Vehicle control device and vehicle control method

Abstract

The invention relates to a vehicle control apparatus and a vehicle control method. A vehicle control device includes a light detection and ranging device (LiDAR), a memory storing map information, and a processor. The processor may filter a data set including a road edge portion related to a position of the vehicle from the map information according to a first specified condition, obtain a plurality of first partial line segments, identify a plurality of second partial line segments from a plurality of line segments formed by contour points corresponding to the road edge portion through LiDAR, pairing of the plurality of second partial line segments with the plurality of first partial line segments is identified, a position of the vehicle in a second coordinate system different from the first coordinate system is output based on applying a specified algorithm to each pairing, and the vehicle is controlled based on the position of the vehicle in the second coordinate system.

Description

[0001] Cross - reference to related applications

[0002] This application claims priority to Korean Patent Application No. 10 - 2023 - 0183514, filed on December 15, 2023, the entire contents of which are incorporated herein for all purposes by this reference. Technical field

[0003] The present invention relates to a vehicle control device and method, and relates to a technology for identifying a road edge portion using LiDAR and map information. Background art

[0004] Various studies are underway to use various sensors to identify external objects to assist in driving a vehicle.

[0005] When a vehicle is traveling in a driver - assistance mode or an autonomous - driving mode, a Light Detection and Ranging (LiDAR) device can be used to identify external objects.

[0006] It is necessary to identify the road edge portion through LiDAR to accurately identify the area where the vehicle can travel. If the vehicle enters a curved road, it is necessary to accurately identify the road edge portion of the curved road and accurately identify the lateral position and / or the amount of change in the traveling direction to generate a path of the vehicle and / or execute control of the vehicle.

[0007] The information included in the background art of the present invention is only intended to enhance the understanding of the overall background of the present invention and should not be regarded as an admission or any form of suggestion that this information constitutes prior art known to those skilled in the art. Summary of the invention

[0008] Aspects of the present invention are directed to providing a vehicle control device and method configured to accurately identify a road edge portion using map information and LiDAR sensor data.

[0009] Aspects of the present invention are directed to providing a vehicle control device and method configured to generate a driving path of a vehicle by accurately identifying an area where the vehicle can travel within a road edge portion, thereby providing a stable driving system.

[0010] Aspects of the present invention are directed to providing a vehicle control device and method configured to improve positioning performance and relatively quickly and accurately perform data processing by not using duplicate data.

[0011] The technical problems solved by the present invention are not limited to the above problems, and those skilled in the art to which the present invention pertains will clearly understand any other technical problems not mentioned herein from the following description.

[0012] According to one aspect of the present invention, a vehicle control device includes a Light Detection and Ranging (LiDAR) device, a memory storing map information, and a processor. The processor filters a data set including a road edge portion related to the position of the vehicle from the map information according to a first predetermined condition, where the first predetermined condition relates to at least one of distance, angle, or any combination thereof; obtains a plurality of first partial line segments by segmenting a line segment corresponding to the road edge portion included in at least one of the data sets based on a predetermined length; identifies a plurality of second partial line segments from a plurality of line segments formed by contour points corresponding to the road edge portion obtained via the LiDAR according to a second predetermined condition, where the second predetermined condition relates to at least one of distance, angle, height, or any combination thereof; identifies pairings of the plurality of second partial line segments and a plurality of first partial line segments that are respectively closest to the plurality of second partial line segments in a vehicle coordinate system represented with the vehicle as the center; outputs the position of the vehicle in a second coordinate system different from the first coordinate system by using a calibration amount based on applying a predetermined algorithm to each pairing, where the calibration amount relates to at least one of a lateral movement of the vehicle based on the first coordinate system, a change amount of the traveling direction of the vehicle, or any combination thereof; and controls the vehicle based on the position of the vehicle in the second coordinate system.

[0013] According to an exemplary embodiment of the present invention, the processor may identify at least one of a plurality of first partial line segments, a plurality of second partial line segments, pairings, or any combination thereof on a plane formed by a first axis and a second axis among a first axis, a second axis, and a third axis.

[0014] According to an exemplary embodiment of the present invention, the processor may identify a plurality of second partial line segments in each layer separated based on a third axis among a first axis, a second axis, and a third axis; and identify a first sub-pairing included in the pairing. The first sub-pairing may include a plurality of first partial line segments that are respectively closest to the plurality of second partial line segments included in each layer.

[0015] According to an exemplary embodiment of the present invention, the processor may identify first identifiers respectively assigned to the plurality of first partial line segments; identify second identifiers respectively assigned to the plurality of second partial line segments; identify a second sub-pairing in the pairing, where the distance between the plurality of first partial line segments and the plurality of second partial line segments in the second sub-pairing is less than a predetermined distance; and sequentially arrange at least one of the first identifiers or the second identifiers included in the identified second sub-pairing, or any combination thereof.

[0016] According to an exemplary embodiment of the present invention, the processor may select a part of a plurality of second partial line segments from among a plurality of layers separated based on a third axis among a first axis, a second axis, and a third axis, based on a construction type of map information.

[0017] According to an exemplary embodiment of the present invention, when the construction type of the map information is a top line construction type, the processor may identify a part of a plurality of second partial line segments identified in a first reference number of top layers among a plurality of layers; and select, from among the parts of the plurality of second partial line segments identified in the first reference number of top layers, a part of the plurality of second partial line segments closest to a construction height of the map information.

[0018] According to an exemplary embodiment of the present invention, the top line construction type may be a construction type in which map information is generated based on a part of a road edge portion identified at the highest height with reference to a third axis.

[0019] According to an exemplary embodiment of the present invention, when the construction type of the map information is a bottom line construction type, the processor may identify a part of a plurality of second partial line segments identified in a second reference number of bottom layers among a plurality of layers.

[0020] According to an exemplary embodiment of the present invention, the bottom line construction type may be a construction type in which map information is generated based on a part of a road edge portion identified at the lowest height with reference to a third axis.

[0021] According to an exemplary embodiment of the present invention, the predetermined algorithm may include at least one of an Iterative Closest Point (ICP) algorithm, a Simultaneous Localization and Mapping (SLAM) algorithm, or any combination thereof.

[0022] According to one aspect of the present invention, a vehicle control method includes: filtering, according to a first predetermined condition, a data set including a road edge portion related to the position of a vehicle from map information, the first predetermined condition relating to at least one of distance, or angle, or any combination thereof; obtaining a plurality of first partial line segments by dividing, based on a predetermined length, a line segment included in at least one of the data sets and corresponding to the road edge portion; identifying, according to a second predetermined condition, a plurality of second partial line segments from a plurality of line segments formed by contour points corresponding to the road edge portion via a light detection and ranging device (LiDAR), the second predetermined condition relating to at least one of distance, angle, or height, or any combination thereof; identifying, in a vehicle coordinate system represented with the vehicle as the center, pairings of the plurality of second partial line segments and the plurality of first partial line segments respectively closest to the plurality of second partial line segments; outputting, based on applying a predetermined algorithm to each pairing, the position of the vehicle in a second coordinate system different from a first coordinate system by using a calibration quantity, the calibration quantity relating to at least one of a lateral movement of the vehicle based on the first coordinate system, or a change amount of the traveling direction of the vehicle, or any combination thereof; and controlling the vehicle based on the position of the vehicle in the second coordinate system.

[0023] According to an exemplary embodiment of the present invention, the vehicle control method may further include: identifying at least one of the plurality of first partial line segments, the plurality of second partial line segments, or the pairings, or any combination thereof, on a plane formed by a first axis and a second axis among a first axis, a second axis, and a third axis.

[0024] According to an exemplary embodiment of the present invention, the vehicle control method may further include: identifying the plurality of second partial line segments in each layer separated based on a third axis among a first axis, a second axis, and a third axis; identifying a first sub-pairing included in the pairing. The first sub-pairing may include a plurality of first partial line segments that are respectively closest to the plurality of second partial line segments included in each layer.

[0025] According to an exemplary embodiment of the present invention, the vehicle control method may further include: identifying first identifiers respectively assigned to the plurality of first partial line segments; identifying second identifiers respectively assigned to the plurality of second partial line segments; identifying a second sub-pairing in the pairing, in which the distance between the plurality of first partial line segments and the plurality of second partial line segments is less than a predetermined distance; and sequentially arranging at least one of the first identifiers or the second identifiers, or any combination thereof, included in the identified second sub-pairing.

[0026] According to an exemplary embodiment of the present invention, the vehicle control method may further include: selecting a part of the plurality of second partial line segments in a plurality of layers separated based on a third axis among a first axis, a second axis, and a third axis based on the construction type of the map information.

[0027] According to an exemplary embodiment of the present invention, the vehicle control method may further include: identifying a part of a plurality of second partial line segments identified in a first reference number of top layers among a plurality of layers based on that the construction type of the map information is a top line construction type; and selecting, among parts of the plurality of second partial line segments identified in the first reference number of top layers, a part of the plurality of second partial line segments closest to the construction height of the map information.

[0028] According to an exemplary embodiment of the present invention, the top line construction type may be a construction type in which map information is generated based on a part of a road edge portion identified at the highest height with reference to a third axis.

[0029] According to an exemplary embodiment of the present invention, the vehicle control method may further include: identifying a part of a plurality of second partial line segments identified in a second reference number of bottom layers among a plurality of layers based on that the construction type of the map information is a bottom line construction type.

[0030] According to an exemplary embodiment of the present invention, the bottom line construction type may be a construction type in which map information is generated based on a part of a road edge portion identified at the lowest height with reference to a third axis.

[0031] According to an exemplary embodiment of the present invention, the predetermined algorithm may include at least one of an Iterative Closest Point (ICP) algorithm, a Simultaneous Localization and Mapping (SLAM) algorithm, or any combination thereof.

[0032] The methods and apparatuses of the present invention have other characteristics and advantages that will be apparent from, or will be described in more detail in, the accompanying drawings and subsequent detailed description incorporated herein. The accompanying drawings and detailed description together are used to explain specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 An example of a block diagram related to a vehicle control device according to an exemplary embodiment of the present invention is shown;

[0034] Figure 2 An example of a process of outputting the position of a vehicle according to an exemplary embodiment of the present invention is shown;

[0035] Figure 3 An example of dividing a line segment corresponding to a road edge portion according to an exemplary embodiment of the present invention is shown;

[0036] Figure 4Shows an example of identifying pairs of a first partial line segment and a second partial line segment according to an exemplary embodiment of the present invention;

[0037] Figure 5 Shows an example of selecting a pair corresponding to a road edge portion according to an exemplary embodiment of the present invention;

[0038] Figure 6 Shows an example of the result before applying the present invention and an example of the result after applying the present invention;

[0039] Figure 7 Shows an example of a flowchart related to a vehicle control method according to an exemplary embodiment of the present invention;

[0040] Figure 8 Shows a computing system related to a vehicle control device or a vehicle control method according to an exemplary embodiment of the present invention.

[0041] It can be understood that the accompanying drawings are not drawn to scale, but present certain simplified representations of various features to illustrate the basic principles of the present invention. Specific design features of the present invention as included herein (including, for example, specific dimensions, orientations, positions, and configurations) will be determined in part by the specific environment in which it is to be applied and used.

[0042] In the drawings, throughout the several views, like reference numerals refer to the same or equivalent parts of the present invention. Detailed Description of the Invention

[0043] Reference will now be made in detail to various embodiments of the present invention, examples of which are illustrated in the accompanying drawings and described below. Although the present invention will be described in conjunction with the exemplary embodiments of the present invention, it will be understood that this specification is not intended to limit the present invention to those exemplary embodiments. On the contrary, the present invention is intended to cover not only the exemplary embodiments of the present invention, but also various alternative forms, modifications, equivalents, and other embodiments that may be included within the spirit and scope of the present invention as defined by the appended claims.

[0044] Hereinafter, various exemplary embodiments of the present invention will be described in detail with reference to the exemplary drawings. When adding reference numerals to the components of each figure, it should be noted that even if the same or equivalent components are shown in other figures, they are designated by the same reference numerals. In addition, when describing the exemplary embodiments of the present invention, detailed descriptions of well-known features or functions will be excluded so as not to unnecessarily obscure the gist of the present invention.

[0045] When describing the components of the exemplary embodiments of the present invention, terms such as first, second, "A", "B", (a), (b), etc. may be used. These terms are only intended to distinguish one component from another component, and these terms do not limit the nature, order or sequence of the components. Unless otherwise defined, all terms (including technical terms or scientific terms) used herein include the same meanings as those commonly understood by those skilled in the technical field to which the present invention belongs. Such terms defined in a commonly used dictionary should be interpreted as having the same meanings as the contextual meanings in the relevant technical field, and should not be interpreted as having ideal or overly formal meanings, unless clearly defined in this application.

[0046] Hereinafter, various exemplary embodiments of the present invention will be described in detail with reference to Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 and Figure 8 .

[0047] Figure 1 FIG. shows an example of a block diagram related to a vehicle control device according to an exemplary embodiment of the present invention.

[0048] Referring to Figure 1 , the vehicle control device 100 according to an exemplary embodiment of the present invention can be implemented inside or outside the vehicle, and a part of the components included in the vehicle control device 100 can be implemented inside or outside the vehicle. In this case, the vehicle control device 100 can be integrated with the internal control unit of the vehicle, or can be implemented as a separate device and connected to the control unit of the vehicle by separate connection means. For example, the vehicle control device 100 can further include Figure 1 components not shown in

[0049] Referring to Figure 1 , the vehicle control device 100 according to various exemplary embodiments of the present invention can include a processor 110, a memory 120, and a light detection and ranging device (LiDAR) 130. The processor 110, the memory 120, or the LiDAR 130 can be electronically and / or operably coupled to each other through electronic components including a communication bus.

[0050] Hereinafter, operably coupling multiple hardware can include establishing a direct and / or indirect connection between the multiple hardware such that the second hardware is controlled by the first hardware among the multiple hardware.

[0051] Different blocks are shown, but the embodiments are not limited thereto. Figure 1A portion of the multiple hardware components may be included in a single integrated circuit including a system-on-chip (SoC).

[0052] The type and / or quantity of the hardware included within the vehicle control device 100 is not limited to Figure 1 that shown. For example, the vehicle control device 100 may include only Figure 1 a portion of the hardware shown.

[0053] The vehicle control device 100 according to various exemplary embodiments of the present invention may include hardware for processing data based on one or more instructions. The hardware for processing data may include a processor 110. For example, the hardware for processing data may include an arithmetic logic unit (ALU), a floating-point processing unit (FPU), a field-programmable gate array (FPGA), a central processing unit (CPU), and / or an application processor (AP).

[0054] For example, the processor 110 may include a structure of a single-core processor or may include a structure of a multi-core (including dual-core, quad-core, hexa-core, or octa-core) processor.

[0055] According to an exemplary embodiment of the present invention, the memory 120 of the vehicle control device 100 may include a hardware component for storing data and / or instructions that are input to the processor 110 of the vehicle control device 100 and / or output from the processor 110 of the vehicle control device 100.

[0056] For example, the memory 120 may include a volatile memory including a random access memory (RAM) or a non-volatile memory including a read-only memory (ROM).

[0057] For example, the volatile memory may include at least one of a dynamic RAM (DRAM), a static RAM (SRAM), a cache RAM, or a pseudo SRAM (PSRAM) or any combination thereof.

[0058] For example, the non-volatile memory may include at least one of a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), a flash memory, a hard disk, an optical disk, a solid-state drive (SSD), or an embedded multimedia card (eMMC) or any combination thereof.

[0059] For example, map information may be stored in the memory 120 of the vehicle control device 100. For example, map information may be generated (or produced) based on at least one of a map creator or a map generator or any combination thereof and stored in the memory.

[0060] For example, map information can be generated based on various types of construction types. For example, map information can be generated based on at least one of a top-line construction type or a bottom-line construction type, or any combination thereof.

[0061] For example, the top-line construction type can include a construction type that generates map information based on the position of an object identified at the highest height with reference to the third axis among the first axis, the second axis, and the third axis.

[0062] For example, the bottom-line construction type can include a construction type that generates map information based on the position of an object identified at the lowest height with reference to the third axis among the first axis, the second axis, and the third axis.

[0063] The LiDAR 130 of the vehicle control device 100 according to various exemplary embodiments of the present invention can obtain a data set for identifying surrounding objects of the vehicle control device 100. For example, the LiDAR 130 can identify the position, moving direction, or speed of a surrounding object, or any combination thereof, based on a pulsed laser signal emitted from the LiDAR 130 being reflected and returned to the surrounding object.

[0064] For example, the LiDAR 130 can obtain a data set based on a pulsed laser signal reflected from a surrounding object, the data set representing an external object in a space formed by the first axis, the second axis, and the third axis. For example, the LiDAR 130 can obtain a data set based on receiving pulsed laser signals at a specified interval, the data set including a plurality of points in a space formed by the first axis, the second axis, and the third axis.

[0065] According to an exemplary embodiment of the present invention, the processor 110 can emit light from the vehicle using the LiDAR 130. For example, the processor 110 can receive the light emitted from the vehicle. For example, the processor 110 can identify at least one of the position, speed, or moving direction of a surrounding object, or any combination thereof, based on the time of emitting light from the vehicle and the time of receiving the light emitted from the vehicle.

[0066] The vehicle control device 100 according to various exemplary embodiments of the present invention can include a communication circuit instead of the memory 120. For example, the communication circuit of the vehicle control device 100 can include hardware components for supporting sending and / or receiving signals between the vehicle control device 100 and an external electronic device. For example, the communication circuit can include at least one of a modem, an antenna, or an optic / electronic (O / E) converter, or any combination thereof. The aforementioned external electronic device can include at least one of hardware components or software components different from the vehicle control device 100 included in the vehicle, or any combination thereof.

[0067] For example, the communication circuit may support the transmission or reception of signals based on various types of protocols, including at least one of Ethernet, local area network (LAN), wide area network (WAN), wireless fidelity (WiFi), Bluetooth, Bluetooth low energy (BLE), ZigBee, long term evolution (LTE), 5G new radio (NR), controller area network (CAN), or local interconnect network (LIN), or any combination thereof.

[0068] For example, the processor 110 of the vehicle control device 100 may perform a process substantially the same as the process of using the map information stored in the memory 120 based on the map information received via the communication circuit.

[0069] The processor 110 of the vehicle control device 100 according to various exemplary embodiments of the present invention may filter a data set including a road edge portion related to the position of the vehicle according to a first specified condition, the first specified condition relating to at least one of a distance, an angle, or any combination thereof from the map information (or the map information received via the communication circuit) stored in the memory 120. For example, the road edge portion related to the position of the vehicle may represent the boundary of the road on which the vehicle is traveling.

[0070] The processor 110 of the vehicle control device 100 according to various exemplary embodiments of the present invention may divide a line segment included in at least one of the data sets of the data set based on a specified length, the data set including a road edge portion related to the position of the vehicle and corresponding to the road edge portion.

[0071] For example, the processor 110 may obtain a plurality of first partial line segments by dividing a line segment included in at least one of the data sets of the data set based on a specified length, the data set including a road edge portion related to the position of the vehicle and corresponding to the road edge portion.

[0072] For example, the processor 110 may obtain a plurality of first partial line segments by dividing a line segment corresponding to the road edge portion into a specified length or shorter. For example, the specified length may include about 10 meters (m).

[0073] According to an exemplary embodiment of the present invention, the processor 110 may obtain a point cloud representing a road edge portion via the LiDAR 130. The processor 110 may identify contour points corresponding to the road edge portion within the point cloud based on obtaining the point cloud representing the road edge portion. The processor 110 may identify a plurality of second partial line segments among a plurality of line segments formed by the contour points corresponding to the road edge portion according to a second specified condition, the second specified condition relating to at least one of a distance, an angle, or a height, or any combination thereof. For example, each of the second partial line segments may be referred to as a segment.

[0074] According to an exemplary embodiment of the present invention, the processor 110 may arrange a plurality of first partial line segments and a plurality of second partial line segments based on the vehicle. For example, the processor 110 may arrange the plurality of first partial line segments and the plurality of second partial line segments in a vehicle coordinate system formed based on the vehicle.

[0075] According to an exemplary embodiment of the present invention, the processor 110 may identify the distances between the plurality of first partial line segments and the plurality of second partial line segments in the vehicle coordinate system. For example, the processor 110 may identify the plurality of first partial line segments that are respectively closest to the plurality of second partial line segments. For example, the processor 110 may identify the pairings of the plurality of first partial line segments and the plurality of second partial line segments that are respectively closest to the plurality of second partial line segments in the vehicle coordinate system.

[0076] According to an exemplary embodiment of the present invention, the processor 110 may identify at least one of the plurality of first partial line segments, the plurality of second partial line segments, or the pairings, or any combination thereof, on a plane formed by the first axis and the second axis among the first axis, the second axis, and the third axis.

[0077] According to an exemplary embodiment of the present invention, the processor 110 may identify the plurality of second partial line segments in each layer separated based on the third axis among the first axis, the second axis, and the third axis. The processor 110 may identify a first sub-pairing included in each layer in the pairing. For example, the first sub-pairing may include the plurality of first partial line segments that are respectively closest to the second partial line segments included in each layer.

[0078] According to an exemplary embodiment of the present invention, the processor 110 may identify first identifiers respectively assigned to the plurality of first partial line segments. The processor 110 may identify second identifiers respectively assigned to the plurality of second partial line segments.

[0079] According to an exemplary embodiment of the present invention, the processor 110 may identify a second sub-pairing in the pairing, in which the distance between the plurality of first partial line segments and the plurality of second partial line segments is less than a specified distance. The processor 110 may sequentially arrange at least one of the first identifiers or the second identifiers, or any combination thereof, included in the identified second sub-pairing.

[0080] According to an exemplary embodiment of the present invention, the processor 110 may select a part of the plurality of second partial line segments in a plurality of layers separated based on the third axis among the first axis, the second axis, and the third axis, based on the construction type of the map information.

[0081] For example, if the map information-based construction type is the top line construction type, the processor 110 may select a part of a plurality of second partial line segments identified in the first reference number of top layers among multiple layers. For example, the top line construction type may include map information generated based on a part of a road edge portion identified at the highest height with reference to the third axis.

[0082] For example, if the map information-based construction type is the top line construction type, the processor 110 may select a part of a plurality of second partial line segments that are closest to the construction height of the map information among the plurality of second partial line segments identified in the first reference number of layers.

[0083] For example, if the map information-based construction type is the bottom line construction type, the processor 110 may select a part of a plurality of second partial line segments identified in the second reference number of bottom layers among multiple layers. For example, the bottom line construction type may include map information generated based on a part of a road edge portion identified at the lowest height with reference to the third axis.

[0084] According to an exemplary embodiment of the present invention, the processor 110 may apply a specified algorithm to each of the pairs based on identifying a plurality of first partial line segments that are respectively closest to the plurality of second partial line segments in the vehicle coordinate system. For example, the specified algorithm may include at least one of an iterative closest point (ICP) algorithm, a simultaneous localization and mapping (SLAM) algorithm, or any combination thereof.

[0085] For example, the processor 110 may obtain a calibration quantity based on applying the specified algorithm to each of the pairs, where the calibration quantity relates to at least one of a lateral movement of the vehicle in the first coordinate system, or a change amount of the traveling direction of the vehicle, or any combination thereof. For example, the first coordinate system may include a relative coordinate system of the vehicle center.

[0086] According to an exemplary embodiment of the present invention, the processor 110 may be configured to predict the position of the vehicle based on the LiDAR 130. For example, the processor 110 may be configured to predict the position of the vehicle in the next frame based on a data set obtained by the LiDAR 130.

[0087] According to an exemplary embodiment of the present invention, the processor 110 may apply the obtained calibration amount to the predicted vehicle position. For example, the processor 110 may identify the position of the vehicle in a second coordinate system different from the first coordinate system based on applying the obtained calibration amount to the predicted vehicle position. For example, the second coordinate system may include a vehicle absolute coordinate system. For example, the second coordinate system may include a latitude-longitude-height (LLH) coordinate system.

[0088] For example, the processor 110 may output the position of the vehicle in a second coordinate system different from the first coordinate system based on applying the obtained calibration amount to the predicted vehicle position.

[0089] As described above, the vehicle control device 100 according to various exemplary embodiments of the present invention may avoid using duplicate data by using sequentially set pairings. By avoiding using duplicate data, the vehicle control device 100 may identify the road edge portion based on far-field data to improve the positioning performance.

[0090] Furthermore, by continuously ensuring the positioning performance, the vehicle control device 100 may improve the control performance of the vehicle in the driver assistance mode or the autonomous driving mode and provide an effect of enabling the autonomous driving system to operate stably.

[0091] Figure 2 An example of a process of outputting the position of a vehicle according to an exemplary embodiment of the present invention is shown.

[0092] Reference Figure 2 , according to various exemplary embodiments of the present invention, a processor (e.g., Figure 1 the processor 110) of a vehicle control device (e.g., Figure 1 the vehicle control device 100) may perform preprocessing 201 on sensor data from a light detection and ranging device (LiDAR) (e.g., Figure 1 the LiDAR 130).

[0093] For example, the processor is configured to perform preprocessing 201 on sensor data obtained via LiDAR. For example, the processor may obtain a plurality of points corresponding to an external object based on identifying pulsed laser signals reflected from the external object via LiDAR. The processor is configured to generate a point cloud representing the external object based on obtaining a plurality of points corresponding to the external object.

[0094] For example, the processor may identify contour points in a point cloud based on generating a point cloud representing an external object. For example, contour points may be identified in each layer formed along a third axis among a first axis, a second axis, and a third axis. For example, the first axis may include the x-axis. For example, the second axis may include the y-axis. For example, the third axis may include the z-axis.

[0095] For example, contour points may be obtained based on representative points included in the point cloud in each layer formed along the third axis among the first axis, the second axis, and the third axis. For example, the representative points may include all or a part of the points among the plurality of points included in the point cloud that are relatively far from the central part of the point cloud. For example, a point cloud may be obtained by performing clustering based on each point obtained by LiDAR identified within a specified distance.

[0096] According to an exemplary embodiment of the present invention, the processor may identify a point cloud corresponding to a road edge portion. The processor may identify contour points in the point cloud corresponding to the road edge portion. The processor may identify a line segment connecting the contour points based on identifying the contour points representing all or a part of the road edge portion. For example, the processor may identify a line segment connecting two contour points, and the line segment includes the minimum distance among the contour points representing all or a part of the road edge portion.

[0097] According to an exemplary embodiment of the present invention, the processor may obtain map information from the map provider 203. For example, the map provider 203 may include a map creator that generates (or constructs) map information and stores the map information in a memory of the vehicle control device (e.g., Figure 1 the memory 120), or at least one of a hardware component or a software component different from the vehicle control device, or any combination thereof.

[0098] For example, the map provider 203 may be configured to generate map information based on at least one of a first construction type, or a second construction type, or any combination thereof. For example, the first construction type may include a top line construction type. For example, the second construction type may include a bottom line construction type.

[0099] For example, the first construction type may include a construction type that represents the position of an external object based on the highest layer of the external object used to generate the map information.

[0100] For example, the second construction type may include a construction type that represents the position of an external object based on the lowest layer of the external object used to generate the map information.

[0101] According to an exemplary embodiment of the present invention, the processor may input the first data obtained by performing preprocessing 201 on the sensor data of the LiDAR and the map information generated by the map provider 203 into the map matching road edge portion 210.

[0102] For example, the map matching road edge portion 210 may be included in the vehicle control device. For example, the map matching road edge portion 210 may perform at least one of data filtering 211, data selection 213, or map matching 215, or any combination thereof.

[0103] For example, the data filtering 211 may include a function of filtering all or a part of the sensor data obtained by performing preprocessing 201 on the sensor data of the LiDAR. For example, the data filtering 211 may include a function of filtering all or a part of the road edge portion from the map information generated by the map provider 203.

[0104] For example, the data filtering 211 may include a function of filtering a data set related to the road edge portion from the map information based on a first specified condition, where the first specified condition relates to at least one of distance, or angle, or any combination thereof.

[0105] The data set related to the road edge portion included in the map information may include the length of the road edge portion.

[0106] For example, the data filtering 211 may include a function of filtering the data set obtained by the LiDAR based on a second specified condition, where the second specified condition relates to at least one of distance, angle, or height, or any combination thereof in the data set obtained by the LiDAR.

[0107] For example, in the first specified condition (which relates to at least one of distance, or angle, or any combination thereof) and / or the second specified condition (which relates to at least one of distance, angle, or height, or any combination thereof), the first specified condition and / or the second specified condition related to the distance may be related to the distance between a first partial line segment (which corresponds to the road edge portion represented in the map information) and a second partial line segment (which corresponds to the road edge portion identified by the LiDAR).

[0108] For example, if the distance between the first partial line segment and the second partial line segment exceeds a reference distance, the processor may delete (or exclude) the first partial line segment and the second partial line segment whose distance therebetween exceeds the reference distance from the candidate data set.

[0109] For example, the first specified condition and / or the second specified condition related to an angle may be related to an angle formed by each line segment and each second partial line segment corresponding to the road edge portion represented in the map information (corresponding to the road edge portion identified by LiDAR).

[0110] For example, if the angle formed by the line segment corresponding to the road edge portion and the second partial line segment exceeds a reference angle, the processor may delete (or exclude) the line segment and the second partial line segment whose angles exceed the reference angle from the candidate set.

[0111] For example, the second specified condition related to a height may be related to the height of the second partial line segment identified corresponding to the road edge portion identified by LiDAR.

[0112] For example, if the second partial line segment exceeds a reference height, the processor may delete (or exclude) the second partial line segment that exceeds the reference height from the candidate set.

[0113] According to an exemplary embodiment of the present invention, the processor is configured to perform data selection 213. For example, the processor is configured to perform data selection 213 when selecting at least a part of the data set obtained from data filtering 211.

[0114] For example, the processor may divide a line segment that exceeds a reference length among the line segments corresponding to the road edge portion obtained from data filtering 211. For example, the reference length may include approximately 10 meters (m).

[0115] For example, since the length of the road edge portion represented by the data set related to the road edge portion may be several hundred meters, the processor may obtain the first partial line segments based on dividing the line segments that exceed the reference length among the line segments corresponding to the road edge portion.

[0116] According to an exemplary embodiment of the present invention, the processor may identify the distance between the first partial line segments and the second partial line segments. For example, the processor may identify the distance between each first partial line segment and each second partial line segment.

[0117] For example, the processor may identify at least a part of the first partial line segments that are respectively closest to the second partial line segments. Based on identifying at least a part of the first partial line segments that are respectively closest to the second partial line segments, the processor may obtain (or select) pairs including the second partial line segments and at least a part of the first partial line segments that are respectively closest to the second partial line segments.

[0118] The above operations may be referred to as data selection 213.

[0119] According to an exemplary embodiment of the present invention, the processor is configured to perform map matching 215 based on the execution data selection 213. The processor is configured to perform map matching 215 by utilizing the selected pairs based on the execution data selection 213.

[0120] For example, the processor may apply a specified algorithm to the pairs. For example, the specified algorithm may include at least one of an Iterative Closest Point (ICP) algorithm, a Simultaneous Localization and Mapping (SLAM) algorithm, or any combination thereof. However, the specified algorithm is not limited to the above algorithms.

[0121] According to an exemplary embodiment of the present invention, the processor is configured to determine a calibration amount based on applying the specified algorithm to the pairs, where the calibration amount relates to at least one of a lateral movement of the vehicle, or a change amount of the traveling direction of the vehicle, or any combination thereof.

[0122] For example, the processor may obtain the calibration amount based on at least one of a lateral movement of the vehicle, or a change amount of the traveling direction of the vehicle, or any combination thereof. For example, the processor may apply at least one of a lateral movement of the vehicle, or a change amount of the traveling direction of the vehicle, or any combination thereof to the predicted vehicle position.

[0123] For example, the predicted vehicle position may include the position of the vehicle identified in a frame different from the first frame obtained by LiDAR. For example, the frame different from the first frame may include a second time point identifying the first time point of the first frame.

[0124] Figure 3 An example of segmenting a line segment corresponding to a road edge portion according to an exemplary embodiment of the present invention is shown.

[0125] Reference Figure 3 , according to various exemplary embodiments of the present invention, a processor (e.g., Figure 1 processor 110 of the vehicle control device 100) of a vehicle control device (e.g., Figure 1 ) may identify a line segment 310 corresponding to a road edge portion of the road on which the vehicle 300 is traveling from the map information.

[0126] According to an exemplary embodiment of the present invention, the processor 110 may segment the line segment 310 corresponding to the road edge portion related to the position of the vehicle 300 in the map information.

[0127] For example, the processor may segment the line segment 310 corresponding to the road edge portion based on a specified length. For example, if the line segment 310 corresponding to the road edge portion exceeds the specified length, the processor may segment the line segment 310 corresponding to the road edge portion into a specified length or less. For example, the specified length may include approximately 10 m.

[0128] In Figure 3 , the first line segment 311 is a line segment that exceeds a specified length and may refer to the line segment before segmentation. The second line segment 313 is also a line segment before segmentation but is less than or equal to the specified length, and the length of the second line segment 313 before and after segmentation may be substantially the same. The third line segment 315 is Figure 1 and Figure 2 a part of the plurality of first partial line segments described in Figure 3 and may include at least a part obtained by segmenting the first line segment 311 that exceeds the specified length. Figure 1 and Figure 2 The partial line segment 320 shown in

[0129] Figure 4 shows an example of identifying a pair of a first partial line segment and a second partial line segment according to an exemplary embodiment of the present invention.

[0130] Referring to Figure 4 , according to various exemplary embodiments of the present invention, a processor (e.g., Figure 1 the processor 110 of the vehicle control device 100) of a vehicle control device (e.g., Figure 1 ) can identify a plurality of first partial line segments 410 corresponding to at least a part of a road edge portion related to the position of the vehicle 400. According to an exemplary embodiment of the present invention, the processor can identify a plurality of second partial line segments 411 and 413 corresponding to at least a part of the road edge portion through an optical detection and ranging device (LiDAR).

[0131] For example, the plurality of second partial line segments 411 and 413 may include partial line segments identified in each layer. For example, the first part 411 of the plurality of second partial line segments 411 and 413 may include a second partial line segment corresponding to at least a part of the road edge portion identified in the first layer. For example, the second part 413 of the plurality of second partial line segments 411 and 413 may include a second partial line segment corresponding to at least a part of the road edge portion identified in a second layer different from the first layer. For ease of description, a separation is made between the first layer and the second layer, and the number of layers is not limited to the above number.

[0132] According to an exemplary embodiment of the present invention, the processor may identify the pairing of the first partial line segment 410 with the second partial line segments 411 and 413. For example, the processor may identify the first partial line segment 410 that is closest to the second partial line segments 411 and 413, respectively. For example, in the vehicle coordinate system, based on identifying the first partial line segment 410 that is closest to the second partial line segments 411 and 413, respectively, the processor may identify the pairing of the second partial line segments 411 and 413 with the first partial line segment 410 that is closest to the second partial line segments 411 and 413, respectively. For example, each pairing may be referred to as a matching pair.

[0133] According to an exemplary embodiment of the present invention, the processor may identify the identifiers of the first partial line segment 410 and the second partial line segments 411 and 413 included in the pairing based on the identified pairing. For example, the first identifier assigned to the first partial line segment 410 may be represented in a format such as "Map ID 1000", "Map ID 1001", "Map ID 1002", and "Map ID 2002". For example, the second identifier assigned to the second partial line segments 411 and 413 may be represented in a format such as "Obj#1", "Obj#2", and "Obj#3".

[0134] According to an exemplary embodiment of the present invention, the processor may sequentially arrange the identifiers with respect to one of the first partial line segment 410 or the second partial line segments 411 and 413 included in the pairing.

[0135] As described above, the processor of the vehicle control device according to various exemplary embodiments of the present invention may easily manage data by identifying the matching pairs and sequentially arranging the identifiers included in the identified matching pairs. Further, by sequentially arranging and outputting the identifiers, assistance may be provided when selecting the driving path of the vehicle if the vehicle is operating in at least one of a driving assistance mode, or a driver assistance mode, or any combination thereof.

[0136] Figure 5 An example of selecting a pairing corresponding to a road edge portion according to an exemplary embodiment of the present invention is shown.

[0137] Reference Figure 5 According to various exemplary embodiments of the present invention, the processor (e.g., Figure 1 the processor 110 of the vehicle control device 100) of the vehicle control device (e.g., Figure 1 may identify a plurality of first partial line segments 510 corresponding to the road edge portions included in the map information.

[0138] According to an exemplary embodiment of the present invention, the processor may identify pairings of a plurality of second partial line segments 520 and a plurality of first partial line segments 510 that are respectively closest to the plurality of second partial line segments 520 in a vehicle coordinate system. The processor may sequentially arrange the pairings. For example, the processor may identify at least one of a first identifier of the plurality of first partial line segments 510, a second identifier of the plurality of second partial line segments 520, or any combination thereof. The processor may sequentially arrange the pairings based on the first identifier or the second identifier.

[0139] According to an exemplary embodiment of the present invention, the processor may identify sub-pairings including a plurality of first partial line segments 510 and a plurality of second partial line segments 520, the plurality of first partial line segments 510 being respectively identified within a specified distance from the plurality of second partial line segments 520. For example, the above-specified distance may include approximately 0.5 m.

[0140] According to an exemplary embodiment of the present invention, the processor may arrange the identified sub-pairings based on a second identifier of the plurality of second partial line segments 520. For example, the processor may sequentially arrange the identified sub-pairings based on the order of the second identifiers of the plurality of second partial line segments 520.

[0141] According to an exemplary embodiment of the present invention, the processor may identify at least one of a top layer, a bottom layer, or any combination thereof with reference to a third axis among a first axis, a second axis, and a third axis based on the second identifier, a construction type of map information, or any combination thereof.

[0142] For example, the processor may identify a portion of the plurality of second partial line segments 520 that is relatively the longest in at least one of a top layer, a bottom layer, or any combination thereof.

[0143] For example, if the construction type of the map information is a top line construction type, the processor may select a portion of the second partial line segments 520 identified in the top layer having the highest height and the upper layer adjacent to the top layer among the plurality of second partial line segments 520.

[0144] For example, if the construction type of the map information is a top line construction type, the processor may select a portion of the plurality of second partial line segments 520 that is closest to the map construction height among the plurality of second partial line segments 520.

[0145] For example, if the construction type of the map information is a bottom line construction type, the processor may select a portion of the plurality of second partial line segments 520 identified in the bottom layer.

[0146] According to an exemplary embodiment of the present invention, a processor may apply a specified algorithm to a part of a plurality of second partial line segments 520 selected according to the construction type of map information. The processor may identify at least one of a lateral movement of the vehicle, or a change amount of the traveling direction of the vehicle, or any combination thereof by applying the specified algorithm to a part of the selected plurality of second partial line segments 520.

[0147] According to an exemplary embodiment of the present invention, a processor is configured to predict and output a position of a vehicle based on identifying at least one of a lateral movement of the vehicle, or a change amount of the traveling direction of the vehicle, or any combination thereof.

[0148] As described above, the processor of the vehicle control device may sequentially list identifiers and use pairings corresponding to the sequentially listed identifiers to predict and output the position of the vehicle, thereby avoiding using duplicate data. The processor is configured to perform the process relatively quickly by not using duplicate data.

[0149] In addition, the processor is configured to output the predicted vehicle position and operate in a driving assistance mode or an autonomous driving mode based on the predicted vehicle position, improving the positioning performance and enhancing the user experience by providing a stable operation to the user.

[0150] Figure 6 Examples of results before applying the present invention and examples of results after applying the present invention are shown.

[0151] Reference Figure 6 , Figure 6 The first example 601 in Figure 6 The second example 603 in

[0152] It can be seen that in the first example 601, the road edge part is not clearly identified, while in the second example 603, the road edge part is accurately identified. In addition, the road edge part can be identified relatively quickly by applying the present invention.

[0153] By quickly and accurately identifying the road edge part, if the vehicle is running in a driver assistance mode or an autonomous driving mode, it can travel stably on a curved road.

[0154] Hereinafter, Figure 7 A vehicle control method according to an exemplary embodiment of the present invention will be described in detail. Figure 7 An example of a flowchart related to a vehicle control method according to an exemplary embodiment of the present invention is shown.

[0155] Hereinafter, it is assumed that Figure 1 the vehicle control device 100 ofFigure 7 process. In addition, in Figure 7 description, the operations described as being performed by the device can be understood as being controlled by the processor 110 of the vehicle control device 100.

[0156] Figure 7 At least one operation in Figure 1 can be performed by the vehicle control device 100 in Figure 7 At least one operation in Figure 1 can be controlled by the processor 110 in Figure 7 The operations in

[0157] Referring to Figure 7 , in step S701, the vehicle control method according to various exemplary embodiments of the present invention may include: filtering a data set including a road edge portion related to the position of the vehicle according to a first specified condition, the first specified condition relating to at least one of distance, or angle, or any combination thereof.

[0158] In step S703, the vehicle control method according to various exemplary embodiments of the present invention may include: obtaining a plurality of first partial line segments by dividing a line segment corresponding to the road edge portion included in at least one of the data sets based on a specified length.

[0159] In step S705, the vehicle control method according to various exemplary embodiments of the present invention may include: identifying a plurality of second partial line segments from a plurality of line segments formed by contour points corresponding to the road edge portion via a light detection and ranging device (LiDAR) according to a second predetermined condition, the second predetermined condition being related to at least one of distance, angle, or height, or any combination thereof.

[0160] In step S707, the vehicle control method according to various exemplary embodiments of the present invention may include: in a vehicle coordinate system represented with the vehicle as the center, identifying a pairing of a plurality of second partial line segments and a plurality of first partial line segments that are respectively closest to the plurality of second partial line segments based on the identification of the plurality of second partial line segments and the plurality of first partial line segments.

[0161] According to an exemplary embodiment of the present invention, the vehicle control method may include: identifying at least one of a plurality of first partial line segments, a plurality of second partial line segments, or a pairing, or any combination thereof, on a plane formed by a first axis and a second axis among a first axis, a second axis, and a third axis.

[0162] According to an exemplary embodiment of the present invention, a vehicle control method may include: identifying a plurality of second partial line segments in each layer separated based on a third axis among a first axis, a second axis, and a third axis. The vehicle control method may include identifying a first sub-pairing included in each layer in a pairing. For example, the first sub-pairing may include a plurality of first partial line segments included in each layer, and the plurality of first partial line segments are respectively closest to the plurality of second partial line segments.

[0163] According to an exemplary embodiment of the present invention, a vehicle control method may include identifying first identifiers respectively assigned to the plurality of first partial line segments. The vehicle control method may include identifying second identifiers respectively assigned to the plurality of second partial line segments.

[0164] According to an exemplary embodiment of the present invention, a vehicle control method may include identifying a second sub-pairing in the pairing, in which the distance between the plurality of first partial line segments and the plurality of second partial line segments is less than a specified distance.

[0165] According to an exemplary embodiment of the present invention, a vehicle control method may include: sequentially arranging at least one of the first identifiers or the second identifiers, or any combination thereof, included in the identified second sub-pairing.

[0166] According to an exemplary embodiment of the present invention, a vehicle control method may include: selecting a part of the plurality of second partial line segments based on a construction type of map information among a plurality of layers separated based on a third axis among a first axis, a second axis, and a third axis.

[0167] According to an exemplary embodiment of the present invention, a vehicle control method may include: selecting a part of the plurality of second partial line segments identified in a first reference number of top layers among the plurality of layers based on the construction type of the map information being a top-line construction type. For example, the top-line construction type may include map information generated based on a part of a road edge portion identified at the highest height with reference to the third axis. For example, the first reference number may include approximately 2.

[0168] According to the vehicle control method of various exemplary embodiments of the present invention, it may include: identifying a part of the plurality of second partial line segments identified in the first reference number of layers. The vehicle control method may include: among the parts of the plurality of second partial line segments identified in the first reference number of layers, selecting a part of the plurality of second partial line segments closest to the construction height of the map information. According to an exemplary embodiment of the present invention, a vehicle control method may include: storing a part of the plurality of second partial line segments identified in the first reference number of layers in a memory.

[0169] According to an exemplary embodiment of the present invention, a vehicle control method may include: selecting a part of a plurality of second partial line segments identified in a second reference number of layers located at the bottom among a plurality of layers, based on the map information construction type being a bottom line construction type. For example, the bottom line construction type may include map information generated based on a part of a road edge portion identified at the lowest height with reference to a third axis. For example, the second reference number may include approximately 1.

[0170] In step S709, a vehicle control method according to various exemplary embodiments of the present invention may include: obtaining a calibration amount based on applying an algorithm designated for each pair, the calibration amount relating to at least one of a lateral movement of the vehicle based on a first coordinate system, or a change amount of the traveling direction of the vehicle, or any combination thereof.

[0171] For example, the designated algorithm may include at least one of an Iterative Closest Point (ICP) algorithm, or a Simultaneous Localization and Mapping (SLAM) algorithm, or any combination thereof.

[0172] In step S711, the vehicle control method may include: outputting a vehicle position in a second coordinate system different from the first coordinate system based on applying the obtained calibration amount to a predicted vehicle position.

[0173] As described above, the vehicle control method according to various exemplary embodiments of the present invention can improve the positioning performance by identifying a road edge portion based on long-distance data without using duplicate data.

[0174] In addition, the vehicle control method can improve the control performance of the vehicle in a driver assistance mode or an autonomous driving mode, and promote the stable operation of the autonomous driving system by continuously ensuring the positioning performance.

[0175] Figure 8 A computing system related to a vehicle control device or a vehicle control method according to an exemplary embodiment of the present invention is shown.

[0176] Reference Figure 8 , the computing system 1000 may include at least one processor 1100, a memory 1300, a user interface input device 1400, a user interface output device 1500, a storage device 1600, and a network interface 1700 that are connected to each other via a bus 1200.

[0177] The processor 1100 may be a central processing unit (CPU) or a semiconductor device for processing instructions stored in the memory 1300 and / or the storage device 1600. The memory 1300 and the storage device 1600 may include various types of volatile or non-volatile storage media. For example, the memory 1300 may include a read-only memory (ROM) 1310 and a random access memory (RAM) 1320.

[0178] Accordingly, the operations of the methods or algorithms described in connection with the exemplary embodiments included herein can be implemented directly in hardware or software modules executed by the processor 1100, or in a combination thereof. The software modules can reside on a storage medium (i.e., the memory 1300 and / or the storage device 1600), such as RAM, flash memory, ROM, EPROM, EEPROM, registers, a hard disk, a removable disk, and a CD-ROM.

[0179] The exemplary storage medium can be coupled to the processor 1100, the processor 1100 can read information from the storage medium, and can record information in the storage medium. Alternatively, the storage medium can be integrated with the processor 1100. The processor and the storage medium can be present in an application specific integrated circuit (ASIC). The ASIC can be present within a user terminal. In another case, the processor and the storage medium can exist as separate components in the user terminal.

[0180] The above description is only an illustration of the technical idea of the present invention, and those skilled in the art to which the present invention pertains can make various modifications and variations without departing from the essential features of the present invention.

[0181] Accordingly, the exemplary embodiments included in the exemplary embodiments of the present invention are not intended to limit the technical idea of the present invention, but to describe the present invention, and the scope of the technical idea of the present invention is not limited by the embodiments either. The protection scope of the present invention should be interpreted by the appended claims, and all technical ideas within the equivalent scope thereof should be interpreted as being included within the scope of the present invention.

[0182] This technology can use map information and LiDAR sensor data to accurately identify the road edge portion.

[0183] In addition, this technology can be configured to generate a driving path of the vehicle by accurately identifying an area within the road edge portion where the vehicle is configured to travel, thereby providing a stable driving system.

[0184] In addition, this technology can improve the positioning performance and perform data processing relatively quickly and accurately by not using duplicate data.

[0185] In addition, various effects directly or indirectly understood through the present invention can be provided.

[0186] In various exemplary embodiments of the present invention, each of the above operations can be performed by a control device, and the control device can be configured by a plurality of control devices or an integrated single control device.

[0187] In various exemplary embodiments of the present invention, the memory and the processor may be provided on one chip or on separate chips.

[0188] In various exemplary embodiments of the present invention, the scope of the present invention includes software or machine-executable instructions (e.g., operating systems, applications, firmware, programs, etc.) for enabling the operation of the methods according to the various embodiments to be executed on a device or a computer, and the scope of the present invention includes non-volatile computer-readable media that include such software or instructions stored on and executable on the device or the computer.

[0189] In various exemplary embodiments of the present invention, the control device may be implemented in the form of hardware or software, or may be implemented in a combination of hardware and software.

[0190] Furthermore, terms such as "unit" and "module" included in the specification denote units for performing at least one function or operation, and may be implemented by hardware, software, or a combination thereof.

[0191] In an exemplary embodiment of the present invention, a vehicle may be referred to as being based on the concept including various means of transportation. In some cases, a vehicle may be interpreted as being based on the concept including not only various land vehicles (e.g., cars, motorcycles, trucks, and buses) traveling on roads but also various means of transportation such as airplanes, drones, ships, etc.

[0192] For the convenience of explanation and to precisely define the appended claims, the terms "above", "below", "inside", "outside", "upper", "lower", "upward", "downward", "front", "rear", "back", "inner", "outer", "inwardly", "outwardly", "internal", "external", "inner side", "outer side", "forward", and "backward" are used to describe the features of the exemplary embodiments with reference to the positions of these features shown in the drawings. It will be further understood that the term "connected" or its derivatives refer to both direct connection and indirect connection.

[0193] The term "and / or" may include combinations of multiple related recited items or any one of the multiple related recited items. For example, "A and / or B" includes all three cases, such as "A", "B", and "A and B".

[0194] In an exemplary embodiment of the present invention, "at least one of A and B" may refer to "at least one of A or B" or "at least one of a combination of at least one of A and B". Furthermore, "one or more of A and B" may refer to "one or more of A or B" or "one or more of a combination of one or more of A and B".

[0195] In this specification, unless otherwise specified, singular expressions include plural expressions, unless the context clearly indicates otherwise.

[0196] In the exemplary embodiments of the present invention, it should be understood that terms such as "including" or "having" are intended to indicate the presence of the features, quantities, steps, operations, elements, components, or combinations thereof described in the specification, and do not exclude the possibility of adding or having one or more other features, quantities, steps, operations, elements, components, or combinations thereof.

[0197] According to the exemplary embodiments of the present invention, components may be combined with each other to be implemented as one, or some components may be omitted.

[0198] Hereinafter, the fact that hardware is operably coupled may include the fact that a direct and / or indirect connection between the hardware is established in a wired and / or wireless manner.

[0199] The foregoing description of specific exemplary embodiments of the present invention has been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the invention to the precise forms disclosed, and obviously many modifications and changes are possible in light of the above teachings. The exemplary embodiments were chosen and described in order to explain specific principles of the invention and its practical application so as to enable others skilled in the art to implement and utilize the invention in its various exemplary embodiments and various alternative forms and modifications. The scope of the invention is intended to be defined by the appended claims and their equivalents.

Claims

1. A vehicle control device, comprising: LiDAR; a memory configured to store map information; as well as a processor operably connected to the LiDAR and the memory, Wherein, the processor is configured as follows: filtering a data set including a road edge portion associated with a position of the vehicle from the map information according to a first predetermined condition, the first predetermined condition being associated with at least one of a distance, or an angle, or any combination thereof; obtaining a plurality of first partial line segments by segmenting line segments included in at least one of the data sets and corresponding to the road edge portion based on a predetermined length in the map information; identifying a plurality of second partial line segments from a plurality of line segments formed by contour points corresponding to the road edge portion obtained via LiDAR according to a second predetermined condition, wherein the second predetermined condition is related to at least one of a distance, an angle, or a height, or any combination thereof; In a vehicle coordinate system represented by the vehicle as a center, identifying pairs of a plurality of second partial line segments and a plurality of first partial line segments that are respectively closest to the plurality of second partial line segments; outputting a position of the vehicle in a second coordinate system different from the first coordinate system by using a calibration quantity related to at least one of a lateral movement of the vehicle based on the first coordinate system, or an amount of change in the direction of travel of the vehicle, or any combination thereof, based on applying a predetermined algorithm to each pairing, The vehicle is controlled based on the position of the vehicle in the second coordinate system.

2. The vehicle control device according to claim 1, wherein: The processor is further configured to identify at least one of a plurality of first partial line segments, a plurality of second partial line segments, or pairs, or any combination thereof on a plane formed by the first axis and the second axis among the first axis, the second axis, and the third axis.

3. The vehicle control device according to claim 1, wherein: The processor is further configured to: identifying a plurality of second portion line segments in each layer separated based on the third axis among the first axis, the second axis, and the third axis; identifying, in the pairing, a first sub-pairing included in each layer; The first sub-pair includes a plurality of first partial line segments, and the plurality of first partial line segments are respectively closest to a plurality of second partial line segments included in each layer.

4. The vehicle control device according to claim 1, wherein: The processor is further configured to: identifying first identifiers respectively assigned to the plurality of first partial line segments; identifying second identifiers respectively assigned to the plurality of second partial line segments; identifying a second sub-pairing in the pairing, in which a distance between the plurality of first partial line segments and the plurality of second partial line segments is less than a predetermined distance; At least one of the first identifier or the second identifier, or any combination thereof, included in the identified second sub-pair is sequentially arranged.

5. The vehicle control device according to claim 1, wherein: The processor is further configured to select, based on the construction type of the map information, a portion of the plurality of second portion line segments in a plurality of layers separated based on the third axis among the first axis, the second axis, and the third axis.

6. The vehicle control device according to claim 5, wherein: The processor is further configured to: The construction type based on the map information is a top line construction type, identifying a portion of the plurality of second portion line segments identified in a first reference number of layers located at the top of the plurality of layers; Among the parts of the plurality of second partial line segments identified in the first reference number of layers, a part of the plurality of second partial line segments closest to the construction height of the map information is selected.

7. The vehicle control device according to claim 6, wherein: The top line build type is a build type in which map information is generated based on a portion of a road edge portion identified at a highest altitude with reference to a third axis.

8. The vehicle control device according to claim 5, wherein: The processor is further configured to: based on the construction type of the map information being a bottom line construction type, identify a portion of the plurality of second partial line segments identified in a second reference number of layers located at the bottom of the plurality of layers.

9. The vehicle control device according to claim 8, wherein: The bottom line build type is a build type in which map information is generated based on a portion of a road edge portion identified at a lowest height with reference to a third axis.

10. The vehicle control device according to claim 1, wherein: The predetermined algorithm comprises at least one of an iterative closest point algorithm, or a simultaneous positioning and mapping algorithm, or any combination thereof.

11. A vehicle control method, comprising: filtering, by the processor, from the map information a data set including a road edge portion associated with the position of the vehicle according to a first predetermined condition, the first predetermined condition relating to at least one of a distance, or an angle, or any combination thereof; obtaining, by a processor, a plurality of first partial line segments by segmenting line segments included in at least one of the data sets and corresponding to road edge portions based on a predetermined length in the map information; The processor identifies a plurality of second partial line segments from a plurality of line segments formed by contour points corresponding to the road edge portion obtained by the LiDAR according to a second predetermined condition, wherein the second predetermined condition is related to at least one of a distance, an angle, or a height, or any combination thereof; identifying, by the processor, in a vehicle coordinate system represented by the vehicle as a center, pairs of a plurality of second partial line segments and a plurality of first partial line segments that are respectively closest to the plurality of second partial line segments; outputting, by the processor, a position of the vehicle in a second coordinate system different from the first coordinate system based on applying a predetermined algorithm to each pairing by using a calibration quantity related to at least one of a lateral movement of the vehicle based on the first coordinate system, or an amount of change in the direction of travel of the vehicle, or any combination thereof; The vehicle is controlled by the processor based on the position of the vehicle in the second coordinate system.

12. The vehicle control method according to claim 11, further comprising: At least one of a plurality of first partial line segments, a plurality of second partial line segments, or pairs, or any combination thereof is identified, by the processor, on a plane formed by the first axis and the second axis among the first axis, the second axis, and the third axis.

13. The vehicle control method according to claim 11, further comprising: identifying, by the processor, a plurality of second partial line segments in each of the layers separated based on the third axis among the first axis, the second axis, and the third axis; identifying, by the processor, in the pairing, a first sub-pair included in each layer, The first sub-pair includes a plurality of first partial line segments, and the plurality of first partial line segments are respectively closest to a plurality of second partial line segments included in each layer.

14. The vehicle control method according to claim 11, further comprising: identifying, by a processor, first identifiers respectively assigned to the plurality of first partial line segments; identifying, by the processor, second identifiers respectively assigned to the plurality of second partial line segments; identifying, by the processor, a second sub-pairing in the pairing, in which a distance between the plurality of first partial line segments and the plurality of second partial line segments is less than a predetermined distance; At least one of the first identifier or the second identifier, or any combination thereof, included in the identified second sub-pair is sequentially arranged, by the processor.

15. The vehicle control method according to claim 11, further comprising: A portion of the plurality of second portion line segments is selected, by the processor, in a plurality of layers separated based on the third axis among the first axis, the second axis, and the third axis based on the construction type of the map information.

16. The vehicle control method according to claim 15, further comprising: The construction type based on the map information is a top line construction type, and the processor identifies a portion of the plurality of second portion line segments identified in a first reference number of layers located at the top of the plurality of layers; Among the parts of the plurality of second partial line segments identified in the first reference number of layers, a part of the plurality of second partial line segments closest to the construction height of the map information is selected by the processor.

17. The vehicle control method according to claim 16, wherein: The top line build type is a build type in which map information is generated based on a portion of a road edge portion identified at a highest altitude with reference to a third axis.

18. The vehicle control method according to claim 15, further comprising: The construction type based on the map information is a bottom line construction type, and a portion of the plurality of second partial line segments identified in a second reference number of layers located at the bottom of the plurality of layers is identified by the processor.

19. The vehicle control method according to claim 18, wherein: The bottom line build type is a build type in which map information is generated based on a portion of a road edge portion identified at a lowest height with reference to a third axis.

20. The vehicle control method according to claim 11, wherein: The predetermined algorithm comprises at least one of an iterative closest point algorithm, or a simultaneous positioning and mapping algorithm, or any combination thereof.

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