System

Abstract

To provide real-time advice and support in order to improve the safety during driving.SOLUTION: A system according to an embodiment includes an obstacle detection unit, an alarm unit, a fatigue sensing unit, a suggestion unit, a parking suggestion unit, and an automatic parking unit. The obstacle detection unit detects an obstacle in front of or behind the vehicle. Based on the obstacle detected by the obstacle detection unit, the alarm unit alarms a driver. The fatigue sensing unit senses fatigue and distraction of the driver. Based on information sensed by the fatigue sensing unit, the suggestion unit suggests that the driver takes a rest. The parking suggestion unit suggests a parking spot to the driver. The automatic parking unit supports automatic parking.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Existing technologies leave room for improvement as they do not adequately provide real-time advice and support to improve driving safety.

[0005] The system of the embodiment aims to provide real-time advice and support to increase safety while driving. [Means for solving the problem]

[0006] The system according to the embodiment includes an obstacle detection unit, a warning unit, a fatigue detection unit, a suggestion unit, a parking suggestion unit, and an automatic parking unit. The obstacle detection unit detects obstacles in front of or behind the vehicle. The warning unit issues a warning to the driver based on the obstacle detected by the obstacle detection unit. The fatigue detection unit detects fatigue and distraction of the driver. The suggestion unit suggests a break to the driver based on the information sensed by the fatigue detection unit. The parking suggestion unit presents a parking spot to the driver. The automatic parking unit supports automatic parking. Effect of the Invention

[0007] Embodiments of the system can provide real-time advice and support to enhance safety while driving. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A driving assistance system according to an embodiment of the present invention is a system that provides real-time safety advice while driving. The system detects obstacles in front of or behind the vehicle and issues a warning to the driver based on the detected obstacle. The system also detects the driver's fatigue level or distraction and suggests a break. Furthermore, the system suggests optimal parking spots and supports automatic parking. For example, ultrasonic sensors, cameras, radar, LiDAR, etc. are used to detect obstacles in front of or behind the vehicle. When there is a pedestrian in front or an obstacle in the rear, these sensors detect the obstacle. Next, a warning is issued to the driver based on the detected obstacle. The warning is audio or visual. For example, when there is a pedestrian in front, the system issues an audio warning saying "There is a pedestrian in front" and visually displays the pedestrian's location. Furthermore, the system detects the driver's fatigue level and distraction. In addition to facial recognition technology and eye movement tracking technology, the system uses technology to measure heart rate and skin temperature to detect the driver's fatigue level and distraction. For example, if the driver's eyes are closed for a long time or the driver's gaze is fixed in a certain direction, these technologies detect the driver's level of fatigue or distraction. Based on the detected information, the system suggests a break to the driver. For example, it makes a suggestion such as "The driver is fatigued. Please take a break." It also suggests parking spots. It detects vacant spaces in the parking lot and suggests the optimal parking spot to the driver. For example, it obtains the congestion status of the parking lot in real time and suggests the most vacant parking spot. It also supports automatic parking. When approaching a parking space, it automatically parks the vehicle in the parking space. For example, it suggests a safe parking spot taking into account the distance to other vehicles in the parking lot and automatically parks the vehicle. As a result, the driving assistance system can improve driving safety by providing real-time safety advice during driving, detecting the driver's fatigue or distraction and suggesting a break, suggesting the optimal parking spot, and supporting automatic parking.

[0029] The driving assistance system according to the embodiment includes an obstacle detection unit, a warning unit, a fatigue detection unit, a suggestion unit, a parking suggestion unit, and an automatic parking unit. The obstacle detection unit detects an obstacle in front of or behind the vehicle. The obstacle detection unit can detect an obstacle using, for example, an ultrasonic sensor, a camera, a radar, or a LiDAR. For example, the ultrasonic sensor measures the distance of the obstacle by emitting a sound wave and receiving the reflected wave. The camera detects the obstacle using an image processing technique. The radar identifies the position of the obstacle by emitting a radio wave and receiving the reflected wave. The LiDAR grasps the shape of the obstacle by emitting a laser light and receiving the reflected light. The warning unit issues a warning to the driver based on the obstacle detected by the obstacle detection unit. The warning unit can issue, for example, an audio warning or a visual warning. For example, the audio warning issues an audio warning to the driver using a speaker. The visual warning visually displays a warning to the driver using a display. The fatigue detection unit detects the driver's fatigue level and distraction. The fatigue detection unit can detect the driver's condition using, for example, face recognition technology, eye movement tracking technology, heart rate, and skin temperature measurement technology. For example, the face recognition technology uses a camera to recognize the driver's face and analyzes the open / closed state of the eyes and facial expressions. The eye movement tracking technology detects the driver's gaze movement using an eye tracking sensor. The heart rate is measured using a heart rate sensor. The skin temperature is measured using a temperature sensor. The suggestion unit suggests a break to the driver based on the information sensed by the fatigue detection unit. The suggestion unit can suggest a break by voice or visual, for example. For example, the suggestion unit can suggest by voice, "The driver seems tired. Please take a break." The parking suggestion unit detects vacant spaces in the parking lot and suggests a parking spot to the driver. The parking suggestion unit can, for example, obtain the congestion status of the parking lot in real time and suggest the vacant parking spot. The automatic parking unit automatically parks the vehicle in the parking space when approaching the parking space. The automatic parking unit can, for example, suggest a safe parking spot taking into account the distance to other vehicles in the parking lot and automatically park the vehicle.As a result, the driving assistance system of the embodiment can improve driving safety by providing real-time safety advice while driving, detecting driver fatigue or distraction and suggesting breaks, suggesting optimal parking spots, and supporting automatic parking.

[0030] The obstacle detection unit detects obstacles in front of or behind the vehicle. The obstacle detection unit can detect obstacles using, for example, an ultrasonic sensor, a camera, a radar, or LiDAR. Specifically, an ultrasonic sensor measures the distance of an obstacle by emitting sound waves and receiving the reflected waves. This makes it possible to accurately grasp the position and distance of obstacles around the vehicle. The camera detects obstacles using image processing technology. For example, the camera acquires high-resolution images and identifies the shape and type of obstacles using image recognition algorithms. This makes it possible to identify pedestrians, bicycles, other vehicles, etc. in front of or behind the vehicle. The radar identifies the position of an obstacle by emitting radio waves and receiving the reflected waves. Radar is suitable for all-weather obstacle detection because it can detect obstacles with high accuracy even in bad weather or in the dark. LiDAR grasps the shape of an obstacle by emitting laser light and receiving the reflected light. LiDAR can scan the surrounding environment in three dimensions with extremely high resolution and grasp the detailed shape and position of obstacles in real time. This allows the obstacle detection unit to detect any obstacles around the vehicle with high accuracy and provide appropriate information to the driver. Furthermore, the obstacle detection unit integrates these sensor data and combines information obtained from multiple sensors to achieve more reliable obstacle detection. For example, by combining camera and radar data, visual information and distance information can be integrated to more accurately grasp the position and movement of obstacles. This allows the obstacle detection unit to provide accurate obstacle information to the driver in real time, improving driving safety.

[0031] The warning unit issues a warning to the driver based on the obstacle detected by the obstacle detection unit. The warning unit can, for example, issue a voice warning or a visual warning. Specifically, the voice warning issues a voice warning to the driver using a speaker. For example, a specific message such as "There is an obstacle ahead. Please be careful" can be conveyed to the driver to instantly draw his / her attention. The visual warning visually displays a warning to the driver using a display. For example, a warning icon or message can be displayed on the dashboard or head-up display of the vehicle, and the warning can be conveyed without the driver's gaze being distracted. Furthermore, the warning unit can also use a vibration warning. For example, a vibration motor is built into the driver's seat or steering wheel, and vibrations are generated when an obstacle is detected, thereby providing physical feedback to the driver. This allows the driver to receive the warning not only through sight and hearing, but also through touch. The warning unit can adjust the strength and content of the warning depending on the type and position of the obstacle. For example, when an obstacle is very close to the vehicle or is approaching rapidly, a stronger warning can be issued to convey urgency to the driver. This enables the warning unit to provide an appropriate warning to the driver at an appropriate time, thereby reducing the risk of an accident.

[0032] The fatigue detection unit detects the driver's degree of fatigue and distraction. The fatigue detection unit can detect the driver's condition using, for example, face recognition technology, eye movement tracking technology, heart rate, and skin temperature measurement technology. Specifically, the face recognition technology uses a camera to recognize the driver's face and analyzes the eye opening and closing state and facial expression. This makes it possible to measure the frequency of opening and closing of the driver's eyelids and the number of blinks, and detect signs of fatigue. The eye movement tracking technology detects the driver's gaze movement using an eye tracking sensor. This makes it possible to determine whether the driver is concentrating on the road and detect signs of distraction. The heart rate is measured by using a heart rate sensor. Since the fluctuation in the heart rate is an indicator of the driver's stress and degree of fatigue, the driver's condition can be grasped by monitoring this. The skin temperature is measured by using a temperature sensor. Since the fluctuation in the skin temperature indicates the driver's physical condition and degree of fatigue, the driver's condition can be sensed by monitoring this. As a result, the fatigue detection unit can accurately detect the driver's level of fatigue and distraction by collecting various biological information of the driver in real time and comprehensively analyzing it. Furthermore, the fatigue detection unit can perform more accurate detection by taking into account past data and the individual characteristics of the driver. For example, an individual fatigue detection model can be constructed based on the driver's past fatigue pattern and driving history, and optimal detection can be performed for each driver. As a result, the fatigue detection unit can accurately detect the driver's state in real time and improve driving safety.

[0033] The suggestion unit suggests a break to the driver based on the information sensed by the fatigue sensing unit. The suggestion unit can suggest a break by voice or visual, for example. Specifically, the suggestion unit suggests by voice, "The driver is fatigued. Please take a break." The visual suggestion displays a message or icon indicating the need for a break on the vehicle display. For example, a message such as "It's time to take a break" can be displayed to visually encourage the driver to take a break. Furthermore, the suggestion unit can suggest an optimal rest location in consideration of the driver's current location and driving route. For example, information on nearby service areas and parking areas is provided to suggest specific rest locations to the driver. This allows the driver to take a break at an appropriate time, and the risk of an accident due to fatigue can be reduced. The suggestion unit can collect feedback from the driver and continuously improve the accuracy and effectiveness of the suggestion content. For example, if the driver takes a break according to the suggestion, the suggestion unit monitors the driver's driving performance thereafter and evaluates the effectiveness of the suggestion. This allows the suggestion unit to make more effective rest suggestions to the driver.

[0034] The parking suggestion unit detects vacant spaces in the parking lot and suggests a parking spot to the driver. For example, the parking suggestion unit can obtain the congestion status of the parking lot in real time and suggest the vacant parking spot. Specifically, the presence or absence of vacant spaces is detected using sensors and cameras installed in the parking lot. This makes it possible to accurately grasp which areas in the parking lot have vacant spaces. Furthermore, the parking suggestion unit can suggest the optimal parking spot taking into consideration the driver's current location and the distance from the entrance to the parking lot. For example, if the driver wants to park in a place close to the entrance of the parking lot, a parking spot according to the driver's desire can be suggested. The parking suggestion unit can convey the suggested content to the driver by voice or visually. For example, a voice suggestion is made such as "Please park in the vacant space on the right side." The visual suggestion displays a map or an icon showing the location of the parking spot on the display of the vehicle. This makes it easier for the driver to visually confirm the location of the parking spot. Furthermore, the parking suggestion unit can monitor the congestion status of the parking lot and changes in vacant spaces in real time and update the suggested content based on the latest information. This allows the parking suggestion unit to always suggest the optimal parking spot to the driver and reduce the stress of parking.

[0035] The automatic parking unit automatically parks the vehicle in the parking space when the vehicle approaches the parking space. For example, the automatic parking unit can suggest a safe parking spot by considering the distance to other vehicles in the parking lot, and automatically park the vehicle. Specifically, the automatic parking unit detects the position of the parking space and surrounding obstacles using sensors and cameras installed around the vehicle. This makes it possible to accurately grasp the shape of the parking space and the distance to other vehicles. The automatic parking unit calculates the optimal parking path based on the detected information and automatically guides the vehicle to the parking space. For example, the steering, accelerator, and brakes can be automatically controlled, allowing the vehicle to be safely parked without the driver having to operate the vehicle. Furthermore, the automatic parking unit can monitor the movements of obstacles and other vehicles during parking in real time and correct the parking path as necessary. This makes it possible to minimize the risk of accidents during parking. The automatic parking unit can notify the driver of the parking progress by voice or visual. For example, messages such as "Parking begins" and "Parking is complete" are conveyed by voice. Visual notifications display graphics and messages indicating the parking progress on the vehicle's display. This allows the driver to check the parking progress in real time. The automated parking unit can reduce the burden on the driver and improve the safety and efficiency of parking.

[0036] The obstacle detection unit can detect obstacles in front or behind using an ultrasonic sensor, a camera, a radar, or a LiDAR. The obstacle detection unit detects obstacles using, for example, an ultrasonic sensor. The ultrasonic sensor measures the distance of an obstacle by emitting sound waves and receiving the reflected waves. For example, the ultrasonic sensor is arranged at the front and rear of the vehicle and detects the presence or absence of an obstacle. The obstacle detection unit can also detect obstacles using a camera. The camera detects obstacles using image processing technology. For example, the camera is arranged at the front and rear of the vehicle, acquires image data, and identifies the position of the obstacle. Furthermore, the obstacle detection unit can also detect obstacles using a radar. The radar identifies the position of the obstacle by emitting radio waves and receiving the reflected waves. For example, the radar is arranged at the front and rear of the vehicle and detects the presence or absence of an obstacle. Furthermore, the obstacle detection unit can also detect obstacles using LiDAR. The LiDAR grasps the shape of an obstacle by emitting laser light and receiving the reflected light. For example, the LiDAR is arranged at the front and rear of the vehicle and detects the presence or absence of an obstacle. This allows the obstacle detection unit to detect obstacles in front or behind the vehicle with high accuracy by using a variety of sensors.

[0037] The fatigue detection unit can detect the driver's fatigue and distraction using face recognition technology, eye movement tracking technology, heart rate, and skin temperature measurement technology. The fatigue detection unit detects the driver's fatigue using, for example, face recognition technology. The face recognition technology uses a camera to recognize the driver's face and analyzes the open / closed state of the eyes and facial expressions. For example, when the driver's eyes are closed for a long time or the driver's facial expression is tired, the face recognition technology detects the driver's fatigue. The fatigue detection unit can also detect the driver's distraction using eye movement tracking technology. The eye movement tracking technology detects the driver's gaze movement using an eye tracking sensor. For example, when the driver's gaze is fixed in a certain direction or when the gaze moves frequently, the eye movement tracking technology detects the driver's distraction. Furthermore, the fatigue detection unit can also detect the driver's fatigue using a technology to measure the heart rate. The heart rate is measured by using a heart rate sensor. For example, when the driver's heart rate is high or irregular, the technology for measuring the heart rate detects the driver's fatigue. Furthermore, the fatigue detection unit can also detect the driver's fatigue using a technology for measuring the skin temperature. The skin temperature is measured using a temperature sensor. For example, when the driver's skin temperature is high or low, the technology for measuring the skin temperature detects the driver's fatigue. In this way, the fatigue detection unit can detect the driver's fatigue and distraction with high accuracy by using various technologies.

[0038] The parking suggestion unit can detect vacant spaces in the parking lot and present parking spots to the driver. The parking suggestion unit uses, for example, a camera or a sensor to detect vacant spaces in the parking lot. The camera acquires an image of the parking lot and detects vacant spaces using image processing technology. For example, the camera is installed on the ceiling or wall of the parking lot and acquires an image of the entire parking lot. The sensor is installed in each space in the parking lot and detects the presence or absence of vacant spaces. For example, an ultrasonic sensor or an infrared sensor is used to detect the occupancy status of the parking space. The parking suggestion unit proposes the optimal parking spot to the driver based on the information on the detected vacant spaces. For example, the parking suggestion unit can acquire the congestion status of the parking lot in real time and propose the most vacant parking spot. The parking suggestion unit presents the parking spot to the driver by voice or visual. For example, the parking suggestion unit announces by voice that "there is a vacant space on the right side of the parking lot" and visually displays the location of the parking spot. In this way, the parking suggestion unit can detect vacant spaces in the parking lot and propose the optimal parking spot to the driver.

[0039] The automatic parking unit can automatically park the vehicle in the parking space when the vehicle approaches the parking space. The automatic parking unit detects the position of the parking space and the distance to other vehicles using sensors and cameras arranged around the vehicle. For example, ultrasonic sensors and LiDAR are used to measure the shape of the parking space and the distance to other vehicles. The automatic parking unit controls the steering and braking of the vehicle based on the detected information to automatically park the vehicle. For example, the automatic parking unit makes the vehicle enter the parking space at an appropriate angle and operates the steering to put the vehicle into the parking space. In addition, the automatic parking unit can monitor the surrounding situation in real time and apply the brakes as necessary to avoid collisions with other vehicles or obstacles during parking. As a result, the automatic parking unit can reduce the burden on the driver and achieve safe parking by automatically parking the vehicle.

[0040] The warning unit can issue a warning to the driver by voice and visually. The warning unit uses, for example, a speaker to issue a voice warning. The speaker issues a voice warning to the driver. For example, when there is a pedestrian ahead, a warning of "There is a pedestrian ahead" is issued through the speaker. The warning unit also uses a display to issue a visual warning. The display visually displays a warning to the driver. For example, when there is an obstacle ahead, the position of the obstacle is displayed on the display to alert the driver. The warning unit can effectively alert the driver by combining a voice warning and a visual warning. For example, a voice warning of "There is an obstacle ahead" is issued and the position of the obstacle is simultaneously displayed on the display. In this way, the warning unit can effectively alert the driver by issuing a voice and visual warning to the driver.

[0041] The obstacle detection unit can predict the movement of the obstacle when detecting the obstacle and change the detection range based on the predicted movement. The obstacle detection unit uses, for example, a movement pattern analysis or a prediction algorithm to predict the movement of the obstacle. The movement pattern analysis analyzes the movement pattern of the obstacle based on past data and predicts the future movement. For example, when there is a possibility that a pedestrian may enter the roadway, the movement is predicted and the detection range is expanded. The prediction algorithm analyzes the movement of the obstacle in real time and predicts the future movement. For example, when a bicycle is heading in the traveling direction of the vehicle, the movement is predicted and the detection range is adjusted. The obstacle detection unit dynamically changes the detection range based on the predicted movement. For example, when there is a possibility that another vehicle will suddenly change lanes, the movement is predicted and the detection range is changed. As a result, the obstacle detection unit can provide safer driving assistance by predicting the movement of the obstacle and dynamically changing the detection range.

[0042] The obstacle detection unit can optimize the detection algorithm based on weather conditions when detecting an obstacle. The obstacle detection unit uses, for example, a weather sensor and weather data to take into account weather conditions. The weather sensor is mounted on the vehicle and detects weather conditions such as rain, snow, and fog. For example, in rainy weather, the detection algorithm is adjusted to eliminate the influence of raindrops. The weather data is obtained from an external weather information service to grasp the weather conditions in real time. For example, in the case of fog, visibility is reduced, so the detection algorithm is strengthened. The obstacle detection unit optimizes the detection algorithm based on weather conditions. For example, on a snowy day, the detection algorithm is optimized taking into account the reflection of snow. As a result, the obstacle detection unit can provide high-precision obstacle detection even in bad weather by optimizing the detection algorithm taking into account weather conditions.

[0043] The warning unit can generate a different warning message according to the type of obstacle when issuing a warning. The warning unit uses, for example, image recognition technology and sensor data to identify the type of obstacle. The image recognition technology uses a camera to acquire an image of the obstacle and uses image processing technology to identify the type of obstacle. For example, the type is identified as a pedestrian, bicycle, other vehicle, etc. The sensor data uses an ultrasonic sensor or LiDAR to detect the shape and position of the obstacle and identify the type of obstacle. The warning unit generates a different warning message according to the type of obstacle identified. For example, if a pedestrian is in front, the warning unit issues a warning that "There is a pedestrian in front," and if a bicycle is on the side, the warning unit issues a warning that "There is a bicycle on the side." If another vehicle is behind, the warning unit issues a warning that "There is a vehicle behind." As a result, the warning unit can provide a more specific warning to the driver by generating a warning message according to the type of obstacle.

[0044] The warning unit can select an optimal warning method based on the driver's past reaction history when issuing a warning. The warning unit uses a database to store and refer to the driver's past reaction history, for example. The database stores a history of how the driver responded to what warnings in the past. For example, audio warning is given priority to a driver who responded quickly to an audio warning. A visual warning is given priority to a driver who responded quickly to a visual warning. A composite warning method is selected for a driver for whom a combination of multiple warning methods was effective. The warning unit refers to the driver's past reaction history from the database when issuing a warning and selects an optimal warning method. For example, audio warning is given priority to a driver who responded quickly to an audio warning in the past. A visual warning is given priority to a driver who responded quickly to a visual warning in the past. A combination of audio and visual warnings is given to a driver for whom a combination of multiple warning methods was effective in the past. In this way, the warning unit can select an optimal warning method by referring to the driver's past reaction history and provide an effective warning.

[0045] When fatigue is detected, the fatigue detection unit can analyze the breathing pattern of the driver to evaluate the fatigue. The fatigue detection unit uses, for example, a breathing sensor to analyze the breathing pattern of the driver. The breathing sensor measures the rhythm and depth of the driver's breathing. For example, when the driver's breathing becomes shallow, the fatigue level is evaluated as high. When the driver's breathing becomes fast, the fatigue level is evaluated as high. When the driver's breathing becomes irregular, the fatigue level is evaluated as high. The fatigue detection unit analyzes the breathing pattern of the driver based on the data acquired from the breathing sensor and evaluates the fatigue level. In this way, the fatigue detection unit can evaluate the fatigue level with high accuracy by analyzing the breathing pattern of the driver.

[0046] The suggestion unit can suggest an optimal rest timing based on the driver's past rest history at the time of suggestion. The suggestion unit uses a database to store and refer to the driver's past rest history, for example. The database stores a history of when the driver took breaks in the past. For example, the suggestion unit suggests a break based on a time period when the driver frequently took breaks in the past. The suggestion unit suggests an optimal rest location based on rest locations used in the past. The suggestion unit suggests a rest location preferred by the driver from the past rest history at the time of suggestion. The suggestion unit refers to the driver's past rest history from the database and suggests an optimal rest timing at the time of suggestion. For example, the suggestion unit suggests a break based on a time period when the driver frequently took breaks in the past. The suggestion unit suggests an optimal rest location based on rest locations used in the past. The suggestion unit suggests a rest location preferred by the driver from the past rest history. In this way, the suggestion unit can suggest an optimal rest timing by referring to the driver's past rest history and provide effective rest suggestions.

[0047] The parking suggestion unit can suggest an optimal parking spot based on a congestion prediction of the parking lot when suggesting parking. For example, the parking suggestion unit acquires past congestion data and real-time congestion status in order to predict congestion in the parking lot. The past congestion data is based on the usage history of the parking lot, and analyzes congestion patterns during specific times of the day and days of the week. The real-time congestion status is acquired using a sensor or a camera in the parking lot. For example, a sensor installed in each space of the parking lot detects the presence or absence of an empty space and grasps the congestion status in real time. The parking suggestion unit predicts congestion based on these data and suggests an optimal parking spot. For example, the parking suggestion unit acquires the congestion status of the parking lot in real time and suggests an empty parking spot. Based on the past congestion data, the parking suggestion unit predicts congestion and suggests an optimal parking spot. Based on the congestion prediction of the parking lot, the parking suggestion unit suggests a reservation of a parking spot. In this way, the parking suggestion unit can suggest an optimal parking spot by predicting congestion in the parking lot.

[0048] The parking suggestion unit can suggest a safe parking spot based on the security information of the parking lot when suggesting parking. For example, the parking suggestion unit grasps the installation status of security cameras and the deployment status of security guards in order to acquire the security information of the parking lot. The installation status of security cameras is based on the positions and coverage ranges of cameras installed in each area of ​​the parking lot. For example, an area where security cameras are installed is determined to have high security. The deployment status of security guards is based on the patrol routes and deployment locations of security guards in the parking lot. For example, an area where security guards frequently patrol is determined to have high security. The parking suggestion unit suggests a safe parking spot based on these security information. For example, the parking suggestion unit suggests a safe parking spot based on the installation status of security cameras. The parking suggestion unit suggests a safe parking spot based on the deployment status of security guards. The parking suggestion unit suggests a safe parking spot based on the lighting status of the parking lot. In this way, the parking suggestion unit can suggest a safe parking spot by taking into account the security information of the parking lot.

[0049] During automatic parking, the automatic parking unit can analyze the shape of the parking space and select the optimal parking method. The automatic parking unit uses, for example, a camera and a sensor to analyze the shape of the parking space. The camera acquires an image of the parking space and analyzes the shape using image processing technology. For example, if the parking space is narrow, reverse parking is selected. The sensor measures the dimensions and shape of the parking space and selects the optimal parking method. For example, if the parking space is wide, forward parking is selected. If the parking space is diagonal, diagonal parking is selected. The automatic parking unit selects the optimal parking method based on the analyzed shape of the parking space. For example, if the parking space is narrow, reverse parking is selected and the vehicle enters at an appropriate angle. If the parking space is wide, forward parking is selected and the vehicle is parked forward. If the parking space is diagonal, diagonal parking is selected and the vehicle enters diagonally. In this way, the automatic parking unit can select the optimal parking method by analyzing the shape of the parking space and provide safe parking.

[0050] The automatic parking unit can predict the movement of surrounding vehicles during automatic parking to perform safe parking. The automatic parking unit uses, for example, movement pattern analysis and a prediction algorithm to predict the movement of surrounding vehicles. The movement pattern analysis analyzes the movement pattern of surrounding vehicles based on past data and predicts future movement. For example, when surrounding vehicles are moving, the movement is predicted to perform safe parking. The prediction algorithm analyzes the movement of surrounding vehicles in real time and predicts future movement. For example, when surrounding vehicles are stopped, the position is taken into consideration to perform safe parking. The automatic parking unit performs safe parking based on the predicted movement of surrounding vehicles. For example, when surrounding vehicles enter a parking space, the movement is predicted to perform safe parking. In this way, the automatic parking unit can provide safe parking by predicting the movement of surrounding vehicles.

[0051] The automatic parking unit can adjust the parking method based on the state of the tires of the vehicle during automatic parking. The automatic parking unit uses, for example, sensors that measure tire pressure and wear in order to take into account the state of the tires of the vehicle. The tire pressure sensor measures the tire pressure in real time, and adjusts the parking method when the tire pressure is low. For example, when the tire pressure is low, the parking method is adjusted to provide safe parking. The tire wear sensor measures the tire wear, and adjusts the parking method when the tire wear is advanced. For example, when the tire wear is advanced, the parking method is adjusted to provide safe parking. The tire temperature sensor measures the tire temperature, and adjusts the parking method when the temperature is high. For example, when the tire temperature is high, the parking method is adjusted to provide safe parking. In this way, the automatic parking unit can provide safe parking by taking into account the state of the tires of the vehicle.

[0052] The automatic parking unit can adjust the parking method based on the lighting conditions of the parking space during automatic parking. The automatic parking unit uses, for example, a lighting sensor to take into account the lighting conditions of the parking space. The lighting sensor measures the brightness of the parking space and adjusts the parking method based on the lighting conditions. For example, if the parking space is dark, the parking method is adjusted to provide safe parking. If the parking space is bright, a normal parking method is selected. If the parking space is partially dark, parking is performed while avoiding that part. The automatic parking unit analyzes the lighting conditions of the parking space based on the data acquired from the lighting sensor and selects the optimal parking method. In this way, the automatic parking unit can provide safe parking by taking into account the lighting conditions of the parking space.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The obstacle detection unit can further include a function for identifying the material of the obstacle. For example, a camera or LiDAR can be used to analyze the surface reflection characteristics of the obstacle and identify the material, such as metal, plastic, or wood. Based on the material, the danger level of the obstacle can be evaluated and an appropriate warning can be issued to the driver. For example, a metal obstacle has a high danger level, so a strong warning is issued. On the other hand, a plastic obstacle has a relatively low danger level, so a normal warning is issued. This allows the obstacle detection unit to provide an appropriate warning according to the material of the obstacle.

[0055] The parking suggestion unit can further suggest parking spots taking into account the energy efficiency of the parking lot. For example, the location of an electric vehicle charging station in the parking lot and the installation status of solar power generation panels are taken into account. For electric vehicles, parking spots close to the charging station are suggested. In areas where solar power generation panels are installed, energy-efficient parking spots are suggested. This allows the parking suggestion unit to suggest parking spots taking energy efficiency into account.

[0056] The automatic parking unit can further analyze the condition of the ground in the parking space and adjust the parking method. For example, it can use a camera or a sensor to detect the unevenness or slipperiness of the ground. If the ground is slippery, it can set the parking speed slower and park carefully. If the ground is uneven, it can adjust the vehicle suspension to park safely. This allows the automatic parking unit to provide safe parking that takes the ground condition into account.

[0057] The warning unit can further adjust the timing of the warning based on the driver's past driving history. For example, if a warning was ignored in a particular situation in the past, the warning will be issued earlier in that situation. Conversely, if a quick reaction was made in the past, the warning will be issued at a normal timing in that situation. This allows the warning unit to provide appropriate warning timing that takes into account the driver's past driving history.

[0058] The obstacle detection unit can further predict the movement of the obstacle and change the detection range based on the predicted movement. For example, if there is a possibility that a pedestrian will enter the roadway, the detection range is expanded by predicting the movement. If a bicycle is heading in the direction of travel of the vehicle, the detection range is adjusted by predicting the movement. In this way, the obstacle detection unit can provide safer driving assistance by predicting the movement of the obstacle and dynamically changing the detection range.

[0059] The obstacle detection unit can further optimize the detection algorithm based on weather conditions. For example, in rainy weather, the detection algorithm is adjusted to eliminate the effect of raindrops. In foggy weather, the detection algorithm is strengthened because visibility is reduced. On snowy days, the detection algorithm is optimized to take into account snow reflections. Thus, the obstacle detection unit can provide highly accurate obstacle detection even in bad weather by optimizing the detection algorithm taking into account weather conditions.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The obstacle detection unit detects obstacles in front of or behind the vehicle. The obstacle detection unit can detect obstacles using, for example, an ultrasonic sensor, a camera, a radar, or LiDAR. For example, an ultrasonic sensor measures the distance to an obstacle by emitting sound waves and receiving the reflected waves. A camera detects obstacles using image processing technology. A radar identifies the position of an obstacle by emitting radio waves and receiving the reflected waves. A LiDAR identifies the shape of an obstacle by emitting laser light and receiving the reflected light. Step 2: The warning unit issues a warning to the driver based on the obstacle detected by the obstacle detection unit. The warning unit can issue, for example, an audio warning or a visual warning. For example, the audio warning issues an audio warning to the driver using a speaker. For the visual warning, a visual warning is displayed to the driver using a display. Step 3: The fatigue detection unit detects the driver's fatigue level and distraction. The fatigue detection unit can detect the driver's condition using, for example, face recognition technology, eye movement tracking technology, heart rate, and skin temperature measurement technology. For example, face recognition technology uses a camera to recognize the driver's face and analyzes the eye opening / closing state and facial expression. Eye movement tracking technology detects the driver's gaze movement using an eye tracking sensor. Heart rate is measured by using a heart rate sensor to measure the driver's heart rate. Skin temperature is measured by using a temperature sensor to measure the driver's skin temperature. Step 4: The suggestion unit suggests to the driver to take a break based on the information sensed by the fatigue sensing unit. The suggestion unit can suggest the break by voice or visual means, for example. For example, the suggestion unit suggests by voice, "The driver appears tired. Please take a break." Step 5: The parking suggestion unit detects vacant spaces in the parking lot and suggests parking spots to the driver. For example, the parking suggestion unit can obtain the congestion status of the parking lot in real time and suggest the vacant parking spot. Step 6: The automatic parking unit automatically parks the vehicle in the parking space when the vehicle approaches the parking space. The automatic parking unit can, for example, take into account the distance to other vehicles in the parking lot and suggest a safe parking spot to park the vehicle automatically.

[0062] (Example 2) A driving assistance system according to an embodiment of the present invention is a system that provides real-time safety advice while driving. The system detects obstacles in front of or behind the vehicle and issues a warning to the driver based on the detected obstacle. The system also detects the driver's fatigue level or distraction and suggests a break. Furthermore, the system suggests optimal parking spots and supports automatic parking. For example, ultrasonic sensors, cameras, radar, LiDAR, etc. are used to detect obstacles in front of or behind the vehicle. When there is a pedestrian in front or an obstacle in the rear, these sensors detect the obstacle. Next, a warning is issued to the driver based on the detected obstacle. The warning is audio or visual. For example, when there is a pedestrian in front, the system issues an audio warning saying "There is a pedestrian in front" and visually displays the pedestrian's location. Furthermore, the system detects the driver's fatigue level and distraction. In addition to facial recognition technology and eye movement tracking technology, the system uses technology to measure heart rate and skin temperature to detect the driver's fatigue level and distraction. For example, if the driver's eyes are closed for a long time or the driver's gaze is fixed in a certain direction, these technologies detect the driver's level of fatigue or distraction. Based on the detected information, the system suggests a break to the driver. For example, it makes a suggestion such as "The driver is fatigued. Please take a break." It also suggests parking spots. It detects vacant spaces in the parking lot and suggests the optimal parking spot to the driver. For example, it obtains the congestion status of the parking lot in real time and suggests the most vacant parking spot. It also supports automatic parking. When approaching a parking space, it automatically parks the vehicle in the parking space. For example, it suggests a safe parking spot taking into account the distance to other vehicles in the parking lot and automatically parks the vehicle. As a result, the driving assistance system can improve driving safety by providing real-time safety advice during driving, detecting the driver's fatigue or distraction and suggesting a break, suggesting the optimal parking spot, and supporting automatic parking.

[0063] The driving assistance system according to the embodiment includes an obstacle detection unit, a warning unit, a fatigue detection unit, a suggestion unit, a parking suggestion unit, and an automatic parking unit. The obstacle detection unit detects an obstacle in front of or behind the vehicle. The obstacle detection unit can detect an obstacle using, for example, an ultrasonic sensor, a camera, a radar, or a LiDAR. For example, the ultrasonic sensor measures the distance of the obstacle by emitting a sound wave and receiving the reflected wave. The camera detects the obstacle using an image processing technique. The radar identifies the position of the obstacle by emitting a radio wave and receiving the reflected wave. The LiDAR grasps the shape of the obstacle by emitting a laser light and receiving the reflected light. The warning unit issues a warning to the driver based on the obstacle detected by the obstacle detection unit. The warning unit can issue, for example, an audio warning or a visual warning. For example, the audio warning issues an audio warning to the driver using a speaker. The visual warning visually displays a warning to the driver using a display. The fatigue detection unit detects the driver's fatigue level and distraction. The fatigue detection unit can detect the driver's condition using, for example, face recognition technology, eye movement tracking technology, heart rate, and skin temperature measurement technology. For example, the face recognition technology uses a camera to recognize the driver's face and analyzes the open / closed state of the eyes and facial expressions. The eye movement tracking technology detects the driver's gaze movement using an eye tracking sensor. The heart rate is measured using a heart rate sensor. The skin temperature is measured using a temperature sensor. The suggestion unit suggests a break to the driver based on the information sensed by the fatigue detection unit. The suggestion unit can suggest a break by voice or visual, for example. For example, the suggestion unit can suggest by voice, "The driver seems tired. Please take a break." The parking suggestion unit detects vacant spaces in the parking lot and suggests a parking spot to the driver. The parking suggestion unit can, for example, obtain the congestion status of the parking lot in real time and suggest the vacant parking spot. The automatic parking unit automatically parks the vehicle in the parking space when approaching the parking space. The automatic parking unit can, for example, suggest a safe parking spot taking into account the distance to other vehicles in the parking lot and automatically park the vehicle.As a result, the driving assistance system of the embodiment can improve driving safety by providing real-time safety advice while driving, detecting driver fatigue or distraction and suggesting breaks, suggesting optimal parking spots, and supporting automatic parking.

[0064] The obstacle detection unit detects obstacles in front of or behind the vehicle. The obstacle detection unit can detect obstacles using, for example, an ultrasonic sensor, a camera, a radar, or LiDAR. Specifically, an ultrasonic sensor measures the distance of an obstacle by emitting sound waves and receiving the reflected waves. This makes it possible to accurately grasp the position and distance of obstacles around the vehicle. The camera detects obstacles using image processing technology. For example, the camera acquires high-resolution images and identifies the shape and type of obstacles using image recognition algorithms. This makes it possible to identify pedestrians, bicycles, other vehicles, etc. in front of or behind the vehicle. The radar identifies the position of an obstacle by emitting radio waves and receiving the reflected waves. Radar is suitable for all-weather obstacle detection because it can detect obstacles with high accuracy even in bad weather or in the dark. LiDAR grasps the shape of an obstacle by emitting laser light and receiving the reflected light. LiDAR can scan the surrounding environment in three dimensions with extremely high resolution and grasp the detailed shape and position of obstacles in real time. This allows the obstacle detection unit to detect any obstacles around the vehicle with high accuracy and provide appropriate information to the driver. Furthermore, the obstacle detection unit integrates these sensor data and combines information obtained from multiple sensors to achieve more reliable obstacle detection. For example, by combining camera and radar data, visual information and distance information can be integrated to more accurately grasp the position and movement of obstacles. This allows the obstacle detection unit to provide accurate obstacle information to the driver in real time, improving driving safety.

[0065] The warning unit issues a warning to the driver based on the obstacle detected by the obstacle detection unit. The warning unit can, for example, issue a voice warning or a visual warning. Specifically, the voice warning issues a voice warning to the driver using a speaker. For example, a specific message such as "There is an obstacle ahead. Please be careful" can be conveyed to the driver to instantly draw his / her attention. The visual warning visually displays a warning to the driver using a display. For example, a warning icon or message can be displayed on the dashboard or head-up display of the vehicle, and the warning can be conveyed without the driver's gaze being distracted. Furthermore, the warning unit can also use a vibration warning. For example, a vibration motor is built into the driver's seat or steering wheel, and vibrations are generated when an obstacle is detected, thereby providing physical feedback to the driver. This allows the driver to receive the warning not only through sight and hearing, but also through touch. The warning unit can adjust the strength and content of the warning depending on the type and position of the obstacle. For example, when an obstacle is very close to the vehicle or is approaching rapidly, a stronger warning can be issued to convey urgency to the driver. This enables the warning unit to provide an appropriate warning to the driver at an appropriate time, thereby reducing the risk of an accident.

[0066] The fatigue detection unit detects the driver's degree of fatigue and distraction. The fatigue detection unit can detect the driver's condition using, for example, face recognition technology, eye movement tracking technology, heart rate, and skin temperature measurement technology. Specifically, the face recognition technology uses a camera to recognize the driver's face and analyzes the eye opening and closing state and facial expression. This makes it possible to measure the frequency of opening and closing of the driver's eyelids and the number of blinks, and detect signs of fatigue. The eye movement tracking technology detects the driver's gaze movement using an eye tracking sensor. This makes it possible to determine whether the driver is concentrating on the road and detect signs of distraction. The heart rate is measured by using a heart rate sensor. Since the fluctuation in the heart rate is an indicator of the driver's stress and degree of fatigue, the driver's condition can be grasped by monitoring this. The skin temperature is measured by using a temperature sensor. Since the fluctuation in the skin temperature indicates the driver's physical condition and degree of fatigue, the driver's condition can be sensed by monitoring this. As a result, the fatigue detection unit can accurately detect the driver's level of fatigue and distraction by collecting various biological information of the driver in real time and comprehensively analyzing it. Furthermore, the fatigue detection unit can perform more accurate detection by taking into account past data and the individual characteristics of the driver. For example, an individual fatigue detection model can be constructed based on the driver's past fatigue pattern and driving history, and optimal detection can be performed for each driver. As a result, the fatigue detection unit can accurately detect the driver's state in real time and improve driving safety.

[0067] The suggestion unit suggests a break to the driver based on the information sensed by the fatigue sensing unit. The suggestion unit can suggest a break by voice or visual, for example. Specifically, the suggestion unit suggests by voice, "The driver is fatigued. Please take a break." The visual suggestion displays a message or icon indicating the need for a break on the vehicle display. For example, a message such as "It's time to take a break" can be displayed to visually encourage the driver to take a break. Furthermore, the suggestion unit can suggest an optimal rest location in consideration of the driver's current location and driving route. For example, information on nearby service areas and parking areas is provided to suggest specific rest locations to the driver. This allows the driver to take a break at an appropriate time, and the risk of an accident due to fatigue can be reduced. The suggestion unit can collect feedback from the driver and continuously improve the accuracy and effectiveness of the suggestion content. For example, if the driver takes a break according to the suggestion, the suggestion unit monitors the driver's driving performance thereafter and evaluates the effectiveness of the suggestion. This allows the suggestion unit to make more effective rest suggestions to the driver.

[0068] The parking suggestion unit detects vacant spaces in the parking lot and suggests a parking spot to the driver. For example, the parking suggestion unit can obtain the congestion status of the parking lot in real time and suggest the vacant parking spot. Specifically, the presence or absence of vacant spaces is detected using sensors and cameras installed in the parking lot. This makes it possible to accurately grasp which areas in the parking lot have vacant spaces. Furthermore, the parking suggestion unit can suggest the optimal parking spot taking into consideration the driver's current location and the distance from the entrance to the parking lot. For example, if the driver wants to park in a place close to the entrance of the parking lot, a parking spot according to the driver's desire can be suggested. The parking suggestion unit can convey the suggested content to the driver by voice or visually. For example, a voice suggestion is made such as "Please park in the vacant space on the right side." The visual suggestion displays a map or an icon showing the location of the parking spot on the display of the vehicle. This makes it easier for the driver to visually confirm the location of the parking spot. Furthermore, the parking suggestion unit can monitor the congestion status of the parking lot and changes in vacant spaces in real time and update the suggested content based on the latest information. This allows the parking suggestion unit to always suggest the optimal parking spot to the driver and reduce the stress of parking.

[0069] The automatic parking unit automatically parks the vehicle in the parking space when the vehicle approaches the parking space. For example, the automatic parking unit can suggest a safe parking spot by considering the distance to other vehicles in the parking lot, and automatically park the vehicle. Specifically, the automatic parking unit detects the position of the parking space and surrounding obstacles using sensors and cameras installed around the vehicle. This makes it possible to accurately grasp the shape of the parking space and the distance to other vehicles. The automatic parking unit calculates the optimal parking path based on the detected information and automatically guides the vehicle to the parking space. For example, the steering, accelerator, and brakes can be automatically controlled, allowing the vehicle to be safely parked without the driver having to operate the vehicle. Furthermore, the automatic parking unit can monitor the movements of obstacles and other vehicles during parking in real time and correct the parking path as necessary. This makes it possible to minimize the risk of accidents during parking. The automatic parking unit can notify the driver of the parking progress by voice or visual. For example, messages such as "Parking begins" and "Parking is complete" are conveyed by voice. Visual notifications display graphics and messages indicating the parking progress on the vehicle's display. This allows the driver to check the parking progress in real time. The automated parking unit can reduce the burden on the driver and improve the safety and efficiency of parking.

[0070] The obstacle detection unit can detect obstacles in front or behind using an ultrasonic sensor, a camera, a radar, or a LiDAR. The obstacle detection unit detects obstacles using, for example, an ultrasonic sensor. The ultrasonic sensor measures the distance of an obstacle by emitting sound waves and receiving the reflected waves. For example, the ultrasonic sensor is arranged at the front and rear of the vehicle and detects the presence or absence of an obstacle. The obstacle detection unit can also detect obstacles using a camera. The camera detects obstacles using image processing technology. For example, the camera is arranged at the front and rear of the vehicle, acquires image data, and identifies the position of the obstacle. Furthermore, the obstacle detection unit can also detect obstacles using a radar. The radar identifies the position of the obstacle by emitting radio waves and receiving the reflected waves. For example, the radar is arranged at the front and rear of the vehicle and detects the presence or absence of an obstacle. Furthermore, the obstacle detection unit can also detect obstacles using LiDAR. The LiDAR grasps the shape of an obstacle by emitting laser light and receiving the reflected light. For example, the LiDAR is arranged at the front and rear of the vehicle and detects the presence or absence of an obstacle. This allows the obstacle detection unit to detect obstacles in front or behind the vehicle with high accuracy by using a variety of sensors.

[0071] The fatigue detection unit can detect the driver's fatigue and distraction using face recognition technology, eye movement tracking technology, heart rate, and skin temperature measurement technology. The fatigue detection unit detects the driver's fatigue using, for example, face recognition technology. The face recognition technology uses a camera to recognize the driver's face and analyzes the open / closed state of the eyes and facial expressions. For example, when the driver's eyes are closed for a long time or the driver's facial expression is tired, the face recognition technology detects the driver's fatigue. The fatigue detection unit can also detect the driver's distraction using eye movement tracking technology. The eye movement tracking technology detects the driver's gaze movement using an eye tracking sensor. For example, when the driver's gaze is fixed in a certain direction or when the gaze moves frequently, the eye movement tracking technology detects the driver's distraction. Furthermore, the fatigue detection unit can also detect the driver's fatigue using a technology to measure the heart rate. The heart rate is measured by using a heart rate sensor. For example, when the driver's heart rate is high or irregular, the technology for measuring the heart rate detects the driver's fatigue. Furthermore, the fatigue detection unit can also detect the driver's fatigue using a technology for measuring the skin temperature. The skin temperature is measured using a temperature sensor. For example, when the driver's skin temperature is high or low, the technology for measuring the skin temperature detects the driver's fatigue. In this way, the fatigue detection unit can detect the driver's fatigue and distraction with high accuracy by using various technologies.

[0072] The parking suggestion unit can detect vacant spaces in the parking lot and present parking spots to the driver. The parking suggestion unit uses, for example, a camera or a sensor to detect vacant spaces in the parking lot. The camera acquires an image of the parking lot and detects vacant spaces using image processing technology. For example, the camera is installed on the ceiling or wall of the parking lot and acquires an image of the entire parking lot. The sensor is installed in each space in the parking lot and detects the presence or absence of vacant spaces. For example, an ultrasonic sensor or an infrared sensor is used to detect the occupancy status of the parking space. The parking suggestion unit proposes the optimal parking spot to the driver based on the information on the detected vacant spaces. For example, the parking suggestion unit can acquire the congestion status of the parking lot in real time and propose the most vacant parking spot. The parking suggestion unit presents the parking spot to the driver by voice or visual. For example, the parking suggestion unit announces by voice that "there is a vacant space on the right side of the parking lot" and visually displays the location of the parking spot. In this way, the parking suggestion unit can detect vacant spaces in the parking lot and propose the optimal parking spot to the driver.

[0073] The automatic parking unit can automatically park the vehicle in the parking space when the vehicle approaches the parking space. The automatic parking unit detects the position of the parking space and the distance to other vehicles using sensors and cameras arranged around the vehicle. For example, ultrasonic sensors and LiDAR are used to measure the shape of the parking space and the distance to other vehicles. The automatic parking unit controls the steering and braking of the vehicle based on the detected information to automatically park the vehicle. For example, the automatic parking unit makes the vehicle enter the parking space at an appropriate angle and operates the steering to put the vehicle into the parking space. In addition, the automatic parking unit can monitor the surrounding situation in real time and apply the brakes as necessary to avoid collisions with other vehicles or obstacles during parking. As a result, the automatic parking unit can reduce the burden on the driver and achieve safe parking by automatically parking the vehicle.

[0074] The warning unit can issue a warning to the driver by voice and visually. The warning unit uses, for example, a speaker to issue a voice warning. The speaker issues a voice warning to the driver. For example, when there is a pedestrian ahead, a warning of "There is a pedestrian ahead" is issued through the speaker. The warning unit also uses a display to issue a visual warning. The display visually displays a warning to the driver. For example, when there is an obstacle ahead, the position of the obstacle is displayed on the display to alert the driver. The warning unit can effectively alert the driver by combining a voice warning and a visual warning. For example, a voice warning of "There is an obstacle ahead" is issued and the position of the obstacle is simultaneously displayed on the display. In this way, the warning unit can effectively alert the driver by issuing a voice and visual warning to the driver.

[0075] The obstacle detection unit can estimate the driver's emotions and adjust the sensitivity of the obstacle detection based on the estimated driver's emotions. The obstacle detection unit uses, for example, face recognition technology and voice analysis technology to estimate the driver's emotions. The face recognition technology uses a camera to analyze the driver's facial expression and estimate the driver's emotions. For example, when the driver is nervous, the driver's emotions are estimated by analyzing the degree of tension in the facial muscles and the movement of the eyes. The voice analysis technology uses a microphone to analyze the driver's voice tone and speed and estimate the driver's emotions. For example, when the driver is relaxed, the driver's emotions are estimated by analyzing that the voice tone is gentle and the speed is slow. The obstacle detection unit adjusts the sensitivity of the obstacle detection based on the estimated driver's emotions. For example, when the driver is nervous, the obstacle detection sensitivity is increased to issue an earlier warning. When the driver is relaxed, the obstacle detection sensitivity is set to normal to avoid excessive warnings. When the driver is tired, the obstacle detection sensitivity is increased to compensate for the distraction. This allows the obstacle detection unit to provide a more appropriate warning by adjusting the sensitivity of obstacle detection according to the driver's emotions. Estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0076] The obstacle detection unit can predict the movement of the obstacle when detecting the obstacle and change the detection range based on the predicted movement. The obstacle detection unit uses, for example, a movement pattern analysis or a prediction algorithm to predict the movement of the obstacle. The movement pattern analysis analyzes the movement pattern of the obstacle based on past data and predicts the future movement. For example, when there is a possibility that a pedestrian may enter the roadway, the movement is predicted and the detection range is expanded. The prediction algorithm analyzes the movement of the obstacle in real time and predicts the future movement. For example, when a bicycle is heading in the traveling direction of the vehicle, the movement is predicted and the detection range is adjusted. The obstacle detection unit dynamically changes the detection range based on the predicted movement. For example, when there is a possibility that another vehicle will suddenly change lanes, the movement is predicted and the detection range is changed. As a result, the obstacle detection unit can provide safer driving assistance by predicting the movement of the obstacle and dynamically changing the detection range.

[0077] The obstacle detection unit can optimize the detection algorithm based on weather conditions when detecting an obstacle. The obstacle detection unit uses, for example, a weather sensor and weather data to take into account weather conditions. The weather sensor is mounted on the vehicle and detects weather conditions such as rain, snow, and fog. For example, in rainy weather, the detection algorithm is adjusted to eliminate the influence of raindrops. The weather data is obtained from an external weather information service to grasp the weather conditions in real time. For example, in the case of fog, visibility is reduced, so the detection algorithm is strengthened. The obstacle detection unit optimizes the detection algorithm based on weather conditions. For example, on a snowy day, the detection algorithm is optimized taking into account the reflection of snow. As a result, the obstacle detection unit can provide high-precision obstacle detection even in bad weather by optimizing the detection algorithm taking into account weather conditions.

[0078] The warning unit can estimate the driver's emotions and adjust the strength of the warning based on the estimated driver's emotions. The warning unit uses, for example, face recognition technology and voice analysis technology to estimate the driver's emotions. The face recognition technology uses a camera to analyze the driver's facial expression and estimate the driver's emotions. For example, when the driver is nervous, the driver's emotions are estimated by analyzing the degree of tension in the facial muscles and the movement of the eyes. The voice analysis technology uses a microphone to analyze the driver's voice tone and speed and estimate the driver's emotions. For example, when the driver is relaxed, the driver's emotions are estimated by analyzing that the voice tone is gentle and the speed is slow. The warning unit adjusts the strength of the warning based on the estimated driver's emotions. For example, when the driver is nervous, the warning strength is increased to call attention. When the driver is relaxed, the warning strength is set to normal to avoid excessive warning. When the driver is tired, the warning strength is increased to compensate for distraction. In this way, the warning unit can provide a more effective warning by adjusting the warning strength according to the driver's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0079] The warning unit can generate a different warning message according to the type of obstacle when issuing a warning. The warning unit uses, for example, image recognition technology and sensor data to identify the type of obstacle. The image recognition technology uses a camera to acquire an image of the obstacle and uses image processing technology to identify the type of obstacle. For example, the type is identified as a pedestrian, bicycle, other vehicle, etc. The sensor data uses an ultrasonic sensor or LiDAR to detect the shape and position of the obstacle and identify the type of obstacle. The warning unit generates a different warning message according to the type of obstacle identified. For example, if a pedestrian is in front, the warning unit issues a warning that "There is a pedestrian in front," and if a bicycle is on the side, the warning unit issues a warning that "There is a bicycle on the side." If another vehicle is behind, the warning unit issues a warning that "There is a vehicle behind." As a result, the warning unit can provide a more specific warning to the driver by generating a warning message according to the type of obstacle.

[0080] The warning unit can select an optimal warning method based on the driver's past reaction history when issuing a warning. The warning unit uses a database to store and refer to the driver's past reaction history, for example. The database stores a history of how the driver responded to what warnings in the past. For example, audio warning is given priority to a driver who responded quickly to an audio warning. A visual warning is given priority to a driver who responded quickly to a visual warning. A composite warning method is selected for a driver for whom a combination of multiple warning methods was effective. The warning unit refers to the driver's past reaction history from the database when issuing a warning and selects an optimal warning method. For example, audio warning is given priority to a driver who responded quickly to an audio warning in the past. A visual warning is given priority to a driver who responded quickly to a visual warning in the past. A combination of audio and visual warnings is given to a driver for whom a combination of multiple warning methods was effective in the past. In this way, the warning unit can select an optimal warning method by referring to the driver's past reaction history and provide an effective warning.

[0081] The fatigue detection unit can estimate the driver's emotions and adjust the sensitivity of fatigue detection based on the estimated driver's emotions. The fatigue detection unit uses, for example, face recognition technology and voice analysis technology to estimate the driver's emotions. The face recognition technology uses a camera to analyze the driver's facial expression and estimate the driver's emotions. For example, when the driver is nervous, the driver's emotions are estimated by analyzing the degree of tension in the facial muscles and the movement of the eyes. The voice analysis technology uses a microphone to analyze the driver's voice tone and speed and estimate the driver's emotions. For example, when the driver is relaxed, the driver's emotions are estimated by analyzing that the voice tone is gentle and the speed is slow. The fatigue detection unit adjusts the sensitivity of fatigue detection based on the estimated driver's emotions. For example, when the driver is nervous, the fatigue detection sensitivity is increased and an early break is suggested. When the driver is relaxed, the fatigue detection sensitivity is set to normal. When the driver is tired, the fatigue detection sensitivity is increased to compensate for distraction. This allows the fatigue detection unit to provide a more appropriate rest suggestion by adjusting the sensitivity of fatigue detection according to the driver's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0082] When fatigue is detected, the fatigue detection unit can analyze the breathing pattern of the driver to evaluate the fatigue. The fatigue detection unit uses, for example, a breathing sensor to analyze the breathing pattern of the driver. The breathing sensor measures the rhythm and depth of the driver's breathing. For example, when the driver's breathing becomes shallow, the fatigue level is evaluated as high. When the driver's breathing becomes fast, the fatigue level is evaluated as high. When the driver's breathing becomes irregular, the fatigue level is evaluated as high. The fatigue detection unit analyzes the breathing pattern of the driver based on the data acquired from the breathing sensor and evaluates the fatigue level. In this way, the fatigue detection unit can evaluate the fatigue level with high accuracy by analyzing the breathing pattern of the driver.

[0083] The suggestion unit can estimate the driver's emotion and adjust the content of the rest suggestion based on the estimated driver's emotion. The suggestion unit uses, for example, face recognition technology and voice analysis technology to estimate the driver's emotion. The face recognition technology uses a camera to analyze the driver's facial expression and estimate the driver's emotion. For example, when the driver is nervous, the driver's emotion is estimated by analyzing the degree of tension in the facial muscles and the movement of the eyes. The voice analysis technology uses a microphone to analyze the tone and speed of the driver's voice and estimate the driver's emotion. For example, when the driver is relaxed, the driver's emotion is estimated by analyzing that the tone of the voice is gentle and the speed is slow. The suggestion unit adjusts the content of the rest suggestion based on the estimated driver's emotion. For example, when the driver is nervous, the suggestion unit suggests a rest place where the driver can relax. When the driver is relaxed, the suggestion unit suggests a normal rest place. When the driver is tired, the suggestion unit suggests a rest place with a longer rest time. As a result, the suggestion unit can provide a more appropriate rest suggestion by adjusting the content of the rest suggestion according to the driver's emotion. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0084] The suggestion unit can suggest an optimal rest timing based on the driver's past rest history at the time of suggestion. The suggestion unit uses a database to store and refer to the driver's past rest history, for example. The database stores a history of when the driver took breaks in the past. For example, the suggestion unit suggests a break based on a time period when the driver frequently took breaks in the past. The suggestion unit suggests an optimal rest location based on rest locations used in the past. The suggestion unit suggests a rest location preferred by the driver from the past rest history at the time of suggestion. The suggestion unit refers to the driver's past rest history from the database and suggests an optimal rest timing at the time of suggestion. For example, the suggestion unit suggests a break based on a time period when the driver frequently took breaks in the past. The suggestion unit suggests an optimal rest location based on rest locations used in the past. The suggestion unit suggests a rest location preferred by the driver from the past rest history. In this way, the suggestion unit can suggest an optimal rest timing by referring to the driver's past rest history and provide effective rest suggestions.

[0085] The parking suggestion unit can estimate the driver's emotions and adjust the contents of the parking suggestion based on the estimated driver's emotions. The parking suggestion unit uses, for example, face recognition technology and voice analysis technology to estimate the driver's emotions. The face recognition technology uses a camera to analyze the driver's facial expression and estimate the driver's emotions. For example, when the driver is nervous, the driver's emotions are estimated by analyzing the degree of tension in the facial muscles and the movement of the eyes. The voice analysis technology uses a microphone to analyze the driver's voice tone and speed and estimate the driver's emotions. For example, when the driver is relaxed, the driver's emotions are estimated by analyzing that the voice tone is gentle and the speed is slow. The parking suggestion unit adjusts the contents of the parking suggestion based on the estimated driver's emotions. For example, when the driver is nervous, a large parking space is suggested. When the driver is relaxed, a normal parking space is suggested. When the driver is tired, a nearby parking space is suggested. In this way, the parking suggestion unit can provide a more appropriate parking suggestion by adjusting the contents of the parking suggestion according to the driver's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0086] The parking suggestion unit can suggest an optimal parking spot based on a congestion prediction of the parking lot when suggesting parking. For example, the parking suggestion unit acquires past congestion data and real-time congestion status in order to predict congestion in the parking lot. The past congestion data is based on the usage history of the parking lot, and analyzes congestion patterns during specific times of the day and days of the week. The real-time congestion status is acquired using a sensor or a camera in the parking lot. For example, a sensor installed in each space of the parking lot detects the presence or absence of an empty space and grasps the congestion status in real time. The parking suggestion unit predicts congestion based on these data and suggests an optimal parking spot. For example, the parking suggestion unit acquires the congestion status of the parking lot in real time and suggests an empty parking spot. Based on the past congestion data, the parking suggestion unit predicts congestion and suggests an optimal parking spot. Based on the congestion prediction of the parking lot, the parking suggestion unit suggests a reservation of a parking spot. In this way, the parking suggestion unit can suggest an optimal parking spot by predicting congestion in the parking lot.

[0087] The parking suggestion unit can suggest a safe parking spot based on the security information of the parking lot when suggesting parking. For example, the parking suggestion unit grasps the installation status of security cameras and the deployment status of security guards in order to acquire the security information of the parking lot. The installation status of security cameras is based on the positions and coverage ranges of cameras installed in each area of ​​the parking lot. For example, an area where security cameras are installed is determined to have high security. The deployment status of security guards is based on the patrol routes and deployment locations of security guards in the parking lot. For example, an area where security guards frequently patrol is determined to have high security. The parking suggestion unit suggests a safe parking spot based on these security information. For example, the parking suggestion unit suggests a safe parking spot based on the installation status of security cameras. The parking suggestion unit suggests a safe parking spot based on the deployment status of security guards. The parking suggestion unit suggests a safe parking spot based on the lighting status of the parking lot. In this way, the parking suggestion unit can suggest a safe parking spot by taking into account the security information of the parking lot.

[0088] The automatic parking unit can estimate the driver's emotions and adjust the automatic parking speed based on the estimated driver's emotions. The automatic parking unit uses, for example, face recognition technology and voice analysis technology to estimate the driver's emotions. The face recognition technology uses a camera to analyze the driver's facial expression and estimate the driver's emotions. For example, when the driver is nervous, the driver's emotions are estimated by analyzing the degree of tension in the facial muscles and the movement of the eyes. The voice analysis technology uses a microphone to analyze the driver's voice tone and speed and estimate the driver's emotions. For example, when the driver is relaxed, the driver's emotions are estimated by analyzing that the voice tone is gentle and the speed is slow. The automatic parking unit adjusts the automatic parking speed based on the estimated driver's emotions. For example, when the driver is nervous, the automatic parking speed is set to slow. When the driver is relaxed, the automatic parking speed is set to normal. When the driver is tired, the automatic parking speed is set to slow. In this way, the automatic parking unit can provide safer parking by adjusting the automatic parking speed according to the driver's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0089] During automatic parking, the automatic parking unit can analyze the shape of the parking space and select the optimal parking method. The automatic parking unit uses, for example, a camera and a sensor to analyze the shape of the parking space. The camera acquires an image of the parking space and analyzes the shape using image processing technology. For example, if the parking space is narrow, reverse parking is selected. The sensor measures the dimensions and shape of the parking space and selects the optimal parking method. For example, if the parking space is wide, forward parking is selected. If the parking space is diagonal, diagonal parking is selected. The automatic parking unit selects the optimal parking method based on the analyzed shape of the parking space. For example, if the parking space is narrow, reverse parking is selected and the vehicle enters at an appropriate angle. If the parking space is wide, forward parking is selected and the vehicle is parked forward. If the parking space is diagonal, diagonal parking is selected and the vehicle enters diagonally. In this way, the automatic parking unit can select the optimal parking method by analyzing the shape of the parking space and provide safe parking.

[0090] The automatic parking unit can predict the movement of surrounding vehicles during automatic parking to perform safe parking. The automatic parking unit uses, for example, movement pattern analysis and a prediction algorithm to predict the movement of surrounding vehicles. The movement pattern analysis analyzes the movement pattern of surrounding vehicles based on past data and predicts future movement. For example, when surrounding vehicles are moving, the movement is predicted to perform safe parking. The prediction algorithm analyzes the movement of surrounding vehicles in real time and predicts future movement. For example, when surrounding vehicles are stopped, the position is taken into consideration to perform safe parking. The automatic parking unit performs safe parking based on the predicted movement of surrounding vehicles. For example, when surrounding vehicles enter a parking space, the movement is predicted to perform safe parking. In this way, the automatic parking unit can provide safe parking by predicting the movement of surrounding vehicles.

[0091] The automatic parking unit can estimate the driver's emotions and adjust the start timing of automatic parking based on the estimated driver's emotions. The automatic parking unit uses, for example, face recognition technology and voice analysis technology to estimate the driver's emotions. The face recognition technology uses a camera to analyze the driver's facial expression and estimate the driver's emotions. For example, when the driver is nervous, the driver's emotions are estimated by analyzing the degree of tension in the facial muscles and the movement of the eyes. The voice analysis technology uses a microphone to analyze the driver's voice tone and speed and estimates the driver's emotions. For example, when the driver is relaxed, the driver's emotions are estimated by analyzing that the voice tone is gentle and the speed is slow. The automatic parking unit adjusts the start timing of automatic parking based on the estimated driver's emotions. For example, when the driver is nervous, automatic parking is started early. When the driver is relaxed, automatic parking is started at a normal timing. When the driver is tired, automatic parking is started early. In this way, the automatic parking unit can provide safer parking by adjusting the start timing of automatic parking according to the driver's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0092] The automatic parking unit can adjust the parking method based on the state of the tires of the vehicle during automatic parking. The automatic parking unit uses, for example, sensors that measure tire pressure and wear in order to take into account the state of the tires of the vehicle. The tire pressure sensor measures the tire pressure in real time, and adjusts the parking method when the tire pressure is low. For example, when the tire pressure is low, the parking method is adjusted to provide safe parking. The tire wear sensor measures the tire wear, and adjusts the parking method when the tire wear is advanced. For example, when the tire wear is advanced, the parking method is adjusted to provide safe parking. The tire temperature sensor measures the tire temperature, and adjusts the parking method when the temperature is high. For example, when the tire temperature is high, the parking method is adjusted to provide safe parking. In this way, the automatic parking unit can provide safe parking by taking into account the state of the tires of the vehicle.

[0093] The automatic parking unit can adjust the parking method based on the lighting conditions of the parking space during automatic parking. The automatic parking unit uses, for example, a lighting sensor to take into account the lighting conditions of the parking space. The lighting sensor measures the brightness of the parking space and adjusts the parking method based on the lighting conditions. For example, if the parking space is dark, the parking method is adjusted to provide safe parking. If the parking space is bright, a normal parking method is selected. If the parking space is partially dark, parking is performed while avoiding that part. The automatic parking unit analyzes the lighting conditions of the parking space based on the data acquired from the lighting sensor and selects the optimal parking method. In this way, the automatic parking unit can provide safe parking by taking into account the lighting conditions of the parking space.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The driving assistance system may further include a stress sensing unit that monitors the driver's stress level. The stress sensing unit measures the driver's heart rate variability and electrodermal activity to evaluate the stress level. For example, if the heart rate variability decreases and the electrodermal activity increases, it is determined that the driver is in a high stress state. The stress sensing unit may make suggestions to the driver to relax based on the stress level. For example, it may play audio guidance encouraging deep breathing or relaxing music. In addition, if the stress level is high, it may suggest to the driver to take a break. In this way, the driving assistance system can reduce the driver's stress and support safe driving.

[0096] The obstacle detection unit can further include a function for identifying the material of the obstacle. For example, a camera or LiDAR can be used to analyze the surface reflection characteristics of the obstacle and identify the material, such as metal, plastic, or wood. Based on the material, the danger level of the obstacle can be evaluated and an appropriate warning can be issued to the driver. For example, a metal obstacle has a high danger level, so a strong warning is issued. On the other hand, a plastic obstacle has a relatively low danger level, so a normal warning is issued. This allows the obstacle detection unit to provide an appropriate warning according to the material of the obstacle.

[0097] The fatigue detection unit can further analyze the driver's voice pattern to evaluate fatigue. Using voice analysis technology, the tone, speed, and frequency of speech of the driver are analyzed. For example, if the tone of the voice is low and the frequency of speech is decreasing, it is determined that the driver is tired. Based on the voice pattern, it can suggest that the driver take a break. For example, it can make a suggestion such as "The driver's voice sounds tired. Please take a break." It can also monitor changes in the voice pattern in real time to detect the progression of fatigue at an early stage. This allows the fatigue detection unit to perform a more accurate fatigue evaluation by analyzing the driver's voice pattern.

[0098] The parking suggestion unit can further suggest parking spots taking into account the energy efficiency of the parking lot. For example, the location of an electric vehicle charging station in the parking lot and the installation status of solar power generation panels are taken into account. For electric vehicles, parking spots close to the charging station are suggested. In areas where solar power generation panels are installed, energy-efficient parking spots are suggested. This allows the parking suggestion unit to suggest parking spots taking energy efficiency into account.

[0099] The automatic parking unit can further analyze the condition of the ground in the parking space and adjust the parking method. For example, it can use a camera or a sensor to detect the unevenness or slipperiness of the ground. If the ground is slippery, it can set the parking speed slower and park carefully. If the ground is uneven, it can adjust the vehicle suspension to park safely. This allows the automatic parking unit to provide safe parking that takes the ground condition into account.

[0100] The warning unit can further adjust the timing of the warning based on the driver's past driving history. For example, if a warning was ignored in a particular situation in the past, the warning will be issued earlier in that situation. Conversely, if a quick reaction was made in the past, the warning will be issued at a normal timing in that situation. This allows the warning unit to provide appropriate warning timing that takes into account the driver's past driving history.

[0101] The obstacle detection unit can further estimate the driver's emotions and adjust the sensitivity of the obstacle detection based on the estimated driver's emotions. For example, when the driver is tense, the obstacle detection sensitivity is increased to issue an earlier warning. When the driver is relaxed, the obstacle detection sensitivity is set to normal to avoid excessive warnings. When the driver is tired, the obstacle detection sensitivity is increased to compensate for the distraction. In this way, the obstacle detection unit can provide more appropriate warnings by adjusting the obstacle detection sensitivity according to the driver's emotions.

[0102] The obstacle detection unit can further predict the movement of the obstacle and change the detection range based on the predicted movement. For example, if there is a possibility that a pedestrian will enter the roadway, the detection range is expanded by predicting the movement. If a bicycle is heading in the direction of travel of the vehicle, the detection range is adjusted by predicting the movement. In this way, the obstacle detection unit can provide safer driving assistance by predicting the movement of the obstacle and dynamically changing the detection range.

[0103] The obstacle detection unit can further optimize the detection algorithm based on weather conditions. For example, in rainy weather, the detection algorithm is adjusted to eliminate the effect of raindrops. In foggy weather, the detection algorithm is strengthened because visibility is reduced. On snowy days, the detection algorithm is optimized to take into account snow reflections. Thus, the obstacle detection unit can provide highly accurate obstacle detection even in bad weather by optimizing the detection algorithm taking into account weather conditions.

[0104] The warning unit can further estimate the driver's emotions and adjust the strength of the warning based on the estimated driver's emotions. For example, when the driver is nervous, the warning strength is increased to call attention. When the driver is relaxed, the warning strength is set to normal to avoid excessive warning. When the driver is tired, the warning strength is increased to compensate for the distraction. In this way, the warning unit can provide a more effective warning by adjusting the warning strength according to the driver's emotions.

[0105] The process flow of the second embodiment will be briefly described below.

[0106] Step 1: The obstacle detection unit detects obstacles in front of or behind the vehicle. The obstacle detection unit can detect obstacles using, for example, an ultrasonic sensor, a camera, a radar, or LiDAR. For example, an ultrasonic sensor measures the distance to an obstacle by emitting sound waves and receiving the reflected waves. A camera detects obstacles using image processing technology. A radar identifies the position of an obstacle by emitting radio waves and receiving the reflected waves. A LiDAR identifies the shape of an obstacle by emitting laser light and receiving the reflected light. Step 2: The warning unit issues a warning to the driver based on the obstacle detected by the obstacle detection unit. The warning unit can issue, for example, an audio warning or a visual warning. For example, the audio warning issues an audio warning to the driver using a speaker. For the visual warning, a visual warning is displayed to the driver using a display. Step 3: The fatigue detection unit detects the driver's fatigue level and distraction. The fatigue detection unit can detect the driver's condition using, for example, face recognition technology, eye movement tracking technology, heart rate, and skin temperature measurement technology. For example, face recognition technology uses a camera to recognize the driver's face and analyzes the eye opening / closing state and facial expression. Eye movement tracking technology detects the driver's gaze movement using an eye tracking sensor. Heart rate is measured by using a heart rate sensor to measure the driver's heart rate. Skin temperature is measured by using a temperature sensor to measure the driver's skin temperature. Step 4: The suggestion unit suggests to the driver to take a break based on the information sensed by the fatigue sensing unit. The suggestion unit can suggest the break by voice or visual means, for example. For example, the suggestion unit suggests by voice, "The driver appears tired. Please take a break." Step 5: The parking suggestion unit detects vacant spaces in the parking lot and suggests parking spots to the driver. For example, the parking suggestion unit can obtain the congestion status of the parking lot in real time and suggest the vacant parking spot. Step 6: The automatic parking unit automatically parks the vehicle in the parking space when the vehicle approaches the parking space. The automatic parking unit can, for example, take into account the distance to other vehicles in the parking lot and suggest a safe parking spot to park the vehicle automatically.

[0107] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0109] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0110] For example, the obstacle detection unit can detect an obstacle in front of or behind the vehicle using the camera 42 or ultrasonic sensor of the smart device 14. The specific processing unit 290 of the data processing device 12 can also process these sensor data to detect an obstacle. The warning unit can issue a warning to the driver using the speaker 40B or display 40A of the smart device 14. The specific processing unit 290 of the data processing device 12 can also generate an audio warning or a visual warning. The fatigue detection unit can detect the driver's fatigue level or distraction using the camera 42 or heart rate sensor of the smart device 14. The specific processing unit 290 of the data processing device 12 can also analyze these data to detect the driver's condition. The suggestion unit can suggest a break to the driver using the speaker 40B or display 40A of the smart device 14. The specific processing unit 290 of the data processing device 12 can also generate a break suggestion. The parking suggestion unit can detect an empty space in the parking lot using the camera 42 or sensor of the smart device 14 and suggest a parking spot to the driver. The specific processing unit 290 of the data processing device 12 can also suggest parking spots. The automatic parking unit can automatically park the vehicle using the control unit 46A of the smart device 14. The specific processing unit 290 of the data processing device 12 can also control automatic parking. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] 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.

[0115] 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 the voice according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0117] 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 performed in a secure state.

[0118] Fig. 4 shows an example of 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.

[0119] 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.

[0120] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0123] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0124] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0125] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0126] For example, the obstacle detection unit can detect an obstacle in front of or behind the vehicle using the camera 42 or ultrasonic sensor of the smart glasses 214. The specific processing unit 290 of the data processing device 12 can also process these sensor data to detect an obstacle. The warning unit can issue a warning to the driver using the speaker 240 or display of the smart glasses 214. The specific processing unit 290 of the data processing device 12 can also generate an audio warning or a visual warning. The fatigue detection unit can detect the driver's fatigue level or distraction using the camera 42 or heart rate sensor of the smart glasses 214. The specific processing unit 290 of the data processing device 12 can also analyze these data to detect the driver's condition. The suggestion unit can suggest a break to the driver using the speaker 240 or display of the smart glasses 214. The specific processing unit 290 of the data processing device 12 can also generate a break suggestion. The parking suggestion unit can detect an empty space in the parking lot using the camera 42 or sensor of the smart glasses 214 and suggest a parking spot to the driver. The specific processing unit 290 of the data processing device 12 can also suggest parking spots. The automatic parking unit can automatically park the vehicle using the control unit 46A of the smart glasses 214. The specific processing unit 290 of the data processing device 12 can also control automatic parking. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] 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.

[0131] 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 the voice according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0133] 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 performed in a secure state.

[0134] 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.

[0135] 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.

[0136] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0137] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0138] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0139] 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 voice indicating a user input for 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.

[0140] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0141] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0142] For example, the obstacle detection unit can detect an obstacle in front of or behind the vehicle using the camera 42 or ultrasonic sensor of the headset type terminal 314. The specific processing unit 290 of the data processing device 12 can also process these sensor data to detect an obstacle. The warning unit can issue a warning to the driver using the speaker 240 or display 343 of the headset type terminal 314. The specific processing unit 290 of the data processing device 12 can also generate an audio warning or a visual warning. The fatigue detection unit can detect the driver's fatigue level or distraction using the camera 42 or heart rate sensor of the headset type terminal 314. The specific processing unit 290 of the data processing device 12 can also analyze these data to detect the driver's condition. The suggestion unit can suggest a break to the driver using the speaker 240 or display 343 of the headset type terminal 314. The specific processing unit 290 of the data processing device 12 can also generate a break suggestion. The parking suggestion unit can detect an empty space in the parking lot using the camera 42 or sensor of the headset type terminal 314 and suggest a parking spot to the driver. The specific processing unit 290 of the data processing device 12 can also suggest parking spots. The automatic parking unit can automatically park the vehicle using the control unit 46A of the headset type terminal 314. The specific processing unit 290 of the data processing device 12 can also control automatic parking. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] 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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] 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.

[0147] 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 the voice according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0149] 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 performed in a secure state.

[0150] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0151] 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.

[0152] 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.

[0153] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0154] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0155] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0156] 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 a voice indicating a user input for 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.

[0157] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0158] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0159] For example, the obstacle detection unit can detect an obstacle in front of or behind the vehicle using the camera 42 or ultrasonic sensor of the robot 414. The specific processing unit 290 of the data processing device 12 can also process these sensor data to detect an obstacle. The warning unit can issue a warning to the driver using the speaker 240 or display of the robot 414. The specific processing unit 290 of the data processing device 12 can also generate an audio warning or a visual warning. The fatigue detection unit can detect the driver's fatigue level or distraction using the camera 42 or heart rate sensor of the robot 414. The specific processing unit 290 of the data processing device 12 can also analyze these data to detect the driver's condition. The suggestion unit can suggest a break to the driver using the speaker 240 or display of the robot 414. The specific processing unit 290 of the data processing device 12 can also generate a break suggestion. The parking suggestion unit can detect an empty space in the parking lot using the camera 42 or sensor of the robot 414 and suggest a parking spot to the driver. The specific processing unit 290 of the data processing device 12 can also make a parking spot suggestion. The automatic parking unit can automatically park the vehicle using the control unit 46A of the robot 414. The specific processing unit 290 of the data processing device 12 can also control the automatic parking. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0160] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0161] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0162] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0163] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0164] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0165] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0167] 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.

[0168] 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 Universal Serial Bus (USB) 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.

[0169] In addition, 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 upon request from the data processing device 12.

[0170] 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.

[0171] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0172] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0173] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0174] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0175] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0176] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0177] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0178] (Appendix 1) an obstacle detection unit that detects an obstacle in front of or behind the vehicle; a warning unit that issues a warning to a driver based on the obstacle detected by the obstacle detection unit; A fatigue detection unit that detects fatigue and distraction of the driver; A suggestion unit that suggests a break to the driver based on the information sensed by the fatigue sensing unit; a parking suggestion unit that suggests parking spots to the driver; An automatic parking unit that supports automatic parking. A system characterized by: (Appendix 2) The obstacle detection unit is Detects obstacles in front or behind the vehicle using ultrasonic sensors, cameras, radar, and LiDAR 2. The system of claim 1, (Appendix 3) The fatigue sensing unit is Detect driver fatigue and distraction using facial recognition technology, eye movement tracking technology, heart rate and skin temperature measurement technology 2. The system of claim 1, (Appendix 4) The parking suggestion unit includes: Detects vacant spaces in parking lots and presents parking spots to drivers 2. The system of claim 1, (Appendix 5) The automatic parking unit includes: When the vehicle approaches a parking space, the vehicle automatically parks in the parking space. 2. The system of claim 1, (Appendix 6) The warning unit is Issue audio and visual warnings to the driver 2. The system of claim 1, (Appendix 7) The obstacle detection unit is Estimate the driver's emotions and adjust the sensitivity of obstacle detection based on the estimated driver's emotions. 2. The system of claim 1, (Appendix 8) The obstacle detection unit is When detecting an obstacle, predict the obstacle's movement and change the detection range based on the predicted movement. 2. The system of claim 1, (Appendix 9) The obstacle detection unit is Optimize obstacle detection algorithms based on weather conditions 2. The system of claim 1, (Appendix 10) The warning unit is Estimate the driver's emotions and adjust the intensity of the warning based on the estimated driver's emotions 2. The system of claim 1, (Appendix 11) The warning unit is When a warning occurs, generate different warning messages depending on the type of obstacle. 2. The system of claim 1, (Appendix 12) The warning unit is When issuing a warning, the system selects the most appropriate warning method based on the driver's past reaction history. 2. The system of claim 1, (Appendix 13) The fatigue sensing unit is Estimating the driver's emotions and adjusting the sensitivity of fatigue detection based on the estimated driver's emotions 2. The system of claim 1, (Appendix 14) The fatigue sensing unit is When fatigue is detected, the driver's breathing pattern is analyzed to assess fatigue. 2. The system of claim 1, (Appendix 15) The suggestion unit, Estimate the driver's emotions and adjust the rest suggestion content based on the estimated driver's emotions 2. The system of claim 1, (Appendix 16) The suggestion unit, When making a proposal, the system suggests optimal rest timing based on the driver's past rest history 2. The system of claim 1, (Appendix 17) The parking suggestion unit includes: Estimate the driver's emotions and adjust parking suggestions based on the estimated driver's emotions 2. The system of claim 1, (Appendix 18) The parking suggestion unit includes: When suggesting parking spots, the system suggests optimal parking spots based on predicted parking congestion. 2. The system of claim 1, (Appendix 19) The parking suggestion unit includes: When suggesting parking, suggest safe parking spots based on parking lot security information 2. The system of claim 1, (Appendix 20) The automatic parking unit includes: Estimate the driver's emotions and adjust the speed of automatic parking based on the estimated driver's emotions. 2. The system of claim 1, (Appendix 21) The automatic parking unit includes: When automatically parking, the shape of the parking space is analyzed to select the optimal parking method. 2. The system of claim 1, (Appendix 22) The automatic parking unit includes: During automatic parking, the system performs safe parking by predicting the movements of surrounding vehicles. 2. The system of claim 1, (Appendix 23) The automatic parking unit includes: Estimating the driver's emotions and adjusting the timing of automatic parking based on the estimated driver's emotions 2. The system of claim 1, (Appendix 24) The automatic parking unit includes: When automatically parking, the parking strategy is adjusted based on the condition of the vehicle's tires 2. The system of claim 1, (Appendix 25) The automatic parking unit includes: When parking automatically, the car adjusts its parking strategy based on the lighting conditions in the parking space. 2. The system of claim 1, [Explanation of symbols]

[0179] 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 obstacle detection unit that detects an obstacle in front of or behind the vehicle; a warning unit that issues a warning to a driver based on the obstacle detected by the obstacle detection unit; A fatigue detection unit that detects fatigue and distraction of the driver; A suggestion unit that suggests a break to the driver based on the information sensed by the fatigue sensing unit; a parking suggestion unit that suggests parking spots to the driver; An automatic parking unit that supports automatic parking. A system characterized by:

2. The obstacle detection unit is Detects obstacles in front or behind the vehicle using ultrasonic sensors, cameras, radar, and LiDAR 2. The system of claim 1 .

3. The fatigue sensing unit is Detecting fatigue and distraction in the driver using facial recognition technology, eye movement tracking technology, heart rate and skin temperature measurement technology 2. The system of claim 1 .

4. The parking suggestion unit includes: Detects vacant spaces in a parking lot and presents parking spots to the driver 2. The system of claim 1 .

5. The automatic parking unit includes: When the vehicle approaches a parking space, the vehicle automatically parks in the parking space.

2. The system of claim 1 .

6. The warning unit is Issue audio and visual warnings to the driver 2. The system of claim 1 .

7. The obstacle detection unit is The emotion of the driver is estimated, and the sensitivity of the obstacle detection is adjusted based on the estimated emotion of the driver.

2. The system of claim 1 .

8. The obstacle detection unit is When detecting an obstacle, predict the obstacle's movement and change the detection range based on the predicted movement.

2. The system of claim 1 .

9. The obstacle detection unit is Optimize obstacle detection algorithms based on weather conditions 2. The system of claim 1 .

10. The warning unit is Estimating the driver's emotion and adjusting the intensity of the warning based on the estimated driver's emotion.

2. The system of claim 1 .

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