A road sensor arrangement method suitable for car-road cooperation

Abstract

The application relates to a road sensor arrangement method suitable for vehicle-road cooperation, which comprises the following steps: constructing a road coordinate system, selecting road key nodes, and constructing a road geometric model; considering a camera coverage area, constructing a multi-objective optimization model of camera arrangement, optimizing camera installation height, angle and spacing between adjacent cameras based on an MOGWO algorithm, and determining camera position coordinates; determining sensor fixed support position coordinates and support height based on the camera position coordinates and the installation height; constructing a multi-objective optimization model of millimeter wave radar arrangement, optimizing and solving millimeter wave radar position coordinates and installation height based on the MOGWO algorithm; determining laser radar position coordinates based on the road geometric model, the sensor fixed support position coordinates and the support height, and constructing a laser radar arrangement optimization model to optimize and solve the installation height of the laser radar. Compared with the prior art, the application has the advantages of complete arrangement parameters and strong practicability.

Description

Technical Field

[0001] This invention relates to the field of vehicle-road cooperation, and in particular to a method for deploying road sensors suitable for vehicle-road cooperation. Background Technology

[0002] In recent years, with the continuous updates to intelligent driving assistance technologies, intelligent vehicles have experienced rapid development. However, currently, intelligent vehicles can only perform some low-level driving assistance tasks. To truly achieve autonomous driving, the support of vehicle-to-infrastructure (V2I) technology is required. V2I technology necessitates the use of numerous roadside sensors, such as cameras, millimeter-wave radar, and lidar, to acquire target information. Furthermore, urban traffic scenarios are relatively complex, and different road conditions have different characteristics regarding sensor installation and requirements. Therefore, a well-planned sensor deployment method can achieve better perception results and reliably realize V2I.

[0003] Currently, some scholars have conducted relevant research on the deployment of road sensors. For example, patent number CN114640800A, entitled "Method and System for Camera Deployment," obtains a three-dimensional model of the real space, determines the initial installation range of the camera within the model, and determines the target installation position of the camera corresponding to the three-dimensional model scene based on the initial installation range and the scene. Patent number CN115499848A, entitled "An Optimization Method for Roadside Sensor Deployment Applied to Vehicle-Road Cooperative Systems," generates initial values ​​for a multi-objective genetic algorithm randomly and obtains the optimal solution for the objective function G based on the fitness function Y. This allows for the early confirmation of the sensor deployment scheme and meets the functional, coverage, and cost requirements of the roadside perception system. Patent number CN115223361A, entitled "A Method for Optimizing the Deployment of Roadside Sensors in a Vehicle-Road Cooperative System," extracts vehicle detection and tracking data from simulation experiments to establish a sensor performance regression model. It uses the performance evaluation indicators of roadside sensors on the target road segment as constraints and the lowest sensor network cost as the objective function. By solving the deployment optimization model, the optimal roadside sensor deployment scheme is determined. From the above, it can be seen that existing sensor deployment methods have the following shortcomings:

[0004] 1) Most existing road sensor deployment methods are designed to optimize the same type of sensor (such as camera or lidar) in a single road environment, without considering the comprehensive consideration of cameras, millimeter-wave radar and lidar.

[0005] 2) Optimizing only the sensor placement coordinates or installation angle is not comprehensive enough;

[0006] 3) It ignores the influence of the previous sensor's position on the sensor's placement;

[0007] 4) The blind spots of the sensor were ignored. Summary of the Invention

[0008] The purpose of this invention is to provide a road sensor layout method suitable for vehicle-road cooperation. It takes into account the synergistic effect between various sensors and their respective blind spots, optimizes multiple parameters such as sensor installation position and angle, and provides a better sensor layout scheme to better facilitate the application of vehicle-road cooperation.

[0009] The objective of this invention can be achieved through the following technical solutions:

[0010] A method for deploying road sensors suitable for vehicle-road cooperation includes the following steps:

[0011] Construct a road coordinate system and select key road nodes;

[0012] Construct a road geometric model based on key road nodes;

[0013] Based on the road geometry model and considering the camera coverage area, a multi-objective optimization model for camera placement is constructed. The MOGWO algorithm is used to optimize the camera installation height, angle, and spacing between adjacent cameras to determine the camera position coordinates.

[0014] The position coordinates and height of the sensor mounting bracket are determined based on the camera's position coordinates and camera installation height, wherein the camera, millimeter-wave radar, and lidar are all mounted on the sensor mounting bracket;

[0015] Based on the road geometry model, the position coordinates of the sensor mounting bracket and the bracket height, a multi-objective optimization model for millimeter-wave radar deployment is constructed. The deployment parameters of the millimeter-wave radar are optimized based on the MOGWO algorithm to obtain the position coordinates and installation height of the millimeter-wave radar.

[0016] Based on the road geometry model, the position coordinates of the sensor mounting bracket, and the bracket height, the position coordinates of the lidar are determined, and an optimization model for lidar layout is constructed to optimize the installation height of the lidar. The lidar is installed on the sensor mounting bracket at the intersection.

[0017] The key road nodes include the center points at both ends of the road and the center points of each intersection.

[0018] The projection of the camera's coverage area onto the road's horizontal plane is a fan-shaped ring, and the parameters of the fan-shaped ring are as follows:

[0019]

[0020]

[0021]

[0022] α c =H c

[0023] Among them, R c r is the camera coverage radius. c Z represents the radius of the camera's blind spot. c For the camera installation height, H c V c These are the horizontal and vertical field of view, respectively, β c For pitch angle, W c α is the angle between the pitch angle and half of the vertical field of view. c It is the apex of the fan-shaped ring.

[0024] Considering the impact of blind spots on the camera's coverage area, the distance from the camera to the near side of the road must meet the following conditions:

[0025]

[0026]

[0027] Among them, X c D is the distance projected from the camera position to the edge of the road, and D is the road width.

[0028] The objective function of the multi-objective optimization model for camera deployment is:

[0029]

[0030] The constraints are:

[0031]

[0032]

[0033]

[0034]

[0035] 0 < Z c ≤H

[0036] Among them, M c1 B represents the effective coverage area of ​​the camera during deployment. c L represents the area of ​​the camera's blind spot. c H represents the maximum sensing distance of the camera, and H represents the empirical value of the fixed height of the sensor bracket.

[0037] The MOGWO algorithm is used to solve the objective function. The optimal solution is selected from the Pareto solution set to obtain the camera installation height and angle. Then, based on the M corresponding to the optimal solution... c1 The value determines the length of the effective coverage area, which serves as the spacing between adjacent cameras.

[0038] The relevant parameters of the projection of the millimeter-wave radar coverage area onto the road horizontal plane are as follows:

[0039]

[0040]

[0041]

[0042] α m1 =H m1

[0043] α m2 =H m2

[0044] Among them, R m2 r is one of the coverage radii of millimeter-wave radar. m Z is the blind zone radius of the millimeter-wave radar. m For the installation height of millimeter-wave radar, β m H is the pitch angle. m1 H m2 V m For the field of view, W m α is the angle between the pitch angle and half of the vertical angle. m1 and α m2 These are the two vertices of the covered area.

[0045] When the blind spot of the millimeter-wave radar falls on the road, and the road at the far end of the field of view needs to be covered by the millimeter-wave radar, the following constraints must be met:

[0046]

[0047]

[0048] α m1 >α m2

[0049] Among them, X m D is the distance projected from the millimeter-wave radar installation location onto the road edge, where D is the road width.

[0050] The objective function of the multi-objective optimization model for the millimeter-wave radar deployment is:

[0051]

[0052] The constraints are:

[0053]

[0054]

[0055]

[0056]

[0057] Z c -h≤Z m ≤Z c -h

[0058] Among them, M m1 B represents the effective coverage area of ​​the millimeter-wave radar during deployment. m L represents the blind zone area of ​​millimeter-wave radar. m Z represents the maximum sensing range of millimeter-wave radar. c h represents the camera installation height, and h represents the height of the external rod that can be added to the sensor mounting bracket, which is the height that can be adjusted from the existing sensor mounting rod height.

[0059] The MOGWO algorithm is used to solve the objective function, and the optimal solution is selected from the Pareto solution set to obtain the position coordinates and installation height of the millimeter-wave radar. The position coordinates of the millimeter-wave radar are determined based on the position of the sensor mounting bracket and the spacing between adjacent millimeter-wave radars. The spacing between adjacent millimeter-wave radars is determined according to the following rule: based on the M corresponding to the optimal solution... m1 The length of the effective coverage area is determined by the value. The greatest common divisor of this length and the spacing between the sensor mounting brackets is taken. The greatest common divisor is multiplied by the spacing between the sensor mounting brackets to obtain the spacing between adjacent millimeter-wave radars.

[0060] The relevant parameters of the projection of the lidar coverage area onto the road horizontal plane are as follows:

[0061]

[0062] r l =Z l ×cotV l

[0063] Among them, R l r is the coverage radius of the lidar. l Z is the blind zone radius of the lidar. l For the installation height of the lidar, L l To meet the maximum distance required for lidar resolution, V l This is the vertical field of view angle.

[0064] The objective function of the lidar deployment optimization model is to maximize the lidar deployment height.

[0065] Maximize(Z l )

[0066] The constraints are:

[0067]

[0068] Among them, y A ,y B ,y C and y D These are the four coordinates of the intersection, X l Where is the distance projected from the sensor location to the road edge, D is the road width, h is the height of the external rod that can be added to the sensor mounting bracket (i.e., the adjustable height based on the existing sensor mounting rod height), and y is the distance from the sensor location projected to the road edge. L1 Here are the position coordinates of lidar L1;

[0069] The installation height of the lidar is obtained by solving the lidar layout optimization model using a general numerical optimization method.

[0070] Compared with the prior art, the present invention has the following beneficial effects:

[0071] (1) This invention fully considers the arrangement of different roadside sensors used in vehicle-road cooperation, including cameras, millimeter-wave radar and lidar, and considers the influence of the installation order of different road sensors on the sensor placement position, reducing the use of sensor fixing rods.

[0072] (2) This invention optimizes both the sensor coverage area and the blind zone formed by the sensor through a multi-objective optimization method, and obtains the Pareto solution set of the sensor installation height and angle, providing a certain range of choices for the layout of road sensors.

[0073] (3) The present invention takes into account the placement coordinates, installation height and installation angle of the roadside sensor at the same time, making it more complete. Attached Figure Description

[0074] Figure 1 This is a flowchart of the method of the present invention;

[0075] Figure 2 Here are model diagrams of the sensor coverage area of ​​the present invention, wherein (2a) is a model diagram of the camera coverage area, (2b) is a model diagram of the millimeter-wave radar coverage area, and (2c) is a model diagram of the lidar coverage area.

[0076] Figure 3This is a coverage relationship diagram of the sensors of the present invention, wherein (3a) represents the coverage relationship between cameras, (3b) represents the coverage relationship between millimeter-wave radars, and (3c) represents the coverage relationship between lidars;

[0077] Figure 4 The following is a diagram showing the multi-objective solution results in an embodiment of the present invention, wherein (4a) is the solution result of the multi-objective optimization model for camera arrangement, and (4b) is the solution result of the multi-objective optimization model for millimeter-wave radar arrangement;

[0078] Figure 5 The following is a diagram showing the actual scene and corresponding sensor arrangement in an embodiment of the present invention, wherein (5a) is a diagram of the actual scene and (5b) is a schematic diagram of the sensor arrangement corresponding to the actual scene. Detailed Implementation

[0079] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0080] This embodiment provides a method for deploying road sensors suitable for vehicle-road cooperative systems, such as... Figure 1 As shown, it includes the following steps:

[0081] 1) Construct a road coordinate system and select key road nodes.

[0082] In this embodiment, key road nodes include the center points at both ends of the road and the center points of each intersection.

[0083] 2) Construct a road geometry model based on key road nodes.

[0084] Specifically, the road network centerline is constructed by identifying key road nodes, and a road geometry model is built based on the location of the road network centerline and the road width.

[0085] 3) Based on the road geometry model and considering the camera coverage area, a multi-objective optimization model for camera placement is constructed. The MOGWO algorithm is used to optimize the camera installation height, angle, and spacing between adjacent cameras to determine the camera position coordinates.

[0086] Based on the installation characteristics of the camera, the field of view of the camera will extend beyond the road. This invention only focuses on the covered road area. The installation diagram and coverage range of the camera are shown in Figure (2a), and the coverage relationship between the cameras is shown in Figure (3a).

[0087] The projection of the camera's coverage area onto the road's horizontal plane is a fan-shaped ring, with the following parameters:

[0088]

[0089]

[0090]

[0091] α c =H c

[0092] Among them, R c r is the camera coverage radius. c Z represents the radius of the camera's blind spot. c For the camera installation height, H c V c These are the horizontal and vertical field of view, respectively, β c For pitch angle, W c α is the angle between the pitch angle and half of the vertical field of view. c It is the apex angle of the sector ring, generally considered to be equal to H. c .

[0093] Considering the impact of blind spots on camera coverage, since traffic volume in the middle lane is higher than in the edge lanes, the camera is placed very close to the center of the road. The blind spot below the camera will be distributed across the entire road. At the same time, it must be considered that the camera can cover the lowest edge of the road. In this case, the distance from the camera to the near side of the road satisfies the following condition:

[0094]

[0095]

[0096] Among them, X c D is the distance projected from the camera position to the edge of the road, and D is the road width.

[0097] During the optimization of camera deployment, for road sensors, the farther the area is from the sensor, the larger the blind spot caused by vehicle obstruction. Therefore, for cameras, the effective coverage area M during the deployment process is crucial. c1 It should be as large as possible, while the camera blind spot area B c The objective function of the multi-objective optimization model for camera placement should be as small as possible.

[0098]

[0099] The height of the fixed bracket must meet certain conditions; excessive height will lead to cost and safety issues. It is also necessary to ensure that the distance between the camera and the farthest point in the image area does not exceed the camera's maximum sensing distance. Therefore, the constraints of the multi-objective optimization model for camera placement are:

[0100]

[0101]

[0102]

[0103]

[0104] 0 < Z c ≤H

[0105] Among them, L c H represents the maximum sensing distance of the camera; H represents the empirical value of the fixed height of the sensor bracket, which is a known quantity.

[0106] The MOGWO algorithm is used to solve the objective function. The optimal solution is selected from the Pareto solution set to obtain the camera installation height and angle. Then, based on the M corresponding to the optimal solution... c1 The value determines the length of the effective coverage area, which serves as the spacing between adjacent cameras.

[0107] 4) Determine the position coordinates and height of the sensor mounting bracket based on the camera's position coordinates and installation height.

[0108] In this embodiment, the camera position coordinates are used as the sensor mounting bracket position coordinates, and the camera mounting height is used as the sensor mounting bracket height. This is because each camera mounting location is equipped with at least one bracket for mounting. The camera, millimeter-wave radar, and lidar are all mounted on the sensor mounting bracket. The mounting locations of the millimeter-wave radar and lidar are determined according to methods 5) and 6), but their locations can only be selected from the sensor mounting bracket location (i.e., the camera mounting location coordinates).

[0109] 5) Based on the road geometry model, the position coordinates of the sensor mounting bracket and the bracket height, a multi-objective optimization model for millimeter-wave radar deployment is constructed. The deployment parameters of the millimeter-wave radar are optimized based on the MOGWO algorithm to obtain the position coordinates and installation height of the millimeter-wave radar.

[0110] Based on the installation characteristics of millimeter-wave radar, its installation diagram and coverage range are shown in Figure (2b), and the coverage relationship between millimeter-wave radars is shown in Figure (3b).

[0111] Highway millimeter-wave radar typically has a long detection range and two field of view (FOV), corresponding to near and far distances respectively. Similar to the modeling method for camera coverage areas, the projection parameters of the millimeter-wave radar coverage area onto the road surface are as follows:

[0112]

[0113]

[0114]

[0115]

[0116] α m2 =H m2

[0117] Among them, R m2 r is one of the coverage radii of millimeter-wave radar. m Z is the blind zone radius of the millimeter-wave radar. m For the installation height of millimeter-wave radar, β m H is the pitch angle. m1 H m2 V m For the field of view, W m α is the angle between the pitch angle and half of the vertical angle. m1 and α m2 These are the two vertices of the covered area.

[0118] When the blind spot of the millimeter-wave radar falls on the road, and the road at the far end of the field of view needs to be covered by the millimeter-wave radar, the following constraints must be met:

[0119]

[0120]

[0121] α m1 >α m2

[0122] Among them, X m D is the distance projected from the millimeter-wave radar installation location onto the road edge, where D is the road width.

[0123] In this embodiment, the objective function of the multi-target optimization model for millimeter-wave radar deployment is:

[0124]

[0125] The constraints are:

[0126]

[0127]

[0128]

[0129]

[0130] Z c -h≤Zm ≤Z c -h

[0131] Among them, M m1 B represents the effective coverage area of ​​the millimeter-wave radar during deployment. m L represents the blind zone area of ​​millimeter-wave radar. m Z represents the maximum sensing range of millimeter-wave radar. c h represents the camera installation height, and h represents the height of the external rod that can be added to the sensor mounting bracket, which is the adjustable height based on the existing sensor mounting rod height.

[0132] The MOGWO algorithm is used to solve the objective function. The optimal solution is selected from the Pareto solution set to obtain the position coordinates and installation height of the millimeter-wave radar. The position coordinates of the millimeter-wave radar are determined based on the position of the sensor mounting bracket and the spacing between adjacent millimeter-wave radars. The spacing between adjacent millimeter-wave radars is determined according to the following rule: based on the M corresponding to the optimal solution... m1 The length of the effective coverage area is determined by the value. The greatest common divisor of this length and the spacing between the sensor mounting brackets is taken. Multiplying the greatest common divisor by the spacing between the sensor mounting brackets yields the spacing between adjacent millimeter-wave radars. This rule is based on the principle of satisfying the requirements of the effective coverage area while meeting the positional constraints of the sensor mounting brackets, and minimizing the number of millimeter-wave radars deployed to save costs.

[0133] 6) Based on the road geometry model, the position coordinates of the sensor mounting bracket and the bracket height, determine the position coordinates of the lidar, and construct a lidar layout optimization model to optimize the installation height of the lidar. The lidar is installed on the sensor mounting bracket at the intersection.

[0134] Most roadside lidar has a horizontal field of view of 360° and is typically placed horizontally to provide a wider coverage area. Although the field of view of lidar can cover objects at very far distances, the range of lidar is limited by its resolution. Its installation diagram and coverage range are shown in Figure (2c), and the coverage relationship between lidars is shown in Figure (3c).

[0135] The relevant parameters of the projection of the lidar coverage area onto the road horizontal plane are as follows:

[0136]

[0137] r l =Z l ×cotV l

[0138] Among them, R l r is the coverage radius of the lidar. l Z is the blind zone radius of the lidar.l For the installation height of the lidar, L l To meet the maximum distance required for lidar resolution, V l This is the vertical field of view angle.

[0139] The lidar units are placed near the intersection and mounted on fixed brackets established by optimizing the camera installation positions. An intersection typically requires at least two lidar units because intersections are prone to accidents and redundant sensing is necessary. Therefore, the position coordinates of the lidar units can be determined.

[0140] Regarding the installation height of LiDAR, the lower the LiDAR is installed, the larger the sensing area, and the larger the area obstructed by vehicles. Therefore, LiDAR needs to be placed at the highest possible position. Thus, the objective function of the LiDAR placement optimization model is to maximize the LiDAR placement height.

[0141] Maximize(Z l )

[0142] The lidar L1 needs to cover the intersection area, therefore the following constraints must be met:

[0143]

[0144] Among them, y A ,y B ,y C and y D These are the four coordinates of the intersection, X l The distance from the sensor location projected onto the road edge. These are the position coordinates of lidar L1.

[0145] The lidar layout optimization model is linear. The lidar layout optimization model is solved using a general numerical optimization method to obtain the installation height of the lidar.

[0146] Referring to Figure (5a), this embodiment selects a street in Shanghai, China as the optimization scene. The satellite view of the scene is taken from Baidu Maps. This street segment includes three straight road segments and two intersections. The lengths of the three straight road segments are: Y... A =600m,Y B =560m,Y C =500m, width is: D A =24m,D B =24m,D C =10m.

[0147] The multi-objective solution result obtained in this embodiment based on the above method is shown in the figure below. Figure 4As shown, the obtained sensor layout scheme uses a total of 54 cameras, 14 millimeter-wave radars, and 4 lidars, with their specific locations as shown in Figure (5b), to achieve 100% coverage (excluding the start and end points of the road) and sensor redundancy at 5 intersections. Taking road A as an example, the specific sensor layout scheme is shown in Table 1.

[0148] Table 1 Sensor Layout Scheme

[0149]

[0150] This embodiment optimizes the sensor placement on straight sections and intersections, but for other types of road networks, straight sections and intersections can be referenced. For example, ramps and roundabouts can be considered simplified intersections, thereby achieving sensor placement optimization for complex and diverse roads.

[0151] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for arranging road sensors suitable for cooperative vehicle infrastructure systems, characterized in that, Includes the following steps: Construct a road coordinate system and select key road nodes; Construct a road geometric model based on key road nodes; Based on the road geometry model and considering the camera coverage area, a multi-objective optimization model for camera placement is constructed. The MOGWO algorithm is used to optimize the camera installation height, angle, and spacing between adjacent cameras to determine the camera position coordinates. The position coordinates and height of the sensor mounting bracket are determined based on the camera's position coordinates and camera installation height, wherein the camera, millimeter-wave radar, and lidar are all mounted on the sensor mounting bracket; Based on the road geometry model, the position coordinates of the sensor mounting bracket and the bracket height, a multi-objective optimization model for millimeter-wave radar deployment is constructed. The deployment parameters of the millimeter-wave radar are optimized based on the MOGWO algorithm to obtain the position coordinates and installation height of the millimeter-wave radar. Based on the road geometry model, the position coordinates of the sensor mounting bracket and the bracket height, the position coordinates of the lidar are determined, and a lidar layout optimization model is constructed to optimize the installation height of the lidar. The lidar is installed on the sensor mounting bracket at the intersection. The projection of the camera's coverage area onto the road's horizontal plane is a fan-shaped ring, and the parameters of the fan-shaped ring are as follows: wherein, is the camera coverage radius, is the camera blind spot radius, is the camera mounting height, , are the horizontal and vertical field of view angles, respectively, is the pitch angle, is the included angle between the pitch angle and half the vertical field of view angle, is the top angle of the sector ring; Considering the impact of blind spots on the camera's coverage area, the distance from the camera to the near side of the road must meet the following conditions: wherein, is the distance of the camera position projected to the road edge, is the road width; The objective function of the multi-objective optimization model for camera deployment is: The constraints are: wherein, represents the effective coverage area of the camera during the arrangement process, represents the camera blind area, represents the maximum sensing distance of the camera, H represents the empirical value of the sensor support fixed height; The MOGWO algorithm is used to solve the objective function, optimal solutions are selected from the Pareto solutions, camera installation height and angle are obtained, and the length of the effective coverage area is determined as the spacing between adjacent cameras according to the optimal solution corresponding to the value.

2. The method for arranging a road sensor for cooperative vehicle infrastructure according to claim 1, characterized in that, The key road nodes include the center points at both ends of the road and the center points of each intersection.

3. The method for deploying road sensors suitable for vehicle-road cooperation according to claim 1, characterized in that, The relevant parameters of the projection of the millimeter-wave radar coverage area onto the road horizontal plane are as follows: in, One of the coverage radii of millimeter-wave radar. This refers to the blind zone radius of millimeter-wave radar. Installation height for millimeter-wave radar The pitch angle, , , Where is the field of view, The horizontal field of view for a short-range, wide-beam millimeter-wave radar; The horizontal field of view for long-range narrow beam millimeter-wave radar; For the vertical field of view of millimeter-wave radar, It is the angle between the pitch angle and half of the vertical angle. and These are the two vertices of the covered area.

4. A method for deploying road sensors suitable for vehicle-road cooperation according to claim 3, characterized in that, When the blind spot of the millimeter-wave radar falls on the road, and the road at the far end of the field of view needs to be covered by the millimeter-wave radar, the following constraints must be met: in, The distance projected from the installation location of the millimeter-wave radar to the edge of the road. It refers to the road width.

5. A method for deploying road sensors suitable for vehicle-road cooperation according to claim 4, characterized in that, The objective function of the multi-objective optimization model for the millimeter-wave radar deployment is: The constraints are: in, This represents the effective coverage area of ​​the millimeter-wave radar during deployment. Represents the blind zone area of ​​millimeter-wave radar. This indicates the maximum sensing range of the millimeter-wave radar. Determine the installation height of the camera. h The height of the external rod that can be added to the sensor mounting bracket; The MOGWO algorithm is used to solve the objective function, and the optimal solution is selected from the Pareto solution set to obtain the position coordinates and installation height of the millimeter-wave radar. The position coordinates of the millimeter-wave radar are determined based on the position of the sensor mounting bracket and the spacing between adjacent millimeter-wave radars. The spacing between adjacent millimeter-wave radars is determined according to the following rules: based on the optimal solution... The length of the effective coverage area is determined by the value. The greatest common divisor of this length and the spacing between the sensor mounting brackets is taken. The greatest common divisor is multiplied by the spacing between the sensor mounting brackets to obtain the spacing between adjacent millimeter-wave radars.

6. A method for deploying road sensors suitable for vehicle-road cooperation according to claim 1, characterized in that, The relevant parameters of the projection of the lidar coverage area onto the road horizontal plane are as follows: in, The coverage radius of the lidar. The radius of the lidar blind zone. For the installation height of the lidar, To meet the maximum distance required for lidar resolution, This is the vertical field of view angle.

7. A method for deploying road sensors suitable for vehicle-road cooperation according to claim 6, characterized in that, The objective function of the lidar deployment optimization model is to maximize the lidar deployment height. Maximize The constraints are: in, , , and These are the coordinates of the four intersections of the crossroads. The distance from the sensor location projected onto the road edge. For road width, h The height of the external rod that can be added to the sensor mounting bracket. For lidar L The position coordinates of 1; The installation height of the lidar was obtained by solving the lidar layout optimization model using numerical optimization methods.

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