Vehicle-occlusion-based roadside lidar beam distribution optimization method and device

Abstract

The application provides a roadside laser radar beam distribution optimization method and device based on vehicle shielding, relates to the technical field of laser radar optimization, and comprises the following steps: based on the shielding relationship between a laser radar and a dynamic vehicle in road testing, a desired shielding effect of a target vehicle is constructed; the sensing capability of the laser radar is quantified, and based on the desired shielding effect and the energy attenuation of the laser radar, the expected value of the number of point clouds that can be recognized by the laser radar is calculated; a target function is constructed based on the expected value of the number of point clouds, and an optimization parameter is set, the optimization parameter being a vertical angle distribution function of the laser radar; the optimization parameter is optimized through the target function and an interval search algorithm, the optimal vertical angle distribution function is obtained, and the beam distribution of the laser radar is determined through the optimal vertical angle distribution function. The application solves the problem of unreasonable internal beam distribution of the existing roadside laser radar.

Description

Technical Field

[0001] This invention relates to the field of lidar optimization technology, and more specifically, to a method and apparatus for optimizing the beam distribution of roadside lidar based on vehicle occlusion. Background Technology

[0002] With the rapid development of autonomous driving technology, perception systems play a crucial role in ensuring vehicle safety and reliability. Roadside lidar, as a supplement to onboard sensors, can provide a wider field of view, reduce blind spots, and enable multi-vehicle cooperative perception in complex traffic scenarios such as intersections and highways.

[0003] However, the perception performance of roadside lidar is affected by various factors, especially traffic density and the lidar's own configuration. In high-density traffic flow, dynamic occlusion effects between vehicles significantly reduce lidar detection performance. Furthermore, the lidar beam distribution has a significant impact on its perception performance; an unreasonable beam distribution leads to sparse target point cloud coverage, further limiting its perception capabilities. Existing research on lidar beam distribution optimization mainly focuses on static occlusion models, which cannot accurately reflect the occlusion effects in dynamic traffic flow. In addition, existing optimization methods typically require substantial computational resources and are difficult to optimize for high-beam-count lidar configurations in a short time. These problems limit the effectiveness of lidar applications in real-world traffic environments and make it difficult to meet the needs of autonomous driving and intelligent transportation systems. Summary of the Invention

[0004] The purpose of this invention is to provide a method and apparatus for optimizing the beam distribution of roadside lidar based on vehicle occlusion, in order to improve the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0005] Firstly, this application provides a method for optimizing the beam distribution of roadside lidar based on vehicle occlusion, including:

[0006] Based on the occlusion relationship between the road-tested lidar and dynamic vehicles, the desired occlusion effect of the target vehicle is constructed.

[0007] The sensing capability of lidar is quantified, and the expected number of point clouds that lidar can identify is calculated based on the expected occlusion effect and the energy attenuation of lidar.

[0008] An objective function is constructed based on the expected value of the number of points in the cloud, and optimization parameters are set, wherein the optimization parameters are the vertical angle distribution function of the lidar.

[0009] The optimization parameters are optimized by using an objective function and an interval search algorithm to obtain the optimal vertical angle distribution function, and the laser radar beam distribution is determined by the optimal vertical angle distribution function.

[0010] Secondly, this application also provides a roadside lidar beam distribution optimization device based on vehicle occlusion, including:

[0011] The first construction module is used to construct the desired occlusion effect of the target vehicle based on the occlusion relationship between the road-based lidar and dynamic vehicles.

[0012] The calculation module is used to quantify the sensing capability of the lidar. Based on the expected occlusion effect and the energy attenuation of the lidar, it calculates the expected value of the number of point clouds that the lidar can identify.

[0013] The second construction module is used to construct an objective function based on the expected value of the point cloud number and set optimization parameters, wherein the optimization parameters are the vertical angle distribution function of the lidar;

[0014] The optimization module is used to optimize the parameters through the objective function and the interval search algorithm to obtain the optimal vertical angle distribution function, and then determine the laser radar beam distribution through the optimal vertical angle distribution function.

[0015] The beneficial effects of this invention are as follows: This invention uses a geometric method to calculate the occlusion area between the target vehicle and LiDAR. By constructing a projection plane and calculating the normal vector, the three-dimensional occlusion problem is transformed into a two-dimensional problem, achieving accurate quantification of the occlusion effect and computability of the occlusion area. The LiDAR sensing capability is evaluated based on the number of point clouds acquired by the target vehicle, and the introduction of distance energy attenuation and occlusion effects makes the evaluation more comprehensive. An interval search algorithm is designed to optimize the angle distribution of the laser beam as a whole, avoiding the complex operation of optimizing each vertical angle individually, significantly improving optimization efficiency, and finding the optimal solution within a fewer iterations. The optimal vertical angle distribution function is then obtained to determine the LiDAR beam distribution.

[0016] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the roadside lidar beam distribution optimization method based on vehicle occlusion as described in an embodiment of the present invention;

[0019] Figure 2 This is a schematic diagram of vehicle occlusion relationships in traffic flow according to an embodiment of the present invention;

[0020] Figure 3 This is a schematic diagram of vehicle occlusion relationships based on projection in an embodiment of the present invention;

[0021] Figure 4 This is a schematic diagram showing the location of vehicles obstructing the obstructed area in an embodiment of the present invention;

[0022] Figure 5 This is a schematic diagram of the laser radar beam distribution in an embodiment of the present invention;

[0023] Figure 6 This is a schematic diagram of the lidar's field of view on the vehicle in an embodiment of the present invention;

[0024] Figure 7 This is a schematic diagram illustrating the sensing capability of the lidar in an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0026] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] Example 1:

[0028] This embodiment provides a method for optimizing the beam distribution of roadside lidar based on vehicle occlusion.

[0029] See Figure 1 The figure shows that the method includes steps S1, S2, S3 and S4.

[0030] Step S1: Based on the occlusion relationship between the road-tested LiDAR and dynamic vehicles, construct the desired occlusion effect of the target vehicle;

[0031] In this embodiment, the vehicle occlusion relationships in dynamic traffic flow are first modeled, such as... Figure 2 As shown in the top view, with the LiDAR as the origin, there is a triangular occlusion region between the target vehicle and the LiDAR, i.e., the occlusion region, Ω in the figure. q Let q represent the obstruction area between the q-th target vehicle and the LiDAR, where q = 1, 2, ..., n, and n represents the total number of target vehicles. LiDAR represents the laser radar, and X, Y, and Z represent the established x-axis, y-axis, and z-axis, respectively. veh and w veh Let x represent the length and width of the target vehicle, respectively. t and y t These represent the x-coordinate and y-coordinate of the center point of the target vehicle, respectively.

[0032] In this embodiment, the position of the corresponding vehicle is represented by the coordinates of the vehicle's center point.

[0033] In step S1, constructing the desired occlusion effect of the target vehicle includes:

[0034] Step S11: Determine the coordinates of the diagonal vertices of the bounding box of the target vehicle based on its position and size;

[0035] In this embodiment, as Figure 2 As shown, located in (x t ,y t The coordinates of the diagonal vertices of the bounding box of the target vehicle are respectively and The specific calculation formula is as follows:

[0036]

[0037] In the formula, x t and y t The x and y coordinates of the center point of the target vehicle are represented by l. veh and w veh These represent the length and width of the target vehicle, respectively.

[0038] Step S12: Calculate the area of ​​the occlusion region of the target vehicle based on the coordinates of the diagonal vertices of the bounding box and the position of the lidar;

[0039] In this embodiment, the formula for calculating the area of ​​the obstructed region of the target vehicle is:

[0040]

[0041] In the formula, τ represents the area of ​​the target vehicle's obstructed region. and These represent the x-coordinates of the two diagonal vertices of the bounding boxes of the target vehicle. and These represent the ordinates of the diagonal vertices of the two bounding boxes of the target vehicle, respectively, and |·| indicates taking the absolute value.

[0042] Step S13: Based on the Poisson point process, calculate the probability that the obstacle vehicle appears in the occluded area;

[0043] In this embodiment, when other vehicles are located within the occlusion area, they may obstruct the target vehicle. Since vehicle arrivals in the traffic flow follow a Poisson distribution, a Poisson point process is used to model the probability of an obstructing vehicle appearing within the occlusion area. The formula is as follows:

[0044]

[0045] In the formula, Let λ represent the probability that the obstacle vehicle appears in the obstructed area, λ represent the traffic density, and τ represent the area of ​​the obstructed area of ​​the target vehicle.

[0046] Step S14: Construct the projection plane and calculate the occlusion effect of the target vehicle based on the projection plane and the obstacle vehicles;

[0047] In this embodiment, as Figure 3 As shown, the occlusion effect is modeled as the overlapping area between the obstacle vehicle and the target vehicle on the projection plane (the diagonal section of the target vehicle's bounding box).

[0048] First, construct the general expression for the projection plane:

[0049]

[0050] In the formula, and Both represent plane normal vectors The amount, The constants that determine the position on the plane are represented by x, y, and z, which represent the abscissa, ordinate, and ordinate of the point on the projection plane, respectively.

[0051] In step S14, the calculation of the occlusion effect of the target vehicle based on the projection plane and obstacle vehicles includes:

[0052] Step S141: Calculate the normal vector of the projection plane based on the coordinates of the center point and two diagonal vertices of the target vehicle;

[0053] In this embodiment, the normal vector of the projection plane needs to be solved. You need to select three points on the plane, such as Figure 3As shown, the center point (p1) and two diagonal vertices (p2 and p3) of the target vehicle are selected, specifically as follows:

[0054] p1=(x t ,y t ,z t )

[0055]

[0056] In the formula, x t y t and z t Let l represent the x-coordinate, y-coordinate, and vertices of the center point of the target vehicle, respectively. veh w veh and h veh These represent the length, width, and height of the target vehicle, respectively.

[0057] Based on p1, p2, and p3, the normal vector of the projection plane can be expressed as:

[0058]

[0059] in, and Both represent two vectors on the plane. and The solution is obtained by substituting p1 into the general expression for the projection plane.

[0060] Step S142: Using the lidar as the light source, construct the projection equation of the obstacle vehicle on the projection plane;

[0061] In this embodiment, the projection equation of the obstacle vehicle on the projection plane is:

[0062]

[0063] In the formula, and These represent the coordinates of the obstacle vehicle before and after its projection. This indicates the location of the lidar, and ρ represents the scaling factor. and Both represent the normal vector of the projection plane. The amount, x represents the constant that determines the position of the projection plane. l y l and z l Let x represent the x, y, and y coordinates of the lidar, respectively. o y o and z o These represent the x-coordinate, y-coordinate, and vertical coordinate of a diagonal vertex of the obstacle vehicle, respectively.

[0064] Step S143: Calculate the projected coordinates of the two diagonal vertices of the obstacle vehicle according to the projection equation;

[0065] In this embodiment, as Figure 3 As shown, and Let be the coordinates of the two diagonal vertices of the obstacle vehicle. and Given the coordinates of the two diagonal vertices of the target vehicle, the projected coordinates of the two diagonal vertices of the obstacle vehicle are obtained through the projection equation. and And (x) l ,y l ,z l () represents the coordinates of the lidar.

[0066] Step S144: Calculate the area of ​​the overlapping region between the obstacle vehicle and the target vehicle based on the coordinates of the two diagonal vertices of the target vehicle and the projected coordinates of the two diagonal vertices of the obstacle vehicle.

[0067] In this embodiment, the overlapping area between the obstacle vehicle and the target vehicle is approximated as a rectangle, and the area of ​​the overlapping area can be calculated as follows:

[0068] S lap =w lap ×l lap

[0069]

[0070] In the formula, S lap w represents the area of ​​the overlapping region between the obstacle vehicle and the target vehicle. lap Indicates the width of the overlapping region, l lap The value represents the length of the overlapping region, min(·) represents the minimum value, and max(·) represents the maximum value. veh Indicates the height of the target vehicle. and These represent the x-coordinates of the two diagonal vertices of the target vehicle. and These represent the ordinates of the two diagonal vertices of the target vehicle. and These represent the projected x-coordinates of the two diagonal vertices of the obstacle vehicle. and These represent the projected ordinates of the two diagonal vertices of the obstacle vehicle, respectively. This represents the projected vertical coordinate of one diagonal vertex of the obstacle vehicle.

[0071] Step S145: Calculate the occlusion ratio of the target vehicle based on the overlapping area, and define the occlusion effect of the target vehicle based on the occlusion ratio.

[0072] In this embodiment, in order to quantify the impact of occlusion, an occlusion ratio is defined to represent the proportion of the total area of ​​the target vehicle on the projection plane that is occluded.

[0073] The formula for the shading ratio is:

[0074]

[0075] In the formula, S lap w represents the area of ​​the overlapping region between the obstacle vehicle and the target vehicle. lap Indicates the width of the overlapping region, l lap The length of the overlapping region, l veh w veh and h veh These represent the length, width, and height of the target vehicle, respectively.

[0076] Since the occlusion effect of obstacle vehicles at different locations affects the perception of the target vehicle, the occlusion effect is represented as a conditional function, as follows:

[0077]

[0078] In the formula, This indicates the occlusion effect of the target vehicle. Represents the shading benefit function. and λ represents the location of the obstacle vehicle, the target vehicle, and the lidar, respectively, and λ represents the traffic density.

[0079] Step S15: Discretize the occlusion region, and calculate the expected occlusion effect of the target vehicle by using the probability density function of the obstacle vehicle at different positions in the occlusion region, the probability of the obstacle vehicle appearing in the occlusion region, and the occlusion effect of the target vehicle.

[0080] In this embodiment, The occlusion effect of obstacle vehicles on target vehicles under static positional relationships was quantified, and its impact was quantified based on the geometric occlusion ratio. The occlusion effect under static positional relationships was further extended by introducing a probability density function to characterize the spatial distribution of obstacle vehicles within the occlusion area. Specifically, using... Let represent the probability density function of the obstacle vehicle at different positions within the occluded region Ω. Assume the probability of the obstacle vehicle appearing in the occluded region is... The desired occlusion effect of the target vehicle can then be expressed as:

[0081]

[0082] In the formula, This represents the desired occlusion effect of the target vehicle. This indicates the probability that an obstructing vehicle appears in the obstructed area. Represents the shading benefit function. Let x represent the probability density function of the obstacle vehicle at different positions within the obstructed area Ω. o and y o These represent the x-coordinate and y-coordinate of the center point of the obstacle vehicle, respectively. and λ represents the location of the obstacle vehicle, the target vehicle, and the lidar, respectively, and λ represents the traffic density.

[0083] Therefore, through It can be described as located in The target vehicle at the location, under traffic flow density λ, from the LiDAR perspective The expected occlusion effect caused by obstacle vehicles within the occlusion region Ω is observed. However, the expected occlusion effect of the target vehicle in the above formula is difficult to solve directly because... It is a complex function derived through projection, whose precise form cannot be explicitly defined, making analytical integration difficult to implement.

[0084] To overcome this difficulty, this step simplifies the calculation by discretizing the occluded area into a series of candidate obstacle vehicle locations. For example... Figure 4 As shown, the positions of the obstructing vehicles are generated at fixed intervals (e.g., 1 meter) along each lane within the occlusion area to calculate the expected occlusion effect of the target vehicle, where k represents the total number of lanes.

[0085] The desired occlusion effect of the discretized target vehicle can be approximated as follows:

[0086]

[0087] In the formula, Let N represent the expected occlusion effect of the target vehicle, and let N represent the number of discretized obstacle vehicle locations within the occlusion region Ω. This indicates the probability that an obstructing vehicle appears in the obstructed area. Represents the shading benefit function. This represents the position of the discretized a-th obstacle vehicle. The positions of the target vehicle and the lidar are represented respectively, and λ represents the traffic density. This indicates that the obstructing vehicle is located within the obstructed area Ω. The probability density function is given by ΔΩ, which represents the discrete unit region.

[0088] Since the probability of an obstacle vehicle appearing at any different location on the lane is the same, this means that the locations of obstacle vehicles follow a uniform distribution. Therefore... Therefore, the desired occlusion effect of the target vehicle can be further simplified as:

[0089]

[0090] In the formula, Let N represent the expected occlusion effect of the target vehicle, and let N represent the number of discretized obstacle vehicle locations within the occlusion region Ω. This indicates the probability that an obstructing vehicle appears in the obstructed area. Represents the shading benefit function. This represents the position of the discretized a-th obstacle vehicle. and λ represents the location of the target vehicle and the lidar, respectively, and λ represents the traffic density.

[0091] Step S2: Quantify the sensing capability of the lidar. Based on the expected occlusion effect and the energy attenuation of the lidar, calculate the expected value of the number of point clouds that the lidar can identify.

[0092] In this embodiment, a quantitative relationship between the lidar beam distribution and sensing capability is first established, using the number of point clouds acquired by the target vehicle as a key indicator for evaluating the lidar's sensing capability. The lidar is modeled as a set of rays defined by horizontal and vertical emission angles, where the horizontal emission angle is represented by a horizontal angle and the vertical emission angle by a vertical angle, as shown below. Figure 5 As shown, its mathematical representation is as follows:

[0093] α={α i |α i ∈[0,2π), i=1,2,…,N α}

[0094] β={β j |β j ∈[β min ,β max ],j=1,2,…,N β}

[0095] In the formula, α and β represent the horizontal and vertical angles of the LiDAR laser, respectively, and α i Let β represent the i-th horizontal angle of the LiDAR laser. j Let β represent the j-th vertical angle of the LiDAR laser. min and β max N represents the minimum and maximum perpendicular angles, respectively. ɑ and N β These represent the number of horizontal and vertical angles, respectively. Δɑ represents the horizontal angular resolution.

[0096] like Figure 6As shown, for the location (x t ,y t ,z t For the target vehicle, the effective sensing range of the LiDAR beam in the horizontal and vertical angles is as follows:

[0097]

[0098] In the formula, Δα t and Δβ t These represent the horizontal and vertical angular sensing ranges of the lidar beam for the target vehicle, respectively. t and y t Represent the x and y coordinates of the center point of the target vehicle. and These represent the x-coordinates of the two diagonal vertices of the target vehicle. and Let h represent the ordinates of the two diagonal vertices of the target vehicle, respectively. veh Z represents the height of the target vehicle. l The vertical coordinate of the lidar is represented by arctan(·), which represents the arctangent function, and α is the vertical coordinate of the lidar. t and β t These represent the horizontal and vertical angles of the lidar beam relative to the target vehicle, respectively.

[0099] In step S2, the calculation steps for the expected value of the point cloud number are as follows:

[0100] Step S21: Calculate the theoretical number of point clouds on the target vehicle based on the first information and the vertical angle distribution function. The first information is the horizontal angle sensing range and vertical angle sensing range of the lidar beam on the target vehicle.

[0101] In this embodiment, the focus is on a mechanically rotating LiDAR, whose horizontal angular resolution Δα is fixed, while its vertical angular distribution is determined by its internal design and characterized by a distribution function or model. Therefore, the theoretical number of point clouds for the target vehicle can be expressed as:

[0102]

[0103] In the formula, Δα represents the theoretical number of point clouds for the target vehicle. t and Δβ t These represent the horizontal and vertical angular sensing ranges of the lidar beam for the target vehicle, respectively, where Δα represents the horizontal angular resolution. Let β represent the vertical angle distribution function, and let β represent the vertical angle of the LiDAR laser.

[0104] Step S22: Calculate the first point cloud loss ratio on the target vehicle based on the energy attenuation of the lidar;

[0105] In this embodiment, as Figure 7 As shown, LiDAR's ability to detect vehicle point clouds is affected by laser reflectivity and occlusion. Regarding reflectivity, laser energy attenuates with increasing distance, significantly increasing the probability that distant targets cannot be detected. Therefore, this energy attenuation is modeled based on the distance-based loss probability, expressed as follows:

[0106]

[0107] In the formula, This represents the percentage of point cloud loss, which is actually the percentage of point cloud loss on the target vehicle caused by energy decay. γ represents the decay rate, characterizing the rate at which the loss probability increases with distance. t y t and z t These represent the x-coordinate, y-coordinate, and vertices of the center point of the target vehicle, respectively.

[0108] Step S23: Calculate the proportion of the second point cloud loss on the target vehicle based on the expected occlusion effect;

[0109] In this embodiment, regarding the occlusion problem, obstructed vehicles within the occlusion area reduce the effective perception area of ​​the target vehicle. Specifically, the occlusion area on the projection plane represents the portion of the target vehicle that LiDAR cannot directly observe due to occlusion. Therefore, the reduction in the number of detectable point clouds by LiDAR caused by occlusion can be quantified by the occlusion effect of the target vehicle, as expressed below:

[0110]

[0111] In the formula, This represents the percentage of point cloud loss on the second point, which is actually the percentage of point cloud loss on the target vehicle caused by the occlusion effect. This represents the desired occlusion effect of the target vehicle.

[0112] Step S24: Calculate the second information based on the theoretical number of point clouds, the first point cloud loss ratio, and the second point cloud loss ratio. The second information is the number of detectable effective point clouds on the target vehicle.

[0113] In this embodiment, to further quantify this point cloud loss relationship, a function is established to correlate the reduction in the number of detectable point clouds on the target vehicle with energy attenuation and occlusion effects, and its expression is as follows:

[0114]

[0115] In the formula, Represents the horizontal angular resolution Δα and the vertical angular distribution function. Below, target vehicle v t The number of detectable valid point clouds. This represents the theoretical number of point clouds for the target vehicle. This indicates the percentage of cloud loss at the first point. This indicates the percentage of cloud loss at point two.

[0116] Step S25: Calculate the expected number of point clouds that the lidar can identify using the second information.

[0117] In this embodiment, to expand the analysis and cover all possible target vehicle locations within the LiDAR detection range, the spatial distribution of target vehicles is considered. The expected value. Spatial probability density function. Describes the target vehicle in the detection area any position within (x) t ,y t The probability of ). Therefore, considering the change in the target vehicle's position, the expected value of the number of points in the cloud that can be identified by LiDAR can be expressed as:

[0118]

[0119] In the formula, This represents the expected number of point clouds that the lidar can identify. Indicates the detection area. Represents the horizontal angular resolution Δα and the vertical angular distribution function. Below, located at (x t ,y t The number of detectable point clouds on the target vehicle. This represents the spatial probability density function.

[0120] in, This actually describes the expected number of point clouds that a LiDAR can identify when considering all possible target vehicle positions within the LiDAR detection range. To simplify... The calculation uses a discretization method to divide the detection area. The detection area is divided into a finite number of discrete points. Specifically, the LiDAR detection range is gridded based on the lane and a specific length interval (e.g., 5 meters). The discretized formula is expressed as follows:

[0121]

[0122] In the formula, This represents the expected number of point clouds that the lidar can identify, where M represents the total number of possible locations of the target vehicle. Represents the horizontal angular resolution Δα and the vertical angular distribution function. Below, located The number of effective point clouds that can be detected on the target vehicle. This represents the coordinates of the b-th discrete position. This represents the area corresponding to each discrete point. This represents the probability density function.

[0123] Since the probability of a target vehicle being located at any point within the same lane is uniform, that is... Therefore, it can be further simplified to obtain the expected occlusion effect of the target vehicle as follows:

[0124]

[0125] In the formula This represents the expected number of point clouds that the lidar can identify, where M represents the total number of possible locations of the target vehicle. Represents the horizontal angular resolution Δα and the vertical angular distribution function. Below, located The number of effective point clouds that can be detected on the target vehicle. This represents the coordinates of the b-th discrete position.

[0126] Step S3: Construct an objective function based on the expected value of the point cloud number and set optimization parameters, wherein the optimization parameters are the vertical angle distribution function of the lidar;

[0127] In step S3, constructing the objective function based on the expected value of the point cloud number includes:

[0128] Step S31: Construct an initial objective function using the expected value of the point cloud number;

[0129] In this embodiment, to optimize the laser beam distribution of LiDAR to maximize its sensing capability, and focusing on the vertical angle distribution function, an optimization model is constructed using the expected value of the point cloud number as the initial objective function. The optimization model is expressed as:

[0130]

[0131] In the formula, M represents the total number of possible locations of the target vehicle. Represents the horizontal angular resolution Δα and the vertical angular distribution function. Below, located The number of effective point clouds that can be detected on the target vehicle. This represents the coordinates of the b-th discrete position. This indicates the time when the objective function reaches its maximum value. The value, This represents the optimal vertical angle distribution function.

[0132] Step S32: Construct a weighted function for distance, and modify the initial objective function using the weighted function to obtain the objective function;

[0133] In this embodiment, the optimization model aims to maximize the expected perception capability of the vehicle point cloud. However, simply optimizing the overall perception capability may lead to a bias in the LiDAR laser beam distribution towards closer targets, as occlusion and laser attenuation reduce the point cloud gain for distant vehicles. To address this issue, a distance-based weighting function is introduced to adjust the contribution weights according to the distance to the target vehicle's location, resulting in an objective function. The corrected optimization model based on this objective function is expressed as follows:

[0134]

[0135] In the formula, M represents the total number of possible locations of the target vehicle. Represents the horizontal angular resolution Δα and the vertical angular distribution function. Below, located The number of effective point clouds that can be detected on the target vehicle. This represents the coordinates of the b-th discrete position. This indicates the time when the objective function reaches its maximum value. The value, Represents the optimal vertical angle distribution function. Indicates about The weighting function, where k represents the slope of weight growth, determines the strength of the influence of distance growth on the weights.

[0136] Step S33: Set the constraints of the objective function. The constraints include beam angle limitation constraints and minimum angular resolution limitation constraints. The beam angle limitation constraints stipulate that the vertical angle of each laser beam is not less than the minimum vertical angle and not greater than the maximum vertical angle.

[0137] In this embodiment, the beam angle constraint restricts the laser's field of view, ensuring β min Allows detection of the underside of the nearest vehicle, while β max The formula for making the top of the furthest vehicle visible is as follows:

[0138] β min ≤β j ≤β max

[0139]

[0140] In the formula, β min and β max These represent the minimum and maximum perpendicular angles, respectively, and arctan(·) represents the arctangent function. l h represents the vertical coordinate of the lidar.veh d represents the height of the target vehicle. min and d max β represents the minimum and maximum detection range of LiDAR, respectively. j This represents the j-th vertical angle of the LiDAR laser.

[0141] The formula for the minimum angular resolution constraint is:

[0142] β j+1 -β j ≥Δ

[0143] In the formula, β j and β j+1 These represent the j-th and (j+1)-th vertical angles of the LiDAR laser, respectively, and Δ represents the preset resolution.

[0144] Step S4: Optimize the optimization parameters using the objective function and interval search algorithm to obtain the optimal vertical angle distribution function, and determine the lidar beam distribution using the optimal vertical angle distribution function.

[0145] In this embodiment, since the modified optimization model involves multiple variables, namely multiple vertical angles, the interdependence of multiple vertical angles makes it difficult to find the global optimal solution. Therefore, an interval search algorithm is designed to solve the problem based on the objective function to obtain the optimal vertical angle distribution function.

[0146] In step S4, obtaining the optimal vertical angle distribution function includes:

[0147] Step S41: Calculate the first vertical angle and the second vertical angle of each laser beam based on the uniform angle distribution and the uniform distance distribution, respectively;

[0148] In this embodiment, the LiDAR has multiple vertical angles, each corresponding to a laser beam. The LiDAR's vehicle detection capabilities differ under two different laser beam distribution modes: uniform angular distribution and uniform distance distribution. When the LiDAR's laser beams are uniformly distributed at equal angular intervals within the field of view, its detection capability for nearby vehicles is strong, but it struggles to detect distant targets. In contrast, a LiDAR laser beam distributed at a uniform distance relative to the detection range can effectively detect distant vehicles, but its detection capability for nearby targets is reduced.

[0149] The formulas for calculating the first and second perpendicular angles are:

[0150]

[0151] In the formula, and Let N represent the first and second vertical angles of the j′-th laser beam, respectively.β β represents the number of vertical angles. min and β max These represent the minimum and maximum perpendicular angles, respectively, and arctan(·) represents the arctangent function. l The vertical coordinate of the lidar is d. min and d max d represents the minimum and maximum detection ranges of LiDAR, respectively. j′ This represents the detection distance of the j′-th laser beam.

[0152] Step S42: Calculate the lower and upper bounds of the vertical angle of each laser beam using the first and second vertical angles;

[0153] In this embodiment, the formulas for calculating the lower and upper bounds of the vertical angle of each laser beam are as follows:

[0154]

[0155] In the formula, and Let represent the lower and upper bounds of the vertical angle of the j′-th laser beam, respectively. and Let represent the first vertical angle and the second vertical angle of the j′-th laser beam, respectively. min(·) represents the minimum value and max(·) represents the maximum value.

[0156] Step S43: Define the lower and upper bounds of the initial vertical angle distribution function by the lower and upper bounds of the vertical angle of each laser beam;

[0157] In this embodiment, the boundary of the initial vertical angle distribution function is as follows:

[0158]

[0159] In the formula, and Let these represent the lower and upper bounds of the initial vertical angle distribution function, respectively. and Let N represent the lower and upper bounds of the vertical angle of the j′-th laser beam, respectively. β Indicates the number of vertical angles.

[0160] Step S44: Use the lower and upper bounds of the initial vertical angle distribution function as the search interval, and iteratively optimize the search interval based on the segmentation ratio to obtain the optimal search interval;

[0161] In this embodiment, the search interval is in, and These represent the lower and upper bounds of the initial vertical angular distribution function, respectively.

[0162] In step S44, the optimal search interval is obtained, including:

[0163] Step S441: Generate the first laser beam distribution and the second laser beam distribution for each laser beam based on the segmentation ratio and the search space;

[0164] In this embodiment, the segmentation ratio is preset. In each iteration, two new laser beams are obtained according to the segmentation ratio, specifically:

[0165]

[0166] j′=1,2,…,N β

[0167] In the formula, and Let the first laser beam distribution and the second laser beam distribution of the j′-th laser beam be represented respectively. and Let N represent the lower and upper bounds of the j′-th laser beam, respectively, where φ represents the segmentation ratio, and N represents the upper bound of the lower bound of ... β Indicates the number of vertical angles.

[0168] Step S442: Combine all the first laser beam distributions into the first trial distribution, and combine all the second laser beam distributions into the second trial distribution;

[0169] Step S443: For the first and second trial distributions, calculate the corresponding first and second function values ​​using the objective function;

[0170] In this embodiment, for the first trial distribution and the second trial distribution Calculate the corresponding first function value using the objective function. Second function value

[0171] Step S444: Update the search space by updating the strategy, the first function value, and the second function value;

[0172] In this embodiment, if Then the second trial distribution As Otherwise, the first trial distribution will be... As This update strategy is used to continuously update the boundaries of the search space.

[0173] Step S445: Determine whether the termination threshold has been reached. If so, use the updated search interval as the optimal search interval; otherwise, proceed to the next iteration based on the updated search interval.

[0174] Step S45: Calculate the optimal vertical angle distribution function based on the lower and upper bounds of the optimal search interval.

[0175] In this embodiment, the formula for calculating the optimal vertical angle distribution function is:

[0176]

[0177] In the formula, This represents the optimal vertical angle distribution function.

[0178] In this embodiment, the interval search algorithm optimizes the LiDAR laser beam angles as a whole distribution, rather than optimizing the angle of each laser beam individually. This simplifies the inherently complex multidimensional optimization problem and significantly improves the efficiency of the optimization process. Compared to the traditional method of adjusting the angle of each laser beam individually, this holistic optimization method can consider the interrelationships between multiple angles simultaneously in a single search. Therefore, this algorithm can quickly find the optimal laser beam angle distribution in fewer iterations, thereby significantly improving optimization efficiency and maintaining stable convergence in large-scale problems.

[0179] In summary, this invention calculates the occlusion region between the target vehicle and LiDAR using a geometric method, enabling accurate quantification of the occlusion effect. By constructing a projection plane and calculating normal vectors, the occlusion problem in three-dimensional space can be transformed into a geometric problem on a two-dimensional plane, simplifying the analysis of complex occlusion relationships and making the calculation of the occlusion area feasible.

[0180] Secondly, evaluating LiDAR's perception capability by measuring the number of point clouds acquired from the target vehicle provides an intuitive and quantifiable evaluation standard. Furthermore, the introduction of distance-based energy attenuation and occlusion effects makes the evaluation of LiDAR's perception capability more comprehensive, reflecting the complex influencing factors in the real-world environment.

[0181] Finally, the laser beam angle distribution is optimized as a whole by using an interval search algorithm. When obtaining the optimal lidar beam distribution, the complexity of optimizing each laser beam angle individually is avoided, which significantly improves the optimization efficiency. Furthermore, the algorithm can find the optimal solution in a relatively small number of iterations.

[0182] Example 2:

[0183] This embodiment provides a roadside lidar beam distribution optimization device based on vehicle occlusion, the device comprising:

[0184] The first construction module is used to construct the desired occlusion effect of the target vehicle based on the occlusion relationship between the road-based lidar and dynamic vehicles.

[0185] The calculation module is used to quantify the sensing capability of the lidar. Based on the expected occlusion effect and the energy attenuation of the lidar, it calculates the expected value of the number of point clouds that the lidar can identify.

[0186] The second construction module is used to construct an objective function based on the expected value of the point cloud number and set optimization parameters, wherein the optimization parameters are the vertical angle distribution function of the lidar;

[0187] The optimization module is used to optimize the parameters through the objective function and the interval search algorithm to obtain the optimal vertical angle distribution function, and then determine the laser radar beam distribution through the optimal vertical angle distribution function.

[0188] The first building module includes:

[0189] The determining unit is used to determine the coordinates of the diagonal vertices of the bounding box of the target vehicle based on the position and size of the target vehicle;

[0190] The first calculation unit is used to calculate the area of ​​the occluded region of the target vehicle based on the coordinates of the diagonal vertices of the bounding box and the position of the lidar.

[0191] The second calculation unit is used to calculate the probability of an obstacle vehicle appearing in the occluded area based on the Poisson point process.

[0192] The first building unit is used to construct the projection plane and calculate the occlusion effect of the target vehicle based on the projection plane and the obstacle vehicle;

[0193] The third calculation unit is used to discretize the occlusion area and calculate the expected occlusion effect of the target vehicle by using the probability density function of the obstacle vehicle at different positions in the occlusion area, the probability of the obstacle vehicle appearing in the occlusion area, and the occlusion effect of the target vehicle.

[0194] The computing module includes:

[0195] The fourth calculation unit is used to calculate the theoretical number of point clouds on the target vehicle based on the first information and the vertical angle distribution function. The first information is the horizontal angle sensing range and vertical angle sensing range of the lidar beam on the target vehicle.

[0196] The fifth calculation unit is used to calculate the first point cloud loss ratio on the target vehicle based on the energy attenuation of the lidar.

[0197] The sixth calculation unit is used to calculate the proportion of the second point cloud loss on the target vehicle based on the expected occlusion effect;

[0198] The seventh calculation unit is used to calculate the second information based on the theoretical number of point clouds, the first point cloud loss ratio, and the second point cloud loss ratio. The second information is the number of detectable effective point clouds on the target vehicle.

[0199] The eighth calculation unit is used to calculate the expected number of point clouds that can be identified by the lidar using the second information.

[0200] It should be noted that the specific manner in which each module performs its operation in the apparatus described in the above embodiments has been described in detail in the embodiments of the method, and will not be elaborated here.

[0201] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0202] 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 variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included 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 optimizing the beam distribution of roadside lidar based on vehicle occlusion, characterized in that, include: Based on the occlusion relationship between the road-tested lidar and dynamic vehicles, the desired occlusion effect of the target vehicle is constructed. The sensing capability of lidar is quantified, and the expected number of point clouds that lidar can identify is calculated based on the expected occlusion effect and the energy attenuation of lidar. An objective function is constructed based on the expected value of the number of points in the cloud, and optimization parameters are set, wherein the optimization parameters are the vertical angle distribution function of the lidar. The optimization parameters are optimized by using an objective function and an interval search algorithm to obtain the optimal vertical angle distribution function, and the laser radar beam distribution is determined by the optimal vertical angle distribution function. Among them, based on the occlusion relationship between road-tested LiDAR and dynamic vehicles, the desired occlusion effect of the target vehicle is constructed, including: Calculate the normal vector of the projection plane based on the coordinates of the center point and two diagonal vertices of the target vehicle; Using lidar as the light source, the projection equation of the obstacle vehicle on the projection plane is constructed. The projection equation is as follows: ； ； In the formula, and These represent the coordinates of the obstacle vehicle before and after its projection. Indicates the location of the lidar. Indicates the scaling factor. , and Both represent the normal vector of the projection plane. The amount, This represents a constant that determines the position of the projection plane. , and These represent the horizontal, vertical, and triangular coordinates of the lidar, respectively. , and These represent the x-coordinate, y-coordinate, and vertical coordinate of a diagonal vertex of the obstacle vehicle, respectively. Calculate the projected coordinates of the two diagonal vertices of the obstacle vehicle based on the projection equation; The overlapping area between the target vehicle and the obstacle vehicle is calculated based on the coordinates of the two diagonal vertices of the target vehicle and the projected coordinates of the two diagonal vertices of the obstacle vehicle. The formula for calculating the overlapping area is: ； ； ； In the formula, This indicates the area of ​​the overlapping region between the obstacle vehicle and the target vehicle. Indicates the width of the overlapping region. Indicates the length of the overlapping region. This indicates taking the minimum value. This indicates taking the maximum value. Indicates the height of the target vehicle. and These represent the x-coordinates of the two diagonal vertices of the target vehicle. and These represent the ordinates of the two diagonal vertices of the target vehicle. and These represent the projected x-coordinates of the two diagonal vertices of the obstacle vehicle. and These represent the projected ordinates of the two diagonal vertices of the obstacle vehicle, respectively. Represents the projected vertical coordinate of one diagonal vertex of the obstacle vehicle; The occlusion ratio of the target vehicle is calculated based on the area of ​​the overlapping region, and the occlusion effect of the target vehicle is defined based on the occlusion ratio. The formula for calculating the shading ratio is: ； In the formula, This indicates the area of ​​the overlapping region between the obstacle vehicle and the target vehicle. Indicates the width of the overlapping region. Indicates the length of the overlapping region. , and These represent the length, width, and height of the target vehicle, respectively. The occlusion effect function is: ； In the formula, This indicates the occlusion effect of the target vehicle. Represents the shading benefit function. and These represent the positions of the obstacle vehicle, the target vehicle, and the lidar, respectively. Indicates traffic density; The occlusion region is discretized, and the expected occlusion effect of the target vehicle is calculated using the probability density function of the obstacle vehicle at different positions within the occlusion region, the probability of the obstacle vehicle appearing in the occlusion region, and the occlusion effect of the target vehicle. The expression for the expected occlusion effect is as follows: ； In the formula, This represents the desired occlusion effect of the target vehicle. Indicates the area to be covered The number of obstacle vehicle locations in the internal discretization. This indicates the probability that an obstructing vehicle appears in the obstructed area. Represents the shading benefit function. Represents the discrete order of the first... The location of the obstructing vehicle. and These represent the positions of the target vehicle and the lidar, respectively. Indicates traffic density.

2. The method for optimizing roadside lidar beam distribution based on vehicle occlusion according to claim 1, characterized in that... The construction of the desired occlusion effect of the target vehicle includes: Determine the coordinates of the diagonal vertices of the bounding box of the target vehicle based on its position and size; The area of ​​the target vehicle's obstruction is calculated based on the coordinates of the diagonal vertices of the bounding box and the position of the lidar. Based on the Poisson point process, the probability of an obstacle vehicle appearing in the occluded area is calculated.

3. The method for optimizing roadside lidar beam distribution based on vehicle occlusion according to claim 1, characterized in that... The calculation steps for the expected value of the point cloud number are as follows: The theoretical number of point clouds on the target vehicle is calculated based on the first information and the vertical angle distribution function. The first information is the horizontal angle sensing range and the vertical angle sensing range of the laser radar beam on the target vehicle. Calculate the first point cloud loss ratio on the target vehicle based on the energy attenuation of lidar; Calculate the proportion of second point cloud loss on the target vehicle based on the expected occlusion effect; The second information is calculated based on the theoretical number of point clouds, the first point cloud loss ratio, and the second point cloud loss ratio. The second information is the number of detectable effective point clouds on the target vehicle. The expected number of point clouds that the lidar can identify is calculated using the second information.

4. The method for optimizing roadside lidar beam distribution based on vehicle occlusion according to claim 1, characterized in that... The objective function constructed based on the expected value of point cloud data includes: Construct an initial objective function based on the expected value of the point cloud data; Construct a distance-based weighted function, and then modify the initial objective function using the weighted function to obtain the objective function; The objective function is constrained by the following constraints: beam angle constraint and minimum angular resolution constraint. The beam angle constraint ensures that the vertical angle of each laser beam is not less than the minimum vertical angle and not greater than the maximum vertical angle.

5. The method for optimizing roadside lidar beam distribution based on vehicle occlusion according to claim 1, characterized in that... The process of obtaining the optimal vertical angle distribution function includes: The first vertical angle and the second vertical angle of each laser beam are calculated based on the uniform angle distribution and the uniform distance distribution, respectively. The lower and upper bounds of the vertical angle of each laser beam are calculated using the first and second vertical angles. The lower and upper bounds of the initial vertical angle distribution function are defined by the lower and upper bounds of the vertical angle of each laser beam; The lower and upper bounds of the initial vertical angle distribution function are used as the search interval, and the search interval is iteratively optimized based on the segmentation ratio to obtain the optimal search interval; The optimal vertical angle distribution function is calculated based on the lower and upper bounds of the optimal search interval.

6. The method for optimizing roadside lidar beam distribution based on vehicle occlusion according to claim 5, characterized in that... The optimal search interval is obtained, including: The first and second laser beam distributions for each laser beam are generated based on the segmentation ratio and the search space. Combine all the first laser beam distributions into the first trial distribution, and combine all the second laser beam distributions into the second trial distribution; For the first and second trial distributions, calculate the corresponding first and second function values ​​using the objective function; The search space is updated by updating the strategy, the first function value, and the second function value; Determine whether the termination threshold has been reached. If so, use the updated search interval as the optimal search interval; otherwise, proceed to the next iteration based on the updated search interval.

7. A roadside lidar beam distribution optimization device based on vehicle occlusion, characterized in that, include: The first construction module is used to construct the desired occlusion effect of the target vehicle based on the occlusion relationship between the road-based lidar and dynamic vehicles. The calculation module is used to quantify the sensing capability of the lidar. Based on the expected occlusion effect and the energy attenuation of the lidar, it calculates the expected value of the number of point clouds that the lidar can identify. The second construction module is used to construct an objective function based on the expected value of the point cloud number and set optimization parameters, wherein the optimization parameters are the vertical angle distribution function of the lidar; The optimization module is used to optimize the optimization parameters through the objective function and the interval search algorithm to obtain the optimal vertical angle distribution function, and to determine the laser radar beam distribution through the optimal vertical angle distribution function; Among them, based on the occlusion relationship between road-tested LiDAR and dynamic vehicles, the desired occlusion effect of the target vehicle is constructed, including: Calculate the normal vector of the projection plane based on the coordinates of the center point and two diagonal vertices of the target vehicle; Using lidar as the light source, the projection equation of the obstacle vehicle on the projection plane is constructed. The projection equation is as follows: ； ； In the formula, and These represent the coordinates of the obstacle vehicle before and after its projection. Indicates the location of the lidar. Indicates the scaling factor. , and Both represent the normal vector of the projection plane. The amount, This represents a constant that determines the position of the projection plane. , and These represent the horizontal, vertical, and triangular coordinates of the lidar, respectively. , and These represent the x-coordinate, y-coordinate, and vertical coordinate of a diagonal vertex of the obstacle vehicle, respectively. Calculate the projected coordinates of the two diagonal vertices of the obstacle vehicle based on the projection equation; The overlapping area between the target vehicle and the obstacle vehicle is calculated based on the coordinates of the two diagonal vertices of the target vehicle and the projected coordinates of the two diagonal vertices of the obstacle vehicle. The formula for calculating the overlapping area is: ； ； ； In the formula, This indicates the area of ​​the overlapping region between the obstacle vehicle and the target vehicle. Indicates the width of the overlapping region. Indicates the length of the overlapping region. This indicates taking the minimum value. This indicates taking the maximum value. Indicates the height of the target vehicle. and These represent the x-coordinates of the two diagonal vertices of the target vehicle. and These represent the ordinates of the two diagonal vertices of the target vehicle. and These represent the projected x-coordinates of the two diagonal vertices of the obstacle vehicle. and These represent the projected ordinates of the two diagonal vertices of the obstacle vehicle, respectively. Represents the projected vertical coordinate of one diagonal vertex of the obstacle vehicle; The occlusion ratio of the target vehicle is calculated based on the area of ​​the overlapping region, and the occlusion effect of the target vehicle is defined based on the occlusion ratio. The formula for calculating the shading ratio is: ； In the formula, This indicates the area of ​​the overlapping region between the obstacle vehicle and the target vehicle. Indicates the width of the overlapping region. Indicates the length of the overlapping region. , and These represent the length, width, and height of the target vehicle, respectively. The occlusion effect function is: ； In the formula, This indicates the occlusion effect of the target vehicle. Represents the shading benefit function. and These represent the positions of the obstacle vehicle, the target vehicle, and the lidar, respectively. Indicates traffic density; The occlusion region is discretized, and the expected occlusion effect of the target vehicle is calculated using the probability density function of the obstacle vehicle at different positions within the occlusion region, the probability of the obstacle vehicle appearing in the occlusion region, and the occlusion effect of the target vehicle. The expression for the expected occlusion effect is as follows: ； In the formula, This represents the desired occlusion effect of the target vehicle. Indicates the area to be covered The number of obstacle vehicle locations in the internal discretization. This indicates the probability that an obstructing vehicle appears in the obstructed area. Represents the shading benefit function. Represents the discrete order of the first... The location of the obstructing vehicle. and These represent the positions of the target vehicle and the lidar, respectively. Indicates traffic density.

8. The roadside lidar beam distribution optimization device based on vehicle occlusion according to claim 7, characterized in that, The first building module includes: The determining unit is used to determine the coordinates of the diagonal vertices of the bounding box of the target vehicle based on the position and size of the target vehicle; The first calculation unit is used to calculate the area of ​​the occluded region of the target vehicle based on the coordinates of the diagonal vertices of the bounding box and the position of the lidar. The second calculation unit is used to calculate the probability of an obstacle vehicle appearing in the occluded area based on the Poisson point process. The first building unit is used to construct the projection plane and calculate the occlusion effect of the target vehicle based on the projection plane and the obstacle vehicle.

9. The roadside lidar beam distribution optimization device based on vehicle occlusion according to claim 7, characterized in that, The computing module includes: The fourth calculation unit is used to calculate the theoretical number of point clouds on the target vehicle based on the first information and the vertical angle distribution function. The first information is the horizontal angle sensing range and vertical angle sensing range of the lidar beam on the target vehicle. The fifth calculation unit is used to calculate the first point cloud loss ratio on the target vehicle based on the energy attenuation of the lidar. The sixth calculation unit is used to calculate the proportion of the second point cloud loss on the target vehicle based on the expected occlusion effect; The seventh calculation unit is used to calculate the second information based on the theoretical number of point clouds, the first point cloud loss ratio, and the second point cloud loss ratio. The second information is the number of detectable effective point clouds on the target vehicle. The eighth calculation unit is used to calculate the expected number of point clouds that can be identified by the lidar using the second information.

Images