Method for calculating effective detection distance of lidar along entire tunnel route, device, and medium

Abstract

The present invention relates to a method for calculating the effective detection distance of a lidar along the entire tunnel route, a device, and a medium. The method comprises: performing tunnel modeling on the basis of as-built drawings for post-construction survey of a track; and calculating the effective detection distance of a radar at each point on the track on the basis of an established tunnel model, wherein the calculation comprises: setting a certain point as a starting point, and the center of an object in a tunnel cross-section at a first set distance from the point as an end point; calculating, within the distance from the starting point to the end point, included angles between the lines connecting all tunnel points to the starting point and the line connecting the starting point to the end point; if there is an included angle smaller than the radar resolution, setting a point obtained by moving the end point back toward the starting point by a set distance as a new end point; and then repeating the calculation process until within the distance from the starting point to the end point, the included angles between the lines connecting all the tunnel points to the starting point and the line connecting the starting point to the end point are all greater than the radar resolution, and in this case, the distance from the end point to the starting point is the effective detection distance at the starting point. Compared with the prior art, the present invention has the advantages of high calculation precision.

Description

Methods, equipment, and media for calculating the effective detection distance of lidar along the entire tunnel route Technical Field

[0001] This invention relates to rail transit signaling systems, and in particular to a method, equipment, and medium for calculating the effective detection distance of a lidar system along the entire tunnel route. Background Technology

[0002] With the continuous development of automatic driving technology in rail transit, the use of lidar for forward active detection of trains has gradually become a powerful means to ensure driving safety. Tunnels are a very common scenario in rail transit. Due to the physical structure of tunnels and the obstruction of radar's field of view by installation facilities, the effective detection range of lidar changes constantly as the train moves. This change leads to abrupt and unstable detection results, which is very detrimental to the effectiveness of lidar-based train safety protection. Therefore, it is essential to determine the effective detection range of the radar at each location within the tunnel. In addition, different radars have different detection performance, and different installation locations also have different detection performance. Furthermore, the structural characteristics of the object being detected and the obstruction caused by the tunnel structure also result in significant differences in detection range. Therefore, being able to deduce the effective detection range of the radar based on radar characteristics, installation location, tunnel structure, and the structural characteristics of the object being detected, and thereby limit the output of the radar detection results to ensure the effectiveness and safety of the output results, is of great value.

[0003] Research on tunnel modeling currently includes: reading and modeling from 2D CAD drawings of tunnel construction plans (e.g., patent CN117934751A), and 3D modeling considering tunnel walls and internal pipelines (e.g., patent CN114399600B). Calculations of tunnel visual distances include: estimation calculations on a 2D plane (e.g., patent CN117688642A), and actual detection using a rangefinder (e.g., patent CN117934751A). However, there is still no method for determining the effective detection range of lidar within tunnels, and even less so a method that comprehensively considers the radar installation location, the actual detection performance of the lidar, and the obstruction caused by the actual structure within the tunnel, enabling automated calculation of the effective detection range of the entire tunnel. Summary of the Invention

[0004] The purpose of this invention is to overcome the defects of the prior art by providing a method, equipment and medium for calculating the effective detection distance of a full-route lidar in a tunnel.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] According to a first aspect of the present invention, a method for calculating the effective detection range of a lidar system along the entire tunnel route is provided. This method involves modeling the tunnel based on drawings obtained after track construction and calculating the effective detection range of the lidar at each point on the track based on the established tunnel model. The calculation process includes:

[0007] First, set a point as the starting point, and the center of the object in the tunnel profile at a first set distance from that point as the ending point. Calculate the angles between the lines connecting all tunnel points to the starting point and the lines connecting the starting and ending points within the range from the starting point to the ending point. If any angle is smaller than the radar resolution, it means that the ending point is outside the radar's effective detection range at that location. Then, set a new ending point by moving the ending point back a set distance from the starting point. Repeat the calculation process until the angles between all tunnel points to the starting point and the lines connecting the starting point and the lines connecting the starting and ending points within the range from the starting point to the ending point are all greater than the radar resolution. At this point, the distance between the ending point and the starting point is the effective detection range at that starting point.

[0008] As a preferred technical solution, the tunnel modeling process specifically includes:

[0009] Step S1: Determine the size of the object to be detected and calculate the maximum linear detection performance D of the lidar when detecting the object. max ;

[0010] Step S2: Organize the mileage-elevation data and perform elevation calculations;

[0011] Step S3: Organize the mileage-plane data and calculate the horizontal coordinates;

[0012] Step S4: Correct the mileage-plane information data based on the length of the chain;

[0013] Step S5: Combine the vertical height and two-dimensional coordinates of each point on the line to obtain the three-dimensional coordinates of each point;

[0014] Step S6: Perform tunnel profile modeling;

[0015] Step S7: Construct the overall tunnel model.

[0016] As a preferred technical solution, in step S1, the maximum detection performance D of the straight line max It equals the minimum value among the maximum detection distance calculated by horizontal resolution, the maximum detection distance calculated by vertical resolution, and the nominal farthest straight-line detection distance.

[0017] As a preferred technical solution, the D max The specific calculations are as follows:

[0018] Where L O W OD represents the length and width dimensions of the object to be detected facing the radar. m This represents the maximum straight-line detection range of the lidar. The horizontal and vertical resolutions of the lidar.

[0019] As a preferred technical solution, in step S2, the track construction drawings are organized into a data table, and the longitudinal profile is organized into mileage-elevation information data, thereby calculating the vertical height of each point along the route.

[0020] As a preferred technical solution, the mileage-elevation information data includes the starting mileage value of each straight slope, the ending mileage value of the straight slope, the ending mileage value of the variable slope, the slope of the straight slope, the radius of curvature of the variable slope, and the direction of the variable slope.

[0021] As a preferred technical solution, in step S3, the track construction plan is organized into mileage-plan information data, thereby calculating the two-dimensional planar coordinates of each point along the route.

[0022] As a preferred technical solution, the mileage-plane information data includes the starting mileage value of each straight segment, the ending mileage value of the straight segment, the ending mileage value of the curve, the radius of curvature of the curve, the rotation angle of the curve, the length of the round section, the length of the round section, and the rotation direction of the curve.

[0023] As a preferred technical solution, in step S4, if there is a long chain of k meters at mileage A, then k meters are added to the mileage information after mileage A in all mileage-elevation information, and the corresponding elevation value is recalculated; at the same time, k meters are added to the mileage information after mileage A in all mileage-plane information, and the two-dimensional plane coordinates are recalculated.

[0024] As a preferred technical solution, in step S6, the tunnel profile modeling specifically involves:

[0025] Based on the tunnel cross-section diagram of the modeled tunnel, determine the relative positional relationship between the tunnel center, the two tracks, the train center, and the radar installation position. Discretize the tunnel cross-section at set angles, and add the radar position point and the center point of the detected object to form the tunnel cross-section model.

[0026] As a preferred technical solution, in step S7, the discrete points of the tunnel profile at each second predetermined distance along the mileage are summarized to form a point set, which is the overall tunnel model.

[0027] As a preferred technical solution, the second set distance is 1m.

[0028] As a preferred technical solution, the first set distance is the maximum detection performance D in a straight line. max .

[0029] According to a second aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described thereon.

[0030] According to a third aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described thereon.

[0031] Compared with the prior art, the present invention has the following advantages:

[0032] 1) This invention addresses the problem of difficulty in measuring the effective detection range of radar under different performances, installation locations, and varying tunnel structures. It uses mathematical modeling of the relative positions of the tunnel structure, track construction drawings, tunnel profile, radar, train, and track, as well as the radar's linear detection performance. It also considers the radar's detection performance, installation location, structural characteristics of the detected object, and field-of-view obstruction caused by facilities installed in the tunnel. The calculated maximum visible distance is closer to the actual radar detection.

[0033] 2) This invention limits the safe detection range of the detection system by calculating the maximum visible distance, making the output radar detection data safer and more reliable;

[0034] 3) This invention is based on fixed-measurement tunnel mapping and modeling, taking into account transition curves and long and short chains. The model has high accuracy, and the error with the drawings is on the order of 10^-7. Attached Figure Description

[0035] Figure 1 is a flowchart of the calculation of the full-line safe detection distance of lidar based on the tunnel construction map of the present invention;

[0036] Figure 2 is a side view of the object detected by the vehicle-mounted lidar of the present invention.

[0037] Figure 3 is a top view of the object detected by the vehicle-mounted lidar of the present invention.

[0038] Figure 4 is a cross-sectional model diagram of the shield tunnel of the present invention;

[0039] Figure 5 is a model diagram of the cross-sectional view of the square tunnel excavated according to the present invention;

[0040] Figure 6 is a modeling effect diagram of the tunnel according to the present invention;

[0041] Figure 7 is a flowchart of the calculation of the maximum effective detection distance of the present invention;

[0042] Figure 8 is a schematic diagram of the effective detection distance calculation of the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0044] This invention constructs a track model based on the drawings obtained after track construction and takes into account radar characteristics, installation location, actual tunnel structure, and structural characteristics of the object to be inspected, and calculates the effective detection range of the lidar for the entire route.

[0045] As shown in Figure 1, the specific processing steps of this invention are as follows:

[0046] Step S001: Determine the linear detection performance of the lidar: Based on the length and width dimensions (L) of the object to be detected facing the lidar. O W O ), the furthest straight-line detection range of lidar (D m The horizontal and vertical resolution of the lidar Calculations were performed on the linear detection performance (D) of the lidar when detecting this object. max The maximum detection range calculated using horizontal resolution, the maximum detection range calculated using vertical resolution, and the nominal maximum straight-line detection range are equal to the minimum value among:

[0047] Step S002, Elevation Calculation: Organize the track construction drawings into a data table. Organize the longitudinal profile drawings into mileage-slope information data. Each piece of information includes the following in the vertical direction: the starting mileage of each straight slope segment, the ending mileage of the straight slope segment (which is also the starting point of the gradient change segment), the ending mileage of the gradient change segment, the slope of the straight slope (positive for uphill, negative for downhill), the radius of curvature of the gradient change segment, and the direction of the gradient change (clockwise / counterclockwise). From this, calculate the vertical elevation (relative to the starting point of the line) of each point along the mileage.

[0048] Step S003, Horizontal Coordinate Calculation: By organizing the plan view into mileage-planar information data, each piece of information is contained on the plane: the starting mileage value of each straight segment, the ending mileage value of the straight segment (the starting point of the curve), the ending mileage value of the curve, the radius of curvature of the curve, the curve rotation angle, the length of the roundabout segment, the length of the roundabout segment, and the direction of curve rotation (counterclockwise rotation around the Z-axis is positive, clockwise is negative). From this, the two-dimensional coordinates of each point on the route (relative to the starting point of the route) are calculated.

[0049] Step S004, Long and Short Chain Correction: For areas with long and short chains on the plan drawing, the mileage-plan information data is corrected according to the value of the long and short chains. For example, if there is a long chain of k meters at mileage A, then k meters need to be added to the mileage information after mileage A in all mileage-elevation information, and the corresponding elevation value is recalculated. k meters need to be added to the mileage information after mileage A in all mileage-plan information, and the two-dimensional planar coordinates are recalculated.

[0050] Step S005: Obtain the three-dimensional coordinates: By combining the vertical height and two-dimensional coordinates of each point, the three-dimensional coordinates of each point on the route can be obtained;

[0051] Step S006, Tunnel profile modeling: Based on the tunnel profile diagram of the modeled tunnel, determine the relative positional relationship between the tunnel center, the two tracks, the train center, and the radar installation position. Discretize the tunnel profile at certain angles, and add the radar position point and the center point of the detected object to form the tunnel profile model.

[0052] Step S007, Tunnel Modeling: According to the mileage value from the starting point to the end point, at every mileage value, calculate the direction vector and three-dimensional coordinates of the track at this point, and then calculate the tunnel profile model perpendicular to the direction vector at that point. All the points constitute the point set model of the tunnel. Here, the profile models of different tunnel sections can be defined according to the tunnel's engineering clearance drawings, taking into account the facilities and structures inside the tunnel that have a greater impact on the field of vision, such as emergency platforms, catenary, third rails, etc.

[0053] Step S008, Effective Detection Range Calculation: When calculating the effective detection range of the radar at each point on the track, first set that point as the starting point, and then set a certain distance away from that point (the maximum detection performance D calculated in step S001). max Taking the center of the object in the tunnel profile at point S008 as the endpoint, calculate the angles between the lines connecting all tunnel points to the starting point and the lines connecting the starting and ending points within the range from the starting point to the endpoint. If any angle is smaller than the radar resolution, it indicates that for the radar at that starting point, the object at the endpoint is obstructed by the tunnel wall, meaning that the endpoint is outside the radar's effective detection range at that point. Then, set a point a certain distance away from the starting point as the new endpoint, and repeat step S008 until the angles between all tunnel points to the starting point and the lines connecting the starting and ending points within the range from the starting point to the endpoint are greater than the radar resolution. The distance between the endpoint and the starting point at this point is the effective detection range at the starting point.

[0054] Specific Implementation

[0055] Figure 1 shows the steps for calculating the safe detection range of a lidar system along the entire tunnel based on the tunnel construction map. First, the size of the object to be detected is determined, and the maximum linear detection performance of the lidar is calculated when detecting the object. Then, the mileage-elevation data is organized, and the elevation is calculated. The mileage-horizontal data is organized, and the horizontal coordinate is calculated. The coordinates are then merged into three-dimensional coordinates. Errors caused by long and short chains on the route are corrected. Mathematical modeling of the tunnel profile is performed. Mathematical modeling of typical tunnel structures and tunnel walls is performed. Finally, the maximum effective detection range is calculated.

[0056] Figures 2 and 3 are the side and top views of an object detected by a vehicle-mounted lidar, respectively. If the size of the object directly facing the lidar is L... o ×W o The horizontal and vertical resolution of the lidar is To ensure the object can be detected (the object must have at least 2×2 points in the laser point cloud), what is the maximum straight-line detection range of the laser radar based on horizontal resolution? The maximum linear detection range of the object based on vertical resolution lidar. The nominal detection range of a lidar is the value D specified in its technical manual. m The minimum value among the three is taken as the maximum straight-line detection range of the lidar for the object.

[0057] Figures 4 and 5 are cross-sectional model diagrams of tunnels with different structures. Figure 4 shows a shield tunnel, and Figure 5 shows a cut-and-cover rectangular tunnel. Figure 4 also considers an emergency platform in the tunnel. Therefore, compared to the circular tunnel cross-section, the lower left corner is divided according to the size of the manual maintenance platform, and the tunnel cross-section is discretized (discrete into 360 points in the figure). Then, the projection points of the radar center and the object center on this plane are added. It should be noted that two tunnel cross-sectional models were designed here based on the tunnel structure, and the appropriate model was selected according to the actual tunnel scenario. Other types of tunnels can also be modeled and discretized using this method.

[0058] Figure 6 is a tunnel modeling effect diagram (one section). It is formed by summing up the discrete points of the tunnel profile at 1m intervals along the mileage to form a point set, which is the overall tunnel model.

[0059] Figure 7 is a flowchart for calculating the maximum effective detection range, and Figure 8 is a schematic diagram for calculating the effective detection range. For each point on the entire line, the corresponding maximum effective radar detection range is calculated: the point is determined as the starting point S. i After setting the initial endpoint as the starting point D max Let E be the point at which the distance is calculated. iThe process involves taking all tunnel model points between the starting and ending points as a point set P. Each point in P forms an angle with both the starting and ending points. Each angle is calculated and compared to the angular resolution of the lidar (using the larger of the horizontal and vertical angular resolutions). If any angle is smaller than the lidar's angular resolution, the ending point is outside the maximum effective detection range. The endpoint is then adjusted towards the starting point, i.e., E. i =E i -d (where d is the adjusted mileage value, which is also the resolution of the maximum effective detection range, generally set to 1 meter), repeat step S008 until all included angles are greater than the radar angular resolution, at which point E i -S i This is the maximum effective detection distance at that point.

[0060] The above is an introduction to the method embodiments. The following embodiments using electronic devices and storage media will further illustrate the solution of the present invention.

[0061] This invention also provides an electronic device including a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0062] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0063] The processing unit performs the various methods and processes described above, such as the methods of the present invention. For example, in some embodiments, the methods of the present invention may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the methods of the present invention described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute the methods of the present invention by any other suitable means (e.g., by means of firmware).

[0064] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0065] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0066] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0067] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for calculating the effective detection distance of a lidar system along the entire tunnel route, characterized in that, This method involves modeling the tunnel based on the surveyed drawings obtained after the track construction, and calculating the effective detection range of the radar at each point on the track based on the established tunnel model. The calculation process includes: First, set a point as the starting point, and the center of the object in the tunnel profile at a first set distance from that point as the ending point. Calculate the angles between the lines connecting all tunnel points to the starting point and the lines connecting the starting and ending points within the range from the starting point to the ending point. If any angle is smaller than the radar resolution, it means that the ending point is outside the radar's effective detection range at that location. Then, set a new ending point by moving the ending point back a set distance from the starting point. Repeat the calculation process until the angles between all tunnel points to the starting point and the lines connecting the starting point and the lines connecting the starting and ending points within the range from the starting point to the ending point are all greater than the radar resolution. At this point, the distance between the ending point and the starting point is the effective detection range at that starting point.

2. The method for calculating the effective detection distance of a full-route lidar in a tunnel according to claim 1, characterized in that, The tunnel modeling process specifically includes: Step S1: Determine the size of the object to be detected and calculate the maximum linear detection performance D of the lidar when detecting the object. max ; Step S2: Organize the mileage-elevation data and perform elevation calculations; Step S3: Organize the mileage-plane data and calculate the horizontal coordinates; Step S4: Correct the mileage-plane information data based on the length of the chain; Step S5: Combine the vertical height and two-dimensional coordinates of each point on the line to obtain the three-dimensional coordinates of each point; Step S6: Perform tunnel profile modeling; Step S7: Construct the overall tunnel model.

3. The method for calculating the effective detection distance of a full-route lidar in a tunnel according to claim 2, characterized in that, In step S1, the maximum detection performance of the straight line D max It equals the minimum value among the maximum detection distance calculated by horizontal resolution, the maximum detection distance calculated by vertical resolution, and the nominal farthest straight-line detection distance.

4. The method for calculating the effective detection distance of a full-route lidar in a tunnel according to claim 3, characterized in that, The D max The specific calculations are as follows: Where L O W O D represents the length and width dimensions of the object to be detected facing the radar. m This represents the maximum straight-line detection range of the lidar. The horizontal and vertical resolutions of the lidar.

5. The method for calculating the effective detection distance of a full-route lidar in a tunnel according to claim 2, characterized in that, In step S2, the track construction drawings are organized into a data table, and the longitudinal profile is organized into mileage-elevation information data, thereby calculating the vertical height of each point along the route.

6. The method for calculating the effective detection distance of a full-route lidar in a tunnel according to claim 5, characterized in that, The mileage-elevation information data includes the starting mileage of each straight slope, the ending mileage of the straight slope, the ending mileage of the slope with a change of slope, the slope of the straight slope, the radius of curvature of the slope with a change of slope, and the direction of the slope change.

7. The method for calculating the effective detection distance of a full-route lidar in a tunnel according to claim 2, characterized in that, In step S3, the track construction plan is organized into mileage-plan information data, thereby calculating the two-dimensional planar coordinates of each point along the route.

8. The method for calculating the effective detection distance of a full-route lidar in a tunnel according to claim 7, characterized in that, The mileage-plane information data includes the starting mileage value of each straight segment, the ending mileage value of the straight segment, the ending mileage value of the curve, the radius of curvature of the curve, the rotation angle of the curve, the length of the transition curve, the length of the transition curve, and the direction of rotation of the curve.

9. The method for calculating the effective detection distance of a full-route lidar in a tunnel according to claim 2, characterized in that, In step S4, if there is a long chain of k meters at mileage A, then k meters are added to the mileage information after mileage A in all mileage-elevation information, and the corresponding elevation value is recalculated; at the same time, k meters are added to the mileage information after mileage A in all mileage-plane information, and the two-dimensional plane coordinates are recalculated.

10. The method for calculating the effective detection distance of a full-route lidar in a tunnel according to claim 2, characterized in that, In step S6, the tunnel profile modeling specifically involves: Based on the tunnel cross-section diagram of the modeled tunnel, determine the relative positional relationship between the tunnel center, the two tracks, the train center, and the radar installation position. Discretize the tunnel cross-section at set angles, and add the radar position point and the center point of the detected object to form the tunnel cross-section model.

11. The method for calculating the effective detection distance of a full-route lidar in a tunnel according to claim 2, characterized in that, In step S7, the discrete points of the tunnel profile at each second predetermined distance along the mileage are summarized to form a point set, which is the overall tunnel model.

12. The method for calculating the effective detection distance of a full-route lidar in a tunnel according to claim 11, characterized in that, The second set distance is 1m.

13. The method for calculating the effective detection distance of a full-route lidar in a tunnel according to claim 2, characterized in that, The first set distance is the maximum detection performance D in a straight line. max .

14. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 13.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 13.

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