Method, apparatus, and system for generating vehicle export information and vehicle state modeling information by using artificial intelligence model and ar

Abstract

According to an embodiment, provided is a method for generating vehicle export information and vehicle state modeling information by using an artificial intelligence model and AR, which is performed by an apparatus, the method comprising the steps of: when image capturing is being performed on a first area through a first camera, receiving, from the first camera, first image information generated by capturing an image of the first area; on the basis of the first image information, analyzing whether or not a vehicle to be exported is parked in the first area; if it is determined from the analyzing that the vehicle is parked in the first area, recognizing, on the basis of the first image information, a license plate number in a license plate of the vehicle parked in the first area; on the basis of the recognized license plate number, identifying the vehicle parked in the first area as a first vehicle; acquiring first vehicle information including a vehicle type, year, mileage, accident history, insurance history, and estimated market price of the first vehicle; applying the first vehicle information to an artificial intelligence model to select an export country for the first vehicle on the basis of an output of the artificial intelligence model; and when the export country for the first vehicle is selected as a first country, generating export information of the first vehicle, recommending to export the first vehicle to the first country.

Description

Method, device, and system for generating vehicle export information and vehicle condition modeling information using artificial intelligence models and AR

[0001] The following examples relate to a technique for generating vehicle export information and vehicle condition modeling information using an artificial intelligence model and AR.

[0002] As exports of used vehicles gradually expand, the used vehicle export market is also continuously growing.

[0003] The export price of these used vehicles may be determined based on factors such as the vehicle type, year, mileage, accident history, and condition. Unlike new vehicles, the price is not fixed, so the export price of used vehicles is determined differently depending on the situation.

[0004] Accordingly, there is a growing demand for determining the export price of used vehicles based on their condition and for providing export strategies for vehicles, including which countries are most advantageous for exporting vehicles, and research on related technologies is required.

[0005] According to one embodiment, the purpose is to provide a method, device and system for generating vehicle export information and vehicle condition modeling information by utilizing an artificial intelligence model and AR.

[0006] The purpose of the present invention is not limited to the purposes mentioned above, and other purposes not mentioned can be clearly understood from the description below.

[0007] According to one embodiment, a method for generating vehicle export information and vehicle condition modeling information using an artificial intelligence model and AR, which is performed by a device, comprises: a step of receiving first image information generated by capturing a first area from a first camera when a first area is being captured through the first camera; a step of analyzing, based on the first image information, whether a vehicle to be exported is parked in the first area; a step of recognizing, based on the first image information, a license plate number of a vehicle parked in the first area, based on the first image information, when it is analyzed that a vehicle is parked in the first area; a step of identifying, based on the recognized license plate number, a vehicle parked in the first area as a first vehicle; a step of acquiring first vehicle information including a vehicle type, model year, mileage, accident history, insurance history, and expected market price of the first vehicle; a step of applying the first vehicle information to an artificial intelligence model and selecting an export country of the first vehicle based on an output of the artificial intelligence model; And when the export country of the first vehicle is selected as the first country, a method for generating vehicle export information and vehicle condition modeling information using an artificial intelligence model and AR is provided, including a step of generating export information of the first vehicle that recommends exporting the first vehicle to the first country.

[0008] The method for generating vehicle export information and vehicle condition modeling information using the artificial intelligence model and AR comprises the steps of: receiving a plurality of image information generated by photographing the first vehicle from various directions from the first camera when the first vehicle is photographed from various directions; collating the plurality of image information to generate a first image which is a 3D image of the first vehicle; analyzing the condition of the first vehicle based on the first image; determining whether repair is required for the first vehicle based on the first image; extracting a portion where the first portion is located from the first image as a 1-1 image when it is determined that repair is required for a first portion of the first vehicle; generating a 2-1 image representing a state in which the first portion is repaired based on the 1-1 image; generating a second image by replacing a portion where the first portion is located in the first image with the 2-1 image; The method may further include: calculating an export price of the first vehicle as a first amount before repairing the first part based on the first image and the first vehicle information; calculating an export price of the first vehicle as a second amount after repairing the first part based on the second image and the first vehicle information; and generating condition modeling information of the first vehicle based on the first image, the second image, the first amount, and the first amount.

[0009] According to one embodiment, by utilizing an artificial intelligence model and AR to generate vehicle export information and vehicle condition modeling information, it is possible to provide a vehicle export strategy and help export vehicles to each country.

[0010] Meanwhile, the effects according to the embodiments are not limited to those mentioned above, and other effects not mentioned can be clearly understood by a person having ordinary skill in the art from the description below.

[0011] Figure 1 is a schematic diagram showing the configuration of a system according to one embodiment.

[0012] Figure 2 is a flowchart illustrating a process of generating vehicle export information using an artificial intelligence model according to an embodiment.

[0013] Figure 3 is a flowchart illustrating a process for generating vehicle state modeling information according to an embodiment.

[0014] Figure 4 is an exemplary diagram of the configuration of a device according to one embodiment.

[0015] Hereinafter, embodiments are described in detail with reference to the attached drawings. However, the embodiments may be modified in various ways, and the scope of the patent application is not limited or restricted by these embodiments. It should be understood that all modifications, equivalents, or alternatives to the embodiments are included within the scope of the patent application.

[0016] Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only and may be modified and implemented in various forms. Accordingly, the embodiments are not limited to the specific disclosed form, and the scope of this specification includes modifications, equivalents, or alternatives that fall within the technical concept.

[0017] Although terms such as "first" or "second" may be used to describe various components, these terms should be interpreted solely to distinguish one component from another. For example, a first component may be referred to as a second component, and similarly, a second component may also be referred to as a first component.

[0018] When it is said that a component is "connected" to another component, it should be understood that it may be directly connected or connected to that other component, but there may also be other components in between.

[0019] The terms used in the examples are for illustrative purposes only and should not be construed as limiting. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as "comprise" or "have" are intended to indicate the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, but should be understood to not preclude the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0020] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments pertain. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense unless explicitly defined herein.

[0021] In addition, when describing with reference to the attached drawings, identical components will be assigned the same reference numerals regardless of the drawing numbers, and redundant descriptions thereof will be omitted. When describing embodiments, if a detailed description of a related known technology is judged to unnecessarily obscure the gist of the embodiment, the detailed description will be omitted.

[0022] The embodiments may be implemented in various forms of products, such as personal computers, laptop computers, tablet computers, smart phones, televisions, smart home appliances, intelligent vehicles, kiosks, and wearable devices.

[0023] In practice, an artificial intelligence (AI) system is a computer system that demonstrates human-level intelligence. Unlike existing rule-based smart systems, it is a machine-based system that learns and makes decisions on its own. As AI systems become more widely used, their recognition rates improve and their ability to more accurately understand seller preferences increases. As a result, existing rule-based smart systems are gradually being replaced by deep learning-based AI systems.

[0024] AI technology consists of machine learning and its underlying technologies. Machine learning is an algorithmic technology that autonomously classifies and learns the characteristics of input data. Elementary technologies utilize machine learning algorithms, such as deep learning, to mimic the cognitive and judgmental functions of the human brain. These technologies encompass areas such as linguistic understanding, visual understanding, inference / prediction, knowledge representation, and motion control.

[0025] The various fields in which AI technology is applied are as follows. Linguistic understanding refers to the technology that recognizes, applies, and processes human language / text, including natural language processing, machine translation, dialogue systems, question-answering, and speech recognition / synthesis. Visual understanding refers to the technology that recognizes and processes objects similar to human vision, including object recognition, object tracking, image search, person recognition, scene understanding, spatial understanding, and image enhancement. Inference prediction refers to the technology that logically infers and predicts information by judging it, including knowledge / probability-based inference, optimization prediction, preference-based planning, and recommendations. Knowledge representation refers to the technology that automatically processes human experience information into knowledge data, including knowledge construction (data creation / classification) and knowledge management (data utilization). Motion control refers to the technology that controls the movement of autonomous vehicles and robots, including movement control (navigation, collision, driving), and manipulation control (behavior control).

[0026] Typically, applying machine learning algorithms to real-world applications requires a trial-and-error approach, due to the fundamental nature of machine learning methodology. Deep learning, in particular, requires hundreds of thousands of iterations. Because this is impossible to implement in an actual physical environment, learning is performed through simulations, where the actual physical environment is virtually implemented on a computer.

[0027] Figure 1 is a schematic diagram showing the configuration of a system according to one embodiment.

[0028] Referring to FIG. 1, a system according to one embodiment may include a plurality of cameras (100) and devices (200) that can communicate with each other through a communication network.

[0029] First, the communication network can be configured regardless of the communication mode, such as wired or wireless, and can be implemented in various forms to perform communication between servers and between servers and terminals.

[0030] A plurality of cameras (100) are installed in a specific location by zone and are cameras that perform filming for the installed zones, and may include a first camera (110) installed in a first zone and performs filming for the first zone, a second camera (120) installed in a second zone and performs filming for the second zone, etc. Here, each zone, such as the first zone and the second zone, is a zone where a vehicle to be exported is parked, and one vehicle is parked in each zone so that the status of the parked vehicle can be analyzed.

[0031] Each of the plurality of cameras (100) may be formed by a combination of a motion detector camera having a function of detecting moving objects within a shooting area, an RGB camera that captures high-quality images, a camera equipped with a more reliable radar, a pan-tilt camera for performing automatic tracking, a network camera capable of performing IP communication, etc.

[0032] A plurality of cameras (100) can be connected to the device (200) via a network and can be operated by control signals received from the device (200).

[0033] For convenience of explanation, the following description focuses on the operation of the first camera (110) among the multiple cameras (100), but the operation of the first camera (110) can be performed instead by another camera, such as the second camera (120).

[0034] The device (200) may be a private server owned by an entity or organization providing services using the device (200), a cloud server, or a peer-to-peer (p2p) collection of distributed nodes. The device (200) may be configured to perform all or part of the computational functions, storage / reference functions, input / output functions, and control functions of a typical computer. The device (200) may be equipped with at least one artificial intelligence model that performs an inference function.

[0035] The device (200) can be configured to communicate with a plurality of cameras (100) via wired or wireless communication, and can control the operation of each of the plurality of cameras (100) to control whether to shoot, the shooting angle, the shooting direction, whether to save image information, etc.

[0036] Meanwhile, for convenience of explanation, only the first camera (110) and the second camera (120) among the multiple cameras (100) are illustrated in FIG. 1, but the number of terminals may vary depending on the embodiment. There is no particular limitation on the number of terminals as long as the processing capacity of the device (200) allows.

[0037] According to one embodiment, the device (200) can analyze which country it is advantageous to export the vehicle to based on artificial intelligence and select the country to which the vehicle is to be exported.

[0038] In the present invention, artificial intelligence (AI) refers to a technology that mimics human learning, reasoning, and perception abilities and implements them on a computer. It may include concepts such as machine learning and symbolic logic. Machine learning (ML) is an algorithmic technology that classifies or learns the characteristics of input data on its own. AI technology analyzes input data using machine learning algorithms, learns the results of that analysis, and makes judgments or predictions based on the results of that learning. Furthermore, technologies that utilize machine learning algorithms to mimic human brain functions such as cognition and judgment can also be understood as falling under the category of AI. For example, this may include technical fields such as linguistic understanding, visual understanding, inference / prediction, knowledge representation, and motion control.

[0039] Machine learning can refer to the process of training a neural network model using data processing experience. Through machine learning, computer software can improve its data processing capabilities. Neural network models are built by modeling correlations between data, and these correlations can be expressed by multiple parameters. Neural network models extract and analyze features from given data to derive correlations between data. This process of iteratively optimizing the parameters of a neural network model can be defined as machine learning. For example, a neural network model can learn the mapping (correlation) between inputs and outputs for data presented as input-output pairs. Alternatively, a neural network model can learn the relationships between inputs and outputs by deriving regularities between the given data, even when presented with only input data.

[0040] An artificial intelligence learning model or neural network model can be designed to implement the structure of the human brain on a computer, and can include multiple network nodes that simulate the neurons of a human neural network and have weights. The multiple network nodes can have connections with each other by simulating the synaptic activity of neurons that exchange signals through synapses. In the artificial intelligence learning model, the multiple network nodes can be located at layers of different depths and exchange data according to convolutional connections. The artificial intelligence learning model can be, for example, an artificial neural network model, a convolutional neural network (CNN), etc. In one embodiment, the artificial intelligence learning model can be machine-learned according to a method such as supervised learning, unsupervised learning, or reinforcement learning. Machine learning algorithms that can be used to perform machine learning include decision trees, Bayesian networks, support vector machines, artificial neural networks, Ada-boost, perceptrons, genetic programming, and clustering.

[0041] CNNs are a type of multilayer perceptron designed to utilize minimal preprocessing. They consist of one or more convolutional layers stacked on top of regular artificial neural network layers, with additional weight and pooling layers. This structure allows CNNs to fully utilize two-dimensional input data. Compared to other deep learning architectures, CNNs demonstrate excellent performance in both image and audio domains. CNNs can also be trained using standard backpropagation. Compared to other feedforward artificial neural network techniques, CNNs are easier to train and have fewer parameters.

[0042] Convolutional networks are neural networks that contain sets of nodes with bounded parameters. The increasing availability of training data and computational power, combined with advances in algorithms such as piecewise linear units and dropout training, have led to significant improvements in many computer vision tasks. With the massive datasets available for many tasks today, overfitting is less of a concern, and increasing network size improves test accuracy. Optimal utilization of computing resources becomes a limiting factor. To address this, distributed, scalable implementations of deep neural networks can be utilized.

[0043] Figure 2 is a flowchart illustrating a process of generating vehicle export information using an artificial intelligence model according to an embodiment.

[0044] Referring to FIG. 2, first, in step S201, when a photographing of a first area is being performed through a first camera (110), the device (200) can receive first image information generated by photographing the first area from the first camera (110).

[0045] In step S202, the device (200) can analyze whether a vehicle to be exported is parked in the first zone based on the first image information.

[0046] In step S203, if the device (200) analyzes that a vehicle is parked in the first zone, it can recognize the vehicle number from the license plate of the vehicle parked in the first zone based on the first image information.

[0047] At step S204, the device (200) can identify a vehicle parked in the first zone as the first vehicle based on the recognized vehicle number.

[0048] At step S205, the device (200) can obtain first vehicle information including the vehicle type, year, mileage, accident history, insurance history, and expected market price of the first vehicle.

[0049] At step S206, the device (200) can apply the first vehicle information to the pre-learned artificial intelligence model.

[0050] At step S207, the device (200) can select an export country for the first vehicle based on the output of the artificial intelligence model.

[0051] At step S208, if the export country of the first vehicle is selected as the first country, the device (200) can generate export information of the first vehicle recommending exporting the first vehicle to the first country.

[0052] Figure 3 is a flowchart illustrating a process for generating vehicle state modeling information according to an embodiment.

[0053] Referring to FIG. 3, first, in step S301, when the device (200) photographs the first vehicle from various directions through the first camera (110), it can receive a plurality of image information generated by photographing the first vehicle from various directions from the first camera (110).

[0054] In step S302, the device (200) can collect multiple pieces of image information to generate a first image, which is a 3D image of the first vehicle.

[0055] At step S303, the device (200) can analyze the status of the first vehicle based on the first image.

[0056] At step S304, the device (200) can determine whether repairs are required for the first vehicle based on the first image.

[0057] At step S305, if the device (200) determines that repair is required for the first part of the first vehicle, the device can extract the part where the first part is located from the first image as the first-1 image.

[0058] At step S306, the device (200) can generate a 2-1 image showing the state in which the 1st part has been repaired based on the 1-1 image.

[0059] At step S307, the device (200) can generate a second image by replacing the portion where the first part is located in the first image with the second-1 image.

[0060] At step S308, the device (200) can calculate the export price of the first vehicle as the first amount before repairing the first part based on the first image and the first vehicle information.

[0061] At step S309, the device (200) can calculate the export price of the first vehicle as the second amount after repairing the first part based on the second image and the first vehicle information.

[0062] At step S310, the device (200) can generate status modeling information of the first vehicle based on the first image, the second image, the first amount, and the first amount.

[0063] Figure 4 is an exemplary diagram of the configuration of a device according to one embodiment.

[0064] A device (200) according to one embodiment includes a processor (210) and a memory (220). The processor (210) may include at least one of the devices described above with reference to FIGS. 1 to 3, or may perform at least one method described above with reference to FIGS. 1 to 3. A person or organization using the device (200) may provide services related to some or all of the methods described above with reference to FIGS. 1 to 3.

[0065] The memory (220) can store information related to the methods described above or store a program in which the methods described below are implemented. The memory (220) can be a volatile memory or a non-volatile memory.

[0066] The processor (210) can execute a program and control the device (200). The code of the program executed by the processor (210) can be stored in the memory (220). The device (200) can be connected to an external device (e.g., a personal computer or a network) through an input / output device (not shown in the drawing) and exchange data through wired or wireless communication.

[0067] The device (200) can be used to train an artificial intelligence model or utilize a trained artificial intelligence model. The memory (220) can include an artificial intelligence model that is being trained or has been trained. The processor (210) can train or execute an artificial intelligence model algorithm stored in the memory (220). The training device that trains an artificial intelligence model and the device (200) that utilizes the trained artificial intelligence model may be the same or separate.

[0068] The embodiments described above may be implemented using hardware components, software components, and / or a combination of hardware components and software components. For example, the devices, methods, and components described in the embodiments may be implemented using one or more general-purpose computers or special-purpose computers, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing instructions and responding to them. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing device is sometimes described as being used alone; however, one of ordinary skill in the art will recognize that the processing device may include multiple processing elements and / or multiple types of processing elements. For example, a processing unit may include multiple processors, or a processor and a controller. Other processing configurations, such as parallel processors, are also possible.

[0069] The method according to the embodiment may be implemented in the form of program commands that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program commands, data files, data structures, etc., alone or in combination. The program commands recorded on the medium may be those specially designed and configured for the embodiment or may be those known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specially configured to store and execute program commands, such as ROMs, RAMs, and flash memories. Examples of the program commands include not only machine language codes generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiment, and vice versa.

[0070] Software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing device to perform a desired operation or may, independently or collectively, command the processing device. The software and / or data may be permanently or temporarily embodied in any type of machine, component, physical device, virtual equipment, computer storage medium or device, or transmitted signal wave, for interpretation by the processing device or for providing instructions or data to the processing device. The software may also be distributed over networked computer systems and stored or executed in a distributed manner. The software and data may be stored on one or more computer-readable recording media.

[0071] Although the embodiments described above have been described with limited drawings, those skilled in the art will appreciate that various technical modifications and variations can be applied based on the above. For example, appropriate results can still be achieved even if the described techniques are performed in a different order than described, and / or components of the described systems, structures, devices, circuits, etc. are combined or combined in a different manner than described, or are replaced or substituted with other components or equivalents.

[0072] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims described below.

Claims

1. A method for generating vehicle export information and vehicle status modeling information using an artificial intelligence model and AR, performed by a device, When filming of a first area is being performed through a first camera, a step of receiving first image information generated by filming of the first area from the first camera; A step of analyzing whether a vehicle to be exported is parked in the first zone based on the first image information; If it is analyzed that a vehicle is parked in the first zone, a step of recognizing a vehicle number from the license plate of the vehicle parked in the first zone based on the first image information; A step of identifying a vehicle parked in the first zone as the first vehicle based on the recognized vehicle number; A step of obtaining first vehicle information including the vehicle type, year, mileage, accident history, insurance history, and expected market price of the first vehicle; A step of applying the above first vehicle information to an artificial intelligence model and selecting an export country of the first vehicle based on the output of the artificial intelligence model; and If the export country of the first vehicle is selected as the first country, a step of generating export information of the first vehicle that recommends exporting the first vehicle to the first country is included. Method for generating vehicle export information and vehicle condition modeling information using artificial intelligence models and AR.

2. In paragraph 1, When the first vehicle is photographed from various directions through the first camera, a step of receiving a plurality of image information generated by photographing the first vehicle from various directions from the first camera; A step of generating a first image, which is a 3D image of the first vehicle, by collating the plurality of image information; A step of analyzing the condition of the first vehicle based on the first image; A step of determining whether repair is required for the first vehicle based on the first image; When it is determined that repair is required for the first part in the first vehicle, a step of extracting a part where the first part is located from the first image as a first-1 image; A step of generating a 2-1 image representing a state in which the 1st part has been repaired based on the 1-1 image; A step of generating a second image by replacing a portion where the first part is located in the first image with the second-1 image; A step of calculating an export price of the first vehicle as a first amount before repairing the first part based on the first image and the first vehicle information; A step of calculating the export price of the first vehicle as the second amount after repairing the first part based on the second image and the first vehicle information; and Further comprising a step of generating status modeling information of the first vehicle based on the first image, the second image, the first amount, and the first amount. Method for generating vehicle export information and vehicle condition modeling information using artificial intelligence models and AR.

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