System and method for evaluating road polyline objects using results of automated point cloud analysis

Abstract

Disclosed are a system and a method for evaluating road polyline objects using the results of automated point cloud analysis. The system for evaluating road polyline objects using the results of automated point cloud analysis comprises: a point cloud data storage unit for storing point cloud (PCL) data collected from roads; a lane extraction unit for extracting lanes by analyzing the PCL data; a determination unit for determining whether a first line, composed of zero-th and first points, and a second line, composed of third and fourth points, extracted by the lane extraction unit are parallel; and an abnormality detection unit for determining abnormality on the basis of adherence to respective preset abnormality detection conditions based on whether the first and second lines are parallel.

Description

Road alignment object evaluation system and method using point cloud automatic analysis results

[0001] The present invention relates to a system and method for evaluating a road linear object using a result of automatic analysis of a point cloud, and more particularly, to a system and method for evaluating a road linear object using a result of automatic analysis of a point cloud, which extracts a lane using point cloud (PCL) data collected on a road, finds a plurality of reference points among the constituent points of the extracted lane linear, extracts a plurality of straight lines, and determines whether each of the plurality of straight lines falls within a preset reference error value based on whether or not they are parallel, thereby detecting whether or not there is an abnormality in a road linear object (Road Polyline).

[0002] Autonomous vehicles are vehicles capable of sensing their surroundings and movements with little or no human intervention or input. To realize these autonomous vehicles, a variety of sensors capable of sensing their surroundings, such as lidar, radar, computer vision, GPS, odometry, and inertial measurement units, must be integrated. More specifically, the sensing behavior of various physical sensors must be combined with logical road information technology pre-configured using HD-Map data authoring technology.

[0003] In particular, real-time data and transmission technologies are needed to provide autonomous vehicles with vehicle operation support information, facilitating their ability to perceive difficult-to-detect surrounding traffic conditions. For example, LDM (Local Dynamic Map) is a technology that links, stores, and manages standardized vehicle operation support information for autonomous cooperative driving. It integrates road traffic and surrounding vehicle conditions (dynamic information) based on a lane-level precision electronic map (or static information) to provide real-time information to autonomous vehicles.

[0004] The unmanned autonomous driving of a vehicle (autonomous vehicle) can be broadly divided into three stages: recognizing the surrounding environment, planning a driving route based on the recognized environment, and driving along the planned route.

[0005] The technology for recognizing the environment around a vehicle will vary depending on the target environment in which the autonomous vehicle is driving. In particular, in order to drive autonomously in a road environment designed and constructed for existing manned vehicles, technology for recognizing various rules existing on the road may be essential.

[0006] In particular, recognizing lanes and following a designated lane is the most basic technology for safe driving with manned vehicles.

[0007] While methods for recognizing road lanes rely on images captured by cameras, they suffer from reduced recognition accuracy when ambient light is insufficient. Consequently, lane recognition technology using Lidar sensors is currently being developed.

[0008] A lidar sensor is a device that precisely draws the surroundings by measuring the distance to objects and other things through the intensity (strength) of the signal that is reflected back from the medium where the pulse collided after firing a laser pulse onto the road surface. It has the same principle as radar using radio waves, but since it uses the frequency of the visible light range, which is called light among electromagnetic waves, the actual technology and scope of use can be seen to be different.

[0009] These lidars are one component of the Mobile Mapping System (MMS), and in addition to lidars, the MMS includes a camera, an inertial measurement unit (IMU), GPS, and a distance measurement indicator (DMI).

[0010] Accordingly, the information acquired through the MMS collection equipment (vehicle) that integrates lidar sensors, image sensors, IMU, and GPS equipment can obtain specific road configuration information (signal reflection intensity of the medium, absolute position of the medium, continuity, etc.), i.e., high-precision digital map data.

[0011] Typically, point cloud data collected by vehicles equipped with MMS equipment contains a variety of information, such as roadside structures, buildings, and vehicles. However, the information actually needed for autonomous driving in point cloud data is road floor data, including lanes, road signs, crosswalks, and stop lines. Providing point cloud data collected via LiDAR equipment directly to autonomous vehicles can lead to errors in automatic detection results due to noise from vehicles in adjacent lanes (e.g., missed sections due to weak signal collection, or traces of vehicles driving alongside in the adjacent lane).

[0012] The purpose of the present invention is to provide a system and method for evaluating a road linear object using the results of automatic analysis of a point cloud, which evaluates the continuity and consistency of a road surface linearity (lane, center line, curb, etc.) cartographed with HD Map data, extracts a lane using point cloud (PCL) data collected on a road, finds a plurality of reference points among the constituent points of the extracted lane linearity, extracts a plurality of straight lines, and determines whether each of the plurality of straight lines falls within a preset reference error value depending on whether they are parallel, thereby detecting an abnormality in the road linearity (Road Polyline), as well as to provide a method for examining the suitability of an entire road linearity created for the purpose of constructing an HD-MAP.

[0013] The purpose of the present invention is not limited to the purposes mentioned above, and other purposes not mentioned will be clearly understood by those skilled in the art from the description below.

[0014] According to one embodiment of the present invention, a road linear object evaluation system using a point cloud automatic analysis result is configured to include: a point cloud data storage unit that stores point cloud (PCL) data collected on a road; a lane extraction unit that analyzes the point cloud data to extract a lane; a parallel determination unit that determines whether a first line composed of a 0th point and a 1st point and a second line composed of a 3rd point and a 4th point extracted from the lane extraction unit are parallel; and an abnormality detection unit that determines whether an abnormality exists based on whether preset abnormality detection criteria conditions are satisfied based on whether the first and second lines are parallel.

[0015] The above-mentioned abnormality detection unit may store a first vertical distance calculated as a vertical distance between the second point and the third line connecting the first point and the third point, as a first error value, if it is determined that the first line and the second line are parallel.

[0016] The above-mentioned abnormality detection unit, when it is determined that the first line and the second line are not parallel, calculates an extended intersection point (pK) of the first line and the second line, and can generate a Bezier curve connecting the first point and the third point using the first point, the third point, and the extended intersection point.

[0017] The above-mentioned abnormality detection unit can store the second vertical distance calculated from the vertical distance between the Bezier curve and the second point as a second error value.

[0018] The road linear object evaluation system using the above point cloud automatic analysis results is configured to further include an error range confirmation unit that confirms whether the first error value and the second error value detected by the above anomaly detection unit are within a preset error range.

[0019] According to one embodiment of the present invention, a road linear object evaluation method using a point cloud automatic analysis result is configured to include a point cloud data storage step, which is performed in a point cloud data storage unit and stores point cloud (PCL) data collected on a road; a lane extraction step, which is performed in a lane extraction unit and analyzes the point cloud data to extract a lane; a parallel determination step, which is performed in a parallel determination unit and determines whether a first line composed of a 0th point and a first point, and a second line composed of a third point and a fourth point, extracted from the lane extraction unit, are parallel; and an abnormality detection step, which is performed in an abnormality detection unit and determines whether an abnormality exists depending on whether preset abnormality detection criteria conditions are satisfied based on whether the first and second lines are parallel.

[0020] The above-mentioned abnormality detection step may include a step of storing a first vertical distance calculated as a vertical distance between the second point and a third line connecting the first point and the third point, as a first error value, if the first line and the second line are determined to be parallel.

[0021] The above-described abnormality detection step may include a step of calculating an extended intersection point (pK) of the first line and the second line when it is determined that the first line and the second line are not parallel, and generating a Bezier curve connecting the first point and the third point using the first point, the third point, and the extended intersection point.

[0022] The above-mentioned abnormality detection step may include a step of storing a second vertical distance calculated as a vertical distance between the Bezier curve and the second point as a second error value.

[0023] The road linear object evaluation method using the above point cloud automatic analysis results is performed in an error range confirmation unit, and may further include an error range confirmation step for confirming whether the first error value and the second error value detected in the above abnormality detection step are within a preset error range.

[0024] According to one aspect of the present invention, a system and method for evaluating a road linear object using a result of automatic analysis of a point cloud are provided, which extracts a lane using point cloud (PCL) data collected on a road, finds a plurality of reference points among constituent points of the extracted lane linearity, extracts a plurality of straight lines, and determines whether each of the plurality of straight lines falls within a preset reference error value depending on whether the straight lines are parallel, thereby detecting whether an abnormality in a road linearity (Road Polyline) is present.

[0025] In addition, the system and method for evaluating road linear objects using the automatic analysis results of point clouds according to one embodiment of the present invention interpolates errors in the automatic detection results caused by noise (missed collection sections due to weak signal collection, driving traces of vehicles driving side by side in the adjacent lane, etc.) from the automatic detection results into an appropriate linear interpolation, thereby detecting and removing road linear error points, thereby enabling more accurate lane extraction.

[0026] The effects that can be obtained from the present invention are not limited to the effects mentioned above, and other effects not mentioned can be clearly understood by a person having ordinary skill in the art to which the present invention belongs from the description below.

[0027] FIG. 1 is a drawing illustrating an example of an automatic analysis evaluation method using a road linear object evaluation system and method using the results of automatic point cloud analysis.

[0028] FIG. 2 is a drawing illustrating a road linear object evaluation system (200) using the results of automatic point cloud analysis.

[0029] FIG. 3 is a drawing illustrating an example of determining whether a first line and a second line are parallel when they are parallel from a parallel detection unit by a road linear object evaluation system and method using a point cloud automatic analysis result according to one embodiment of the present invention.

[0030] FIG. 4 is a diagram schematically illustrating a process of calculating a second point and performing interpolation when a first line and a second line are parallel according to one embodiment of the present invention.

[0031] FIG. 5 is a drawing illustrating an example of determining whether a first line and a second line are parallel when they are not parallel from a parallel detection unit by a road linear object evaluation system and method using a point cloud automatic analysis result according to one embodiment of the present invention.

[0032] FIG. 6 is a diagram schematically illustrating a process of calculating a second point and performing interpolation when the first line and the second line are not parallel according to one embodiment of the present invention.

[0033] FIG. 7 is a block diagram illustrating a computer system for implementing a road linear object evaluation method using point cloud automatic analysis results according to one embodiment of the present invention.

[0034] FIG. 8 is a block diagram showing a computer system for implementing a road linear object evaluation method using point cloud automatic analysis results according to one embodiment of the present invention.

[0035] The advantages and features of the present invention, and the methods for achieving them, will become clear with reference to the embodiments described in detail below together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and these embodiments are provided only to make the disclosure of the present invention complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims. Meanwhile, the terminology used in this specification is for the purpose of describing the embodiments and is not intended to limit the present invention. In this specification, the singular also includes the plural unless specifically stated in the phrase. The terms "comprises" and / or "comprising" as used in the specification do not exclude the presence or addition of one or more other components, steps, operations, and / or elements mentioned.

[0036] In describing the present invention, if it is determined that a detailed description of a related known technology may unnecessarily obscure the gist of the present invention, the detailed description is omitted.

[0037] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings. In order to facilitate an overall understanding in describing the present invention, the same reference numbers will be used for the same means regardless of the drawing numbers.

[0038] FIG. 1 is a drawing illustrating an example of an automatic analysis evaluation method using a road linear object evaluation system and method using the results of automatic point cloud analysis.

[0039] Referring to FIG. 1, according to the system and method for evaluating road linear objects using the automatic analysis results of a point cloud according to an embodiment of the present invention, only data including lane information, which is information practically necessary for autonomous driving, can be extracted. As illustrated, it can be confirmed that point cloud data of structures, buildings, vehicles, etc. around the road that are unnecessary for autonomous driving of an autonomous vehicle have been removed. In this way, the system and method for evaluating road linear objects using the automatic analysis results of a point cloud according to an embodiment of the present invention interpolates automatic detection results errors caused by noise (missed collection sections, weak signal strength) caused by vehicles in adjacent lanes, etc. into an appropriate linear interpolation, thereby detecting and removing road linear error points, thereby enabling more accurate lane extraction.

[0040] FIG. 2 is a drawing illustrating a road linear object evaluation system (200) using the results of automatic point cloud analysis.

[0041] Referring to FIG. 2, a road linear object evaluation system (200) using cloud automatic analysis results is configured to include a point cloud data storage unit (210), a lane extraction unit (220), a parallel determination unit (230), an anomaly detection unit (240), and an error range confirmation unit (250). The road linear object evaluation system (200) using cloud automatic analysis results illustrated in FIG. 2 is according to one embodiment, and the components of the road linear object evaluation system (200) using cloud automatic analysis results according to the present invention are not limited to the embodiment illustrated in FIG. 2, and may be added, changed, or deleted as needed.

[0042] The point cloud data storage unit (210) stores point cloud (PCL) data collected on the road. Point cloud data is data collected by Lidar sensors, RGB-D sensors, etc. These sensors send light / signals to objects and record the time it takes for them to return, calculate distance information for each light / signal, and create a single point. Point cloud data refers to a set cloud of multiple points spread across a three-dimensional space collected from these sensors. In one embodiment, the point cloud data stored in the point cloud data storage unit (210) may be data collected from a Lidar sensor equipped in the MMS.

[0043] In one embodiment, point cloud data may be stored in the point cloud data storage unit (210) in the LAS file format. Due to the characteristics of the LAS file format, when point cloud data is collected on a road using a lidar sensor, the data is collected by dividing it into left and right along the driving path of the MMS vehicle. Accordingly, when point cloud data is collected on a road, the left and right division points are collected as points and converted into lines to form lanes (driving lines) that can be expressed on the point cloud data.

[0044] The lane extraction unit (220) can extract lanes by analyzing information collected from the point cloud data storage unit (210). The lane extraction unit (220) can connect points and express them as lines based on data collected repeatedly along the driving direction from the point cloud data storage unit (210).

[0045] The parallel judgment unit (230) can determine whether the first line formed by the 0th point (p0) and the first point (p1) extracted from the lane extraction unit (220) and the second line formed by the third point (p3) and the fourth point (p4) are parallel. The first line and the second line refer to lines formed at the same distance, and the first and second lines can be determined to be parallel if they are formed horizontally in the driving direction (forward direction). In addition, the first line and the second line can be determined to be not parallel if they are not formed horizontally in the driving direction (forward direction).

[0046] The abnormality detection unit (240) can determine whether there is an abnormality based on whether the first and second lines are parallel and whether the preset abnormality detection criteria are satisfied. Here, when the preset abnormality detection criteria are satisfied, it is determined to be normal, and when the abnormality detection criteria are not satisfied, it is determined to be abnormal.

[0047] If the abnormality detection unit (240) determines that the first line and the second line are parallel, the first vertical distance calculated from the vertical distance between the third line connecting the first point and the third point and the second point can be stored as the first error value.

[0048] If the anomaly detection unit (240) determines that the first line and the second line are not parallel, it can calculate the extended intersection point (pK) of the first line and the second line, and generate a Bezier curve connecting the first point and the third point using the first point, the third point, and the extended intersection point (pK), and store the second vertical distance calculated as the vertical distance of the Bezier curve and the second point as a second error value.

[0049] The error range confirmation unit (250) can confirm whether the first error value and the second error value detected by the abnormality detection unit are within a preset error range.

[0050] In addition, according to one embodiment, a road linear object correction system (200) using the results of automatic point cloud analysis can evaluate the continuity and consistency of linearity (lanes, center lines, curbs, etc.) of a road surface cartographed with HD Map data.

[0051] FIG. 3 is a drawing illustrating an example of determining whether a first line and a second line are parallel when they are parallel from a parallel detection unit by a road linear object evaluation system and method using a point cloud automatic analysis result according to one embodiment of the present invention.

[0052] FIG. 4 is a diagram schematically illustrating a process of calculating a second point and performing interpolation when a first line and a second line are parallel according to one embodiment of the present invention.

[0053] Referring to FIGS. 3 and 4, in one embodiment, the point cloud data storage unit (210) repeatedly collects point cloud data (point cloud data of p0, p1, p2, p3, and p4), and the lane extraction unit (220) extracts a first line by connecting p0 and p1 and a second line by connecting p3 and p4, so that the parallel determination unit (230) can determine whether the first and second lines are parallel. If the parallel determination unit (230) determines that the first and second lines are parallel, the vertical distance between the line connecting p1 and p3 and p2 can be calculated, and if it is within a preset reference range, it can be determined as normal data. Here, the preset reference range may be an error range, and the preset reference range may be changed as needed. In addition, the vertical distance from p2 can be repeatedly stored as an error value.

[0054] FIG. 5 is a drawing illustrating an example of determining whether a first line and a second line are parallel when they are not parallel from a parallel detection unit by a road linear object evaluation system and method using a point cloud automatic analysis result according to one embodiment of the present invention.

[0055] FIG. 6 is a diagram schematically illustrating a process of calculating a second point and performing interpolation when the first line and the second line are not parallel according to one embodiment of the present invention.

[0056] Referring to FIGS. 5 and 6, in one embodiment, the point cloud data storage unit (210) repeatedly collects point cloud data (point cloud data of p3, p4, p5, p6, and p7), the lane extraction unit (220) extracts a first line by connecting p3 and p4, and a second line by connecting p6 and p7, so that the parallel determination unit (230) can determine whether the first and second lines are parallel. If the parallel determination unit (230) determines that the first and second lines are not parallel, the extended intersection point (pK) between the first and second lines is calculated, a Bezier curve connecting p4 and p5 is generated using pK, and the vertical distance between the generated Bezier curve and p2 is calculated, and if it is within a preset reference range, it can be determined as normal data. Here, the preset reference range may be an error range, and the preset reference range may be changed as needed. Additionally, the vertical distance from p2 can be repeatedly stored as an error value.

[0057] A Bezier curve is a mathematically expressed curve used in computer graphics and animation. To draw a quadratic Bezier curve, three points (x0, x1, x2) are used to draw two imaginary lines, one from x0 to x1 and the other from x1 to x2. The starting point of the first imaginary line and the ending point of the second imaginary line move steadily as a third imaginary line is drawn. On this imaginary line, a point that moves steadily from the starting point to the ending point is drawn to form a curve.

[0058] FIG. 7 is a block diagram illustrating a computer system for implementing a road linear object evaluation method using point cloud automatic analysis results according to one embodiment of the present invention.

[0059] As illustrated in FIG. 7, a road linear object evaluation method using a point cloud automatic analysis result according to one embodiment of the present invention may include steps S710 to S750.

[0060] Step S710 is a step of storing point cloud (PCL) data collected on the road in the point cloud data storage unit (210).

[0061] Step S720 is a step for extracting lanes by analyzing information collected from the point cloud data storage unit (210) in the lane extraction unit (220). The lane extraction unit (220) may include a step for connecting points and expressing them as lines based on data collected repeatedly along the driving direction from the point cloud data storage unit (210).

[0062] Step S730 is performed in the parallel determination unit (230), and is a step of determining whether the first line formed by the 0th point and the 1st point extracted from the lane extraction unit (220) and the second line formed by the 3rd point and the 4th point are parallel. The first line and the second line mean lines formed at the same distance, and may include a step of determining that the first and second lines are parallel when they are formed horizontally in the driving direction (forward direction), and a step of determining that the first and second lines are not parallel when they are not formed horizontally in the driving direction (forward direction).

[0063] Step S740 is performed in the abnormality detection unit (240), and includes a step of determining whether there is an abnormality depending on whether the first line and the second line are parallel and whether preset abnormality detection criteria conditions are satisfied. Here, when each preset abnormality detection criteria condition is satisfied, it is determined to be normal, and when the abnormality detection criteria condition is not satisfied, it may include a step of determining that there is an abnormality.

[0064] Step S750 is performed in the error range confirmation unit (250), and is a step for confirming whether the first error value and the second error value detected by the abnormality detection unit are within the preset error range.

[0065] The aforementioned method for evaluating road linear objects using automatic point cloud analysis results according to one embodiment of the present invention has been described with reference to the flowchart presented in the drawings. For simplicity, the method has been illustrated and described as a series of blocks; however, the present invention is not limited to the order of the blocks, and some blocks may occur in a different order or simultaneously with other blocks than illustrated and described herein, and various other branches, flow paths, and block orders that achieve the same or similar results may be implemented. Furthermore, not all illustrated blocks may be required to implement the method described herein.

[0066] In the description with reference to FIG. 7, each step may be further divided into additional steps or combined into fewer steps, depending on the implementation of the present invention. Furthermore, some steps may be omitted as needed, and the order of the steps may be changed. Furthermore, even if other omitted content is included, the content of FIGS. 1 through 6 may be applied to the content of FIG. 7. Furthermore, the content of FIG. 7 may be applied to the content of FIGS. 1 through 6.

[0067] FIG. 8 is a block diagram showing a computer system for implementing a road linear object evaluation method using point cloud automatic analysis results according to one embodiment of the present invention.

[0068] Referring to FIG. 8, a computer system (1000) may include at least one of a processor (1010), a memory (1030), an input interface device (1050), an output interface device (1060), and a storage device (1040) that communicate via a bus (1070). The computer system (1000) may further include a communication device (1020) coupled to a network. The processor (1010) may be a central processing unit (CPU), or a semiconductor device that executes instructions stored in the memory (1030) or the storage device (1040). The memory (1030) and the storage device (1040) may include various forms of volatile or non-volatile storage media. For example, the memory may include a read-only memory (ROM) and a random access memory (RAM). In embodiments of the present disclosure, the memory may be located internally or externally to the processor, and the memory may be connected to the processor via various known means. Memory is a variety of volatile or non-volatile storage media, and may include, for example, read-only memory (ROM) or random access memory (RAM).

[0069] Accordingly, embodiments of the present invention may be implemented as a computer-implemented method or as a non-transitory computer-readable medium storing computer-executable instructions. In one embodiment, when executed by a processor, the computer-readable instructions may perform a method according to at least one aspect of the present disclosure.

[0070] The communication device (1020) can transmit or receive wired or wireless signals.

[0071] In addition, the method according to the embodiment of the present invention may be implemented in the form of program commands that can be executed through various computer means and recorded on a computer-readable medium.

[0072] The computer-readable medium may include program commands, data files, data structures, etc., either singly or in combination. The program commands recorded on the computer-readable medium may be specially designed and configured for embodiments of the present invention, or may be known and usable by those skilled in the art of computer software. The computer-readable recording medium may include a hardware device configured to store and execute the program commands. For example, the computer-readable recording medium may be a magnetic medium such as a hard disk, a floppy disk, and a magnetic tape, an optical medium such as a CD-ROM or a DVD, a magneto-optical medium such as a floptical disk, a ROM, a RAM, a flash memory, etc. The program commands may include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer through an interpreter, etc.

[0073] For reference, components according to embodiments of the present invention may be implemented in the form of software or hardware such as a digital signal processor (DSP), a field programmable gate array (FPGA), or an application specific integrated circuit (ASIC), and may perform certain roles.

[0074] However, 'components' are not limited to software or hardware, and each component may be configured to reside on an addressable storage medium or configured to trigger one or more processors.

[0075] Thus, as an example, components include components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, properties, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables.

[0076] Components and the functionality provided within those components may be combined into a smaller number of components or further separated into additional components.

[0077] Meanwhile, it will be understood that each block of the flowchart drawings and combinations of the flowchart drawings can be performed by computer program instructions. These computer program instructions can be installed on a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment, so that the instructions executed by the processor of the computer or other programmable data processing equipment create a means for performing the functions described in the flowchart block(s). The computer program instructions can also be installed on a computer or other programmable data processing equipment, so that a series of operational steps are performed on the computer or other programmable data processing equipment to create a computer-executable process, so that the instructions executed by the computer or other programmable data processing equipment can also provide steps for performing the functions described in the flowchart block(s).

[0078] Additionally, each block may represent a module, segment, or portion of code that contains one or more executable instructions for performing a specific logical function(s). It should also be noted that in some alternative implementation examples, the functions described in the blocks may occur out of order. For example, two blocks depicted in succession may actually be executed substantially concurrently, or the blocks may sometimes be executed in reverse order, depending on their respective functions.

[0079] The term 'part' or 'module' used in this embodiment means a software or hardware component such as an FPGA or ASIC, and the 'part' or 'module' performs certain roles. However, the 'part' or 'module' is not limited to software or hardware. The 'part' or 'module' may be configured to be on an addressable storage medium and may be configured to play one or more processors. Thus, as an example, the 'part' or 'module' includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functionality provided within the components and '~sub-parts' or '~modules' may be combined into a smaller number of components and '~sub-parts' or '~modules' or further separated into additional components and '~sub-parts' or '~modules'. In addition, the components and '~sub-parts' or '~modules' may be implemented to regenerate one or more CPUs within the device or secure multimedia card.

[0080] Although the present invention has been described above with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various modifications and changes may be made to the present invention without departing from the spirit and scope of the present invention as set forth in the claims below.

Claims

1. A point cloud data storage unit that stores point cloud (PCL) data collected on the road; A lane extraction unit that extracts lanes by analyzing the above point cloud data; A parallel judgment unit that determines whether a first line composed of the 0th point and the 1st point extracted from the above-mentioned lane extraction unit and a second line composed of the 3rd point and the 4th point are parallel; and A road linear object evaluation system using the results of automatic point cloud analysis, characterized in that it includes an abnormality detection unit that determines whether there is an abnormality based on whether preset abnormality detection criteria conditions are satisfied based on whether the first and second lines are parallel.

2. In paragraph 1, The above abnormality detection unit, A road alignment object evaluation system using the results of automatic point cloud analysis, characterized in that if the first line and the second line are determined to be parallel, the first vertical distance calculated from the vertical distance between the third line connecting the first point and the third point and the second point is stored as a first error value.

3. In paragraph 2, The above abnormality detection unit, A road alignment object evaluation system using the results of automatic point cloud analysis, characterized in that if it is determined that the first line and the second line are not parallel, the extended intersection point (pK) of the first line and the second line is calculated, and a Bezier curve connecting the first point and the third point is generated using the first point, the third point, and the extended intersection point.

4. In paragraph 3, The above abnormality detection unit, A road alignment object evaluation system using the results of automatic point cloud analysis, characterized in that the second vertical distance calculated from the vertical distance between the above Bezier curve and the second point is stored as a second error value.

5. In paragraph 1, The road linear object evaluation system using the above point cloud automatic analysis results is A road linear object evaluation system using the results of automatic point cloud analysis, characterized in that it further includes an error range verification unit that verifies whether the first error value and the second error value detected by the above-mentioned abnormality detection unit are within a preset error range.

6. A point cloud data storage step that is performed in the point cloud data storage unit and stores point cloud (PCL) data collected on the road; A lane extraction step performed in a lane extraction unit, wherein the lane is extracted by analyzing the point cloud data; A parallel judgment step performed in a parallel judgment unit, which determines whether a first line composed of the 0th point and the 1st point extracted from the lane extraction unit and a second line composed of the 3rd point and the 4th point are parallel; and A road linear object evaluation method using the results of automatic point cloud analysis, characterized in that it includes an abnormality detection step performed in an abnormality detection unit and determining whether an abnormality exists based on whether preset abnormality detection criteria conditions are satisfied based on whether the first and second lines are parallel.

7. In paragraph 6, The above abnormality detection step is, A method for evaluating a road linear object using the results of automatic point cloud analysis, characterized in that it comprises the step of: if it is determined that the first line and the second line are parallel, storing the first vertical distance calculated from the vertical distance between the third line connecting the first point and the third point and the second point as a first error value; 8. In paragraph 7, The above abnormality detection step is, A method for evaluating a road alignment object using the results of automatic point cloud analysis, characterized in that it comprises the steps of: calculating an extended intersection point (pK) of the first line and the second line if it is determined that the first line and the second line are not parallel, and generating a Bezier curve connecting the first point and the third point using the first point, the third point, and the extended intersection point; 9. In paragraph 8, The above abnormality detection step is, A method for evaluating a road linear object using the results of automatic point cloud analysis, characterized in that it comprises a step of calculating a second vertical distance between the Bezier curve and the second point and storing the second vertical distance as a second error value.

10. In paragraph 6, The road linear object evaluation method using the above point cloud automatic analysis results is as follows: A road linear object evaluation method using the results of automatic point cloud analysis, characterized in that it further includes an error range verification step performed in an error range verification unit and verifying whether the first error value and the second error value detected in the above-detected abnormality detection step are within a preset error range.

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