Vehicle running track determination method and device, computer equipment and storage medium

Abstract

The invention relates to a vehicle running track determination method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring three-dimensional point cloud data and a plurality of image data of a target vehicle; performing feature matching on the three-dimensional point cloud data and the multiple pieces of image data to obtain a matching result, and determining geometric parameter data of the target vehicle according to the matching result; obtaining contour data of the target vehicle according to the geometric parameter data; and determining a motion track of the target vehicle according to the outline data. By adopting the method, the accuracy of the geometric parameter data of the target vehicle can be improved, the accuracy of determining the actual moving trajectory of the target vehicle is ensured, and the accuracy of the trafficability test result of the target vehicle is further improved.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle whole vehicle detection, and in particular to a method, device, computer equipment and storage medium for determining a vehicle running trajectory. Background Art

[0002] After the vehicle is assembled, it is necessary to conduct various performance tests on the vehicle. The comprehensive performance test line and road simulation test are both key technical means of performance testing, and are important guarantees for the various performance of the vehicle to meet relevant standards and enterprise process specifications when it leaves the factory.

[0003] At present, the vehicle comprehensive performance test line test items include non-contact four-wheel alignment, Advanced Driving Assistance System (ADAS) calibration, 360-degree surround view system calibration, brake and anti-lock brake system (ABS) detection, headlamp high beam luminous intensity and high and low beam offset, steering wheel lateral side slip, steering angle detection, external dimension measurement, etc. Due to the large number of items and in order to improve the detection rhythm and detection efficiency, the general detection line adopts a distributed measurement and control method, that is, these detection items are distributed in the detection workshop in the form of multiple stations. The specific implementation method of the detection project is that the driver drives the vehicle and, under the guidance of the driver's assistant, follows the operation prompts set by the detection program step by step. During the implementation of the above-mentioned vehicle comprehensive performance test line test project, the vehicle needs to be kept centered as much as possible, that is, the vehicle's driving centerline must be parallel to or coincide with the centerline of the test line, and as perpendicular to the test equipment (such as headlight running guides, brake platforms, ADAS calibration frames, etc.) as possible. If the vehicle's driving trajectory does not meet the above requirements, that is, the vehicle's driving centerline deviates from the centerline of the test line, on the one hand, it will lead to low accuracy and poor repeatability of the passability test results and misjudgment of the test results. On the other hand, it will also lead to sampling, detection, and test failures due to excessive vehicle position deviations, which will cause repetitive testing or vehicle return to the line, seriously affecting production efficiency.

[0004] The road simulation test mainly involves the driver adjusting the vehicle's steering wheel to a horizontal level on a straight road, and then driving at high speed along the ground markings. The driver observes the relationship between the vehicle and the ground markings, and at the same time observes whether the steering wheel deviates to the left or right to judge whether the vehicle has a tendency to deviate. This is entirely based on personal subjective experience, and there is a problem of vehicles that deviate "leaving the factory with problems", which also poses certain risks.

[0005] Therefore, during the vehicle passability test, the vehicle's driving trajectory is monitored and the vehicle's actual motion trajectory is measured and analyzed, which can ensure the accuracy of the vehicle's posture during the detection process, and thus ensure the accuracy of the vehicle passability test.

[0006] In traditional technology, satellite positioning is used, and the motion trajectory of the target vehicle is obtained by algorithm fitting.

[0007] However, when conducting passability tests on various performance of the target vehicle, since satellite positioning has signal blind spots in indoor obstructed environments and the irregular left and right tilting of the target vehicle may cause unstable positioning signal reception, the positioning accuracy of the target vehicle is low, and the accuracy of the actual operating trajectory of the target vehicle cannot be ensured, thereby reducing the accuracy of the target vehicle's passability test results. Summary of the invention

[0008] Based on this, it is necessary to provide a vehicle running trajectory determination method, device, computer equipment and storage medium that can ensure the accuracy of the actual running trajectory of the target vehicle and thereby improve the accuracy of the target vehicle's passability test results in response to the above technical problems.

[0009] In a first aspect, the present application provides a method for determining a vehicle running trajectory, comprising:

[0010] Acquire three-dimensional point cloud data and multiple image data of the target vehicle;

[0011] Perform feature matching on the three-dimensional point cloud data and the multiple image data to obtain a matching result, and determine the geometric parameter data of the target vehicle according to the matching result;

[0012] According to the geometric parameter data, the outer contour data of the target vehicle is obtained;

[0013] According to the contour data, the motion trajectory of the target vehicle is determined.

[0014] In one embodiment, feature matching is performed on the three-dimensional point cloud data and the plurality of image data to obtain a matching result, and the geometric parameter data of the target vehicle is determined according to the matching result, including:

[0015] Perform feature matching on the three-dimensional point cloud data and the multiple image data to obtain matching result data; the matching result data includes multiple position and posture data of the target vehicle;

[0016] According to the matching result data and the fusion model, the three-dimensional point cloud data and multiple image data are fused to obtain the fused data of the target vehicle;

[0017] The geometric parameter data of the target vehicle is extracted from the fused data.

[0018] In one embodiment, the three-dimensional point cloud data is feature matched with a plurality of image data, and the method also includes:

[0019] Extracting three-dimensional feature data of the target vehicle from the three-dimensional point cloud data, and extracting two-dimensional feature data of the target vehicle from the multiple image data;

[0020] Projecting the three-dimensional point cloud data of the target vehicle onto a plurality of image data;

[0021] According to the three-dimensional feature data and two-dimensional feature data of the target vehicle, feature matching is performed between the three-dimensional point cloud data and the multiple image data.

[0022] In one embodiment, extracting three-dimensional feature data of a target vehicle from three-dimensional point cloud data and extracting two-dimensional feature data of a target vehicle from a plurality of image data comprises:

[0023] Convert the three-dimensional point cloud data of the target vehicle into voxel grid data, and extract the three-dimensional feature data of the target vehicle from the voxel grid data;

[0024] A feature extraction algorithm is used to extract key points and descriptors of the target vehicle in each image data to obtain two-dimensional feature data of the target vehicle.

[0025] In one embodiment, obtaining three-dimensional point cloud data and multiple image data of a target vehicle includes:

[0026] Acquire the original point cloud data of the target vehicle transmitted by the radar, and capture the original image data of the target vehicle transmitted by multiple visual sensors;

[0027] According to the timestamps of the original point cloud data and the original image data, the original point cloud data and the original image data are adjusted to obtain the original point cloud data and the original image data after time synchronization;

[0028] The time-synchronized original point cloud data and original image data are screened to obtain the screened original point cloud data and original image data.

[0029] In one embodiment, after the original point cloud data and the original image data after time synchronization are screened to obtain the screened original point cloud data and the original image data, the following further comprises:

[0030] The screened original point cloud data and original image data are preprocessed to obtain three-dimensional point cloud data and multiple image data of the target vehicle.

[0031] In one embodiment, the method further comprises:

[0032] Performing a preliminary inspection on the geometric parameter data of the target vehicle to obtain geometric parameter data after the preliminary inspection;

[0033] Acquire historical parameter data of the target vehicle, and correct the geometric parameter data after the initial inspection according to the historical parameter data to obtain corrected geometric parameter data;

[0034] According to the corrected geometric parameter data, the outline data of the target vehicle is obtained;

[0035] According to the outline data, the center point of the outline of the target vehicle is obtained, and the motion trajectory of the target vehicle is determined according to the center point of the outline.

[0036] In a second aspect, the present application also provides a vehicle running trajectory determination device, comprising:

[0037] A data acquisition module, used to acquire three-dimensional point cloud data and multiple image data of the target vehicle;

[0038] A fusion module is used to perform feature matching between the three-dimensional point cloud data and the multiple image data to obtain a matching result, and determine the geometric parameter data of the target vehicle according to the matching result;

[0039] An outline determination module is used to obtain outline data of the target vehicle according to the geometric parameter data;

[0040] The trajectory determination module is used to determine the motion trajectory of the target vehicle according to the outline data.

[0041] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0042] Acquire three-dimensional point cloud data and multiple image data of the target vehicle;

[0043] Perform feature matching on the three-dimensional point cloud data and the multiple image data to obtain a matching result, and determine the geometric parameter data of the target vehicle according to the matching result;

[0044] According to the geometric parameter data, the outer contour data of the target vehicle is obtained;

[0045] According to the contour data, the motion trajectory of the target vehicle is determined.

[0046] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:

[0047] Acquire three-dimensional point cloud data and multiple image data of the target vehicle;

[0048] Perform feature matching on the three-dimensional point cloud data and the multiple image data to obtain a matching result, and determine the geometric parameter data of the target vehicle according to the matching result;

[0049] According to the geometric parameter data, the outer contour data of the target vehicle is obtained;

[0050] According to the contour data, the motion trajectory of the target vehicle is determined.

[0051] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0052] Acquire three-dimensional point cloud data and multiple image data of the target vehicle;

[0053] Perform feature matching on the three-dimensional point cloud data and the multiple image data to obtain a matching result, and determine the geometric parameter data of the target vehicle according to the matching result;

[0054] According to the geometric parameter data, the outer contour data of the target vehicle is obtained;

[0055] According to the contour data, the motion trajectory of the target vehicle is determined.

[0056] The above-mentioned vehicle running trajectory determination method, device, computer equipment, storage medium and computer program product obtain the three-dimensional point cloud data and multiple image data of the target vehicle, can obtain the relevant data of the target vehicle from different dimensions and angles, and improve the reliability of the relevant data of the target vehicle; perform feature matching on the three-dimensional point cloud data and the multiple image data to obtain a matching result, and determine the geometric parameter data of the target vehicle based on the matching result, which can improve the accuracy of the geometric parameter data; obtain the outline data of the target vehicle based on the geometric parameter data; determine the motion trajectory of the target vehicle based on the outline data, which can ensure the accuracy of the determination of the actual running trajectory of the target vehicle, thereby improving the accuracy of the target vehicle's passability test results. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0058] FIG1 is a diagram showing an application environment of a method for determining a vehicle running trajectory in one embodiment;

[0059] FIG2 is a schematic diagram of a flow chart of a method for determining a vehicle running trajectory in one embodiment;

[0060] FIG3 is a schematic flow chart of a step of determining geometric parameter data of a target vehicle in one embodiment;

[0061] FIG4 is a structural block diagram of a vehicle running track determination device in one embodiment;

[0062] FIG. 5 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0064] The vehicle running track determination method provided in the embodiment of the present application can be applied in the application environment shown in Figure 1. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The terminal 102 sends a vehicle running track determination request to the server 104, and the server 104 receives the vehicle running track determination request, obtains the three-dimensional point cloud data and multiple image data of the target vehicle; performs feature matching on the three-dimensional point cloud data and the multiple image data to obtain a matching result, and determines the geometric parameter data of the target vehicle according to the matching result; According to the geometric parameter data, the motion trajectory of the target vehicle is determined. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices, and the Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0065] In an exemplary embodiment, as shown in FIG2 , a method for determining a vehicle running trajectory is provided, which is described by taking the method applied to the server in FIG1 as an example, and includes the following steps 202 to 208 . Among them:

[0066] Step 202, obtaining three-dimensional point cloud data and multiple image data of the target vehicle.

[0067] Among them, the target vehicle refers to a vehicle that needs to undergo various performance pass tests. Three-dimensional point cloud data is a data form obtained by laser radar or other sensors. The three-dimensional point cloud data of the target vehicle can represent the spatial structure of the target vehicle and the surrounding environment. The multiple image data of the target vehicle refers to the two-dimensional image data related to the target vehicle captured from different perspectives or different time points. The multiple two-dimensional image data include two-dimensional visual information of the target vehicle and the surrounding environment. The multiple image data can be obtained by multiple visual sensors or multiple cameras.

[0068] For example, the three-dimensional point cloud data around the target vehicle is obtained by laser radar, and multiple two-dimensional image data of the target vehicle and the surrounding environment of the target vehicle are captured by multiple visual sensors. The three-dimensional point cloud data obtained by the laser radar can provide accurate depth and spatial information of the target vehicle, and the multiple two-dimensional image data captured by the visual sensor can capture more details and texture features related to the target vehicle. Therefore, according to the three-dimensional point cloud data obtained by the laser radar and the multiple two-dimensional image data captured by multiple visual sensors, rich information about the target vehicle and the surrounding environment can be obtained from different dimensions and angles of the target vehicle, thereby improving the reliability of the data related to the target vehicle.

[0069] Step 204 , feature matching is performed on the three-dimensional point cloud data and the plurality of image data to obtain a matching result, and geometric parameter data of the target vehicle is determined based on the matching result.

[0070] Among them, feature matching of the three-dimensional point cloud data with the multiple image data means that after determining the corresponding relationship between the three-dimensional point cloud data and the multiple image data, the spatial points in the three-dimensional point cloud data are connected with the pixel points in the image data, so that the information extracted from the image data can be combined with the point cloud in the three-dimensional data. Exemplarily, the matching result may include the position and posture data of the target vehicle, etc.

[0071] Optionally, after extracting key feature points and feature surface data related to the target vehicle from the three-dimensional point cloud data and extracting feature data related to the target vehicle from each image data, the three-dimensional point cloud data is projected into each image data, so that the key feature points and feature surface data related to the target vehicle extracted from the three-dimensional point cloud data are matched with the feature data related to the target vehicle extracted from each image data, and the matched feature points and feature surface data are obtained. The three-dimensional coordinate information of the target vehicle is extracted from the three-dimensional point cloud data obtained by the laser radar, and the corresponding relationship between these three-dimensional points in the two-dimensional image is determined by using the matched feature points and feature surface data, and the two-dimensional projection information of these feature points is aligned with the three-dimensional coordinate data, and the geometric parameters such as the size, shape and posture of the vehicle are calculated by combining the geometric algorithm to obtain the geometric parameter data of the target vehicle, thereby ensuring the accurate registration between the three-dimensional point cloud data and multiple image data, and being able to obtain the feature information of the target vehicle more comprehensively and accurately, and thus obtain more accurate geometric parameter data.

[0072] Step 206, obtaining the outline data of the target vehicle according to the geometric parameter data.

[0073] The target vehicle's outline data includes the target vehicle's boundary shape, body contour line and other data.

[0074] Optionally, the geometric parameter data of the target vehicle may include basic information such as the length, width, and height of the target vehicle. The body contour line of the target vehicle is constructed based on the geometric parameter data of the target vehicle, wherein the body contour line of the target vehicle may be a rectangular frame outline, which can effectively define the body range of the target vehicle, is not affected by irregular movements of the target vehicle such as tilting left or right, and stably represents the position and range of the target vehicle.

[0075] Step 208, determining the motion trajectory of the target vehicle based on the outline data.

[0076] The coordinates of the center point of the outer contour of the target vehicle are obtained according to the outer contour data of the target vehicle, and the coordinates of the center point of the outer contour of the target vehicle are fitted and connected to obtain the motion trajectory of the target vehicle.

[0077] Exemplarily, for each frame of the target vehicle's outline data, the center point of the target vehicle's outline in the frame is calculated. In the continuous time frame, the position change of the center point of the target vehicle's outline is tracked frame by frame, and the fitting of the center point of the target vehicle's outline is connected to obtain the real-time motion trajectory of the target vehicle. Since the center point of the target vehicle's outline is relatively stable and is not affected by the local morphological changes caused by the left and right tilting of the target vehicle, the real-time motion trajectory of the target vehicle is formed by fitting and connecting these center points of the target vehicle's outline, which can more accurately reflect the overall moving path of the vehicle, thereby effectively solving the problem of the trajectory accuracy being affected by the left and right tilting of the target vehicle.

[0078] In the above-mentioned method for determining the vehicle running trajectory, the three-dimensional point cloud data and multiple image data of the target vehicle are obtained, so as to obtain rich information about the target vehicle and the surrounding environment from different dimensions and angles of the target vehicle, thereby improving the reliability of the target vehicle related data; the three-dimensional point cloud data and the multiple image data are feature matched to obtain the matching result, and the geometric parameter data of the target vehicle is determined according to the matching result, so as to obtain the characteristic information of the target vehicle more comprehensively and accurately, thereby obtaining more accurate geometric parameter data; according to the geometric parameter data, the outline data of the target vehicle is obtained, which can stably represent the position and range of the target vehicle; according to the outline data, the motion trajectory of the target vehicle is determined, which can effectively solve the problem of the trajectory accuracy being affected by the left and right tilting of the target vehicle, thereby ensuring the accuracy of the determination of the actual running trajectory of the target vehicle, thereby improving the accuracy of the target vehicle passability test results.

[0079] In an exemplary embodiment, obtaining three-dimensional point cloud data and multiple image data of a target vehicle includes: obtaining original point cloud data of the target vehicle transmitted by a radar, and capturing original image data of the target vehicle transmitted by multiple visual sensors; adjusting the original point cloud data and the original image data according to the timestamps of the original point cloud data and the original image data to obtain time-synchronized original point cloud data and original image data; and screening the time-synchronized original point cloud data and original image data to obtain screened original point cloud data and original image data.

[0080] Exemplarily, the original three-dimensional point cloud data of the target vehicle is obtained by a laser radar sensor, and these point cloud data include the point coordinate information of the target vehicle in the three-dimensional space obtained from different angles. At the same time, multiple visual sensors are used to capture the original image data of the target vehicle from different perspectives. These image data contain the two-dimensional visual information and appearance feature data of the target vehicle. The original point cloud data and the original image data are adjusted using the timestamps of the laser radar sensor and the multiple visual sensors to ensure the temporal synchronization of the original point cloud data and the original image data. By aligning the data captured at different time points, the original point cloud data and the original image data after time synchronization can be obtained. The original point cloud data and the original image data after time synchronization are screened. Among them, the screening of the original point cloud data and the original image data after time synchronization includes applying a data preprocessing algorithm to filter out irrelevant parts and abnormal values ​​of the original point cloud data and the original image data after time synchronization to obtain the screened original point cloud data and the original image data. The quality and accuracy of the original point cloud data and the original image data after screening can be ensured.

[0081] In this embodiment, the original point cloud data of the target vehicle transmitted by the radar and the original image data of the target vehicle transmitted by multiple visual sensors are time-synchronized, and the original point cloud data and original image data after time synchronization are screened, which can ensure the quality and accuracy of the original point cloud data and original image data after screening.

[0082] In an exemplary embodiment, the original point cloud data and original image data after time synchronization are screened to obtain the screened original point cloud data and original image data, and then the method also includes: preprocessing the screened original point cloud data and original image data to obtain three-dimensional point cloud data and multiple image data of the target vehicle.

[0083] Optionally, the screened point cloud data and image data are preprocessed, which may include segmenting the original point cloud data after screening, removing outliers, filling data gaps, and denoising, image enhancement and feature extraction of the original image data after screening, so as to obtain three-dimensional point cloud data and multiple image data of the target vehicle.

[0084] In this embodiment, preprocessing the original point cloud data and original image data after screening can improve the reliability of the target vehicle related data.

[0085] In an exemplary embodiment, as shown in FIG3 , feature matching is performed on the three-dimensional point cloud data and the multiple image data to obtain a matching result, and the geometric parameter data of the target vehicle is determined according to the matching result, including steps 302 to 306. Among them:

[0086] Step 302, feature matching is performed on the three-dimensional point cloud data and the multiple image data to obtain matching result data; the matching result data includes multiple position and posture data of the target vehicle.

[0087] Step 304 , according to the matching result data and the fusion model, the three-dimensional point cloud data and the multiple image data are fused to obtain fused data of the target vehicle.

[0088] Step 306, extracting geometric parameter data of the target vehicle from the fused data.

[0089] Exemplarily, a feature matching algorithm is used to extract and match key feature points of three-dimensional point cloud data and multiple image data, and the key feature points may include data such as vehicle body contour, edge, corner point, etc. Matching result data is generated, and the matching result data includes multiple posture data of the target vehicle at different viewing angles and time points, and these posture data describe the spatial position, direction and posture change of the target vehicle. The three-dimensional point cloud data and the image data are fused using the matching result data and a preset fusion model. Among them, the fusion model can be obtained by training a deep learning model. The fusion model integrates the position, posture, direction, geometric features, visual information and other data in the three-dimensional point cloud data and the image data to generate fused data of the target vehicle, and the fused data provides more complete and consistent relevant data of the target vehicle. The geometric parameter data of the target vehicle is extracted from the fused data, and the geometric parameter data may include data such as the size (such as length, width, height), shape, wheel position, body edge, and other geometric features of the target vehicle.

[0090] In the previous exemplary embodiment, before feature matching the three-dimensional point cloud data with the multiple image data, it also includes: extracting three-dimensional feature data of the target vehicle from the three-dimensional point cloud data, extracting two-dimensional feature data of the target vehicle from the multiple image data; projecting the three-dimensional point cloud data of the target vehicle to the multiple image data; and feature matching the three-dimensional point cloud data with the multiple image data based on the three-dimensional feature data and the two-dimensional feature data of the target vehicle.

[0091] Exemplarily, the three-dimensional feature data may include data such as the geometric shape, body edge, and wheel position of the target vehicle, and the two-dimensional feature data may include the body contour and other visual feature data of the target vehicle. The three-dimensional point cloud data of the target vehicle is projected into multiple image data so as to visualize the three-dimensional geometric information of the target vehicle on the two-dimensional image data. Using a feature matching algorithm, based on the extracted three-dimensional feature data and two-dimensional feature data, the three-dimensional point cloud data is feature matched with the multiple image data to identify and match the corresponding feature points of the vehicle at different viewing angles.

[0092] In this embodiment, by extracting features from three-dimensional point cloud data and multiple image data, and performing feature matching and data fusion, the fused data of the target vehicle can be more comprehensively and accurately identified and acquired, and the vehicle geometric parameters extracted from the fused data can improve the accuracy of the target vehicle geometric parameter data.

[0093] In the previous exemplary embodiment, three-dimensional feature data of the target vehicle is extracted from three-dimensional point cloud data, and two-dimensional feature data of the target vehicle is extracted from multiple image data, including: converting the three-dimensional point cloud data of the target vehicle into voxel grid data, and extracting the three-dimensional feature data of the target vehicle in the voxel grid data; using a feature extraction algorithm to extract key points and descriptors of the target vehicle in each image data to obtain the two-dimensional feature data of the target vehicle.

[0094] Exemplarily, voxel grid data is a representation method that divides a three-dimensional space into uniform volume units (i.e., voxels). The three-dimensional point cloud data is converted into voxel grid data through voxelization. In the voxel grid data, a three-dimensional feature extraction algorithm is used to extract the three-dimensional feature data of the target vehicle. Optionally, the three-dimensional feature extraction algorithm can use voxel grid surface feature extraction (such as surface normals, curvature, etc.), three-dimensional shape descriptors, etc. From multiple image data, a feature extraction algorithm is used to extract key points and descriptors of the target vehicle. Among them, the feature extraction algorithm includes SIFT (Scale Invariant Feature Transform), SURF (Speeded Up Robust Features) or ORB (Oriented Gradient Features).

[0095] In this embodiment, the three-dimensional point cloud data is converted into voxel grid data, and three-dimensional feature data is extracted therefrom, and a feature extraction algorithm is used to extract two-dimensional feature data from the image data, which can make the feature matching of the three-dimensional point cloud data and multiple image data more accurate, thereby making the geometric parameter data of the target vehicle more comprehensive.

[0096] In an exemplary embodiment, the method also includes: performing an initial inspection on the geometric parameter data of the target vehicle to obtain geometric parameter data after the initial inspection; obtaining historical parameter data of the target vehicle, and correcting the geometric parameter data after the initial inspection based on the historical parameter data to obtain corrected geometric parameter data; obtaining contour data of the target vehicle based on the corrected geometric parameter data; obtaining a contour center point of the target vehicle based on the contour data, and determining a motion trajectory of the target vehicle based on the contour center point.

[0097] Exemplarily, the geometric parameter data of the target vehicle is initially inspected to obtain the geometric parameter data after the initial inspection, which can ensure the integrity and accuracy of the geometric parameter data of the target vehicle. The historical parameter data of the target vehicle is obtained, wherein the historical parameter data of the target vehicle can come from the vehicle model data in the database. The geometric parameter data after the initial inspection is corrected using the historical parameter data, and the correction process can include comparing the geometric parameter data after the initial inspection with the corresponding historical parameter data, adjusting and correcting the errors or deviations in the geometric parameter data after the initial inspection, and obtaining more accurate and consistent corrected geometric parameter data.

[0098] According to the corrected geometric parameter data, the contour data of the target vehicle is further generated. According to the contour data, the contour center point of the target vehicle is calculated, and the contour center point can be the position data of the geometric center of the vehicle. By tracking the changes of the contour center point in the continuous time frame, the motion trajectory of the target vehicle is obtained.

[0099] In this embodiment, an initial check is performed on the geometric parameter data of the target vehicle to ensure the integrity and accuracy of the geometric parameter data of the target vehicle. The motion trajectory of the target vehicle is determined based on the contour data, which can effectively solve the problem of trajectory accuracy being affected by the left and right tilting of the target vehicle, ensure the accuracy of the determination of the actual operating trajectory of the target vehicle, and thus improve the accuracy of the target vehicle's passability test results.

[0100] In another embodiment, a method for determining a vehicle running trajectory is provided, the method comprising:

[0101] The original point cloud data of the target vehicle transmitted by the radar is obtained, and the original image data of the target vehicle transmitted by multiple visual sensors is captured; the original point cloud data and the original image data are adjusted according to the timestamps of the original point cloud data and the original image data to obtain the original point cloud data and the original image data after time synchronization; the original point cloud data and the original image data after time synchronization are screened to obtain the screened original point cloud data and the original image data; the screened original point cloud data and the original image data are pre-processed to obtain the three-dimensional point cloud data and multiple image data of the target vehicle.

[0102] The three-dimensional point cloud data of the target vehicle is converted into voxel grid data, and the three-dimensional feature data of the target vehicle is extracted from the voxel grid data; the key points and descriptors of the target vehicle are extracted from each image data using a feature extraction algorithm to obtain the two-dimensional feature data of the target vehicle.

[0103] The three-dimensional point cloud data of the target vehicle is projected onto a plurality of image data; according to the three-dimensional feature data and the two-dimensional feature data of the target vehicle, the three-dimensional point cloud data is feature matched with the plurality of image data to obtain matching result data; the matching result data includes a plurality of position and posture data of the target vehicle.

[0104] According to the matching result data and the fusion model, the three-dimensional point cloud data and the multiple image data are fused to obtain the fused data of the target vehicle; and the geometric parameter data of the target vehicle is extracted from the fused data.

[0105] Perform an initial inspection on the geometric parameter data of the target vehicle to obtain geometric parameter data after the initial inspection; obtain historical parameter data of the target vehicle, and correct the geometric parameter data after the initial inspection according to the historical parameter data to obtain corrected geometric parameter data; obtain the contour data of the target vehicle according to the corrected geometric parameter data; obtain the center point of the contour of the target vehicle according to the contour data, and determine the motion trajectory of the target vehicle according to the center point of the contour

[0106] In this embodiment, the original point cloud data of the target vehicle transmitted by the radar and the original image data of the target vehicle transmitted by multiple visual sensors are processed in time synchronization, and the original point cloud data and original image data after time synchronization are screened, and the original point cloud data and original image data after screening are preprocessed, which can ensure the quality and accuracy of the original point cloud data and original image data, and improve the reliability of the target vehicle related data. By extracting features from the three-dimensional point cloud data and multiple image data, and performing feature matching and data fusion, the fused data of the target vehicle can be more comprehensively and accurately identified and obtained, and the vehicle geometric parameters extracted from the fused data can improve the accuracy of the target vehicle geometric parameter data. The initial inspection of the geometric parameter data of the target vehicle can ensure the integrity and accuracy of the geometric parameter data of the target vehicle, and determine the motion trajectory of the target vehicle according to the contour data, which can effectively solve the problem of the trajectory accuracy being affected by the left and right tilting of the target vehicle, and ensure the accuracy of the determination of the actual running trajectory of the target vehicle, thereby improving the accuracy of the target vehicle passability test results.

[0107] Through the above-mentioned vehicle running trajectory determination method, the actual parameters of the target vehicle can be obtained, and the actual running trajectory of the target vehicle can be monitored. Deviation warning can be carried out to ensure vehicle centering, and it can also be used in vehicle passability testing. For example, after receiving the geometric parameter data sent by the vehicle from the production management system, a theoretical vehicle contour is generated, and then the contour of the vehicle in the actual production environment is obtained through the lidar system. After verification, it can be found whether a certain model has production passability risks and problems, including but not limited to length, height, width, and chassis clearance, and whether the vehicle contour interferes with the site. After the risk source is identified, it can be quickly processed. By monitoring the actual running trajectory of the vehicle, the driver can be guided to drive in an accurate direction and confirm whether there are problems during the actual test of the vehicle detection line.

[0108] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0109] Based on the same inventive concept, the embodiment of the present application also provides a vehicle running track determination device for implementing the vehicle running track determination method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more vehicle running track determination device embodiments provided below can refer to the limitations of the vehicle running track determination method above, and will not be repeated here.

[0110] In an exemplary embodiment, as shown in FIG4 , a vehicle running track determination device is provided, comprising: a data acquisition module 402 , a fusion module 404 , an outline determination module 406 and a track determination module 408 , wherein:

[0111] The data acquisition module 402 is used to acquire the three-dimensional point cloud data and multiple image data of the target vehicle.

[0112] The fusion module 404 is used to perform feature matching on the three-dimensional point cloud data and the multiple image data to obtain matching results, and determine the geometric parameter data of the target vehicle according to the matching results.

[0113] The outline determination module 406 is used to obtain the outline data of the target vehicle according to the geometric parameter data.

[0114] The trajectory determination module 408 is used to determine the motion trajectory of the target vehicle according to the outline data.

[0115] In an exemplary embodiment, the data acquisition module 402 includes a laser radar interface unit, a visual sensor interface unit, a data buffer unit, a synchronization unit and a data processing unit. The laser radar interface unit is responsible for communicating with the laser radar sensor and receiving the raw point cloud data or distance data sent by it. The laser radar interface unit is also used to process the laser radar specific communication protocol and data format. The visual sensor interface unit is responsible for communicating with the visual sensor (such as a camera) and receiving the raw image data captured by it. The visual sensor interface unit is also used to process the synchronization, decompression and format conversion of the image data. The data buffer unit is responsible for temporarily storing the raw point cloud data and raw image data received from the laser radar sensor and the visual sensor. The data buffer unit is also used to smooth the difference between the data transmission rate and the processing speed. When the data processing unit is ready to process the data, the data buffer unit sends the raw point cloud data and the raw image data to the processing unit. The synchronization unit is responsible for ensuring that the data received from the laser radar sensor and the visual sensor are synchronized in time. The synchronization unit is used to process the timestamp, frame rate, etc. of the laser radar sensor and the visual sensor. The data processing unit is used to verify the integrity of the raw point cloud data and the raw image data, and to filter noise or abnormal values.

[0116] The laser radar interface unit receives raw point cloud data from the laser radar sensor and stores the raw point cloud data in the data buffer unit. The visual sensor interface unit receives raw image data from the visual sensor and stores the raw image data in the data buffer unit. The synchronization unit reads the raw point cloud data and raw image data from the data buffer unit and adjusts or marks the timestamp as needed to ensure the synchronization of the raw point cloud data and the raw image data. The synchronization unit sends the synchronized raw point cloud data and raw image data to the data processing unit for preliminary processing. After the data processing unit completes preliminary data inspection and screening of the raw point cloud data and raw image data, it sends the processed raw point cloud data and raw image data to the preprocessing module.

[0117] In an exemplary embodiment, the vehicle running trajectory determination device further includes:

[0118] The preprocessing module includes a data receiving unit, a denoising unit, a compression unit and a data output unit. The data receiving unit is responsible for receiving the processed raw point cloud data and raw image data to ensure the integrity and accuracy of the processed raw point cloud data and raw image data. The denoising unit is responsible for denoising the received processed raw point cloud data and raw image data to remove noise and outliers and obtain the denoised raw point cloud data and raw image data. The denoising processing algorithm may include statistical filtering, median filtering, etc. The compression unit is responsible for compressing the denoised raw point cloud data and raw image data to reduce the overhead of data transmission and storage. The compression algorithm may include point cloud compression, image compression, etc. After the above preprocessing, the three-dimensional point cloud data and multiple image data of the target vehicle are obtained. The data output unit is responsible for sending the three-dimensional point cloud data and multiple image data of the target vehicle to the fusion module, and at the same time, the three-dimensional point cloud data and multiple image data of the target vehicle are stored in the intelligent cache module.

[0119] The data receiving unit receives the processed original point cloud data and original image data, and pre-processes the processed original point cloud data and original image data. The pre-processing process includes sending the processed original point cloud data and original image data to the denoising unit for denoising, and the denoising unit sends the denoised original point cloud data and original image data to the compression unit for compression operation to obtain the three-dimensional point cloud data and multiple image data of the target vehicle. The data output unit then sends the three-dimensional point cloud data and multiple image data of the target vehicle to the fusion module and the intelligent cache module.

[0120] In an exemplary embodiment, the vehicle running trajectory determination device further includes:

[0121] The intelligent cache module includes a cache storage unit, a cache management unit, a cache interface unit and a cache strategy unit. The cache storage unit is responsible for actually storing data, which can be a memory, a hard disk or other storage media. The cache storage unit supports fast data read and write operations. The cache management unit is responsible for managing the data in the cache storage unit, including adding, retrieving, replacing and updating data, and deciding which data should be retained or replaced according to the cache strategy. The cache interface unit is used to provide an interface for interacting with other modules (such as a fusion module and a post-processing module). The cache interface unit receives requests from other modules, such as data retrieval or data update, and calls the cache management unit to perform corresponding operations. The cache strategy unit is responsible for defining and maintaining cache strategies, such as LRU (least recently used), LFU (least frequently used), etc. The cache strategy unit selects a suitable cache strategy based on the data access mode and system requirements. Among them, the data may include three-dimensional point cloud data and multiple image data of the target vehicle.

[0122] The cache interface unit receives data retrieval requests from the fusion module or the post-processing module. The cache interface unit forwards the request to the cache management unit and returns the retrieved data to the request module. When new data needs to be stored, the cache management unit is responsible for writing the data to the cache storage unit and deciding whether to replace the old data based on the cache strategy. The cache management unit queries the cache strategy unit when necessary to obtain the currently used cache strategy. The cache management unit decides on data replacement and update based on the cache strategy. The preprocessing module sends the three-dimensional point cloud data and multiple image data of the target vehicle to the cache interface unit for storage.

[0123] In this embodiment, commonly used data or calculation results are cached through an intelligent cache module, and the calculation and transmission time are reduced by storing and reusing data, thereby improving the real-time performance and response speed of the device, so as to provide the required data in a timely and accurate manner during vehicle driving.

[0124] In an exemplary embodiment, the fusion module 404 includes a data acquisition unit, a data fusion processing unit, a cache access unit and a fusion result output unit. The data acquisition unit is responsible for receiving the three-dimensional point cloud data and multiple image data of the target vehicle sent by the pre-processing module, ensuring the integrity and accuracy of the three-dimensional point cloud data and multiple image data of the target vehicle, and performing necessary data verification. The data fusion processing unit is responsible for using the trained model to perform multi-sensor data fusion, accessing the data in the intelligent cache module to reduce repeated calculations, combining the three-dimensional point cloud data and multiple image data of the target vehicle, extracting and fusing the geometric parameter data of the target vehicle. The cache access unit is responsible for interacting with the intelligent cache module, retrieving or updating the three-dimensional point cloud data and multiple image data of the target vehicle in the cache, and in the process of fusing the three-dimensional point cloud data and multiple image data of the target vehicle, quickly retrieving historical data or intermediate results from the intelligent cache module as needed. The fusion result output unit is responsible for sending the geometric parameter data of the target vehicle obtained by the data fusion processing unit to the post-processing module, and storing the fusion results in the intelligent cache module for subsequent use or analysis.

[0125] The data acquisition unit receives the three-dimensional point cloud data and multiple image data of the target vehicle from the preprocessing module, and passes them to the data fusion processing unit for fusion processing. The data fusion processing unit retrieves the three-dimensional point cloud data and multiple image data of the target vehicle from the intelligent cache module through the cache access unit when needed. The data fusion processing unit stores the intermediate result or the final fusion result in the intelligent cache module through the cache access unit. After the data fusion processing unit completes the data fusion, it sends the fusion result to the fusion result output unit.

[0126] In an exemplary embodiment, the vehicle running trajectory determination device further includes:

[0127] The post-processing module includes a fusion data receiving unit, a fusion data processing unit, an intelligent cache access unit and a post-processing result output unit. The fusion data receiving unit is responsible for receiving the geometric parameter data of the target vehicle output by the fusion module, and performing preliminary verification on the received geometric parameter data of the target vehicle to ensure the integrity and accuracy of the geometric parameter data of the target vehicle. The fusion data processing unit is responsible for further processing and analyzing the received geometric parameter data of the target vehicle. The fusion data processing unit accesses the data in the intelligent cache module to obtain historical measurement results for comparison or analysis, and applies various algorithms and models to optimize, correct or verify the geometric parameter data of the target vehicle. The intelligent cache access unit is responsible for interacting with the intelligent cache module to retrieve historical measurement data or other related information. The intelligent cache access unit provides a data retrieval function so that the fusion data processing unit can obtain the required historical data. The post-processing result output unit is responsible for sending the processed geometric parameter data of the target vehicle to the terminal so that it can be displayed on the user interface or used by other systems, and storing the processed results in the intelligent cache module for subsequent analysis and query.

[0128] In an exemplary embodiment, the vehicle running trajectory determination device further includes:

[0129] The model management module includes a model storage unit, a model management unit, a model call interface unit, and a model update and maintenance unit. The model storage unit is responsible for storing the trained models, including model parameters, structure, and related metadata, providing an efficient storage solution to ensure the integrity and accessibility of model data. The model management unit is responsible for managing the models in the model storage unit, including adding, deleting, modifying, and querying models, maintaining model version information, ensuring that the system can use the latest model version, monitoring the performance and status of the model, and providing suggestions for model update and maintenance. The model call interface unit is responsible for providing an interface for interacting with other modules (such as the fusion module), allowing them to call the model for data processing as needed, verifying the authority and legitimacy of the requesting module, and ensuring the safe use of the model. The model update and maintenance unit is responsible for updating and maintaining the model, including downloading new model versions, replacing old versions, performing necessary model verification and testing, monitoring the performance and stability of the model, and promptly discovering and fixing potential problems. Among them, the model may include a fusion model.

[0130] The model management unit adds, deletes, modifies and queries model data through the model storage unit. When other modules (such as the fusion module) need to call the model, they send requests to the model management unit through the model call interface unit. The model update and maintenance unit receives update and maintenance instructions from the model management unit and performs corresponding operations. The model update and maintenance unit obtains the new version of the model data from an external source and stores it in the model storage unit.

[0131] In this embodiment, the trained model is stored and managed by the model management unit, and when other modules need it, the model can be called for data processing. The efficient calling mechanism can reduce the time required for model loading and initialization.

[0132] In an exemplary embodiment, the trajectory determination module 408 is also used to perform an initial inspection on the geometric parameter data of the target vehicle to obtain geometric parameter data after the initial inspection; obtain historical parameter data of the target vehicle, and correct the geometric parameter data after the initial inspection based on the historical parameter data to obtain corrected geometric parameter data; obtain the outline data of the target vehicle based on the corrected geometric parameter data; obtain the center point of the outline of the target vehicle based on the outline data, and determine the motion trajectory of the target vehicle based on the center point of the outline.

[0133] In this embodiment, through the trajectory determination module, trajectory prompts and monitoring can be performed at the four-wheel alignment, driving assistance, lighting detection, corner detection and other stations on the vehicle comprehensive performance testing line to ensure the accuracy and precision of the vehicle comprehensive performance testing and improve vehicle production efficiency; at the same time, the vehicle's geometric parameter data can also be used to determine the passability problems of the vehicle model production.

[0134] Each module in the above-mentioned vehicle running trajectory determination device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0135] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG5. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store three-dimensional point cloud data and multiple image data, geometric parameter data, contour data, etc. of the target vehicle. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for determining a vehicle running trajectory is implemented.

[0136] Those skilled in the art will understand that the structure shown in FIG. 5 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0137] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0138] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0139] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0140] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0141] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0142] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0143] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining a vehicle running trajectory, characterized in that: The method comprises: Acquire three-dimensional point cloud data and multiple image data of the target vehicle; Performing feature matching on the three-dimensional point cloud data and the plurality of image data to obtain a matching result, and determining geometric parameter data of the target vehicle according to the matching result; Obtaining the outline data of the target vehicle according to the geometric parameter data; The motion trajectory of the target vehicle is determined according to the outline data.

2. The method according to claim 1, characterized in that The performing feature matching on the three-dimensional point cloud data and the plurality of image data to obtain a matching result, and determining the geometric parameter data of the target vehicle according to the matching result comprises: Performing feature matching on the three-dimensional point cloud data and the plurality of image data to obtain matching result data; the matching result data includes a plurality of position and posture data of the target vehicle; According to the matching result data and the fusion model, the three-dimensional point cloud data and the plurality of image data are fused to obtain fused data of the target vehicle; The geometric parameter data of the target vehicle is extracted from the fused data.

3. The method according to claim 2, characterized in that Before performing feature matching between the three-dimensional point cloud data and the plurality of image data, the method further includes: Extracting three-dimensional feature data of the target vehicle from the three-dimensional point cloud data, and extracting two-dimensional feature data of the target vehicle from the plurality of image data; Projecting the three-dimensional point cloud data of the target vehicle onto the plurality of image data; According to the three-dimensional feature data and the two-dimensional feature data of the target vehicle, feature matching is performed between the three-dimensional point cloud data and the plurality of image data.

4. The method according to claim 3, characterized in that: The step of extracting the three-dimensional feature data of the target vehicle from the three-dimensional point cloud data and extracting the two-dimensional feature data of the target vehicle from the plurality of image data comprises: Converting the three-dimensional point cloud data of the target vehicle into voxel grid data, and extracting the three-dimensional feature data of the target vehicle from the voxel grid data; A feature extraction algorithm is used to extract key points and descriptors of the target vehicle from each of the image data to obtain two-dimensional feature data of the target vehicle.

5. The method according to claim 1, characterized in that: The step of acquiring the three-dimensional point cloud data and multiple image data of the target vehicle includes: Acquire the original point cloud data of the target vehicle transmitted by the radar, and capture the original image data of the target vehicle transmitted by multiple visual sensors; Adjusting the original point cloud data and the original image data according to the timestamps of the original point cloud data and the original image data to obtain the time-synchronized original point cloud data and the original image data; The time-synchronized original point cloud data and original image data are screened to obtain screened original point cloud data and original image data.

6. The method according to claim 5, characterized in that After screening the time-synchronized original point cloud data and original image data to obtain the screened original point cloud data and original image data, the method further includes: The screened original point cloud data and original image data are preprocessed to obtain three-dimensional point cloud data and multiple image data of the target vehicle.

7. The method according to claim 1, characterized in that The method further comprises: Performing a preliminary inspection on the geometric parameter data of the target vehicle to obtain geometric parameter data after the preliminary inspection; Acquire historical parameter data of the target vehicle, and correct the geometric parameter data after the initial inspection according to the historical parameter data to obtain corrected geometric parameter data; Obtaining the outline data of the target vehicle according to the corrected geometric parameter data; The center point of the outer contour of the target vehicle is obtained according to the outer contour data, and the motion trajectory of the target vehicle is determined according to the outer contour center point.

8. A vehicle running track determination device, characterized in that: The device comprises: A data acquisition module, used to acquire three-dimensional point cloud data and multiple image data of the target vehicle; A fusion module, used for performing feature matching between the three-dimensional point cloud data and the plurality of image data to obtain a matching result, and determining geometric parameter data of the target vehicle according to the matching result; An outline determination module, used to obtain outline data of the target vehicle according to the geometric parameter data; The trajectory determination module is used to determine the motion trajectory of the target vehicle according to the outline data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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