Method and device for determining roadside base station installation information

Abstract

This application relates to the field of target detection technology and provides a method and apparatus for determining roadside base station installation information. In this method, a high-precision map is used as the basic simulation environment, combined with sensor simulation techniques, to accurately reconstruct the perception results of roadside base stations within the target area. Furthermore, considering that pedestrian data is typically limited during simulation, potentially leading to the omission of blind spots and affecting the installation layout of roadside base stations, a pedestrian model is incorporated to update the blind spot information. The resulting base station installation locations further enhance the perception capabilities of the roadside base stations.

Description

Technical Field

[0001] This application belongs to the field of intelligent transportation technology, and in particular relates to a method and apparatus for determining roadside base station installation information. Background Technology

[0002] With the rapid development of the economy and society, in order to better support vehicle-road cooperation, it is necessary to improve road perception capabilities and introduce more advanced sensing technologies.

[0003] In related technologies, the installation information of roadside base stations is usually calculated based on the site environment and the coverage capability of the roadside base stations. However, for some special scenarios, simply deploying roadside base stations according to the coverage area cannot obtain the best perception results.

[0004] Therefore, determining the installation information of roadside sensing devices to obtain the best sensing results has become an urgent technical problem to be solved. Summary of the Invention

[0005] This application provides a method and apparatus for determining roadside base station installation information, which can determine the installation information of roadside sensing devices and improve the sensing capability of roadside base stations.

[0006] In a first aspect, embodiments of this application provide a method for determining roadside base station installation information. The method includes: acquiring initial installation information of roadside base stations in a target area; performing simulation based on a high-precision map of the target area and the initial installation information of the roadside base stations to obtain a first simulation result, wherein the first simulation result includes blind spot information in the target area; using a preset pedestrian model to augment the first simulation result to update the blind spot information and obtain a second simulation result; and adjusting the initial installation information of the roadside base stations based on the second simulation result to obtain target installation information of the roadside base stations.

[0007] Optionally, in one possible implementation of the first aspect, the above-mentioned simulation based on the high-precision map of the target area and the initial installation information of the roadside base station to obtain the first simulation result includes: performing simulation based on the high-precision map of the target area, the initial installation information of the roadside base station and the preset calibration parameters to obtain the first simulation result.

[0008] Optionally, in another possible implementation of the first aspect, the above-mentioned simulation based on the high-precision map of the target area, the initial installation information of the roadside base station, and the preset calibration parameters to obtain the first simulation result includes: simulating the sensing range of the roadside base station based on the initial installation information of the roadside base station and the preset calibration parameters to obtain the sensing range simulation area; determining blind spot information based on the high-precision map of the target area and the sensing range simulation area; displaying the sensing range simulation area in the high-precision map, and combining it with the blind spot information to obtain the first simulation result.

[0009] Optionally, in another possible implementation of the first aspect, the roadside base station includes a roadside radar, and the sensing range simulation area includes a point cloud coverage simulation area. The above-mentioned simulation of the sensing range of the roadside base station based on the initial installation information and preset calibration parameters of the roadside base station to obtain the sensing range simulation area includes: simulating the beam of the roadside radar based on the initial installation information and calibration parameters corresponding to the roadside radar to obtain the point cloud coverage simulation area.

[0010] Optionally, in another possible implementation of the first aspect, the roadside base station includes a roadside radar and a camera, and the sensing range simulation area includes a fusion coverage simulation area. The simulation of the sensing range of the roadside base station based on its initial installation information and preset calibration parameters to obtain the sensing range simulation area includes: simulating the beam of the roadside radar based on the initial installation information of the roadside radar, the initial installation information of the camera, and calibration parameters to obtain a point cloud coverage simulation area; and simulating the range that the camera can collect to obtain an image coverage simulation area. The fusion coverage simulation area is then determined based on the point cloud coverage simulation area and the image coverage simulation area.

[0011] Optionally, in another possible implementation of the first aspect, the calibration parameters mentioned above include a rotation matrix and a translation matrix.

[0012] Optionally, in another possible implementation of the first aspect, adjusting the initial installation information of the roadside base station based on the second simulation result to obtain the target installation information of the roadside base station includes: determining the standard coverage area based on the second simulation result; adjusting the initial installation information of the roadside base station based on the standard coverage area to make the sensing range of the roadside base station match the standard coverage area to obtain the third simulation result; and outputting the target installation information of the roadside base station based on the third simulation result.

[0013] Optionally, in another possible implementation of the first aspect, obtaining the initial installation information of the roadside base station in the target area includes: receiving the initial installation information of the roadside base station input by the user; or, determining the attribute information of the roadside base station according to the received simulation instructions, and obtaining the initial installation information of the roadside base station from a preset data storage repository according to the attribute information of the roadside base station.

[0014] Optionally, in another possible implementation of the first aspect, the target installation information includes the target installation quantity, target installation location, target installation height, and target installation direction.

[0015] Secondly, embodiments of this application provide a device for determining roadside base station installation information, the device comprising:

[0016] The acquisition module is used to acquire the initial installation information of roadside base stations within the target area;

[0017] The simulation module is used to perform simulation based on the high-precision map of the target area and the initial installation information of the roadside base station to obtain the first simulation result, wherein the first simulation result includes blind spot information in the target area;

[0018] An expansion module is used to expand the data of the first simulation result using a preset pedestrian model to update the blind spot information and obtain the second simulation result;

[0019] The processing module is used to adjust the initial installation information of the roadside base station based on the second simulation results to obtain the target installation information of the roadside base station.

[0020] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the electronic device is able to implement any of the methods described in the first aspect above.

[0021] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on an electronic device, enable the electronic device to perform any of the methods described in the first aspect.

[0022] Fifthly, embodiments of this application provide a computer program product, which includes a computer program. When the computer program is executed by an electronic device, the electronic device is able to implement any of the methods described in the first aspect.

[0023] The beneficial effects of this application embodiment compared with the prior art are as follows: This application discloses a method and apparatus for determining roadside base station installation information. In this method, the initial installation information of roadside base stations in the target area is first obtained. Then, simulation is performed based on the high-precision map of the target area and the initial installation information of the roadside base stations to obtain a first simulation result, wherein the first simulation result includes blind spot information in the target area. Next, the first simulation result is augmented with a pedestrian model to update the blind spot information, resulting in a second simulation result. Finally, the initial installation information of the roadside base stations is adjusted based on the second simulation result to obtain the target installation information of the roadside base stations. Thus, by using a high-precision map as the basic simulation environment and combining it with sensor simulation methods, the perception results of roadside base stations in the target area are accurately reproduced. In addition, considering that pedestrian data is usually scarce during the simulation process, which may lead to some potential blind spots not being considered and affecting the installation layout of roadside base stations, by adding a pedestrian model to update the blind spot information, the final base station installation location can further improve the perception capability of the roadside base stations. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart illustrating a method for determining roadside base station installation information provided in an embodiment of this application;

[0026] Figure 2 This is a schematic diagram of a process for determining a first simulation result provided in an embodiment of this application;

[0027] Figure 3 This is a schematic diagram of the perception of a lidar in a radar pose calibration software interface provided in an embodiment of this application;

[0028] Figure 4 This is a schematic diagram of the structure of a device for determining roadside base station installation information provided in an embodiment of this application;

[0029] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0030] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0031] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0032] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0033] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0034] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0035] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0036] It should be understood that the sequence number of each step in this embodiment does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of this application embodiment.

[0037] In related technologies, the installation information of roadside base stations is usually calculated based on the site environment and the coverage capability of the roadside base stations. However, for some special scenarios, simply deploying roadside base stations according to the coverage area cannot obtain the best sensing results. For example, if a roadside base station is installed on a pole and there is a sign on the pole obstructing the view, the installation height of the roadside base station needs to be recalculated. The higher the installation position of the roadside base station, the larger the blind spot. If there are multiple possible installation positions on site, it is difficult to determine the optimal installation position. A poor installation position of the roadside base station will affect the sensing accuracy of the roadside base station, leading to the need to reinstall the roadside base station, increasing the complexity and cost of roadside base station installation.

[0038] In view of this, embodiments of this application provide a method and apparatus for determining roadside base station installation information. In this method, firstly, initial installation information of roadside base stations within a target area is acquired. Then, simulation is performed based on a high-precision map of the target area and the initial installation information of the roadside base stations to obtain a first simulation result, which includes blind spot information within the target area. Next, a pedestrian model is used to augment the first simulation result to update the blind spot information, resulting in a second simulation result. Finally, based on the second simulation result, the initial installation information of the roadside base stations is adjusted to obtain the target installation information of the roadside base stations. Thus, by using a high-precision map as the basic simulation environment and combining it with sensor simulation methods, the perception results of roadside base stations within the target area are accurately reproduced. Furthermore, considering that pedestrian data is usually limited during simulation, potentially leading to the omission of certain blind spots and affecting the installation layout of roadside base stations, the addition of a pedestrian model to update the blind spot information further enhances the perception capability of the roadside base stations by ultimately obtaining the base station installation location.

[0039] To illustrate the technical solution of this application, specific embodiments are described below.

[0040] Reference Figure 1 The diagram illustrates a flowchart of a method for determining roadside base station installation information according to an embodiment of this application. Figure 1 As shown, the method may include the following steps:

[0041] Step 101: Obtain the initial installation information of roadside base stations within the target area.

[0042] It should be noted that the target area is the area that requires collaborative sensing by roadside base stations. Roadside base stations may include at least one of LiDAR, millimeter-wave radar, and cameras. This application does not specifically limit the types of sensors included in the roadside base stations.

[0043] In this embodiment of the application, it is necessary to obtain the initial installation information of roadside base stations within the target area before simulation. This initial installation information can be the installation information of the roadside base stations in a high-precision map of the target area. In other words, the initial installation information can be obtained by first determining the initial installation information of the roadside base stations in the target area, and then mapping this initial installation information to a high-precision map of the target area to obtain the initial installation information of the roadside base stations in the high-precision map of the target area.

[0044] It's important to note that high-precision maps are maps with sub-meter level accuracy. They contain richer road elements, such as detailed lane lines, road signs, traffic lights, lane curvature, and slope. In practical applications, elements existing in the physical world, such as road width, traffic light height, lane lines, and road signs, can be extracted using devices like smart base stations, LiDAR, or sensors. This information is then used as the precision data for generating a high-precision map. The high-precision map is then obtained by merging this precision data with an original map.

[0045] In one embodiment, the initial installation information may include the type of sensor contained in the roadside base station and the location information of the roadside base station in the target area. The number of roadside base stations is at least one; therefore, the initial installation information also includes at least one location information, i.e., one roadside base station corresponds to one location information, which includes the location, altitude, and orientation of the roadside base station in the target area.

[0046] In one embodiment, the initial installation information of the roadside base station can be obtained by receiving user input; or, based on a received simulation command, the attribute information of the roadside base station can be determined, and the initial installation information of the roadside base station can be retrieved from a preset data repository based on the attribute information of the roadside base station. The simulation command is an instruction to simulate installation information in a target area. After receiving the simulation command, the electronic device determines the attribute information of the roadside base station, including information such as the type and model of the roadside base station. The data repository pre-stores the correspondence between the attribute information and installation information of multiple roadside base stations. Therefore, the installation information corresponding to the roadside base station can be retrieved from the data repository based on the attribute information of the roadside base station, and the installation information retrieved from the data repository can be determined as the initial installation information.

[0047] Step 102: Perform simulation based on the high-precision map of the target area and the initial installation information of the roadside base station to obtain the first simulation result.

[0048] In this embodiment of the application, the roadside base station is simulated and perceived using a high-precision map of the target area, which can accurately determine the first simulation result of the roadside base station in the target area and ensure the accuracy of the first simulation result.

[0049] In one embodiment, a simulation can be performed based on a high-precision map of the target area, the initial installation information of the roadside base station, and preset calibration parameters to obtain a first simulation result. The preset calibration parameters refer to the calibration parameters of the roadside base station. These calibration parameters can be rotation and translation matrices, specifically the rotation and translation matrices between the roadside base station's own coordinate system and the Earth's coordinate system.

[0050] It should be noted that before determining the installation information of the roadside base station, it is first necessary to select a coordinate origin and establish a coordinate system based on the selected origin. Based on the embodiments of this application, the Earth coordinate system can be used as the required coordinate system. Therefore, the obtained location of the roadside base station is its coordinate position in the Earth coordinate system. This application does not limit the location of the selected coordinate origin.

[0051] This can be achieved using perception simulation software. Specifically, the calibration parameters and initial installation information of the roadside base station can be input into the perception simulation software. The software can then perform a simulation based on these parameters and obtain a first simulation result of the roadside base station's perception of the target area. This first simulation result can include the perception area of ​​the roadside base station, which can be displayed on a high-precision map of the target area.

[0052] In one embodiment, the first simulation result includes blind zone information within the target area. This blind zone information may include pre-defined blind zone location information. The blind zone can be an area that does not affect roadside sensing, i.e., an area typically not considered during the installation of roadside base stations, such as building areas.

[0053] In one possible implementation, the specific steps of step 102 are as follows: Figure 2 As shown, it includes:

[0054] Step 201: Based on the initial installation information and preset calibration parameters of the roadside base station, simulate the sensing range of the roadside base station to obtain the simulated sensing range area.

[0055] It should be noted that, based on the initial installation information of the roadside base station and its calibration parameters, the sensing range of the roadside base station can be simulated. Then, based on the initial installation information of the roadside base station, the sensing range can be mapped to the target area to obtain the simulated sensing range area of ​​the roadside base station.

[0056] In one embodiment, when the roadside base station includes a roadside radar, the radar beam can be simulated based on the initial installation information and calibration parameters corresponding to the roadside radar to obtain a simulated point cloud coverage area. The roadside radar can be a lidar, millimeter-wave radar, or similar type.

[0057] In many LiDAR applications, datasets are primarily generated and labeled based on a single LiDAR sensor. While such datasets offer advantages in labeling and training models, they also have limitations. For example, single-LiDAR point cloud data may struggle to capture all details in complex scenes, especially with occlusion, reflections, or distant objects. By controlling the configuration of multiple LiDAR sensors (e.g., dual-LiDAR, triple-LiDAR), the sparsity of the generated point cloud can be influenced. Specifically, increasing the number of LiDARs increases the point cloud density, helping to capture more details and complex environmental features; adjusting the distance between LiDARs alters the coverage and sparsity of the point cloud, with smaller spacing providing a denser point cloud, while larger spacing may result in sparser points in certain areas. Through these controls, data from different scenarios can be simulated, facilitating training and testing under various conditions.

[0058] Furthermore, when using multiple lidar systems, the point cloud data from different lidar systems can first be converted to the same coordinate system for fusion. Then, the point clouds can be registered using an algorithm to ensure that the data from different laser sources can be accurately aligned. Next, the registered point cloud data can be fused to generate a unified, dense point cloud representation. Finally, by controlling the laser configuration and adjusting the stitching algorithm, the point cloud sparsity and stitching problems in the multi-lidar system can be effectively solved, thereby improving the accuracy and reliability of environmental perception.

[0059] In one embodiment, when the roadside base station includes a roadside radar and a camera, the radar beam can be simulated based on the initial installation information of the roadside radar, the initial installation information of the camera, and calibration parameters to obtain a point cloud coverage simulation area, and the range that the camera can collect can be simulated to obtain an image coverage simulation area. A fused coverage simulation area is then determined based on the point cloud coverage simulation area and the image coverage simulation area. Therefore, by fusing the point cloud coverage simulation area and the image coverage simulation area of ​​the roadside radar and camera in the target area, a fused coverage simulation area is obtained, ensuring the accuracy of the roadside base station's sensing coverage range in the target area and further guaranteeing the sensing effect of the roadside base station.

[0060] Step 202: Determine blind spot information based on the high-precision map of the target area and the simulated area of ​​the perception range.

[0061] In this embodiment of the application, the area outside the simulated area of ​​the perception range in the high-precision map of the target area is the blind zone. Furthermore, the location information of the blind zone in the Earth coordinate system can be determined as the blind zone information.

[0062] Step 203: Display the simulated area of ​​the perception range on the high-precision map, and combine it with blind spot information to obtain the first simulation result.

[0063] Specifically, the simulated area of ​​the perception range obtained above is displayed in the high-precision map corresponding to the target area, that is, a schematic picture of the simulated area of ​​the perception range is displayed in the high-precision map, and the schematic picture of the simulated area of ​​the perception range in the high-precision map is determined as the first simulation result.

[0064] In one embodiment, the simulated sensing range of the roadside base station can be displayed by the pose calibration software. Specifically, a high-precision map of the target area is input into the pose calibration software, the origin of the coordinate system is selected, and a coordinate system is established. The initial installation information and calibration parameters of the roadside base station are set in the pose calibration software, and the actual sensing range simulation area of ​​the roadside base station can be determined and overlapped with the high-precision map to obtain the first simulation result.

[0065] Step 103: Use a preset pedestrian model to augment the data of the first simulation result to update the blind spot information and obtain the second simulation result.

[0066] It's important to note that the scarcity of pedestrian and other target data impacts the installation of roadside base stations. A lack of pedestrian data may prevent roadside base stations from identifying potential blind spots, especially in complex urban environments where pedestrians can appear unexpectedly, leading to insufficient visual coverage of certain areas and affecting their sensing capabilities. Furthermore, the accuracy of pedestrian identification is crucial in subsequent traffic event analysis using roadside base stations. The scarcity of pedestrian and other target data may prevent timely pedestrian identification, increasing the risk of accidents. Therefore, it's necessary to augment the initial simulation results with a pedestrian model to update blind spot information. In other words, areas where pedestrians frequently appear can be included in the initial blind spot information as areas requiring roadside base station sensing.

[0067] In this embodiment, the pedestrian model is a mathematical or computational model used to simulate and analyze pedestrian behavior. During data augmentation, the pedestrian model can simulate pedestrian behavior in different scenarios, generating virtual pedestrian data to supplement the deficiencies of actual data. This augmented data can be used to update blind spot information, improve the identification capabilities of roadside base stations, thereby enhancing the accuracy and timeliness of traffic event (e.g., collision event) analysis and reducing accident risks. Through the reasonable application of the pedestrian model, pedestrian behavior can be better understood and predicted in complex urban environments, thereby optimizing traffic management and safety measures.

[0068] Step 104: Based on the second simulation results, adjust the initial installation information of the roadside base station to obtain the target installation information of the roadside base station.

[0069] In one embodiment, the target installation information may include the type of sensor contained in the roadside base station and the location information of the roadside base station in the target area. The number of roadside base stations is at least one; therefore, the target installation information also includes at least one location information, i.e., one roadside base station corresponds to one location information, which includes the location, altitude, and orientation of the roadside base station in the target area.

[0070] In this embodiment, the second simulation result is obtained based on the initial installation information of the roadside base station in the high-precision map of the target area and blind spot updates. At this time, the installation information of the roadside base station has not yet been updated, and the second simulation result may not meet the perception requirements of the target area. That is, the simulated area of ​​the roadside base station's perception range in the second simulation result does not completely cover the area that needs to be perceived by the roadside base station. Therefore, a standard coverage area can be determined based on the second simulation result, where the standard coverage area is the area that needs to be perceived by the roadside base station. Based on the standard coverage area, the initial installation information of the roadside base station is adjusted to match the perception range of the roadside base station with the standard coverage area, resulting in a third simulation result. Based on the third simulation result, the target installation information of the roadside base station is output. In other words, it is necessary to ensure that the perception range of the roadside base station is greater than or equal to the standard coverage area so that the roadside base station can meet the perception requirements of the target area.

[0071] The method for determining roadside base station installation information disclosed in the above embodiments of this application first obtains the initial installation information of roadside base stations in the target area. Then, it performs simulation based on a high-precision map of the target area and the initial installation information of the roadside base stations to obtain a first simulation result, wherein the first simulation result includes blind spot information in the target area. Next, it uses a pedestrian model to augment the first simulation result to update the blind spot information, obtaining a second simulation result. Finally, based on the second simulation result, it adjusts the initial installation information of the roadside base stations to obtain the target installation information of the roadside base stations. Thus, by using a high-precision map as the basic simulation environment and combining sensor simulation methods, the perception results of roadside base stations in the target area are accurately reproduced. In addition, considering that pedestrian data is usually scarce during the simulation process, which may lead to some potential blind spots not being taken into account and affecting the installation layout of roadside base stations, by adding a pedestrian model to update the blind spot information, the final base station installation location can further improve the perception capability of the roadside base stations.

[0072] Taking lidar as an example, such as Figure 3 As shown, Figure 3This is a schematic diagram of the LiDAR's perception in the radar pose calibration software interface. In practical applications, a high-precision map of the LiDAR's location can be loaded into the radar pose calibration software. A point can be selected as the origin, and then the latitude and longitude coordinates of the LiDAR can be added. By setting the LiDAR's rotation and translation matrices, the actual LiDAR beam can be simulated and overlapped with the points on the high-precision map. By observing the LiDAR's coverage area and adjusting parameters such as the LiDAR's installation height and position, the optimal LiDAR installation location, height, and number of LiDARs can be obtained.

[0073] See Figure 4 The diagram shows a schematic of a device for determining roadside base station installation information according to an embodiment of this application. For ease of explanation, only the parts related to the embodiment of this application are shown.

[0074] The device 400 for determining roadside base station installation information may specifically include the following modules:

[0075] The acquisition module 401 is used to acquire the initial installation information of roadside base stations in the target area.

[0076] The simulation module 402 is used to perform simulation based on the high-precision map of the target area and the initial installation information of the roadside base station to obtain the first simulation result, wherein the first simulation result includes blind spot information in the target area.

[0077] The expansion module 403 is used to expand the data of the first simulation result using a preset pedestrian model to update the blind spot information and obtain the second simulation result.

[0078] The processing module 404 is used to adjust the initial installation information of the roadside base station based on the second simulation results to obtain the target installation information of the roadside base station.

[0079] The device for determining roadside base station installation information disclosed in the above embodiments of this application first acquires the initial installation information of roadside base stations in a target area. Then, it performs simulation based on a high-precision map of the target area and the initial installation information of the roadside base stations to obtain a first simulation result, wherein the first simulation result includes blind spot information in the target area. Next, it uses a pedestrian model to augment the first simulation result to update the blind spot information, obtaining a second simulation result. Finally, based on the second simulation result, it adjusts the initial installation information of the roadside base stations to obtain the target installation information of the roadside base stations. Thus, by using a high-precision map as the basic simulation environment and combining sensor simulation methods, the perception results of roadside base stations in the target area are accurately reproduced. In addition, considering that pedestrian data is usually scarce during the simulation process, which may lead to some potential blind spots not being taken into account and affecting the installation layout of roadside base stations, by adding a pedestrian model to update the blind spot information, the final base station installation location can further improve the perception capability of the roadside base stations.

[0080] Furthermore, in one possible implementation of this application embodiment, the simulation module 402 may specifically include the following units:

[0081] The first simulation unit is used to perform simulation based on the high-precision map of the target area, the initial installation information of the roadside base station, and the preset calibration parameters to obtain the first simulation result.

[0082] Furthermore, in another possible implementation of the embodiments of this application, the first simulation unit is specifically used to: simulate the sensing range of the roadside base station according to the initial installation information of the roadside base station and the preset calibration parameters to obtain the sensing range simulation area; determine the blind spot information according to the high-precision map of the target area and the sensing range simulation area; display the sensing range simulation area in the high-precision map, and combine it with the blind spot information to obtain the first simulation result.

[0083] Furthermore, in another possible implementation of this application embodiment, the roadside base station includes a roadside radar, the sensing range simulation area includes a point cloud coverage simulation area, and the first simulation unit is specifically used to: simulate the beam of the roadside radar according to the initial installation information and calibration parameters corresponding to the roadside radar to obtain the point cloud coverage simulation area.

[0084] Furthermore, in another possible implementation of this application embodiment, the roadside base station includes a roadside radar and a camera, and the sensing range simulation area includes a fused coverage simulation area. The first simulation unit is specifically used to: simulate the beam of the roadside radar based on the initial installation information corresponding to the roadside radar, the initial installation information corresponding to the camera, and calibration parameters to obtain a point cloud coverage simulation area; and simulate the range that the camera can collect to obtain an image coverage simulation area. The fused coverage simulation area is then determined based on the point cloud coverage simulation area and the image coverage simulation area.

[0085] Furthermore, in another possible implementation of the embodiments of this application, the above calibration parameters include a rotation matrix and a translation matrix.

[0086] Furthermore, in another possible implementation of this application embodiment, the above-mentioned processing module 404 may specifically include the following units:

[0087] The first processing unit is used to determine the standard coverage area based on the second simulation results.

[0088] The second processing unit is used to adjust the initial installation information of the roadside base station according to the standard coverage area so that the sensing range of the roadside base station matches the standard coverage area, and obtain the third simulation result.

[0089] The third processing unit is used to output the target installation information of the roadside base station based on the third simulation results.

[0090] Furthermore, in another possible implementation of this application embodiment, the above-mentioned acquisition module 401 may specifically include the following units:

[0091] The first acquisition unit is used to receive the initial installation information of the roadside base station input by the user; or, based on the received simulation instructions, to determine the attribute information of the roadside base station, and to acquire the initial installation information of the roadside base station from a preset data storage repository based on the attribute information of the roadside base station.

[0092] Furthermore, in another possible implementation of the embodiments of this application, the above-mentioned target installation information includes the target installation quantity, target installation location, target installation height, and target installation direction.

[0093] Figure 5 This is a schematic diagram of the structure of the electronic device provided in an embodiment of this application. For example... Figure 5 As shown, the electronic device 500 of this embodiment includes: at least one processor 510 ( Figure 5(Only one is shown in the diagram) a processor, a memory 520, and a computer program 521 stored in the memory 520 and executable on the at least one processor 510, wherein the processor 510 executes the computer program 521 to implement the steps in the above-described method embodiment for determining roadside base station installation information.

[0094] The electronic device 500 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. This electronic device may include, but is not limited to, a processor 510 and a memory 520. Those skilled in the art will understand that... Figure 5 This is merely an example of electronic device 500 and does not constitute a limitation on electronic device 500. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0095] The processor 510 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0096] In some embodiments, the memory 520 may be an internal storage unit of the electronic device 500, such as a hard disk or memory of the electronic device 500. In other embodiments, the memory 520 may be an external storage device of the electronic device 500, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 500. Furthermore, the memory 520 may include both internal and external storage units of the electronic device 500. The memory 520 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 520 can also be used to temporarily store data that has been output or will be output.

[0097] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0098] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0099] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0100] In the embodiments provided in this application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings or direct couplings or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0101] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0102] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0103] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0104] The implementation of all or part of the processes in the methods of the above embodiments can also be accomplished by a computer program product. When the computer program product is run on an electronic device, the electronic device can implement the steps in the various method embodiments described above.

[0105] The embodiments described above are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for determining roadside base station installation information, characterized in that, include: Obtain initial installation information of roadside base stations within the target area; Simulation is performed based on the high-precision map of the target area and the initial installation information of the roadside base station to obtain a first simulation result, wherein the first simulation result includes blind spot information within the target area; The first simulation result is augmented using a preset pedestrian model to update the blind spot information, thereby obtaining the second simulation result; Based on the second simulation results, the initial installation information of the roadside base station is adjusted to obtain the target installation information of the roadside base station.

2. The method according to claim 1, characterized in that, The simulation, based on the high-precision map of the target area and the initial installation information of the roadside base station, yields a first simulation result, including: Based on the high-precision map of the target area, the initial installation information of the roadside base station, and the preset calibration parameters, a simulation is performed to obtain the first simulation result.

3. The method according to claim 2, characterized in that, The simulation is performed based on the high-precision map of the target area, the initial installation information of the roadside base station, and preset calibration parameters to obtain a first simulation result, including: Based on the initial installation information and preset calibration parameters of the roadside base station, the sensing range of the roadside base station is simulated to obtain the simulated sensing range area. The blind spot information is determined based on the high-precision map of the target area and the simulated area of ​​the perception range; The simulated area of ​​the perception range is displayed in the high-precision map, and the first simulation result is obtained by combining the blind spot information.

4. The method according to claim 3, characterized in that, The roadside base station includes a roadside radar, and the simulated sensing range area includes a point cloud coverage simulated area. The sensing range of the roadside base station is simulated based on its initial installation information and preset calibration parameters to obtain the simulated sensing range area, which includes: Based on the initial installation information and calibration parameters of the roadside radar, the beam of the roadside radar is simulated to obtain the simulated area of ​​point cloud coverage.

5. The method according to claim 3, characterized in that, The roadside base station includes a roadside radar and a camera. The simulated sensing range area includes a fused coverage simulated area. The sensing range of the roadside base station is simulated based on its initial installation information and preset calibration parameters to obtain the simulated sensing range area, which includes: Based on the initial installation information of the roadside radar, the initial installation information of the camera, and the calibration parameters, the beam of the roadside radar is simulated to obtain the point cloud coverage simulation area, and the range that the camera can collect is simulated to obtain the image coverage simulation area. The fused coverage simulation area is determined based on the point cloud coverage simulation area and the image coverage simulation area.

6. The method according to any one of claims 2-5, characterized in that, The calibration parameters include rotation matrix and translation matrix.

7. The method according to claim 1, characterized in that, The step of adjusting the initial installation information of the roadside base station based on the second simulation result to obtain the target installation information of the roadside base station includes: Based on the second simulation results, the standard coverage area is determined; Based on the standard coverage area, the initial installation information of the roadside base station is adjusted so that the sensing range of the roadside base station matches the standard coverage area, thus obtaining the third simulation result; Based on the third simulation results, the target installation information of the roadside base station is output.

8. The method according to any one of claims 1-5, characterized in that, The acquisition of initial installation information of roadside base stations within the target area includes: Receive the initial installation information of the roadside base station input by the user; or, Based on the received simulation instructions, the attribute information of the roadside base station is determined, and based on the attribute information of the roadside base station, the initial installation information of the roadside base station is obtained from a preset data storage repository.

9. The method according to any one of claims 1-5, characterized in that, The target installation information includes the target installation quantity, target installation location, target installation height, and target installation direction.

10. A device for determining roadside base station installation information, characterized in that, include: The acquisition module is used to acquire the initial installation information of roadside base stations within the target area; The simulation module is used to perform simulation based on the high-precision map of the target area and the initial installation information of the roadside base station to obtain a first simulation result, wherein the first simulation result includes blind spot information within the target area; An expansion module is used to expand the first simulation result with a preset pedestrian model to update the blind spot information and obtain a second simulation result. The processing module is used to adjust the initial installation information of the roadside base station based on the second simulation result to obtain the target installation information of the roadside base station.

11. An electronic device, characterized in that, The electronic device includes: one or more processors, and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the electronic device to perform the method as described in any one of claims 1 to 9.

12. A computer program product, characterized in that, The computer program product includes a computer program that, when run on an electronic device, causes the electronic device to perform the method as described in any one of claims 1 to 9.

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