system

Abstract

We provide the system. [Solution] A data collection means for acquiring vehicle status information and location information, An advice generation means analyzes the information acquired by the data collection means and generates driving advice for the driver, A presentation means for providing driving advice generated by the advice generation means to the driver, A hazard prediction method that predicts potential hazards and generates warnings based on traffic conditions and weather information ahead, A warning presentation means that presents a warning generated by the aforementioned hazard prediction means to the driver, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, the occurrence of traffic accidents and the increase in greenhouse gas emissions have become serious problems. Especially for novice and elderly drivers, these risks are high, and effective support means for realizing safe driving are required. Also, in order to reduce the environmental burden, efforts to improve fuel efficiency are necessary. Therefore, there is a need to develop a technology that can support drivers to take appropriate driving actions and achieve both the improvement of traffic safety and environmental protection.

Means for Solving the Problems

[0005] This invention comprises means for acquiring vehicle status information and location information, means for analyzing the acquired information to generate driving advice, and means for providing the advice to the driver. Furthermore, it is a system that supports safe driving by predicting potential hazards based on the traffic conditions and weather information ahead, and generating and presenting warnings. In addition, by providing means for calculating and providing a driving score at the end of driving, and eco-driving support means for providing advice for improving fuel efficiency, it achieves a reduction in traffic accidents and a reduction in environmental impact.

[0006] "Vehicle status information" refers to data that indicates the current operation and behavior of the vehicle, such as vehicle speed, acceleration, steering status, and brake usage.

[0007] "Location information" refers to data obtained using GPS or similar methods that indicates the geographical location and movement trajectory of a vehicle.

[0008] "Data collection means" refers to a mechanism that uses sensors, GPS modules, etc., to acquire vehicle status information and location information.

[0009] "Advice generation means" refers to a mechanism that analyzes acquired vehicle status and location information and generates appropriate driving advice for the driver.

[0010] "Presentation means" refers to a mechanism for providing the generated driving advice to the driver visually or audibly.

[0011] A "hazard prediction system" refers to a mechanism that predicts potential hazards based on traffic conditions and weather information ahead, and generates warnings.

[0012] A "warning presentation means" refers to a mechanism that provides the driver with a generated warning and prompts them to pay attention.

[0013] A "driving score" refers to a score generated to quantitatively evaluate a driver's driving behavior.

[0014] The "score calculation means" refers to a mechanism for calculating a driving score based on driving information and providing it to the driver.

[0015] The "eco-drive support means" refers to a mechanism for generating and providing specific driving advice to the driver in order to improve fuel efficiency and reduce environmental load.

Brief Description of the Drawings

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

Mode for Carrying Out the Invention

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

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

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

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

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

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

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

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

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

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0037] This invention is a system for achieving traffic safety and environmental protection. This system has the function of acquiring vehicle status and location information, and a server analyzes this information to provide appropriate advice to the driver. Specifically, this system collects data from sensors mounted on the vehicle in real time and generates driving advice and warnings based on that data.

[0038] The server acquires vehicle status information such as speed, acceleration, and brake usage, and also uses GPS to determine the vehicle's location. This allows the server to evaluate the driver's current driving situation in detail. Based on the evaluation results, the server generates advice to encourage the driver to take appropriate driving actions, such as adjusting speed or checking the distance between vehicles. This advice is presented to the driver visually or audibly through a terminal.

[0039] The server also takes into account traffic conditions and weather information ahead to predict potential hazards. For example, if there is a possibility of sudden braking on the road ahead, the server will immediately issue a warning to the driver. This can prevent potential accidents.

[0040] Upon completion of the drive, the server analyzes the driving information in detail and generates a driving score. This score is an indicator of how safely and efficiently the driver drove and is provided to the driver via the terminal. Furthermore, the server includes an eco-driving support function that can suggest specific advice to the driver on how to improve fuel efficiency.

[0041] Specific example

[0042] For example, when a driver is driving on a highway and approaches a sharp curve, the server analyzes the curve's location and the vehicle's speed. If it detects a possible speeding violation, it will issue a voice command from the terminal saying, "Slow down in preparation for the next curve." Also, if driving in heavy rain, the server will predict slippery road conditions based on weather information and display a warning saying, "Maintain a safe distance from other vehicles and drive safely."

[0043] Overall, this system aims to support safe driving by providing drivers with useful information in real time, thereby reducing traffic accidents and environmental impact.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server acquires real-time status information from sensors mounted on the vehicle. This information includes speed, acceleration, brake usage, and steering angle. It also acquires the vehicle's location information using GPS.

[0047] Step 2:

[0048] The server preprocesses the acquired raw data. This includes noise reduction, data format conversion, and handling of missing values. This preprocessing prepares the data in a state suitable for analysis.

[0049] Step 3:

[0050] The server inputs pre-processed data into an AI model and analyzes the driving situation. Here, the driver's behavior is evaluated, taking into account driving behavior, road conditions, and the influence of other traffic participants.

[0051] Step 4:

[0052] Based on the analysis results, the server generates driving advice to provide to the driver. This advice may include instructions such as when speed adjustments are needed or when a safe following distance should be maintained.

[0053] Step 5:

[0054] The terminal provides the user with driving advice transmitted from the server, either verbally or visually. This allows the user to take appropriate driving actions through real-time navigation.

[0055] Step 6:

[0056] The server predicts potential hazards based on traffic conditions and weather information ahead. For example, this includes predicting changes in weather and traffic congestion.

[0057] Step 7:

[0058] If a potential threat is detected, the server will immediately generate a warning and present it to the user via the terminal. The warning will be delivered in visual or audio format.

[0059] Step 8:

[0060] Once the operation is complete, the server calculates an operation score based on the data from the entire operation session. This score is an indicator used to evaluate the safety and efficiency of the operation.

[0061] Step 9:

[0062] The device provides the user with a calculated driving score and feedback, including strengths to highlight and areas for improvement.

[0063] Step 10:

[0064] The server generates and provides to users via terminals advice on reducing environmental impact, such as driving methods to improve fuel efficiency.

[0065] (Example 1)

[0066] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0067] Balancing safety and environmental protection is crucial in automobile driving. However, drivers often lack access to timely and appropriate information, leading to inappropriate driving behavior. Therefore, there is a need for effective and immediate support systems to prevent traffic accidents and improve fuel efficiency.

[0068] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0069] In this invention, the server includes an information gathering means for acquiring vehicle status information and location information, an instruction generation means for analyzing the information acquired by the information gathering means and generating driving instructions for the driver, and an information presentation means for providing the driving instructions generated by the instruction generation means to the driver. As a result, the driver can receive information in real time to support safe and efficient driving, thereby preventing traffic accidents and reducing environmental impact.

[0070] "Information gathering means" refers to functions that use various sensors and GPS installed in the vehicle to acquire vehicle status information and location information in real time.

[0071] The "instruction generation means" is a function that analyzes data acquired by the information gathering means and generates specific driving instructions and advice for the driver.

[0072] "Information presentation means" refers to a device or method for providing generated driving instructions or warnings to the driver visually or audibly.

[0073] A "predictive warning system" is a function that analyzes traffic conditions and weather information ahead to predict potential dangers in advance and generate warnings.

[0074] "Warning display means" refers to a device or method for presenting a warning generated by a predictive warning means to the driver.

[0075] The "evaluation calculation means" is a function that calculates a driving evaluation based on driving data acquired after the end of the drive and provides that evaluation as feedback to the driver.

[0076] "Environmental protection support measures" refer to functions that generate and provide specific instructions for reducing the environmental impact of driver actions and improving fuel efficiency.

[0077] This invention is a support system for achieving both safety and environmental protection in vehicle operation. The server uses various sensors and GPS devices mounted on the vehicle to acquire status information such as vehicle speed, acceleration, and brake usage, as well as location information, in real time. This information is acquired by information collection means.

[0078] The server analyzes the collected data and generates specific driving instructions for the driver based on the results. The generation AI model used here employs deep learning technology to detect abnormal driving patterns and potential hazards. The driving instructions generated after the analysis are provided by an instruction generation system. For example, the AI ​​generates instructions using prompts such as, "Do you need to adjust your speed for the next curve?"

[0079] The terminal displays driving instructions generated by the server to the driver. This display is delivered visually or audibly using speech synthesis technology and a display. By following the instructions provided through the terminal, the driver can reduce the risk of traffic accidents and minimize environmental impact. A specific example is when driving on a highway, the terminal issues a voice instruction such as, "Slow down in preparation for the next curve."

[0080] Once the drive is complete, the server calculates a driving evaluation based on the data acquired during the drive and provides feedback to the driver. This allows the driver to objectively evaluate the safety and efficiency of their driving behavior. Furthermore, driving improvement instructions for eco-driving are provided, promoting improved fuel efficiency. In this way, combining the server and terminal makes it possible to provide drivers with real-time support for safe driving and environmental protection.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The server acquires real-time speed, acceleration, brake usage, and location information from sensors and GPS installed in the vehicle. This data is aggregated on the server by various data collection methods. The server records this data chronologically to form an overall picture of the driving conditions.

[0084] Step 2:

[0085] The server analyzes the collected data. Specifically, it uses a generative AI model to process and calculate data to detect abnormal driving patterns and potential hazards. By analyzing the driving data as input, if abnormalities such as speeding or sudden acceleration are detected, the server incorporates this information into the generation of driving instructions.

[0086] Step 3:

[0087] The server generates appropriate driving instructions for the driver based on the analysis results. The generating AI model outputs specific instructions based on prompts such as "Do you need to adjust your speed for the next curve?". This provides the driver with advice on speed adjustments and maintaining a safe distance from other vehicles.

[0088] Step 4:

[0089] The terminal displays driving instructions sent from the server to the driver. Specifically, the terminal uses speech synthesis technology to play instructions such as "Slow down in preparation for the next curve" aloud. It also visually alerts the driver by displaying warning messages on the screen.

[0090] Step 5:

[0091] After the drive is complete, the server calculates a driving evaluation based on the data collected during the drive. It analyzes the driving data as input and generates an output that includes a driving evaluation along with fuel efficiency and safety evaluations. This evaluation is then fed back to the driver via a terminal to help improve future driving.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] With the proliferation of autonomous vehicles, the increasingly complex traffic environment necessitates simultaneous improvements in both safety and driving efficiency. Conventional technologies struggle to provide immediate driving advice and warnings based on real-time driving data, and have not adequately improved the accuracy of predicting potential hazards or fuel efficiency. To address these challenges, there is a need for systems that enable safer and more environmentally conscious driving in autonomous vehicles.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes measuring means for acquiring vehicle status information and location information; command generation means for analyzing the information acquired by the measuring means and autonomously generating driving advice; notification means for providing the driving advice generated by the command generation means to an automatic control device; risk prediction means for predicting potential hazards and generating warnings based on the traffic environment and weather data ahead; and warning notification means for providing the warning generated by the risk prediction means to an automatic control device. This enables the autonomous vehicle to drive safely and efficiently in real time.

[0097] A "measuring means" is a device that uses sensors installed on the vehicle to acquire state information such as the vehicle's speed, acceleration, and position.

[0098] A "command generation means" is a device that analyzes data acquired by a measurement means and autonomously generates driving advice necessary to improve driving safety and efficiency.

[0099] A "notification means" is a device that receives driving advice generated by a command generation means and provides it to an automatic control device.

[0100] A "risk prediction device" is a device that analyzes traffic conditions and weather data ahead to predict potential dangers and generate warnings.

[0101] A "warning notification means" is a device that provides warnings generated by a risk prediction means to an automatic control device and notifies the driver visually or audibly.

[0102] This invention is a system for achieving safe and efficient driving in autonomous vehicles. The system is implemented with the following configuration.

[0103] The server uses multiple sensors installed in the vehicle, such as speed sensors, acceleration sensors, and GPS modules, to measure the vehicle's status and location in real time. This data is collected by the measurement devices and continuously monitored and recorded by the server.

[0104] On the server side, a command generation mechanism is used to analyze the acquired data. This mechanism utilizes machine learning algorithms, particularly libraries such as TENSORFLOW®, to analyze and predict operating conditions. Based on the results of the data analysis, driving advice is generated.

[0105] The server provides the generated driving advice to the vehicle's automatic control system via a notification device. The notification device can visually display the driving advice on the vehicle's display or inform the driver via voice.

[0106] Furthermore, the server is equipped with a risk prediction system. This system collects data from weather information services and traffic information databases to predict potential hazards. The resulting warnings are provided to the automatic control system by an alert notification system, and the driver is alerted when necessary.

[0107] For example, if a vehicle ahead suddenly slows down in rainy weather, the server immediately detects the situation. The risk prediction system takes into account the slipperiness of the road surface and generates a warning such as "Increase your following distance and prepare to brake," which is then communicated to the driver through a notification system. Throughout this entire process, the vehicle can maintain driving efficiency while enhancing safety.

[0108] An example of a prompt would be, "What real-time advice can an autonomous vehicle provide to safely navigate sharp curves in rainy weather?" Using such prompts allows generative AI models to effectively create driving advice.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] To obtain vehicle status and location information, a group of sensors installed on the vehicle measures speed, acceleration, GPS data, and other parameters. This data is transmitted to a server in real time. At this stage, the raw data from the sensors is used as input, which is then converted into a structured data format and stored in a database.

[0112] Step 2:

[0113] The server uses machine learning algorithms to analyze the acquired data. It analyzes the input data using the TensorFlow library and evaluates the driving situation. As a result of the analysis, it outputs driving advice to improve driving safety and efficiency. Through this analysis process, the server generates the necessary commands for the driver.

[0114] Step 3:

[0115] The generated driving advice is sent from the server to the vehicle's terminal. The terminal uses the notification means to provide advice to the driver through visual displays or audio guidance. The input at this stage is the generated advice data, and the output is visual or auditory instructions to the driver.

[0116] Step 4:

[0117] The server obtains traffic conditions and weather information from an external database and predicts potential hazards using risk prediction tools. The input is the obtained environmental data, and calculations are performed based on this data to determine the presence or absence of potential hazards. The output is generated as a warning message.

[0118] Step 5:

[0119] Warnings generated by the risk prediction system are sent from the server to the terminal and provided to the driver via the warning notification system. The input is warning data, and the output is an alert that draws the driver's attention. The terminal processes these warnings instantly and communicates them to the driver visually or audibly when necessary.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] This invention is an in-vehicle system aimed at supporting safe driving by the driver, and further incorporates an emotion engine that recognizes the user's emotions in real time and enables interaction in accordance with those emotions. In addition to basic functions such as collecting and analyzing vehicle status and location information and generating and presenting driving advice, this system has advanced functions that take the user's emotions into consideration.

[0122] The server continuously acquires vehicle status and location information from sensors and GPS, and analyzes it. Based on the analysis results, it generates necessary driving advice and warnings for the driver and provides them to the driver via a terminal. The content of the warnings and advice comprehensively takes into account road conditions and weather changes.

[0123] The emotion engine uses in-car cameras and microphones to monitor the user's facial expressions and voice, and analyzes their emotions in real time. The emotional information obtained from this analysis is taken into consideration when generating driving advice. For example, if the user is feeling stressed, it can present advice in a tone that promotes relaxation.

[0124] Furthermore, by analyzing accumulated emotional data, it becomes possible to understand drivers' long-term behavioral patterns and reactions, and provide more personalized advice. In this way, interactions optimized for each individual driver are realized.

[0125] Specific example

[0126] For example, if a driver gets caught in traffic during their commute, the server recognizes the traffic information and provides normal route advice. On the other hand, if the emotion engine detects frustration or anxiety from the driver's facial expressions and voice, the device will provide advice in a relaxed tone, such as, "Relax, take a deep breath. Safe driving is the most important thing."

[0127] In this way, by utilizing the emotion engine of this system, flexible responses tailored to the driver's psychological state become possible, contributing to improved traffic safety. Furthermore, since advice for improving fuel efficiency is presented in a way that is appropriate to the driver's emotional state, more effective eco-driving support is possible.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The server continuously acquires vehicle status and location information from sensors and GPS installed in the vehicle. This information includes speed, acceleration, steering angle, and current position.

[0131] Step 2:

[0132] The server acquires the user's facial expressions and voice from the in-car camera and microphone. Based on this data, the emotion engine analyzes the user's emotions in real time. For example, it can determine whether the user is feeling stressed based on their facial expressions.

[0133] Step 3:

[0134] The server performs preprocessing to prepare the data for analysis. For vehicle data, it removes noise and detects outliers, while for emotion data, it extracts facial expressions and vocal characteristics.

[0135] Step 4:

[0136] The server evaluates driving conditions based on analyzed vehicle and emotional data. This evaluation includes determining whether the speed is appropriate, selecting future driving routes, and assessing the user's psychological state.

[0137] Step 5:

[0138] The server generates driving advice, adjusting the content and tone of the advice based on the user's emotional state. For example, if the user is relaxed, it generates standard advice; if the user is stressed, it generates advice in a more calming tone.

[0139] Step 6:

[0140] The terminal provides the user with driving advice and warnings received from the server, either visually or audibly. This allows the user to take appropriate actions in real time based on the driving conditions.

[0141] Step 7:

[0142] After a driving session ends, the server calculates a driving score based on the collected driving and emotional data and presents it to the user via the terminal. The score includes evaluations of safety and eco-driving.

[0143] Step 8:

[0144] The device provides users with feedback based on driving and emotional data, including specific advice on how to improve driving and stress management.

[0145] Step 9:

[0146] The server analyzes accumulated emotional data using an emotion engine, updates the advice generation logic to be optimal for each individual user, and prepares to provide personalized advice in future driving sessions.

[0147] (Example 2)

[0148] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0149] Preventing traffic accidents and improving driver safety are crucial challenges. However, conventional systems have struggled to grasp road conditions and drivers' emotional states in real time and take appropriate action based on that information. Furthermore, because they do not take into account the driver's mental state, they cannot mitigate the impact of tension and stress on driving. Therefore, there is a need to enhance driver safety while simultaneously providing support tailored to individual psychological states.

[0150] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0151] In this invention, the server includes information acquisition means for acquiring vehicle operation information and location information, emotion analysis means for monitoring the user's facial expressions and voice and analyzing their emotions, and hazard prediction means for predicting potential hazards based on the traffic conditions and weather conditions ahead and generating warnings. This enables real-time understanding of various traffic situations faced by the driver and driving assistance tailored to the emotional state of each individual driver. This provides a safe and comfortable driving environment.

[0152] "Information acquisition means" refers to devices and methods for acquiring vehicle operation information and location information.

[0153] "Advice generation means" refers to a device or method for analyzing information collected by information acquisition means and generating driving advice for the driver.

[0154] "Information presentation means" refers to devices or methods for providing drivers with driving advice generated by advice generation means.

[0155] "Hazard prediction means" refers to devices or methods for predicting potential hazards based on traffic conditions and weather conditions ahead and generating warnings.

[0156] "Warning information means" refers to devices or methods for presenting warnings generated by hazard prediction means to the driver.

[0157] "Emotion analysis means" refers to devices and methods for monitoring a user's facial expressions and voice and analyzing their emotions.

[0158] "Driving support means" refers to devices and methods for generating advice that reflects the driver's psychological state, taking into account emotions obtained through emotion analysis means.

[0159] "Score calculation means" refers to a device or method for calculating a driving score based on driving information.

[0160] "Environmentally friendly driving support means" refers to devices and methods for generating and providing advice on improving fuel consumption in order to reduce the environmental impact of a driver's driving.

[0161] This invention provides an in-vehicle system to enhance driver safety and reduce the environmental impact while driving. The server first acquires information about the vehicle's operation and location from sensors and GPS devices. This information includes vehicle speed, engine operating status, road conditions, and weather information.

[0162] Next, the server analyzes the acquired information in real time. This analysis process involves advanced data processing to generate advice on optimal driving routes and driving conditions. The latest artificial intelligence technology is used at this stage, particularly by leveraging generative AI models to improve the driver experience.

[0163] In addition, emotion analysis is performed using the vehicle's cameras and microphones. The server analyzes the driver's facial expressions and voice in real time to determine their emotions. Once the emotional state is determined, advice is generated that takes the driver's psychological state into account and is provided to the driver through the terminal. For example, if the system detects that the user is tense, it will provide a message encouraging them to relax.

[0164] The terminal's role is to present the user with analysis data and generated advice sent from the server. The terminal provides information to the driver using voice output devices and screen displays, and features improved usability to ensure the user can quickly understand and act upon the information.

[0165] As a concrete example, when a user encounters traffic congestion, the server suggests an alternative route based on traffic information. On the other hand, if stress is detected through emotion analysis, advice such as "Take a deep breath and calm down. Please drive safely" is provided.

[0166] In this way, the system can provide driving assistance optimized for each individual driver in real time, enabling safe driving and environmental considerations.

[0167] An example of a prompt message is, "Analyze the driver's emotions in real time and generate driving advice based on those emotions."

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The server collects information from sensors and GPS devices installed in the vehicle. It receives vehicle speed, engine status, location information, and weather data as input. This input data is used to understand the vehicle's operating status and environmental conditions in real time. The collected information is output as foundational data for subsequent analysis. Specifically, the server periodically polls data from the devices and stores it in a database.

[0171] Step 2:

[0172] The server analyzes the collected information. This analysis uses a generative AI model to prepare the foundational data for generating optimal driving routes and driving advice. The input data includes all the data collected in Step 1. During the analysis, statistical algorithms and machine learning models are used to predict traffic flow and road conditions. The output is a list of candidate driving advice to provide to the driver. Specifically, the server calculates the results of the real-time analysis and passes them on to the next step.

[0173] Step 3:

[0174] The server receives the user's facial expressions and voice as input from the in-car camera and microphone, and performs emotion analysis. Using a generative AI model, it analyzes this input data to identify the user's emotional state. If the analysis determines, for example, that the user is in a stressed state, emotion data is generated as output. Specifically, the server processes the video data from the camera and the audio data from the microphone every second and applies the emotion recognition algorithm.

[0175] Step 4:

[0176] The server integrates candidate driving advice and emotion data to generate optimal driving advice. The inputs are the list of candidate advice from step 2 and the emotion data from step 3. This integration process outputs personalized driving advice that is appropriate for the user's emotional state. Specifically, the server applies multiple inference rules to select the most appropriate message for the driver.

[0177] Step 5:

[0178] The terminal notifies the user of the final generated driving advice. The input is the output from step 4. The terminal uses speech synthesis technology and a display to provide the driver with information visually and audibly. This allows the user to receive information to perform appropriate driving actions. Specifically, the terminal uses its speaker and display to deliver messages that promote relaxation.

[0179] (Application Example 2)

[0180] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0181] While modern vehicles offer numerous systems to support safe driving, few possess interaction features that take into account the emotions of the driver and passengers. Since a driver's psychological state significantly impacts their performance and safety, there is a growing need for systems that can provide emotion-based feedback. Furthermore, there is a demand for a more comfortable and safer driving experience through emotion-based adjustments to the in-car environment.

[0182] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0183] In this invention, the server includes data collection means for acquiring vehicle status information and location information, advice generation means for analyzing the information acquired by the data collection means and generating driving advice for the driver, and emotion analysis means for detecting the emotional state of the occupants and providing emotion-based feedback. This makes it possible to provide appropriate feedback and adjust the in-vehicle environment according to the emotions of the driver and occupants.

[0184] "Data collection means" refers to devices and methods for acquiring vehicle status information and location information.

[0185] The "advice generation means" is a function that analyzes collected information and generates driving advice for the driver.

[0186] "Presentation means" refers to devices or methods for providing generated driving advice or warnings to the driver.

[0187] A "hazard prediction system" is a function that predicts potential hazards based on traffic conditions and weather information ahead and generates warnings.

[0188] A "warning notification means" refers to a method or device for communicating a generated warning to the driver.

[0189] "Emotional analysis means" refers to a function that detects the emotional state of the crew and provides feedback based on that.

[0190] "Environmental adjustment means" refers to devices or methods for appropriately adjusting the vehicle's environment based on data obtained through emotion analysis.

[0191] In this invention, the server acquires vehicle status information and location information from data collection means installed in the vehicle. This makes it possible to collect environmental data in real time while driving. The data collection means consists of in-vehicle sensors, GPS modules, and the like.

[0192] The server analyzes this data using an advice generation system and generates necessary driving advice for the driver. The advice is then provided to the driver via a presentation system, such as a digital display or voice assistant.

[0193] Furthermore, the vehicle uses emotion analysis capabilities to monitor the occupants' emotional state in real time through facial recognition cameras and microphones. Based on this information, it generates emotional feedback and, if necessary, modifies the in-vehicle environment using environmental adjustment mechanisms. For example, if an occupant is feeling stressed, it automatically plays relaxing music or adjusts the temperature.

[0194] Furthermore, the hazard prediction system predicts potential hazards based on road conditions and weather information, and communicates them to the driver through a warning system. This allows the driver to take appropriate action in advance.

[0195] For example, if the system detects that the occupants are fatigued during long drives, it will use emotion analysis to reduce stress levels by adjusting the lighting to a slightly dimmer setting and playing relaxation music to create a more comfortable driving environment.

[0196] An example of a prompt message for the generating AI model is the instruction, "Generate a program that takes into account the emotional state of the occupants and creates a safe and comfortable driving environment."

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] The server acquires vehicle status and location information from data collection devices installed in the vehicle. Specifically, it acquires data such as current speed, location, and engine status in real time from on-board sensors and GPS modules, and stores this data in a database. The input is raw data from the sensors, and the output is status and location information in an analyzable format.

[0200] Step 2:

[0201] The server analyzes the acquired data using an advice generation system and generates driving advice for the driver. Here, a data analysis algorithm is used to derive optimal advice that takes into account the driver's driving style and current traffic conditions. Inputs are vehicle status information and location information, and output is specific driving advice. The advice is expressed in text or audio format.

[0202] Step 3:

[0203] The terminal provides the driver with generated driving advice. It supports driving decision-making by displaying the advice on a screen or communicating it to the driver via voice. The input is the generated advice, and the output is presented to the driver.

[0204] Step 4:

[0205] The server uses emotion analysis tools to analyze the emotions of the crew members from their facial expressions and voice data. Using facial recognition and voice recognition technologies, it quantifies the crew members' emotions such as stress, fatigue, and sense of security, and generates analysis results. Input is data from cameras and microphones, and output is the analyzed emotion data.

[0206] Step 5:

[0207] The server, based on the analyzed emotional data, generates necessary feedback through environmental adjustment mechanisms to adjust the in-car environment. Examples include adjusting the lighting and selecting music. The input is emotional data, and the output is specific instructions for adjusting the environment.

[0208] Step 6:

[0209] The device uses hazard prediction tools to analyze traffic conditions and weather information ahead and predict potential hazards. Based on the prediction results, it issues a warning to the driver. The input is traffic and weather information, and the output is a warning message conveyed to the driver.

[0210] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0211] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0212] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0216] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0217] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0218] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0219] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0220] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0221] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0222] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0223] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0224] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0225] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0226] This invention is a system for achieving traffic safety and environmental protection. This system has the function of acquiring vehicle status and location information, and a server analyzes this information to provide appropriate advice to the driver. Specifically, this system collects data from sensors mounted on the vehicle in real time and generates driving advice and warnings based on that data.

[0227] The server acquires vehicle status information such as speed, acceleration, and brake usage, and also uses GPS to determine the vehicle's location. This allows the server to evaluate the driver's current driving situation in detail. Based on the evaluation results, the server generates advice to encourage the driver to take appropriate driving actions, such as adjusting speed or checking the distance between vehicles. This advice is presented to the driver visually or audibly through a terminal.

[0228] The server also takes into account traffic conditions and weather information ahead to predict potential hazards. For example, if there is a possibility of sudden braking on the road ahead, the server will immediately issue a warning to the driver. This can prevent potential accidents.

[0229] Upon completion of the drive, the server analyzes the driving information in detail and generates a driving score. This score is an indicator of how safely and efficiently the driver drove and is provided to the driver via the terminal. Furthermore, the server includes an eco-driving support function that can suggest specific advice to the driver on how to improve fuel efficiency.

[0230] Specific example

[0231] For example, when a driver is driving on a highway and approaches a sharp curve, the server analyzes the curve's location and the vehicle's speed. If it detects a possible speeding violation, it will issue a voice command from the terminal saying, "Slow down in preparation for the next curve." Also, if driving in heavy rain, the server will predict slippery road conditions based on weather information and display a warning saying, "Maintain a safe distance from other vehicles and drive safely."

[0232] Overall, this system aims to support safe driving by providing drivers with useful information in real time, thereby reducing traffic accidents and environmental impact.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The server acquires real-time status information from sensors mounted on the vehicle. This information includes speed, acceleration, brake usage, and steering angle. It also acquires the vehicle's location information using GPS.

[0236] Step 2:

[0237] The server preprocesses the acquired raw data. This includes noise reduction, data format conversion, and handling of missing values. This preprocessing prepares the data in a state suitable for analysis.

[0238] Step 3:

[0239] The server inputs pre-processed data into an AI model and analyzes the driving situation. Here, the driver's behavior is evaluated, taking into account driving behavior, road conditions, and the influence of other traffic participants.

[0240] Step 4:

[0241] Based on the analysis results, the server generates driving advice to provide to the driver. This advice may include instructions such as when speed adjustments are needed or when a safe following distance should be maintained.

[0242] Step 5:

[0243] The terminal provides the user with driving advice transmitted from the server, either verbally or visually. This allows the user to take appropriate driving actions through real-time navigation.

[0244] Step 6:

[0245] The server predicts potential hazards based on traffic conditions and weather information ahead. For example, this includes predicting changes in weather and traffic congestion.

[0246] Step 7:

[0247] If a potential threat is detected, the server will immediately generate a warning and present it to the user via the terminal. The warning will be delivered in visual or audio format.

[0248] Step 8:

[0249] Once the operation is complete, the server calculates an operation score based on the data from the entire operation session. This score is an indicator used to evaluate the safety and efficiency of the operation.

[0250] Step 9:

[0251] The device provides the user with a calculated driving score and feedback, including strengths to highlight and areas for improvement.

[0252] Step 10:

[0253] The server generates and provides to users via terminals advice on reducing environmental impact, such as driving methods to improve fuel efficiency.

[0254] (Example 1)

[0255] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0256] Balancing safety and environmental protection is crucial in automobile driving. However, drivers often lack access to timely and appropriate information, leading to inappropriate driving behavior. Therefore, there is a need for effective and immediate support systems to prevent traffic accidents and improve fuel efficiency.

[0257] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0258] In this invention, the server includes an information gathering means for acquiring vehicle status information and location information, an instruction generation means for analyzing the information acquired by the information gathering means and generating driving instructions for the driver, and an information presentation means for providing the driving instructions generated by the instruction generation means to the driver. As a result, the driver can receive information in real time to support safe and efficient driving, thereby preventing traffic accidents and reducing environmental impact.

[0259] "Information gathering means" refers to functions that use various sensors and GPS installed in the vehicle to acquire vehicle status information and location information in real time.

[0260] The "instruction generation means" is a function that analyzes data acquired by the information gathering means and generates specific driving instructions and advice for the driver.

[0261] "Information presentation means" refers to a device or method for providing generated driving instructions or warnings to the driver visually or audibly.

[0262] A "predictive warning system" is a function that analyzes traffic conditions and weather information ahead to predict potential dangers in advance and generate warnings.

[0263] "Warning display means" refers to a device or method for presenting a warning generated by a predictive warning means to the driver.

[0264] The "evaluation calculation means" is a function that calculates a driving evaluation based on driving data acquired after the end of the drive and provides that evaluation as feedback to the driver.

[0265] "Environmental protection support measures" refer to functions that generate and provide specific instructions for reducing the environmental impact of driver actions and improving fuel efficiency.

[0266] This invention is a support system for achieving both safety and environmental protection in vehicle operation. The server uses various sensors and GPS devices mounted on the vehicle to acquire status information such as vehicle speed, acceleration, and brake usage, as well as location information, in real time. This information is acquired by information collection means.

[0267] The server analyzes the collected data and generates specific driving instructions for the driver based on the results. The generation AI model used here employs deep learning technology to detect abnormal driving patterns and potential hazards. The driving instructions generated after the analysis are provided by an instruction generation system. For example, the AI ​​generates instructions using prompts such as, "Do you need to adjust your speed for the next curve?"

[0268] The terminal displays driving instructions generated by the server to the driver. This display is delivered visually or audibly using speech synthesis technology and a display. By following the instructions provided through the terminal, the driver can reduce the risk of traffic accidents and minimize environmental impact. A specific example is when driving on a highway, the terminal issues a voice instruction such as, "Slow down in preparation for the next curve."

[0269] Once the drive is complete, the server calculates a driving evaluation based on the data acquired during the drive and provides feedback to the driver. This allows the driver to objectively evaluate the safety and efficiency of their driving behavior. Furthermore, driving improvement instructions for eco-driving are provided, promoting improved fuel efficiency. In this way, combining the server and terminal makes it possible to provide drivers with real-time support for safe driving and environmental protection.

[0270] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0271] Step 1:

[0272] The server acquires real-time speed, acceleration, brake usage, and location information from sensors and GPS installed in the vehicle. This data is aggregated on the server by various data collection methods. The server records this data chronologically to form an overall picture of the driving conditions.

[0273] Step 2:

[0274] The server analyzes the collected data. Specifically, it uses a generative AI model to process and calculate data to detect abnormal driving patterns and potential hazards. By analyzing the driving data as input, if abnormalities such as speeding or sudden acceleration are detected, the server incorporates this information into the generation of driving instructions.

[0275] Step 3:

[0276] The server generates appropriate driving instructions for the driver based on the analysis results. The generating AI model outputs specific instructions based on prompts such as "Do you need to adjust your speed for the next curve?". This provides the driver with advice on speed adjustments and maintaining a safe distance from other vehicles.

[0277] Step 4:

[0278] The terminal displays driving instructions sent from the server to the driver. Specifically, the terminal uses speech synthesis technology to play instructions such as "Slow down in preparation for the next curve" aloud. It also visually alerts the driver by displaying warning messages on the screen.

[0279] Step 5:

[0280] After the drive is complete, the server calculates a driving evaluation based on the data collected during the drive. It analyzes the driving data as input and generates an output that includes a driving evaluation along with fuel efficiency and safety evaluations. This evaluation is then fed back to the driver via a terminal to help improve future driving.

[0281] (Application Example 1)

[0282] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0283] With the spread of autonomous vehicles, there is a growing need to improve both safety and driving efficiency in an increasingly complex traffic environment. Conventional technologies have difficulty providing immediate driving advice and warnings based on real-time driving data, and have not sufficiently improved the prediction accuracy of potential risks and fuel efficiency. To solve such problems, it is necessary to provide a system that realizes safer and more environmentally considerate driving in autonomous vehicles.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0285] In this invention, the server includes a measurement means for acquiring the state information and position information of the vehicle, a command generation means for analyzing the information acquired by the measurement means and autonomously generating a driving advice, a notification means for providing the driving advice generated by the command generation means to the automatic control device, a risk prediction means for predicting potential risks based on the traffic environment and weather data ahead and generating a warning, and an alarm notification means for providing the warning generated by the risk prediction means to the automatic control device. Thereby, it becomes possible for the autonomous vehicle to drive safely and efficiently in real time.

[0286] The "measurement means" is a device for acquiring state information such as the speed, acceleration, and position information of the vehicle using sensors installed in the vehicle.

[0287] The "command generation means" is a device that analyzes the data acquired by the measurement means and autonomously generates a driving advice necessary to improve the safety and efficiency of driving.

[0288] A "notification means" is a device that receives driving advice generated by a command generation means and provides it to an automatic control device.

[0289] A "risk prediction device" is a device that analyzes traffic conditions and weather data ahead to predict potential dangers and generate warnings.

[0290] A "warning notification means" is a device that provides warnings generated by a risk prediction means to an automatic control device and notifies the driver visually or audibly.

[0291] This invention is a system for achieving safe and efficient driving in autonomous vehicles. The system is implemented with the following configuration.

[0292] The server uses multiple sensors installed in the vehicle, such as speed sensors, acceleration sensors, and GPS modules, to measure the vehicle's status and location in real time. This data is collected by the measurement devices and continuously monitored and recorded by the server.

[0293] On the server side, a command generation mechanism is used to analyze the acquired data. This mechanism utilizes machine learning algorithms, particularly libraries such as TensorFlow, to analyze and predict operating conditions. Based on the results of the data analysis, driving advice is generated.

[0294] The server provides the generated driving advice to the vehicle's automatic control system via a notification device. The notification device can visually display the driving advice on the vehicle's display or inform the driver via voice.

[0295] Furthermore, the server is equipped with a risk prediction system. This system collects data from weather information services and traffic information databases to predict potential hazards. The resulting warnings are provided to the automatic control system by an alert notification system, and the driver is alerted when necessary.

[0296] For example, if a vehicle ahead suddenly slows down in rainy weather, the server immediately detects the situation. The risk prediction system takes into account the slipperiness of the road surface and generates a warning such as "Increase your following distance and prepare to brake," which is then communicated to the driver through a notification system. Throughout this entire process, the vehicle can maintain driving efficiency while enhancing safety.

[0297] An example of a prompt would be, "What real-time advice can an autonomous vehicle provide to safely navigate sharp curves in rainy weather?" Using such prompts allows generative AI models to effectively create driving advice.

[0298] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0299] Step 1:

[0300] To obtain vehicle status and location information, a group of sensors installed on the vehicle measures speed, acceleration, GPS data, and other parameters. This data is transmitted to a server in real time. At this stage, the raw data from the sensors is used as input, which is then converted into a structured data format and stored in a database.

[0301] Step 2:

[0302] The server uses machine learning algorithms to analyze the acquired data. It analyzes the input data using the TensorFlow library and evaluates the driving situation. As a result of the analysis, it outputs driving advice to improve driving safety and efficiency. Through this analysis process, the server generates the necessary commands for the driver.

[0303] Step 3:

[0304] The generated driving advice is notified from the server to the vehicle terminal. The terminal uses a notification means to provide the driver with advice through visual displays or voice guidance. The input at this stage is the generated advice data, and the output is a visual or auditory instruction for the driver.

[0305] Step 4:

[0306] The server obtains the traffic situation ahead and weather information from an external database, and predicts potential dangers using risk prediction means. The input is the obtained environmental data, and calculations are performed based on this data to determine the presence or absence of potential dangers. The output is generated as a warning message.

[0307] Step 5:

[0308] The warning generated by the risk prediction means is sent from the server to the terminal and provided to the driver via an alarm notification means. The input is the warning data, and an alert prompting the driver to pay attention is issued as the output. The terminal processes these warnings instantaneously and communicates them to the driver visually or audibly when necessary.

[0309] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0310] The present invention is an in-vehicle system aimed at assisting the safe driving of a driver, and further includes an emotion engine for recognizing the user's emotion in real time and realizing an interaction according to the emotion. In addition to the basic functions of collecting and analyzing the vehicle's state information and position information, generating and presenting driving advice, this system has advanced functions that take into account the user's emotion.

[0311] The server continuously acquires vehicle status and location information from sensors and GPS, and analyzes it. Based on the analysis results, it generates necessary driving advice and warnings for the driver and provides them to the driver via a terminal. The content of the warnings and advice comprehensively takes into account road conditions and weather changes.

[0312] The emotion engine uses in-car cameras and microphones to monitor the user's facial expressions and voice, and analyzes their emotions in real time. The emotional information obtained from this analysis is taken into consideration when generating driving advice. For example, if the user is feeling stressed, it can present advice in a tone that promotes relaxation.

[0313] Furthermore, by analyzing accumulated emotional data, it becomes possible to understand drivers' long-term behavioral patterns and reactions, and provide more personalized advice. In this way, interactions optimized for each individual driver are realized.

[0314] Specific example

[0315] For example, if a driver gets caught in traffic during their commute, the server recognizes the traffic information and provides normal route advice. On the other hand, if the emotion engine detects frustration or anxiety from the driver's facial expressions and voice, the device will provide advice in a relaxed tone, such as, "Relax, take a deep breath. Safe driving is the most important thing."

[0316] In this way, by utilizing the emotion engine of this system, flexible responses tailored to the driver's psychological state become possible, contributing to improved traffic safety. Furthermore, since advice for improving fuel efficiency is presented in a way that is appropriate to the driver's emotional state, more effective eco-driving support is possible.

[0317] The following describes the processing flow.

[0318] Step 1:

[0319] The server continuously acquires vehicle status and location information from sensors and GPS installed in the vehicle. This information includes speed, acceleration, steering angle, and current position.

[0320] Step 2:

[0321] The server acquires the user's facial expressions and voice from the in-car camera and microphone. Based on this data, the emotion engine analyzes the user's emotions in real time. For example, it can determine whether the user is feeling stressed based on their facial expressions.

[0322] Step 3:

[0323] The server performs preprocessing to prepare the data for analysis. For vehicle data, it removes noise and detects outliers, while for emotion data, it extracts facial expressions and vocal characteristics.

[0324] Step 4:

[0325] The server evaluates driving conditions based on analyzed vehicle and emotional data. This evaluation includes determining whether the speed is appropriate, selecting future driving routes, and assessing the user's psychological state.

[0326] Step 5:

[0327] The server generates driving advice, adjusting the content and tone of the advice based on the user's emotional state. For example, if the user is relaxed, it generates standard advice; if the user is stressed, it generates advice in a more calming tone.

[0328] Step 6:

[0329] The terminal provides the user with driving advice and warnings received from the server, either visually or audibly. This allows the user to take appropriate actions in real time based on the driving conditions.

[0330] Step 7:

[0331] After a driving session ends, the server calculates a driving score based on the collected driving and emotional data and presents it to the user via the terminal. The score includes evaluations of safety and eco-driving.

[0332] Step 8:

[0333] The device provides users with feedback based on driving and emotional data, including specific advice on how to improve driving and stress management.

[0334] Step 9:

[0335] The server analyzes accumulated emotional data using an emotion engine, updates the advice generation logic to be optimal for each individual user, and prepares to provide personalized advice in future driving sessions.

[0336] (Example 2)

[0337] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0338] Preventing traffic accidents and improving driver safety are crucial challenges. However, conventional systems have struggled to grasp road conditions and drivers' emotional states in real time and take appropriate action based on that information. Furthermore, because they do not take into account the driver's mental state, they cannot mitigate the impact of tension and stress on driving. Therefore, there is a need to enhance driver safety while simultaneously providing support tailored to individual psychological states.

[0339] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0340] In this invention, the server includes information acquisition means for acquiring vehicle operation information and location information, emotion analysis means for monitoring the user's facial expressions and voice and analyzing their emotions, and hazard prediction means for predicting potential hazards based on the traffic conditions and weather conditions ahead and generating warnings. This enables real-time understanding of various traffic situations faced by the driver and driving assistance tailored to the emotional state of each individual driver. This provides a safe and comfortable driving environment.

[0341] "Information acquisition means" refers to devices and methods for acquiring vehicle operation information and location information.

[0342] "Advice generation means" refers to a device or method for analyzing information collected by information acquisition means and generating driving advice for the driver.

[0343] "Information presentation means" refers to devices or methods for providing drivers with driving advice generated by advice generation means.

[0344] "Hazard prediction means" refers to devices or methods for predicting potential hazards based on traffic conditions and weather conditions ahead and generating warnings.

[0345] "Warning information means" refers to devices or methods for presenting warnings generated by hazard prediction means to the driver.

[0346] "Emotion analysis means" refers to devices and methods for monitoring a user's facial expressions and voice and analyzing their emotions.

[0347] "Driving support means" refers to devices and methods for generating advice that reflects the driver's psychological state, taking into account emotions obtained through emotion analysis means.

[0348] "Score calculation means" refers to a device or method for calculating a driving score based on driving information.

[0349] "Environmentally friendly driving support means" refers to devices and methods for generating and providing advice on improving fuel consumption in order to reduce the environmental impact of a driver's driving.

[0350] This invention provides an in-vehicle system to enhance driver safety and reduce the environmental impact while driving. The server first acquires information about the vehicle's operation and location from sensors and GPS devices. This information includes vehicle speed, engine operating status, road conditions, and weather information.

[0351] Next, the server analyzes the acquired information in real time. This analysis process involves advanced data processing to generate advice on optimal driving routes and driving conditions. The latest artificial intelligence technology is used at this stage, particularly by leveraging generative AI models to improve the driver experience.

[0352] In addition, emotion analysis is performed using the vehicle's cameras and microphones. The server analyzes the driver's facial expressions and voice in real time to determine their emotions. Once the emotional state is determined, advice is generated that takes the driver's psychological state into account and is provided to the driver through the terminal. For example, if the system detects that the user is tense, it will provide a message encouraging them to relax.

[0353] The terminal's role is to present the user with analysis data and generated advice sent from the server. The terminal provides information to the driver using voice output devices and screen displays, and features improved usability to ensure the user can quickly understand and act upon the information.

[0354] As a concrete example, when a user encounters traffic congestion, the server suggests an alternative route based on traffic information. On the other hand, if stress is detected through emotion analysis, advice such as "Take a deep breath and calm down. Please drive safely" is provided.

[0355] In this way, the system can provide driving assistance optimized for each individual driver in real time, enabling safe driving and environmental considerations.

[0356] An example of a prompt message is, "Analyze the driver's emotions in real time and generate driving advice based on those emotions."

[0357] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0358] Step 1:

[0359] The server collects information from sensors and GPS devices installed in the vehicle. It receives vehicle speed, engine status, location information, and weather data as input. This input data is used to understand the vehicle's operating status and environmental conditions in real time. The collected information is output as foundational data for subsequent analysis. Specifically, the server periodically polls data from the devices and stores it in a database.

[0360] Step 2:

[0361] The server analyzes the collected information. This analysis uses a generative AI model to prepare the foundational data for generating optimal driving routes and driving advice. The input data includes all the data collected in Step 1. During the analysis, statistical algorithms and machine learning models are used to predict traffic flow and road conditions. The output is a list of candidate driving advice to provide to the driver. Specifically, the server calculates the results of the real-time analysis and passes them on to the next step.

[0362] Step 3:

[0363] The server receives the user's facial expressions and voice as input from the in-car camera and microphone, and performs emotion analysis. Using a generative AI model, it analyzes this input data to identify the user's emotional state. If the analysis determines, for example, that the user is in a stressed state, emotion data is generated as output. Specifically, the server processes the video data from the camera and the audio data from the microphone every second and applies the emotion recognition algorithm.

[0364] Step 4:

[0365] The server integrates candidate driving advice and emotion data to generate optimal driving advice. The inputs are the list of candidate advice from step 2 and the emotion data from step 3. This integration process outputs personalized driving advice that is appropriate for the user's emotional state. Specifically, the server applies multiple inference rules to select the most appropriate message for the driver.

[0366] Step 5:

[0367] The terminal notifies the user of the final generated driving advice. The input is the output from step 4. The terminal uses speech synthesis technology and a display to provide the driver with information visually and audibly. This allows the user to receive information to perform appropriate driving actions. Specifically, the terminal uses its speaker and display to deliver messages that promote relaxation.

[0368] (Application Example 2)

[0369] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0370] While modern vehicles offer numerous systems to support safe driving, few possess interaction features that take into account the emotions of the driver and passengers. Since a driver's psychological state significantly impacts their performance and safety, there is a growing need for systems that can provide emotion-based feedback. Furthermore, there is a demand for a more comfortable and safer driving experience through emotion-based adjustments to the in-car environment.

[0371] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0372] In this invention, the server includes data collection means for acquiring vehicle status information and location information, advice generation means for analyzing the information acquired by the data collection means and generating driving advice for the driver, and emotion analysis means for detecting the emotional state of the occupants and providing emotion-based feedback. This makes it possible to provide appropriate feedback and adjust the in-vehicle environment according to the emotions of the driver and occupants.

[0373] "Data collection means" refers to devices and methods for acquiring vehicle status information and location information.

[0374] The "advice generation means" is a function that analyzes collected information and generates driving advice for the driver.

[0375] "Presentation means" refers to devices or methods for providing generated driving advice or warnings to the driver.

[0376] A "hazard prediction system" is a function that predicts potential hazards based on traffic conditions and weather information ahead and generates warnings.

[0377] A "warning notification means" refers to a method or device for communicating a generated warning to the driver.

[0378] "Emotional analysis means" refers to a function that detects the emotional state of the crew and provides feedback based on that.

[0379] "Environmental adjustment means" refers to devices or methods for appropriately adjusting the vehicle's environment based on data obtained through emotion analysis.

[0380] In this invention, the server acquires vehicle status information and location information from data collection means installed in the vehicle. This makes it possible to collect environmental data in real time while driving. The data collection means consists of in-vehicle sensors, GPS modules, and the like.

[0381] The server analyzes this data using an advice generation system and generates necessary driving advice for the driver. The advice is then provided to the driver via a presentation system, such as a digital display or voice assistant.

[0382] Furthermore, the vehicle uses emotion analysis capabilities to monitor the occupants' emotional state in real time through facial recognition cameras and microphones. Based on this information, it generates emotional feedback and, if necessary, modifies the in-vehicle environment using environmental adjustment mechanisms. For example, if an occupant is feeling stressed, it automatically plays relaxing music or adjusts the temperature.

[0383] Furthermore, the hazard prediction system predicts potential hazards based on road conditions and weather information, and communicates them to the driver through a warning system. This allows the driver to take appropriate action in advance.

[0384] For example, if the system detects that the occupants are fatigued during long drives, it will use emotion analysis to reduce stress levels by adjusting the lighting to a slightly dimmer setting and playing relaxation music to create a more comfortable driving environment.

[0385] An example of a prompt message for the generating AI model is the instruction, "Generate a program that takes into account the emotional state of the occupants and creates a safe and comfortable driving environment."

[0386] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0387] Step 1:

[0388] The server acquires vehicle status and location information from data collection devices installed in the vehicle. Specifically, it acquires data such as current speed, location, and engine status in real time from on-board sensors and GPS modules, and stores this data in a database. The input is raw data from the sensors, and the output is status and location information in an analyzable format.

[0389] Step 2:

[0390] The server analyzes the acquired data using an advice generation system and generates driving advice for the driver. Here, a data analysis algorithm is used to derive optimal advice that takes into account the driver's driving style and current traffic conditions. Inputs are vehicle status information and location information, and output is specific driving advice. The advice is expressed in text or audio format.

[0391] Step 3:

[0392] The terminal provides the driver with generated driving advice. It supports driving decision-making by displaying the advice on a screen or communicating it to the driver via voice. The input is the generated advice, and the output is presented to the driver.

[0393] Step 4:

[0394] The server uses emotion analysis tools to analyze the emotions of the crew members from their facial expressions and voice data. Using facial recognition and voice recognition technologies, it quantifies the crew members' emotions such as stress, fatigue, and sense of security, and generates analysis results. Input is data from cameras and microphones, and output is the analyzed emotion data.

[0395] Step 5:

[0396] The server, based on the analyzed emotional data, generates necessary feedback through environmental adjustment mechanisms to adjust the in-car environment. Examples include adjusting the lighting and selecting music. The input is emotional data, and the output is specific instructions for adjusting the environment.

[0397] Step 6:

[0398] The device uses hazard prediction tools to analyze traffic conditions and weather information ahead and predict potential hazards. Based on the prediction results, it issues a warning to the driver. The input is traffic and weather information, and the output is a warning message conveyed to the driver.

[0399] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0400] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0401] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0402] [Third Embodiment]

[0403] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0404] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0405] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0406] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0407] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0408] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0409] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0410] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0411] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0412] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0413] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0414] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0415] This invention is a system for achieving traffic safety and environmental protection. This system has the function of acquiring vehicle status and location information, and a server analyzes this information to provide appropriate advice to the driver. Specifically, this system collects data from sensors mounted on the vehicle in real time and generates driving advice and warnings based on that data.

[0416] The server acquires vehicle status information such as speed, acceleration, and brake usage, and also uses GPS to determine the vehicle's location. This allows the server to evaluate the driver's current driving situation in detail. Based on the evaluation results, the server generates advice to encourage the driver to take appropriate driving actions, such as adjusting speed or checking the distance between vehicles. This advice is presented to the driver visually or audibly through a terminal.

[0417] The server also takes into account traffic conditions and weather information ahead to predict potential hazards. For example, if there is a possibility of sudden braking on the road ahead, the server will immediately issue a warning to the driver. This can prevent potential accidents.

[0418] Upon completion of the drive, the server analyzes the driving information in detail and generates a driving score. This score is an indicator of how safely and efficiently the driver drove and is provided to the driver via the terminal. Furthermore, the server includes an eco-driving support function that can suggest specific advice to the driver on how to improve fuel efficiency.

[0419] Specific example

[0420] For example, when a driver is driving on a highway and approaches a sharp curve, the server analyzes the curve's location and the vehicle's speed. If it detects a possible speeding violation, it will issue a voice command from the terminal saying, "Slow down in preparation for the next curve." Also, if driving in heavy rain, the server will predict slippery road conditions based on weather information and display a warning saying, "Maintain a safe distance from other vehicles and drive safely."

[0421] Overall, this system aims to support safe driving by providing drivers with useful information in real time, thereby reducing traffic accidents and environmental impact.

[0422] The following describes the processing flow.

[0423] Step 1:

[0424] The server acquires real-time status information from sensors mounted on the vehicle. This information includes speed, acceleration, brake usage, and steering angle. It also acquires the vehicle's location information using GPS.

[0425] Step 2:

[0426] The server preprocesses the acquired raw data. This includes noise reduction, data format conversion, and handling of missing values. This preprocessing prepares the data in a state suitable for analysis.

[0427] Step 3:

[0428] The server inputs pre-processed data into an AI model and analyzes the driving situation. Here, the driver's behavior is evaluated, taking into account driving behavior, road conditions, and the influence of other traffic participants.

[0429] Step 4:

[0430] Based on the analysis results, the server generates driving advice to provide to the driver. This advice may include instructions such as when speed adjustments are needed or when a safe following distance should be maintained.

[0431] Step 5:

[0432] The terminal provides the user with driving advice transmitted from the server, either verbally or visually. This allows the user to take appropriate driving actions through real-time navigation.

[0433] Step 6:

[0434] The server predicts potential hazards based on traffic conditions and weather information ahead. For example, this includes predicting changes in weather and traffic congestion.

[0435] Step 7:

[0436] If a potential threat is detected, the server will immediately generate a warning and present it to the user via the terminal. The warning will be delivered in visual or audio format.

[0437] Step 8:

[0438] Once the operation is complete, the server calculates an operation score based on the data from the entire operation session. This score is an indicator used to evaluate the safety and efficiency of the operation.

[0439] Step 9:

[0440] The device provides the user with a calculated driving score and feedback, including strengths to highlight and areas for improvement.

[0441] Step 10:

[0442] The server generates and provides to users via terminals advice on reducing environmental impact, such as driving methods to improve fuel efficiency.

[0443] (Example 1)

[0444] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0445] Balancing safety and environmental protection is crucial in automobile driving. However, drivers often lack access to timely and appropriate information, leading to inappropriate driving behavior. Therefore, there is a need for effective and immediate support systems to prevent traffic accidents and improve fuel efficiency.

[0446] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0447] In this invention, the server includes an information gathering means for acquiring vehicle status information and location information, an instruction generation means for analyzing the information acquired by the information gathering means and generating driving instructions for the driver, and an information presentation means for providing the driving instructions generated by the instruction generation means to the driver. As a result, the driver can receive information in real time to support safe and efficient driving, thereby preventing traffic accidents and reducing environmental impact.

[0448] "Information gathering means" refers to functions that use various sensors and GPS installed in the vehicle to acquire vehicle status information and location information in real time.

[0449] The "instruction generation means" is a function that analyzes data acquired by the information gathering means and generates specific driving instructions and advice for the driver.

[0450] "Information presentation means" refers to a device or method for providing generated driving instructions or warnings to the driver visually or audibly.

[0451] A "predictive warning system" is a function that analyzes traffic conditions and weather information ahead to predict potential dangers in advance and generate warnings.

[0452] "Warning display means" refers to a device or method for presenting a warning generated by a predictive warning means to the driver.

[0453] The "evaluation calculation means" is a function that calculates a driving evaluation based on driving data acquired after the end of the drive and provides that evaluation as feedback to the driver.

[0454] "Environmental protection support measures" refer to functions that generate and provide specific instructions for reducing the environmental impact of driver actions and improving fuel efficiency.

[0455] This invention is a support system for achieving both safety and environmental protection in vehicle operation. The server uses various sensors and GPS devices mounted on the vehicle to acquire status information such as vehicle speed, acceleration, and brake usage, as well as location information, in real time. This information is acquired by information collection means.

[0456] The server analyzes the collected data and generates specific driving instructions for the driver based on the results. The generation AI model used here employs deep learning technology to detect abnormal driving patterns and potential hazards. The driving instructions generated after the analysis are provided by an instruction generation system. For example, the AI ​​generates instructions using prompts such as, "Do you need to adjust your speed for the next curve?"

[0457] The terminal displays driving instructions generated by the server to the driver. This display is delivered visually or audibly using speech synthesis technology and a display. By following the instructions provided through the terminal, the driver can reduce the risk of traffic accidents and minimize environmental impact. A specific example is when driving on a highway, the terminal issues a voice instruction such as, "Slow down in preparation for the next curve."

[0458] Once the drive is complete, the server calculates a driving evaluation based on the data acquired during the drive and provides feedback to the driver. This allows the driver to objectively evaluate the safety and efficiency of their driving behavior. Furthermore, driving improvement instructions for eco-driving are provided, promoting improved fuel efficiency. In this way, combining the server and terminal makes it possible to provide drivers with real-time support for safe driving and environmental protection.

[0459] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0460] Step 1:

[0461] The server acquires real-time speed, acceleration, brake usage, and location information from sensors and GPS installed in the vehicle. This data is aggregated on the server by various data collection methods. The server records this data chronologically to form an overall picture of the driving conditions.

[0462] Step 2:

[0463] The server analyzes the collected data. Specifically, it uses a generative AI model to process and calculate data to detect abnormal driving patterns and potential hazards. By analyzing the driving data as input, if abnormalities such as speeding or sudden acceleration are detected, the server incorporates this information into the generation of driving instructions.

[0464] Step 3:

[0465] The server generates appropriate driving instructions for the driver based on the analysis results. The generating AI model outputs specific instructions based on prompts such as "Do you need to adjust your speed for the next curve?". This provides the driver with advice on speed adjustments and maintaining a safe distance from other vehicles.

[0466] Step 4:

[0467] The terminal displays driving instructions sent from the server to the driver. Specifically, the terminal uses speech synthesis technology to play instructions such as "Slow down in preparation for the next curve" aloud. It also visually alerts the driver by displaying warning messages on the screen.

[0468] Step 5:

[0469] After the drive is complete, the server calculates a driving evaluation based on the data collected during the drive. It analyzes the driving data as input and generates an output that includes a driving evaluation along with fuel efficiency and safety evaluations. This evaluation is then fed back to the driver via a terminal to help improve future driving.

[0470] (Application Example 1)

[0471] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0472] With the proliferation of autonomous vehicles, the increasingly complex traffic environment necessitates simultaneous improvements in both safety and driving efficiency. Conventional technologies struggle to provide immediate driving advice and warnings based on real-time driving data, and have not adequately improved the accuracy of predicting potential hazards or fuel efficiency. To address these challenges, there is a need for systems that enable safer and more environmentally conscious driving in autonomous vehicles.

[0473] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0474] In this invention, the server includes measuring means for acquiring vehicle status information and location information; command generation means for analyzing the information acquired by the measuring means and autonomously generating driving advice; notification means for providing the driving advice generated by the command generation means to an automatic control device; risk prediction means for predicting potential hazards and generating warnings based on the traffic environment and weather data ahead; and warning notification means for providing the warning generated by the risk prediction means to an automatic control device. This enables the autonomous vehicle to drive safely and efficiently in real time.

[0475] A "measuring means" is a device that uses sensors installed on the vehicle to acquire state information such as the vehicle's speed, acceleration, and position.

[0476] A "command generation means" is a device that analyzes data acquired by a measurement means and autonomously generates driving advice necessary to improve driving safety and efficiency.

[0477] A "notification means" is a device that receives driving advice generated by a command generation means and provides it to an automatic control device.

[0478] A "risk prediction device" is a device that analyzes traffic conditions and weather data ahead to predict potential dangers and generate warnings.

[0479] A "warning notification means" is a device that provides warnings generated by a risk prediction means to an automatic control device and notifies the driver visually or audibly.

[0480] This invention is a system for achieving safe and efficient driving in autonomous vehicles. The system is implemented with the following configuration.

[0481] The server uses multiple sensors installed in the vehicle, such as speed sensors, acceleration sensors, and GPS modules, to measure the vehicle's status and location in real time. This data is collected by the measurement devices and continuously monitored and recorded by the server.

[0482] On the server side, a command generation mechanism is used to analyze the acquired data. This mechanism utilizes machine learning algorithms, particularly libraries such as TensorFlow, to analyze and predict operating conditions. Based on the results of the data analysis, driving advice is generated.

[0483] The server provides the generated driving advice to the vehicle's automatic control system via a notification device. The notification device can visually display the driving advice on the vehicle's display or inform the driver via voice.

[0484] Furthermore, the server is equipped with a risk prediction system. This system collects data from weather information services and traffic information databases to predict potential hazards. The resulting warnings are provided to the automatic control system by an alert notification system, and the driver is alerted when necessary.

[0485] For example, if a vehicle ahead suddenly slows down in rainy weather, the server immediately detects the situation. The risk prediction system takes into account the slipperiness of the road surface and generates a warning such as "Increase your following distance and prepare to brake," which is then communicated to the driver through a notification system. Throughout this entire process, the vehicle can maintain driving efficiency while enhancing safety.

[0486] An example of a prompt would be, "What real-time advice can an autonomous vehicle provide to safely navigate sharp curves in rainy weather?" Using such prompts allows generative AI models to effectively create driving advice.

[0487] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0488] Step 1:

[0489] To obtain vehicle status and location information, a group of sensors installed on the vehicle measures speed, acceleration, GPS data, and other parameters. This data is transmitted to a server in real time. At this stage, the raw data from the sensors is used as input, which is then converted into a structured data format and stored in a database.

[0490] Step 2:

[0491] The server uses machine learning algorithms to analyze the acquired data. It analyzes the input data using the TensorFlow library and evaluates the driving situation. As a result of the analysis, it outputs driving advice to improve driving safety and efficiency. Through this analysis process, the server generates the necessary commands for the driver.

[0492] Step 3:

[0493] The generated driving advice is sent from the server to the vehicle's terminal. The terminal uses the notification means to provide advice to the driver through visual displays or audio guidance. The input at this stage is the generated advice data, and the output is visual or auditory instructions to the driver.

[0494] Step 4:

[0495] The server obtains traffic conditions and weather information from an external database and predicts potential hazards using risk prediction tools. The input is the obtained environmental data, and calculations are performed based on this data to determine the presence or absence of potential hazards. The output is generated as a warning message.

[0496] Step 5:

[0497] Warnings generated by the risk prediction system are sent from the server to the terminal and provided to the driver via the warning notification system. The input is warning data, and the output is an alert that draws the driver's attention. The terminal processes these warnings instantly and communicates them to the driver visually or audibly when necessary.

[0498] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0499] This invention is an in-vehicle system aimed at supporting safe driving by the driver, and further incorporates an emotion engine that recognizes the user's emotions in real time and enables interaction in accordance with those emotions. In addition to basic functions such as collecting and analyzing vehicle status and location information and generating and presenting driving advice, this system has advanced functions that take the user's emotions into consideration.

[0500] The server continuously acquires vehicle status and location information from sensors and GPS, and analyzes it. Based on the analysis results, it generates necessary driving advice and warnings for the driver and provides them to the driver via a terminal. The content of the warnings and advice comprehensively takes into account road conditions and weather changes.

[0501] The emotion engine uses in-car cameras and microphones to monitor the user's facial expressions and voice, and analyzes their emotions in real time. The emotional information obtained from this analysis is taken into consideration when generating driving advice. For example, if the user is feeling stressed, it can present advice in a tone that promotes relaxation.

[0502] Furthermore, by analyzing accumulated emotional data, it becomes possible to understand drivers' long-term behavioral patterns and reactions, and provide more personalized advice. In this way, interactions optimized for each individual driver are realized.

[0503] Specific example

[0504] For example, if a driver gets caught in traffic during their commute, the server recognizes the traffic information and provides normal route advice. On the other hand, if the emotion engine detects frustration or anxiety from the driver's facial expressions and voice, the device will provide advice in a relaxed tone, such as, "Relax, take a deep breath. Safe driving is the most important thing."

[0505] In this way, by utilizing the emotion engine of this system, flexible responses tailored to the driver's psychological state become possible, contributing to improved traffic safety. Furthermore, since advice for improving fuel efficiency is presented in a way that is appropriate to the driver's emotional state, more effective eco-driving support is possible.

[0506] The following describes the processing flow.

[0507] Step 1:

[0508] The server continuously acquires vehicle status and location information from sensors and GPS installed in the vehicle. This information includes speed, acceleration, steering angle, and current position.

[0509] Step 2:

[0510] The server acquires the user's facial expressions and voice from the in-car camera and microphone. Based on this data, the emotion engine analyzes the user's emotions in real time. For example, it can determine whether the user is feeling stressed based on their facial expressions.

[0511] Step 3:

[0512] The server performs preprocessing to prepare the data for analysis. For vehicle data, it removes noise and detects outliers, while for emotion data, it extracts facial expressions and vocal characteristics.

[0513] Step 4:

[0514] The server evaluates driving conditions based on analyzed vehicle and emotional data. This evaluation includes determining whether the speed is appropriate, selecting future driving routes, and assessing the user's psychological state.

[0515] Step 5:

[0516] The server generates driving advice, adjusting the content and tone of the advice based on the user's emotional state. For example, if the user is relaxed, it generates standard advice; if the user is stressed, it generates advice in a more calming tone.

[0517] Step 6:

[0518] The terminal provides the user with driving advice and warnings received from the server, either visually or audibly. This allows the user to take appropriate actions in real time based on the driving conditions.

[0519] Step 7:

[0520] After a driving session ends, the server calculates a driving score based on the collected driving and emotional data and presents it to the user via the terminal. The score includes evaluations of safety and eco-driving.

[0521] Step 8:

[0522] The device provides users with feedback based on driving and emotional data, including specific advice on how to improve driving and stress management.

[0523] Step 9:

[0524] The server analyzes accumulated emotional data using an emotion engine, updates the advice generation logic to be optimal for each individual user, and prepares to provide personalized advice in future driving sessions.

[0525] (Example 2)

[0526] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0527] Preventing traffic accidents and improving driver safety are crucial challenges. However, conventional systems have struggled to grasp road conditions and drivers' emotional states in real time and take appropriate action based on that information. Furthermore, because they do not take into account the driver's mental state, they cannot mitigate the impact of tension and stress on driving. Therefore, there is a need to enhance driver safety while simultaneously providing support tailored to individual psychological states.

[0528] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0529] In this invention, the server includes information acquisition means for acquiring vehicle operation information and location information, emotion analysis means for monitoring the user's facial expressions and voice and analyzing their emotions, and hazard prediction means for predicting potential hazards based on the traffic conditions and weather conditions ahead and generating warnings. This enables real-time understanding of various traffic situations faced by the driver and driving assistance tailored to the emotional state of each individual driver. This provides a safe and comfortable driving environment.

[0530] "Information acquisition means" refers to devices and methods for acquiring vehicle operation information and location information.

[0531] "Advice generation means" refers to a device or method for analyzing information collected by information acquisition means and generating driving advice for the driver.

[0532] "Information presentation means" refers to devices or methods for providing drivers with driving advice generated by advice generation means.

[0533] "Hazard prediction means" refers to devices or methods for predicting potential hazards based on traffic conditions and weather conditions ahead and generating warnings.

[0534] "Warning information means" refers to devices or methods for presenting warnings generated by hazard prediction means to the driver.

[0535] "Emotion analysis means" refers to devices and methods for monitoring a user's facial expressions and voice and analyzing their emotions.

[0536] "Driving support means" refers to devices and methods for generating advice that reflects the driver's psychological state, taking into account emotions obtained through emotion analysis means.

[0537] "Score calculation means" refers to a device or method for calculating a driving score based on driving information.

[0538] "Environmentally friendly driving support means" refers to devices and methods for generating and providing advice on improving fuel consumption in order to reduce the environmental impact of a driver's driving.

[0539] This invention provides an in-vehicle system to enhance driver safety and reduce the environmental impact while driving. The server first acquires information about the vehicle's operation and location from sensors and GPS devices. This information includes vehicle speed, engine operating status, road conditions, and weather information.

[0540] Next, the server analyzes the acquired information in real time. This analysis process involves advanced data processing to generate advice on optimal driving routes and driving conditions. The latest artificial intelligence technology is used at this stage, particularly by leveraging generative AI models to improve the driver experience.

[0541] In addition, emotion analysis is performed using the vehicle's cameras and microphones. The server analyzes the driver's facial expressions and voice in real time to determine their emotions. Once the emotional state is determined, advice is generated that takes the driver's psychological state into account and is provided to the driver through the terminal. For example, if the system detects that the user is tense, it will provide a message encouraging them to relax.

[0542] The terminal's role is to present the user with analysis data and generated advice sent from the server. The terminal provides information to the driver using voice output devices and screen displays, and features improved usability to ensure the user can quickly understand and act upon the information.

[0543] As a concrete example, when a user encounters traffic congestion, the server suggests an alternative route based on traffic information. On the other hand, if stress is detected through emotion analysis, advice such as "Take a deep breath and calm down. Please drive safely" is provided.

[0544] In this way, the system can provide driving assistance optimized for each individual driver in real time, enabling safe driving and environmental considerations.

[0545] An example of a prompt message is, "Analyze the driver's emotions in real time and generate driving advice based on those emotions."

[0546] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0547] Step 1:

[0548] The server collects information from sensors and GPS devices installed in the vehicle. It receives vehicle speed, engine status, location information, and weather data as input. This input data is used to understand the vehicle's operating status and environmental conditions in real time. The collected information is output as foundational data for subsequent analysis. Specifically, the server periodically polls data from the devices and stores it in a database.

[0549] Step 2:

[0550] The server analyzes the collected information. This analysis uses a generative AI model to prepare the foundational data for generating optimal driving routes and driving advice. The input data includes all the data collected in Step 1. During the analysis, statistical algorithms and machine learning models are used to predict traffic flow and road conditions. The output is a list of candidate driving advice to provide to the driver. Specifically, the server calculates the results of the real-time analysis and passes them on to the next step.

[0551] Step 3:

[0552] The server receives the user's facial expressions and voice as input from the in-car camera and microphone, and performs emotion analysis. Using a generative AI model, it analyzes this input data to identify the user's emotional state. If the analysis determines, for example, that the user is in a stressed state, emotion data is generated as output. Specifically, the server processes the video data from the camera and the audio data from the microphone every second and applies the emotion recognition algorithm.

[0553] Step 4:

[0554] The server integrates candidate driving advice and emotion data to generate optimal driving advice. The inputs are the list of candidate advice from step 2 and the emotion data from step 3. This integration process outputs personalized driving advice that is appropriate for the user's emotional state. Specifically, the server applies multiple inference rules to select the most appropriate message for the driver.

[0555] Step 5:

[0556] The terminal notifies the user of the final generated driving advice. The input is the output from step 4. The terminal uses speech synthesis technology and a display to provide the driver with information visually and audibly. This allows the user to receive information to perform appropriate driving actions. Specifically, the terminal uses its speaker and display to deliver messages that promote relaxation.

[0557] (Application Example 2)

[0558] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0559] While modern vehicles offer numerous systems to support safe driving, few possess interaction features that take into account the emotions of the driver and passengers. Since a driver's psychological state significantly impacts their performance and safety, there is a growing need for systems that can provide emotion-based feedback. Furthermore, there is a demand for a more comfortable and safer driving experience through emotion-based adjustments to the in-car environment.

[0560] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0561] In this invention, the server includes data collection means for acquiring vehicle status information and location information, advice generation means for analyzing the information acquired by the data collection means and generating driving advice for the driver, and emotion analysis means for detecting the emotional state of the occupants and providing emotion-based feedback. This makes it possible to provide appropriate feedback and adjust the in-vehicle environment according to the emotions of the driver and occupants.

[0562] "Data collection means" refers to devices and methods for acquiring vehicle status information and location information.

[0563] The "advice generation means" is a function that analyzes collected information and generates driving advice for the driver.

[0564] "Presentation means" refers to devices or methods for providing generated driving advice or warnings to the driver.

[0565] A "hazard prediction system" is a function that predicts potential hazards based on traffic conditions and weather information ahead and generates warnings.

[0566] A "warning notification means" refers to a method or device for communicating a generated warning to the driver.

[0567] "Emotional analysis means" refers to a function that detects the emotional state of the crew and provides feedback based on that.

[0568] "Environmental adjustment means" refers to devices or methods for appropriately adjusting the vehicle's environment based on data obtained through emotion analysis.

[0569] In this invention, the server acquires vehicle status information and location information from data collection means installed in the vehicle. This makes it possible to collect environmental data in real time while driving. The data collection means consists of in-vehicle sensors, GPS modules, and the like.

[0570] The server analyzes this data using an advice generation system and generates necessary driving advice for the driver. The advice is then provided to the driver via a presentation system, such as a digital display or voice assistant.

[0571] Furthermore, the vehicle uses emotion analysis capabilities to monitor the occupants' emotional state in real time through facial recognition cameras and microphones. Based on this information, it generates emotional feedback and, if necessary, modifies the in-vehicle environment using environmental adjustment mechanisms. For example, if an occupant is feeling stressed, it automatically plays relaxing music or adjusts the temperature.

[0572] Furthermore, the hazard prediction system predicts potential hazards based on road conditions and weather information, and communicates them to the driver through a warning system. This allows the driver to take appropriate action in advance.

[0573] For example, if the system detects that the occupants are fatigued during long drives, it will use emotion analysis to reduce stress levels by adjusting the lighting to a slightly dimmer setting and playing relaxation music to create a more comfortable driving environment.

[0574] An example of a prompt message for the generating AI model is the instruction, "Generate a program that takes into account the emotional state of the occupants and creates a safe and comfortable driving environment."

[0575] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0576] Step 1:

[0577] The server acquires vehicle status and location information from data collection devices installed in the vehicle. Specifically, it acquires data such as current speed, location, and engine status in real time from on-board sensors and GPS modules, and stores this data in a database. The input is raw data from the sensors, and the output is status and location information in an analyzable format.

[0578] Step 2:

[0579] The server analyzes the acquired data using an advice generation system and generates driving advice for the driver. Here, a data analysis algorithm is used to derive optimal advice that takes into account the driver's driving style and current traffic conditions. Inputs are vehicle status information and location information, and output is specific driving advice. The advice is expressed in text or audio format.

[0580] Step 3:

[0581] The terminal provides the driver with generated driving advice. It supports driving decision-making by displaying the advice on a screen or communicating it to the driver via voice. The input is the generated advice, and the output is presented to the driver.

[0582] Step 4:

[0583] The server uses emotion analysis tools to analyze the emotions of the crew members from their facial expressions and voice data. Using facial recognition and voice recognition technologies, it quantifies the crew members' emotions such as stress, fatigue, and sense of security, and generates analysis results. Input is data from cameras and microphones, and output is the analyzed emotion data.

[0584] Step 5:

[0585] The server, based on the analyzed emotional data, generates necessary feedback through environmental adjustment mechanisms to adjust the in-car environment. Examples include adjusting the lighting and selecting music. The input is emotional data, and the output is specific instructions for adjusting the environment.

[0586] Step 6:

[0587] The device uses hazard prediction tools to analyze traffic conditions and weather information ahead and predict potential hazards. Based on the prediction results, it issues a warning to the driver. The input is traffic and weather information, and the output is a warning message conveyed to the driver.

[0588] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0589] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0590] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0591] [Fourth Embodiment]

[0592] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0593] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0594] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0595] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0596] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0597] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0598] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0599] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0600] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0601] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0602] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0603] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0604] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0605] This invention is a system for achieving traffic safety and environmental protection. This system has the function of acquiring vehicle status and location information, and a server analyzes this information to provide appropriate advice to the driver. Specifically, this system collects data from sensors mounted on the vehicle in real time and generates driving advice and warnings based on that data.

[0606] The server acquires vehicle status information such as speed, acceleration, and brake usage, and also uses GPS to determine the vehicle's location. This allows the server to evaluate the driver's current driving situation in detail. Based on the evaluation results, the server generates advice to encourage the driver to take appropriate driving actions, such as adjusting speed or checking the distance between vehicles. This advice is presented to the driver visually or audibly through a terminal.

[0607] The server also takes into account traffic conditions and weather information ahead to predict potential hazards. For example, if there is a possibility of sudden braking on the road ahead, the server will immediately issue a warning to the driver. This can prevent potential accidents.

[0608] Upon completion of the drive, the server analyzes the driving information in detail and generates a driving score. This score is an indicator of how safely and efficiently the driver drove and is provided to the driver via the terminal. Furthermore, the server includes an eco-driving support function that can suggest specific advice to the driver on how to improve fuel efficiency.

[0609] Specific example

[0610] For example, when a driver is driving on a highway and approaches a sharp curve, the server analyzes the curve's location and the vehicle's speed. If it detects a possible speeding violation, it will issue a voice command from the terminal saying, "Slow down in preparation for the next curve." Also, if driving in heavy rain, the server will predict slippery road conditions based on weather information and display a warning saying, "Maintain a safe distance from other vehicles and drive safely."

[0611] Overall, this system aims to support safe driving by providing drivers with useful information in real time, thereby reducing traffic accidents and environmental impact.

[0612] The following describes the processing flow.

[0613] Step 1:

[0614] The server acquires real-time status information from sensors mounted on the vehicle. This information includes speed, acceleration, brake usage, and steering angle. It also acquires the vehicle's location information using GPS.

[0615] Step 2:

[0616] The server preprocesses the acquired raw data. This includes noise reduction, data format conversion, and handling of missing values. This preprocessing prepares the data in a state suitable for analysis.

[0617] Step 3:

[0618] The server inputs pre-processed data into an AI model and analyzes the driving situation. Here, the driver's behavior is evaluated, taking into account driving behavior, road conditions, and the influence of other traffic participants.

[0619] Step 4:

[0620] Based on the analysis results, the server generates driving advice to provide to the driver. This advice may include instructions such as when speed adjustments are needed or when a safe following distance should be maintained.

[0621] Step 5:

[0622] The terminal provides the user with driving advice transmitted from the server, either verbally or visually. This allows the user to take appropriate driving actions through real-time navigation.

[0623] Step 6:

[0624] The server predicts potential hazards based on traffic conditions and weather information ahead. For example, this includes predicting changes in weather and traffic congestion.

[0625] Step 7:

[0626] If a potential threat is detected, the server will immediately generate a warning and present it to the user via the terminal. The warning will be delivered in visual or audio format.

[0627] Step 8:

[0628] Once the operation is complete, the server calculates an operation score based on the data from the entire operation session. This score is an indicator used to evaluate the safety and efficiency of the operation.

[0629] Step 9:

[0630] The device provides the user with a calculated driving score and feedback, including strengths to highlight and areas for improvement.

[0631] Step 10:

[0632] The server generates and provides to users via terminals advice on reducing environmental impact, such as driving methods to improve fuel efficiency.

[0633] (Example 1)

[0634] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0635] Balancing safety and environmental protection is crucial in automobile driving. However, drivers often lack access to timely and appropriate information, leading to inappropriate driving behavior. Therefore, there is a need for effective and immediate support systems to prevent traffic accidents and improve fuel efficiency.

[0636] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0637] In this invention, the server includes an information gathering means for acquiring vehicle status information and location information, an instruction generation means for analyzing the information acquired by the information gathering means and generating driving instructions for the driver, and an information presentation means for providing the driving instructions generated by the instruction generation means to the driver. As a result, the driver can receive information in real time to support safe and efficient driving, thereby preventing traffic accidents and reducing environmental impact.

[0638] "Information gathering means" refers to functions that use various sensors and GPS installed in the vehicle to acquire vehicle status information and location information in real time.

[0639] The "instruction generation means" is a function that analyzes data acquired by the information gathering means and generates specific driving instructions and advice for the driver.

[0640] "Information presentation means" refers to a device or method for providing generated driving instructions or warnings to the driver visually or audibly.

[0641] A "predictive warning system" is a function that analyzes traffic conditions and weather information ahead to predict potential dangers in advance and generate warnings.

[0642] "Warning display means" refers to a device or method for presenting a warning generated by a predictive warning means to the driver.

[0643] The "evaluation calculation means" is a function that calculates a driving evaluation based on driving data acquired after the end of the drive and provides that evaluation as feedback to the driver.

[0644] "Environmental protection support measures" refer to functions that generate and provide specific instructions for reducing the environmental impact of driver actions and improving fuel efficiency.

[0645] This invention is a support system for achieving both safety and environmental protection in vehicle operation. The server uses various sensors and GPS devices mounted on the vehicle to acquire status information such as vehicle speed, acceleration, and brake usage, as well as location information, in real time. This information is acquired by information collection means.

[0646] The server analyzes the collected data and generates specific driving instructions for the driver based on the results. The generation AI model used here employs deep learning technology to detect abnormal driving patterns and potential hazards. The driving instructions generated after the analysis are provided by an instruction generation system. For example, the AI ​​generates instructions using prompts such as, "Do you need to adjust your speed for the next curve?"

[0647] The terminal displays driving instructions generated by the server to the driver. This display is delivered visually or audibly using speech synthesis technology and a display. By following the instructions provided through the terminal, the driver can reduce the risk of traffic accidents and minimize environmental impact. A specific example is when driving on a highway, the terminal issues a voice instruction such as, "Slow down in preparation for the next curve."

[0648] Once the drive is complete, the server calculates a driving evaluation based on the data acquired during the drive and provides feedback to the driver. This allows the driver to objectively evaluate the safety and efficiency of their driving behavior. Furthermore, driving improvement instructions for eco-driving are provided, promoting improved fuel efficiency. In this way, combining the server and terminal makes it possible to provide drivers with real-time support for safe driving and environmental protection.

[0649] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0650] Step 1:

[0651] The server acquires real-time speed, acceleration, brake usage, and location information from sensors and GPS installed in the vehicle. This data is aggregated on the server by various data collection methods. The server records this data chronologically to form an overall picture of the driving conditions.

[0652] Step 2:

[0653] The server analyzes the collected data. Specifically, it uses a generative AI model to process and calculate data to detect abnormal driving patterns and potential hazards. By analyzing the driving data as input, if abnormalities such as speeding or sudden acceleration are detected, the server incorporates this information into the generation of driving instructions.

[0654] Step 3:

[0655] The server generates appropriate driving instructions for the driver based on the analysis results. The generating AI model outputs specific instructions based on prompts such as "Do you need to adjust your speed for the next curve?". This provides the driver with advice on speed adjustments and maintaining a safe distance from other vehicles.

[0656] Step 4:

[0657] The terminal displays driving instructions sent from the server to the driver. Specifically, the terminal uses speech synthesis technology to play instructions such as "Slow down in preparation for the next curve" aloud. It also visually alerts the driver by displaying warning messages on the screen.

[0658] Step 5:

[0659] After the drive is complete, the server calculates a driving evaluation based on the data collected during the drive. It analyzes the driving data as input and generates an output that includes a driving evaluation along with fuel efficiency and safety evaluations. This evaluation is then fed back to the driver via a terminal to help improve future driving.

[0660] (Application Example 1)

[0661] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0662] With the proliferation of autonomous vehicles, the increasingly complex traffic environment necessitates simultaneous improvements in both safety and driving efficiency. Conventional technologies struggle to provide immediate driving advice and warnings based on real-time driving data, and have not adequately improved the accuracy of predicting potential hazards or fuel efficiency. To address these challenges, there is a need for systems that enable safer and more environmentally conscious driving in autonomous vehicles.

[0663] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0664] In this invention, the server includes measuring means for acquiring vehicle status information and location information; command generation means for analyzing the information acquired by the measuring means and autonomously generating driving advice; notification means for providing the driving advice generated by the command generation means to an automatic control device; risk prediction means for predicting potential hazards and generating warnings based on the traffic environment and weather data ahead; and warning notification means for providing the warning generated by the risk prediction means to an automatic control device. This enables the autonomous vehicle to drive safely and efficiently in real time.

[0665] A "measuring means" is a device that uses sensors installed on the vehicle to acquire state information such as the vehicle's speed, acceleration, and position.

[0666] A "command generation means" is a device that analyzes data acquired by a measurement means and autonomously generates driving advice necessary to improve driving safety and efficiency.

[0667] A "notification means" is a device that receives driving advice generated by a command generation means and provides it to an automatic control device.

[0668] A "risk prediction device" is a device that analyzes traffic conditions and weather data ahead to predict potential dangers and generate warnings.

[0669] A "warning notification means" is a device that provides warnings generated by a risk prediction means to an automatic control device and notifies the driver visually or audibly.

[0670] This invention is a system for achieving safe and efficient driving in autonomous vehicles. The system is implemented with the following configuration.

[0671] The server uses multiple sensors installed in the vehicle, such as speed sensors, acceleration sensors, and GPS modules, to measure the vehicle's status and location in real time. This data is collected by the measurement devices and continuously monitored and recorded by the server.

[0672] On the server side, a command generation mechanism is used to analyze the acquired data. This mechanism utilizes machine learning algorithms, particularly libraries such as TensorFlow, to analyze and predict operating conditions. Based on the results of the data analysis, driving advice is generated.

[0673] The server provides the generated driving advice to the vehicle's automatic control system via a notification device. The notification device can visually display the driving advice on the vehicle's display or inform the driver via voice.

[0674] Furthermore, the server is equipped with a risk prediction system. This system collects data from weather information services and traffic information databases to predict potential hazards. The resulting warnings are provided to the automatic control system by an alert notification system, and the driver is alerted when necessary.

[0675] For example, if a vehicle ahead suddenly slows down in rainy weather, the server immediately detects the situation. The risk prediction system takes into account the slipperiness of the road surface and generates a warning such as "Increase your following distance and prepare to brake," which is then communicated to the driver through a notification system. Throughout this entire process, the vehicle can maintain driving efficiency while enhancing safety.

[0676] An example of a prompt would be, "What real-time advice can an autonomous vehicle provide to safely navigate sharp curves in rainy weather?" Using such prompts allows generative AI models to effectively create driving advice.

[0677] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0678] Step 1:

[0679] To obtain vehicle status and location information, a group of sensors installed on the vehicle measures speed, acceleration, GPS data, and other parameters. This data is transmitted to a server in real time. At this stage, the raw data from the sensors is used as input, which is then converted into a structured data format and stored in a database.

[0680] Step 2:

[0681] The server uses machine learning algorithms to analyze the acquired data. It analyzes the input data using the TensorFlow library and evaluates the driving situation. As a result of the analysis, it outputs driving advice to improve driving safety and efficiency. Through this analysis process, the server generates the necessary commands for the driver.

[0682] Step 3:

[0683] The generated driving advice is sent from the server to the vehicle's terminal. The terminal uses the notification means to provide advice to the driver through visual displays or audio guidance. The input at this stage is the generated advice data, and the output is visual or auditory instructions to the driver.

[0684] Step 4:

[0685] The server obtains traffic conditions and weather information from an external database and predicts potential hazards using risk prediction tools. The input is the obtained environmental data, and calculations are performed based on this data to determine the presence or absence of potential hazards. The output is generated as a warning message.

[0686] Step 5:

[0687] Warnings generated by the risk prediction system are sent from the server to the terminal and provided to the driver via the warning notification system. The input is warning data, and the output is an alert that draws the driver's attention. The terminal processes these warnings instantly and communicates them to the driver visually or audibly when necessary.

[0688] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0689] This invention is an in-vehicle system aimed at supporting safe driving by the driver, and further incorporates an emotion engine that recognizes the user's emotions in real time and enables interaction in accordance with those emotions. In addition to basic functions such as collecting and analyzing vehicle status and location information and generating and presenting driving advice, this system has advanced functions that take the user's emotions into consideration.

[0690] The server continuously acquires vehicle status and location information from sensors and GPS, and analyzes it. Based on the analysis results, it generates necessary driving advice and warnings for the driver and provides them to the driver via a terminal. The content of the warnings and advice comprehensively takes into account road conditions and weather changes.

[0691] The emotion engine uses in-car cameras and microphones to monitor the user's facial expressions and voice, and analyzes their emotions in real time. The emotional information obtained from this analysis is taken into consideration when generating driving advice. For example, if the user is feeling stressed, it can present advice in a tone that promotes relaxation.

[0692] Furthermore, by analyzing accumulated emotional data, it becomes possible to understand drivers' long-term behavioral patterns and reactions, and provide more personalized advice. In this way, interactions optimized for each individual driver are realized.

[0693] Specific example

[0694] For example, if a driver gets caught in traffic during their commute, the server recognizes the traffic information and provides normal route advice. On the other hand, if the emotion engine detects frustration or anxiety from the driver's facial expressions and voice, the device will provide advice in a relaxed tone, such as, "Relax, take a deep breath. Safe driving is the most important thing."

[0695] In this way, by utilizing the emotion engine of this system, flexible responses tailored to the driver's psychological state become possible, contributing to improved traffic safety. Furthermore, since advice for improving fuel efficiency is presented in a way that is appropriate to the driver's emotional state, more effective eco-driving support is possible.

[0696] The following describes the processing flow.

[0697] Step 1:

[0698] The server continuously acquires vehicle status and location information from sensors and GPS installed in the vehicle. This information includes speed, acceleration, steering angle, and current position.

[0699] Step 2:

[0700] The server acquires the user's facial expressions and voice from the in-car camera and microphone. Based on this data, the emotion engine analyzes the user's emotions in real time. For example, it can determine whether the user is feeling stressed based on their facial expressions.

[0701] Step 3:

[0702] The server performs preprocessing to prepare the data for analysis. For vehicle data, it removes noise and detects outliers, while for emotion data, it extracts facial expressions and vocal characteristics.

[0703] Step 4:

[0704] The server evaluates driving conditions based on analyzed vehicle and emotional data. This evaluation includes determining whether the speed is appropriate, selecting future driving routes, and assessing the user's psychological state.

[0705] Step 5:

[0706] The server generates driving advice, adjusting the content and tone of the advice based on the user's emotional state. For example, if the user is relaxed, it generates standard advice; if the user is stressed, it generates advice in a more calming tone.

[0707] Step 6:

[0708] The terminal provides the user with driving advice and warnings received from the server, either visually or audibly. This allows the user to take appropriate actions in real time based on the driving conditions.

[0709] Step 7:

[0710] After a driving session ends, the server calculates a driving score based on the collected driving and emotional data and presents it to the user via the terminal. The score includes evaluations of safety and eco-driving.

[0711] Step 8:

[0712] The device provides users with feedback based on driving and emotional data, including specific advice on how to improve driving and stress management.

[0713] Step 9:

[0714] The server analyzes accumulated emotional data using an emotion engine, updates the advice generation logic to be optimal for each individual user, and prepares to provide personalized advice in future driving sessions.

[0715] (Example 2)

[0716] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0717] Preventing traffic accidents and improving driver safety are crucial challenges. However, conventional systems have struggled to grasp road conditions and drivers' emotional states in real time and take appropriate action based on that information. Furthermore, because they do not take into account the driver's mental state, they cannot mitigate the impact of tension and stress on driving. Therefore, there is a need to enhance driver safety while simultaneously providing support tailored to individual psychological states.

[0718] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0719] In this invention, the server includes information acquisition means for acquiring vehicle operation information and location information, emotion analysis means for monitoring the user's facial expressions and voice and analyzing their emotions, and hazard prediction means for predicting potential hazards based on the traffic conditions and weather conditions ahead and generating warnings. This enables real-time understanding of various traffic situations faced by the driver and driving assistance tailored to the emotional state of each individual driver. This provides a safe and comfortable driving environment.

[0720] "Information acquisition means" refers to devices and methods for acquiring vehicle operation information and location information.

[0721] "Advice generation means" refers to a device or method for analyzing information collected by information acquisition means and generating driving advice for the driver.

[0722] "Information presentation means" refers to devices or methods for providing drivers with driving advice generated by advice generation means.

[0723] "Hazard prediction means" refers to devices or methods for predicting potential hazards based on traffic conditions and weather conditions ahead and generating warnings.

[0724] "Warning information means" refers to devices or methods for presenting warnings generated by hazard prediction means to the driver.

[0725] "Emotion analysis means" refers to devices and methods for monitoring a user's facial expressions and voice and analyzing their emotions.

[0726] "Driving support means" refers to devices and methods for generating advice that reflects the driver's psychological state, taking into account emotions obtained through emotion analysis means.

[0727] "Score calculation means" refers to a device or method for calculating a driving score based on driving information.

[0728] "Environmentally friendly driving support means" refers to devices and methods for generating and providing advice on improving fuel consumption in order to reduce the environmental impact of a driver's driving.

[0729] This invention provides an in-vehicle system to enhance driver safety and reduce the environmental impact while driving. The server first acquires information about the vehicle's operation and location from sensors and GPS devices. This information includes vehicle speed, engine operating status, road conditions, and weather information.

[0730] Next, the server analyzes the acquired information in real time. This analysis process involves advanced data processing to generate advice on optimal driving routes and driving conditions. The latest artificial intelligence technology is used at this stage, particularly by leveraging generative AI models to improve the driver experience.

[0731] In addition, emotion analysis is performed using the vehicle's cameras and microphones. The server analyzes the driver's facial expressions and voice in real time to determine their emotions. Once the emotional state is determined, advice is generated that takes the driver's psychological state into account and is provided to the driver through the terminal. For example, if the system detects that the user is tense, it will provide a message encouraging them to relax.

[0732] The terminal's role is to present the user with analysis data and generated advice sent from the server. The terminal provides information to the driver using voice output devices and screen displays, and features improved usability to ensure the user can quickly understand and act upon the information.

[0733] As a concrete example, when a user encounters traffic congestion, the server suggests an alternative route based on traffic information. On the other hand, if stress is detected through emotion analysis, advice such as "Take a deep breath and calm down. Please drive safely" is provided.

[0734] In this way, the system can provide driving assistance optimized for each individual driver in real time, enabling safe driving and environmental considerations.

[0735] An example of a prompt message is, "Analyze the driver's emotions in real time and generate driving advice based on those emotions."

[0736] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0737] Step 1:

[0738] The server collects information from sensors and GPS devices installed in the vehicle. It receives vehicle speed, engine status, location information, and weather data as input. This input data is used to understand the vehicle's operating status and environmental conditions in real time. The collected information is output as foundational data for subsequent analysis. Specifically, the server periodically polls data from the devices and stores it in a database.

[0739] Step 2:

[0740] The server analyzes the collected information. This analysis uses a generative AI model to prepare the foundational data for generating optimal driving routes and driving advice. The input data includes all the data collected in Step 1. During the analysis, statistical algorithms and machine learning models are used to predict traffic flow and road conditions. The output is a list of candidate driving advice to provide to the driver. Specifically, the server calculates the results of the real-time analysis and passes them on to the next step.

[0741] Step 3:

[0742] The server receives the user's facial expressions and voice as input from the in-car camera and microphone, and performs emotion analysis. Using a generative AI model, it analyzes this input data to identify the user's emotional state. If the analysis determines, for example, that the user is in a stressed state, emotion data is generated as output. Specifically, the server processes the video data from the camera and the audio data from the microphone every second and applies the emotion recognition algorithm.

[0743] Step 4:

[0744] The server integrates candidate driving advice and emotion data to generate optimal driving advice. The inputs are the list of candidate advice from step 2 and the emotion data from step 3. This integration process outputs personalized driving advice that is appropriate for the user's emotional state. Specifically, the server applies multiple inference rules to select the most appropriate message for the driver.

[0745] Step 5:

[0746] The terminal notifies the user of the final generated driving advice. The input is the output from step 4. The terminal uses speech synthesis technology and a display to provide the driver with information visually and audibly. This allows the user to receive information to perform appropriate driving actions. Specifically, the terminal uses its speaker and display to deliver messages that promote relaxation.

[0747] (Application Example 2)

[0748] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0749] While modern vehicles offer numerous systems to support safe driving, few possess interaction features that take into account the emotions of the driver and passengers. Since a driver's psychological state significantly impacts their performance and safety, there is a growing need for systems that can provide emotion-based feedback. Furthermore, there is a demand for a more comfortable and safer driving experience through emotion-based adjustments to the in-car environment.

[0750] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0751] In this invention, the server includes data collection means for acquiring vehicle status information and location information, advice generation means for analyzing the information acquired by the data collection means and generating driving advice for the driver, and emotion analysis means for detecting the emotional state of the occupants and providing emotion-based feedback. This makes it possible to provide appropriate feedback and adjust the in-vehicle environment according to the emotions of the driver and occupants.

[0752] "Data collection means" refers to devices and methods for acquiring vehicle status information and location information.

[0753] The "advice generation means" is a function that analyzes collected information and generates driving advice for the driver.

[0754] "Presentation means" refers to devices or methods for providing generated driving advice or warnings to the driver.

[0755] A "hazard prediction system" is a function that predicts potential hazards based on traffic conditions and weather information ahead and generates warnings.

[0756] A "warning notification means" refers to a method or device for communicating a generated warning to the driver.

[0757] "Emotional analysis means" refers to a function that detects the emotional state of the crew and provides feedback based on that.

[0758] "Environmental adjustment means" refers to devices or methods for appropriately adjusting the vehicle's environment based on data obtained through emotion analysis.

[0759] In this invention, the server acquires vehicle status information and location information from data collection means installed in the vehicle. This makes it possible to collect environmental data in real time while driving. The data collection means consists of in-vehicle sensors, GPS modules, and the like.

[0760] The server analyzes this data using an advice generation system and generates necessary driving advice for the driver. The advice is then provided to the driver via a presentation system, such as a digital display or voice assistant.

[0761] Furthermore, the vehicle uses emotion analysis capabilities to monitor the occupants' emotional state in real time through facial recognition cameras and microphones. Based on this information, it generates emotional feedback and, if necessary, modifies the in-vehicle environment using environmental adjustment mechanisms. For example, if an occupant is feeling stressed, it automatically plays relaxing music or adjusts the temperature.

[0762] Furthermore, the hazard prediction system predicts potential hazards based on road conditions and weather information, and communicates them to the driver through a warning system. This allows the driver to take appropriate action in advance.

[0763] For example, if the system detects that the occupants are fatigued during long drives, it will use emotion analysis to reduce stress levels by adjusting the lighting to a slightly dimmer setting and playing relaxation music to create a more comfortable driving environment.

[0764] An example of a prompt message for the generating AI model is the instruction, "Generate a program that takes into account the emotional state of the occupants and creates a safe and comfortable driving environment."

[0765] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0766] Step 1:

[0767] The server acquires vehicle status and location information from data collection devices installed in the vehicle. Specifically, it acquires data such as current speed, location, and engine status in real time from on-board sensors and GPS modules, and stores this data in a database. The input is raw data from the sensors, and the output is status and location information in an analyzable format.

[0768] Step 2:

[0769] The server analyzes the acquired data using an advice generation system and generates driving advice for the driver. Here, a data analysis algorithm is used to derive optimal advice that takes into account the driver's driving style and current traffic conditions. Inputs are vehicle status information and location information, and output is specific driving advice. The advice is expressed in text or audio format.

[0770] Step 3:

[0771] The terminal provides the driver with generated driving advice. It supports driving decision-making by displaying the advice on a screen or communicating it to the driver via voice. The input is the generated advice, and the output is presented to the driver.

[0772] Step 4:

[0773] The server uses emotion analysis tools to analyze the emotions of the crew members from their facial expressions and voice data. Using facial recognition and voice recognition technologies, it quantifies the crew members' emotions such as stress, fatigue, and sense of security, and generates analysis results. Input is data from cameras and microphones, and output is the analyzed emotion data.

[0774] Step 5:

[0775] The server, based on the analyzed emotional data, generates necessary feedback through environmental adjustment mechanisms to adjust the in-car environment. Examples include adjusting the lighting and selecting music. The input is emotional data, and the output is specific instructions for adjusting the environment.

[0776] Step 6:

[0777] The device uses hazard prediction tools to analyze traffic conditions and weather information ahead and predict potential hazards. Based on the prediction results, it issues a warning to the driver. The input is traffic and weather information, and the output is a warning message conveyed to the driver.

[0778] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0779] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0780] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0781] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0782] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0783] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0784] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0785] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0786] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0787] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0788] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0789] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0790] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0791] 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.

[0792] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0793] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0794] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0795] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0796] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0797] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0798] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0799] The following is further disclosed regarding the embodiments described above.

[0800] (Claim 1)

[0801] A data collection means for acquiring vehicle status information and location information,

[0802] An advice generation means analyzes the information acquired by the data collection means and generates driving advice for the driver,

[0803] A presentation means for providing driving advice generated by the advice generation means to the driver,

[0804] A hazard prediction method that predicts potential hazards and generates warnings based on traffic conditions and weather information ahead,

[0805] A warning presentation means that presents a warning generated by the aforementioned hazard prediction means to the driver,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, further comprising a score calculation means that calculates a driving score based on driving information at the end of driving and provides the score to the driver.

[0809] (Claim 3)

[0810] The system according to claim 1, comprising an eco-driving support means that generates and provides advice for improving fuel efficiency in order to reduce the environmental impact caused by the driver's driving.

[0811] "Example 1"

[0812] (Claim 1)

[0813] A means for collecting information to acquire vehicle status information and location information,

[0814] An instruction generation means analyzes the information acquired by the information collection means and generates driving instructions for the driver,

[0815] Information presentation means for providing driving instructions generated by the instruction generation means to the driver,

[0816] A predictive warning system that predicts potential hazards and generates warnings based on traffic conditions and weather information ahead,

[0817] A warning display means that presents a warning generated by the predictive warning means to the driver,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, further comprising an evaluation calculation means for calculating a driving evaluation based on driving information at the end of driving and providing the evaluation to the driver.

[0821] (Claim 3)

[0822] The system according to claim 1, comprising an environmental protection support means for generating and providing instructions for improving fuel efficiency in order to reduce the environmental impact caused by the driver's driving.

[0823] "Application Example 1"

[0824] (Claim 1)

[0825] A measurement means for acquiring vehicle status information and location information,

[0826] A command generation means analyzes the information acquired by the measurement means and autonomously generates driving advice,

[0827] A notification means for providing the driving advice generated by the command generation means to the automatic control device,

[0828] A risk prediction means that predicts potential hazards and generates warnings based on the traffic environment and weather data ahead,

[0829] Alarm notification means that provides a warning generated by the risk prediction means to an automatic control device,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, further comprising an evaluation calculation means for calculating an operational evaluation regarding safety and efficiency at the end of operation and providing the evaluation to an automatic control device.

[0833] (Claim 3)

[0834] The system according to claim 1, comprising an environmentally conscious driving support means that generates and provides commands for improving fuel efficiency to an automatic control device in order to reduce environmental impact.

[0835] "Example 2 of combining an emotion engine"

[0836] (Claim 1)

[0837] Information acquisition means for acquiring vehicle operation information and location information,

[0838] An advice generation means analyzes the information collected by the information acquisition means and generates driving advice for the driver,

[0839] Information presentation means for providing driving advice generated by the advice generation means to the driver,

[0840] A hazard prediction means that predicts potential hazards and generates warnings based on the traffic conditions and weather conditions ahead,

[0841] Warning information means that presents a warning generated by the aforementioned hazard prediction means to the driver,

[0842] An emotion analysis means that monitors the user's facial expressions and voice and analyzes their emotions,

[0843] A driving support means that considers the emotions obtained by the emotion analysis means and generates advice that reflects the driver's psychological state,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, further comprising a score calculation means for calculating a driving score based on driving information at the end of driving.

[0847] (Claim 3)

[0848] The system according to claim 1, comprising an environmentally conscious driving support means that generates and provides advice for improving fuel consumption in order to reduce the environmental impact of the driver's driving.

[0849] "Application example 2 when combining with an emotional engine"

[0850] (Claim 1)

[0851] A data collection means for acquiring vehicle status information and location information,

[0852] An advice generation means analyzes the information acquired by the data collection means and generates driving advice for the driver,

[0853] A presentation means for providing driving advice generated by the advice generation means to the driver,

[0854] A hazard prediction method that predicts potential hazards and generates warnings based on traffic conditions and weather information ahead,

[0855] A warning presentation means that presents a warning generated by the aforementioned hazard prediction means to the driver,

[0856] An emotion analysis means that detects the emotional state of the crew and provides emotion-based feedback,

[0857] An environmental adjustment means that adjusts the vehicle environment based on the data acquired by the emotion analysis means,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, further comprising a score calculation means that calculates a driving score based on driving information at the end of driving and provides the score to the driver.

[0861] (Claim 3)

[0862] The system according to claim 1, comprising an eco-driving support means that generates and provides advice for improving fuel efficiency in order to reduce the environmental impact caused by the driver's driving. [Explanation of symbols]

[0863] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A data collection means for acquiring vehicle status information and location information, An advice generation means analyzes the information acquired by the data collection means and generates driving advice for the driver, A presentation means for providing driving advice generated by the advice generation means to the driver, A hazard prediction method that predicts potential hazards and generates warnings based on traffic conditions and weather information ahead, A warning presentation means that presents a warning generated by the aforementioned hazard prediction means to the driver, A system that includes this.

2. The system according to claim 1, further comprising a score calculation means that calculates a driving score based on driving information at the end of driving and provides the score to the driver.

3. The system according to claim 1, comprising an eco-driving support means that generates and provides advice for improving fuel efficiency in order to reduce the environmental impact caused by the driver's driving.

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