Vehicle-mounted laser point cloud pavement evaluation method and system based on continuous longitudinal section analysis

Abstract

The application discloses a vehicle-mounted laser point cloud road surface evaluation method based on continuous longitudinal section analysis, which comprises the following steps: obtaining point cloud data and a vehicle driving track line of a target road; converting the point cloud data to a local coordinate system with the vehicle driving track line as a reference, wherein the mileage direction along the vehicle driving track line is the longitudinal direction, the direction deviating from the vehicle driving track line is the transverse direction, and the elevation information of each point is kept; setting a transverse interval in the local coordinate system based on the vehicle driving track line, eliminating points located outside the transverse interval, and obtaining road candidate area point cloud; performing road surface point extraction on the road candidate area point cloud to obtain target road surface point cloud; obtaining longitudinal section elevation data corresponding to at least one wheel trace from the target road surface point cloud along the longitudinal direction; dividing the longitudinal section elevation data into multiple sections, and calculating the road surface flatness index of each section by using a vehicle model as a road surface quality evaluation result.

Description

Technical Field

[0001] This invention relates to the field of road engineering inspection technology, specifically to a vehicle-mounted laser point cloud road surface evaluation method based on continuous longitudinal profile analysis. Background Technology

[0002] Road surface smoothness is a key performance indicator for measuring road surface quality and affecting driving safety and comfort. It is typically quantified using international smoothness indices. Traditional methods for road surface smoothness testing mainly include a three-meter straightedge, continuous smoothness meters, and various vehicle-mounted laser cross-section inspection devices. While the three-meter straightedge method is simple to operate, it is inefficient, has discrete sampling, and struggles to objectively represent the overall appearance of long road sections. Continuous smoothness meters, although capable of obtaining continuous longitudinal sections, are slow, significantly interfere with normal traffic, and their mechanical components are susceptible to wear, affecting long-term measurement accuracy. These traditional methods have significant limitations in terms of testing efficiency, data continuity, and environmental adaptability.

[0003] In recent years, vehicle-mounted mobile laser scanning systems have been introduced into the field of road condition detection due to their ability to rapidly and densely acquire 3D point cloud data of roads and the environment. However, existing methods for evaluating road surface smoothness based on vehicle-mounted laser point clouds still have the following shortcomings: First, most methods mesh the road surface point cloud or perform overall surface fitting, and then calculate the elevation deviation of each point relative to the fitted surface, failing to focus on the longitudinal profile corresponding to the actual vehicle wheel tracks, resulting in a discrepancy between the smoothness evaluation results and the actual driving experience. Second, the original point cloud data contains a large number of non-road surface points such as roadside guardrails, signs, and vehicles. If elevation statistics are directly calculated based on unrefined point cloud data, a large amount of noise will be introduced, seriously interfering with the accuracy of the smoothness evaluation. Third, existing methods mostly use simple statistical indicators such as elevation variance and standard deviation to measure smoothness, lacking the means to use vehicle dynamic response models to simulate and calculate the longitudinal profile elevation sequence. This makes it difficult to establish a direct and comparable relationship with standardized international smoothness indices, making it difficult to directly use the evaluation results for road quality grade assessment and maintenance decisions. Summary of the Invention

[0004] To overcome the above-mentioned technical defects, the present invention provides a vehicle-mounted laser point cloud road surface evaluation method and system based on continuous longitudinal section analysis.

[0005] To solve the above problems, the present invention is implemented according to the following technical solution:

[0006] In a first aspect, the present invention provides a vehicle-mounted laser point cloud road surface evaluation method based on continuous longitudinal profile analysis, comprising:

[0007] Acquire point cloud data of the target road and vehicle trajectory lines;

[0008] The point cloud data is converted to a local coordinate system based on the vehicle's driving trajectory line, wherein the mileage direction along the vehicle's driving trajectory line is the longitudinal direction, the direction deviating from the vehicle's driving trajectory line is the lateral direction, and the elevation information of each point is preserved.

[0009] Based on the vehicle's trajectory line, a horizontal interval is set in the local coordinate system, and points located outside the horizontal interval are eliminated to obtain a point cloud of the road candidate region.

[0010] Road surface points are extracted from the point cloud of the candidate road area to obtain the target road surface point cloud;

[0011] Acquire longitudinal profile elevation data for at least one wheel track from the target road surface point cloud along the longitudinal direction;

[0012] The longitudinal profile elevation data is divided into multiple sections, and the road surface smoothness index of each section is calculated using a vehicle model as the road surface quality evaluation result.

[0013] In conjunction with the first aspect, the present invention provides a first specific implementation of the first aspect, specifically, the acquisition of point cloud data of the target road and vehicle trajectory lines specifically includes:

[0014] Acquire raw point cloud data of the target road collected by an on-board laser scanning system. The raw point cloud data includes road surface point cloud and point cloud of ground features around the road.

[0015] The vehicle position trajectory data of the vehicle-mounted laser scanning system during the scanning process is acquired, and the vehicle position trajectory data records the three-dimensional spatial coordinates of the vehicle-mounted laser scanner at each acquisition time.

[0016] Extract the horizontal coordinates of the vehicle position trajectory data, and construct a vehicle driving trajectory reference line using the horizontal coordinates.

[0017] In conjunction with the first aspect, the present invention provides a second specific implementation of the first aspect, specifically, the step of converting the point cloud data to a local coordinate system based on the vehicle's trajectory line includes:

[0018] The three-dimensional coordinates of each point in the road point cloud data are projected onto the horizontal plane of the vehicle driving trajectory reference line to obtain the projected coordinates of each point on the horizontal plane.

[0019] Along the travel direction of the vehicle travel trajectory reference line, calculate the cumulative mileage value of each projection point from the starting point of the trajectory line, and use the cumulative mileage value as the longitudinal coordinate of each point in the local coordinate system.

[0020] Calculate the normal distance from each projection point to the vehicle trajectory reference line, and use the normal distance as the lateral coordinate of each point in the local coordinate system;

[0021] The original elevation values ​​of each point in the road point cloud data are retained as the vertical coordinates of each point in the local coordinate system;

[0022] Based on the longitudinal, lateral, and vertical coordinates, a local coordinate mapping relationship is established between each point cloud data and the vehicle's driving trajectory line.

[0023] In conjunction with the first aspect, the present invention provides a third specific implementation of the first aspect. Specifically, the step of setting a horizontal interval in the local coordinate system based on the vehicle's driving trajectory line and eliminating points located outside the horizontal interval to obtain a road candidate region point cloud specifically includes:

[0024] Using the vehicle driving trajectory reference line as the baseline center line, a lateral clipping interval is set in the lateral direction. The lateral clipping interval is preset according to the number of road lanes.

[0025] Obtain the lateral coordinates of each point in the local coordinate system;

[0026] Determine whether each point is located within the horizontal clipping interval based on its horizontal coordinates.

[0027] Point cloud data whose lateral coordinates are outside the lateral clipping interval are removed, while point cloud data whose lateral coordinates are within the lateral clipping interval are retained. The retained point cloud data constitutes the road candidate region point cloud.

[0028] In conjunction with the first aspect, the present invention provides a fourth specific implementation of the first aspect, specifically, the step of extracting road surface points from the road candidate region point cloud to obtain the target road surface point cloud includes:

[0029] Divide the point cloud of the road candidate area into grids in the longitudinal and transverse planes, extract the lowest elevation point in each grid, set an elevation tolerance range based on the lowest elevation point, and extract the points that fall within the elevation tolerance range as candidate road surface points.

[0030] Within a local spatial window, the candidate road surface points are fitted to a plane. Based on the residual values ​​of each candidate road surface point to the fitted plane, outliers are removed to obtain the target road surface point cloud.

[0031] In conjunction with the first aspect, the present invention provides a fifth specific implementation of the first aspect. Specifically, the step of dividing the point cloud of the road candidate region into grids on the longitudinal and transverse planes, extracting the lowest elevation point within each grid, setting an elevation tolerance range based on the lowest elevation point, and extracting points falling within the elevation tolerance range as candidate road surface points specifically includes:

[0032] On the longitudinal and transverse planes of the road candidate area point cloud, longitudinal grid lines are divided along the longitudinal direction at a first sampling interval, and transverse grid lines are divided along the transverse direction at a second sampling interval. The intersection of the longitudinal grid lines and the transverse grid lines forms a grid array. Each point in the road candidate area point cloud falls into a unique grid cell according to its longitudinal coordinates and transverse coordinates.

[0033] Traverse each grid cell and extract the point cloud data point with the smallest elevation value in each grid cell as the lowest elevation point of that grid cell;

[0034] Centered on the elevation value of the lowest elevation point, a preset height threshold is extended upwards and downwards along the elevation direction to form the elevation tolerance range corresponding to the grid cell.

[0035] Traverse all point cloud data points within the current grid cell, mark points whose elevation values ​​are within the elevation tolerance range, and extract them as candidate road surface points;

[0036] Perform the minimum elevation point extraction and candidate road surface point extraction operations on each grid cell, and the candidate road surface points extracted from all grid cells constitute the candidate road surface point set.

[0037] In conjunction with the first aspect, the present invention provides a sixth specific implementation of the first aspect. Specifically, the step of performing planar fitting on the candidate road surface points within a local spatial window, and removing outliers based on the residual values ​​of each candidate road surface point to the fitting plane to obtain the target road surface point cloud, specifically includes:

[0038] With the current candidate road point as the center, a local spatial window is constructed by extending a preset window length in both the longitudinal and transverse directions, with the current point to be processed as the center, and all neighboring candidate road points falling within the local spatial window are obtained.

[0039] Perform plane fitting on all neighboring candidate road surface points that fall within the local spatial window to obtain the local fitting plane corresponding to the current point to be processed.

[0040] Calculate the spatial distance from the current point to be processed to the local fitting plane, and use it as the fitting residual of the current point to be processed;

[0041] The fitting residual is compared with a preset residual threshold. If the fitting residual is less than the preset residual threshold, the current point to be processed is marked and retained as a target road surface point; if the fitting residual is greater than or equal to the preset residual threshold, the current point to be processed is marked and removed.

[0042] Traverse all candidate road points in the candidate road point set, and the target road point cloud is formed by all marked and retained candidate road points.

[0043] In conjunction with the first aspect, the present invention provides a seventh specific implementation of the first aspect, specifically, the step of obtaining longitudinal profile elevation data corresponding to at least one wheel track from the target road surface point cloud along the longitudinal direction specifically includes:

[0044] Based on the preset wheel track and the position of the vehicle driving trajectory reference line, the lateral position of the target wheel track is determined in the lateral direction of the local coordinate system.

[0045] Based on the lateral position of the target wheel track, point cloud data whose lateral coordinates match the lateral position of the target wheel track are selected from the target road surface point cloud and used as candidate point clouds for the target wheel track;

[0046] In the longitudinal direction, at a preset longitudinal sampling interval, the elevation values ​​at each sampling position are extracted from the candidate point cloud of the target wheel track to generate the longitudinal profile elevation data corresponding to the target wheel track.

[0047] In conjunction with the first aspect, the present invention provides an eighth specific implementation of the first aspect. Specifically, the step of dividing the longitudinal profile elevation data into multiple sections and using a vehicle model to calculate the road surface smoothness index of each section as the road surface quality evaluation result specifically includes:

[0048] The acquired longitudinal profile elevation data is filtered to obtain filtered longitudinal profile elevation data.

[0049] The filtered longitudinal profile elevation data is divided into multiple continuous longitudinal profile segments according to a preset segment length along the longitudinal direction.

[0050] For each longitudinal section, the international roughness index corresponding to that section is calculated using a quarter-vehicle model.

[0051] The pavement quality evaluation results are generated based on the international roughness index corresponding to each longitudinal section.

[0052] Secondly, the present invention also provides a vehicle-mounted laser point cloud road surface evaluation system based on continuous longitudinal profile analysis, comprising:

[0053] The data acquisition module is used to acquire point cloud data of the target road and vehicle trajectory lines;

[0054] The coordinate transformation module is used to transform the point cloud data to a local coordinate system based on the vehicle's driving trajectory line, wherein the mileage direction along the vehicle's driving trajectory line is the longitudinal direction, the direction deviating from the vehicle's driving trajectory line is the lateral direction, and the elevation information of each point is maintained.

[0055] The region clipping module is used to set a horizontal interval in the local coordinate system based on the vehicle's driving trajectory line, and remove points located outside the horizontal interval to obtain a point cloud of road candidate regions.

[0056] The road surface point extraction module is used to extract road surface points from the point cloud of the road candidate region to obtain the target road surface point cloud;

[0057] The wheel track longitudinal profile extraction module is used to obtain longitudinal profile elevation data corresponding to at least one wheel track from the target road surface point cloud along the longitudinal direction.

[0058] The road surface evaluation module is used to divide the longitudinal profile elevation data into multiple sections and use a vehicle model to calculate the road surface smoothness index of each section as the road surface quality evaluation result.

[0059] Compared with the prior art, the beneficial effects of the present invention are:

[0060] By converting the vehicle-mounted laser point cloud to a local coordinate system based on the driving trajectory line and extracting the longitudinal section elevation data corresponding to the wheel tracks along the longitudinal direction, it is possible to directly focus on the road surface undulations on the actual rolling path of the vehicle tires, so that the smoothness evaluation results are highly consistent with the actual driving comfort and safety, overcoming the defect of traditional overall point cloud statistical methods that ignore the wheel track zone and cause evaluation distortion.

[0061] By setting a lateral interval in the local coordinate system to eliminate interference points at the far ends of the trajectory line, and by refining the road surface point extraction in the candidate area of ​​the road, noise from non-road surface point clouds such as guardrails, signs, and vehicles is effectively filtered out, ensuring the purity of the longitudinal profile elevation data from the source, thereby significantly improving the accuracy and reliability of road surface smoothness evaluation.

[0062] The continuous longitudinal profile is divided into multiple sections, and the pavement smoothness index of each section is calculated using a vehicle dynamics response model. This achieves end-to-end automatic calculation from the original laser point cloud to the standardized international smoothness index. The evaluation results can be directly used for pavement quality grade assessment and maintenance decisions, solving the problem that the evaluation indicators of existing methods are out of sync with industry standards and difficult to benchmark. Attached Figure Description

[0063] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings, wherein:

[0064] Figure 1 This is a flowchart of the vehicle-mounted laser point cloud road surface evaluation method based on continuous longitudinal section analysis of the present invention.

[0065] Figure 2 This is a block diagram of an electronic device used to implement embodiments of the present invention;

[0066] In the picture:

[0067] 100 - Electronic device, 101 - Computing unit, 102 - ROM, 103 - RAM, 104 - Bus, 105 - I / O interface, 106 - Input unit, 107 - Output unit, 108 - Storage unit, 109 - Communication unit. Detailed Implementation

[0068] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0069] like Figures 1-2 As shown, the present invention relates to a vehicle-mounted laser point cloud road surface evaluation method and system based on continuous longitudinal profile analysis.

[0070] Example 1

[0071] refer to Figure 1 Embodiment 1 of this invention provides a flowchart illustrating a method for evaluating road surface quality based on continuous longitudinal profile analysis using vehicle-mounted laser point clouds. This invention provides a road surface quality evaluation technology that automatically extracts road wheel track point clouds from road point cloud data and vehicle trajectories acquired by a vehicle-mounted laser scanning system. The International Roughness Index (IRI) is calculated based on fixed segment lengths, thereby achieving continuous road surface quality evaluation for urban roads and curved roads. This method can handle both straight and curved roads, and is particularly suitable for road surface quality evaluation in urban roads, curved sections, and complex traffic environments. It reduces the workload of manual measurement and data analysis in traditional road surface inspection, improving the efficiency and accuracy of road surface quality evaluation.

[0072] The data processing method of this invention can be executed by a road surface quality evaluation system, which can be implemented in hardware and / or software. This system can be configured in the electronic equipment of an onboard laser scanning system, such as an onboard controller or a backend data processing server. The data processing method of this invention is based on an onboard laser scanning system. Specifically, the onboard laser scanning system includes a laser scanner, a Global Navigation Satellite System (GNSS) receiver, an inertial measurement unit (IMU), and positioning and attitude determination devices such as an odometer, enabling rapid acquisition of high-density three-dimensional point cloud data of the road and its surrounding environment. Figure 1 As shown, the data processing method includes:

[0073] S100: Acquire point cloud data of the target road and vehicle trajectory lines.

[0074] In practice, a vehicle-mounted laser scanning system collects 3D point cloud data of the road and its surrounding environment while the vehicle is in motion, and simultaneously records the vehicle's position trajectory information. The point cloud data contains spatial information of the road surface and surrounding features such as buildings, vehicles, and trees. The vehicle's trajectory records the 3D spatial coordinates of the laser scanner at each acquisition moment, which is used for establishing the local road coordinate system and extracting wheel tracks.

[0075] In one implementation, step S100 specifically includes the following steps:

[0076] S110: Acquire raw point cloud data of the target road collected by the vehicle-mounted laser scanning system. The raw point cloud data includes road surface point cloud and point cloud of surrounding features.

[0077] In practice, the vehicle-mounted laser scanning system is mounted on the measuring vehicle. While the vehicle is in motion, the laser scanner emits a high-frequency pulse laser beam toward the road and its surrounding environment and receives the echo signal to acquire high-density three-dimensional point cloud data. The raw point cloud data includes the three-dimensional coordinate information (x, y, z) and reflection intensity information of various ground features such as road surfaces, buildings, vehicles, trees, and curbs.

[0078] S120: Acquire vehicle position trajectory data of the vehicle-mounted laser scanning system during the scanning process. The vehicle position trajectory data records the three-dimensional spatial coordinates of the vehicle-mounted laser scanner at each acquisition time.

[0079] In practice, vehicle position trajectory data is provided by the onboard GNSS / IMU integrated navigation system, which records the three-dimensional spatial coordinates (x, y, z) of the laser scanner center at each acquisition moment during vehicle movement. The z coordinate represents the device's altitude above the ground, and the trajectory data is recorded at a fixed frequency (e.g., 1Hz~10Hz) to form a continuous vehicle position sequence.

[0080] S130: Extract the horizontal coordinates of the vehicle position trajectory data, and construct a vehicle driving trajectory reference line using the horizontal coordinates.

[0081] In practice, the horizontal coordinates (x, y) of each trajectory point are extracted from the vehicle position trajectory data, while the z-coordinate (equipment height information) is discarded. The extracted horizontal coordinates are then used to construct a two-dimensional vehicle trajectory reference line. For curved roads, this trajectory reference line accurately reflects the vehicle's actual driving path, replacing the traditional road centerline and ensuring the accuracy of wheel track extraction in curved areas.

[0082] S200: Convert the point cloud data to a local coordinate system based on the vehicle's driving trajectory line, wherein the mileage direction along the vehicle's driving trajectory line is the longitudinal direction, the direction deviating from the vehicle's driving trajectory line is the lateral direction, and the elevation information of each point is maintained.

[0083] In practice, a local STRIZ coordinate system is established by projecting the original point cloud onto the horizontal plane of the vehicle's trajectory reference line. Within this coordinate system, the correspondence between the position of each point cloud data point and the vehicle trajectory is clear, facilitating subsequent wheel track point cloud extraction and longitudinal profile analysis.

[0084] In one implementation, step S200 specifically includes the following steps:

[0085] S210: Project the three-dimensional coordinates of each point in the road point cloud data onto the horizontal plane of the vehicle driving trajectory reference line to obtain the projected coordinates of each point on the horizontal plane.

[0086] S220: Along the travel direction of the vehicle travel trajectory reference line, calculate the cumulative mileage value of each projection point from the trajectory starting point along the trajectory line, and use the cumulative mileage value as the longitudinal coordinate s of each point in the local coordinate system.

[0087] In practice, for each point cloud data point, the point closest to it on the vehicle's trajectory reference line is found. Then, the cumulative path length to this closest point is calculated along the trajectory line from the trajectory starting point, and this path length is used as the longitudinal coordinate s of that point. This longitudinal coordinate s reflects the mileage position of the point cloud along the road direction.

[0088] S230: Calculate the normal distance from each projection point to the vehicle driving trajectory reference line, and use the normal distance as the lateral coordinate t of each point in the local coordinate system.

[0089] In practice, for each point cloud data point, the vertical distance from its projected position to the vehicle's trajectory reference line is calculated, and it is determined whether the point is located to the left or right of the trajectory line, assigning a positive or negative sign as the point's lateral coordinate t. The lateral coordinate t reflects the lateral distance of the point cloud from the trajectory line.

[0090] S240: Retain the original elevation values ​​of each point in the road point cloud data as the vertical coordinates z of each point in the local coordinate system.

[0091] In practice, the original elevation values ​​of each point in the point cloud data are not changed; instead, they are directly used as the vertical coordinate z in the local coordinate system, thus preserving the true elevation information of the road surface.

[0092] S250: Based on the longitudinal, transverse, and vertical coordinates, establish a local coordinate mapping relationship between each point cloud data and the vehicle's driving trajectory line.

[0093] After the above steps, each point cloud data point has complete coordinates (s, t, z) in the local coordinate system, where s represents the mileage position along the road direction, t represents the lateral distance from the trajectory line, and z represents the elevation value, thus establishing a spatial correspondence between the point cloud data and the vehicle driving trajectory line.

[0094] S300: Based on the vehicle's driving trajectory line, a horizontal interval is set in the local coordinate system, and points located outside the horizontal interval are eliminated to obtain a point cloud of the road candidate area.

[0095] In practice, the vehicle trajectory reference line is used as the baseline center line. A reasonable clipping interval is set in the lateral direction to remove non-road point clouds (such as buildings, trees, vehicles, etc.) located in the far areas on both sides of the trajectory line, and only retain the point cloud data near the road surface, so as to reduce the amount of computation in subsequent processing and improve the accuracy of road surface point extraction.

[0096] In one implementation, step S300 specifically includes the following steps:

[0097] S310: Using the vehicle driving trajectory reference line as the baseline center line, a lateral clipping interval is set in the lateral direction, and the lateral clipping interval is preset according to the number of road lanes.

[0098] In practice, the width of the lateral trimming interval is set according to the actual number of lanes on the road. For typical urban roads, the lateral range can be set to [-4 m, +4 m] (4 meters to the left and right of the trajectory line), which can cover the width of a regular lane. For multi-lane roads, the trimming width can be adjusted according to the actual number of lanes. For example, a two-way four-lane road can be extended to [-8 m, +8 m].

[0099] S320: Obtain the horizontal coordinate t of each point in the local coordinate system.

[0100] S330: Determine whether each point is located within the horizontal clipping interval based on its horizontal coordinate t.

[0101] S340: Remove point cloud data whose lateral coordinates are outside the lateral clipping interval, and retain point cloud data whose lateral coordinates are within the lateral clipping interval, and construct the road candidate area point cloud from the retained point cloud data.

[0102] This step removes non-road point clouds such as buildings, trees, and vehicles that are far away on both sides of the trajectory line, resulting in a road candidate area point cloud that mainly contains road surface information. This effectively reduces the amount of data and eliminates most of the non-road surface interference.

[0103] S400: Extract road surface points from the point cloud of the candidate road area to obtain the target road surface point cloud.

[0104] In practice, a two-stage pavement point extraction strategy combining "local minimum point method for initial screening" and "local plane fitting method for fine screening" is adopted. First, candidate pavement points are extracted using the local minimum point method, and then further fine screening is performed using the local plane fitting method to remove non-pavement points such as vehicles, trees, curbs, and buildings, resulting in a clean and accurate target pavement point cloud that reflects the continuity of pavement elevation.

[0105] In one implementation, step S400 specifically includes the following steps:

[0106] S410: Divide the point cloud of the candidate road area into grids on the longitudinal-transverse plane (st plane), extract the lowest elevation point in each grid, set the elevation tolerance range according to the lowest elevation point, and extract the points that fall within the elevation tolerance range as candidate road surface points.

[0107] In practice, the road surface is usually the lowest surface in a local area (excluding depressions), so the lowest elevation point within each grid cell is highly likely to be a road surface point. Setting an elevation tolerance range centered on the lowest elevation point can effectively extract the candidate point set of the road surface from each grid.

[0108] In one implementation, step S410 specifically includes the following steps:

[0109] S411: On the longitudinal-transverse plane (st plane) of the road candidate area point cloud, longitudinal grid lines are divided along the longitudinal direction at a first sampling interval, and transverse grid lines are divided along the transverse direction at a second sampling interval. The intersection of the longitudinal grid lines and the transverse grid lines forms a grid array. Each point in the road candidate area point cloud falls into a unique grid cell according to its longitudinal coordinate s and transverse coordinate t.

[0110] In practice, the longitudinal sampling interval (first sampling interval) is set to 10mm (0.01m) to ensure the high-frequency road surface texture information required for subsequent IRI calculations; the transverse grid width (second sampling interval) is set to 0.2m. This grid division ensures both the accuracy of road surface point extraction and computational efficiency.

[0111] S412: Traverse each grid cell and extract the point cloud data point with the smallest elevation value in each grid cell as the lowest elevation point of that grid cell.

[0112] S413: Using the elevation value of the lowest elevation point as the center, extend the preset height thresholds upward and downward along the elevation direction to form the elevation tolerance range corresponding to the grid cell.

[0113] In practice, the preset height threshold can be set to ±0.10m. The basis for setting this threshold is that the elevation change of the road surface within the same grid cell is usually small, while the difference between the elevation of non-road points such as the bottom of vehicles and the top of curbs and the road surface elevation is usually greater than this threshold.

[0114] S414: Traverse all point cloud data points within the current grid cell, mark points whose elevation values ​​are within the elevation tolerance range, and extract them as candidate road surface points.

[0115] S415: Perform the above-mentioned minimum elevation point extraction and candidate road surface point extraction operations on each grid cell, and the candidate road surface points extracted from all grid cells constitute the candidate road surface point set.

[0116] The local minimum point method can be used to quickly remove most of the point clouds that are significantly higher than the road surface (such as vehicle roofs, trees, buildings, etc.) and obtain a candidate road surface point set.

[0117] S420: Perform planar fitting on the candidate road surface points within a local spatial window, and remove outlier points based on the residuals from each candidate road surface point to the fitted plane to obtain the target road surface point cloud.

[0118] In practice, even after initial screening using the local minimum point method, candidate road surface points may still contain a small number of residual non-road surface points (such as curb edges, vehicle chassis, etc.). By using the local plane fitting method to calculate the distance (residual) from each point to its neighborhood fitting plane, these outliers can be further identified and eliminated, improving the purity of the road surface point cloud.

[0119] In one implementation, step S420 specifically includes the following steps:

[0120] S421: Using the current candidate road surface point to be processed as the center, extend the preset window length in both the longitudinal and transverse directions to construct a local spatial window centered on the current candidate road surface point, and obtain all neighboring candidate road surface points that fall within the local spatial window.

[0121] In practice, the local spatial window is set to 1m in the s-direction and 0.5m in the t-direction. This window size is sufficient to include a large number of neighborhood points for plane fitting, while also being small enough to reflect the local geometric features of the road surface.

[0122] S422: Perform plane fitting on all neighboring candidate road surface points that fall within the local spatial window to obtain the local fitting plane corresponding to the current point to be processed.

[0123] In practice, the least squares method is used to fit the neighborhood points within the window to the plane, and the equation parameters of the local fitted plane are obtained.

[0124] S423: Calculate the spatial distance from the current point to be processed to the local fitting plane, and use it as the fitting residual of the current point to be processed.

[0125] S424: Compare the fitting residual with a preset residual threshold. If the fitting residual is less than the preset residual threshold, mark the current point to be processed and retain it as the target road surface point. If the fitting residual is greater than or equal to the preset residual threshold, mark the current point to be processed and remove it.

[0126] In practice, the preset residual threshold is set to 0.05m. This threshold can effectively identify and eliminate outliers that deviate far from the local road surface plane, while retaining the true elevation changes on the road surface (such as road texture, slight undulations, etc.).

[0127] S425: Traverse all candidate road points in the candidate road point set, and the target road point cloud is formed by all marked and retained candidate road points.

[0128] After fine screening using the local plane fitting method, the obtained target road surface point cloud has eliminated most of the non-road surface interference, and can truly and accurately reflect the continuous elevation information of the road surface, providing a high-quality data foundation for subsequent wheel track longitudinal profile extraction.

[0129] S500: Obtain longitudinal profile elevation data corresponding to at least one wheel track from the target road surface point cloud along the longitudinal direction.

[0130] In practice, the lateral positions of the left and right wheel tracks are determined based on the vehicle's trajectory reference line and the preset wheelbase. Point cloud data at the corresponding lateral positions are extracted from the target road surface point cloud, and samples are taken at equal intervals along the longitudinal direction according to a preset sampling interval to obtain the longitudinal profile elevation data corresponding to the wheel tracks. The left and right wheel tracks are extracted separately and used for IRI calculations.

[0131] In one implementation, step S500 specifically includes the following steps:

[0132] S510: Determine the lateral position of the target wheel track in the lateral direction of the local coordinate system based on the preset wheel track and the position of the vehicle driving trajectory reference line.

[0133] In practice, the vehicle track width is set to 1.5m (this is a typical value for the front track width of a vehicle; the rear track width is usually slightly larger, around 1.6m to 1.8m). Using the vehicle's driving trajectory reference line as the center line, the lateral position of the left wheel track is tL = -0.75m, and the lateral position of the right wheel track is tR = +0.75m.

[0134] S520: Based on the lateral position of the target wheel track, select point cloud data from the target road surface point cloud whose lateral coordinates match the lateral position of the target wheel track, and use them as candidate point clouds for the target wheel track.

[0135] In practice, for left wheel tracks, all points in the target road surface point cloud with a lateral coordinate t close to -0.75m are selected; for right wheel tracks, all points with a lateral coordinate t close to +0.75m are selected. Due to the discreteness of the point cloud, slight deviations in the lateral coordinates are allowed (e.g., ±0.01m).

[0136] S530: Extract the elevation values ​​at each sampling position from the candidate point cloud of the target wheel track according to a preset longitudinal sampling interval in the longitudinal direction, and generate the longitudinal profile elevation data corresponding to the target wheel track.

[0137] In practice, the longitudinal sampling interval is set to 10 mm. The smaller the sampling interval, the smaller the calculation error of IRI, because a smaller sampling interval can more accurately measure the road surface texture and more realistically reflect the road surface condition. For each sampling location s_i (i = 0, 10 mm, 20 mm, ...), the corresponding elevation value z_i is obtained from the candidate point cloud of the target wheel track through interpolation or nearest neighbor search, forming a longitudinal profile elevation data sequence {(s_i, z_i)}.

[0138] In one specific implementation, if the candidate point cloud of the target wheel track has multiple points at a certain sampling location (due to the non-uniform density of the point cloud), the average elevation of these points is taken as the elevation value of that sampling location. If there is no point cloud data near a certain sampling location, linear interpolation is performed using the elevations of adjacent sampling points to fill in the gaps.

[0139] S600: Divide the longitudinal profile elevation data into multiple sections and use a vehicle model to calculate the road surface smoothness index of each section as the road surface quality evaluation result.

[0140] In practice, the acquired wheel track longitudinal profile elevation data is filtered to reduce the influence of long-wavelength components on IRI calculation. Then, the longitudinal profile is divided into multiple continuous sections according to a fixed segment length (10m). The IRI value of each section is calculated using a quarter vehicle model to obtain the road surface smoothness evaluation results that are continuously distributed along the road direction.

[0141] In one implementation, step S600 specifically includes the following steps:

[0142] S610: Filter the acquired longitudinal profile elevation data to obtain filtered longitudinal profile elevation data.

[0143] In practice, a moving average filter is used to filter the longitudinal profile elevation data. In actual contour measurements, filtering of the longitudinal profile elevation data that constitutes the contour is almost always necessary. The most obvious features of the unfiltered longitudinal profile (slope and overall curvature) are the least accurate parts of the data. When calculating the IRI, filtering is needed to reduce wavelength components beyond 1.3m to 29.4m, as these will cause the calculated IRI value to be overestimated.

[0144] In one specific implementation, the window length of the moving average filter is adjusted according to the road grade and vehicle speed to effectively filter out elevation fluctuation components with wavelengths less than 1.3m and greater than 29.4m.

[0145] S620: Divide the filtered longitudinal profile elevation data into multiple continuous longitudinal profile segments along the longitudinal direction according to a preset segment length.

[0146] In its implementation, the Ministry of Transport added a 10m step distance IRI index to the 2018 standard JTG 5210-2018. This is beneficial for meeting the needs of daily highway maintenance and can more accurately reflect the pavement smoothness in local areas. Therefore, the preset segment length is set at 10m, and starting from the longitudinal profile starting point, every 10m is divided into an independent calculation segment. If the remaining segment is less than 10m, it is treated as the actual length.

[0147] S630: For each longitudinal section, calculate the International Roughness Index (IRI) corresponding to that section using a quarter vehicle model.

[0148] In practical implementation, for each longitudinal section, a quarter-car model is used to simulate the dynamic response of the vehicle on the road longitudinal section. The cumulative relative motion between the sprung and unsprung mass of the vehicle body is calculated to obtain the IRI value for that section. The formula for calculating IRI is: in, For the sprung displacement of the vehicle body, Let L be the unsprung displacement of the wheel and L be the segment length (10m). Dividing this integral value by the segment length yields the IRI value in m / km.

[0149] In practice, the longitudinal section of the left wheel track is calculated separately. and the longitudinal section of the right wheel track Then, the average of the two values ​​is taken as the composite IRI value for that segment: .

[0150] S640: Generate pavement quality evaluation results based on the international roughness index corresponding to each longitudinal section.

[0151] In practice, based on the IRI values ​​calculated for each section, and referring to the road surface driving quality evaluation standards (Excellent: IRI 2~4 m / km; Good: IRI 4~6 m / km; Average: IRI 6~8 m / km; Substandard: IRI 8~10 m / km; Poor: IRI 10~15.3 m / km), the road surface quality of each section is graded, generating a road surface quality evaluation result continuously distributed along the road mileage, including a data table of IRI values ​​for each section and a road surface quality grade distribution map.

[0152] S700: Based on the road surface quality evaluation results, output a road surface smoothness index distribution data table and a road surface quality evaluation map for each section.

[0153] In the specific implementation, the IRI values ​​of each segment generated in step S640 are output in the form of a data table. Each row records the starting mileage, ending mileage, left wheel track IRI, right wheel track IRI, average IRI, and corresponding quality level of a segment. At the same time, a road surface quality evaluation map is generated in a visual manner, with the road mileage on the horizontal axis and the IRI value on the vertical axis. Different colors are used to mark each level interval, intuitively displaying the distribution of road surface smoothness of the entire road.

[0154] The data processing method of this invention, through steps S100 to S700, realizes a complete automated processing flow from the original point cloud of vehicle-mounted laser scanning to the road surface smoothness evaluation result. The method uses the vehicle's trajectory line as a spatial reference, establishes a local coordinate system for point cloud organization, and accurately extracts the road surface point cloud using a two-stage strategy of "local minimum point method + local plane fitting method." Combined with wheel track analysis and a quarter-vehicle model, the IRI is calculated, ultimately generating the road surface quality evaluation result. This method does not rely on expensive dedicated profile measuring equipment; it can be completed using only conventional point cloud data from a vehicle-mounted laser scanning system. It is applicable to straight roads, curves, and complex urban road environments, and has significant advantages such as low cost, high accuracy, and wide applicability.

[0155] Example 2

[0156] The vehicle-mounted laser point cloud road evaluation system based on continuous longitudinal profile analysis includes: a data acquisition module, used to acquire point cloud data of the target road and vehicle driving trajectory lines;

[0157] The coordinate transformation module is used to transform the point cloud data to a local coordinate system based on the vehicle's driving trajectory line, wherein the mileage direction along the vehicle's driving trajectory line is the longitudinal direction, the direction deviating from the vehicle's driving trajectory line is the lateral direction, and the elevation information of each point is maintained.

[0158] The region clipping module is used to set a horizontal interval in the local coordinate system based on the vehicle's driving trajectory line, and remove points located outside the horizontal interval to obtain a point cloud of road candidate regions.

[0159] The road surface point extraction module is used to extract road surface points from the point cloud of the road candidate region to obtain the target road surface point cloud;

[0160] The wheel track longitudinal profile extraction module is used to obtain longitudinal profile elevation data corresponding to at least one wheel track from the target road surface point cloud along the longitudinal direction.

[0161] The road surface evaluation module is used to divide the longitudinal profile elevation data into multiple sections and use a vehicle model to calculate the road surface smoothness index of each section as the road surface quality evaluation result.

[0162] Example 3

[0163] According to embodiments of the present invention, the present invention also provides an electronic device, a readable storage medium, and a computer program product.

[0164] Figure 2 A schematic block diagram of an example electronic device 100 that can be used to implement embodiments of the present invention is shown. Electronic device 100 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 100 may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their links and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0165] like Figure 2 As shown, the electronic device 100 includes a computing unit 101, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 102 or a computer program loaded from a storage unit 108 into a random access memory (RAM) 103. The RAM 103 may also store various programs and data required for the operation of the device 100. The computing unit 101, ROM 102, and RAM 103 are interconnected via a bus 104. An input / output (I / O) interface 105 is also linked to the bus 104.

[0166] Multiple components in electronic device 100 are linked to I / O interface 105, including: input unit 106, such as keyboard, mouse, etc.; output unit 107, such as various types of displays, speakers, etc.; storage unit 108, such as disk, optical disk, etc.; and communication unit 109, such as network card, modem, wireless transceiver, etc. Communication unit 109 allows electronic device 100 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0167] The computing unit 101 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 101 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 101 performs the various methods and processes described above, such as the vehicle-mounted laser point cloud road surface evaluation method based on continuous longitudinal profile analysis. For example, in some embodiments, the vehicle-mounted laser point cloud road surface evaluation method based on continuous longitudinal profile analysis can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 108. In some embodiments, part or all of the computer program can be loaded and / or installed on device 100 via ROM 102 and / or communication unit 109. When the computer program is loaded into RAM 103 and executed by the computing unit 101, one or more steps of the vehicle-mounted laser point cloud road surface evaluation method based on continuous longitudinal profile analysis described above can be performed. Alternatively, in other embodiments, computing unit 101 may be configured in any other suitable manner (e.g., by means of firmware) to perform a vehicle-mounted laser point cloud road surface evaluation method based on continuous longitudinal profile analysis.

[0168] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0169] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0170] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical links based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0171] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0172] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0173] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0174] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0175] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Therefore, any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for evaluating road surface using vehicle-mounted laser point cloud based on continuous longitudinal profile analysis, characterized in that, include: Acquire point cloud data of the target road and vehicle trajectory lines; The point cloud data is converted to a local coordinate system based on the vehicle's driving trajectory line, wherein the mileage direction along the vehicle's driving trajectory line is the longitudinal direction, the direction deviating from the vehicle's driving trajectory line is the lateral direction, and the elevation information of each point is preserved. Based on the vehicle's trajectory line, a horizontal interval is set in the local coordinate system, and points located outside the horizontal interval are eliminated to obtain a point cloud of the road candidate region. Road surface points are extracted from the point cloud of the candidate road area to obtain the target road surface point cloud; Acquire longitudinal profile elevation data for at least one wheel track from the target road surface point cloud along the longitudinal direction; The longitudinal profile elevation data is divided into multiple sections, and the road surface smoothness index of each section is calculated using a vehicle model as the road surface quality evaluation result.

2. The vehicle-mounted laser point cloud road surface evaluation method based on continuous longitudinal profile analysis according to claim 1, characterized in that, The acquisition of point cloud data of the target road and vehicle trajectory lines specifically includes: Acquire raw point cloud data of the target road collected by an on-board laser scanning system. The raw point cloud data includes road surface point cloud and point cloud of ground features around the road. The vehicle position trajectory data of the vehicle-mounted laser scanning system during the scanning process is acquired, and the vehicle position trajectory data records the three-dimensional spatial coordinates of the vehicle-mounted laser scanner at each acquisition time. Extract the horizontal coordinates of the vehicle position trajectory data, and construct a vehicle driving trajectory reference line using the horizontal coordinates.

3. The vehicle-mounted laser point cloud road surface evaluation method based on continuous longitudinal profile analysis according to claim 1, characterized in that, The step of converting the point cloud data to a local coordinate system based on the vehicle's trajectory line specifically includes: The three-dimensional coordinates of each point in the road point cloud data are projected onto the horizontal plane of the vehicle driving trajectory reference line to obtain the projected coordinates of each point on the horizontal plane. Along the travel direction of the vehicle travel trajectory reference line, calculate the cumulative mileage value of each projection point from the starting point of the trajectory line, and use the cumulative mileage value as the longitudinal coordinate of each point in the local coordinate system. Calculate the normal distance from each projection point to the vehicle trajectory reference line, and use the normal distance as the lateral coordinate of each point in the local coordinate system; The original elevation values ​​of each point in the road point cloud data are retained as the vertical coordinates of each point in the local coordinate system; Based on the longitudinal, lateral, and vertical coordinates, a local coordinate mapping relationship is established between each point cloud data and the vehicle's driving trajectory line.

4. The vehicle-mounted laser point cloud road surface evaluation method based on continuous longitudinal profile analysis according to claim 1, characterized in that, The process of defining a horizontal interval in the local coordinate system based on the vehicle's trajectory line, and eliminating points located outside the horizontal interval to obtain a road candidate region point cloud, specifically includes: Using the vehicle driving trajectory reference line as the baseline center line, a lateral clipping interval is set in the lateral direction. The lateral clipping interval is preset according to the number of road lanes. Obtain the lateral coordinates of each point in the local coordinate system; Determine whether each point is located within the horizontal clipping interval based on its horizontal coordinates. Point cloud data whose lateral coordinates are outside the lateral clipping interval are removed, while point cloud data whose lateral coordinates are within the lateral clipping interval are retained. The retained point cloud data constitutes the road candidate region point cloud.

5. The vehicle-mounted laser point cloud road surface evaluation method based on continuous longitudinal profile analysis according to claim 1, characterized in that, The step of extracting road surface points from the point cloud of the road candidate region to obtain the target road surface point cloud specifically includes: Divide the point cloud of the road candidate area into grids in the longitudinal and transverse planes, extract the lowest elevation point in each grid, set an elevation tolerance range based on the lowest elevation point, and extract the points that fall within the elevation tolerance range as candidate road surface points. Within a local spatial window, the candidate road surface points are fitted to a plane. Based on the residual values ​​of each candidate road surface point to the fitted plane, outliers are removed to obtain the target road surface point cloud.

6. The vehicle-mounted laser point cloud road surface evaluation method based on continuous longitudinal profile analysis according to claim 5, characterized in that, The process of dividing the point cloud of the road candidate area into grids on the longitudinal and transverse planes, extracting the lowest elevation point within each grid, setting an elevation tolerance range based on the lowest elevation point, and extracting points falling within the elevation tolerance range as candidate road surface points specifically includes: On the longitudinal and transverse planes of the road candidate area point cloud, longitudinal grid lines are divided along the longitudinal direction at a first sampling interval, and transverse grid lines are divided along the transverse direction at a second sampling interval. The intersection of the longitudinal grid lines and the transverse grid lines forms a grid array. Each point in the road candidate area point cloud falls into a unique grid cell according to its longitudinal coordinates and transverse coordinates. Traverse each grid cell and extract the point cloud data point with the smallest elevation value in each grid cell as the lowest elevation point of that grid cell; Centered on the elevation value of the lowest elevation point, a preset height threshold is extended upwards and downwards along the elevation direction to form the elevation tolerance range corresponding to the grid cell. Traverse all point cloud data points within the current grid cell, mark points whose elevation values ​​are within the elevation tolerance range, and extract them as candidate road surface points; Perform the minimum elevation point extraction and candidate road surface point extraction operations on each grid cell, and the candidate road surface points extracted from all grid cells constitute the candidate road surface point set.

7. The vehicle-mounted laser point cloud road surface evaluation method based on continuous longitudinal profile analysis according to claim 5, characterized in that, The step of performing planar fitting on the candidate road surface points within a local spatial window, and removing outliers based on the residuals from each candidate road surface point to the fitted plane to obtain the target road surface point cloud, specifically includes: With the current candidate road point as the center, a local spatial window is constructed by extending a preset window length in both the longitudinal and transverse directions, with the current point to be processed as the center, and all neighboring candidate road points falling within the local spatial window are obtained. Perform plane fitting on all neighboring candidate road surface points that fall within the local spatial window to obtain the local fitting plane corresponding to the current point to be processed. Calculate the spatial distance from the current point to be processed to the local fitting plane, and use it as the fitting residual of the current point to be processed; The fitting residual is compared with a preset residual threshold. If the fitting residual is less than the preset residual threshold, the current point to be processed is marked and retained as a target road surface point; if the fitting residual is greater than or equal to the preset residual threshold, the current point to be processed is marked and removed. Traverse all candidate road points in the candidate road point set, and the target road point cloud is formed by all marked and retained candidate road points.

8. The vehicle-mounted laser point cloud road surface evaluation method based on continuous longitudinal profile analysis according to claim 1, characterized in that, The step of obtaining longitudinal profile elevation data corresponding to at least one wheel track from the target road surface point cloud along the longitudinal direction specifically includes: Based on the preset wheel track and the position of the vehicle driving trajectory reference line, the lateral position of the target wheel track is determined in the lateral direction of the local coordinate system. Based on the lateral position of the target wheel track, point cloud data whose lateral coordinates match the lateral position of the target wheel track are selected from the target road surface point cloud and used as candidate point clouds for the target wheel track; In the longitudinal direction, at a preset longitudinal sampling interval, the elevation values ​​at each sampling position are extracted from the candidate point cloud of the target wheel track to generate the longitudinal profile elevation data corresponding to the target wheel track.

9. The vehicle-mounted laser point cloud road surface evaluation method based on continuous longitudinal profile analysis according to claim 1, characterized in that, The process of dividing the longitudinal profile elevation data into multiple sections and calculating the road surface smoothness index of each section using a vehicle model as the road surface quality evaluation result specifically includes: The acquired longitudinal profile elevation data is filtered to obtain filtered longitudinal profile elevation data. The filtered longitudinal profile elevation data is divided into multiple continuous longitudinal profile segments according to a preset segment length along the longitudinal direction. For each longitudinal section, the international roughness index corresponding to that section is calculated using a quarter-vehicle model. The pavement quality evaluation results are generated based on the international roughness index corresponding to each longitudinal section.

10. A vehicle-mounted laser point cloud road surface evaluation system based on continuous longitudinal profile analysis, characterized in that, include: The data acquisition module is used to acquire point cloud data of the target road and vehicle trajectory lines; The coordinate transformation module is used to transform the point cloud data to a local coordinate system based on the vehicle's driving trajectory line, wherein the mileage direction along the vehicle's driving trajectory line is the longitudinal direction, the direction deviating from the vehicle's driving trajectory line is the lateral direction, and the elevation information of each point is maintained. The region clipping module is used to set a horizontal interval in the local coordinate system based on the vehicle's driving trajectory line, and remove points located outside the horizontal interval to obtain a point cloud of road candidate regions. The road surface point extraction module is used to extract road surface points from the point cloud of the road candidate region to obtain the target road surface point cloud; The wheel track longitudinal profile extraction module is used to obtain longitudinal profile elevation data corresponding to at least one wheel track from the target road surface point cloud along the longitudinal direction. The road surface evaluation module is used to divide the longitudinal profile elevation data into multiple sections and use a vehicle model to calculate the road surface smoothness index of each section as the road surface quality evaluation result.

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