System

Abstract

An object of a system according to an embodiment is to propose an accurate driving operation even under an irregular situation.SOLUTION: A system according to an embodiment includes an image generation unit, a prompt input unit, and a driving operation suggestion unit. The image generation unit generates an image of an irregular situation. The prompt input unit inputs a situation during driving as a language prompt. The driving operation suggestion section suggests the driving operation based on the prompt input by the prompt input section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the AI's decision-making criteria for irregular situations in autonomous driving were unclear, posing a risk of misunderstanding.

[0005] The system according to the embodiment aims to propose accurate driving operations even under irregular circumstances. [Means for solving the problem]

[0006] The system according to the embodiment includes an image generation unit, a prompt input unit, and a driving maneuver suggestion unit. The image generation unit generates an image of an irregular situation. The prompt input unit inputs a driving situation as a verbal prompt. The driving maneuver suggestion unit suggests a driving maneuver based on the prompt input by the prompt input unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest accurate driving maneuvers even under irregular circumstances. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The automated driving system according to the embodiment of the present invention is a system that improves the recognition ability and response ability to irregular situations. This system proposes two methods using image generation AI and verbal prompts. This reduces the automated driving system's misrecognition and realizes a safe and secure automated driving world.

[0029] An autonomous driving system according to an embodiment includes an image generation unit, a prompt input unit, and a driving maneuver suggestion unit. The image generation unit generates images of irregular situations. For example, the image generation unit uses a generation AI to generate images of obstacles that suddenly appear on the road, unexpected weather changes, traffic accident scenes, and the like. The image generation unit can also input a prompt such as "An obstacle appeared on the road" to the generation AI and generate images of irregular situations based on the prompt. The prompt input unit inputs a situation during driving as a verbal prompt. For example, the prompt input unit can input a situation such as "An obstacle suddenly appeared ahead" to the generation AI as a verbal prompt while driving. The driving maneuver suggestion unit suggests driving maneuvers based on the prompt input by the prompt input unit. For example, the generation AI analyzes the prompt "An obstacle suddenly appeared ahead" and suggests the optimal driving maneuver for the situation. This enables the autonomous driving system according to an embodiment to improve its ability to recognize and respond to irregular situations. For example, it can take appropriate action even when an obstacle suddenly appears on the road or unexpected weather changes occur. In addition, by understanding the situation while driving in real time and taking appropriate action, misperceptions can be reduced, realizing a world of safe and secure autonomous driving.

[0030] The image generation unit can generate additional images from different viewpoints and angles for images of irregular situations. For example, the image generation unit generates additional images from different viewpoints and angles for images of irregular situations generated by the generation AI. For example, images of an obstacle that suddenly appears on the road can be generated from viewpoints in front, behind, and diagonally, thereby diversifying the dataset. This allows for the construction of a diverse dataset and improved recognition capabilities.

[0031] The image generation unit can automatically add detailed annotations to images of irregular situations. For example, the image generation unit automatically adds detailed annotations, such as the position and type of object and the direction of movement, to images of irregular situations generated by the generation AI. For example, annotations can be added that clearly indicate the position and type of obstacles that appear on the road. This can improve the quality of the training dataset.

[0032] The image generation unit generates videos of irregular situations, thereby improving the autonomous driving system's ability to respond to dynamic scenarios. The image generation unit, for example, uses a generation AI to generate videos of irregular situations and simulate dynamic scenarios. For example, a video of an obstacle suddenly appearing on the road and moving can be generated to verify the autonomous driving system's ability to respond. This improves the system's ability to respond to dynamic scenarios.

[0033] The image generation unit uses the generated image of the irregular situation to perform simulations under different weather conditions and time periods, thereby enhancing the ability to respond in a variety of environments. The image generation unit, for example, uses the generated image of the irregular situation to perform simulations under different weather conditions. For example, simulations are performed under conditions such as sunny weather, rainy weather, and snowy weather, thereby enhancing the ability to respond in a variety of environments.

[0034] The prompt input unit allows the generation AI to refer to similar situations in the past and suggest optimal driving maneuvers based on past response results. For example, in response to the input of a verbal prompt, the generation AI refers to similar situations in the past and suggests optimal driving maneuvers based on past response results. For example, in response to the prompt "An obstacle suddenly appeared ahead," the generation AI suggests optimal maneuvers based on past response results. This enables more effective driving by suggesting optimal driving maneuvers based on past response results.

[0035] The prompt input unit enables the generation AI to accept voice input in response to the input of a verbal prompt, thereby enabling a more intuitive input of the situation while driving. For example, the prompt input unit enables the generation AI to accept voice input in response to the input of a verbal prompt, thereby enabling a more intuitive input of the situation while driving. For example, a driver may input "There is an obstacle ahead" by voice. In this way, by accepting voice input, the situation while driving can be input more intuitively.

[0036] The prompt input unit allows the generation AI to provide visual feedback in response to the input of a linguistic prompt, thereby enabling the driver to visually confirm the proposed driving maneuvers. For example, when the driver inputs "there is an obstacle ahead," the prompt input unit allows the generation AI to provide visual feedback in response to the input of a linguistic prompt, thereby enabling the driver to visually confirm the proposed driving maneuvers. For example, when the driver inputs "there is an obstacle ahead," an avoidance maneuver is visually displayed. In this way, by providing visual feedback, the driver can visually confirm the proposed driving maneuvers.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The autonomous driving system also includes a voice recognition unit. The voice recognition unit recognizes the driver's voice instructions in real time while driving and can suggest driving maneuvers based on those instructions. For example, if the driver says "there is an obstacle ahead," the voice recognition unit analyzes the instruction and suggests appropriate avoidance maneuvers. The voice recognition unit can also record the driver's voice instructions and analyze them later. This allows for more intuitive and faster response by suggesting driving maneuvers based on voice instructions.

[0039] The autonomous driving system further includes an environment recognition unit. The environment recognition unit can acquire information about the surrounding environment in real time and suggest driving maneuvers based on that information. For example, the environment recognition unit acquires information about road conditions, weather, traffic volume, etc., and suggests optimal driving maneuvers based on that information. The environment recognition unit can also record the acquired environment information and analyze it later. This allows for safer and more efficient driving by suggesting driving maneuvers based on the surrounding environment information.

[0040] The automated driving system further includes a user interface unit. The user interface unit visually displays the driving situation and proposed driving operations, and can provide feedback to the driver. For example, if there is an obstacle ahead, the user interface unit displays its location and evasive operations on the display. The user interface unit can also provide real-time feedback on the driver's operations, improving driving safety. This visual feedback helps the driver understand the situation and enables safer driving.

[0041] The autonomous driving system further includes a prediction unit. The prediction unit can predict future situations based on the current driving situation and suggest driving maneuvers based on the prediction. For example, the prediction unit can predict changes in traffic conditions and weather ahead and suggest optimal driving maneuvers based on the predictions. The prediction unit can also predict future situations based on past data. This allows for safer and more efficient driving by predicting future situations.

[0042] The autonomous driving system also includes a communication unit. The communication unit can communicate with other vehicles and infrastructure and share information in real time. For example, the communication unit can receive information from the vehicle ahead and suggest driving maneuvers based on that information. The communication unit can also receive information from road infrastructure and suggest optimal driving maneuvers based on that information. This communication with other vehicles and infrastructure enables safer and more efficient driving.

[0043] The autonomous driving system further includes a learning unit. The learning unit collects data during driving and can improve the system's performance based on that data. For example, the learning unit records the driving situation and the driver's operations and improves the system's algorithm based on that data. The learning unit can also collect data from other vehicles and improve the system's performance based on that data. This allows for safer and more efficient driving by improving the system's performance based on data during driving.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The image generation unit generates images of irregular situations. For example, the generation AI can be used to generate images of obstacles that suddenly appear on the road, unexpected weather changes, traffic accident scenes, etc. It is also possible to input a prompt such as "an obstacle has appeared on the road" into the generation AI and generate an image of the irregular situation based on that prompt. Step 2: The prompt input unit inputs the situation during driving as a verbal prompt. For example, a situation such as "an obstacle suddenly appeared ahead" can be input to the generation AI as a verbal prompt while driving. Step 3: The driving maneuver suggestion unit suggests driving maneuvers based on the prompt input by the prompt input unit. For example, the generation AI analyzes the prompt "An obstacle suddenly appeared ahead" and suggests the optimal driving maneuver for that situation.

[0046] (Example 2) The automated driving system according to the embodiment of the present invention is a system that improves the recognition ability and response ability to irregular situations. This system proposes two methods using image generation AI and verbal prompts. This reduces the automated driving system's misrecognition and realizes a safe and secure automated driving world.

[0047] An autonomous driving system according to an embodiment includes an image generation unit, a prompt input unit, and a driving maneuver suggestion unit. The image generation unit generates images of irregular situations. For example, the image generation unit uses a generation AI to generate images of obstacles that suddenly appear on the road, unexpected weather changes, traffic accident scenes, and the like. The image generation unit can also input a prompt such as "An obstacle appeared on the road" to the generation AI and generate images of irregular situations based on the prompt. The prompt input unit inputs a situation during driving as a verbal prompt. For example, the prompt input unit can input a situation such as "An obstacle suddenly appeared ahead" to the generation AI as a verbal prompt while driving. The driving maneuver suggestion unit suggests driving maneuvers based on the prompt input by the prompt input unit. For example, the generation AI analyzes the prompt "An obstacle suddenly appeared ahead" and suggests the optimal driving maneuver for the situation. This enables the autonomous driving system according to an embodiment to improve its ability to recognize and respond to irregular situations. For example, it can take appropriate action even when an obstacle suddenly appears on the road or unexpected weather changes occur. In addition, by understanding the situation while driving in real time and taking appropriate action, misperceptions can be reduced, realizing a world of safe and secure autonomous driving.

[0048] The image generation unit can generate additional images from different viewpoints and angles for images of irregular situations. For example, the image generation unit generates additional images from different viewpoints and angles for images of irregular situations generated by the generation AI. For example, images of an obstacle that suddenly appears on the road can be generated from viewpoints in front, behind, and diagonally, thereby diversifying the dataset. This allows for the construction of a diverse dataset and improved recognition capabilities.

[0049] The image generation unit can automatically add detailed annotations to images of irregular situations. For example, the image generation unit automatically adds detailed annotations, such as the position and type of object and the direction of movement, to images of irregular situations generated by the generation AI. For example, annotations can be added that clearly indicate the position and type of obstacles that appear on the road. This can improve the quality of the training dataset.

[0050] The image generation unit can collect the user's emotional reactions to images of irregular situations and prioritize generating emotionally significant scenarios. For example, the image generation unit collects the user's emotional reactions to generated images of irregular situations in real time and identifies emotionally significant scenarios. For example, it prioritizes generating scenarios in which the user feels strong surprise or fear. This prioritizes generating emotionally significant scenarios, enabling more effective learning.

[0051] The image generation unit generates videos of irregular situations, thereby improving the autonomous driving system's ability to respond to dynamic scenarios. The image generation unit, for example, uses a generation AI to generate videos of irregular situations and simulate dynamic scenarios. For example, a video of an obstacle suddenly appearing on the road and moving can be generated to verify the autonomous driving system's ability to respond. This improves the system's ability to respond to dynamic scenarios.

[0052] The image generation unit uses the generated image of the irregular situation to perform simulations under different weather conditions and time periods, thereby enhancing the ability to respond in a variety of environments. The image generation unit, for example, uses the generated image of the irregular situation to perform simulations under different weather conditions. For example, simulations are performed under conditions such as sunny weather, rainy weather, and snowy weather, thereby enhancing the ability to respond in a variety of environments.

[0053] The image generation unit can analyze the user's emotional response to the generated image of the irregular situation and identify a scenario that is likely to elicit emotional empathy. The image generation unit, for example, collects the user's emotional response to the generated image of the irregular situation and identifies a scenario that is likely to elicit emotional empathy based on that data. For example, it preferentially generates scenarios that the user strongly empathizes with. This allows for more effective learning by identifying a scenario that is likely to elicit emotional empathy.

[0054] The prompt input unit allows the generation AI to refer to similar situations in the past and suggest optimal driving maneuvers based on past response results. For example, in response to the input of a verbal prompt, the generation AI refers to similar situations in the past and suggests optimal driving maneuvers based on past response results. For example, in response to the prompt "An obstacle suddenly appeared ahead," the generation AI suggests optimal maneuvers based on past response results. This enables more effective driving by suggesting optimal driving maneuvers based on past response results.

[0055] The prompt input unit can use the emotion estimation function to estimate the driver's emotion regarding the situation while driving in real time and suggest optimal driving operations based on the emotion. The prompt input unit, for example, uses the emotion estimation function to estimate the driver's emotion regarding the situation while driving in real time and suggest optimal driving operations based on the emotion. For example, if the driver is nervous, the prompt input unit suggests a safer operation. In this way, by suggesting optimal driving operations based on the driver's emotion, safer driving is possible.

[0056] The prompt input unit enables the generation AI to accept voice input in response to the input of a verbal prompt, thereby enabling a more intuitive input of the situation while driving. For example, the prompt input unit enables the generation AI to accept voice input in response to the input of a verbal prompt, thereby enabling a more intuitive input of the situation while driving. For example, a driver may input "There is an obstacle ahead" by voice. In this way, by accepting voice input, the situation while driving can be input more intuitively.

[0057] The prompt input unit allows the generation AI to provide visual feedback in response to the input of a linguistic prompt, thereby enabling the driver to visually confirm the proposed driving maneuvers. For example, when the driver inputs "there is an obstacle ahead," the prompt input unit allows the generation AI to provide visual feedback in response to the input of a linguistic prompt, thereby enabling the driver to visually confirm the proposed driving maneuvers. For example, when the driver inputs "there is an obstacle ahead," an avoidance maneuver is visually displayed. In this way, by providing visual feedback, the driver can visually confirm the proposed driving maneuvers.

[0058] The prompt input unit can use the emotion estimation function to analyze the driver's emotions regarding the driving situation and assist the driver in performing driving operations in an emotionally stable state. For example, the prompt input unit uses the emotion estimation function to analyze the driver's emotions regarding the driving situation in real time and assist the driver in performing driving operations in an emotionally stable state. For example, if the driver is nervous, the prompt input unit provides advice on how to relax. In this way, the driver's emotions are analyzed and the driver is supported to perform driving operations in an emotionally stable state.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The autonomous driving system also includes a voice recognition unit. The voice recognition unit recognizes the driver's voice instructions in real time while driving and can suggest driving maneuvers based on those instructions. For example, if the driver says "there is an obstacle ahead," the voice recognition unit analyzes the instruction and suggests appropriate avoidance maneuvers. The voice recognition unit can also record the driver's voice instructions and analyze them later. This allows for more intuitive and faster response by suggesting driving maneuvers based on voice instructions.

[0061] The autonomous driving system further includes an environment recognition unit. The environment recognition unit can acquire information about the surrounding environment in real time and suggest driving maneuvers based on that information. For example, the environment recognition unit acquires information about road conditions, weather, traffic volume, etc., and suggests optimal driving maneuvers based on that information. The environment recognition unit can also record the acquired environment information and analyze it later. This allows for safer and more efficient driving by suggesting driving maneuvers based on the surrounding environment information.

[0062] The automated driving system further includes a user interface unit. The user interface unit visually displays the driving situation and proposed driving operations, and can provide feedback to the driver. For example, if there is an obstacle ahead, the user interface unit displays its location and evasive operations on the display. The user interface unit can also provide real-time feedback on the driver's operations, improving driving safety. This visual feedback helps the driver understand the situation and enables safer driving.

[0063] The autonomous driving system can further use emotion estimation to adjust driving behavior based on the driver's emotions. For example, if the driver is feeling stressed, the system can suggest a more relaxed driving behavior. Also, if the driver is tired, the system can suggest taking a break. This allows for safer and more comfortable driving by adjusting driving behavior based on the driver's emotions.

[0064] The autonomous driving system can also use emotion estimation to provide entertainment content based on the driver's emotions. For example, if the driver wants to relax, the system can provide relaxing music or videos. If the driver wants to improve their concentration, the system can provide content to improve their concentration. By providing entertainment content based on the driver's emotions, it is possible to reduce stress while driving and create a comfortable driving environment.

[0065] The autonomous driving system can further use emotion estimation to optimize the driving route based on the driver's emotions. For example, if the driver is in a hurry, the system can suggest the shortest route. If the driver wants to relax, the system can suggest a scenic route. This allows the system to optimize the driving route based on the driver's emotions, resulting in a more comfortable and efficient driving experience.

[0066] The autonomous driving system can further use emotion estimation to adjust the in-car environment based on the driver's emotions. For example, if the driver wants to relax, the system can adjust the lighting and temperature inside the car. If the driver wants to increase their concentration, the system can also adjust the music and air conditioning inside the car. In this way, by adjusting the in-car environment based on the driver's emotions, a more comfortable driving environment can be achieved.

[0067] The autonomous driving system further includes a prediction unit. The prediction unit can predict future situations based on the current driving situation and suggest driving maneuvers based on the prediction. For example, the prediction unit can predict changes in traffic conditions and weather ahead and suggest optimal driving maneuvers based on the predictions. The prediction unit can also predict future situations based on past data. This allows for safer and more efficient driving by predicting future situations.

[0068] The autonomous driving system also includes a communication unit. The communication unit can communicate with other vehicles and infrastructure and share information in real time. For example, the communication unit can receive information from the vehicle ahead and suggest driving maneuvers based on that information. The communication unit can also receive information from road infrastructure and suggest optimal driving maneuvers based on that information. This communication with other vehicles and infrastructure enables safer and more efficient driving.

[0069] The autonomous driving system further includes a learning unit. The learning unit collects data during driving and can improve the system's performance based on that data. For example, the learning unit records the driving situation and the driver's operations and improves the system's algorithm based on that data. The learning unit can also collect data from other vehicles and improve the system's performance based on that data. This allows for safer and more efficient driving by improving the system's performance based on data during driving.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The image generation unit generates images of irregular situations. For example, the generation AI can be used to generate images of obstacles that suddenly appear on the road, unexpected weather changes, traffic accident scenes, etc. It is also possible to input a prompt such as "an obstacle has appeared on the road" into the generation AI and generate an image of the irregular situation based on that prompt. Step 2: The prompt input unit inputs the situation during driving as a verbal prompt. For example, a situation such as "an obstacle suddenly appeared ahead" can be input to the generation AI as a verbal prompt while driving. Step 3: The driving maneuver suggestion unit suggests driving maneuvers based on the prompt input by the prompt input unit. For example, the generation AI analyzes the prompt "An obstacle suddenly appeared ahead" and suggests the optimal driving maneuver for that situation.

[0072] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0073] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0074] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0077] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0078] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0079] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0080] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0081] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0082] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0083] 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.

[0084] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0085] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0086] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0087] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0088] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0089] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0092] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0093] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0094] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0095] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0096] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0097] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0098] 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.

[0099] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0100] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0101] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0102] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 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.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0113] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0122] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0123] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0124] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0125] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0126] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0127] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0128] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0129] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0130] 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.

[0131] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0132] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0133] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0134] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0135] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0136] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0137] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0138] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0139] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image generation unit that generates an image of an irregular situation; a prompt input unit for inputting a driving situation as a linguistic prompt; a driving operation suggestion unit that suggests a driving operation based on the prompt input by the prompt input unit. A system characterized by:

2. The image generation unit Generate additional images from different viewpoints or angles for the image of the irregular situation.

2. The system of claim 1.

3. The image generation unit Generate videos of irregular situations to improve the autonomous driving system's ability to respond to dynamic scenarios 2. The system of claim 1.

4. The prompt input unit Estimates the driver's emotions in real time regarding the driving situation and suggests optimal driving maneuvers based on those emotions.

2. The system of claim 1.

5. The image generation unit Collecting the user's emotional reactions to the images of the irregular situations and generating emotionally significant scenarios with priority.

2. The system of claim 1.

6. The image generation unit The generated images of irregular situations are used to conduct simulations under different weather conditions and time periods, strengthening the ability to respond to diverse environments.

2. The system of claim 1.

7. The prompt input unit The generation AI will also accept voice input in response to language prompts, allowing for more intuitive input of driving situations.

2. The system of claim 1.

8. The prompt input unit Analyzes the driver's emotions regarding the driving situation and helps them drive in an emotionally stable state 2. The system of claim 1.

Images