system

Abstract

The system according to the embodiment aims to acquire and analyze traffic volume information, provide congestion information, and detect speeding in an integrated manner. [Solution] The system according to the embodiment includes an acquisition unit, an analysis unit, a linking unit, and a detection unit. The acquisition unit acquires traffic volume information. The analysis unit analyzes the information acquired by the acquisition unit and switches traffic lights. The linking unit performs telemetry with the SIM card installed in the vehicle and links and displays congestion information on the car navigation system. The detection unit detects speeding and issues a warning.

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] With conventional technology, the acquisition and analysis of traffic volume information, provision of congestion information, and detection of speeding were all carried out separately, making integrated traffic management difficult.

[0005] The system according to the embodiment aims to acquire and analyze traffic volume information, provide congestion information, and detect speeding in an integrated manner. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a linking unit, and a detection unit. The acquisition unit acquires traffic volume information. The analysis unit analyzes the information acquired by the acquisition unit and switches traffic lights. The linking unit performs telemetry with the SIM installed in the vehicle and links with the car navigation system to display traffic congestion information. The detection unit detects speeding and issues a warning. [Effects of the Invention]

[0007] The system according to the embodiment can acquire and analyze traffic volume information, provide congestion information, and detect speeding in an integrated manner. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A traffic efficiency system according to an embodiment of the present invention utilizes generation AI to incorporate cameras into traffic signals to improve traffic efficiency. The traffic efficiency system uses cameras built into traffic signals to acquire traffic volume information for specific time periods, and the generation AI analyzes this information to optimally switch signals. Next, telemetry is performed with the vehicle's SIM card, and traffic congestion information and other information are displayed on the car navigation system, allowing the system to select the optimal route. The camera also detects speeding and acts as a substitute for speed cameras, preventing drivers from exceeding the speed limit and reducing traffic accidents. Furthermore, images captured by the camera are stored for several days to preserve evidence of traffic accidents. When an earthquake is detected, nearby vehicles are advised to stop to avoid secondary disasters. The system also provides guidance to refueling points for electric vehicles and hydrogen-powered vehicles, taking into account the remaining energy of those vehicles. Finally, the system detects and analyzes the direction of sunlight to automatically move the roof to improve vehicle visibility. This allows the traffic efficiency system to acquire, analyze, and connect traffic volume information, detect speeding, preserve evidence, respond to earthquakes, provide energy refueling guidance, and improve traffic signal visibility. For example, during times of heavy traffic, the green time for traffic lights will be extended to smooth traffic flow. If a traffic jam occurs, the generation AI will analyze the information and suggest a detour route. If the speed limit is exceeded, the generation AI will analyze the information and display a warning to the driver. If a traffic accident occurs, the generation AI will analyze images and provide them as evidence. If an earthquake occurs, the generation AI will analyze the information and advise surrounding vehicles to stop. If an EV vehicle's battery is low, the generation AI will analyze the information and guide the driver to the nearest charging station. If the sunlight is strong, the generation AI will analyze the information and adjust the roof so that the traffic light is easier to see.

[0029] A traffic efficiency improvement system according to an embodiment includes an acquisition unit, an analysis unit, a linking unit, and a detection unit. The acquisition unit acquires traffic volume information. The traffic volume information includes, for example, the number of vehicles, their speeds, and vehicle types, but is not limited to these examples. The acquisition unit counts the number of vehicles using, for example, a camera. The acquisition unit can also measure the vehicle speeds using a speed sensor. The acquisition unit can also identify the vehicle types using vehicle type identification technology. The analysis unit analyzes the information acquired by the acquisition unit and switches the traffic lights. The analysis is performed using, for example, an algorithm, but is not limited to these examples. For example, the analysis unit extends the green time of a traffic light during times of heavy traffic. The analysis unit can also shorten the green time of a traffic light during times of light traffic. The analysis unit can also consider priority passage for certain vehicles (such as emergency vehicles). The linking unit performs telemetry with a SIM card installed in the vehicle and links with the car navigation system to display traffic congestion information. The telemetry is performed using, for example, a communication protocol, but is not limited to these examples. For example, the coordination unit acquires congestion information in real time and displays it on the car navigation system. The coordination unit can also suggest a detour route. The coordination unit can also suggest an optimal route taking into account the remaining fuel level of the vehicle. The detection unit detects speeding and issues a warning. Speeding detection is performed, for example, using a speed sensor, but is not limited to this example. For example, the detection unit displays a warning when the speed limit is exceeded. The detection unit can also analyze the cause of speeding and suggest appropriate measures. The detection unit can also issue a warning taking into account the driver's attribute information. As a result, the traffic efficiency system according to the embodiment can efficiently acquire, analyze, and coordinate traffic volume information, and detect speeding.

[0030] The traffic efficiency system includes a preservation unit that preserves evidence of traffic accidents by storing images acquired by a camera for seven days. The preservation unit stores images acquired by the camera. The storage may be, for example, in digital format, but is not limited to such an example. For example, the preservation unit stores images in the cloud. The preservation unit can also store images locally. The preservation unit can also adjust the storage period for images. For example, the preservation unit stores images for seven days. The preservation unit can also determine storage priorities based on the importance of the images. For example, images that serve as evidence of traffic accidents are stored with the highest priority. The preservation unit can also apply different storage algorithms depending on the image category. For example, an algorithm for long-term storage is applied to images of traffic accidents. This makes it possible to preserve evidence of traffic accidents.

[0031] The traffic efficiency system includes an advisory unit that advises nearby vehicles to stop when an earthquake is detected. The advisory unit advises nearby vehicles to stop when an earthquake is detected. Earthquake detection is performed, for example, using an earthquake sensor, but is not limited to this example. For example, the advisory unit adjusts the strength of the advisory based on the magnitude of the earthquake. The advisory unit can also make the advisory taking into account the status of surrounding buildings and infrastructure. For example, if there are many high-rise buildings in the vicinity, the advisory unit will advise the vehicle to stop using a stronger voice. The advisory unit can also estimate the user's emotions and adjust the method of advising the vehicle to stop based on the estimated user's emotions. For example, if the user is feeling stressed, the advisory unit will advise the vehicle to stop using a gentler voice. This makes it possible to avoid secondary disasters when an earthquake occurs.

[0032] The traffic efficiency system includes a guidance unit that provides guidance on refueling points based on the remaining energy of an electric vehicle or a hydrogen engine vehicle. The guidance unit provides guidance on refueling points based on the remaining energy of the electric vehicle or the hydrogen engine vehicle. The remaining energy can be measured, for example, using a battery remaining capacity sensor, but is not limited to such an example. For example, the guidance unit can suggest the nearest charging station when the remaining energy is low. The guidance unit can also analyze the vehicle's driving history to suggest refueling points. For example, it can prioritize suggesting refueling points that have been used in the past. The guidance unit can also estimate the user's emotions and adjust the method of providing guidance on refueling points based on the estimated user's emotions. For example, if the user is feeling stressed, it can provide guidance on refueling points in a calm voice. This enables efficient energy refueling of electric vehicles or hydrogen engine vehicles.

[0033] The traffic efficiency system includes a movable unit that detects and analyzes the direction of sunlight and automatically moves the roof portion to make traffic signals more visible from the vehicle. The movable unit detects and analyzes the direction of sunlight and automatically moves the roof portion to make traffic signals more visible from the vehicle. The detection of the direction of sunlight is performed, for example, using a light sensor, but is not limited to such an example. For example, the movable unit adjusts the movable range of the roof portion based on the intensity of sunlight. The movable unit can also move the roof portion taking into account the vehicle's location information. For example, if the vehicle is in a sunny area, the roof portion can be moved widely to block the sunlight. The movable unit can also estimate the user's emotions and adjust the method of moving the roof portion based on the estimated user's emotions. For example, if the user is feeling stressed, the roof portion can be moved with gentle movements. This makes it easier to see traffic signals without being affected by sunlight.

[0034] The acquisition unit can analyze past traffic volume data and select an optimal acquisition method. The acquisition unit analyzes the past traffic volume data. The analysis is performed using, for example, data mining technology, but is not limited to such an example. For example, the acquisition unit can predict peak traffic volume during a specific time period based on the past traffic volume data and acquire information focusing on that time period. The acquisition unit can also analyze the past traffic volume data and acquire information taking into account fluctuations in traffic volume on specific days of the week or during events. The acquisition unit can also predict fluctuations in traffic volume in a specific area based on the past traffic volume data and acquire information focusing on that area. This allows traffic volume information to be acquired efficiently based on past data.

[0035] When acquiring traffic volume information, the acquisition unit can perform filtering taking into account the influence of specific events, holidays, and the like. When acquiring traffic volume information, the acquisition unit performs filtering taking into account the influence of specific events, holidays, and the like. Filtering is performed, for example, using data filtering technology, but is not limited to such an example. For example, the acquisition unit can prioritize acquiring traffic volume information for areas where specific events are held and provide information taking into account the influence of the event. The acquisition unit can also acquire traffic volume information for holidays and consecutive holidays and provide information taking into account patterns that differ from those on normal weekdays. The acquisition unit can also focus on acquiring traffic volume information for surrounding areas when large-scale sporting events, concerts, and the like are held, and provide information taking into account the influence of the event. This makes it possible to provide information taking into account the influence of specific events, holidays, and the like.

[0036] The acquisition unit can select an acquisition means taking into consideration weather information when acquiring traffic volume information. The acquisition unit selects an acquisition means taking into consideration weather information when acquiring traffic volume information. Consideration of weather information is performed, for example, using a weather sensor, but is not limited to this example. For example, the acquisition unit strengthens real-time information acquisition during rainy weather because traffic volume is likely to increase. Furthermore, the acquisition unit can also focus on acquiring information on snowy days because there is a high possibility of road closures and congestion and provide it quickly. Furthermore, the acquisition unit can acquire normal traffic volume information during sunny weather, and reduce the frequency of information acquisition if there are no particular problems. This makes it possible to acquire appropriate traffic volume information taking into consideration weather information.

[0037] When acquiring traffic volume information, the acquisition unit can prioritize acquiring highly relevant information by taking geographical location information into consideration. When acquiring traffic volume information, the acquisition unit prioritizes acquiring highly relevant information by taking geographical location information into consideration. Consideration of geographical location information is performed, for example, using GPS data, but is not limited to this example. For example, the acquisition unit prioritizes acquiring traffic volume information around the user's current location and provides it in real time. The acquisition unit can also acquire traffic volume information around the user's destination and predict the situation at the time of arrival and provide it. The acquisition unit can also acquire traffic volume information for areas the user plans to pass through and suggest an optimal route. This makes it possible to acquire appropriate traffic volume information by taking geographical location information into consideration.

[0038] The acquisition unit can analyze social media activity and acquire related information when acquiring traffic volume information. The acquisition unit can analyze social media activity and acquire related information when acquiring traffic volume information. The analysis of social media activity is performed using, for example, text mining technology, but is not limited to this example. For example, the acquisition unit can analyze the content of posts on social media and acquire information about traffic conditions in a specific area. The acquisition unit can also acquire traffic volume information for surrounding areas based on event information on social media. The acquisition unit can also analyze real-time posts on social media and acquire information about traffic accidents and congestion. This makes it possible to acquire appropriate traffic volume information that takes social media activity into consideration.

[0039] The acquisition unit can customize the acquisition method by reflecting past feedback when acquiring traffic volume information. The acquisition unit customizes the acquisition method by reflecting past feedback when acquiring traffic volume information. The feedback is reflected, for example, using a machine learning algorithm, but is not limited to this example. For example, the acquisition unit can adjust the frequency of information acquisition in a specific time period or area based on feedback from the user. The acquisition unit can also analyze past feedback and prioritize acquisition of information desired by the user. The acquisition unit can also customize the means and timing of information acquisition based on feedback to meet the user's needs. This makes it possible to acquire appropriate traffic volume information that reflects past feedback.

[0040] The analysis unit can detect sudden fluctuations in traffic volume during analysis and take immediate action. The analysis unit can detect sudden fluctuations in traffic volume during analysis and take immediate action. The detection of sudden fluctuations is performed, for example, using real-time data analysis technology, but is not limited to such an example. For example, if traffic volume increases suddenly, the analysis unit can extend the green time of the traffic light to smooth traffic flow. Furthermore, if traffic volume decreases suddenly, the analysis unit can shorten the green time of the traffic light to reduce unnecessary waiting time. Furthermore, if traffic volume changes due to a traffic accident, road construction, or the like, the analysis unit can immediately adjust the switching timing of the traffic light. This makes it possible to immediately respond to sudden fluctuations in traffic volume.

[0041] The analysis unit can take into consideration priority passage for specific vehicles (such as emergency vehicles) during analysis. The analysis unit can take into consideration priority passage for specific vehicles (such as emergency vehicles) during analysis. Consideration of priority passage is performed, for example, using vehicle identification technology, but is not limited to such an example. For example, when an emergency vehicle is approaching, the analysis unit can turn the traffic light green to allow priority passage. Furthermore, when a public transportation vehicle is approaching, the analysis unit can extend the green time of the traffic light to support smooth passage. Furthermore, when a specific vehicle (such as a VIP vehicle) is approaching, the analysis unit can adjust the switching timing of the traffic light to allow priority passage. This allows priority passage for the specific vehicle.

[0042] During analysis, the analysis unit can optimize the traffic light switching algorithm by referring to past analysis results. During analysis, the analysis unit optimizes the traffic light switching algorithm by referring to past analysis results. Optimization is performed using, for example, a machine learning algorithm, but is not limited to this example. For example, the analysis unit can optimize the traffic light switching timing for a specific time period or day of the week based on past analysis results. The analysis unit can also analyze past analysis results and adjust the traffic light switching algorithm by taking into account traffic volume fluctuation patterns. The analysis unit can also optimize the traffic light switching timing in a specific area by referring to past analysis results. This makes it possible to optimize the traffic light switching algorithm by referring to past analysis results.

[0043] During the analysis, the analysis unit can predict the risk of a traffic accident and adjust the traffic light switching. During the analysis, the analysis unit predicts the risk of a traffic accident and adjusts the traffic light switching. The risk prediction is performed, for example, using a risk assessment algorithm, but is not limited to this example. For example, the analysis unit can shorten the green time of a traffic light at an intersection where the risk of a traffic accident is high, thereby reducing the number of vehicles at the intersection. The analysis unit can also extend the green time of a traffic light during times when the risk of a traffic accident is low, thereby smoothing traffic flow. Furthermore, when the risk of a traffic accident is high, the analysis unit can adjust the traffic light switching timing to prevent accidents from occurring. In this way, accidents can be prevented from occurring by predicting the risk of a traffic accident and adjusting the traffic light switching.

[0044] The analysis unit can switch traffic lights during analysis, taking into account the operation status of public transportation. The analysis unit can switch traffic lights during analysis, taking into account the operation status of public transportation. The operation status can be taken into account, for example, using real-time data, but is not limited to this example. For example, the analysis unit can extend the green time of a traffic light when public transportation such as a bus or train is approaching, thereby supporting smooth passage. The analysis unit can also acquire the operation status of public transportation in real time and adjust the timing of switching traffic lights. The analysis unit can also extend the green time of a traffic light when a delay in public transportation occurs, thereby supporting the elimination of the delay. This makes it possible to switch traffic lights while taking into account the operation status of public transportation.

[0045] During analysis, the analysis unit can adjust the traffic light switching taking into account the business hours of surrounding commercial facilities. During analysis, the analysis unit adjusts the traffic light switching taking into account the business hours of surrounding commercial facilities. Consideration of business hours is performed, for example, using data on the commercial facilities, but is not limited to this example. For example, the analysis unit extends the green time of the traffic light during business hours of the commercial facilities to smooth traffic flow. The analysis unit can also shorten the green time of the traffic light outside of business hours of the commercial facilities to reduce unnecessary waiting time. The analysis unit can also adjust the traffic light switching timing to match the business hours of the commercial facilities and optimize traffic flow. This makes it possible to switch traffic lights taking into account the business hours of the commercial facilities.

[0046] During collaboration, the collaboration unit can propose an optimal route taking into account the remaining fuel level of the vehicle. During collaboration, the collaboration unit proposes an optimal route taking into account the remaining fuel level of the vehicle. Consideration of the remaining fuel level is performed, for example, using a fuel gauge, but is not limited to this example. For example, when the remaining fuel level of the vehicle is low, the collaboration unit proposes a route that passes through the nearest gas station. Furthermore, when the remaining fuel level of the vehicle is sufficient, the collaboration unit can also propose the shortest route. Furthermore, the collaboration unit can monitor the remaining fuel level of the vehicle in real time and propose an appropriate route when refueling is necessary. This makes it possible to propose an appropriate route taking into account the remaining fuel level of the vehicle.

[0047] The coordination unit can adjust the route by taking into account vehicle maintenance information during coordination. The coordination unit adjusts the route by taking into account vehicle maintenance information during coordination. Consideration of maintenance information is performed, for example, using the vehicle's maintenance history, but is not limited to this example. For example, if the vehicle requires maintenance, the coordination unit can propose a route that passes through the nearest repair shop. The coordination unit can also propose a route that avoids long-distance driving based on the vehicle maintenance information. The coordination unit can also monitor vehicle maintenance information in real time and propose an appropriate route as necessary. This makes it possible to adjust the route appropriately by taking into account vehicle maintenance information.

[0048] During collaboration, the collaboration unit can improve the accuracy of route proposals by referring to past collaboration results. During collaboration, the collaboration unit improves the accuracy of route proposals by referring to past collaboration results. The improvement in accuracy is performed, for example, using a machine learning algorithm, but is not limited to such an example. For example, the collaboration unit can propose an optimal route for a specific time period or day of the week based on past collaboration results. The collaboration unit can also analyze past collaboration results and propose a route that suits the user's preferences. The collaboration unit can also propose an optimal route in a specific area by referring to past collaboration results. This makes it possible to propose an appropriate route by referring to past collaboration results.

[0049] During collaboration, the collaboration unit can propose an optimal route taking into account the number of occupants in the vehicle. During collaboration, the collaboration unit proposes an optimal route taking into account the number of occupants in the vehicle. Consideration of the number of occupants is performed, for example, using the number of seats in the vehicle, but is not limited to this example. For example, when the number of occupants in the vehicle is large, the collaboration unit proposes a route that prioritizes wide roads. Furthermore, when the number of occupants in the vehicle is small, the collaboration unit can also propose the shortest route. Furthermore, the collaboration unit can monitor the number of occupants in the vehicle in real time and propose an optimal route according to the number of occupants. This makes it possible to propose an appropriate route taking into account the number of occupants in the vehicle.

[0050] The coordination unit can adjust the route by taking into account the luggage information of the vehicle when coordinating. The coordination unit adjusts the route by taking into account the luggage information of the vehicle when coordinating. Taking into account the luggage information is performed, for example, using a luggage sensor in the vehicle, but is not limited to this example. For example, when the vehicle has a lot of luggage, the coordination unit proposes a route that takes into account the safety of the luggage. Furthermore, when the vehicle has a small amount of luggage, the coordination unit can also propose the shortest route. Furthermore, the coordination unit can monitor the luggage information of the vehicle in real time and propose the optimal route according to the luggage status. This makes it possible to adjust the route appropriately by taking into account the luggage information of the vehicle.

[0051] During collaboration, the collaboration unit can analyze the vehicle's driving history and propose an optimal route. During collaboration, the collaboration unit analyzes the vehicle's driving history and proposes an optimal route. The analysis of the driving history is performed, for example, using data mining technology, but is not limited to this example. For example, the collaboration unit prioritizes and proposes routes that have been used in the past based on the vehicle's driving history. The collaboration unit can also analyze the vehicle's driving history and propose routes that suit the driver's preferences. The collaboration unit can also propose an optimal route for a specific time period or day of the week based on the vehicle's driving history. This makes it possible to propose an appropriate route that takes into account the vehicle's driving history.

[0052] The detection unit can issue a warning by taking into consideration attribute information of the driver of the vehicle upon detection. The detection unit can issue a warning by taking into consideration attribute information of the driver of the vehicle upon detection. Consideration of attribute information is performed, for example, using the driver's age and driving experience, but is not limited to such examples. For example, the detection unit can issue a warning including a detailed explanation for a young driver. Furthermore, the detection unit can issue a highly visible warning for an elderly driver. Furthermore, the detection unit can issue a concise warning for a driver with extensive driving experience. This makes it possible to issue an appropriate warning by taking into consideration the driver's attribute information.

[0053] Upon detection, the detection unit can analyze the driving history of the vehicle and adjust the frequency of the warning. Upon detection, the detection unit can analyze the driving history of the vehicle and adjust the frequency of the warning. The analysis of the driving history is performed, for example, using data mining technology, but is not limited to such an example. For example, the detection unit can frequently issue warnings to drivers who have a history of speeding frequently in the past. Furthermore, the detection unit can also issue warnings only when necessary to drivers who have a history of speeding in the past infrequently. Furthermore, the detection unit can monitor the driving history in real time and issue warnings at an appropriate frequency. This makes it possible to set an appropriate warning frequency taking into account the driving history.

[0054] The detection unit can optimize the warning algorithm by referring to past detection results when detecting. The detection unit optimizes the warning algorithm by referring to past detection results when detecting. The optimization is performed using, for example, a machine learning algorithm, but is not limited to such an example. For example, the detection unit adjusts the warning frequency in a specific time period or area based on past detection results. The detection unit can also analyze past detection results and adjust the warning algorithm according to the driver's attributes. The detection unit can also optimize the warning method for a specific situation by referring to past detection results. This makes it possible to optimize an appropriate warning algorithm by referring to past detection results.

[0055] The detection unit can analyze the cause of the vehicle speeding at the time of detection and propose appropriate countermeasures. The detection unit can analyze the cause of the vehicle speeding at the time of detection and propose appropriate countermeasures. The analysis of the cause is performed using, for example, data mining technology, but is not limited to such an example. For example, the detection unit can propose the shortest route if the cause of the speeding is hurry. The detection unit can also display a warning message if the cause of the speeding is carelessness. The detection unit can also propose a detour route if the cause of the speeding is road conditions. In this way, speeding can be prevented by analyzing the cause of the speeding and proposing appropriate countermeasures.

[0056] The detection unit can issue a warning taking into consideration the health condition of the driver of the vehicle upon detection. The detection unit issues a warning taking into consideration the health condition of the driver of the vehicle upon detection. Consideration of the health condition is performed using, for example, heart rate or blood pressure, but is not limited to such examples. For example, the detection unit issues a warning early if the driver is tired. Furthermore, the detection unit can also issue a warning at a normal timing if the driver is healthy. Furthermore, the detection unit can issue a warning including a detailed explanation if the driver is in poor health. This makes it possible to issue an appropriate warning taking into consideration the driver's health condition.

[0057] The detection unit can adjust the strength of the warning when detecting a violation by taking into account the vehicle driver's past violation history. The detection unit can adjust the strength of the warning when detecting a violation by taking into account the vehicle driver's past violation history. Taking into account the violation history is done, for example, by using past violation data, but is not limited to this example. For example, the detection unit can issue a stronger warning to a driver with many past violation histories. The detection unit can also issue a normal warning to a driver with few past violation histories. The detection unit can also monitor the violation history in real time and issue a warning with an appropriate strength. This makes it possible to set an appropriate warning strength by taking into account the past violation history.

[0058] The preservation unit can determine the storage priority based on the importance of the images when storing them. The preservation unit determines the storage priority based on the importance of the images when storing them. The importance is evaluated using, for example, the content and evidentiary value of the images, but is not limited to such examples. For example, the preservation unit may store images that serve as evidence of a traffic accident as a top priority. The preservation unit may also store images that serve as evidence of a traffic violation as a second priority. The preservation unit may also store images of normal traffic conditions as needed. This enables appropriate storage prioritization based on the importance of the images.

[0059] The preservation unit can apply different storage algorithms depending on the category of the image when storing the images. The preservation unit applies different storage algorithms depending on the category of the image when storing the images. The application of the algorithm is performed, for example, using the type or content of the image, but is not limited to such an example. For example, the preservation unit can apply an algorithm for long-term storage to images of traffic accidents. The preservation unit can also apply an algorithm for medium-term storage to images of traffic violations. The preservation unit can also apply an algorithm for short-term storage to images of normal traffic conditions. This makes it possible to apply an appropriate storage algorithm depending on the category of the image.

[0060] The preservation unit can determine the storage priority based on the time of submission of the images when the images are stored. The preservation unit can determine the storage priority based on the time of submission of the images when the images are stored. The submission time is considered, for example, using the timing of image acquisition, but is not limited to this example. For example, the preservation unit stores the most recently acquired image as the highest priority. The preservation unit can also store images acquired in the past as the next highest priority. The preservation unit can also set an appropriate storage period based on the time of submission. This makes it possible to set an appropriate storage priority based on the time of image submission.

[0061] The preservation unit can adjust the order of storage based on the relevance of images when storing them. The preservation unit adjusts the order of storage based on the relevance of images when storing them. The evaluation of relevance is performed using, for example, the content of the image or related events, but is not limited to such examples. For example, the preservation unit may store images that serve as evidence of a traffic accident as a top priority. The preservation unit may also store images that serve as evidence of a traffic violation as a second priority. The preservation unit may also store images of normal traffic conditions as needed. This enables an appropriate order of storage based on the relevance of images.

[0062] The recommendation unit can adjust the strength of the recommendation based on the scale of the earthquake when making a recommendation. The recommendation unit can adjust the strength of the recommendation based on the scale of the earthquake when making a recommendation. The scale of the earthquake is considered using, for example, seismic intensity or magnitude, but is not limited to such examples. For example, in the case of a small earthquake, the recommendation unit can recommend pausing with a gentle voice. In addition, in the case of a medium-sized earthquake, the recommendation unit can also recommend pausing with a normal voice. In addition, in the case of a large earthquake, the recommendation unit can recommend pausing with a stronger voice. This makes it possible to provide an appropriate strength of recommendation according to the scale of the earthquake.

[0063] The recommendation unit can make a recommendation taking into consideration the status of surrounding buildings and infrastructure when making a recommendation. The recommendation unit makes a recommendation taking into consideration the status of surrounding buildings and infrastructure when making a recommendation. Consideration of the status is performed, for example, using the earthquake resistance of buildings and the status of infrastructure, but is not limited to such examples. For example, if there are many high-rise buildings in the vicinity, the recommendation unit can recommend pausing in a stronger voice. Also, if there are many infrastructure facilities in the vicinity, the recommendation unit can recommend pausing in a normal voice. Also, if there are many residential areas in the vicinity, the recommendation unit can recommend pausing in a gentler voice. This makes it possible to make appropriate recommendations taking into consideration the status of surrounding buildings and infrastructure.

[0064] The recommendation unit can determine the priority of the recommendation by taking into consideration the surrounding traffic conditions when making a recommendation. The recommendation unit can determine the priority of the recommendation by taking into consideration the surrounding traffic conditions when making a recommendation. Consideration of the traffic conditions is performed, for example, using traffic volume and congestion information, but is not limited to such examples. For example, the recommendation unit can recommend an early stop when there is a lot of traffic in the surrounding area. Furthermore, the recommendation unit can also recommend a stop at a normal timing when there is little traffic in the surrounding area. Furthermore, the recommendation unit can monitor the surrounding traffic conditions in real time and recommend a stop at an appropriate timing. This makes it possible to appropriately prioritize the recommendation by taking into consideration the surrounding traffic conditions.

[0065] The recommendation unit can improve the accuracy of the recommendation by referring to past earthquake data when making a recommendation. The recommendation unit can improve the accuracy of the recommendation by referring to past earthquake data when making a recommendation. The improvement in accuracy is performed, for example, using data mining technology, but is not limited to such an example. For example, the recommendation unit can optimize the timing of a temporary stop recommendation in a specific area based on past earthquake data. The recommendation unit can also analyze past earthquake data and adjust the recommendation method according to the magnitude of the earthquake. The recommendation unit can also improve the accuracy of the temporary stop recommendation in specific situations by referring to past earthquake data. This enables appropriate accuracy of recommendations by referring to past earthquake data.

[0066] The guidance unit can suggest an optimal refueling point based on the remaining energy amount of the vehicle when providing guidance. The guidance unit can suggest an optimal refueling point based on the remaining energy amount of the vehicle when providing guidance. The remaining energy amount can be taken into consideration using, for example, a battery remaining amount sensor, but is not limited to this example. For example, the guidance unit can suggest the nearest charging station when the remaining energy amount of the vehicle is low. Furthermore, the guidance unit can also suggest the shortest route when the remaining energy amount of the vehicle is sufficient. Furthermore, the guidance unit can monitor the remaining energy amount of the vehicle in real time and suggest an appropriate refueling point. This makes it possible to suggest an appropriate refueling point based on the remaining energy amount of the vehicle.

[0067] The guidance unit can analyze the driving history of the vehicle and suggest replenishment points when providing guidance. The guidance unit can analyze the driving history of the vehicle and suggest replenishment points when providing guidance. The analysis of the driving history is performed, for example, using data mining technology, but is not limited to this example. For example, the guidance unit can prioritize and suggest replenishment points that have been used in the past based on the driving history of the vehicle. The guidance unit can also analyze the driving history of the vehicle and suggest replenishment points that suit the driver's preferences. The guidance unit can also suggest optimal replenishment points for specific time periods or days of the week based on the driving history of the vehicle. This makes it possible to suggest appropriate replenishment points taking into account the driving history of the vehicle.

[0068] The guidance unit can suggest replenishment points by taking into consideration attribute information of the driver of the vehicle when providing guidance. The guidance unit can suggest replenishment points by taking into consideration attribute information of the driver of the vehicle when providing guidance. Consideration of attribute information is performed, for example, using the driver's age and driving experience, but is not limited to such examples. For example, the guidance unit can suggest replenishment points that include detailed explanations for a young driver. Furthermore, the guidance unit can suggest replenishment points that are highly visible for an elderly driver. Furthermore, the guidance unit can suggest simple replenishment points for a driver with extensive driving experience. This makes it possible to suggest appropriate replenishment points by taking into consideration the driver's attribute information.

[0069] When providing guidance, the guidance unit can suggest the optimal replenishment point by referring to the vehicle driver's past replenishment history. ... Consideration of the replenishment history is performed, for example, using past replenishment data, but is not limited to this example. For example, the guidance unit prioritizes suggesting replenishment points that have been used in the past. The guidance unit can also analyze the past replenishment history and suggest replenishment points that suit the driver's preferences. The guidance unit can also suggest the optimal replenishment point for a specific time period or day of the week by referring to the past replenishment history. This makes it possible to suggest appropriate replenishment points by taking the past replenishment history into consideration.

[0070] When in motion, the movable unit can adjust the movable range of the roof portion based on the intensity of sunlight. When in motion, the movable unit adjusts the movable range of the roof portion based on the intensity of sunlight. Consideration of the intensity of sunlight is performed, for example, using a light sensor, but is not limited to such an example. For example, when sunlight is strong, the movable unit can move the roof portion more widely to block the sunlight. Furthermore, when sunlight is weak, the movable unit can move the roof portion less widely to ensure visibility. Furthermore, the movable unit can monitor the intensity of sunlight in real time and set an appropriate movable range. This enables the movable range of the roof portion to be appropriate according to the intensity of sunlight.

[0071] When moving, the movable unit can move the roof portion while taking into account vehicle position information. When moving, the movable unit moves the roof portion while taking into account vehicle position information. Consideration of the position information is performed, for example, using GPS data, but is not limited to this example. For example, when the vehicle is in an area with strong sunlight, the movable unit can move the roof portion more widely to block the sunlight. Furthermore, when the vehicle is in an area with weak sunlight, the movable unit can move the roof portion less widely to ensure visibility. Furthermore, the movable unit can monitor vehicle position information in real time and set an appropriate range of movement. This enables the roof portion to be moved appropriately while taking into account the vehicle position information.

[0072] When moving, the movable unit can move the roof portion while taking into account the status of surrounding buildings and obstacles. When moving, the movable unit can move the roof portion while taking into account the status of surrounding buildings and obstacles. Consideration of the status can be performed, for example, using the height of the building and the position of the obstacle, but is not limited to such examples. For example, when there are many high-rise buildings in the vicinity, the movable unit can move the roof portion widely to block the sunlight. Furthermore, when there are many obstacles in the vicinity, the movable unit can move the roof portion narrowly to ensure visibility. Furthermore, the movable unit can monitor the status of surrounding buildings and obstacles in real time and set an appropriate range of movement. This enables the roof portion to be moved appropriately while taking into account the status of surrounding buildings and obstacles.

[0073] When moving, the movable unit can optimize the movement of the roof portion by referring to past sunlight data. When moving, the movable unit optimizes the movement of the roof portion by referring to past sunlight data. Optimization is performed using, for example, data mining technology, but is not limited to such an example. For example, the movable unit can optimize the movement range of the roof portion for a specific time period or area based on past sunlight data. The movable unit can also analyze past sunlight data and set the movement range according to the intensity of sunlight. The movable unit can also optimize the movement method of the roof portion for a specific situation by referring to past sunlight data. This makes it possible to move the roof portion appropriately by referring to past sunlight data.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] When acquiring traffic volume information, the acquisition unit can perform filtering taking into account the influence of specific events, holidays, and the like. Filtering can be performed, for example, using data filtering technology, but is not limited to such examples. For example, the acquisition unit can prioritize acquiring traffic volume information for areas where specific events are held and provide information taking into account the influence of the events. The acquisition unit can also acquire traffic volume information for holidays and consecutive weekends and provide information taking into account patterns that differ from those on normal weekdays. The acquisition unit can also focus on acquiring traffic volume information for surrounding areas when large-scale sporting events, concerts, and the like are held, and provide information taking into account the influence of the events. This makes it possible to provide information taking into account the influence of specific events, holidays, and the like.

[0076] The analysis unit can detect sudden fluctuations in traffic volume during analysis and take immediate action. Detection of sudden fluctuations is performed, for example, using real-time data analysis technology, but is not limited to such an example. For example, if traffic volume increases suddenly, the analysis unit extends the green time of the traffic light to smooth traffic flow. Furthermore, if traffic volume decreases suddenly, the analysis unit can shorten the green time of the traffic light to reduce unnecessary waiting time. Furthermore, if traffic volume changes due to a traffic accident, road construction, or the like, the analysis unit can immediately adjust the timing of switching the traffic light. This makes it possible to immediately respond to sudden fluctuations in traffic volume.

[0077] During collaboration, the collaboration unit can propose an optimal route taking into account the vehicle's remaining fuel. Consideration of the remaining fuel is performed, for example, using a fuel gauge, but is not limited to this example. For example, when the vehicle's remaining fuel is low, the collaboration unit proposes a route that passes through the nearest gas station. Furthermore, when the vehicle's remaining fuel is sufficient, the collaboration unit can also propose the shortest route. Furthermore, the collaboration unit can monitor the vehicle's remaining fuel in real time and propose an appropriate route when refueling is necessary. This makes it possible to propose an appropriate route taking into account the vehicle's remaining fuel.

[0078] Upon detection, the detection unit can issue a warning by taking into consideration attribute information of the driver of the vehicle. Consideration of the attribute information is performed using, for example, the driver's age and driving experience, but is not limited to such examples. For example, the detection unit can issue a warning including a detailed explanation for a young driver. Furthermore, the detection unit can issue a highly visible warning for an elderly driver. Furthermore, the detection unit can issue a concise warning for a driver with extensive driving experience. This makes it possible to issue an appropriate warning by taking into consideration the driver's attribute information.

[0079] The preservation unit can determine the priority of storage based on the importance of the images when storing them. The evaluation of the importance is performed, for example, using the content and evidentiary value of the images, but is not limited to such examples. For example, the preservation unit may store images that serve as evidence of a traffic accident as a first priority. The preservation unit may also store images that serve as evidence of a traffic violation as a second priority. The preservation unit may also store images of normal traffic conditions as needed. This enables appropriate prioritization of storage based on the importance of the images.

[0080] The processing flow of the first embodiment will be briefly explained below.

[0081] Step 1: The acquisition unit acquires traffic volume information. The traffic volume information includes the number of vehicles, their speeds, vehicle types, etc. The acquisition unit counts the number of vehicles using a camera, measures the vehicle speeds using a speed sensor, and identifies the vehicle types using vehicle type identification technology. Step 2: The analysis unit analyzes the information acquired by the acquisition unit and switches the traffic lights. The analysis is performed using an algorithm, which extends the green time of the signal during times of heavy traffic and shortens the green time during times of light traffic. It also takes into account priority for certain vehicles (such as emergency vehicles). Step 3: The linking unit performs telemetry with the SIM card installed in the vehicle and displays traffic congestion information on the car navigation system. Telemetry is performed using a communication protocol, and traffic congestion information is acquired in real time and displayed on the car navigation system. It can also suggest detour routes and the optimal route taking into account the vehicle's remaining fuel. Step 4: The detection unit detects speeding and issues a warning. Speeding is detected using a speed sensor, and a warning is displayed if the speed limit is exceeded. Furthermore, the system can analyze the cause of speeding, suggest appropriate countermeasures, and issue a warning taking into account the driver's attribute information.

[0082] (Example 2) A traffic efficiency system according to an embodiment of the present invention utilizes generation AI to incorporate cameras into traffic signals to improve traffic efficiency. The traffic efficiency system uses cameras built into traffic signals to acquire traffic volume information for specific time periods, and the generation AI analyzes this information to optimally switch signals. Next, telemetry is performed with the vehicle's SIM card, and traffic congestion information and other information are displayed on the car navigation system, allowing the system to select the optimal route. The camera also detects speeding and acts as a substitute for speed cameras, preventing drivers from exceeding the speed limit and reducing traffic accidents. Furthermore, images captured by the camera are stored for several days to preserve evidence of traffic accidents. When an earthquake is detected, nearby vehicles are advised to stop to avoid secondary disasters. The system also provides guidance to refueling points for electric vehicles and hydrogen-powered vehicles, taking into account the remaining energy of those vehicles. Finally, the system detects and analyzes the direction of sunlight to automatically move the roof to improve vehicle visibility. This allows the traffic efficiency system to acquire, analyze, and connect traffic volume information, detect speeding, preserve evidence, respond to earthquakes, provide energy refueling guidance, and improve traffic signal visibility. For example, during times of heavy traffic, the green time for traffic lights will be extended to smooth traffic flow. If a traffic jam occurs, the generation AI will analyze the information and suggest a detour route. If the speed limit is exceeded, the generation AI will analyze the information and display a warning to the driver. If a traffic accident occurs, the generation AI will analyze images and provide them as evidence. If an earthquake occurs, the generation AI will analyze the information and advise surrounding vehicles to stop. If an EV vehicle's battery is low, the generation AI will analyze the information and guide the driver to the nearest charging station. If the sunlight is strong, the generation AI will analyze the information and adjust the roof so that the traffic light is easier to see.

[0083] A traffic efficiency improvement system according to an embodiment includes an acquisition unit, an analysis unit, a linking unit, and a detection unit. The acquisition unit acquires traffic volume information. The traffic volume information includes, for example, the number of vehicles, their speeds, and vehicle types, but is not limited to these examples. The acquisition unit counts the number of vehicles using, for example, a camera. The acquisition unit can also measure the vehicle speeds using a speed sensor. The acquisition unit can also identify the vehicle types using vehicle type identification technology. The analysis unit analyzes the information acquired by the acquisition unit and switches the traffic lights. The analysis is performed using, for example, an algorithm, but is not limited to these examples. For example, the analysis unit extends the green time of a traffic light during times of heavy traffic. The analysis unit can also shorten the green time of a traffic light during times of light traffic. The analysis unit can also consider priority passage for certain vehicles (such as emergency vehicles). The linking unit performs telemetry with a SIM card installed in the vehicle and links with the car navigation system to display traffic congestion information. The telemetry is performed using, for example, a communication protocol, but is not limited to these examples. For example, the coordination unit acquires congestion information in real time and displays it on the car navigation system. The coordination unit can also suggest a detour route. The coordination unit can also suggest an optimal route taking into account the remaining fuel level of the vehicle. The detection unit detects speeding and issues a warning. Speeding detection is performed, for example, using a speed sensor, but is not limited to this example. For example, the detection unit displays a warning when the speed limit is exceeded. The detection unit can also analyze the cause of speeding and suggest appropriate measures. The detection unit can also issue a warning taking into account the driver's attribute information. As a result, the traffic efficiency system according to the embodiment can efficiently acquire, analyze, and coordinate traffic volume information, and detect speeding.

[0084] The traffic efficiency system includes a preservation unit that preserves evidence of traffic accidents by storing images acquired by a camera for seven days. The preservation unit stores images acquired by the camera. The storage may be, for example, in digital format, but is not limited to such an example. For example, the preservation unit stores images in the cloud. The preservation unit can also store images locally. The preservation unit can also adjust the storage period for images. For example, the preservation unit stores images for seven days. The preservation unit can also determine storage priorities based on the importance of the images. For example, images that serve as evidence of traffic accidents are stored with the highest priority. The preservation unit can also apply different storage algorithms depending on the image category. For example, an algorithm for long-term storage is applied to images of traffic accidents. This makes it possible to preserve evidence of traffic accidents.

[0085] The traffic efficiency system includes an advisory unit that advises nearby vehicles to stop when an earthquake is detected. The advisory unit advises nearby vehicles to stop when an earthquake is detected. Earthquake detection is performed, for example, using an earthquake sensor, but is not limited to this example. For example, the advisory unit adjusts the strength of the advisory based on the magnitude of the earthquake. The advisory unit can also make the advisory taking into account the status of surrounding buildings and infrastructure. For example, if there are many high-rise buildings in the vicinity, the advisory unit will advise the vehicle to stop using a stronger voice. The advisory unit can also estimate the user's emotions and adjust the method of advising the vehicle to stop based on the estimated user's emotions. For example, if the user is feeling stressed, the advisory unit will advise the vehicle to stop using a gentler voice. This makes it possible to avoid secondary disasters when an earthquake occurs.

[0086] The traffic efficiency system includes a guidance unit that provides guidance on refueling points based on the remaining energy of an electric vehicle or a hydrogen engine vehicle. The guidance unit provides guidance on refueling points based on the remaining energy of the electric vehicle or the hydrogen engine vehicle. The remaining energy can be measured, for example, using a battery remaining capacity sensor, but is not limited to such an example. For example, the guidance unit can suggest the nearest charging station when the remaining energy is low. The guidance unit can also analyze the vehicle's driving history to suggest refueling points. For example, it can prioritize suggesting refueling points that have been used in the past. The guidance unit can also estimate the user's emotions and adjust the method of providing guidance on refueling points based on the estimated user's emotions. For example, if the user is feeling stressed, it can provide guidance on refueling points in a calm voice. This enables efficient energy refueling of electric vehicles or hydrogen engine vehicles.

[0087] The traffic efficiency system includes a movable unit that detects and analyzes the direction of sunlight and automatically moves the roof portion to make traffic signals more visible from the vehicle. The movable unit detects and analyzes the direction of sunlight and automatically moves the roof portion to make traffic signals more visible from the vehicle. The detection of the direction of sunlight is performed, for example, using a light sensor, but is not limited to such an example. For example, the movable unit adjusts the movable range of the roof portion based on the intensity of sunlight. The movable unit can also move the roof portion taking into account the vehicle's location information. For example, if the vehicle is in a sunny area, the roof portion can be moved widely to block the sunlight. The movable unit can also estimate the user's emotions and adjust the method of moving the roof portion based on the estimated user's emotions. For example, if the user is feeling stressed, the roof portion can be moved with gentle movements. This makes it easier to see traffic signals without being affected by sunlight.

[0088] The acquisition unit can estimate the user's emotions using a machine learning algorithm and adjust the timing of acquiring traffic volume information based on the estimated user's emotions. The acquisition unit estimates the user's emotions using a machine learning algorithm. The emotion estimation is performed, for example, using facial expression recognition technology, but is not limited to such an example. For example, when the user is feeling stressed, the acquisition unit can increase the frequency of acquiring traffic volume information and provide the information in real time. Furthermore, when the user is relaxed, the acquisition unit can reduce the frequency of acquiring traffic volume information and provide only the necessary information. Furthermore, when the user is in a hurry, the acquisition unit can quickly acquire traffic volume information and immediately suggest the optimal route. This makes it possible to acquire traffic volume information at an appropriate timing according to the user's emotions.

[0089] The acquisition unit can analyze past traffic volume data and select an optimal acquisition method. The acquisition unit analyzes the past traffic volume data. The analysis is performed using, for example, data mining technology, but is not limited to such an example. For example, the acquisition unit can predict peak traffic volume during a specific time period based on the past traffic volume data and acquire information focusing on that time period. The acquisition unit can also analyze the past traffic volume data and acquire information taking into account fluctuations in traffic volume on specific days of the week or during events. The acquisition unit can also predict fluctuations in traffic volume in a specific area based on the past traffic volume data and acquire information focusing on that area. This allows traffic volume information to be acquired efficiently based on past data.

[0090] When acquiring traffic volume information, the acquisition unit can perform filtering taking into account the influence of specific events, holidays, and the like. When acquiring traffic volume information, the acquisition unit performs filtering taking into account the influence of specific events, holidays, and the like. Filtering is performed, for example, using data filtering technology, but is not limited to such an example. For example, the acquisition unit can prioritize acquiring traffic volume information for areas where specific events are held and provide information taking into account the influence of the event. The acquisition unit can also acquire traffic volume information for holidays and consecutive holidays and provide information taking into account patterns that differ from those on normal weekdays. The acquisition unit can also focus on acquiring traffic volume information for surrounding areas when large-scale sporting events, concerts, and the like are held, and provide information taking into account the influence of the event. This makes it possible to provide information taking into account the influence of specific events, holidays, and the like.

[0091] The acquisition unit can select an acquisition means taking into consideration weather information when acquiring traffic volume information. The acquisition unit selects an acquisition means taking into consideration weather information when acquiring traffic volume information. Consideration of weather information is performed, for example, using a weather sensor, but is not limited to this example. For example, the acquisition unit strengthens real-time information acquisition during rainy weather because traffic volume is likely to increase. Furthermore, the acquisition unit can also focus on acquiring information on snowy days because there is a high possibility of road closures and congestion and provide it quickly. Furthermore, the acquisition unit can acquire normal traffic volume information during sunny weather, and reduce the frequency of information acquisition if there are no particular problems. This makes it possible to acquire appropriate traffic volume information taking into consideration weather information.

[0092] The acquisition unit can estimate the user's emotions and determine the priority of traffic volume information to be acquired based on the estimated user's emotions. The acquisition unit can estimate the user's emotions and determine the priority of traffic volume information to be acquired based on the estimated user's emotions. Emotion estimation is performed, for example, using facial expression recognition technology, but is not limited to such an example. For example, when the user is feeling stressed, the acquisition unit can prioritize acquiring traffic congestion information and suggest a detour route. Furthermore, when the user is relaxed, the acquisition unit can acquire normal traffic volume information and refrain from providing information if there are no particular problems. Furthermore, when the user is in a hurry, the acquisition unit can prioritize acquiring traffic volume information for the shortest route and providing it quickly. This makes it possible to acquire traffic volume information in accordance with the user's emotions.

[0093] When acquiring traffic volume information, the acquisition unit can prioritize acquiring highly relevant information by taking geographical location information into consideration. When acquiring traffic volume information, the acquisition unit prioritizes acquiring highly relevant information by taking geographical location information into consideration. Consideration of geographical location information is performed, for example, using GPS data, but is not limited to this example. For example, the acquisition unit prioritizes acquiring traffic volume information around the user's current location and provides it in real time. The acquisition unit can also acquire traffic volume information around the user's destination and predict the situation at the time of arrival and provide it. The acquisition unit can also acquire traffic volume information for areas the user plans to pass through and suggest an optimal route. This makes it possible to acquire appropriate traffic volume information by taking geographical location information into consideration.

[0094] The acquisition unit can analyze social media activity and acquire related information when acquiring traffic volume information. The acquisition unit can analyze social media activity and acquire related information when acquiring traffic volume information. The analysis of social media activity is performed using, for example, text mining technology, but is not limited to this example. For example, the acquisition unit can analyze the content of posts on social media and acquire information about traffic conditions in a specific area. The acquisition unit can also acquire traffic volume information for surrounding areas based on event information on social media. The acquisition unit can also analyze real-time posts on social media and acquire information about traffic accidents and congestion. This makes it possible to acquire appropriate traffic volume information that takes social media activity into consideration.

[0095] The acquisition unit can customize the acquisition method by reflecting past feedback when acquiring traffic volume information. The acquisition unit customizes the acquisition method by reflecting past feedback when acquiring traffic volume information. The feedback is reflected, for example, using a machine learning algorithm, but is not limited to this example. For example, the acquisition unit can adjust the frequency of information acquisition in a specific time period or area based on feedback from the user. The acquisition unit can also analyze past feedback and prioritize acquisition of information desired by the user. The acquisition unit can also customize the means and timing of information acquisition based on feedback to meet the user's needs. This makes it possible to acquire appropriate traffic volume information that reflects past feedback.

[0096] The analysis unit can estimate the user's emotions and adjust the traffic light switching timing based on the estimated user's emotions. The analysis unit can estimate the user's emotions and adjust the traffic light switching timing based on the estimated user's emotions. Emotion estimation is performed, for example, using facial expression recognition technology, but is not limited to such an example. For example, if the user is feeling stressed, the analysis unit can extend the green time of the traffic light to smooth traffic flow. Furthermore, if the user is relaxed, the analysis unit can maintain the normal traffic light switching timing. Furthermore, if the user is in a hurry, the analysis unit can shorten the green time of the traffic light to allow quick passage. This enables appropriate traffic light switching timing according to the user's emotions.

[0097] The analysis unit can detect sudden fluctuations in traffic volume during analysis and take immediate action. The analysis unit can detect sudden fluctuations in traffic volume during analysis and take immediate action. The detection of sudden fluctuations is performed, for example, using real-time data analysis technology, but is not limited to such an example. For example, if traffic volume increases suddenly, the analysis unit can extend the green time of the traffic light to smooth traffic flow. Furthermore, if traffic volume decreases suddenly, the analysis unit can shorten the green time of the traffic light to reduce unnecessary waiting time. Furthermore, if traffic volume changes due to a traffic accident, road construction, or the like, the analysis unit can immediately adjust the switching timing of the traffic light. This makes it possible to immediately respond to sudden fluctuations in traffic volume.

[0098] The analysis unit can take into consideration priority passage for specific vehicles (such as emergency vehicles) during analysis. The analysis unit can take into consideration priority passage for specific vehicles (such as emergency vehicles) during analysis. Consideration of priority passage is performed, for example, using vehicle identification technology, but is not limited to such an example. For example, when an emergency vehicle is approaching, the analysis unit can turn the traffic light green to allow priority passage. Furthermore, when a public transportation vehicle is approaching, the analysis unit can extend the green time of the traffic light to support smooth passage. Furthermore, when a specific vehicle (such as a VIP vehicle) is approaching, the analysis unit can adjust the switching timing of the traffic light to allow priority passage. This allows priority passage for the specific vehicle.

[0099] During analysis, the analysis unit can optimize the traffic light switching algorithm by referring to past analysis results. During analysis, the analysis unit optimizes the traffic light switching algorithm by referring to past analysis results. Optimization is performed using, for example, a machine learning algorithm, but is not limited to this example. For example, the analysis unit can optimize the traffic light switching timing for a specific time period or day of the week based on past analysis results. The analysis unit can also analyze past analysis results and adjust the traffic light switching algorithm by taking into account traffic volume fluctuation patterns. The analysis unit can also optimize the traffic light switching timing in a specific area by referring to past analysis results. This makes it possible to optimize the traffic light switching algorithm by referring to past analysis results.

[0100] The analysis unit can estimate the user's emotions and adjust the traffic light switching pattern based on the estimated user's emotions. The analysis unit can estimate the user's emotions and adjust the traffic light switching pattern based on the estimated user's emotions. Emotion estimation is performed, for example, using facial expression recognition technology, but is not limited to such an example. For example, if the user is feeling stressed, the analysis unit can extend the green time of the traffic light to smooth traffic flow. Furthermore, if the user is relaxed, the analysis unit can maintain the normal traffic light switching pattern. Furthermore, if the user is in a hurry, the analysis unit can shorten the green time of the traffic light to allow quick passage. This makes it possible to create an appropriate traffic light switching pattern according to the user's emotions.

[0101] During the analysis, the analysis unit can predict the risk of a traffic accident and adjust the traffic light switching. During the analysis, the analysis unit predicts the risk of a traffic accident and adjusts the traffic light switching. The risk prediction is performed, for example, using a risk assessment algorithm, but is not limited to this example. For example, the analysis unit can shorten the green time of a traffic light at an intersection where the risk of a traffic accident is high, thereby reducing the number of vehicles at the intersection. The analysis unit can also extend the green time of a traffic light during times when the risk of a traffic accident is low, thereby smoothing traffic flow. Furthermore, when the risk of a traffic accident is high, the analysis unit can adjust the traffic light switching timing to prevent accidents from occurring. In this way, accidents can be prevented from occurring by predicting the risk of a traffic accident and adjusting the traffic light switching.

[0102] The analysis unit can switch traffic lights during analysis, taking into account the operation status of public transportation. The analysis unit can switch traffic lights during analysis, taking into account the operation status of public transportation. The operation status can be taken into account, for example, using real-time data, but is not limited to this example. For example, the analysis unit can extend the green time of a traffic light when public transportation such as a bus or train is approaching, thereby supporting smooth passage. The analysis unit can also acquire the operation status of public transportation in real time and adjust the timing of switching traffic lights. The analysis unit can also extend the green time of a traffic light when a delay in public transportation occurs, thereby supporting the elimination of the delay. This makes it possible to switch traffic lights while taking into account the operation status of public transportation.

[0103] During analysis, the analysis unit can adjust the traffic light switching taking into account the business hours of surrounding commercial facilities. During analysis, the analysis unit adjusts the traffic light switching taking into account the business hours of surrounding commercial facilities. Consideration of business hours is performed, for example, using data on the commercial facilities, but is not limited to this example. For example, the analysis unit extends the green time of the traffic light during business hours of the commercial facilities to smooth traffic flow. The analysis unit can also shorten the green time of the traffic light outside of business hours of the commercial facilities to reduce unnecessary waiting time. The analysis unit can also adjust the traffic light switching timing to match the business hours of the commercial facilities and optimize traffic flow. This makes it possible to switch traffic lights taking into account the business hours of the commercial facilities.

[0104] The linking unit can estimate the user's emotions and adjust the display method of the traffic congestion information based on the estimated user's emotions. The linking unit can estimate the user's emotions and adjust the display method of the traffic congestion information based on the estimated user's emotions. Emotion estimation is performed, for example, using facial expression recognition technology, but is not limited to such an example. For example, the linking unit can provide a simple, highly visible display method when the user is feeling stressed. Furthermore, the linking unit can provide a display method including detailed information when the user is relaxed. Furthermore, the linking unit can provide a display method that focuses on the main points when the user is in a hurry. This makes it possible to display appropriate traffic congestion information according to the user's emotions.

[0105] During collaboration, the collaboration unit can propose an optimal route taking into account the remaining fuel level of the vehicle. During collaboration, the collaboration unit proposes an optimal route taking into account the remaining fuel level of the vehicle. Consideration of the remaining fuel level is performed, for example, using a fuel gauge, but is not limited to this example. For example, when the remaining fuel level of the vehicle is low, the collaboration unit proposes a route that passes through the nearest gas station. Furthermore, when the remaining fuel level of the vehicle is sufficient, the collaboration unit can also propose the shortest route. Furthermore, the collaboration unit can monitor the remaining fuel level of the vehicle in real time and propose an appropriate route when refueling is necessary. This makes it possible to propose an appropriate route taking into account the remaining fuel level of the vehicle.

[0106] The coordination unit can adjust the route by taking into account vehicle maintenance information during coordination. The coordination unit adjusts the route by taking into account vehicle maintenance information during coordination. Consideration of maintenance information is performed, for example, using the vehicle's maintenance history, but is not limited to this example. For example, if the vehicle requires maintenance, the coordination unit can propose a route that passes through the nearest repair shop. The coordination unit can also propose a route that avoids long-distance driving based on the vehicle maintenance information. The coordination unit can also monitor vehicle maintenance information in real time and propose an appropriate route as necessary. This makes it possible to adjust the route appropriately by taking into account vehicle maintenance information.

[0107] During collaboration, the collaboration unit can improve the accuracy of route proposals by referring to past collaboration results. During collaboration, the collaboration unit improves the accuracy of route proposals by referring to past collaboration results. The improvement in accuracy is performed, for example, using a machine learning algorithm, but is not limited to such an example. For example, the collaboration unit can propose an optimal route for a specific time period or day of the week based on past collaboration results. The collaboration unit can also analyze past collaboration results and propose a route that suits the user's preferences. The collaboration unit can also propose an optimal route in a specific area by referring to past collaboration results. This makes it possible to propose an appropriate route by referring to past collaboration results.

[0108] The linking unit can estimate the user's emotions and determine the priority of route suggestions based on the estimated user emotions. The linking unit can estimate the user's emotions and determine the priority of route suggestions based on the estimated user emotions. Emotion estimation is performed, for example, using facial expression recognition technology, but is not limited to such an example. For example, if the user is feeling stressed, the linking unit can prioritize suggesting a route that avoids traffic jams. Furthermore, if the user is relaxed, the linking unit can prioritize suggesting a scenic route. Furthermore, if the user is in a hurry, the linking unit can prioritize suggesting the shortest route. This enables appropriate route suggestion prioritization according to the user's emotions.

[0109] During collaboration, the collaboration unit can propose an optimal route taking into account the number of occupants in the vehicle. During collaboration, the collaboration unit proposes an optimal route taking into account the number of occupants in the vehicle. Consideration of the number of occupants is performed, for example, using the number of seats in the vehicle, but is not limited to this example. For example, when the number of occupants in the vehicle is large, the collaboration unit proposes a route that prioritizes wide roads. Furthermore, when the number of occupants in the vehicle is small, the collaboration unit can also propose the shortest route. Furthermore, the collaboration unit can monitor the number of occupants in the vehicle in real time and propose an optimal route according to the number of occupants. This makes it possible to propose an appropriate route taking into account the number of occupants in the vehicle.

[0110] The coordination unit can adjust the route by taking into account the luggage information of the vehicle when coordinating. The coordination unit adjusts the route by taking into account the luggage information of the vehicle when coordinating. Taking into account the luggage information is performed, for example, using a luggage sensor in the vehicle, but is not limited to this example. For example, when the vehicle has a lot of luggage, the coordination unit proposes a route that takes into account the safety of the luggage. Furthermore, when the vehicle has a small amount of luggage, the coordination unit can also propose the shortest route. Furthermore, the coordination unit can monitor the luggage information of the vehicle in real time and propose the optimal route according to the luggage status. This makes it possible to adjust the route appropriately by taking into account the luggage information of the vehicle.

[0111] During collaboration, the collaboration unit can analyze the vehicle's driving history and propose an optimal route. During collaboration, the collaboration unit analyzes the vehicle's driving history and proposes an optimal route. The analysis of the driving history is performed, for example, using data mining technology, but is not limited to this example. For example, the collaboration unit prioritizes and proposes routes that have been used in the past based on the vehicle's driving history. The collaboration unit can also analyze the vehicle's driving history and propose routes that suit the driver's preferences. The collaboration unit can also propose an optimal route for a specific time period or day of the week based on the vehicle's driving history. This makes it possible to propose an appropriate route that takes into account the vehicle's driving history.

[0112] The detection unit can estimate the user's emotion and adjust the speeding warning method based on the estimated user's emotion. The detection unit can estimate the user's emotion and adjust the speeding warning method based on the estimated user's emotion. Emotion estimation is performed, for example, using facial expression recognition technology, but is not limited to this example. For example, the detection unit can notify the user of speeding with a gentle warning sound if the user is feeling stressed. Furthermore, the detection unit can notify the user of speeding with a normal warning sound if the user is relaxed. Furthermore, the detection unit can notify the user of speeding with a stronger warning sound if the user is in a hurry. This enables an appropriate speeding warning method to be provided according to the user's emotion.

[0113] The detection unit can issue a warning by taking into consideration attribute information of the driver of the vehicle upon detection. The detection unit can issue a warning by taking into consideration attribute information of the driver of the vehicle upon detection. Consideration of attribute information is performed, for example, using the driver's age and driving experience, but is not limited to such examples. For example, the detection unit can issue a warning including a detailed explanation for a young driver. Furthermore, the detection unit can issue a highly visible warning for an elderly driver. Furthermore, the detection unit can issue a concise warning for a driver with extensive driving experience. This makes it possible to issue an appropriate warning by taking into consideration the driver's attribute information.

[0114] Upon detection, the detection unit can analyze the driving history of the vehicle and adjust the frequency of the warning. Upon detection, the detection unit can analyze the driving history of the vehicle and adjust the frequency of the warning. The analysis of the driving history is performed, for example, using data mining technology, but is not limited to such an example. For example, the detection unit can frequently issue warnings to drivers who have a history of speeding frequently in the past. Furthermore, the detection unit can also issue warnings only when necessary to drivers who have a history of speeding in the past infrequently. Furthermore, the detection unit can monitor the driving history in real time and issue warnings at an appropriate frequency. This makes it possible to set an appropriate warning frequency taking into account the driving history.

[0115] The detection unit can optimize the warning algorithm by referring to past detection results when detecting. The detection unit optimizes the warning algorithm by referring to past detection results when detecting. The optimization is performed using, for example, a machine learning algorithm, but is not limited to such an example. For example, the detection unit adjusts the warning frequency in a specific time period or area based on past detection results. The detection unit can also analyze past detection results and adjust the warning algorithm according to the driver's attributes. The detection unit can also optimize the warning method for a specific situation by referring to past detection results. This makes it possible to optimize an appropriate warning algorithm by referring to past detection results.

[0116] The detection unit can estimate the user's emotions and adjust the timing of the speeding warning based on the estimated user emotions. The detection unit can estimate the user's emotions and adjust the timing of the speeding warning based on the estimated user emotions. Emotion estimation is performed, for example, using facial expression recognition technology, but is not limited to this example. For example, the detection unit can issue an early warning if the user is feeling stressed. Furthermore, the detection unit can issue a warning at a normal timing if the user is relaxed. Furthermore, the detection unit can issue a delayed warning if the user is in a hurry. This makes it possible to appropriately time the speeding warning according to the user's emotions.

[0117] The detection unit can analyze the cause of the vehicle speeding at the time of detection and propose appropriate countermeasures. The detection unit can analyze the cause of the vehicle speeding at the time of detection and propose appropriate countermeasures. The analysis of the cause is performed using, for example, data mining technology, but is not limited to such an example. For example, the detection unit can propose the shortest route if the cause of the speeding is hurry. The detection unit can also display a warning message if the cause of the speeding is carelessness. The detection unit can also propose a detour route if the cause of the speeding is road conditions. In this way, speeding can be prevented by analyzing the cause of the speeding and proposing appropriate countermeasures.

[0118] The detection unit can issue a warning taking into consideration the health condition of the driver of the vehicle upon detection. The detection unit issues a warning taking into consideration the health condition of the driver of the vehicle upon detection. Consideration of the health condition is performed using, for example, heart rate or blood pressure, but is not limited to such examples. For example, the detection unit issues a warning early if the driver is tired. Furthermore, the detection unit can also issue a warning at a normal timing if the driver is healthy. Furthermore, the detection unit can issue a warning including a detailed explanation if the driver is in poor health. This makes it possible to issue an appropriate warning taking into consideration the driver's health condition.

[0119] The detection unit can adjust the strength of the warning when detecting a violation by taking into account the vehicle driver's past violation history. The detection unit can adjust the strength of the warning when detecting a violation by taking into account the vehicle driver's past violation history. Taking into account the violation history is done, for example, by using past violation data, but is not limited to this example. For example, the detection unit can issue a stronger warning to a driver with many past violation histories. The detection unit can also issue a normal warning to a driver with few past violation histories. The detection unit can also monitor the violation history in real time and issue a warning with an appropriate strength. This makes it possible to set an appropriate warning strength by taking into account the past violation history.

[0120] The preservation unit can estimate the user's emotions and adjust the image storage period based on the estimated user emotions. The preservation unit can estimate the user's emotions and adjust the image storage period based on the estimated user emotions. Emotion estimation is performed, for example, using facial expression recognition technology, but is not limited to such an example. For example, if the user is feeling stressed, the preservation unit can extend the image storage period so that the image can be provided when needed. Furthermore, if the user is relaxed, the preservation unit can maintain the normal storage period. Furthermore, if the user is in a hurry, the preservation unit can set a shorter storage period so that the image can be provided quickly. This makes it possible to store images for an appropriate period according to the user's emotions.

[0121] The preservation unit can determine the storage priority based on the importance of the images when storing them. The preservation unit determines the storage priority based on the importance of the images when storing them. The importance is evaluated using, for example, the content and evidentiary value of the images, but is not limited to such examples. For example, the preservation unit may store images that serve as evidence of a traffic accident as a top priority. The preservation unit may also store images that serve as evidence of a traffic violation as a second priority. The preservation unit may also store images of normal traffic conditions as needed. This enables appropriate storage prioritization based on the importance of the images.

[0122] The preservation unit can apply different storage algorithms depending on the category of the image when storing the images. The preservation unit applies different storage algorithms depending on the category of the image when storing the images. The application of the algorithm is performed, for example, using the type or content of the image, but is not limited to such an example. For example, the preservation unit can apply an algorithm for long-term storage to images of traffic accidents. The preservation unit can also apply an algorithm for medium-term storage to images of traffic violations. The preservation unit can also apply an algorithm for short-term storage to images of normal traffic conditions. This makes it possible to apply an appropriate storage algorithm depending on the category of the image.

[0123] The preservation unit can estimate the user's emotion and adjust the image storage method based on the estimated user's emotion. The preservation unit can estimate the user's emotion and adjust the image storage method based on the estimated user's emotion. Emotion estimation is performed, for example, using facial expression recognition technology, but is not limited to such an example. For example, if the user is feeling stressed, the preservation unit can store images in the cloud so that they can be accessed at any time. Furthermore, if the user is relaxed, the preservation unit can maintain the normal storage method. Furthermore, if the user is in a hurry, the preservation unit can store images locally so that they can be accessed quickly. This enables an appropriate image storage method according to the user's emotion.

[0124] The preservation unit can determine the storage priority based on the time of submission of the images when the images are stored. The preservation unit can determine the storage priority based on the time of submission of the images when the images are stored. The submission time is considered, for example, using the timing of image acquisition, but is not limited to this example. For example, the preservation unit stores the most recently acquired image as the highest priority. The preservation unit can also store images acquired in the past as the next highest priority. The preservation unit can also set an appropriate storage period based on the time of submission. This makes it possible to set an appropriate storage priority based on the time of image submission.

[0125] The preservation unit can adjust the order of storage based on the relevance of images when storing them. The preservation unit adjusts the order of storage based on the relevance of images when storing them. The evaluation of relevance is performed using, for example, the content of the image or related events, but is not limited to such examples. For example, the preservation unit may store images that serve as evidence of a traffic accident as a top priority. The preservation unit may also store images that serve as evidence of a traffic violation as a second priority. The preservation unit may also store images of normal traffic conditions as needed. This enables an appropriate order of storage based on the relevance of images.

[0126] The recommendation unit can estimate the user's emotion and adjust the method of recommending a pause based on the estimated user's emotion. The recommendation unit can estimate the user's emotion and adjust the method of recommending a pause based on the estimated user's emotion. The emotion estimation is performed using, for example, facial expression recognition technology, but is not limited to such an example. For example, if the user is feeling stressed, the recommendation unit can recommend a pause in a calm voice. Also, if the user is relaxed, the recommendation unit can recommend a pause in a normal voice. Also, if the user is in a hurry, the recommendation unit can recommend a pause in a stronger voice. This makes it possible to provide an appropriate method of recommending a pause according to the user's emotion.

[0127] The recommendation unit can adjust the strength of the recommendation based on the scale of the earthquake when making a recommendation. The recommendation unit can adjust the strength of the recommendation based on the scale of the earthquake when making a recommendation. The scale of the earthquake is considered using, for example, seismic intensity or magnitude, but is not limited to such examples. For example, in the case of a small earthquake, the recommendation unit can recommend pausing with a gentle voice. In addition, in the case of a medium-sized earthquake, the recommendation unit can also recommend pausing with a normal voice. In addition, in the case of a large earthquake, the recommendation unit can recommend pausing with a stronger voice. This makes it possible to provide an appropriate strength of recommendation according to the scale of the earthquake.

[0128] The recommendation unit can make a recommendation taking into consideration the status of surrounding buildings and infrastructure when making a recommendation. The recommendation unit makes a recommendation taking into consideration the status of surrounding buildings and infrastructure when making a recommendation. Consideration of the status is performed, for example, using the earthquake resistance of buildings and the status of infrastructure, but is not limited to such examples. For example, if there are many high-rise buildings in the vicinity, the recommendation unit can recommend pausing in a stronger voice. Also, if there are many infrastructure facilities in the vicinity, the recommendation unit can recommend pausing in a normal voice. Also, if there are many residential areas in the vicinity, the recommendation unit can recommend pausing in a gentler voice. This makes it possible to make appropriate recommendations taking into consideration the status of surrounding buildings and infrastructure.

[0129] The recommendation unit can estimate the user's emotion and adjust the timing of the pause recommendation based on the estimated user's emotion. The recommendation unit can estimate the user's emotion and adjust the timing of the pause recommendation based on the estimated user's emotion. The emotion estimation is performed using, for example, facial expression recognition technology, but is not limited to such an example. For example, the recommendation unit can recommend pausing earlier if the user is feeling stressed. Furthermore, the recommendation unit can also recommend pausing at a normal timing if the user is relaxed. Furthermore, the recommendation unit can also recommend pausing later if the user is in a hurry. This makes it possible to provide an appropriate pause recommendation timing according to the user's emotion.

[0130] The recommendation unit can determine the priority of the recommendation by taking into consideration the surrounding traffic conditions when making a recommendation. The recommendation unit can determine the priority of the recommendation by taking into consideration the surrounding traffic conditions when making a recommendation. Consideration of the traffic conditions is performed, for example, using traffic volume and congestion information, but is not limited to such examples. For example, the recommendation unit can recommend an early stop when there is a lot of traffic in the surrounding area. Furthermore, the recommendation unit can also recommend a stop at a normal timing when there is little traffic in the surrounding area. Furthermore, the recommendation unit can monitor the surrounding traffic conditions in real time and recommend a stop at an appropriate timing. This makes it possible to appropriately prioritize the recommendation by taking into consideration the surrounding traffic conditions.

[0131] The recommendation unit can improve the accuracy of the recommendation by referring to past earthquake data when making a recommendation. The recommendation unit can improve the accuracy of the recommendation by referring to past earthquake data when making a recommendation. The improvement in accuracy is performed, for example, using data mining technology, but is not limited to such an example. For example, the recommendation unit can optimize the timing of a temporary stop recommendation in a specific area based on past earthquake data. The recommendation unit can also analyze past earthquake data and adjust the recommendation method according to the magnitude of the earthquake. The recommendation unit can also improve the accuracy of the temporary stop recommendation in specific situations by referring to past earthquake data. This enables appropriate accuracy of recommendations by referring to past earthquake data.

[0132] The guidance unit can estimate the user's emotions and adjust the method of providing guidance about replenishment points based on the estimated user's emotions. The guidance unit can estimate the user's emotions and adjust the method of providing guidance about replenishment points based on the estimated user's emotions. Estimation of emotions is performed, for example, using facial expression recognition technology, but is not limited to this example. For example, if the user is feeling stressed, the guidance unit can provide guidance about replenishment points in a calm voice. Furthermore, if the user is relaxed, the guidance unit can provide guidance about replenishment points in a normal voice. Furthermore, if the user is in a hurry, the guidance unit can provide guidance about replenishment points in a stronger voice. This enables a method of providing guidance about replenishment points appropriate to the user's emotions.

[0133] The guidance unit can suggest an optimal refueling point based on the remaining energy amount of the vehicle when providing guidance. The guidance unit can suggest an optimal refueling point based on the remaining energy amount of the vehicle when providing guidance. The remaining energy amount can be taken into consideration using, for example, a battery remaining amount sensor, but is not limited to this example. For example, the guidance unit can suggest the nearest charging station when the remaining energy amount of the vehicle is low. Furthermore, the guidance unit can also suggest the shortest route when the remaining energy amount of the vehicle is sufficient. Furthermore, the guidance unit can monitor the remaining energy amount of the vehicle in real time and suggest an appropriate refueling point. This makes it possible to suggest an appropriate refueling point based on the remaining energy amount of the vehicle.

[0134] The guidance unit can analyze the driving history of the vehicle and suggest replenishment points when providing guidance. The guidance unit can analyze the driving history of the vehicle and suggest replenishment points when providing guidance. The analysis of the driving history is performed, for example, using data mining technology, but is not limited to this example. For example, the guidance unit can prioritize and suggest replenishment points that have been used in the past based on the driving history of the vehicle. The guidance unit can also analyze the driving history of the vehicle and suggest replenishment points that suit the driver's preferences. The guidance unit can also suggest optimal replenishment points for specific time periods or days of the week based on the driving history of the vehicle. This makes it possible to suggest appropriate replenishment points taking into account the driving history of the vehicle.

[0135] The guidance unit can estimate the user's emotions and adjust the timing of the replenishment point notification based on the estimated user's emotions. The guidance unit can estimate the user's emotions and adjust the timing of the replenishment point notification based on the estimated user's emotions. Emotion estimation is performed, for example, using facial expression recognition technology, but is not limited to this example. For example, the guidance unit can introduce the replenishment point earlier if the user is feeling stressed. Furthermore, the guidance unit can introduce the replenishment point at a normal timing if the user is relaxed. Furthermore, the guidance unit can introduce the replenishment point later if the user is in a hurry. This makes it possible to appropriately time the replenishment point notification according to the user's emotions.

[0136] The guidance unit can suggest replenishment points by taking into consideration attribute information of the driver of the vehicle when providing guidance. The guidance unit can suggest replenishment points by taking into consideration attribute information of the driver of the vehicle when providing guidance. Consideration of attribute information is performed, for example, using the driver's age and driving experience, but is not limited to such examples. For example, the guidance unit can suggest replenishment points that include detailed explanations for a young driver. Furthermore, the guidance unit can suggest replenishment points that are highly visible for an elderly driver. Furthermore, the guidance unit can suggest simple replenishment points for a driver with extensive driving experience. This makes it possible to suggest appropriate replenishment points by taking into consideration the driver's attribute information.

[0137] When providing guidance, the guidance unit can suggest the optimal replenishment point by referring to the vehicle driver's past replenishment history. ... Consideration of the replenishment history is performed, for example, using past replenishment data, but is not limited to this example. For example, the guidance unit prioritizes suggesting replenishment points that have been used in the past. The guidance unit can also analyze the past replenishment history and suggest replenishment points that suit the driver's preferences. The guidance unit can also suggest the optimal replenishment point for a specific time period or day of the week by referring to the past replenishment history. This makes it possible to suggest appropriate replenishment points by taking the past replenishment history into consideration.

[0138] The moving unit can estimate the user's emotion and adjust the movement method of the roof portion based on the estimated user's emotion. The moving unit can estimate the user's emotion and adjust the movement method of the roof portion based on the estimated user's emotion. Emotion estimation is performed, for example, using facial expression recognition technology, but is not limited to such an example. For example, the moving unit can move the roof portion with gentle movements when the user is feeling stressed. Furthermore, the moving unit can move the roof portion with normal movements when the user is relaxed. Furthermore, the moving unit can move the roof portion with quick movements when the user is in a hurry. This makes it possible to move the roof portion in an appropriate manner according to the user's emotion.

[0139] When in motion, the movable unit can adjust the movable range of the roof portion based on the intensity of sunlight. When in motion, the movable unit adjusts the movable range of the roof portion based on the intensity of sunlight. Consideration of the intensity of sunlight is performed, for example, using a light sensor, but is not limited to such an example. For example, when sunlight is strong, the movable unit can move the roof portion more widely to block the sunlight. Furthermore, when sunlight is weak, the movable unit can move the roof portion less widely to ensure visibility. Furthermore, the movable unit can monitor the intensity of sunlight in real time and set an appropriate movable range. This enables the movable range of the roof portion to be appropriate according to the intensity of sunlight.

[0140] When moving, the movable unit can move the roof portion while taking into account vehicle position information. When moving, the movable unit moves the roof portion while taking into account vehicle position information. Consideration of the position information is performed, for example, using GPS data, but is not limited to this example. For example, when the vehicle is in an area with strong sunlight, the movable unit can move the roof portion more widely to block the sunlight. Furthermore, when the vehicle is in an area with weak sunlight, the movable unit can move the roof portion less widely to ensure visibility. Furthermore, the movable unit can monitor vehicle position information in real time and set an appropriate range of movement. This enables the roof portion to be moved appropriately while taking into account the vehicle position information.

[0141] The movable unit can estimate the user's emotion and adjust the timing of the roof portion movement based on the estimated user's emotion. The movable unit can estimate the user's emotion and adjust the timing of the roof portion movement based on the estimated user's emotion. Emotion estimation is performed, for example, using facial expression recognition technology, but is not limited to such an example. For example, the movable unit can move the roof portion earlier if the user is feeling stressed. Furthermore, the movable unit can move the roof portion at a normal timing if the user is relaxed. Furthermore, the movable unit can move the roof portion later if the user is in a hurry. This makes it possible to move the roof portion at an appropriate timing according to the user's emotion.

[0142] When moving, the movable unit can move the roof portion while taking into account the status of surrounding buildings and obstacles. When moving, the movable unit can move the roof portion while taking into account the status of surrounding buildings and obstacles. Consideration of the status can be performed, for example, using the height of the building and the position of the obstacle, but is not limited to such examples. For example, when there are many high-rise buildings in the vicinity, the movable unit can move the roof portion widely to block the sunlight. Furthermore, when there are many obstacles in the vicinity, the movable unit can move the roof portion narrowly to ensure visibility. Furthermore, the movable unit can monitor the status of surrounding buildings and obstacles in real time and set an appropriate range of movement. This enables the roof portion to be moved appropriately while taking into account the status of surrounding buildings and obstacles.

[0143] When moving, the movable unit can optimize the movement of the roof portion by referring to past sunlight data. When moving, the movable unit optimizes the movement of the roof portion by referring to past sunlight data. Optimization is performed using, for example, data mining technology, but is not limited to such an example. For example, the movable unit can optimize the movement range of the roof portion for a specific time period or area based on past sunlight data. The movable unit can also analyze past sunlight data and set the movement range according to the intensity of sunlight. The movable unit can also optimize the movement method of the roof portion for a specific situation by referring to past sunlight data. This makes it possible to move the roof portion appropriately by referring to past sunlight data. === Hard Collateral 1-1 === Each element of the traffic efficiency improvement system described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires traffic volume information using the camera 42 and speed sensor of the smart device 14, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The link unit performs telemetry with the vehicle's SIM via the communication I / F 44 of the smart device 14 and displays congestion information on the car navigation system. The detection unit detects speeding using the speed sensor of the smart device 14 and issues a warning. The maintenance unit stores images acquired by the camera 42 of the smart device 14 in the storage 32 of the data processing device 12. === Hard Collateral 1-2 === Each element of the traffic efficiency improvement system described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires traffic volume information using the camera 42 and speed sensor of the smart glasses 214, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The link unit performs telemetry with the vehicle's SIM via the communication I / F 44 of the smart glasses 214 and displays congestion information on the car navigation system. The detection unit detects speeding using the speed sensor of the smart glasses 214 and issues a warning. The maintenance unit stores images acquired by the camera 42 of the smart glasses 214 in the storage 32 of the data processing device 12. === Hard Collateral 1-3 === Each element of the traffic efficiency improvement system described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the acquisition unit acquires traffic volume information using the camera 42 and speed sensor of the headset terminal 314, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The linking unit performs telemetry with the vehicle's SIM via the communication I / F 44 of the headset terminal 314 and displays congestion information on the car navigation system. The detection unit detects speeding using the speed sensor of the headset terminal 314 and issues a warning. The maintenance unit stores images acquired by the camera 42 of the headset terminal 314 in the storage 32 of the data processing device 12. === Hard Collateral 1-4 === Each element of the traffic efficiency improvement system described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires traffic volume information using the camera 42 and speed sensor of the robot 414, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The collaboration unit performs telemetry with the vehicle's SIM via the communication I / F 44 of the robot 414 and displays congestion information on the car navigation system. The detection unit detects speeding using the speed sensor of the robot 414 and issues a warning. The maintenance unit stores images acquired by the camera 42 of the robot 414 in the storage 32 of the data processing device 12.

[0144] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0145] When acquiring traffic volume information, the acquisition unit can perform filtering taking into account the influence of specific events, holidays, and the like. Filtering can be performed, for example, using data filtering technology, but is not limited to such examples. For example, the acquisition unit can prioritize acquiring traffic volume information for areas where specific events are held and provide information taking into account the influence of the events. The acquisition unit can also acquire traffic volume information for holidays and consecutive weekends and provide information taking into account patterns that differ from those on normal weekdays. The acquisition unit can also focus on acquiring traffic volume information for surrounding areas when large-scale sporting events, concerts, and the like are held, and provide information taking into account the influence of the events. This makes it possible to provide information taking into account the influence of specific events, holidays, and the like.

[0146] The analysis unit can detect sudden fluctuations in traffic volume during analysis and take immediate action. Detection of sudden fluctuations is performed, for example, using real-time data analysis technology, but is not limited to such an example. For example, if traffic volume increases suddenly, the analysis unit extends the green time of the traffic light to smooth traffic flow. Furthermore, if traffic volume decreases suddenly, the analysis unit can shorten the green time of the traffic light to reduce unnecessary waiting time. Furthermore, if traffic volume changes due to a traffic accident, road construction, or the like, the analysis unit can immediately adjust the timing of switching the traffic light. This makes it possible to immediately respond to sudden fluctuations in traffic volume.

[0147] During collaboration, the collaboration unit can propose an optimal route taking into account the vehicle's remaining fuel. Consideration of the remaining fuel is performed, for example, using a fuel gauge, but is not limited to this example. For example, when the vehicle's remaining fuel is low, the collaboration unit proposes a route that passes through the nearest gas station. Furthermore, when the vehicle's remaining fuel is sufficient, the collaboration unit can also propose the shortest route. Furthermore, the collaboration unit can monitor the vehicle's remaining fuel in real time and propose an appropriate route when refueling is necessary. This makes it possible to propose an appropriate route taking into account the vehicle's remaining fuel.

[0148] Upon detection, the detection unit can issue a warning by taking into consideration attribute information of the driver of the vehicle. Consideration of the attribute information is performed using, for example, the driver's age and driving experience, but is not limited to such examples. For example, the detection unit can issue a warning including a detailed explanation for a young driver. Furthermore, the detection unit can issue a highly visible warning for an elderly driver. Furthermore, the detection unit can issue a concise warning for a driver with extensive driving experience. This makes it possible to issue an appropriate warning by taking into consideration the driver's attribute information.

[0149] The preservation unit can determine the priority of storage based on the importance of the images when storing them. The evaluation of the importance is performed, for example, using the content and evidentiary value of the images, but is not limited to such examples. For example, the preservation unit may store images that serve as evidence of a traffic accident as a first priority. The preservation unit may also store images that serve as evidence of a traffic violation as a second priority. The preservation unit may also store images of normal traffic conditions as needed. This enables appropriate prioritization of storage based on the importance of the images.

[0150] The acquisition unit can estimate the user's emotions using a machine learning algorithm and adjust the timing of acquiring traffic volume information based on the estimated user's emotions. The acquisition unit estimates the user's emotions using a machine learning algorithm. The emotion estimation is performed, for example, using facial expression recognition technology, but is not limited to such an example. For example, when the user is feeling stressed, the acquisition unit can increase the frequency of acquiring traffic volume information and provide the information in real time. Furthermore, when the user is relaxed, the acquisition unit can reduce the frequency of acquiring traffic volume information and provide only the necessary information. Furthermore, when the user is in a hurry, the acquisition unit can quickly acquire traffic volume information and immediately suggest the optimal route. This makes it possible to acquire traffic volume information at an appropriate timing according to the user's emotions.

[0151] The analysis unit can estimate the user's emotions and adjust the traffic light switching timing based on the estimated user's emotions. The analysis unit can estimate the user's emotions and adjust the traffic light switching timing based on the estimated user's emotions. Emotion estimation is performed, for example, using facial expression recognition technology, but is not limited to such an example. For example, if the user is feeling stressed, the analysis unit can extend the green time of the traffic light to smooth traffic flow. Furthermore, if the user is relaxed, the analysis unit can maintain the normal traffic light switching timing. Furthermore, if the user is in a hurry, the analysis unit can shorten the green time of the traffic light to allow quick passage. This enables appropriate traffic light switching timing according to the user's emotions.

[0152] The linking unit can estimate the user's emotions and adjust the display method of the traffic congestion information based on the estimated user's emotions. The linking unit can estimate the user's emotions and adjust the display method of the traffic congestion information based on the estimated user's emotions. Emotion estimation is performed, for example, using facial expression recognition technology, but is not limited to such an example. For example, the linking unit can provide a simple, highly visible display method when the user is feeling stressed. Furthermore, the linking unit can provide a display method including detailed information when the user is relaxed. Furthermore, the linking unit can provide a display method that focuses on the main points when the user is in a hurry. This makes it possible to display appropriate traffic congestion information according to the user's emotions.

[0153] The detection unit can estimate the user's emotion and adjust the speeding warning method based on the estimated user's emotion. The detection unit can estimate the user's emotion and adjust the speeding warning method based on the estimated user's emotion. Emotion estimation is performed, for example, using facial expression recognition technology, but is not limited to this example. For example, the detection unit can notify the user of speeding with a gentle warning sound if the user is feeling stressed. Furthermore, the detection unit can notify the user of speeding with a normal warning sound if the user is relaxed. Furthermore, the detection unit can notify the user of speeding with a stronger warning sound if the user is in a hurry. This enables an appropriate speeding warning method to be provided according to the user's emotion.

[0154] The moving unit can estimate the user's emotion and adjust the movement method of the roof portion based on the estimated user's emotion. The moving unit can estimate the user's emotion and adjust the movement method of the roof portion based on the estimated user's emotion. Emotion estimation is performed, for example, using facial expression recognition technology, but is not limited to such an example. For example, the moving unit can move the roof portion with gentle movements when the user is feeling stressed. Furthermore, the moving unit can move the roof portion with normal movements when the user is relaxed. Furthermore, the moving unit can move the roof portion with quick movements when the user is in a hurry. This makes it possible to move the roof portion in an appropriate manner according to the user's emotion.

[0155] The processing flow of the second embodiment will be briefly explained below.

[0156] Step 1: The acquisition unit acquires traffic volume information. The traffic volume information includes the number of vehicles, their speeds, vehicle types, etc. The acquisition unit counts the number of vehicles using a camera, measures the vehicle speeds using a speed sensor, and identifies the vehicle types using vehicle type identification technology. Step 2: The analysis unit analyzes the information acquired by the acquisition unit and switches the traffic lights. The analysis is performed using an algorithm, which extends the green time of the signal during times of heavy traffic and shortens the green time during times of light traffic. It also takes into account priority for certain vehicles (such as emergency vehicles). Step 3: The linking unit performs telemetry with the SIM card installed in the vehicle and displays traffic congestion information on the car navigation system. Telemetry is performed using a communication protocol, and traffic congestion information is acquired in real time and displayed on the car navigation system. It can also suggest detour routes and the optimal route taking into account the vehicle's remaining fuel. Step 4: The detection unit detects speeding and issues a warning. Speeding is detected using a speed sensor, and a warning is displayed if the speed limit is exceeded. Furthermore, the system can analyze the cause of speeding, suggest appropriate countermeasures, and issue a warning taking into account the driver's attribute information.

[0157] 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.

[0158] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0159] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0162] 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.

[0163] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0164] 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.

[0165] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0167] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0168] 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.

[0169] 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.

[0170] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0171] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0173] 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.

[0174] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0175] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0178] 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.

[0179] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0180] 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.

[0181] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0182] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0183] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0184] 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.

[0185] 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.

[0186] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0187] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0188] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0189] 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.

[0190] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0191] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0192] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0193] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0194] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0195] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0196] 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.

[0197] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0198] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0199] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0200] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0201] 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.

[0202] 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.

[0203] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0204] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0205] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0206] 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.

[0207] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0208] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0209] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0210] 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.

[0211] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0212] 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.

[0213] 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).

[0214] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0215] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0216] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0217] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0218] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0219] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0220] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0221] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0222] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0223] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0224] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0225] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0226] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0227] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0228] [Explanation of symbols]

[0229] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit for acquiring traffic volume information; an analysis unit that analyzes the information acquired by the acquisition unit and switches traffic lights; A linking unit that performs telemetry with the SIM installed in the car and displays traffic congestion information on the car navigation system. A detection unit that detects excessive speed and issues a warning. A system characterized by:

2. The system is equipped with a preservation unit that preserves evidence of traffic accidents by storing images captured by the camera for seven days.

2. The system of claim 1.

3. Equipped with an advisory unit that advises nearby vehicles to stop when an earthquake is detected 2. The system of claim 1.

4. Equipped with a guidance section that provides information on replenishment points based on the remaining energy of EVs and hydrogen-engine vehicles 2. The system of claim 1.

5. The roof is equipped with a movable section that detects and analyzes the direction of sunlight and automatically moves to make traffic signals more visible from the vehicle.

2. The system of claim 1.

6. The acquisition unit The system uses a machine learning algorithm to estimate user emotions and adjusts the timing of traffic information acquisition based on the estimated user emotions.

2. The system of claim 1.

7. The acquisition unit Analyze past traffic volume data and select the optimal acquisition method 2. The system of claim 1.

8. The acquisition unit When obtaining traffic information, filtering is performed taking into account the influence of specific events and holidays.

2. The system of claim 1.

9. The acquisition unit When obtaining traffic information, select the means of obtaining it taking into account weather information.

2. The system of claim 1.

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