KR20230041518A - Method for generating high definition map by using fixed lidar and server using the same - Google Patents

Abstract

According to the present invention, in a method for generating a precise map using a fixed lidar, disclosed is a method, which includes the steps of: (a) obtaining, by a server, object information from specific lidar data; (b) classifying, by the server, objects having the same object ID with reference to object IDs among the object information included in a plurality of consecutive frames, and storing a moving path for each object ID; and (c) generating, by the server, the precise map with reference to each moving path stored for each object ID.

Description

Method for generating precision map using fixed lidar and server using the same

The present invention relates to a method for generating a precise map using a fixed LIDAR and a server using the same.

Lida is a device that accurately draws the surroundings by emitting laser pulses, receiving the light reflected from nearby objects and measuring the distance to the object. Lidar is a compound word made by mixing 'light' and 'radar (radio detection and ranging)' in terms of its origin. In other words, the name LIDAR means a radar that uses light instead of radio waves, and the principle is the same as that of traditional radar, but the wavelength of the electromagnetic wave used is different, so the actual use technology and application range are different.

In recent years, various transportation subjects such as self-driving cars, motorcycles, and electric kickboards as well as general cars are emerging, so a more advanced transportation infrastructure system is needed to reflect this reality.

However, there are not many cases in which a system that can obtain traffic data more accurately and quickly by using lidar in such a transportation infrastructure system has been implemented. It was just a skill to acquire. For example, most cases in which lidar is used in transportation infrastructure systems are equipped with lidar in cars to detect objects around them while the car is driving and give a warning to the driver to avoid them. are using

However, there is almost no technology for obtaining and using complex traffic data of intersections by using LIDAR in a fixed location such as an intersection. In other words, LIDAR is installed at an intersection, etc., and dynamic objects such as cars and pedestrians passing around the intersection and static objects such as facilities are detected, and a precision map is created with reference to the detected data, and it is automatically updated. This is a situation where methods and servers are needed.

The present invention has as its object to solve all the problems mentioned above.

In addition, another object of the present invention is to obtain object information from LiDAR, classify objects having the same object ID among object information, store a moving path for each object ID, and create a detailed map with reference to this.

In addition, the present invention classifies the object into a high-speed moving object and a low-speed moving object, and determines the lane area, vehicle stop line area, pedestrian crosswalk waiting area, and pedestrian crosswalk walking area to more accurately generate a precision map. The purpose.

According to one aspect of the present invention, in a method for generating a precise map using a fixed lidar, the server comprising: acquiring object information from specific lidar data; (b) classifying, by the server, objects having the same object ID with reference to object IDs among the object information included in a plurality of consecutive frames, and storing a moving path for each object ID; and (c) generating, by the server, the precision map with reference to each movement path stored for each object ID.

As an example, in step (a), the server classifies at least a portion of the speed, size, and height data of each object included in the object information into one of a vehicle and a pedestrian, and (b) In step (c), the server stores a movement path for the high-speed-moving object ID identified as the vehicle, and in step (c), the server moves along the movement path of the fast-moving object corresponding to the high-speed object ID. is matched with a plurality of driving cells of the grid map to increase the count of the number of the high-speed moving objects passing through each of the driving cells for each driving cell, and if the count of the driving cells is equal to or greater than a first threshold value, the driving cell is set as a vehicle A method characterized by determining by area is disclosed.

As an example, in the step (c), the server selects a first specific cell in which the high-speed moving object classified as the vehicle stops from among the driving cells with reference to the speed of the object in the object information; A count of the number of the high-speed moving objects stopped for a first time in the first specific cell is increased for each first specific cell, and if the count of the first specific cell is equal to or greater than a second threshold, the first specific cell A method characterized in determining the vehicle stop line area is disclosed.

As an example, in the step (b), the server stores a moving path for the low-speed moving object ID classified as the pedestrian, and in the step (c), the server stores the speed of the object among the object information. With reference to, a second specific cell in which the slow-moving object classified as a pedestrian stops is selected from cells other than the driving cell, and the number of slow-moving objects stopped in the second specific cell for a second time A count is increased for each second specific cell, and when the count of the second specific cell is greater than or equal to a third threshold, the second specific cell is determined as a pedestrian crosswalk waiting area.

As an example, in step (c), the server further determines a pedestrian crosswalk walking area with reference to at least a part of the lane area, the vehicle stop line area, and the pedestrian crosswalk waiting area. A method for doing so is disclosed.

As an example, before the step (a), if (a01) initial lidar data is obtained, the server performs filtering according to a preset criterion to perform fixed lidar data corresponding to a fixed object among the initial lidar data. classifying; and (a02) obtaining residual lidar data as the specific lidar data by removing the fixed lidar data from the initial lidar data.

As an example, in the step (a01), the server obtains information on the fixed object existence area where the fixed object exists with reference to the fixed lidar data, and in the step (c), the server obtains information on the fixed object existence area where the fixed object exists. A method characterized by dividing the moving route into at least one lane having a predetermined width with reference to the route and the information on the fixture presence area is disclosed.

As an example, in the step (a01), the server divides the area covered by the lidar into a virtual 3D grid area, and the initial layer for each cell in the grid area for at least one observation frame. A method characterized in that counting the number of detected data and determining the initial lidar data detected in third specific cells in which the counted number of the cells is equal to or greater than a threshold value as the fixed lidar data is disclosed.

As an example, in the step (a01), the server divides the area covered by the lidar into a virtual 3D grid area, and the initial layer for each cell in the grid area for at least one observation frame. In fourth specific cells, the ratio of the difference between the observation point data number and the average point data number is equal to or less than the threshold ratio by counting the observation point data number of the data and comparing it with the preset average point data number of each cell. A method characterized in that the detected initial lidar data is determined as the fixed lidar data is disclosed.

As an example, in determining the fixed lidar data before removing the fixed lidar data from the initial lidar data, before the step (a01), the server, at least one obtained from the initial lidar data Obtains the traffic volume of a reference frame of and records the reference logging data corresponding to the reference frame when it is determined that the traffic volume is equal to or less than a predetermined value, and in the step (a01), the server is temporally later than the reference frame. A method characterized by determining the fixed lidar data included in the initial lidar data during the observation frame by using the reference logging data during at least one observation frame for the observation frame is disclosed.

As an example, in the step (b), each object age is matched with each of the object IDs, and the higher the value of the object age, the longer the moving path is tracked, and the server, ( i) When the first specific object ID that is not tracked in the (t-1)th frame included in the plurality of consecutive frames is tracked in the tth frame, the first specific object ID of the first specific object ID in the tth frame A specific object age value is generated as an initial value, and (ii) when the second specific object ID tracked in the (t-1)th frame is also tracked in the tth frame, the second specific object ID in the tth frame The second specific object age value of is increased more than the second specific object age value in the (t-1)th frame, and (iii) the (t-1)th frame included in the plurality of consecutive frames. If the third specific object ID tracked in is not tracked in the t th frame, the third specific object age value of the third specific object ID is deleted in the t th frame, (iv) the third specific object age If the value is deleted, the third specific object ID is set not to be used during a frame of a predetermined period from the t th frame, and in the step (c), the server, A method characterized by generating the precise map by mapping each object age value with each movement path is disclosed.

As an example, in step (b), the server stores the movement path of the object with reference to an average value of XY coordinates of points constituting the object.

As an example, a method characterized in that the server corrects the moving path using a polynomial fitting algorithm is disclosed.

As an example, prior to step (a), filtering according to a predetermined criterion is applied to (a11) obtained first lidar data to obtain fixed lidar data corresponding to a fixed object from the first lidar data. In one state, obtaining, by the server, second LiDAR data; and (a12) obtaining, by the server, second residual lidar data with reference to the second lidar data and the fixed lidar data.

According to another aspect of the present invention, a server for generating an accurate map using a fixed LIDAR includes at least one memory for storing instructions; and at least one processor configured to execute the instructions, wherein the processor comprises: (I) a process of obtaining object information from specific lidar data; (II) a process of classifying objects having the same object ID with reference to object IDs among the object information included in a plurality of consecutive frames, and storing a moving path for each object ID; and (III) a process of generating the precise map with reference to each movement path stored for each object ID.

As an example, in the (I) process, the processor classifies at least a portion of the speed, size, and height data of each object included in the object information into one of a vehicle and a pedestrian, and the (II) In the process, the processor stores a moving path for the high-speed moving object ID identified as the vehicle, and in the process (III), the processor stores the moving path along which the high-speed moving object corresponding to the fast-moving object ID has passed. is matched with a plurality of driving cells of the grid map to increase the count of the number of the high-speed moving objects passing through each of the driving cells for each driving cell, and if the count of the driving cells is equal to or greater than a first threshold value, the driving cell is set as a vehicle A server characterized in that the determination is based on the area is provided.

As an example, in the process (III), the processor selects a first specific cell in which the high-speed moving object classified as the vehicle stops from among the traveling cells with reference to the speed of the object in the object information; A count of the number of the high-speed moving objects stopped for a first time in the first specific cell is increased for each first specific cell, and if the count of the first specific cell is equal to or greater than a second threshold, the first specific cell A server characterized in that it is determined as a vehicle stop line area is provided.

As an example, in the (II) process, the processor stores a moving path for the low-speed moving object ID classified as the pedestrian, and in the (III) process, the processor stores the speed of the object among the object information. With reference to, a second specific cell in which the slow-moving object classified as a pedestrian stops is selected from cells other than the driving cell, and the number of slow-moving objects stopped in the second specific cell for a second time The server increases the count for each second specific cell, and determines the second specific cell as a pedestrian crosswalk waiting area when the count of the second specific cell is equal to or greater than a third threshold.

As an example, in the process (III), the processor further determines a pedestrian crosswalk walking area with reference to at least a part of the lane area, the vehicle stop line area, and the pedestrian crosswalk waiting area. A server is provided.

As an example, before the (I) process, if (I01) initial lidar data is obtained, the processor performs filtering according to a predetermined criterion to perform fixed lidar data corresponding to a fixed object among the initial lidar data. the process of classifying; and (I02) a process of obtaining residual lidar data as the specific lidar data by removing the fixed lidar data from the initial lidar data.

As an example, in the process (I01), the processor obtains information on the fixed object existence area where the fixed object exists with reference to the fixed lidar data, and in the process (III), the processor performs the movement A server characterized in that the moving path is divided into at least one lane having a preset width with reference to the path and the fixed object presence area information.

As an example, in the (I01) process, the processor divides the area covered by the LIDAR into a virtual 3D grid area, and the initial layer for each cell in the grid area during at least one observation frame. A server is provided that counts the number of detected data and determines the initial lidar data detected in third specific cells in which the counted number of the cells is equal to or greater than a threshold value as the fixed lidar data.

As an example, in the (I01) process, the processor divides the area covered by the LIDAR into a virtual 3D grid area, and the initial layer for each cell in the grid area during at least one observation frame. In fourth specific cells, the ratio of the difference between the observation point data number and the average point data number is equal to or less than the threshold ratio by counting the observation point data number of the data and comparing it with the preset average point data number of each cell. A server is provided, characterized in that for determining the detected initial lidar data as the fixed lidar data.

As an example, in determining the fixed lidar data before removing the fixed lidar data from the initial lidar data, before the (I01) process, the processor, at least one obtained from the initial lidar data Obtains the traffic volume of a reference frame of and records reference logging data corresponding to the reference frame when it is determined that the traffic volume is less than or equal to a predetermined value, and in the process (I01), the processor is temporally later than the reference frame. A server is provided, characterized in that for determining the fixed lidar data included in the initial lidar data during the observation frame using the reference logging data during at least one observation frame.

As an example, in the (II) process, each object age is matched with each of the object IDs, and the higher the value of the object age, the longer the moving path is tracked, and the processor, ( i) When the first specific object ID that is not tracked in the (t-1)th frame included in the plurality of consecutive frames is tracked in the tth frame, the first specific object ID of the first specific object ID in the tth frame A specific object age value is generated as an initial value, and (ii) when the second specific object ID tracked in the (t-1)th frame is also tracked in the tth frame, the second specific object ID in the tth frame The second specific object age value of is increased more than the second specific object age value in the (t-1)th frame, and (iii) the (t-1)th frame included in the plurality of consecutive frames. If the third specific object ID tracked in is not tracked in the t th frame, the third specific object age value of the third specific object ID is deleted in the t th frame, (iv) the third specific object age If the value is deleted, the third specific object ID is set not to be used for a frame of a predetermined period from the t th frame, and in the process (III), the processor, A server characterized in that for generating the precise map by mapping each object age value with each movement path is provided.

As an example, in the process (II), the processor stores the movement path of the object with reference to an average value of XY coordinates of points constituting the object.

As an example, the server is provided, characterized in that the processor corrects the moving path using a polynomial fitting algorithm.

As an example, before the (I) process, (I11) obtains fixed lidar data corresponding to a fixed object from the first lidar data by applying filtering according to a predetermined criterion to the obtained first lidar data. In one state, the process of obtaining, by the processor, second lidar data; and (I12) a process in which the processor obtains second residual lidar data by referring to the second lidar data and the fixed lidar data.

The present invention has an effect of obtaining object information from LiDAR, classifying objects having the same object ID among object information, storing a moving path for each object ID, and generating a precise map by referring to it.

In addition, the present invention classifies the object into a high-speed moving object and a low-speed moving object, and determines a lane area, a vehicle stop line area, a pedestrian crosswalk waiting area, and a pedestrian crosswalk walking area, so that a precise map can be generated more accurately. there is.

1 is a diagram showing a schematic configuration of a server for generating a precise map using a fixed LIDAR according to an embodiment of the present invention.
2 is a flowchart for explaining an example of performing a determination on a moving object before generating a precision map using a fixed LIDAR according to an embodiment of the present invention.
3 is a flowchart for explaining another example of determining a moving object before generating a precision map using a fixed LIDAR according to an embodiment of the present invention.
4 is an example of a virtual zone corresponding to each predetermined distance based on a lidar in performing determination on a moving object before generating a precision map using a fixed lidar according to an embodiment of the present invention is a drawing representing
5A is a diagram showing an example of a screen before removing fixed lidar data corresponding to a fixed object before generating a precise map using a fixed lidar according to an embodiment of the present invention, and FIG. It is a diagram showing an example of a screen after removing fixed lidar data corresponding to a fixed object before generating a precision map using a fixed lidar according to an embodiment.
6 is a diagram illustrating an example in which a moving object detected using a fixed LIDAR is fitted to a 2D rectangle and a median value is included in the fitted 2D rectangle according to an embodiment of the present invention.
7 is a flowchart for explaining the overall sequence of a method for generating a precision map using a fixed LIDAR according to an embodiment of the present invention.
8 is a diagram illustrating an example of a grid map matching a movement path through which an object has passed according to an embodiment of the present invention.
9A is a diagram showing an example of a movement path of a high-speed moving object according to an embodiment of the present invention, and FIG. 9B is a diagram showing a lane area determined with reference to a high-speed moving object according to an embodiment of the present invention, and a lane area It is a drawing showing an example of
10A is a diagram showing an example of a region where a high-speed moving object stops in a moving path of a high-speed object according to an embodiment of the present invention, and FIG. It is a diagram showing an example of the vehicle stop line area after determining the vehicle stop line area.
11A is a diagram showing an example of a movement path of a slow-moving object according to an embodiment of the present invention, and FIG. 11B is a pedestrian crosswalk waiting area and a pedestrian crossing with reference to a slow-moving object according to an embodiment of the present invention. It is a diagram showing an example of a pedestrian crosswalk waiting area and a pedestrian crosswalk walking area by determining a sidewalk walking area.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The detailed description of the present invention which follows refers to the accompanying drawings which illustrate, by way of illustration, specific embodiments in which the present invention may be practiced. These embodiments are described in sufficient detail to enable one skilled in the art to practice the present invention. It should be understood that the various embodiments of the present invention are different from each other but are not necessarily mutually exclusive. For example, specific shapes, structures, and characteristics described herein may be implemented in one embodiment in another embodiment without departing from the spirit and scope of the invention. Additionally, it should be understood that the location or arrangement of individual components within each disclosed embodiment may be changed without departing from the spirit and scope of the invention. Accordingly, the detailed description set forth below is not to be taken in a limiting sense, and the scope of the present invention, if properly described, is limited only by the appended claims, along with all equivalents as claimed by those claims. Like reference numbers in the drawings indicate the same or similar function throughout the various aspects.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily practice the present invention.

1 is a diagram showing a schematic configuration of a server for generating a precise map using a fixed LIDAR according to an embodiment of the present invention.

As shown in FIG. 1 , the server 100 generating a detailed map using the LIDAR of the present invention may include a memory 110 and a processor 120 .

The memory 110 of the server 100 generating the map may store instructions to be executed by the processor 120, specifically, the instructions cause the server 100 to generate the map to function in a particular way. As code generated for the purpose of doing so, it may be stored in a computer usable or computer readable memory that may be directed to a computer or other programmable data processing equipment. Instructions may perform processes for executing functions described in the specification of the present invention.

In addition, the processor 120 of the server 100 that generates the detailed map includes a hardware configuration such as a micro processing unit (MPU) or a central processing unit (CPU), a cache memory, and a data bus. can do. In addition, it may further include a software configuration of an operating system and an application that performs a specific purpose.

In addition, the server 100 that generates the detailed map may be linked with a database (not shown). Here, the database (not shown) is a flash memory type, a hard disk type, a multimedia card micro type, and a card type memory (for example, SD or XD memory). , RAM (Random Access Memory, RAM), SRAM (Static Random Access Memory), ROM (ReadOnly Memory, ROM), EEPROM (Electrically Erasable Programmable ReadOnly Memory), PROM (Programmable ReadOnly Memory), magnetic memory, magnetic disk, optical disk It may include at least one type of storage medium, but is not limited thereto and may include any medium capable of storing data. In addition, the database (not shown) may be installed separately from the server 100 that generates the detailed map, or may be installed inside the server 100 that generates the detailed map to transmit data or record received data. And, unlike the illustration, it may be implemented by being separated into two or more, which may vary depending on the implementation conditions of the invention.

Meanwhile, in the following, a process of removing a fixed object before generating a precision map will be described with reference to FIGS. 2 to 6 , but this process will not be essential in the present invention. However, for convenience of description, since the processes of FIGS. 2 to 6 have a characteristic that can be performed first as preliminary processes, the main processes of the present invention will be described first before the contents of FIG. 7 and later appear. It should be understood.

2 is a flowchart for explaining an example of performing a determination on a moving object before generating a precision map using a fixed LIDAR according to an embodiment of the present invention.

First, when first lidar data is obtained (S210), filtering (S221) according to a predetermined criterion is performed to remove fixed lidar data corresponding to a fixed object from the first lidar data (S222), and residual lidar data is obtained. data can be obtained.

Thereafter, the moving object may be tracked with reference to the residual lidar data (S230). After the moving object is tracked, it can be stored (S235) and the moving object can be classified (S240).

In addition, when the moving object classification (S240) is completed, a precise map may be generated or an intersection state may be detected (S245).

Here, the first lidar data may be real-time data or already acquired data.

Meanwhile, according to another embodiment of the present invention, determination of a moving object may be performed. This is explained in FIG. 3 as follows.

3 is a flowchart for explaining another example of determining a moving object before generating a precision map using a fixed LIDAR according to an embodiment of the present invention.

That is, FIG. 3 is a flowchart for explaining the overall sequence of a method for determining a moving object using first lidar data and second lidar data according to an embodiment of the present invention.

First, when first lidar data is obtained (S310), first residual lidar data may be obtained by determining fixed lidar data corresponding to a fixed object from the first lidar data (S320). This operation may be performed in advance before obtaining second LiDAR data to be described later. Next, when the second lidar data is obtained (S330), the second residual lidar data may be obtained (S340) with reference to the first residual lidar data and the second lidar data. The second residual lidar data is data from which fixed lidar data has been removed.

Here, when obtaining the second residual lidar data with reference to the second lidar data and the fixed lidar data, (i) a plurality of first point positions corresponding to the fixed lidar data and (ii) the second lidar data It is possible to match the locations of a plurality of second points obtained with each of the data. That is, the second residual lidar data may be obtained by removing specific lidar data detected from a specific point corresponding to a specific position where the first point location and the second point location overlap from the second lidar data.

In other words, by matching the previously acquired point position of the fixed object to the point position of the object acquired in real time, removing the fixed lidar data for the fixed object from the real-time lidar data, the second lidar data in which only the moving object exists. It is possible to acquire the remaining LIDAR data.

Thereafter, the moving object may be tracked with reference to the second residual lidar data (S350). After the moving object is tracked, it can be stored (S355) and the moving object can be classified (S360).

Similarly, when the moving object classification (S360) is completed, a precision map may be generated or an intersection state may be detected (S365).

Here, the first lidar data may be already acquired data, but the second lidar data may correspond to real-time data.

In the above embodiments, an example of a method of determining the fixed lidar data as fixed lidar data before removing the fixed lidar data corresponding to the fixed object is as follows.

In an example of determining fixed lidar data, first, an area covered by lidar is divided into a virtual 3D grid area. Since LIDAR acquires point data from multiple layers, it can be divided into three-dimensional grid areas included in the X-axis, Y-axis, and Z-axis.

In addition, counting the number of first lidar data detected for each cell in the grid area during at least one observation frame, and converting the first lidar data detected in specific cells in which the counted number of cells is equal to or greater than a threshold value to fixed lidar data can be judged by

That is, it is determined that there is a fixed object in a cell where the first lidar data is continuously detected. For example, when first lidar data is detected in 90 observation frames out of 100 observation frames in the first cell, it is determined that a fixed object exists in the first cell. Conversely, when the first lidar data is detected in 80 observation frames out of 100 observation frames in the second cell, it is determined that the fixed object does not exist in the second cell. Here, the threshold may be changed according to the user's setting, but in the above example, it may be assumed that the threshold is set to "more than 90 observation frames".

Here, the threshold may be uniformly set uniformly, but preferably, the threshold may be set differently depending on the distance from the lidar as follows.

For example, in a state in which a corresponding virtual zone is set for each predetermined distance from lidar, the number of specific observation frames in which the first lidar data is detected among the k cells included in the k th zone during the observation frame is greater than or equal to the k th threshold k First lidar data detected in specific cells may be determined as fixed lidar data.

On the other hand, during the observation frame, the number of specific observation frames in which the first lidar data is detected among the (k+1)th cells included in the (k+1)th zone, which is a nearby area farther from lidar than the kth zone, is First lidar data detected in the (k+1)th specific cells that are equal to or greater than the (k+1)th threshold may be determined as fixed lidar data.

Here, the (k+1)th threshold may be set to a value lower than or equal to the kth threshold, the virtual zone includes the first to nth zones, the first zone being the closest to the lidar and the virtual zone being the closest to the lidar. Zone n is the farthest imaginary zone from LiDAR.

This will be explained through FIG. 4 .

4 is an example of a virtual zone corresponding to each predetermined distance based on a lidar in performing determination on a moving object before generating a precision map using a fixed lidar according to an embodiment of the present invention is a drawing representing

In FIG. 4, two zones are illustrated for convenience. There is a kth region 410 and a (k+1)th region 420 . The kth area 410 is an area closer to the LiDAR installation point than the (k+1)th area 420 .

Here, it is assumed that the number of cells included in the kth region 410 is 10 and the number of cells included in the (k+1)th region 420 is 15. Also, it is assumed that the threshold is 90%. That is, it is assumed that it is determined that fixed lidar data is detected from the specific cell only when 90% or more lidar data is detected in the specific cell.

At this time, the number of specific observation frames in which the first lidar data is detected among the 10 cells included in the kth region 410 is 90 (ie, the specific observation frame in which the first lidar data is detected among 100 observation frames). If there are two cells that are 90 or more), the first lidar data detected in the two cells can be determined as fixed lidar data. It may be determined that the first lidar data detected in 8 cells in which the number of specific observation frames is less than 90 is not fixed lidar data.

On the other hand, the number of specific observation frames in which the first lidar data is detected among 15 cells included in the (k+1)th region 420 is 90 (ie, the first lidar data is detected among 100 observation frames). If it is desired to determine cells having 90 or more specific observation frames as cells corresponding to fixed lidar data, there may be more errors than the kth region 410 . This is because, if the data is more likely to be inaccurate as the distance from the lidar increases, the probability of not determining it as fixed lidar data increases even if it is actually fixed lidar data if applied with the same threshold criterion. Therefore, for the (k + 1)th region 420, the threshold is slightly lowered so that if the number of specific observation frames in which the first lidar data is detected is 80 or more, cells corresponding to fixed lidar data can be determined. will be.

On the other hand, in another example of determining fixed lidar data, the area covered by lidar is divided into a virtual 3-dimensional grid area, and the first lidar data for each cell in the grid area during at least one observation frame. The number of observation point data is counted and compared with the preset average number of point data of each cell (for this, information has already been acquired and analyzed in the past frame to secure the average number of point data), the number of observation point data and First lidar data detected in specific cells having a difference ratio of the average point data number equal to or less than a threshold ratio may be determined as fixed lidar data.

More specifically, assuming that the average number of point data in cells judged as fixed lidar data is 10 at a distance corresponding to the kth area 410, point data detected in a specific cell in the kth area 410 In a state where is 9, since it corresponds to 90% compared to the average value of 10, the first lidar data detected in the cell can be determined as fixed lidar data. Here, it is assumed that the critical ratio is set to 90%.

However, assuming that the average number of point data of cells judged as fixed lidar data at a distance corresponding to the (k+1)th area 420 was 5, a specific cell in the (k+1)th area 420 In a state where the point data detected in 4 is 4, (if the threshold ratio corresponds to 90% like the critical ratio of the k th region 410), the 4 points detected in a specific cell in the (k+1)th region 420 Since the point data is only 80% of the average value of 5, it is impossible to determine the first lidar data detected in the cell as fixed lidar data. This is because even though a specific cell may be vulnerable to noise due to its long distance, by applying the same threshold ratio, an irrational decision result that is not judged as fixed LIDAR data occurs. For the cells included in the region 420, the threshold ratio for determining whether or not the LIDAR data is fixed may be lowered to 80%.

Remaining lidar data may be obtained by determining fixed lidar data using at least one of the above examples and removing the fixed lidar data.

Next, let's look at Figures 5a and 5b.

5A is a diagram showing an example of a screen before removing fixed lidar data corresponding to a fixed object before generating a precision map using a fixed lidar according to an embodiment of the present invention.

5B is a diagram showing an example of a screen after removing fixed lidar data corresponding to a fixed object before generating a precision map using a fixed lidar according to an embodiment of the present invention.

As shown in FIG. 5A, before removing the fixed lidar data, a number of yellow points are scattered. In this case, it is not easy to distinguish between fixed lidar data and residual lidar data.

However, in the case of FIG. 5B, since the fixed lidar data is removed using the above-mentioned example, it can be seen that the yellow points are significantly reduced compared to FIG. 5A, and the yellow points shown in FIG. 5B move with higher accuracy. It can be judged as an object.

On the other hand, in determining fixed lidar data, as a pre-process, while monitoring the traffic volume from the first lidar data, frames during which it is determined that the traffic volume is equal to or less than a predetermined value are set as reference frames and correspond to the reference frames. Reference logging data can be recorded.

This is because, when determining fixed lidar data corresponding to a fixed object, accuracy can be further increased only when a frame of a time period in which a moving object is as small as possible is set as a reference frame and measurement is performed. Therefore, by analyzing the traffic volume in the area where the lidar is installed, logging data measured in a time zone with few moving objects (eg, early morning) is recorded as reference logging data, and then using the reference locking data to determine fixed lidar data will be.

Thereafter, in order to more accurately determine a moving object during at least one observation frame temporally following the reference frame, the reference logging data recorded as a pre-process is called and the fixed lidar included in the first lidar data during the observation frame. We can help you determine and remove the data.

Meanwhile, a moving object may be tracked with reference to residual lidar data from which fixed lidar data is removed. Here, in order to track the moving object, a moving path point of the moving object may be determined.

The method for determining the moving path point is to use the MeanX and MeanY values of the moving object, or to fit the moving object to a 2D rectangle and use the median of the fitted 2D rectangle, or to fit the moving object to a 3D rectangle and then fit. The movement route point can be determined using the median value of the 3D rectangle.

In addition, as seen in FIG. 4, a virtual zone corresponding to each predetermined distance from the lidar (the virtual zone includes a first to n-th zone, and the first zone is the closest virtual zone from the lidar) zone, and the n-th zone is a virtual zone farthest from the LIDAR) is set, the server 100 determines the characteristics of the moving object of the first category included in the k-th zone 410 and the second category. The kth reference value for distinguishing the characteristics of the moving object is the first category included in the (k+1)th zone 420 and the second category for distinguishing the characteristics of the moving object from the first category. (k+1) may be set greater than or equal to the reference value. Here, the first category may be a vehicle and the second category may be a pedestrian, but is not limited thereto, and the first category may be a first type of vehicle and the second category may be a second type of vehicle. will be able to assume In addition, the kth reference value and the (k+1)th reference value may be concepts including reference values for at least some factors of, for example, the size factor of an object and the number of points of an object factor. In some cases, each of the two factors It may mean the standard value for In addition, the k th reference value and the (k+1) th reference value may be median values for each of the factors, but will not be limited thereto.

FIG. 6 is a diagram illustrating an example in which a moving object is fitted to a 2D rectangle and a median value is included in the fitted 2D rectangle before generating a precision map using a fixed LIDAR according to an embodiment of the present invention.

As shown in Fig. 6, the moving object was fitted to a 2D rectangle. The center point of a 2D rectangle is the red point, but the median of a 2D rectangle is the yellow point. The moving path point of the moving object can be determined using the median value, which is the yellow dot.

Meanwhile, the previously mentioned moving objects may be classified as vehicles or pedestrians. Here, with reference to the feature data set of the moving object, the median of each feature of the moving object may be obtained, and the moving object may be classified using the median value.

ve Bayes Classification), 딥러닝(Deep Learning), 머신러닝(Machine Learning) 중 적어도 하나를 이용하여 획득될 수 있으며, 이에 한정하는 것은 아니다.Here, the characteristics may include at least some of the size of the moving object, the speed of the moving object, the distance from the lidar, and the number of point data of the first lidar data. In addition, the median value is a support vector machine (Support Vector Machine), a neural network (Neural Network), naive Bayes classification (Na

ve Bayes Classification), deep learning (Deep Learning), and can be obtained using at least one of machine learning (Machine Learning), but is not limited thereto.

As described above, when the classification of the moving object is completed, a precision map may be generated using this. Hereinafter, a method for generating a precision map will be described in detail.

For reference, the part described above is not an essential part, and can be implemented in various other ways.

7 is a flowchart for explaining the overall sequence of a method for generating a precision map using a fixed LIDAR according to an embodiment of the present invention.

As shown in FIG. 7 , object information may be obtained first (S710).

For reference, the object information includes at least a part of object speed, object size, and object height data. In addition, the object may be classified as either a vehicle or a pedestrian by referring to the object information. That is, a vehicle may be referred to as a high-speed moving object, and a pedestrian may be referred to as a low-speed moving object.

Thereafter, with reference to object IDs among object information included in a plurality of consecutive frames, objects having the same object ID may be classified, and moving paths may be stored for each object ID (S720).

Next, with reference to each movement path stored for each object ID, a detailed map may be generated (S730).

Here, an ID number is assigned to the object ID to manage object tracking for each frame, and each object ID is matched with each object age.

The object age counts whether an object is continuously tracked as a plurality of consecutive frames progress, and the higher the object age value, the longer the tracking time.

Here, an example of increasing the object age value is as follows.

First, when a first specific object ID that is not tracked in the (t-1)th frame included in a plurality of consecutive frames is tracked in the tth frame, the first specific object of the first specific object ID in the tth frame The age value can be created as an initial value.

For example, if "ID of high-speed moving object A" is not tracked until the 5th frame and "ID of high-speed moving object A" is first tracked in the 6th frame, "ID of high-speed moving object A" is tracked in the 6th frame. The object age value of can be created as 1. Here, 1 is the initial value.

Second, when the second specific object ID tracked in the (t-1)th frame is also tracked in the tth frame, the second specific object age value of the second specific object ID in the tth frame is It may be increased more than the second specific object age value in the frame.

For example, if the object age value of “ID of slow-moving object B” is 8 in the 8th frame, if “ID of slow-moving object B” is also tracked in the 9th frame, “ID of slow-moving object B” in the 9th frame The object age value of " can be 9. Of course, it may be a number larger than 9, but for convenience, it will be 9 if it is assumed that the object age value is increased by 1 for each successive frame.

Meanwhile, the object age value may be deleted in some cases.

That is, when the third specific object ID tracked in the (t-1)th frame included in a plurality of consecutive frames is not tracked in the tth frame, the third specific object age of the third specific object ID in the tth frame Values can be deleted. Of course, if the third specific object ID is not tracked continuously for x frames without deletion immediately, the corresponding third specific object age value may be deleted.

Here, when the third specific object age value is deleted, the third specific object ID may be set not to be used during a frame of a predetermined period from the deleted frame.

This is to prevent an error that can be determined as the same ID as the deleted ID if the same ID as the deleted ID is used again in a frame recently deleted.

In this way, each object age value included in each object ID is mapped with each movement path to generate a precision map.

In addition, the movement path of the object may be stored with reference to the average value of XY coordinates of points constituting the object. Here, as a method of correcting the movement path, a polynomial fitting algorithm is used, but is not limited thereto.

Hereinafter, generating a precise map by referring to a movement path for each object ID will be described in more detail.

First, in order to generate a precise map using the movement path, it is necessary to match the movement path along which the object moved to the grid map. The grid map configures an area within a certain radius from the location where the lidar is installed as a grid, as shown in FIG. 8 .

8 is a diagram illustrating an example of a grid map matching a movement path through which an object has passed according to an embodiment of the present invention. For reference, although FIG. 8 shows each grid in a large size for convenience, it may actually be configured with a smaller size grid.

A method of generating the lane area 810, vehicle stop line area 820, pedestrian crosswalk waiting area (not shown), and pedestrian crosswalk walking area 830 included in FIG. 8 is as follows.

First, an example of determining a lane area 810 and a vehicle stop line area 820 with reference to a high-speed moving object classified as a vehicle will be described.

9A is a diagram showing an example of a moving path of a high-speed moving object according to an embodiment of the present invention, and FIG. 9B is a diagram showing an example of a lane area determined with reference to a high-speed moving object according to an embodiment of the present invention. it is a drawing

Specifically, a moving path for a high-speed moving object ID classified as a vehicle may be stored, and a moving path passed by a high-speed moving object corresponding to the high-speed moving object ID may be matched with a plurality of driving cells of the grid map. Thereafter, the number of high-speed moving objects passing through each driving cell is counted, and the number is increased for each driving cell. If the count of the driving cell is equal to or greater than a first threshold, the corresponding driving cell is determined to be a lane area.

That is, again referring to FIG. 9A, among the objects of FIG. 9A, the movement path of the high-speed moving object is matched to the driving cell, and, for example, when the first threshold value is 90%, the count of the high-speed moving object is 90% or more. The cell is determined as a vehicle area.

For convenience, in FIG. 9A , driving cells that are equal to or greater than the first threshold are indicated by orange boxes 910 , 920 , and 930 . This is the car area.

A solid line 940 in FIG. 9B obtained from the flow of a high-speed moving object represents a “lane”, and the “lane” refers to a road divided so that vehicles pass through a predetermined part in one line. Meanwhile, when a lane area is created as shown in FIG. 9B , a lane area may be additionally created. Here, the "lane" is a line drawn in a certain direction to help or limit the driving of the vehicle on both sides of the "lane". Since the lane area can be generated identically when the lane area is created, a detailed description thereof will be omitted.

10A is a diagram illustrating an example of a region where a high-speed moving object stops in a moving path of the high-speed object according to an embodiment of the present invention.

FIG. 10B is a diagram illustrating an example of a vehicle stop line area after determining a vehicle stop line area with reference to a high-speed moving object according to an embodiment of the present invention.

That is, by referring to the speed of the object among the object information, a first specific cell in which a high-speed moving object classified as a vehicle stops is selected from driving cells, and the number of high-speed moving objects stopped in the first specific cell for the first time The count is increased for each first specific cell, and if the count of the first specific cell is equal to or greater than the second threshold, the first specific cell is determined to be a vehicle stop line area.

The vehicle stop line area is determined in the same way as the previously described lane area is determined. Referring to FIGS. 10A and 10B , a purple box indicates a vehicle stop line area.

The lane area and the lane area have already been determined, and the number of high-speed moving objects stopping within a set time (eg, within 5 minutes) is counted in the driving cell, and the count is also increased for each cell, for example, the second threshold is 80% In the above case, it is determined that a specific cell among driving cells in which the count of the high-speed moving object stopped is 80% or more as the vehicle stop line area.

Matching the parts determined as the lane area 940, the lane area 950, and the vehicle stop line area 1010 is as shown in FIG. 10B.

11A is a diagram illustrating an example of a movement path of a slow-moving object according to an embodiment of the present invention.

11B is a diagram illustrating an example of a pedestrian crosswalk waiting area and a pedestrian crosswalk walking area by determining a pedestrian crosswalk waiting area and a pedestrian crosswalk walking area with reference to a slow-moving object according to an embodiment of the present invention. .

That is, by referring to the speed of the object among object information, a second specific cell in which a low-speed moving object classified as a pedestrian stops is selected from cells other than the traveling cell, and the low-speed movement stopped in the second specific cell for a second time The count of the number of objects is increased for each second specific cell, and if the count of the second specific cell is greater than or equal to a third threshold, the second specific cell is determined as a pedestrian crosswalk waiting area.

Referring to FIGS. 11A and 11B , in the same way as determining the lane area 940 and the vehicle stop line area 1010 in the previous section, the moving path of the low-speed object is determined this time, and the low-speed object moves at a predetermined time (e.g., 3 minutes), the count is also increased for each cell, and for example, if the third threshold is 50% or more, a specific cell in which the stopped count is 50% or more among cells in which the slow-moving object is not a traveling cell is designated as a pedestrian. It is determined by the crosswalk waiting area 1110.

In addition, with reference to at least a part of the aforementioned lane area 940, lane area 950, vehicle stop line area 1010, and pedestrian crosswalk waiting area 1110, the pedestrian crosswalk walking area 1120 Additional judgments may be made.

Meanwhile, the above-mentioned object information may further include the following information.

These are Max X (maximum value of the X coordinate of the point constituting the object based on the lidar installation point), Max Y (maximum value of the Y coordinate of the point constituting the object based on the lidar installation point), Min X (minimum value of the X coordinate of the point constituting the object based on the lidar installation point), Min Y (minimum value of the Y coordinate of the point constituting the object based on the lidar installation point), MinMax Box Size (minimum value of the X coordinate , the size of the box created using the minimum value of the Y coordinate, the maximum value of the X coordinate, and the maximum value of the Y coordinate), Relative Vx (relative speed of the X axis of the object relative to the lidar), Relative Vy (based on the lidar) Y-axis relative velocity of the object), Absolute Vx (X-axis absolute velocity of the object based on the lidar), Absolute Vy (Y-axis absolute velocity of the object based on the lidar), etc. You can create precise maps. The above object information is not limited to this, and similar things may be assumed.

In addition, the embodiments according to the present invention described above may be implemented in the form of program instructions that can be executed through various computer components and recorded in a computer readable recording medium. The computer readable recording medium may include program instructions, data files, data structures, etc. alone or in combination. Program instructions recorded on the computer-readable recording medium may be specially designed and configured for the present invention, or may be known and usable to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical recording media such as CD-ROMs and DVDs, and magnetooptical media such as floptical disks. , and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter or the like as well as machine language codes such as those produced by a compiler. The hardware device may be configured to act as one or more software modules to perform processing according to the present invention and vice versa.

In the above, the present invention has been described by specific details such as specific components and limited embodiments and drawings, but these are provided to help a more general understanding of the present invention, and the present invention is not limited to the above embodiments. , Those skilled in the art to which the present invention pertains may seek various modifications and variations from these descriptions.

Therefore, the spirit of the present invention should not be limited to the above-described embodiments, and not only the claims described later, but also all modifications equivalent or equivalent to these claims belong to the scope of the spirit of the present invention. will do it

Claims (24)

In the method of generating a precise map using a fixed lidar,
(a) obtaining, by the server, object information from specific lidar data;
(b) classifying, by the server, objects having the same object ID with reference to object IDs among the object information included in a plurality of consecutive frames, and storing a moving path for each object ID; and
(c) generating, by the server, the precise map with reference to each moving path stored for each object ID;
How to include. According to claim 1,
In step (a),
The server distinguishes between a vehicle and a pedestrian by referring to at least a portion of the speed, size, and height data of each object included in the object information,
In step (b),
The server stores a movement path for the high-speed moving object ID classified as the vehicle,
In step (c),
The server matches a movement path passed by a high-speed moving object corresponding to the high-speed object ID with a plurality of driving cells of a grid map, and increases a count of the number of the high-speed moving objects that have passed each of the driving cells for each driving cell. and if the count of the driving cells is equal to or greater than a first threshold, determining the driving cells as a lane area. According to claim 2,
In step (c),
The server selects, among the driving cells, a first specific cell in which the high-speed moving object classified as a vehicle stops with reference to the speed of the object in the object information, and in the first specific cell for a first time A count of the number of stopped high-speed moving objects is increased for each first specific cell, and if the count of the first specific cell is equal to or greater than a second threshold, the first specific cell is determined as a vehicle stop line area. method. According to claim 3,
In step (b),
The server stores a moving path for the slow-moving object ID classified as the pedestrian,
In step (c),
The server selects a second specific cell in which the slow-moving object classified as a pedestrian stops, among cells other than the traveling cell, with reference to the speed of the object among the object information, and selects a second specific cell in the second specific cell The count of the number of slow moving objects stopped for 2 hours is increased for each second specific cell, and if the count of the second specific cell is equal to or greater than a third threshold, the second specific cell is determined as a pedestrian crosswalk waiting area A method characterized by doing. According to claim 4,
In step (c),
The method of claim 1 , wherein the server additionally determines a pedestrian crosswalk walking area by referring to at least a part of the lane area, the vehicle stop line area, and the pedestrian crosswalk waiting area. According to claim 1,
Before step (a),
(a01) classifying, by the server, fixed lidar data corresponding to a fixed object from among the initial lidar data by performing filtering according to a predetermined criterion when initial lidar data is acquired; and
(a02) obtaining residual lidar data as the specific lidar data by removing the fixed lidar data from the initial lidar data;
How to include more. According to claim 6,
In step (a01),
The server divides the area covered by the lidar into a virtual 3D grid area, counts the number of times the initial lidar data is detected for each cell in the grid area during at least one observation frame, And determining the initial lidar data detected in third specific cells in which the counted number of the cells is equal to or greater than a threshold value as the fixed lidar data. According to claim 6,
In step (a01),
The server divides the area covered by the lidar into a virtual three-dimensional grid area, and counts the number of observation point data of the initial lidar data for each cell in the grid area during at least one observation frame, Compared with the preset average number of point data of each cell, the initial lidar data detected in the fourth specific cells in which the ratio of the difference between the number of observation point data and the average number of point data is equal to or less than a threshold ratio is fixed. A method characterized by judging by LiDAR data. According to claim 6,
In determining the fixed lidar data before removing the fixed lidar data from the initial lidar data,
Before the step (a01),
The server obtains the traffic volume of at least one reference frame obtained from the initial lidar data, and records reference logging data corresponding to the reference frame when it is determined that the traffic volume is equal to or less than a predetermined value,
In step (a01),
Characterized in that the server determines the fixed lidar data included in the initial lidar data during the observation frame using the reference logging data for at least one observation frame temporally following the reference frame method. According to claim 1,
In step (b),
Each object age is matched with each of the object IDs, and the higher the value of the object age, the longer the moving path is tracked.
The server, (i) when a first specific object ID that is not tracked in the (t-1)th frame included in the plurality of consecutive frames is tracked in the tth frame, the first specific object ID in the tth frame A first specific object age value of the object ID is generated as an initial value, and (ii) when the second specific object ID tracked in the (t-1)th frame is also tracked in the tth frame, the The second specific object age value of the second specific object ID is increased more than the second specific object age value in the (t-1)th frame, and (iii) the th ( t-1) when the third specific object ID tracked in the frame is not tracked in the t th frame, deleting the third specific object age value of the third specific object ID in the t th frame; (iv) the When the third specific object age value is deleted, setting the third specific object ID not to be used during a frame of a predetermined period from the t th frame;
In step (c),
The method of claim 1 , wherein the server generates the precise map by mapping each object age value included in each object ID with each movement path. According to claim 1,
In step (b),
The server,
and storing the movement path of the object with reference to an average value of XY coordinates of points constituting the object. According to claim 11,
The server,
A method characterized in that for correcting the movement path using a polynomial regression (Polynomial Fitting) algorithm. In a server that generates a precise map using a fixed lidar,
at least one memory for storing instructions; and
at least one processor configured to execute the instructions;
The processor may include (I) a process of obtaining object information from specific lidar data; (II) a process of classifying objects having the same object ID with reference to object IDs among the object information included in a plurality of consecutive frames, and storing a moving path for each object ID; and (III) a process of generating the precise map with reference to each movement path stored for each object ID. According to claim 13,
In the above (I) process,
The processor distinguishes between a vehicle and a pedestrian by referring to at least a portion of speed, size, and height data of each object included in the object information,
In the above (II) process,
The processor stores a moving path for the high-speed moving object ID identified as the vehicle;
In the above (III) process,
The processor matches a movement path along which the high-speed moving object corresponding to the high-speed object ID has passed with a plurality of driving cells of a grid map, and increases a count of the number of the fast-moving objects that have passed each of the driving cells for each driving cell. and if the count of the driving cells is greater than or equal to a first threshold, determining the driving cell as a lane area. According to claim 14,
In the above (III) process,
The processor selects, among the driving cells, a first specific cell in which the high-speed moving object classified as the vehicle stops with reference to the speed of the object in the object information, and in the first specific cell for a first time A count of the number of stopped high-speed moving objects is increased for each first specific cell, and if the count of the first specific cell is equal to or greater than a second threshold, the first specific cell is determined as a vehicle stop line area. server. According to claim 15,
In the above (II) process,
The processor stores a movement path for the slow-moving object ID classified as the pedestrian,
In the above (III) process,
The processor selects a second specific cell in which the slow-moving object classified as a pedestrian stops from among cells other than the traveling cell with reference to the speed of the object among the object information, and selects a second specific cell from the second specific cell. The count of the number of slow moving objects stopped for 2 hours is increased for each second specific cell, and if the count of the second specific cell is equal to or greater than a third threshold, the second specific cell is determined as a pedestrian crosswalk waiting area A server characterized by doing. According to claim 16,
In the above (III) process,
The server, characterized in that the processor further determines a pedestrian crosswalk walking area by referring to at least a part of the lane area, the vehicle stop line area, and the pedestrian crosswalk waiting area. According to claim 13,
Prior to the (I) process,
(I01) a process of classifying, by the processor, fixed lidar data corresponding to a fixed object from among the initial lidar data by performing filtering according to a predetermined criterion when initial lidar data is acquired; and
(I02) a process of removing the fixed lidar data from the initial lidar data to obtain residual lidar data as the specific lidar data;
A server that does more. According to claim 18,
In the above (I01) process,
The processor divides the area covered by the lidar into a virtual 3D grid area, counts the number of times the initial lidar data is detected for each cell in the grid area during at least one observation frame, The server, characterized in that for determining the initial lidar data detected in third specific cells in which the counted number of the cells is equal to or greater than a threshold value as the fixed lidar data. According to claim 18,
In the above (I01) process,
The processor divides the area covered by the lidar into a virtual three-dimensional grid area, counts the number of observation point data of the initial lidar data for each cell in the grid area during at least one observation frame, Compared with the preset average number of point data of each cell, the initial lidar data detected in the fourth specific cells in which the ratio of the difference between the number of observation point data and the average number of point data is equal to or less than a threshold ratio is fixed. A server characterized in that it is judged by lidar data. According to claim 18,
In determining the fixed lidar data before removing the fixed lidar data from the initial lidar data,
Before the (I01) process,
The processor obtains the traffic volume of at least one reference frame obtained from the initial lidar data, and records reference logging data corresponding to the reference frame when it is determined that the traffic volume is equal to or less than a predetermined value,
In the above (I01) process,
Characterized in that the processor determines the fixed lidar data included in the initial lidar data during the observation frame using the reference logging data for at least one observation frame temporally following the reference frame server. According to claim 13,
In the above (II) process,
Each object age is matched with each of the object IDs, and the higher the value of the object age, the longer the moving path is tracked.
The processor may (i) when a first specific object ID that is not tracked in the (t-1)th frame included in the plurality of consecutive frames is tracked in the tth frame, the first specific object ID in the tth frame A first specific object age value of the object ID is generated as an initial value, and (ii) when the second specific object ID tracked in the (t-1)th frame is also tracked in the tth frame, the The second specific object age value of the second specific object ID is increased more than the second specific object age value in the (t-1)th frame, and (iii) the th ( t-1) when the third specific object ID tracked in the frame is not tracked in the t th frame, deleting the third specific object age value of the third specific object ID in the t th frame; (iv) the When the third specific object age value is deleted, setting the third specific object ID not to be used during a frame of a predetermined period from the t th frame;
In the above (III) process,
wherein the processor generates the precise map by mapping each of the object age values included in each of the object IDs with each of the moving paths. According to claim 13,
In the above (II) process,
the processor,
and storing the movement path of the object with reference to an average value of XY coordinates of points constituting the object. According to claim 23,
the processor,
The server, characterized in that for correcting the movement path using a polynomial fitting algorithm.

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