includes means for collecting position data from vehicles, pedestrians, bicycles, and motorcycles, means for controlling traffic lights at intersections, means for calculating the optimal route to a destination, means for predicting the risk of traffic accidents and providing warnings, means for providing driving instructions to improve fuel efficiency, means for calculating and providing evacuation routes in emergencies, means for collecting and analyzing emotional data, and means for providing driving support information based on the emotional state of the user. This allows for the provision of driving support based on the user's emotional state, reducing the risk of traffic accidents, improving driving efficiency, optimizing fuel efficiency, and responding quickly in emergencies.

[0852] "Vehicle" means a means of transportation equipped with an engine or motor for traveling on roads, including automobiles, buses, trucks, etc.

[0853] "Pedestrian" refers to a person walking on a road.

[0854] A "bicycle" is a two-wheeled vehicle that is propelled by pedaling.

[0855] A "bike" is a vehicle equipped with an engine and running on two or three wheels, also known as a motorcycle.

[0856] "Location Data" means information about the geographic location of vehicles, pedestrians, bicycles, motorbikes, etc., obtained by GPS or other means.

[0857] A "traffic light" is a device that emits optical signals to control intersections and vehicle traffic.

[0858] An "optimal route" is the most efficient route to a destination, taking into account factors such as traffic conditions, distance, and time.

[0859] "Risk of traffic accidents" refers to the potential possibility of an accident analyzed from traffic conditions and driving behavior.

[0860] "Warning" is a notification that warns you in advance of the risk of a traffic accident or other high-risk situation.

[0861] "Fuel efficiency advice" is advice to minimize energy consumption and encourage efficient driving.

[0862] An "evacuation route" is a recommended route for safe evacuation in an emergency.

[0863] "Emotion data" is data on the user's emotional state based on information such as facial expressions, tone of voice, and heart rate collected using a camera or microphone.

[0864] "Driving assistance information" refers to various types of information provided based on traffic conditions and the user's emotional state to support driving.

[0865] This invention is a system that combines a traffic management system and an emotion recognition engine to improve traffic efficiency and safety and provide driving assistance based on the user's emotions. The main components of this system will be described below.

[0866] First, vehicles, pedestrians, bicycles, and motorbikes each have GPS functionality, and their location data is sent to a server. The server collects this location data and stores it in a database in real time. Specifically, sensors are used to detect the location, speed, and direction of travel of vehicles, pedestrians, bicycles, and motorbikes.

[0867] The server has a means of controlling the traffic lights at the intersection, and calculates the optimal signal timing taking into account the congestion situation at the intersection and the direction of travel of each participant. The calculation results are sent to the traffic light terminal, and the traffic light changes its signal according to the instructions.

[0868] The server then calculates the optimal route based on the vehicle's current location and destination information. This process includes traffic data, road construction information, and accident information. The calculated results are sent to each vehicle's terminal, and the driver follows the proposed route.

[0869] The server also analyzes the location and speed data collected in real time to predict the risk of traffic accidents. If a high risk is detected, the server sends a warning to the vehicle involved, and the device receives the warning, urging the user to slow down or stop.

[0870] To improve fuel efficiency, the server analyzes each vehicle's fuel consumption data and driving patterns and provides advice on maximizing energy efficiency, which then displays specific driving instructions on the device in real time to improve fuel efficiency.

[0871] In an emergency, the server instantly analyzes the emergency data and calculates the optimal evacuation route, which is then sent to the relevant terminals and displayed to the user in real time.

[0872] The emotion engine collects the user's emotional data from the vehicle's cameras and microphones and evaluates their emotional state. The server analyzes the user's stress level and emotional state and provides driving assistance information based on the analysis. For example, if the user shows a high stress level, the emotion engine detects this and the server sends an instruction to the device to play calming music to provide a more relaxing driving environment.

[0873] Specific examples include extending the red light time at traffic jams to ensure pedestrian safety, or prompting the user to play relaxing music if they are showing high stress levels on their way home.

[0874] Prompt Sentence Examples

[0875] Invention details: A system that incorporates an emotion engine into autonomous driving and traffic management systems to recognize human emotions and improve traffic efficiency and safety based on those emotions. It collects location data from vehicles, pedestrians, bicycles, and motorcycles, controls traffic signals, calculates optimal routes, predicts traffic accidents, improves fuel efficiency, and responds to emergencies. It also incorporates an emotion engine that recognizes the user's emotions to provide driving assistance.

[0876] Application: Autonomous vehicles

[0877] Implemented application: Smart driver assistance assistant

[0878] Application features:

[0879] Real-time traffic situation and user emotional state analysis

[0880] Route guidance for maximizing traffic efficiency

[0881] Signal control, emergency response

[0882] Helps reduce stress and anxiety

[0883] Example: If the user indicates high stress levels, the server sends an instruction to play relaxing music.

[0884] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0885] Step 1:

[0886] Collect location data for vehicles, pedestrians, bicycles, and motorcycles.

[0887] The server obtains location data, speed, and direction of travel from the GPS and sensors installed on each vehicle. This is done in real time and stored in a database. The input is the location and speed data sent from each vehicle, and the output is a database that is updated in real time.

[0888] Step 2:

[0889] Controlling traffic lights at intersections.

[0890] The server analyzes the collected location data and calculates the optimal signal timing taking into account the congestion situation at the intersection and the direction of travel of each moving object. The input is the location data and direction of travel of moving objects near the intersection, and the output is control instructions to the traffic light terminal.

[0891] Step 3:

[0892] Calculate the optimal route.

[0893] The server calculates the optimal route based on the vehicle's current location and destination information, taking into account traffic volume data, road construction information, and accident information. The calculation results are sent to each vehicle's terminal. The input is the vehicle's current location data, destination information, and traffic information, and the output is optimal route information.

[0894] Step 4:

[0895] Predicts the risk of traffic accidents and provides warnings.

[0896] The server analyzes the location and speed data collected in real time to predict the risk of potential traffic accidents. If the risk is determined to be high, it sends a warning to the vehicle involved. The terminal receives the warning and urges the user to slow down or stop. The input is the location and speed data of the moving vehicle, and the output is a risk warning.

[0897] Step 5:

[0898] Provides driving instructions to improve fuel efficiency.

[0899] The server analyzes each vehicle's fuel efficiency data and driving patterns and provides advice on maximizing energy efficiency. The terminal displays specific driving instructions for improving fuel efficiency in real time. The input is the vehicle's fuel efficiency data and driving patterns, and the output is driving instructions.

[0900] Step 6:

[0901] Calculates and provides evacuation routes in case of an emergency.

[0902] The server instantly analyzes emergency data and calculates the optimal evacuation route. The calculation results are sent to the relevant terminals, which then present the evacuation route to the user in real time. The input is the location data of each mobile device in the emergency and evacuation destination information, and the output is evacuation route information.

[0903] Step 7:

[0904] Collect and analyze emotional data.

[0905] The emotion engine collects user emotion data from cameras and microphones in the vehicle and analyzes it. The input is emotion data acquired from the cameras and microphones, and the output is an evaluation of the user's emotional state.

[0906] Step 8:

[0907] Providing driving assistance information based on emotional state.

[0908] After the emotion engine analyzes the user's emotional state, the server provides driving assistance information as needed. For example, if the user indicates a high stress level, the server sends an instruction to the terminal to play relaxing music. The input is the user's emotional state evaluated by the emotion engine, and the output is driving assistance information.

[0909] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0910] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0911] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0912] [Third embodiment]

[0913] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0914] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0915] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0916] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0917] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0918] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0919] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0920] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0921] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0922] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0923] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0924] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0925] This invention integrates various systems and methods related to automated driving and traffic management to improve traffic efficiency and safety in urban areas. The system collects location data from vehicles, pedestrians, bicycles, and motorcycles, and uses this data to control intersection signals, calculate optimal routes to destinations, predict the risk of traffic accidents, provide driving instructions to improve fuel efficiency, and calculate and provide evacuation routes in emergencies.

[0926] Data Collection and Management

[0927] The server collects location data from vehicles and mobile devices. Vehicles and mobile devices are equipped with GPS and sensors to detect speed and direction of travel. This data is sent to the server and stored in a database in real time.

[0928] Examples:

[0929] The server retrieves the intersection data and determines that there are currently 15 cars, 10 pedestrians, and 5 cyclists. The location data for each is updated every few seconds.

[0930] Signal Control

[0931] The server calculates the optimal signal timing based on all the location data sent to the intersection. The traffic light control takes into account the congestion situation at the intersection and the direction of each participant. The calculation results are sent to the traffic light terminal at the intersection, and the traffic light changes its signal accordingly.

[0932] Examples:

[0933] If an intersection is congested, the server will extend the red light time to allow pedestrians to cross the road safely.

[0934] Optimal route calculation

[0935] The server calculates the optimal route based on each vehicle's current location and destination information. This process includes traffic volume data, road construction information, and accident information. The calculated results are sent to each vehicle's terminal, and the driver follows the route displayed on the terminal.

[0936] Examples:

[0937] If the user's usual route to a destination is congested, the server will take this into account and suggest a route that avoids the traffic, thereby improving fuel economy and saving time.

[0938] Traffic accident prediction and avoidance

[0939] The server analyzes the location and speed data collected in real time to predict the risk of traffic accidents. If the risk is determined to be high, the server sends a warning to the vehicle involved. The device receives the warning and urges the user to slow down or stop.

[0940] Examples:

[0941] As the user approaches an intersection, the server analyzes the location and speed of other vehicles to detect potential collision risks, and the device issues a "brake" warning, allowing the user to slow down accordingly.

[0942] Improved fuel efficiency

[0943] The server analyzes each vehicle's fuel economy data and driving patterns and provides advice on maximizing energy efficiency, while the device displays specific driving instructions for improving fuel efficiency in real time.

[0944] Examples:

[0945] While driving, the device will notify the driver to avoid sudden acceleration and deceleration, helping to reduce fuel consumption.

[0946] Emergency response

[0947] The server analyzes emergency data in real time and calculates the optimal evacuation route. The calculation results are sent to the relevant terminals, and the evacuation route is displayed to the user in real time.

[0948] Examples:

[0949] When an earthquake occurs in the city, the server calculates safe evacuation routes and provides accurate information to users, who can then evacuate safely.

[0950] These program processes and their interaction significantly improve traffic optimization and safety. Each step is executed in real time, enabling dynamic management of traffic conditions across the city, resulting in an efficient and safe urban environment.

[0951] The processing flow will be explained below.

[0952] Step 1:

[0953] Terminals (vehicles and mobile devices) periodically collect location and speed data from GPS modules and speed sensors, which are then uploaded to the cloud and received by a server in real time.

[0954] Step 2:

[0955] The server records the received location and speed data in a database to understand the latest traffic conditions. This data is cross-referenced to identify different types of mobility, such as vehicles, pedestrians, bicycles, and motorcycles.

[0956] Step 3:

[0957] The server runs an algorithm to calculate intersection signal timings based on current traffic conditions, using each participant's heading, speed, location, and historical traffic data for analysis.

[0958] Step 4:

[0959] The server transmits the calculated signal timing to the signal terminal, which changes the color and timing of the signal according to the received information.

[0960] Step 5:

[0961] The server receives destination information from each vehicle and calculates the optimal route to that point, incorporating the latest traffic data, road construction information, and accident information, allowing the vehicle to select the best route.

[0962] Step 6:

[0963] The server then sends the calculated optimal route information to the terminals in each vehicle. The terminals receive the information and display the direction and route to the destination on their screens. The driver can follow this information to reach their destination safely and efficiently.

[0964] Step 7:

[0965] The server performs real-time analysis to predict the risk of traffic accidents by analyzing the intersection points, speed, and direction of travel of each vehicle, and if it determines that the risk is high, it sends a warning signal to the terminal of the vehicle in question.

[0966] Step 8:

[0967] The terminal (vehicle) receives the warning signal sent from the server and displays specific instructions to the driver, such as "apply the brakes" or "change direction," thereby preventing potential accidents.

[0968] Step 9:

[0969] The server collects fuel economy data from each vehicle and analyzes driving patterns. It generates advice for improving fuel economy and sends that information to each vehicle's terminal. The advice includes instructions to avoid sudden acceleration and deceleration.

[0970] Step 10:

[0971] The device (vehicle) displays fuel efficiency improvement advice to the driver in real time, and the driver can improve fuel efficiency by adjusting their driving behavior according to the advice.

[0972] Step 11:

[0973] The server quickly calculates evacuation routes in the event of an emergency. In the event of a real emergency (e.g., a natural disaster), the server assesses the situation and determines the safest route based on the latest traffic conditions and geographical information.

[0974] Step 12:

[0975] The server sends emergency evacuation route information to all related devices, which then display the received information to drivers and users to help them evacuate safely.

[0976] By combining these processing steps, the system can optimize traffic, improve safety, and enable efficient emergency response, improving traffic management efficiency across the city.

[0977] Example 1

[0978] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0979] In modern urban transportation systems, multiple traffic participants, including vehicles, pedestrians, bicycles, and motorcycles, use the roads at the same time, resulting in congestion on highways and intersections, which reduces traffic efficiency and compromises safety. In particular, it is difficult to respond appropriately to traffic congestion, accidents, and emergencies. Furthermore, optimizing fuel efficiency is also an important issue.

[0980] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0981] In this invention, the server includes means for collecting location data from vehicles, pedestrians, bicycles, and motorcycles, means for saving the collected location data in a database in real time, means for controlling intersection traffic lights in real time, means for calculating the optimal route to a destination based on traffic volume data, accident information, and road construction information, means for predicting the risk of traffic accidents based on location data and speed data and providing warnings to relevant vehicles, means for analyzing vehicle fuel efficiency data and operation patterns and providing driving instructions to improve fuel efficiency in real time, and means for calculating evacuation routes in emergencies and providing them to relevant vehicles.This enables efficient management of urban traffic, improved safety, and reduced fuel consumption.

[0982] "Vehicle" refers to automobiles, motorcycles, etc. used as a means of transportation in urban transportation systems.

[0983] "Pedestrian" refers to people who travel on foot in urban transportation systems.

[0984] "Bicycle" refers to a two-wheeled, human-powered vehicle in urban transportation systems.

[0985] "Motorcycle" refers to a two-wheeled engine-powered vehicle in urban transportation systems.

[0986] "Location data" refers to the current location information of vehicles, pedestrians, bicycles, and motorcycles obtained from positioning systems such as GPS.

[0987] "Real-time" refers to data collection, analysis, processing, and communication occurring without delay.

[0988] "Database" refers to an information collection system for efficiently storing and managing location data and traffic information.

[0989] A "traffic light" refers to a traffic signal device used to control traffic flow at intersections, etc.

[0990] "Optimal route" refers to the most efficient driving route to a destination, calculated taking into account current traffic conditions.

[0991] "Traffic data" refers to information about the number and flow of vehicles, pedestrians, bicycles, and motorbikes on specific roads or areas.

[0992] "Accident information" refers to information regarding the circumstances, location, scale, and scope of impact of a traffic accident.

[0993] "Road construction information" refers to information such as the status, location, and duration of road repairs and construction.

[0994] "Risk of traffic accident" refers to the possibility of a traffic accident predicted by analyzing location data and speed data.

[0995] "Fuel Economy Data" means information relating to a vehicle's fuel consumption.

[0996] An "operation pattern" refers to the series of movements of a vehicle, including how it actually moves and how it accelerates and decelerates.

[0997] An "emergency" refers to a situation in which normal traffic conditions are significantly disrupted due to a disaster, accident, or other incident.

[0998] An "evacuation route" refers to a route calculated for safe evacuation in an emergency.

[0999] This invention integrates various systems and methods related to automated driving and traffic management to improve traffic efficiency and safety in urban areas. The system collects location data from vehicles, pedestrians, bicycles, and motorcycles, and uses this data to control intersection signals, calculate optimal routes to destinations, predict the risk of traffic accidents, provide driving instructions to improve fuel efficiency, and calculate and provide evacuation routes in emergencies.

[1000] Data Collection and Management

[1001] The server manages location data collected from each vehicle and mobile device in real time. Specifically, vehicles are equipped with GPS, speed sensors, and direction sensors, and the data obtained from these is sent to the server. The server stores this data in a database and grasps the traffic situation throughout the city in real time. For example, the server obtains data on an intersection and confirms that there are currently 15 cars, 10 pedestrians, and 5 cyclists. This information is updated every few seconds.

[1002] Signal Control

[1003] The server calculates the optimal traffic light timing based on all the location data sent to the intersection. The server takes into account the congestion at the intersection and the direction of each participant, and sends the calculation results to the traffic light terminal at the intersection. The traffic light then changes its signal accordingly. For example, if the intersection is congested, the server will extend the red light time to allow pedestrians to cross the road safely.

[1004] Optimal route calculation

[1005] The server calculates the optimal route based on each vehicle's current location and destination information. Traffic volume data, accident information, and road construction information are included in the calculation, and the calculation results are sent to each vehicle's terminal. The driver then follows the route suggested by the terminal. For example, if the user's usual route to their destination is congested, the server will take this into account and suggest a route that avoids the traffic jam. This improves fuel efficiency and saves time.

[1006] Traffic accident prediction and avoidance

[1007] The server analyzes the location and speed data collected in real time to predict the risk of traffic accidents. If the risk is determined to be high, the server sends a warning to the vehicles involved. The device receives the warning and urges the user to slow down or stop. For example, when a user approaches an intersection, the server analyzes the location and speed of other vehicles and detects a potential risk of collision. The device issues a "brake" warning, and the user slows down accordingly.

[1008] Improved fuel efficiency

[1009] The server analyzes each vehicle's fuel economy data and driving patterns and provides advice on maximizing energy efficiency. The device displays specific driving instructions in real time to improve fuel efficiency. For example, the device may notify the driver to avoid sudden acceleration and deceleration while driving, thereby reducing fuel consumption.

[1010] Emergency response

[1011] The server instantly analyzes emergency data and calculates the optimal evacuation route. The calculation results are sent to the relevant devices, which then present the evacuation route to the user in real time. For example, when a disaster occurs in a city, the server calculates the safest evacuation route and provides reliable information to the user. The user can evacuate safely according to this information.

[1012] Prompt Sentence Examples

[1013] "Please create a prompt for an AI model that calculates the optimal route based on location data collected from each vehicle."

[1014] "Generate prompt statements to optimize traffic light control at an intersection."

[1015] "Please provide a prompt to calculate the best evacuation route in an emergency."

[1016] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1017] Step 1:

[1018] Data collection

[1019] The server collects location data from each vehicle and mobile device. As input, it receives location data, speed data, and heading data obtained from the GPS, speed sensor, and heading direction sensor of the vehicle and mobile device. As output, this data is sent to the server. Specifically, the vehicle's GPS periodically updates the location data, and the sensors detect the current speed and heading direction. The data is sent to the server every three seconds.

[1020] Step 2:

[1021] Data storage

[1022] The server stores the collected location data in a database in real time. The inputs are the location data, speed data, and heading data collected in step 1. The output is the location data stored in the database. Specifically, the server creates an entry in the database and stores the latest location and speed information for each vehicle. This allows the latest location information of all vehicles at the intersection to be known.

[1023] Step 3:

[1024] Analysis of intersection congestion

[1025] The server analyzes the collected location data and determines the congestion status of the intersection. The input is the location data stored in the database in step 2. The output is traffic volume information for the intersection. Specifically, it counts the number of vehicles, pedestrians, and bicycles at the intersection and tally the traffic volume heading in each direction. For example, it identifies that 10 cars are traveling from the north.

[1026] Step 4:

[1027] Signal Timing Calculations

[1028] The server calculates the optimal timing for switching traffic lights based on the analysis results. The input is the traffic volume information obtained in step 3. The output is generated signal control data. Specifically, the server determines the optimal signal timing, such as extending the red light to relieve congestion. For example, to ensure pedestrian safety, the crosswalk signal may be extended by 30 seconds.

[1029] Step 5:

[1030] Transmission of signal control data

[1031] The server sends the calculated signal control data to the traffic light terminals at the intersection. The input is the signal control data generated in step 4. The output is instructions sent to each traffic light terminal. In concrete terms, the traffic light terminals control the signals according to the instructions from the server.

[1032] Step 6:

[1033] Receiving route information

[1034] The user enters destination information via the device and sends that information to the server. The input is the destination information entered by the user. The output is the transmission of the destination information to the server. The specific operation is when the user enters the destination into the navigation app and taps the "Calculate route" button.

[1035] Step 7:

[1036] Calculating the best route

[1037] The server analyzes traffic volume data, accident information, and road construction information to calculate the optimal route. The inputs include received route information data and real-time traffic volume data, accident information, and road construction information. The output is optimal route data. Specifically, the server calculates the shortest route in a few seconds based on the current traffic conditions and construction information. For example, it recommends back roads and avoids major roads to avoid traffic jams.

[1038] Step 8:

[1039] Sending route data

[1040] The server sends the calculated optimal route data to the terminal. The input is the optimal route data generated in step 7. The output is the route data sent to the navigation terminal of each vehicle. Specifically, the terminal displays the route shown in the navigation app on a map and guides the user.

[1041] Step 9:

[1042] Traffic accident risk prediction

[1043] The server analyzes location and speed data collected in real time to predict the risk of traffic accidents. The input is real-time location and speed data. The output is risk warning data. Specifically, the server analyzes the speed and direction of vehicles and detects abnormal patterns near intersections. For example, it determines that two cars are approaching at high speed.

[1044] Step 10:

[1045] Sending risk warnings

[1046] If the server determines that the risk is high, it sends a warning to the vehicles involved. The input is the risk warning data generated in step 9. The output is a warning message sent to the terminal of each vehicle. Specifically, the terminal displays an alert saying, "There is a risk of collision ahead. Please slow down."

[1047] Step 11:

[1048] Analysis of fuel consumption data

[1049] The server analyzes each vehicle's fuel efficiency data and driving patterns and generates advice to maximize energy efficiency. The inputs are fuel efficiency data and driving pattern data for each vehicle. The output is advice to improve fuel efficiency. Specifically, the server detects patterns of sudden acceleration and deceleration and generates driving instructions to avoid them.

[1050] Step 12:

[1051] Notification of fuel economy improvement advice

[1052] The terminal displays driving instructions for improving fuel economy in real time. The input is the fuel economy improvement advice generated in step 11. The output is the displayed driving instruction message. Specifically, the terminal displays specific driving instructions in real time, such as "maintain current speed" or "slow down for the next traffic light."

[1053] Step 13:

[1054] Emergency Data Collection

[1055] The server instantly collects and analyzes emergency data. The inputs include disaster information and emergency notification data. The output is damage situation data. Specifically, the server receives emergency information such as earthquakes and fires and plots the damage situation on a map.

[1056] Step 14:

[1057] Evacuation route calculation

[1058] The server calculates the optimal evacuation route based on the collected emergency information. The input is the damage situation data generated in step 13. The output is evacuation route data. Specifically, the server calculates a safe evacuation route based on traffic signal data and traffic information. For example, it selects a route that avoids areas where fires are occurring.

[1059] Step 15:

[1060] Evacuation route notification

[1061] The terminal notifies the user of the optimal evacuation route. The input is the evacuation route data generated in step 14. The output is evacuation route instructions displayed on the user terminal. Specifically, the terminal displays specific instructions such as "Please evacuate through this road" and guides the user to a safe route on a map.

[1062] (Application example 1)

[1063] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1064] Conventional traffic management systems have had difficulty properly integrating location information from vehicles, pedestrians, bicycles, motorbikes, and other moving objects to control traffic signals, calculate optimal routes, and predict traffic accidents in real time. Furthermore, they have not adequately provided appropriate evacuation routes in emergencies or given driving instructions to improve fuel efficiency. For these reasons, there is a need to improve traffic efficiency and safety in urban areas.

[1065] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1066] In this invention, the server includes means for collecting location data from vehicles, mobile objects, motorcycles, and motorbikes, means for controlling traffic signals at intersections, means for calculating the optimal route to a destination, means for predicting the risk of traffic accidents and providing warnings, means for providing driving instructions to improve fuel efficiency, means for calculating and providing evacuation routes in emergencies, communication means for transmitting and receiving data in real time, and high-speed computing means for processing and analyzing the collected data, thereby enabling improved traffic efficiency and safety in urban areas.

[1067] A "vehicle" is a land-based moving object that is powered by a motor or engine.

[1068] "Mobile object" refers to any object that can be moved to transport people or cargo.

[1069] A "two-wheeled vehicle" is a vehicle that typically has two wheels and is steered by a handlebar.

[1070] "Motorcycle" generally refers to a two-wheeled vehicle that can carry one or two people.

[1071] "Location data" refers to coordinate information such as latitude and longitude for a specific location obtained from a positioning system such as a GPS.

[1072] A traffic signal is a device installed at an appropriate location at an intersection or road to control traffic flow using red, yellow, and green lights.

[1073] An "optimal route" is the most efficient route that saves time and fuel when a vehicle or person travels to a destination.

[1074] "Risk of traffic accident" refers to the possibility of a traffic accident occurring, estimated based on traffic conditions and the behavior of other vehicles and pedestrians.

[1075] A "warning" is a notice or guidance to warn of danger.

[1076] "Fuel efficiency" is a measure of the efficient use of fuel consumed during driving.

[1077] "Driving instructions" refer to advice or instructions regarding specific driving actions provided to the driver.

[1078] An "evacuation route" is a route used to safely evacuate in an emergency.

[1079] "Communication means" is a general term for technologies and devices for sending and receiving data.

[1080] "High-speed computing means" refers to computer systems and algorithms for rapidly processing and analyzing large amounts of data in real time.

[1081] "Real-time" refers to the ability to process and react instantly to information about ongoing events.

[1082] To realize this invention, the following elements are required:

[1083] 1. Hardware:

[1084] Vehicles and moving objects are fitted with a GPS module, a communication module, and sensors for detecting speed and direction of travel.

[1085] A traffic light control device is installed at the intersection and manages the traffic lights in cooperation with a server.

[1086] The server is constructed with a computer having high-performance computing power and includes a system for processing large amounts of data in real time.

[1087] 2. Software:

[1088] Server-side software includes Python Flask or Django, MySQL as the database, and a RESTful API for communication.

[1089] On the vehicle terminal side, a smartphone app (such as React Native) or a dedicated in-vehicle terminal is used.

[1090] 3. Data processing and calculation:

[1091] Real-time location data collection and transmission:

[1092] The GPS module collects location information (latitude, longitude, speed, and direction of travel) of vehicles and other moving objects and periodically transmits it to a server.

[1093] Signal Control:

[1094] The server uses the collected location data to assess the congestion situation at intersections and run algorithms to optimize traffic light timing.

[1095] Optimal route calculation:

[1096] The server calculates the optimal route based on each vehicle's current location and destination information, taking into account traffic conditions and road construction and accident information.

[1097] Traffic Accident Prediction and Warning:

[1098] The server analyzes real-time location and speed data collected from mobile devices, predicts the risk of traffic accidents, and sends warnings to relevant devices.

[1099] Improved fuel efficiency:

[1100] The server analyzes each vehicle's driving patterns and fuel consumption data and provides real-time driving instructions to maximize energy efficiency.

[1101] Emergency evacuation route provided:

[1102] The server instantly analyzes the necessary data in the event of an emergency, calculates the optimal evacuation route, and provides it to the user.

[1103] As a concrete example, consider the case of an autonomous vehicle heading towards Shibuya Crossing in Tokyo. The vehicle sends real-time location data to a server, which then calculates traffic signal control and the optimal route based on the congestion situation at the intersection and the positions of other vehicles and pedestrians. The user can reach their destination safely and efficiently by following the information provided by the server.

[1104] Also, as an example of a prompt sentence when using a generative AI model, you can enter the following:

[1105] Example prompt sentence:

[1106] Please explain the overview of a traffic management system for autonomous vehicles. This system collects real-time location information of vehicles, pedestrians, and cyclists, and provides functions such as controlling intersection signals, suggesting optimal routes, and predicting the risk of traffic accidents.

[1107] This will significantly improve traffic efficiency and safety in urban areas.

[1108] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1109] Step 1:

[1110] The server collects location data from vehicles, mobile objects, motorcycles, and single-wheeled vehicles. This location data includes latitude, longitude, speed, and direction of travel collected using a GPS module. The input is real-time location data sent from each mobile object, and the output is location data stored on the server.

[1111] Step 2:

[1112] The server stores the collected location data in a database in real time. Specifically, the server immediately writes the location data it receives into the database. The input is the location data collected in step 1, and the output is the stored location data.

[1113] Step 3:

[1114] The server controls the traffic lights at intersections based on the collected location data. Specifically, the server runs an algorithm that evaluates the congestion situation at each intersection and calculates the optimal signal timing. The input is the location data saved in step 2, and the output is the signal control instructions.

[1115] Step 4:

[1116] The server calculates the optimal route based on each vehicle's current location and destination information. This process includes traffic volume data, road construction information, and accident information. The input is the destination information and the location data saved in step 2, and the output is the optimal route guidance.

[1117] Step 5:

[1118] The server analyzes the collected real-time location and speed data to predict the risk of traffic accidents. If the risk is determined to be high, the server sends a warning to the relevant device. The input is the vehicle's location and speed data, and the output is a warning message.

[1119] Step 6:

[1120] The server analyzes each vehicle's driving patterns and fuel consumption data and provides driving instructions to improve fuel efficiency. These instructions are displayed on the in-vehicle display in real time. The input is driving patterns and fuel consumption data, and the output is driving instructions.

[1121] Step 7:

[1122] The server analyzes emergency data and calculates the optimal evacuation route. The calculation results are sent to the relevant devices, and the evacuation route is presented to the user in real time. The input is emergency incident occurrence data and location data, and the output is evacuation route guidance.

[1123] These steps will improve traffic efficiency and safety in urban areas.

[1124] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1125] This invention combines an emotion engine with an automated driving and traffic management system to recognize human emotions and further improve traffic efficiency and safety. This system collects location data from vehicles, pedestrians, bicycles, and motorcycles, controls intersection signals, calculates optimal routes to destinations, predicts the risk of traffic accidents, provides driving instructions to improve fuel efficiency, and calculates and provides evacuation routes in emergencies. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, more advanced driving assistance can be achieved.

[1126] Data Collection and Management

[1127] The server collects location data from vehicles and mobile devices. Vehicles and mobile devices are equipped with GPS and sensors to detect speed and direction of travel. This data is sent to the server and stored in a database in real time.

[1128] Examples:

[1129] The server retrieves the intersection data and determines that there are currently 15 cars, 10 pedestrians, and 5 cyclists. The location data for each is updated every few seconds.

[1130] Signal Control

[1131] The server calculates the optimal signal timing based on all the location data sent to the intersection. The traffic light control takes into account the congestion situation at the intersection and the direction of each participant. The calculation results are sent to the traffic light terminal at the intersection, and the traffic light changes its signal accordingly.

[1132] Examples:

[1133] If an intersection is congested, the server will extend the red light time to allow pedestrians to cross the road safely.

[1134] Optimal route calculation

[1135] The server calculates the optimal route based on each vehicle's current location and destination information. This process includes traffic volume data, road construction information, and accident information. The calculated results are sent to each vehicle's terminal, and the driver follows the route displayed on the terminal.

[1136] Examples:

[1137] If the user's usual route to a destination is congested, the server will take this into account and suggest a route that avoids the traffic, thereby improving fuel economy and saving time.

[1138] Traffic accident prediction and avoidance

[1139] The server analyzes the location and speed data collected in real time to predict the risk of traffic accidents. If the risk is determined to be high, the server sends a warning to the vehicle involved. The device receives the warning and urges the user to slow down or stop.

[1140] Examples:

[1141] As the user approaches an intersection, the server analyzes the location and speed of other vehicles to detect potential collision risks, and the device issues a "brake" warning, allowing the user to slow down accordingly.

[1142] Improved fuel efficiency

[1143] The server analyzes each vehicle's fuel economy data and driving patterns and provides advice on maximizing energy efficiency, while the device displays specific driving instructions for improving fuel efficiency in real time.

[1144] Examples:

[1145] While driving, the device will notify the driver to avoid sudden acceleration and deceleration, helping to reduce fuel consumption.

[1146] Emergency response

[1147] The server analyzes emergency data in real time and calculates the optimal evacuation route. The calculation results are sent to the relevant terminals, and the evacuation route is displayed to the user in real time.

[1148] Examples:

[1149] When an earthquake occurs in the city, the server calculates safe evacuation routes and provides accurate information to users, who can then evacuate safely.

[1150] Applying the Emotion Engine

[1151] The server collects user emotion data from the vehicle's cameras and microphones, which is then analyzed by the emotion engine, which evaluates the user's stress level and emotional state and provides driving assistance information as needed.

[1152] Example 1:

[1153] If the user exhibits high stress levels while driving, the emotion engine detects this and the server sends instructions to the terminal to play calming music to provide a more relaxing driving environment.

[1154] Example 2:

[1155] If the user is feeling anxious or impatient, the emotion engine will detect this and the server will issue an alert to prevent unsafe driving behavior. For example, if there is a high possibility of distraction, the device will display a message urging the user to focus on driving.

[1156] These steps will enable the system to not only perform traditional traffic management functions but also realize advanced driving assistance that takes into account the user's emotional state, improving traffic efficiency and safety, optimizing fuel efficiency, and improving the accuracy of emergency response.

[1157] The processing flow will be explained below.

[1158] Step 1:

[1159] Terminals (vehicles and mobile devices) periodically collect location and speed data from GPS modules and speed sensors, which are then uploaded to the cloud and received by a server in real time.

[1160] Step 2:

[1161] The server records the received location and speed data in a database to understand the latest traffic conditions. This data is cross-referenced to identify different types of mobility, such as vehicles, pedestrians, bicycles, and motorcycles.

[1162] Step 3:

[1163] The server runs an algorithm to calculate intersection signal timings based on current traffic conditions, using each participant's heading, speed, location, and historical traffic data for analysis.

[1164] Step 4:

[1165] The server transmits the calculated signal timing to the signal terminal, which changes the color and timing of the signal according to the received information.

[1166] Step 5:

[1167] The server receives destination information from each vehicle and calculates the optimal route to that point, incorporating the latest traffic data, road construction information, and accident information, allowing the vehicle to select the best route.

[1168] Step 6:

[1169] The server then sends the calculated optimal route information to the terminals in each vehicle. The terminals receive the information and display the direction and route to the destination on their screens. The driver can follow this information to reach their destination safely and efficiently.

[1170] Step 7:

[1171] The server performs real-time analysis to predict the risk of traffic accidents by analyzing the intersection points, speed, and direction of travel of each vehicle, and if it determines that the risk is high, it sends a warning signal to the terminal of the vehicle in question.

[1172] Step 8:

[1173] The terminal (vehicle) receives the warning signal sent from the server and displays specific instructions to the driver, such as "apply the brakes" or "change direction," thereby preventing potential accidents.

[1174] Step 9:

[1175] The server collects fuel economy data from each vehicle and analyzes driving patterns. It generates advice for improving fuel economy and sends that information to each vehicle's terminal. The advice includes instructions to avoid sudden acceleration and deceleration.

[1176] Step 10:

[1177] The device (vehicle) displays fuel efficiency improvement advice to the driver in real time, and the driver can improve fuel efficiency by adjusting their driving behavior according to the advice.

[1178] Step 11:

[1179] The server quickly calculates evacuation routes in the event of an emergency. In the event of a real emergency (e.g., a natural disaster), the server assesses the situation and determines the safest route based on the latest traffic conditions and geographical information.

[1180] Step 12:

[1181] The server sends emergency evacuation route information to all related devices, which then display the received information to drivers and users to help them evacuate safely.

[1182] Step 13:

[1183] The terminal (vehicle interface) uses the camera and microphone in the vehicle to collect the user's emotional data (e.g., facial expressions and tone of voice), which is then sent to the emotion engine.

[1184] Step 14:

[1185] The emotion engine analyzes the received emotion data and evaluates the user's emotional state, recognizing whether the user is in a high-stress state or relaxed.

[1186] Step 15:

[1187] The server receives the analysis results from the emotion engine and adjusts the driving assistance information based on the user's emotional state, for example, by instructing the playing of calming music if the user is in a high-stress state.

[1188] Step 16:

[1189] The terminal (vehicle) adjusts driving assistance information and entertainment settings based on instructions from the server, providing a relaxing environment for the user.

[1190] Step 17:

[1191] The server adjusts the optimal route and traffic signals according to the user's emotional state. For example, if the user is feeling anxious, the server will provide a route that prioritizes safety.

[1192] By combining these processing steps, the system can optimize traffic, improve safety, respond efficiently to emergencies, and even provide advanced driving assistance that takes into account the user's emotional state.

[1193] Example 2

[1194] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1195] Conventional traffic management systems collect vehicle and pedestrian position data to control traffic signals, suggest optimal routes, predict traffic accidents, improve fuel efficiency, and respond to emergencies. However, they do not provide driving assistance that takes the user's emotional state into consideration. As a result, traffic safety risks caused by users' stress levels and emotional changes are not adequately addressed. To solve this problem, a system that collects and analyzes user emotional data in real time is needed.

[1196] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1197] In this invention, the server includes means for collecting position data from vehicles, pedestrians, bicycles, and motorcycles, means for controlling traffic lights at intersections, means for calculating the optimal route to a destination, means for predicting the risk of traffic accidents and providing warnings, means for providing driving instructions to improve fuel efficiency, means for calculating and providing evacuation routes in emergencies, means for collecting and analyzing user emotion data in real time, and means for providing driving assistance information based on the user's emotion state, thereby enabling advanced driving assistance that takes the user's emotion state into consideration.

[1198] "Vehicle" means a means of transportation for travel over roads using an engine, motor, or other driving device.

[1199] A "pedestrian" is a person who moves on foot on a road or sidewalk.

[1200] A "bicycle" is a two-wheeled vehicle that is human-powered by pedaling.

[1201] A "motorcycle" is a two- or three-wheeled vehicle powered by an engine and used primarily as personal transportation.

[1202] "Location data" is data that indicates the geographic location of an object, typically expressed as latitude and longitude.

[1203] An "intersection traffic light" is a signal device installed at a road intersection to control the passage of vehicles and pedestrians.

[1204] A "destination" is a place that is set as the final purpose of a trip.

[1205] An "optimal route" is a route that is judged to be the most efficient from a departure point to a destination, taking into consideration time, distance, traffic conditions, and the like.

[1206] "Risk of traffic accidents" refers to the danger of collisions between vehicles or contact between vehicles and pedestrians that may occur on the road.

[1207] A "Warning" is a notice or alert issued to inform of a potential hazard or risk.

[1208] "Improving fuel efficiency" refers to the act of reducing energy consumption and mitigating the burden on the environment by improving the efficiency of fuel consumed when a vehicle travels.

[1209] "Driving instructions" are specific instructions for operations and actions provided to the driver to improve fuel efficiency and ensure safe driving.

[1210] An "emergency evacuation route" is a route that is set up for safe evacuation in the event of an emergency such as a disaster or accident.

[1211] "User" refers to an individual who uses the system, particularly a driver or passenger of a vehicle.

[1212] "Emotional data" is data that indicates the user's emotions and psychological state, and is collected through facial recognition, voice analysis, and the like.

[1213] "Analysis" is the process of examining and analyzing collected data in detail to extract meaningful information.

[1214] "Driving assistance information" refers to information and advice provided to drivers to help them drive safely and comfortably.

[1215] "Real-time" means that the time from data collection to the provision of analysis results is very short, almost instantaneous.

[1216] This invention is a traffic management and driving assistance system that collects location data from vehicles, pedestrians, bicycles, and motorcycles. The system also has the function of analyzing users' emotional data in real time and providing appropriate driving assistance information based on their emotional state.

[1217] System Configuration

[1218] This system consists of the following main components:

[1219] Server: Responsible for collecting and analyzing location data and emotion data. The server is installed with database management systems (e.g., MySQL, PostgreSQL), real-time data analysis software (e.g., Apache Kafka, Apache Flink), emotion recognition algorithms (e.g., OpenCV, Google Cloud Speech-to-Text), etc.

[1220] Terminal: Installed in a vehicle or mobile device, it transmits location data and displays driving assistance information.

[1221] User: Drives the vehicle and uses the assistance information provided by the system.

[1222] Data collection

[1223] The server collects location data from vehicles and mobile devices using GPS and various sensors (e.g., accelerometers and gyroscopes). This data is stored in a database in real time.

[1224] Examples:

[1225] For example, the server receives location data such as "Latitude: 35.6895, Longitude: 139.6917" from the vehicle every five seconds and stores it in a database.

[1226] Signal Control

[1227] The server analyzes the location data to calculate optimal signal timings to control traffic lights at intersections, taking into account traffic volume and congestion, and sends instructions to the traffic lights.

[1228] Examples:

[1229] At intersections with congestion, the server extends the green time of the traffic lights to smooth traffic flow.

[1230] Optimal route calculation

[1231] The server calculates the optimal route based on each vehicle's current location and destination information, taking into account traffic volume, construction information, accident information, etc., and sends the proposed route to the terminal.

[1232] Examples:

[1233] If the user's usual route to their destination is congested, the server calculates an alternative route and guides them to a route that avoids the traffic jam.

[1234] Traffic accident prediction and avoidance

[1235] The server analyzes location and speed data to predict the risk of traffic accidents, and if the risk is high, sends a warning to the device to prompt the user to take appropriate measures.

[1236] Examples:

[1237] When approaching an intersection, the server analyzes data from other vehicles, and if there is a risk of collision, the device displays a "brake" warning.

[1238] Improved fuel efficiency

[1239] The server analyzes fuel efficiency data for each vehicle and provides driving instructions to maximize energy efficiency.

[1240] Examples:

[1241] The device will notify the user to avoid sudden acceleration and deceleration, encouraging them to drive calmly.

[1242] Emergency response

[1243] The server analyzes emergency data and calculates the optimal evacuation route, sending that information to the device and encouraging the user to evacuate safely.

[1244] Examples:

[1245] In the event of an earthquake, the server will calculate safe evacuation routes and provide appropriate information to users.

[1246] Emotion Recognition and Driver Assistance

[1247] The server collects and analyzes the user's emotional data from the vehicle's cameras and microphones, evaluates their emotional state, and provides driving assistance information as needed.

[1248] Examples:

[1249] If the user indicates high stress levels, the device will play calming music to help improve the driving environment.

[1250] Prompt Sentence Examples

[1251] Here are some example prompts to input to the generative AI model:

[1252] "Explain how the system collects data, controls traffic lights, calculates optimal routes, predicts traffic accidents, improves fuel efficiency, responds to emergencies, and recognizes emotions."

[1253] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1254] The flow of this system's program processing

[1255] Step 1: Data collection

[1256] Input: Location data from vehicles and mobile devices

[1257] The server collects location data from the GPS and various sensors installed in vehicles and mobile devices every five seconds.

[1258] Output: Collected location data is stored in a database

[1259] Specific operation: The server receives location data such as "latitude: 35.6895, longitude: 139.6917" from each device and stores it in a database in real time.

[1260] Step 2: Signal Control

[1261] Input: Position data of vehicles, pedestrians, and bicycles near the intersection

[1262] The server analyzes location data near intersections and calculates optimal traffic light timings.

[1263] Output: Instructions to the traffic light terminal based on the calculation results

[1264] Specific operation: When congestion occurs, the server sends an instruction to the traffic light terminal to extend the green light time.

[1265] Step 3: Calculate the optimal route

[1266] Input: Current location data and destination information for each vehicle

[1267] The server calculates the optimal route based on traffic volume data, construction information, and accident information.

[1268] Output: The calculated optimal route is sent to the vehicle's terminal.

[1269] Specific operation: When the normal route is congested, the server calculates an alternative route and displays it on the vehicle terminal. The route displayed on the terminal is specific information such as "Please take X street."

[1270] Step 4: Traffic accident prediction and prevention

[1271] Input: Position and velocity data

[1272] The server analyzes location and speed data to predict the risk of traffic accidents.

[1273] Output: Warning message for high-risk vehicles

[1274] Specific operation: When approaching an intersection, the server analyzes data from other vehicles and displays a warning message on the device such as "Please reduce speed as there is a high risk of collision with the vehicle ahead."

[1275] Step 5: Improve fuel economy

[1276] Input: Vehicle fuel consumption data and driving patterns

[1277] The server analyzes the collected fuel economy data and generates driving instructions.

[1278] Output: Driving instructions aimed at improving fuel efficiency are displayed on the vehicle's terminal.

[1279] Specific behavior: The device will display a message such as "Gentle acceleration and deceleration is recommended" as an instruction to reduce sudden acceleration and deceleration.

[1280] Step 6: Emergency response

[1281] Input: Emergency data (disaster information, etc.)

[1282] The server analyzes the emergency data and calculates evacuation routes.

[1283] Output: The calculated evacuation route is sent to the vehicle's terminal.

[1284] Specific operation: In the event of an earthquake, the device will calculate a safe evacuation route and display specific instructions such as "This is the route to the nearest evacuation site."

[1285] Step 7: Emotion Recognition and Driver Assistance

[1286] Input: Emotion data from cameras and microphones in the vehicle

[1287] The server analyzes the collected emotional data and evaluates the user's emotional state.

[1288] Output: Driving assistance information based on the user's emotional state is displayed on the vehicle's terminal.

[1289] Specific behavior: If the user indicates a high stress level, the device will display the message "Your stress level is high, so we will play relaxing music" and actually play music.

[1290] The above is the specific processing flow of this system.

[1291] (Application example 2)

[1292] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1293] Conventional automated driving and traffic management systems aim to improve traffic efficiency and safety, but they are unable to consider the emotional state of drivers and pedestrians. This can result in the inability to provide appropriate support to users who are stressed or anxious, potentially increasing the risk of traffic accidents and reducing driving efficiency. Furthermore, it is difficult to adapt fuel efficiency optimization and emergency response to the emotional state of individual users. Therefore, there is a need for a system that provides advanced driving assistance based on the user's emotional state, further improving traffic efficiency and safety.

[1294] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1295] In this invention, the server includes means for collecting position data from vehicles, pedestrians, bicycles, and motorcycles, means for controlling traffic lights at intersections, means for calculating the optimal route to a destination, means for predicting the risk of traffic accidents and providing warnings, means for providing driving instructions to improve fuel efficiency, means for calculating and providing evacuation routes in emergencies, means for collecting and analyzing emotional data, and means for providing driving support information based on the emotional state of the user. This allows for the provision of driving support based on the user's emotional state, reducing the risk of traffic accidents, improving driving efficiency, optimizing fuel efficiency, and responding quickly in emergencies.

[1296] "Vehicle" means a means of transportation equipped with an engine or motor for traveling on roads, including automobiles, buses, trucks, etc.

[1297] "Pedestrian" refers to a person walking on a road.

[1298] A "bicycle" is a two-wheeled vehicle that is propelled by pedaling.

[1299] A "bike" is a vehicle equipped with an engine and running on two or three wheels, also known as a motorcycle.

[1300] "Location Data" means information about the geographic location of vehicles, pedestrians, bicycles, motorbikes, etc., obtained by GPS or other means.

[1301] A "traffic light" is a device that emits optical signals to control intersections and vehicle traffic.

[1302] An "optimal route" is the most efficient route to a destination, taking into account factors such as traffic conditions, distance, and time.

[1303] "Risk of traffic accidents" refers to the potential possibility of an accident analyzed from traffic conditions and driving behavior.

[1304] "Warning" is a notification that warns you in advance of the risk of a traffic accident or other high-risk situation.

[1305] "Fuel efficiency advice" is advice to minimize energy consumption and encourage efficient driving.

[1306] An "evacuation route" is a recommended route for safe evacuation in an emergency.

[1307] "Emotion data" is data on the user's emotional state based on information such as facial expressions, tone of voice, and heart rate collected using a camera or microphone.

[1308] "Driving assistance information" refers to various types of information provided based on traffic conditions and the user's emotional state to support driving.

[1309] This invention is a system that combines a traffic management system and an emotion recognition engine to improve traffic efficiency and safety and provide driving assistance based on the user's emotions. The main components of this system will be described below.

[1310] First, vehicles, pedestrians, bicycles, and motorbikes each have GPS functionality, and their location data is sent to a server. The server collects this location data and stores it in a database in real time. Specifically, sensors are used to detect the location, speed, and direction of travel of vehicles, pedestrians, bicycles, and motorbikes.

[1311] The server has a means of controlling the traffic lights at the intersection, and calculates the optimal signal timing taking into account the congestion situation at the intersection and the direction of travel of each participant. The calculation results are sent to the traffic light terminal, and the traffic light changes its signal according to the instructions.

[1312] The server then calculates the optimal route based on the vehicle's current location and destination information. This process includes traffic data, road construction information, and accident information. The calculated results are sent to each vehicle's terminal, and the driver follows the proposed route.

[1313] The server also analyzes the location and speed data collected in real time to predict the risk of traffic accidents. If a high risk is detected, the server sends a warning to the vehicle involved, and the device receives the warning, urging the user to slow down or stop.

[1314] To improve fuel efficiency, the server analyzes each vehicle's fuel consumption data and driving patterns and provides advice on maximizing energy efficiency, which then displays specific driving instructions on the device in real time to improve fuel efficiency.

[1315] In an emergency, the server instantly analyzes the emergency data and calculates the optimal evacuation route, which is then sent to the relevant terminals and displayed to the user in real time.

[1316] The emotion engine collects the user's emotional data from the vehicle's cameras and microphones and evaluates their emotional state. The server analyzes the user's stress level and emotional state and provides driving assistance information based on the analysis. For example, if the user shows a high stress level, the emotion engine detects this and the server sends an instruction to the device to play calming music to provide a more relaxing driving environment.

[1317] Specific examples include extending the red light time at traffic jams to ensure pedestrian safety, or prompting the user to play relaxing music if they are showing high stress levels on their way home.

[1318] Prompt Sentence Examples

[1319] Invention details: A system that incorporates an emotion engine into autonomous driving and traffic management systems to recognize human emotions and improve traffic efficiency and safety based on those emotions. It collects location data from vehicles, pedestrians, bicycles, and motorcycles, controls traffic signals, calculates optimal routes, predicts traffic accidents, improves fuel efficiency, and responds to emergencies. It also incorporates an emotion engine that recognizes the user's emotions to provide driving assistance.

[1320] Application: Autonomous vehicles

[1321] Implemented application: Smart driver assistance assistant

[1322] Application features:

[1323] Real-time traffic situation and user emotional state analysis

[1324] Route guidance for maximizing traffic efficiency

[1325] Signal control, emergency response

[1326] Helps reduce stress and anxiety

[1327] Example: If the user indicates high stress levels, the server sends an instruction to play relaxing music.

[1328] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1329] Step 1:

[1330] Collect location data for vehicles, pedestrians, bicycles, and motorcycles.

[1331] The server obtains location data, speed, and direction of travel from the GPS and sensors installed on each vehicle. This is done in real time and stored in a database. The input is the location and speed data sent from each vehicle, and the output is a database that is updated in real time.

[1332] Step 2:

[1333] Controlling traffic lights at intersections.

[1334] The server analyzes the collected location data and calculates the optimal signal timing taking into account the congestion situation at the intersection and the direction of travel of each moving object. The input is the location data and direction of travel of moving objects near the intersection, and the output is control instructions to the traffic light terminal.

[1335] Step 3:

[1336] Calculate the optimal route.

[1337] The server calculates the optimal route based on the vehicle's current location and destination information, taking into account traffic volume data, road construction information, and accident information. The calculation results are sent to each vehicle's terminal. The input is the vehicle's current location data, destination information, and traffic information, and the output is optimal route information.

[1338] Step 4:

[1339] Predicts the risk of traffic accidents and provides warnings.

[1340] The server analyzes the location and speed data collected in real time to predict the risk of potential traffic accidents. If the risk is determined to be high, it sends a warning to the vehicle involved. The terminal receives the warning and urges the user to slow down or stop. The input is the location and speed data of the moving vehicle, and the output is a risk warning.

[1341] Step 5:

[1342] Provides driving instructions to improve fuel efficiency.

[1343] The server analyzes each vehicle's fuel efficiency data and driving patterns and provides advice on maximizing energy efficiency. The terminal displays specific driving instructions for improving fuel efficiency in real time. The input is the vehicle's fuel efficiency data and driving patterns, and the output is driving instructions.

[1344] Step 6:

[1345] Calculates and provides evacuation routes in case of an emergency.

[1346] The server instantly analyzes emergency data and calculates the optimal evacuation route. The calculation results are sent to the relevant terminals, which then present the evacuation route to the user in real time. The input is the location data of each mobile device in the emergency and evacuation destination information, and the output is evacuation route information.

[1347] Step 7:

[1348] Collect and analyze emotional data.

[1349] The emotion engine collects user emotion data from cameras and microphones in the vehicle and analyzes it. The input is emotion data acquired from the cameras and microphones, and the output is an evaluation of the user's emotional state.

[1350] Step 8:

[1351] Providing driving assistance information based on emotional state.

[1352] After the emotion engine analyzes the user's emotional state, the server provides driving assistance information as needed. For example, if the user indicates a high stress level, the server sends an instruction to the terminal to play relaxing music. The input is the user's emotional state evaluated by the emotion engine, and the output is driving assistance information.

[1353] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1354] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1355] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1356] [Fourth embodiment]

[1357] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1358] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1359] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1360] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1361] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1362] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1363] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1364] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1365] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1366] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1367] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1368] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1369] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1370] This invention integrates various systems and methods related to automated driving and traffic management to improve traffic efficiency and safety in urban areas. The system collects location data from vehicles, pedestrians, bicycles, and motorcycles, and uses this data to control intersection signals, calculate optimal routes to destinations, predict the risk of traffic accidents, provide driving instructions to improve fuel efficiency, and calculate and provide evacuation routes in emergencies.

[1371] Data Collection and Management

[1372] The server collects location data from vehicles and mobile devices. Vehicles and mobile devices are equipped with GPS and sensors to detect speed and direction of travel. This data is sent to the server and stored in a database in real time.

[1373] Examples:

[1374] The server retrieves the intersection data and determines that there are currently 15 cars, 10 pedestrians, and 5 cyclists. The location data for each is updated every few seconds.

[1375] Signal Control

[1376] The server calculates the optimal signal timing based on all the location data sent to the intersection. The traffic light control takes into account the congestion situation at the intersection and the direction of each participant. The calculation results are sent to the traffic light terminal at the intersection, and the traffic light changes its signal accordingly.

[1377] Examples:

[1378] If an intersection is congested, the server will extend the red light time to allow pedestrians to cross the road safely.

[1379] Optimal route calculation

[1380] The server calculates the optimal route based on each vehicle's current location and destination information. This process includes traffic volume data, road construction information, and accident information. The calculated results are sent to each vehicle's terminal, and the driver follows the route displayed on the terminal.

[1381] Examples:

[1382] If the user's usual route to a destination is congested, the server will take this into account and suggest a route that avoids the traffic, thereby improving fuel economy and saving time.

[1383] Traffic accident prediction and avoidance

[1384] The server analyzes the location and speed data collected in real time to predict the risk of traffic accidents. If the risk is determined to be high, the server sends a warning to the vehicle involved. The device receives the warning and urges the user to slow down or stop.

[1385] Examples:

[1386] As the user approaches an intersection, the server analyzes the location and speed of other vehicles to detect potential collision risks, and the device issues a "brake" warning, allowing the user to slow down accordingly.

[1387] Improved fuel efficiency

[1388] The server analyzes each vehicle's fuel economy data and driving patterns and provides advice on maximizing energy efficiency, while the device displays specific driving instructions for improving fuel efficiency in real time.

[1389] Examples:

[1390] While driving, the device will notify the driver to avoid sudden acceleration and deceleration, helping to reduce fuel consumption.

[1391] Emergency response

[1392] The server analyzes emergency data in real time and calculates the optimal evacuation route. The calculation results are sent to the relevant terminals, and the evacuation route is displayed to the user in real time.

[1393] Examples:

[1394] When an earthquake occurs in the city, the server calculates safe evacuation routes and provides accurate information to users, who can then evacuate safely.

[1395] These program processes and their interaction significantly improve traffic optimization and safety. Each step is executed in real time, enabling dynamic management of traffic conditions across the city, resulting in an efficient and safe urban environment.

[1396] The processing flow will be explained below.

[1397] Step 1:

[1398] Terminals (vehicles and mobile devices) periodically collect location and speed data from GPS modules and speed sensors, which are then uploaded to the cloud and received by a server in real time.

[1399] Step 2:

[1400] The server records the received location and speed data in a database to understand the latest traffic conditions. This data is cross-referenced to identify different types of mobility, such as vehicles, pedestrians, bicycles, and motorcycles.

[1401] Step 3:

[1402] The server runs an algorithm to calculate intersection signal timings based on current traffic conditions, using each participant's heading, speed, location, and historical traffic data for analysis.

[1403] Step 4:

[1404] The server transmits the calculated signal timing to the signal terminal, which changes the color and timing of the signal according to the received information.

[1405] Step 5:

[1406] The server receives destination information from each vehicle and calculates the optimal route to that point, incorporating the latest traffic data, road construction information, and accident information, allowing the vehicle to select the best route.

[1407] Step 6:

[1408] The server then sends the calculated optimal route information to the terminals in each vehicle. The terminals receive the information and display the direction and route to the destination on their screens. The driver can follow this information to reach their destination safely and efficiently.

[1409] Step 7:

[1410] The server performs real-time analysis to predict the risk of traffic accidents by analyzing the intersection points, speed, and direction of travel of each vehicle, and if it determines that the risk is high, it sends a warning signal to the terminal of the vehicle in question.

[1411] Step 8:

[1412] The terminal (vehicle) receives the warning signal sent from the server and displays specific instructions to the driver, such as "apply the brakes" or "change direction," thereby preventing potential accidents.

[1413] Step 9:

[1414] The server collects fuel economy data from each vehicle and analyzes driving patterns. It generates advice for improving fuel economy and sends that information to each vehicle's terminal. The advice includes instructions to avoid sudden acceleration and deceleration.

[1415] Step 10:

[1416] The device (vehicle) displays fuel efficiency improvement advice to the driver in real time, and the driver can improve fuel efficiency by adjusting their driving behavior according to the advice.

[1417] Step 11:

[1418] The server quickly calculates evacuation routes in the event of an emergency. In the event of a real emergency (e.g., a natural disaster), the server assesses the situation and determines the safest route based on the latest traffic conditions and geographical information.

[1419] Step 12:

[1420] The server sends emergency evacuation route information to all related devices, which then display the received information to drivers and users to help them evacuate safely.

[1421] By combining these processing steps, the system can optimize traffic, improve safety, and enable efficient emergency response, improving traffic management efficiency across the city.

[1422] Example 1

[1423] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1424] In modern urban transportation systems, multiple traffic participants, including vehicles, pedestrians, bicycles, and motorcycles, use the roads at the same time, resulting in congestion on highways and intersections, which reduces traffic efficiency and compromises safety. In particular, it is difficult to respond appropriately to traffic congestion, accidents, and emergencies. Furthermore, optimizing fuel efficiency is also an important issue.

[1425] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1426] In this invention, the server includes means for collecting location data from vehicles, pedestrians, bicycles, and motorcycles, means for saving the collected location data in a database in real time, means for controlling intersection traffic lights in real time, means for calculating the optimal route to a destination based on traffic volume data, accident information, and road construction information, means for predicting the risk of traffic accidents based on location data and speed data and providing warnings to relevant vehicles, means for analyzing vehicle fuel efficiency data and operation patterns and providing driving instructions to improve fuel efficiency in real time, and means for calculating evacuation routes in emergencies and providing them to relevant vehicles.This enables efficient management of urban traffic, improved safety, and reduced fuel consumption.

[1427] "Vehicle" refers to automobiles, motorcycles, etc. used as a means of transportation in urban transportation systems.

[1428] "Pedestrian" refers to people who travel on foot in urban transportation systems.

[1429] "Bicycle" refers to a two-wheeled, human-powered vehicle in urban transportation systems.

[1430] "Motorcycle" refers to a two-wheeled engine-powered vehicle in urban transportation systems.

[1431] "Location data" refers to the current location information of vehicles, pedestrians, bicycles, and motorcycles obtained from positioning systems such as GPS.

[1432] "Real-time" refers to data collection, analysis, processing, and communication occurring without delay.

[1433] "Database" refers to an information collection system for efficiently storing and managing location data and traffic information.

[1434] A "traffic light" refers to a traffic signal device used to control traffic flow at intersections, etc.

[1435] "Optimal route" refers to the most efficient driving route to a destination, calculated taking into account current traffic conditions.

[1436] "Traffic data" refers to information about the number and flow of vehicles, pedestrians, bicycles, and motorbikes on specific roads or areas.

[1437] "Accident information" refers to information regarding the circumstances, location, scale, and scope of impact of a traffic accident.

[1438] "Road construction information" refers to information such as the status, location, and duration of road repairs and construction.

[1439] "Risk of traffic accident" refers to the possibility of a traffic accident predicted by analyzing location data and speed data.

[1440] "Fuel Economy Data" means information relating to a vehicle's fuel consumption.

[1441] An "operation pattern" refers to the series of movements of a vehicle, including how it actually moves and how it accelerates and decelerates.

[1442] An "emergency" refers to a situation in which normal traffic conditions are significantly disrupted due to a disaster, accident, or other incident.

[1443] An "evacuation route" refers to a route calculated for safe evacuation in an emergency.

[1444] This invention integrates various systems and methods related to automated driving and traffic management to improve traffic efficiency and safety in urban areas. The system collects location data from vehicles, pedestrians, bicycles, and motorcycles, and uses this data to control intersection signals, calculate optimal routes to destinations, predict the risk of traffic accidents, provide driving instructions to improve fuel efficiency, and calculate and provide evacuation routes in emergencies.

[1445] Data Collection and Management

[1446] The server manages location data collected from each vehicle and mobile device in real time. Specifically, vehicles are equipped with GPS, speed sensors, and direction sensors, and the data obtained from these is sent to the server. The server stores this data in a database and grasps the traffic situation throughout the city in real time. For example, the server obtains data on an intersection and confirms that there are currently 15 cars, 10 pedestrians, and 5 cyclists. This information is updated every few seconds.

[1447] Signal Control

[1448] The server calculates the optimal traffic light timing based on all the location data sent to the intersection. The server takes into account the congestion at the intersection and the direction of each participant, and sends the calculation results to the traffic light terminal at the intersection. The traffic light then changes its signal accordingly. For example, if the intersection is congested, the server will extend the red light time to allow pedestrians to cross the road safely.

[1449] Optimal route calculation

[1450] The server calculates the optimal route based on each vehicle's current location and destination information. Traffic volume data, accident information, and road construction information are included in the calculation, and the calculation results are sent to each vehicle's terminal. The driver then follows the route suggested by the terminal. For example, if the user's usual route to their destination is congested, the server will take this into account and suggest a route that avoids the traffic jam. This improves fuel efficiency and saves time.

[1451] Traffic accident prediction and avoidance

[1452] The server analyzes the location and speed data collected in real time to predict the risk of traffic accidents. If the risk is determined to be high, the server sends a warning to the vehicles involved. The device receives the warning and urges the user to slow down or stop. For example, when a user approaches an intersection, the server analyzes the location and speed of other vehicles and detects a potential risk of collision. The device issues a "brake" warning, and the user slows down accordingly.

[1453] Improved fuel efficiency

[1454] The server analyzes each vehicle's fuel economy data and driving patterns and provides advice on maximizing energy efficiency. The device displays specific driving instructions in real time to improve fuel efficiency. For example, the device may notify the driver to avoid sudden acceleration and deceleration while driving, thereby reducing fuel consumption.

[1455] Emergency response

[1456] The server instantly analyzes emergency data and calculates the optimal evacuation route. The calculation results are sent to the relevant devices, which then present the evacuation route to the user in real time. For example, when a disaster occurs in a city, the server calculates the safest evacuation route and provides reliable information to the user. The user can evacuate safely according to this information.

[1457] Prompt Sentence Examples

[1458] "Please create a prompt for an AI model that calculates the optimal route based on location data collected from each vehicle."

[1459] "Generate prompt statements to optimize traffic light control at an intersection."

[1460] "Please provide a prompt to calculate the best evacuation route in an emergency."

[1461] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1462] Step 1:

[1463] Data collection

[1464] The server collects location data from each vehicle and mobile device. As input, it receives location data, speed data, and heading data obtained from the GPS, speed sensor, and heading direction sensor of the vehicle and mobile device. As output, this data is sent to the server. Specifically, the vehicle's GPS periodically updates the location data, and the sensors detect the current speed and heading direction. The data is sent to the server every three seconds.

[1465] Step 2:

[1466] Data storage

[1467] The server stores the collected location data in a database in real time. The inputs are the location data, speed data, and heading data collected in step 1. The output is the location data stored in the database. Specifically, the server creates an entry in the database and stores the latest location and speed information for each vehicle. This allows the latest location information of all vehicles at the intersection to be known.

[1468] Step 3:

[1469] Analysis of intersection congestion

[1470] The server analyzes the collected location data and determines the congestion status of the intersection. The input is the location data stored in the database in step 2. The output is traffic volume information for the intersection. Specifically, it counts the number of vehicles, pedestrians, and bicycles at the intersection and tally the traffic volume heading in each direction. For example, it identifies that 10 cars are traveling from the north.

[1471] Step 4:

[1472] Signal Timing Calculations

[1473] The server calculates the optimal timing for switching traffic lights based on the analysis results. The input is the traffic volume information obtained in step 3. The output is generated signal control data. Specifically, the server determines the optimal signal timing, such as extending the red light to relieve congestion. For example, to ensure pedestrian safety, the crosswalk signal may be extended by 30 seconds.

[1474] Step 5:

[1475] Transmission of signal control data

[1476] The server sends the calculated signal control data to the traffic light terminals at the intersection. The input is the signal control data generated in step 4. The output is instructions sent to each traffic light terminal. In concrete terms, the traffic light terminals control the signals according to the instructions from the server.

[1477] Step 6:

[1478] Receiving route information

[1479] The user enters destination information via the device and sends that information to the server. The input is the destination information entered by the user. The output is the transmission of the destination information to the server. The specific operation is when the user enters the destination into the navigation app and taps the "Calculate route" button.

[1480] Step 7:

[1481] Calculating the best route

[1482] The server analyzes traffic volume data, accident information, and road construction information to calculate the optimal route. The inputs include received route information data and real-time traffic volume data, accident information, and road construction information. The output is optimal route data. Specifically, the server calculates the shortest route in a few seconds based on the current traffic conditions and construction information. For example, it recommends back roads and avoids major roads to avoid traffic jams.

[1483] Step 8:

[1484] Sending route data

[1485] The server sends the calculated optimal route data to the terminal. The input is the optimal route data generated in step 7. The output is the route data sent to the navigation terminal of each vehicle. Specifically, the terminal displays the route shown in the navigation app on a map and guides the user.

[1486] Step 9:

[1487] Traffic accident risk prediction

[1488] The server analyzes location and speed data collected in real time to predict the risk of traffic accidents. The input is real-time location and speed data. The output is risk warning data. Specifically, the server analyzes the speed and direction of vehicles and detects abnormal patterns near intersections. For example, it determines that two cars are approaching at high speed.

[1489] Step 10:

[1490] Sending risk warnings

[1491] If the server determines that the risk is high, it sends a warning to the vehicles involved. The input is the risk warning data generated in step 9. The output is a warning message sent to the terminal of each vehicle. Specifically, the terminal displays an alert saying, "There is a risk of collision ahead. Please slow down."

[1492] Step 11:

[1493] Analysis of fuel consumption data

[1494] The server analyzes each vehicle's fuel efficiency data and driving patterns and generates advice to maximize energy efficiency. The inputs are fuel efficiency data and driving pattern data for each vehicle. The output is advice to improve fuel efficiency. Specifically, the server detects patterns of sudden acceleration and deceleration and generates driving instructions to avoid them.

[1495] Step 12:

[1496] Notification of fuel economy improvement advice

[1497] The terminal displays driving instructions for improving fuel economy in real time. The input is the fuel economy improvement advice generated in step 11. The output is the displayed driving instruction message. Specifically, the terminal displays specific driving instructions in real time, such as "maintain current speed" or "slow down for the next traffic light."

[1498] Step 13:

[1499] Emergency Data Collection

[1500] The server instantly collects and analyzes emergency data. The inputs include disaster information and emergency notification data. The output is damage situation data. Specifically, the server receives emergency information such as earthquakes and fires and plots the damage situation on a map.

[1501] Step 14:

[1502] Evacuation route calculation

[1503] The server calculates the optimal evacuation route based on the collected emergency information. The input is the damage situation data generated in step 13. The output is evacuation route data. Specifically, the server calculates a safe evacuation route based on traffic signal data and traffic information. For example, it selects a route that avoids areas where fires are occurring.

[1504] Step 15:

[1505] Evacuation route notification

[1506] The terminal notifies the user of the optimal evacuation route. The input is the evacuation route data generated in step 14. The output is evacuation route instructions displayed on the user terminal. Specifically, the terminal displays specific instructions such as "Please evacuate through this road" and guides the user to a safe route on a map.

[1507] (Application example 1)

[1508] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1509] Conventional traffic management systems have had difficulty properly integrating location information from vehicles, pedestrians, bicycles, motorbikes, and other moving objects to control traffic signals, calculate optimal routes, and predict traffic accidents in real time. Furthermore, they have not adequately provided appropriate evacuation routes in emergencies or given driving instructions to improve fuel efficiency. For these reasons, there is a need to improve traffic efficiency and safety in urban areas.

[1510] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1511] In this invention, the server includes means for collecting location data from vehicles, mobile objects, motorcycles, and motorbikes, means for controlling traffic signals at intersections, means for calculating the optimal route to a destination, means for predicting the risk of traffic accidents and providing warnings, means for providing driving instructions to improve fuel efficiency, means for calculating and providing evacuation routes in emergencies, communication means for transmitting and receiving data in real time, and high-speed computing means for processing and analyzing the collected data, thereby enabling improved traffic efficiency and safety in urban areas.

[1512] A "vehicle" is a land-based moving object that is powered by a motor or engine.

[1513] "Mobile object" refers to any object that can be moved to transport people or cargo.

[1514] A "two-wheeled vehicle" is a vehicle that typically has two wheels and is steered by a handlebar.

[1515] "Motorcycle" generally refers to a two-wheeled vehicle that can carry one or two people.

[1516] "Location data" refers to coordinate information such as latitude and longitude for a specific location obtained from a positioning system such as a GPS.

[1517] A traffic signal is a device installed at an appropriate location at an intersection or road to control traffic flow using red, yellow, and green lights.

[1518] An "optimal route" is the most efficient route that saves time and fuel when a vehicle or person travels to a destination.

[1519] "Risk of traffic accident" refers to the possibility of a traffic accident occurring, estimated based on traffic conditions and the behavior of other vehicles and pedestrians.

[1520] A "warning" is a notice or guidance to warn of danger.

[1521] "Fuel efficiency" is a measure of the efficient use of fuel consumed during driving.

[1522] "Driving instructions" refer to advice or instructions regarding specific driving actions provided to the driver.

[1523] An "evacuation route" is a route used to safely evacuate in an emergency.

[1524] "Communication means" is a general term for technologies and devices for sending and receiving data.

[1525] "High-speed computing means" refers to computer systems and algorithms for rapidly processing and analyzing large amounts of data in real time.

[1526] "Real-time" refers to the ability to process and react instantly to information about ongoing events.

[1527] To realize this invention, the following elements are required:

[1528] 1. Hardware:

[1529] Vehicles and moving objects are fitted with a GPS module, a communication module, and sensors for detecting speed and direction of travel.

[1530] A traffic light control device is installed at the intersection and manages the traffic lights in cooperation with a server.

[1531] The server is constructed with a computer having high-performance computing power and includes a system for processing large amounts of data in real time.

[1532] 2. Software:

[1533] Server-side software includes Python Flask or Django, MySQL as the database, and a RESTful API for communication.

[1534] On the vehicle terminal side, a smartphone app (such as React Native) or a dedicated in-vehicle terminal is used.

[1535] 3. Data processing and calculation:

[1536] Real-time location data collection and transmission:

[1537] The GPS module collects location information (latitude, longitude, speed, and direction of travel) of vehicles and other moving objects and periodically transmits it to a server.

[1538] Signal Control:

[1539] The server uses the collected location data to assess the congestion situation at intersections and run algorithms to optimize traffic light timing.

[1540] Optimal route calculation:

[1541] The server calculates the optimal route based on each vehicle's current location and destination information, taking into account traffic conditions and road construction and accident information.

[1542] Traffic Accident Prediction and Warning:

[1543] The server analyzes real-time location and speed data collected from mobile devices, predicts the risk of traffic accidents, and sends warnings to relevant devices.

[1544] Improved fuel efficiency:

[1545] The server analyzes each vehicle's driving patterns and fuel consumption data and provides real-time driving instructions to maximize energy efficiency.

[1546] Emergency evacuation route provided:

[1547] The server instantly analyzes the necessary data in the event of an emergency, calculates the optimal evacuation route, and provides it to the user.

[1548] As a concrete example, consider the case of an autonomous vehicle heading towards Shibuya Crossing in Tokyo. The vehicle sends real-time location data to a server, which then calculates traffic signal control and the optimal route based on the congestion situation at the intersection and the positions of other vehicles and pedestrians. The user can reach their destination safely and efficiently by following the information provided by the server.

[1549] Also, as an example of a prompt sentence when using a generative AI model, you can enter the following:

[1550] Example prompt sentence:

[1551] Please explain the overview of a traffic management system for autonomous vehicles. This system collects real-time location information of vehicles, pedestrians, and cyclists, and provides functions such as controlling intersection signals, suggesting optimal routes, and predicting the risk of traffic accidents.

[1552] This will significantly improve traffic efficiency and safety in urban areas.

[1553] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1554] Step 1:

[1555] The server collects location data from vehicles, mobile objects, motorcycles, and single-wheeled vehicles. This location data includes latitude, longitude, speed, and direction of travel collected using a GPS module. The input is real-time location data sent from each mobile object, and the output is location data stored on the server.

[1556] Step 2:

[1557] The server stores the collected location data in a database in real time. Specifically, the server immediately writes the location data it receives into the database. The input is the location data collected in step 1, and the output is the stored location data.

[1558] Step 3:

[1559] The server controls the traffic lights at intersections based on the collected location data. Specifically, the server runs an algorithm that evaluates the congestion situation at each intersection and calculates the optimal signal timing. The input is the location data saved in step 2, and the output is the signal control instructions.

[1560] Step 4:

[1561] The server calculates the optimal route based on each vehicle's current location and destination information. This process includes traffic volume data, road construction information, and accident information. The input is the destination information and the location data saved in step 2, and the output is the optimal route guidance.

[1562] Step 5:

[1563] The server analyzes the collected real-time location and speed data to predict the risk of traffic accidents. If the risk is determined to be high, the server sends a warning to the relevant device. The input is the vehicle's location and speed data, and the output is a warning message.

[1564] Step 6:

[1565] The server analyzes each vehicle's driving patterns and fuel consumption data and provides driving instructions to improve fuel efficiency. These instructions are displayed on the in-vehicle display in real time. The input is driving patterns and fuel consumption data, and the output is driving instructions.

[1566] Step 7:

[1567] The server analyzes emergency data and calculates the optimal evacuation route. The calculation results are sent to the relevant devices, and the evacuation route is presented to the user in real time. The input is emergency incident occurrence data and location data, and the output is evacuation route guidance.

[1568] These steps will improve traffic efficiency and safety in urban areas.

[1569] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1570] This invention combines an emotion engine with an automated driving and traffic management system to recognize human emotions and further improve traffic efficiency and safety. This system collects location data from vehicles, pedestrians, bicycles, and motorcycles, controls intersection signals, calculates optimal routes to destinations, predicts the risk of traffic accidents, provides driving instructions to improve fuel efficiency, and calculates and provides evacuation routes in emergencies. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, more advanced driving assistance can be achieved.

[1571] Data Collection and Management

[1572] The server collects location data from vehicles and mobile devices. Vehicles and mobile devices are equipped with GPS and sensors to detect speed and direction of travel. This data is sent to the server and stored in a database in real time.

[1573] Examples:

[1574] The server retrieves the intersection data and determines that there are currently 15 cars, 10 pedestrians, and 5 cyclists. The location data for each is updated every few seconds.

[1575] Signal Control

[1576] The server calculates the optimal signal timing based on all the location data sent to the intersection. The traffic light control takes into account the congestion situation at the intersection and the direction of each participant. The calculation results are sent to the traffic light terminal at the intersection, and the traffic light changes its signal accordingly.

[1577] Examples:

[1578] If an intersection is congested, the server will extend the red light time to allow pedestrians to cross the road safely.

[1579] Optimal route calculation

[1580] The server calculates the optimal route based on each vehicle's current location and destination information. This process includes traffic volume data, road construction information, and accident information. The calculated results are sent to each vehicle's terminal, and the driver follows the route displayed on the terminal.

[1581] Examples:

[1582] If the user's usual route to a destination is congested, the server will take this into account and suggest a route that avoids the traffic, thereby improving fuel economy and saving time.

[1583] Traffic accident prediction and avoidance

[1584] The server analyzes the location and speed data collected in real time to predict the risk of traffic accidents. If the risk is determined to be high, the server sends a warning to the vehicle involved. The device receives the warning and urges the user to slow down or stop.

[1585] Examples:

[1586] As the user approaches an intersection, the server analyzes the location and speed of other vehicles to detect potential collision risks, and the device issues a "brake" warning, allowing the user to slow down accordingly.

[1587] Improved fuel efficiency

[1588] The server analyzes each vehicle's fuel economy data and driving patterns and provides advice on maximizing energy efficiency, while the device displays specific driving instructions for improving fuel efficiency in real time.

[1589] Examples:

[1590] While driving, the device will notify the driver to avoid sudden acceleration and deceleration, helping to reduce fuel consumption.

[1591] Emergency response

[1592] The server analyzes emergency data in real time and calculates the optimal evacuation route. The calculation results are sent to the relevant terminals, and the evacuation route is displayed to the user in real time.

[1593] Examples:

[1594] When an earthquake occurs in the city, the server calculates safe evacuation routes and provides accurate information to users, who can then evacuate safely.

[1595] Applying the Emotion Engine

[1596] The server collects user emotion data from the vehicle's cameras and microphones, which is then analyzed by the emotion engine, which evaluates the user's stress level and emotional state and provides driving assistance information as needed.

[1597] Example 1:

[1598] If the user exhibits high stress levels while driving, the emotion engine detects this and the server sends instructions to the terminal to play calming music to provide a more relaxing driving environment.

[1599] Example 2:

[1600] If the user is feeling anxious or impatient, the emotion engine will detect this and the server will issue an alert to prevent unsafe driving behavior. For example, if there is a high possibility of distraction, the device will display a message urging the user to focus on driving.

[1601] These steps will enable the system to not only perform traditional traffic management functions but also realize advanced driving assistance that takes into account the user's emotional state, improving traffic efficiency and safety, optimizing fuel efficiency, and improving the accuracy of emergency response.

[1602] The processing flow will be explained below.

[1603] Step 1:

[1604] Terminals (vehicles and mobile devices) periodically collect location and speed data from GPS modules and speed sensors, which are then uploaded to the cloud and received by a server in real time.

[1605] Step 2:

[1606] The server records the received location and speed data in a database to understand the latest traffic conditions. This data is cross-referenced to identify different types of mobility, such as vehicles, pedestrians, bicycles, and motorcycles.

[1607] Step 3:

[1608] The server runs an algorithm to calculate intersection signal timings based on current traffic conditions, using each participant's heading, speed, location, and historical traffic data for analysis.

[1609] Step 4:

[1610] The server transmits the calculated signal timing to the signal terminal, which changes the color and timing of the signal according to the received information.

[1611] Step 5:

[1612] The server receives destination information from each vehicle and calculates the optimal route to that point, incorporating the latest traffic data, road construction information, and accident information, allowing the vehicle to select the best route.

[1613] Step 6:

[1614] The server then sends the calculated optimal route information to the terminals in each vehicle. The terminals receive the information and display the direction and route to the destination on their screens. The driver can follow this information to reach their destination safely and efficiently.

[1615] Step 7:

[1616] The server performs real-time analysis to predict the risk of traffic accidents by analyzing the intersection points, speed, and direction of travel of each vehicle, and if it determines that the risk is high, it sends a warning signal to the terminal of the vehicle in question.

[1617] Step 8:

[1618] The terminal (vehicle) receives the warning signal sent from the server and displays specific instructions to the driver, such as "apply the brakes" or "change direction," thereby preventing potential accidents.

[1619] Step 9:

[1620] The server collects fuel economy data from each vehicle and analyzes driving patterns. It generates advice for improving fuel economy and sends that information to each vehicle's terminal. The advice includes instructions to avoid sudden acceleration and deceleration.

[1621] Step 10:

[1622] The device (vehicle) displays fuel efficiency improvement advice to the driver in real time, and the driver can improve fuel efficiency by adjusting their driving behavior according to the advice.

[1623] Step 11:

[1624] The server quickly calculates evacuation routes in the event of an emergency. In the event of a real emergency (e.g., a natural disaster), the server assesses the situation and determines the safest route based on the latest traffic conditions and geographical information.

[1625] Step 12:

[1626] The server sends emergency evacuation route information to all related devices, which then display the received information to drivers and users to help them evacuate safely.

[1627] Step 13:

[1628] The terminal (vehicle interface) uses the camera and microphone in the vehicle to collect the user's emotional data (e.g., facial expressions and tone of voice), which is then sent to the emotion engine.

[1629] Step 14:

[1630] The emotion engine analyzes the received emotion data and evaluates the user's emotional state, recognizing whether the user is in a high-stress state or relaxed.

[1631] Step 15:

[1632] The server receives the analysis results from the emotion engine and adjusts the driving assistance information based on the user's emotional state, for example, by instructing the playing of calming music if the user is in a high-stress state.

[1633] Step 16:

[1634] The terminal (vehicle) adjusts driving assistance information and entertainment settings based on instructions from the server, providing a relaxing environment for the user.

[1635] Step 17:

[1636] The server adjusts the optimal route and traffic signals according to the user's emotional state. For example, if the user is feeling anxious, the server will provide a route that prioritizes safety.

[1637] By combining these processing steps, the system can optimize traffic, improve safety, respond efficiently to emergencies, and even provide advanced driving assistance that takes into account the user's emotional state.

[1638] Example 2

[1639] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1640] Conventional traffic management systems collect vehicle and pedestrian position data to control traffic signals, suggest optimal routes, predict traffic accidents, improve fuel efficiency, and respond to emergencies. However, they do not provide driving assistance that takes the user's emotional state into consideration. As a result, traffic safety risks caused by users' stress levels and emotional changes are not adequately addressed. To solve this problem, a system that collects and analyzes user emotional data in real time is needed.

[1641] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1642] In this invention, the server includes means for collecting position data from vehicles, pedestrians, bicycles, and motorcycles, means for controlling traffic lights at intersections, means for calculating the optimal route to a destination, means for predicting the risk of traffic accidents and providing warnings, means for providing driving instructions to improve fuel efficiency, means for calculating and providing evacuation routes in emergencies, means for collecting and analyzing user emotion data in real time, and means for providing driving assistance information based on the user's emotion state, thereby enabling advanced driving assistance that takes the user's emotion state into consideration.

[1643] "Vehicle" means a means of transportation for travel over roads using an engine, motor, or other driving device.

[1644] A "pedestrian" is a person who moves on foot on a road or sidewalk.

[1645] A "bicycle" is a two-wheeled vehicle that is human-powered by pedaling.

[1646] A "motorcycle" is a two- or three-wheeled vehicle powered by an engine and used primarily as personal transportation.

[1647] "Location data" is data that indicates the geographic location of an object, typically expressed as latitude and longitude.

[1648] An "intersection traffic light" is a signal device installed at a road intersection to control the passage of vehicles and pedestrians.

[1649] A "destination" is a place that is set as the final purpose of a trip.

[1650] An "optimal route" is a route that is judged to be the most efficient from a departure point to a destination, taking into consideration time, distance, traffic conditions, and the like.

[1651] "Risk of traffic accidents" refers to the danger of collisions between vehicles or contact between vehicles and pedestrians that may occur on the road.

[1652] A "Warning" is a notice or alert issued to inform of a potential hazard or risk.

[1653] "Improving fuel efficiency" refers to the act of reducing energy consumption and mitigating the burden on the environment by improving the efficiency of fuel consumed when a vehicle travels.

[1654] "Driving instructions" are specific instructions for operations and actions provided to the driver to improve fuel efficiency and ensure safe driving.

[1655] An "emergency evacuation route" is a route that is set up for safe evacuation in the event of an emergency such as a disaster or accident.

[1656] "User" refers to an individual who uses the system, particularly a driver or passenger of a vehicle.

[1657] "Emotional data" is data that indicates the user's emotions and psychological state, and is collected through facial recognition, voice analysis, and the like.

[1658] "Analysis" is the process of examining and analyzing collected data in detail to extract meaningful information.

[1659] "Driving assistance information" refers to information and advice provided to drivers to help them drive safely and comfortably.

[1660] "Real-time" means that the time from data collection to the provision of analysis results is very short, almost instantaneous.

[1661] This invention is a traffic management and driving assistance system that collects location data from vehicles, pedestrians, bicycles, and motorcycles. The system also has the function of analyzing users' emotional data in real time and providing appropriate driving assistance information based on their emotional state.

[1662] System Configuration

[1663] This system consists of the following main components:

[1664] Server: Responsible for collecting and analyzing location data and emotion data. The server is installed with database management systems (e.g., MySQL, PostgreSQL), real-time data analysis software (e.g., Apache Kafka, Apache Flink), emotion recognition algorithms (e.g., OpenCV, Google Cloud Speech-to-Text), etc.

[1665] Terminal: Installed in a vehicle or mobile device, it transmits location data and displays driving assistance information.

[1666] User: Drives the vehicle and uses the assistance information provided by the system.

[1667] Data collection

[1668] The server collects location data from vehicles and mobile devices using GPS and various sensors (e.g., accelerometers and gyroscopes). This data is stored in a database in real time.

[1669] Examples:

[1670] For example, the server receives location data such as "Latitude: 35.6895, Longitude: 139.6917" from the vehicle every five seconds and stores it in a database.

[1671] Signal Control

[1672] The server analyzes the location data to calculate optimal signal timings to control traffic lights at intersections, taking into account traffic volume and congestion, and sends instructions to the traffic lights.

[1673] Examples:

[1674] At intersections with congestion, the server extends the green time of the traffic lights to smooth traffic flow.

[1675] Optimal route calculation

[1676] The server calculates the optimal route based on each vehicle's current location and destination information, taking into account traffic volume, construction information, accident information, etc., and sends the proposed route to the terminal.

[1677] Examples:

[1678] If the user's usual route to their destination is congested, the server calculates an alternative route and guides them to a route that avoids the traffic jam.

[1679] Traffic accident prediction and avoidance

[1680] The server analyzes location and speed data to predict the risk of traffic accidents, and if the risk is high, sends a warning to the device to prompt the user to take appropriate measures.

[1681] Examples:

[1682] When approaching an intersection, the server analyzes data from other vehicles, and if there is a risk of collision, the device displays a "brake" warning.

[1683] Improved fuel efficiency

[1684] The server analyzes fuel efficiency data for each vehicle and provides driving instructions to maximize energy efficiency.

[1685] Examples:

[1686] The device will notify the user to avoid sudden acceleration and deceleration, encouraging them to drive calmly.

[1687] Emergency response

[1688] The server analyzes emergency data and calculates the optimal evacuation route, sending that information to the device and encouraging the user to evacuate safely.

[1689] Examples:

[1690] In the event of an earthquake, the server will calculate safe evacuation routes and provide appropriate information to users.

[1691] Emotion Recognition and Driver Assistance

[1692] The server collects and analyzes the user's emotional data from the vehicle's cameras and microphones, evaluates their emotional state, and provides driving assistance information as needed.

[1693] Examples:

[1694] If the user indicates high stress levels, the device will play calming music to help improve the driving environment.

[1695] Prompt Sentence Examples

[1696] Here are some example prompts to input to the generative AI model:

[1697] "Explain how the system collects data, controls traffic lights, calculates optimal routes, predicts traffic accidents, improves fuel efficiency, responds to emergencies, and recognizes emotions."

[1698] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1699] The flow of this system's program processing

[1700] Step 1: Data collection

[1701] Input: Location data from vehicles and mobile devices

[1702] The server collects location data from the GPS and various sensors installed in vehicles and mobile devices every five seconds.

[1703] Output: Collected location data is stored in a database

[1704] Specific operation: The server receives location data such as "latitude: 35.6895, longitude: 139.6917" from each device and stores it in a database in real time.

[1705] Step 2: Signal Control

[1706] Input: Position data of vehicles, pedestrians, and bicycles near the intersection

[1707] The server analyzes location data near intersections and calculates optimal traffic light timings.

[1708] Output: Instructions to the traffic light terminal based on the calculation results

[1709] Specific operation: When congestion occurs, the server sends an instruction to the traffic light terminal to extend the green light time.

[1710] Step 3: Calculate the optimal route

[1711] Input: Current location data and destination information for each vehicle

[1712] The server calculates the optimal route based on traffic volume data, construction information, and accident information.

[1713] Output: The calculated optimal route is sent to the vehicle's terminal.

[1714] Specific operation: When the normal route is congested, the server calculates an alternative route and displays it on the vehicle terminal. The route displayed on the terminal is specific information such as "Please take X street."

[1715] Step 4: Traffic accident prediction and prevention

[1716] Input: Position and velocity data

[1717] The server analyzes location and speed data to predict the risk of traffic accidents.

[1718] Output: Warning message for high-risk vehicles

[1719] Specific operation: When approaching an intersection, the server analyzes data from other vehicles and displays a warning message on the device such as "Please reduce speed as there is a high risk of collision with the vehicle ahead."

[1720] Step 5: Improve fuel economy

[1721] Input: Vehicle fuel consumption data and driving patterns

[1722] The server analyzes the collected fuel economy data and generates driving instructions.

[1723] Output: Driving instructions aimed at improving fuel efficiency are displayed on the vehicle's terminal.

[1724] Specific behavior: The device will display a message such as "Gentle acceleration and deceleration is recommended" as an instruction to reduce sudden acceleration and deceleration.

[1725] Step 6: Emergency response

[1726] Input: Emergency data (disaster information, etc.)

[1727] The server analyzes the emergency data and calculates evacuation routes.

[1728] Output: The calculated evacuation route is sent to the vehicle's terminal.

[1729] Specific operation: In the event of an earthquake, the device will calculate a safe evacuation route and display specific instructions such as "This is the route to the nearest evacuation site."

[1730] Step 7: Emotion Recognition and Driver Assistance

[1731] Input: Emotion data from cameras and microphones in the vehicle

[1732] The server analyzes the collected emotional data and evaluates the user's emotional state.

[1733] Output: Driving assistance information based on the user's emotional state is displayed on the vehicle's terminal.

[1734] Specific behavior: If the user indicates a high stress level, the device will display the message "Your stress level is high, so we will play relaxing music" and actually play music.

[1735] The above is the specific processing flow of this system.

[1736] (Application example 2)

[1737] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1738] Conventional automated driving and traffic management systems aim to improve traffic efficiency and safety, but they are unable to consider the emotional state of drivers and pedestrians. This can result in the inability to provide appropriate support to users who are stressed or anxious, potentially increasing the risk of traffic accidents and reducing driving efficiency. Furthermore, it is difficult to adapt fuel efficiency optimization and emergency response to the emotional state of individual users. Therefore, there is a need for a system that provides advanced driving assistance based on the user's emotional state, further improving traffic efficiency and safety.

[1739] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1740] In this invention, the server includes means for collecting position data from vehicles, pedestrians, bicycles, and motorcycles, means for controlling traffic lights at intersections, means for calculating the optimal route to a destination, means for predicting the risk of traffic accidents and providing warnings, means for providing driving instructions to improve fuel efficiency, means for calculating and providing evacuation routes in emergencies, means for collecting and analyzing emotional data, and means for providing driving support information based on the emotional state of the user. This allows for the provision of driving support based on the user's emotional state, reducing the risk of traffic accidents, improving driving efficiency, optimizing fuel efficiency, and responding quickly in emergencies.

[1741] "Vehicle" means a means of transportation equipped with an engine or motor for traveling on roads, including automobiles, buses, trucks, etc.

[1742] "Pedestrian" refers to a person walking on a road.

[1743] A "bicycle" is a two-wheeled vehicle that is propelled by pedaling.

[1744] A "bike" is a vehicle equipped with an engine and running on two or three wheels, also known as a motorcycle.

[1745] "Location Data" means information about the geographic location of vehicles, pedestrians, bicycles, motorbikes, etc., obtained by GPS or other means.

[1746] A "traffic light" is a device that emits optical signals to control intersections and vehicle traffic.

[1747] An "optimal route" is the most efficient route to a destination, taking into account factors such as traffic conditions, distance, and time.

[1748] "Risk of traffic accidents" refers to the potential possibility of an accident analyzed from traffic conditions and driving behavior.

[1749] "Warning" is a notification that warns you in advance of the risk of a traffic accident or other high-risk situation.

[1750] "Fuel efficiency advice" is advice to minimize energy consumption and encourage efficient driving.

[1751] An "evacuation route" is a recommended route for safe evacuation in an emergency.

[1752] "Emotion data" is data on the user's emotional state based on information such as facial expressions, tone of voice, and heart rate collected using a camera or microphone.

[1753] "Driving assistance information" refers to various types of information provided based on traffic conditions and the user's emotional state to support driving.

[1754] This invention is a system that combines a traffic management system and an emotion recognition engine to improve traffic efficiency and safety and provide driving assistance based on the user's emotions. The main components of this system will be described below.

[1755] First, vehicles, pedestrians, bicycles, and motorbikes each have GPS functionality, and their location data is sent to a server. The server collects this location data and stores it in a database in real time. Specifically, sensors are used to detect the location, speed, and direction of travel of vehicles, pedestrians, bicycles, and motorbikes.

[1756] The server has a means of controlling the traffic lights at the intersection, and calculates the optimal signal timing taking into account the congestion situation at the intersection and the direction of travel of each participant. The calculation results are sent to the traffic light terminal, and the traffic light changes its signal according to the instructions.

[1757] The server then calculates the optimal route based on the vehicle's current location and destination information. This process includes traffic data, road construction information, and accident information. The calculated results are sent to each vehicle's terminal, and the driver follows the proposed route.

[1758] The server also analyzes the location and speed data collected in real time to predict the risk of traffic accidents. If a high risk is detected, the server sends a warning to the vehicle involved, and the device receives the warning, urging the user to slow down or stop.

[1759] To improve fuel efficiency, the server analyzes each vehicle's fuel consumption data and driving patterns and provides advice on maximizing energy efficiency, which then displays specific driving instructions on the device in real time to improve fuel efficiency.

[1760] In an emergency, the server instantly analyzes the emergency data and calculates the optimal evacuation route, which is then sent to the relevant terminals and displayed to the user in real time.

[1761] The emotion engine collects the user's emotional data from the vehicle's cameras and microphones and evaluates their emotional state. The server analyzes the user's stress level and emotional state and provides driving assistance information based on the analysis. For example, if the user shows a high stress level, the emotion engine detects this and the server sends an instruction to the device to play calming music to provide a more relaxing driving environment.

[1762] Specific examples include extending the red light time at traffic jams to ensure pedestrian safety, or prompting the user to play relaxing music if they are showing high stress levels on their way home.

[1763] Prompt Sentence Examples

[1764] Invention details: A system that incorporates an emotion engine into autonomous driving and traffic management systems to recognize human emotions and improve traffic efficiency and safety based on those emotions. It collects location data from vehicles, pedestrians, bicycles, and motorcycles, controls traffic signals, calculates optimal routes, predicts traffic accidents, improves fuel efficiency, and responds to emergencies. It also incorporates an emotion engine that recognizes the user's emotions to provide driving assistance.

[1765] Application: Autonomous vehicles

[1766] Implemented application: Smart driver assistance assistant

[1767] Application features:

[1768] Real-time traffic situation and user emotional state analysis

[1769] Route guidance for maximizing traffic efficiency

[1770] Signal control, emergency response

[1771] Helps reduce stress and anxiety

[1772] Example: If the user indicates high stress levels, the server sends an instruction to play relaxing music.

[1773] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1774] Step 1:

[1775] Collect location data for vehicles, pedestrians, bicycles, and motorcycles.

[1776] The server obtains location data, speed, and direction of travel from the GPS and sensors installed on each vehicle. This is done in real time and stored in a database. The input is the location and speed data sent from each vehicle, and the output is a database that is updated in real time.

[1777] Step 2:

[1778] Controlling traffic lights at intersections.

[1779] The server analyzes the collected location data and calculates the optimal signal timing taking into account the congestion situation at the intersection and the direction of travel of each moving object. The input is the location data and direction of travel of moving objects near the intersection, and the output is control instructions to the traffic light terminal.

[1780] Step 3:

[1781] Calculate the optimal route.

[1782] The server calculates the optimal route based on the vehicle's current location and destination information, taking into account traffic volume data, road construction information, and accident information. The calculation results are sent to each vehicle's terminal. The input is the vehicle's current location data, destination information, and traffic information, and the output is optimal route information.

[1783] Step 4:

[1784] Predicts the risk of traffic accidents and provides warnings.

[1785] The server analyzes the location and speed data collected in real time to predict the risk of potential traffic accidents. If the risk is determined to be high, it sends a warning to the vehicle involved. The terminal receives the warning and urges the user to slow down or stop. The input is the location and speed data of the moving vehicle, and the output is a risk warning.

[1786] Step 5:

[1787] Provides driving instructions to improve fuel efficiency.

[1788] The server analyzes each vehicle's fuel efficiency data and driving patterns and provides advice on maximizing energy efficiency. The terminal displays specific driving instructions for improving fuel efficiency in real time. The input is the vehicle's fuel efficiency data and driving patterns, and the output is driving instructions.

[1789] Step 6:

[1790] Calculates and provides evacuation routes in case of an emergency.

[1791] The server instantly analyzes emergency data and calculates the optimal evacuation route. The calculation results are sent to the relevant terminals, which then present the evacuation route to the user in real time. The input is the location data of each mobile device in the emergency and evacuation destination information, and the output is evacuation route information.

[1792] Step 7:

[1793] Collect and analyze emotional data.

[1794] The emotion engine collects user emotion data from cameras and microphones in the vehicle and analyzes it. The input is emotion data acquired from the cameras and microphones, and the output is an evaluation of the user's emotional state.

[1795] Step 8:

[1796] Providing driving assistance information based on emotional state.

[1797] After the emotion engine analyzes the user's emotional state, the server provides driving assistance information as needed. For example, if the user indicates a high stress level, the server sends an instruction to the terminal to play relaxing music. The input is the user's emotional state evaluated by the emotion engine, and the output is driving assistance information.

[1798] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1799] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1800] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1801] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1802] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1803] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1804] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1805] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1806] Th...

Claims

1. A means for collecting location data from vehicles, pedestrians, bicycles, and motorcycles; means for controlling traffic lights at an intersection; a means for calculating an optimal route to a destination; means for predicting the risk of road accidents and providing warnings; means for providing fuel-efficient driving instructions; A means of calculating and providing evacuation routes in emergencies Including system.

2. 10. The system of claim 1, further comprising means for storing the location data in a database in real time.

3. 2. The system of claim 1, further comprising means for calculating optimal timing of the signals.

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