Method and system for measuring road roughness based on multi-angle weighting correction

Abstract

The application discloses a kind of based on multi-angle weighting correction's road surface flatness measurement method and system, it is related to data measurement technical field, method includes: obtaining to be measured section and measurement route, obtain laser sensor, the elevation data of measurement point in measurement route is collected;Obtain initial weight coefficient, obtain the reflection intensity of laser sensor, according to the reflection intensity of the laser sensor corresponding to measurement point the correction coefficient of the laser sensor corresponding to measurement point is obtained;According to the correction coefficient of the laser sensor corresponding to measurement point and initial weight coefficient, the target weight coefficient of the laser sensor corresponding to measurement point is obtained;According to the target weight coefficient of the laser sensor corresponding to measurement point and elevation data, the target elevation data corresponding to measurement point is obtained, according to the target elevation data of measurement point in measurement route, the road surface flatness of to be measured section is obtained.The application has the advantages of dynamic self-adaptability, weighted fusion and tolerance expansion optimization.

Description

Technical Field

[0001] This invention relates to the field of data measurement technology, specifically to a method and system for measuring road surface smoothness based on multi-angle weighted correction. Background Technology

[0002] In the field of road engineering inspection, especially in the measurement of road surface smoothness, laser sensors are widely used to scan the elevation along a preset measurement route to obtain three-dimensional information of the road surface and calculate smoothness indicators. However, the actual measurement environment is complex and variable. The road surface often has water accumulation, residual ice and snow, oil stains or other attachments. These substances can significantly affect the reflection characteristics of the laser beam, causing significant deviations or distortions in the elevation data obtained by a single laser sensor or a fixed-angle sensor array in the contaminated area.

[0003] Specifically, existing methods typically employ single-sensor or fixed-weighted averaging strategies to fuse multi-sensor data. This approach struggles to effectively address the transient and localized road surface pollution interference mentioned above. The reduced reflection intensity from pollution causes abnormal distortion in laser measurements at specific angles. If static weights are applied to all measurement points and sensors for fusion calculations, the erroneous data from these polluted sensors will not be adequately suppressed, thus contaminating the overall calculation results. Ultimately, this leads to a significant deviation between the assessed road surface smoothness and the actual condition. This deviation is particularly pronounced in scenarios requiring refined assessments of polluted and unpolluted areas, as well as areas with varying degrees of interference. This results in an inability to provide accurate and reliable road surface quality reports, consequently impacting the scientific validity of road maintenance decisions and the rationality of resource allocation. Summary of the Invention

[0004] To address the technical problems existing in the background art mentioned above, the present invention provides a road surface smoothness measurement method and system based on multi-angle weighted correction. It can sense abnormal changes in laser reflection intensity in real time (as an indicator of environmental interference), automatically and dynamically adjust the contribution weights of sensors at different angles, effectively reduce the impact of local pollution interference on elevation data, and adaptively adjust the measurement method of the smoothness judgment tolerance standard to improve the reliability of road surface smoothness measurement in complex actual road environments and meet the needs of high-precision road maintenance and inspection.

[0005] A method for measuring road surface smoothness based on multi-angle weighted correction includes: acquiring the road segment to be measured and the measurement route located on the road segment; acquiring multiple laser sensors, all facing the measurement route and having different initial angles; moving each laser sensor along the measurement route and synchronously collecting elevation data of multiple measurement points along the measurement route; acquiring the initial weight coefficients of each laser sensor and acquiring the reflection intensity of each laser sensor corresponding to the i-th measurement point; acquiring the correction coefficients of each laser sensor corresponding to the i-th measurement point based on the reflection intensity of each laser sensor corresponding to the i-th measurement point; acquiring the target weight coefficients of each laser sensor corresponding to the i-th measurement point based on the correction coefficients and initial weight coefficients; acquiring the target elevation data corresponding to the i-th measurement point based on the target weight coefficients and elevation data of each laser sensor corresponding to the i-th measurement point; and acquiring the road surface smoothness of the road segment to be measured based on the target elevation data of each measurement point along the measurement route.

[0006] Optionally, obtaining the correction coefficients for each laser sensor corresponding to the i-th measurement point based on the reflection intensity of each laser sensor corresponding to the i-th measurement point includes: obtaining a standard intensity limit and comparing the reflection intensity of each laser sensor corresponding to the i-th measurement point with the standard intensity limit; if the reflection intensity of the j-th laser sensor corresponding to the i-th measurement point is lower than the standard intensity limit, then subtracting the reflection intensity of the j-th laser sensor from the standard intensity limit and dividing by the standard intensity limit to obtain the correction coefficient of the j-th laser sensor corresponding to the i-th measurement point; if the reflection intensity of the j-th laser sensor corresponding to the i-th measurement point is not lower than the standard intensity limit, then using 0 as the correction coefficient of the j-th laser sensor corresponding to the i-th measurement point.

[0007] Optionally, obtaining the target weight coefficient of each laser sensor corresponding to the i-th measurement point based on the correction coefficient and initial weight coefficient of each laser sensor corresponding to the i-th measurement point includes: multiplying the correction coefficient of the j-th laser sensor corresponding to the i-th measurement point by half of the initial weight coefficient to obtain the correction amount of the j-th laser sensor corresponding to the i-th measurement point; subtracting the correction amount from the initial weight coefficient of the j-th laser sensor corresponding to the i-th measurement point to obtain the target weight coefficient of the j-th laser sensor corresponding to the i-th measurement point.

[0008] Optionally, obtaining the target elevation data corresponding to the i-th measurement point based on the target weight coefficients and elevation data of each laser sensor corresponding to the i-th measurement point includes: obtaining the sum of the target weight coefficients of all laser sensors corresponding to the i-th measurement point, and recording it as the weight sum of the i-th measurement point; dividing the target weight coefficients of each laser sensor corresponding to the i-th measurement point by the weight sum of the i-th measurement point to obtain the dynamic weights of each laser sensor corresponding to the i-th measurement point; multiplying the dynamic weights of each laser sensor corresponding to the i-th measurement point by the elevation data to obtain the sub-data of each laser sensor; and adding the sub-data of all laser sensors corresponding to the i-th measurement point to obtain the target elevation data corresponding to the i-th measurement point.

[0009] Optionally, obtaining the road surface smoothness of the section to be measured based on the target elevation data of each measurement point in the measurement route includes: obtaining the absolute value of the difference between the target elevation data of adjacent measurement points and recording it as the road surface difference of adjacent measurement points; obtaining the standard difference, and obtaining the amplification coefficient of the adjacent measurement point according to the correction coefficient of each laser sensor corresponding to any measurement point in the adjacent measurement points, multiplying the amplification coefficient of the adjacent measurement point by the standard difference to obtain the dynamic difference of the adjacent measurement points; obtaining the number of adjacent measurement points in the measurement route whose road surface difference is lower than the dynamic difference of the corresponding adjacent measurement points and recording it as the smoothness quantity; dividing the smoothness quantity by the number of adjacent measurement points in the measurement route to obtain the road surface smoothness of the section to be measured.

[0010] Optionally, obtaining the amplification factor of an adjacent measurement point based on the correction coefficients of each laser sensor corresponding to any one of the adjacent measurement points includes: obtaining the maximum value among the correction coefficients of each laser sensor corresponding to any one of the adjacent measurement points, and using it as the coefficient to be processed for the adjacent measurement point; adding half of the coefficient to be processed for the adjacent measurement point to 1, and obtaining the amplification factor of the adjacent measurement point.

[0011] A road surface evenness measurement system based on multi-angle weighted correction is also provided. The system includes: an acquisition module for acquiring the road segment to be measured and the measurement route located on the road segment, acquiring multiple laser sensors all facing the measurement route and having different initial angles, moving each laser sensor along the measurement route and synchronously collecting elevation data of multiple measurement points in the measurement route; a first measurement module for acquiring the initial weight coefficients of each laser sensor, acquiring the reflection intensity of each laser sensor corresponding to the i-th measurement point, and acquiring the correction coefficients of each laser sensor corresponding to the i-th measurement point based on the reflection intensity of each laser sensor corresponding to the i-th measurement point; a second measurement module for acquiring the target weight coefficients of each laser sensor corresponding to the i-th measurement point based on the correction coefficients and initial weight coefficients of each laser sensor corresponding to the i-th measurement point; and a third measurement module for acquiring the target elevation data corresponding to the i-th measurement point based on the target weight coefficients and elevation data of each laser sensor corresponding to the i-th measurement point, and acquiring the road surface evenness of the road segment to be measured based on the target elevation data of each measurement point in the measurement route.

[0012] Optionally, the first measurement module is further configured to: obtain a standard intensity limit and compare the reflection intensity of each laser sensor corresponding to the i-th measurement point with the standard intensity limit; if the reflection intensity of the j-th laser sensor corresponding to the i-th measurement point is lower than the standard intensity limit, then subtract the reflection intensity of the j-th laser sensor from the standard intensity limit and divide by the standard intensity limit to obtain the correction coefficient of the j-th laser sensor corresponding to the i-th measurement point; if the reflection intensity of the j-th laser sensor corresponding to the i-th measurement point is not lower than the standard intensity limit, then use 0 as the correction coefficient of the j-th laser sensor corresponding to the i-th measurement point.

[0013] Optionally, the second measurement module is further configured to: multiply the correction coefficient of the j-th laser sensor corresponding to the i-th measurement point by half of the initial weight coefficient to obtain the correction amount of the j-th laser sensor corresponding to the i-th measurement point; subtract the correction amount from the initial weight coefficient of the j-th laser sensor corresponding to the i-th measurement point to obtain the target weight coefficient of the j-th laser sensor corresponding to the i-th measurement point.

[0014] Optionally, the third measurement module is further configured to: obtain the sum of the target weight coefficients of all laser sensors corresponding to the i-th measurement point, and record it as the weight sum of the i-th measurement point; divide the target weight coefficients of each laser sensor corresponding to the i-th measurement point by the weight sum of the i-th measurement point, and obtain the dynamic weights of each laser sensor corresponding to the i-th measurement point; multiply the dynamic weights of each laser sensor corresponding to the i-th measurement point by the elevation data and obtain the sub-data of each laser sensor; and add the sub-data of all laser sensors corresponding to the i-th measurement point to obtain the target elevation data corresponding to the i-th measurement point.

[0015] The beneficial effects of this invention are reflected in:

[0016] In the entire road surface smoothness measurement method based on multi-angle weighted correction, firstly, a correction coefficient is calculated based on the deviation between the reflection intensity and the standard limit, and the initial weight coefficient is dynamically corrected to generate the target weight coefficient, thereby reducing the contribution of interference data from polluted sensors in the fusion. Furthermore, a dynamic difference mechanism is introduced, which uses the maximum correction coefficient to adaptively relax the smoothness judgment tolerance threshold of local polluted areas. The two-level correction works synergistically, which, while ensuring the reliability of multi-angle redundancy of basic data, not only reduces the impact of instantaneous local interference on the elevation fusion results, but also reduces the probability of misjudgment of smoothness caused by apparent changes in pollutants. The final output smoothness index is more in line with the actual physical characteristics of road conditions, providing more reliable data support for road maintenance decisions. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0018] Figure 1 This is a schematic diagram illustrating the steps of the road surface evenness measurement method based on multi-angle weighted correction of the present invention;

[0019] Figure 2 This is a schematic diagram of part of step S2 in the road surface smoothness measurement method based on multi-angle weighted correction of the present invention;

[0020] Figure 3 This is a schematic diagram of part of step S3 in the road surface smoothness measurement method based on multi-angle weighted correction of the present invention;

[0021] Figure 4 This is a schematic diagram of part of step S4 in the road surface smoothness measurement method based on multi-angle weighted correction of the present invention;

[0022] Figure 5 This is a schematic diagram of another part of step S4 in the road surface evenness measurement method based on multi-angle weighted correction of the present invention;

[0023] Figure 6 This is a schematic diagram of part of step S45 in the road surface smoothness measurement method based on multi-angle weighted correction of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0025] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

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

[0027] like Figure 1 As shown, a method for measuring road surface smoothness based on multi-angle weighted correction is provided. In one embodiment, the method includes:

[0028] S1. Obtain the road segment to be measured and the measurement route located on the road segment to be measured. Obtain multiple laser sensors that are all facing the measurement route and have different initial angles. Move each laser sensor along the measurement route and synchronously collect the elevation data of multiple measurement points in the measurement route.

[0029] S2. Obtain the initial weight coefficients of each laser sensor, obtain the reflection intensity of each laser sensor corresponding to the i-th measurement point, and obtain the correction coefficients of each laser sensor corresponding to the i-th measurement point based on the reflection intensity of each laser sensor corresponding to the i-th measurement point.

[0030] S3. Obtain the target weight coefficients of each laser sensor corresponding to the i-th measurement point based on the correction coefficients and initial weight coefficients of each laser sensor corresponding to the i-th measurement point.

[0031] S4. Obtain the target elevation data corresponding to the i-th measurement point based on the target weight coefficients and elevation data of each laser sensor corresponding to the i-th measurement point, and obtain the road surface smoothness of the road section to be measured based on the target elevation data of each measurement point in the measurement route.

[0032] In this embodiment, it should be noted that in S1, the road segment to be measured and the specific measurement route located on that road segment are acquired. The measurement route is typically a pre-defined path for evaluating smoothness, such as the center line of a lane. The key operation is to acquire multiple laser sensors all facing this measurement route, and these laser sensors are pre-set with different initial incident angles (e.g., fixed and different offset angles relative to the vertical direction of the road surface). This multi-angle layout means that the same measurement point will be illuminated and detected by laser beams from multiple different directions.

[0033] Then, each laser sensor is moved synchronously along the measurement route (e.g., mounted on a platform of a mobile vehicle or a towing device). During the movement, all sensors simultaneously collect elevation data (i.e., distance information from the laser sensor to the road surface reflection point) at multiple pre-set measurement points along the measurement route. This step, by deploying multiple observation points at different angles in space and strictly ensuring that they collect data synchronously during movement, provides a rich, spatially correlated raw elevation dataset for subsequent steps. Each measurement point is no longer a single elevation reading, but corresponds to a set of readings from laser sensors at different incident angles. This lays the physical foundation for identifying and mitigating angle-sensitive measurement distortions caused by local road surface contamination (such as water accumulation or oil stains), where water accumulation may significantly interfere with laser reflection at a specific angle. This data acquisition method overcomes the drawback of not being able to effectively identify local interference that may arise from a single sensor or fixed combination, and its design itself provides the possibility for dynamic weight adjustment.

[0034] Furthermore, in implementing S1, for example, five laser sensors can be mounted on a rigid bracket arranged perpendicular to the measurement route. The intersection point of the principal optical axes of these sensors is pre-aligned with a preset point on the measurement route (or the scanning range covers that point), but the angles between their respective optical axes and the road surface normal are set to different initial angles such as -15 degrees, -7.5 degrees, 0 degrees (perpendicular), +7.5 degrees, and +15 degrees. The bracket is mounted on the inspection vehicle, and the sensor array moves along with the vehicle as it travels at a constant speed along the measurement route.

[0035] At this point, through precise timing control or triggering mechanisms, it is ensured that at each preset measurement point location (positioned via vehicle displacement sensors or GPS), all five laser sensors emit laser beams and receive reflected signals at exactly the same physical time point and high spatial position (relative to the measurement point). This allows for the independent acquisition of the elevation values ​​and corresponding laser reflection intensity values ​​of the measurement point at five different angles (although the reflection intensity information is used for correction in S2, its acquisition is also completed synchronously during data acquisition in S1). The basic dataset provided in this step serves as the basis for all subsequent data processing and adaptive weight adjustments. Its design ensures that the effectiveness of the entire method depends on reliable and homogeneous multi-angle observation information.

[0036] In step S2, the initial weighting coefficients of each laser sensor are acquired. Then, using the laser reflection intensity data synchronously collected in step S1 (which represents the intensity of the reflected light signal received by the laser sensor), the degree of interference that may affect the readings of each laser sensor at a specific measurement point is evaluated based on the initial weighting coefficients, and this is quantified into a key parameter—the correction coefficient. The initial weighting coefficients are set comprehensively based on the hardware accuracy of the laser sensor, the importance of the installation angle, and historical calibration data. The basic principles are as follows: vertical sensors have the highest weight (usually 0.3–0.5) because the vertical incident path is the shortest, resulting in minimal atmospheric interference and the highest theoretical accuracy; small-angle sensors are next (within ±10°, usually 0.2–0.3), where measurement errors are controllable when the tilt angle is small; large-angle sensors have the lowest weight (≥±15°, usually 0.1–0.2), as the incident path is longer and they are more susceptible to the diffuse reflection characteristics of the road surface.

[0037] Furthermore, it is first necessary to obtain a preset standard intensity limit value. This limit value is usually derived from calibration experiments on a clean, dry, ideal road surface and represents the expected normal reflection intensity range of the sensor under this standard condition.

[0038] Subsequently, for the i-th measurement point being processed, the actual reflected intensity value measured by each laser sensor (marked as j-th) at this point is checked one by one, and compared one-to-one with this predefined standard intensity limit. This comparison is not a simple pass / fail judgment, but is used to precisely quantify the degree to which the sensor's reading deviates from the ideal state at this point, ultimately generating a specific value between 0 and 1 as the correction coefficient for the j-th sensor at the i-th point. This coefficient reflects the extent to which the reliability of the sensor's reading at the current point is compromised: if the reflected intensity reaches the standard limit, it is considered that there is no significant interference affecting the sensor's reading, and its correction coefficient is zero, indicating that no additional correction is needed; conversely, if the reflected intensity is below the standard limit (this is usually due to the presence of water, ice, oil stains, or other contaminants on the road surface, which leads to enhanced laser scattering or absorption, weakening the effective reflected signal), the proportion of the sensor's below-limit to the standard limit is calculated, and this proportion is directly used as its correction coefficient.

[0039] Therefore, a larger correction coefficient means a more significant decrease in the reflected intensity measured by the sensor at this point, and a higher likelihood that the corresponding elevation data is subject to interference. This requires more careful handling in subsequent weight allocation. The output of S2—calculating a correction coefficient for each sensor at point i—is essentially a preliminary sensor reliability assessment based on the current physical state before multi-angle information fusion. It provides crucial, point-to-point quality judgment for subsequent dynamic weight adjustment.

[0040] Furthermore, to illustrate with an example, suppose at the i-th measurement point, a small patch of water covers part of the road surface. When a laser sensor beam incident perpendicularly (at a 0-degree angle) happens to illuminate this water-covered area, due to the strong reflectivity of water (potentially resulting in specular reflection rather than ideal diffuse reflection) or absorption, the actual received reflection intensity value is likely to be far lower than the standard intensity limit value that the sensor should have on a dry road surface. According to the logic of S2, the calculated correction coefficient for this vertical sensor will be a large value (e.g., close to 1).

[0041] Meanwhile, another laser sensor installed at a -15 degree tilt angle may have its beam avoid this narrow waterlogged area, or its angle of illumination may have caused partial reflection, resulting in a measured reflection intensity that is slightly below the standard limit or close to or even reaches the standard limit. Therefore, the correction factor for this tilt sensor will be a small value (slightly greater than 0) or zero. On the other hand, a sensor at a +15 degree tilt angle, if its beam is positioned entirely on a dry surface, will have a normal reflection intensity, and its correction factor will be zero. This allows for the automatic identification of potential local interference locations and their specific impact on sensors at particular angles, based on actual, objectively measured changes in reflection intensity. This correction factor itself does not directly change the elevation data but is passed as key metadata to subsequent steps, clearly indicating which sensor reading at which point may be unreliable and to what extent.

[0042] In step S3, based on the correction coefficients calculated in step S2 (reflecting the degree of interference each sensor may experience at a specific measurement point), and combined with preset initial weighting coefficients, the final adjusted target weighting coefficients are calculated for each sensor at that measurement point. The initial weighting coefficient is a predefined value, typically set based on the laser sensor's hardware performance, the importance of its installation angle (e.g., a sensor with perpendicular incidence might be assigned a higher initial weight), or other prior knowledge. It represents the proportion of the sensor's contribution to the final elevation value or its initial confidence level under ideal conditions without any interference.

[0043] First, a correction factor is introduced to dynamically revise the initial weights point-to-point and sensor-to-sensor: if a sensor has a high correction factor at measurement point i (indicating a higher probability of its reading being interfered with), its weight in calculating the final elevation of that point should be appropriately reduced; conversely, if a sensor has a zero correction factor at point i (indicating no interference detected), its original reliability at that point is maintained, and its weight will directly use the initial weight factor. This adjustment is achieved through a quantified correction amount, which is the result of multiplying the correction factor by a portion (specifically, half) of the initial weight factor.

[0044] Then, this correction is subtracted from its initial weights to obtain the target weight coefficient. This process ensures that the magnitude of the weight adjustment is positively correlated with the assessment of the degree of interference (the magnitude of the correction coefficient) and also with the initial confidence level of the sensor (the initial weight magnitude). This means that the reduction in weight will have a greater impact on sensors with large initial weights and high interference (because their influence was originally larger), while the impact will be smaller on sensors with small initial weights or low interference levels.

[0045] Finally, the output of S3 is a set of target weighting coefficients for all sensors at the i-th measurement point. These coefficients have been individually adjusted based on the interference status of each sensor at the current point. These target weighting coefficients prepare the data fusion process for the next step (S4), enabling the fusion process to place greater emphasis on the readings of sensors with less interference and reduce the impact of readings of sensors with greater interference on the final result.

[0046] Furthermore, to illustrate with an example, suppose that at the i-th measurement point, after calculation by S2, the correction coefficient for the vertically incident sensor (A) is high (e.g., due to water stains at its laser illumination location causing low reflection intensity), while the correction coefficients for the two tilted sensors (B and C) are zero (no interference detected). The initial weighting coefficients are set as follows: sensor A (weight 0.3), sensor B (weight 0.3), and sensor C (weight 0.4). For sensor A, a correction amount (specifically, the correction coefficient multiplied by half of the initial weight) is calculated based on its higher correction coefficient (reflecting its unreliability). This correction amount represents the portion that is recommended to be deducted from its initial weight (this deduction is less than the correction coefficient multiplied by the initial weight, reflecting the constraint of the correction amount).

[0047] Then, the target weight coefficient is the initial weight (0.3) of sensor A minus this positive correction. The calculation result makes the target weight coefficient of sensor A at point i lower than its initial weight (for example, becoming 0.18), indicating that its contribution is reduced due to potential interference. For sensors B and C, since the correction coefficient is zero, according to the logic of S3, their correction amount is also zero, so their initial weights do not need to be reduced, and the target weight coefficient is directly equal to their respective initial weights (B is still 0.3, and C is still 0.4). After the adjustment of S3, at point i, where there is local water stains, the contribution weight of sensors A and B, which originally had equal weights, is reduced, while the weights of B and C are retained. This adjustment naturally increases the proportion of relatively reliable sensors B and C (especially C, which has a higher weight) when fusing the final elevation data, while the influence of potentially distorted data from sensor A is weakened. S3 enables precise and differentiated dynamic adjustment of sensor weights based on local real-time interference assessment results, avoiding the need for a uniform weight allocation strategy for all points and all sensors. This allows for a more accurate response to the problem of local contamination interference causing distortion of specific sensor data.

[0048] In S4, the target weight coefficients of each sensor calculated for each measurement point in S3 are used to fuse the original elevation data of multiple sensors collected in S1, and obtain the most representative target elevation data value of each measurement point after interference suppression.

[0049] First, the sum of the target weight coefficients of all sensors at the current measurement point (point i) needs to be calculated. This sum serves as a temporary denominator to normalize the target weight coefficient of each sensor, transforming it into a dynamic weight coefficient (the dynamic weight coefficient of each sensor is equal to its target weight coefficient divided by the sum of weights). The sum of the normalized dynamic weight coefficients is 1, representing the voting proportion of all sensors in the fusion at this point.

[0050] Then, the raw elevation data value of each sensor is multiplied by its corresponding dynamic weighting coefficient to obtain a portion (sub-data) of the sensor's final elevation contribution to that point.

[0051] Finally, by summing the sub-data contributed by all sensors, a comprehensive target elevation value representing the location of the measurement point is obtained. The advantage of this method is that sensors assessed as less susceptible to interference and having higher target weight coefficients (i.e., high-reliability sensors) in S2 and S3 have a larger proportion of their elevation data in the final fusion result; conversely, sensors assessed as more likely to be interfered with and having their target weight coefficients lowered (i.e., low-reliability sensors) have a smaller proportion of their data. This dynamically weighted fusion method effectively utilizes the redundant information from multiple angle sensors, potentially weakening the impact of interfered readings on the final elevation value. The resulting target elevation data is closer to the ideal elevation of the point under interference-free conditions, improving the reliability and accuracy of the data and laying a more accurate data foundation for subsequent flatness assessments.

[0052] Furthermore, based on the target elevation value sequence of the continuous measurement points obtained earlier, the comprehensive pavement smoothness index of the entire road section under test is calculated. This process first calculates the absolute difference of the target elevation data between adjacent measurement points (e.g., point i and point i+1). This value is called the pavement difference between the two adjacent points, which intuitively reflects the degree of vertical change between the two points.

[0053] Then, a preset standard deviation is obtained, which represents the maximum permissible elevation change between two points that is considered acceptable for a smooth surface under ideal conditions. However, considering the possibility of local pollution interference on the road surface (even after the previous fusion processing, the target elevation data is theoretically more reliable, but the presence of the pollution source itself may affect the appearance of the road surface), S4 introduces an adaptive adjustment approach: for each pair of adjacent measurement points, it uses the sensor correction coefficient information calculated in the previous steps for those two points or one of them (especially the largest interference indicator) to calculate an amplification factor. This amplification factor is greater than or equal to 1 (its value is greater than 1 when the maximum correction coefficient for that point is greater than 0).

[0054] Next, multiplying this amplification factor by the standard deviation yields the dynamic difference used to determine whether adjacent points are smooth. This means that in areas assessed as highly susceptible to pollution interference (indicated by a larger correction factor for sensors at that point), the tolerance threshold for smoothness is appropriately relaxed (the dynamic difference becomes larger). Conversely, in areas where no interference is detected, the tolerance threshold remains the original standard deviation. Then, among all adjacent measurement point pairs along the measurement route, the number of point pairs whose calculated actual pavement difference is less than or equal to their corresponding dynamic difference is counted. These point pairs are marked as smooth, and the number is recorded as the smoothness quantity. Finally, the pavement smoothness index for the entire road segment is the calculated smoothness quantity divided by the total number of adjacent measurement point pairs along the route (for example, 0.95 represents that 95% of the changes between adjacent points are judged as smooth after considering local interference conditions). This final smoothness value not only reflects the physical elevation changes of the road surface, but also appropriately considers the apparent changes that may be caused by local pollutant coverage through a dynamic difference mechanism, aiming to provide a smoothness assessment result that is closer to the actual road conditions and has more engineering significance.

[0055] In summary, in the road surface evenness measurement method based on multi-angle weighted correction, the correction coefficient is calculated based on the deviation between the reflection intensity and the standard limit, and the initial weight coefficient is dynamically corrected to generate the target weight coefficient, thereby reducing the contribution of interference data from polluted sensors in the fusion process. Furthermore, a dynamic difference mechanism is introduced, which uses the maximum correction coefficient to adaptively relax the evenness judgment tolerance threshold of local polluted areas. The synergistic effect of these two corrections, while ensuring the reliability of multi-angle redundancy of basic data, not only reduces the impact of instantaneous local interference on the elevation fusion results, but also reduces the probability of evenness misjudgment caused by apparent changes in pollutants. The final output evenness index is more in line with the actual physical characteristics of road conditions, providing more reliable data support for road maintenance decisions.

[0056] like Figure 2 As shown, in one embodiment, obtaining the correction coefficients for each laser sensor corresponding to the i-th measurement point in S2 based on the reflection intensity of each laser sensor corresponding to the i-th measurement point includes:

[0057] S21. Obtain the standard intensity limit and compare the reflection intensity of each laser sensor corresponding to the i-th measurement point with the standard intensity limit.

[0058] S22. If the reflection intensity of the j-th laser sensor corresponding to the i-th measurement point is lower than the standard intensity limit, then subtract the reflection intensity of the j-th laser sensor from the standard intensity limit and divide by the standard intensity limit to obtain the correction coefficient of the j-th laser sensor corresponding to the i-th measurement point.

[0059] S23. If the reflection intensity of the j-th laser sensor corresponding to the i-th measurement point is not lower than the standard intensity limit, then 0 is used as the correction coefficient of the j-th laser sensor corresponding to the i-th measurement point.

[0060] In this embodiment, it should be noted that in S21, a value determined in advance through rigorous calibration experiments in a laboratory or on a standard road section is obtained, referred to as the standard intensity limit. This limit represents the lower limit of the normal reference range of the intensity of the reflected laser signal expected to be received by a specific laser sensor under ideal dry and clean road conditions. Specifically, the standard intensity limit is obtained through laboratory calibration. The calibration environment is a constant temperature and humidity chamber with a temperature of 25℃±2℃ and a humidity of 45%±5%. The calibration sample uses a standard diffuse reflection plate (such as a sintered PTFE white board with a reflectivity of 99%) to simulate an ideal road surface. The calibration process is as follows: each sensor is vertically irradiated onto the calibration plate at a fixed distance (e.g., 1.5 meters); the reflection intensity is continuously collected 100 times, and outliers of ±3 times standard deviation are removed before taking the mean; this mean is lowered by 10% as the standard intensity limit.

[0061] During data processing, for the i-th measurement point being evaluated, the raw reflection intensity data actually collected by the j-th laser sensor deployed at that point is compared numerically with this predefined standard intensity limit. This comparison is not a simple binary judgment (qualified or unqualified), but rather provides a quantitative basis for subsequent steps. It aims to initially identify whether the signal of each sensor at the current specific point is within the normal range or below the expected level, thereby indicating whether there are potential pollution or interference factors in that small area of ​​the road surface, preparing for the generation of correction coefficients.

[0062] In S22, when the actual reflected intensity value measured by the j-th laser sensor at the i-th measurement point is lower than the aforementioned standard intensity limit, it indicates that the sensor may be interfered with by road surface anomalies (such as water accumulation, oil stains, ice, etc.) at the current point, resulting in a weakened reflected signal. To quantify the degree of this signal loss relative to the normal state, a calculation process is performed: first, the difference between the standard intensity limit value and the current measured intensity value of the j-th sensor (i.e., the absolute amount by which the actual signal is lower than the standard value) is calculated, and then this difference is divided by the standard intensity limit value itself.

[0063] This division operation converts the signal loss into a proportional value relative to a standard limit, ranging from 0 to 1 (1 when the measured intensity equals 0, and 0 when the limit is reached). This calculated proportional value is directly defined as the correction coefficient for the j-th laser sensor at the i-th measurement point. The larger this coefficient (the closer to 1), the more severe the signal loss, and the higher the risk of distortion in the sensor's elevation data at the current point due to interference.

[0064] In S23, if the measured reflection intensity value of the j-th laser sensor reaches or exceeds the preset standard intensity limit, it is considered that the sensor is in a normal state of measurement environment at the current point, and no obvious road surface attachments or abnormal conditions are detected that adversely affect the laser reflection characteristics.

[0065] In this scenario, the elevation data acquired by the sensor is considered relatively reliable. To reflect this reliability, the correction coefficient for the sensor at this point is set to zero. A zero correction coefficient means that the initial weight of the sensor does not need to be adjusted downwards in subsequent weight adjustments. This ensures that in interference-free areas, all sensors (especially those ideally considered highly accurate, such as sensors with perpendicular incidence) can fully participate in the elevation data fusion calculation with their preset initial weights, avoiding unnecessary weakening of reliable data.

[0066] like Figure 3 As shown, in one embodiment, S3, obtaining the target weight coefficients of each laser sensor corresponding to the i-th measurement point based on the correction coefficients and initial weight coefficients of each laser sensor corresponding to the i-th measurement point includes:

[0067] S31. Multiply the correction coefficient of the j-th laser sensor corresponding to the i-th measurement point by half of the initial weight coefficient to obtain the correction amount of the j-th laser sensor corresponding to the i-th measurement point.

[0068] S32. Subtract the correction amount from the initial weight coefficient of the j-th laser sensor corresponding to the i-th measurement point, and obtain the target weight coefficient of the j-th laser sensor corresponding to the i-th measurement point.

[0069] In this embodiment, it should be noted that in S31, the correction coefficient calculated for the j-th sensor at the i-th measurement point by S2 is obtained. Next, the initial weight coefficient preset for the j-th sensor is retrieved (usually set during initialization based on sensor characteristics or angle importance).

[0070] Then, the correction coefficient of the sensor is multiplied by a preset fixed ratio (specified as one-half in the implementation) of its initial weight coefficient. The result of this multiplication is called the correction amount of the j-th laser sensor at the i-th measurement point. This correction amount essentially represents a suggested weight reduction.

[0071] The calculation logic lies in the fact that the initial weights represent the basic reliability or importance of the sensor, while the correction coefficient characterizes the specific interference risk at the current point. The combination of these two factors considers both the degree of interference and the importance of the sensor itself, making the correction amount targeted. It is worth noting that the maximum correction amount will not exceed half the initial weight multiplied by the correction coefficient. This design avoids the weights being excessively weakened or even reduced to zero at once, maintaining data availability.

[0072] In step S32, based on the correction amount calculated in the previous step, the actual adjustment of the initial weight of the j-th sensor is completed. Specifically, the initial weight coefficient of the sensor is subtracted from the correction amount (a positive value) calculated for it. The result of the subtraction is the final contribution weight of the sensor at the i-th measurement point, called the target weight coefficient. This target weight coefficient is less than or equal to its initial weight (equal to the initial weight when the correction amount is zero).

[0073] Its core objective is to appropriately reduce the influence of a sensor in subsequent elevation fusion calculations when interference is detected at the current point (correction coefficient > 0); and to maintain its original reliability level when no interference is detected. This process achieves dynamic adaptation of sensor weights based on location, which is one of the core mechanisms for dealing with local interference and ensures that the impact of contaminated locations on potentially distorted data is suppressed.

[0074] like Figure 4 As shown, in one embodiment, S4, obtaining the target elevation data corresponding to the i-th measurement point based on the target weight coefficients and elevation data of each laser sensor corresponding to the i-th measurement point includes:

[0075] S41. Obtain the sum of the target weight coefficients of all laser sensors corresponding to the i-th measurement point, and record it as the weight sum of the i-th measurement point;

[0076] S42. Divide the target weight coefficient of each laser sensor corresponding to the i-th measurement point by the sum of the weights of the i-th measurement point, and obtain the dynamic weight of each laser sensor corresponding to the i-th measurement point.

[0077] S43. Multiply the dynamic weights of each laser sensor corresponding to the i-th measurement point by the elevation data to obtain the sub-data of each laser sensor. Add the sub-data of all laser sensors corresponding to the i-th measurement point to obtain the target elevation data corresponding to the i-th measurement point.

[0078] In this embodiment, it should be noted that in S41, the necessary denominator is prepared for weight normalization. After determining the target weight coefficients of all laser sensors at the i-th measurement point (i.e., the weights dynamically adjusted in S3), the target weight coefficients of all sensors need to be summed. This sum is called the weight sum of that measurement point. The value of this weight sum is usually less than or equal to the total number of sensors (because the weight upper limit is 1 and may be lowered). This weight sum serves as an intermediate calculation, used in the following S42 step to convert the target weight coefficients of each sensor into normalized weights (i.e., dynamic weights) with a sum of 1 for the actual fusion operation.

[0079] In S42, the target weight coefficients of each sensor are converted into proportional coefficients that actually participate in the elevation data fusion. For each laser sensor at the i-th measurement point, its target weight coefficient (already adjusted according to interference) is divided by the sum of weights at that point (the result of S41). This division operation is called the normalization process. After normalization, each sensor obtains a dynamic weight coefficient. The sum of the dynamic weight coefficients of all sensors at that point is strictly equal to 1. This means that each dynamic weight coefficient represents the relative contribution or voting weight of that sensor in calculating the final elevation value of the current point.

[0080] In S43, the core operation is to fuse multi-sensor data to obtain the final elevation value representing the i-th measurement point. First, the raw elevation data value collected by each laser sensor (denoted as the j-th) at point i is multiplied by its corresponding dynamic weighting coefficient (the result of S42). The result is called the sub-data of that sensor. Each sub-data represents the contribution share of that sensor to the final point elevation, and its magnitude depends on its raw measurement value and dynamic weight (i.e., the fusion ratio).

[0081] Next, the sub-data from all participating laser sensors at point i are algebraically summed (simply added). This final sum is the target elevation data for the i-th measurement point. Essentially, this result is an elevation value derived from a weighted average based on the relative reliability of each sensor after interference assessment (reflected in dynamic weights). High-weighted, reliable sensors contribute more, while low-weighted, potentially distorted sensors have less impact, thus reducing the overall influence of interfered data on the final elevation value. This improves the accuracy of the elevation representation at that point and provides more reliable basic data for subsequent calculations of flatness differences between points.

[0082] like Figure 5 As shown, in one embodiment, S4, obtaining the road surface smoothness of the section to be measured based on the target elevation data of each measurement point in the measurement route includes:

[0083] S44. Obtain the absolute value of the difference between the target elevation data of adjacent measurement points and record it as the road surface difference between adjacent measurement points;

[0084] S45. Obtain the standard difference, and obtain the amplification coefficient of the adjacent measurement point based on the correction coefficient of each laser sensor corresponding to any measurement point among the adjacent measurement points. Multiply the amplification coefficient of the adjacent measurement point by the standard difference to obtain the dynamic difference of the adjacent measurement points.

[0085] S46. Obtain the number of adjacent measurement points in the measurement route whose road surface difference is lower than the dynamic difference of the corresponding adjacent measurement points, and record it as the leveling quantity;

[0086] S47. Divide the leveling quantity by the number of adjacent measurement points in the measurement route to obtain the road surface smoothness of the section to be measured.

[0087] In this embodiment, it should be noted that in S44, consecutive measurement point pairs (e.g., point k and point k+1) on the measurement route are processed sequentially. For such a pair of adjacent points, the target elevation data values ​​calculated in the previous stage of S4 are obtained. Then, the difference between the target elevation data of this pair of adjacent points is calculated (usually the elevation of point k+1 minus the elevation of point k), and the absolute value of this difference is taken. This absolute difference is called the road surface difference quantity between this pair of adjacent points. This physical quantity intuitively and quantitatively characterizes the magnitude of the vertical height change of the road surface between these two consecutive sampling points in the direction of vehicle travel. The larger the absolute value, the more drastic the height change between points, and the more uneven the road surface. This is the direct input data for subsequent judgment of whether each adjacent road surface segment is smooth.

[0088] In S45, firstly, a preset standard difference value is obtained. This threshold is derived from specifications or engineering experience, defining the maximum allowable height change (i.e., pavement difference) between adjacent points under ideal conditions (judgment without interference). If it is lower than this value, the pavement between those points is judged to be smooth. Specifically, the standard difference value is first obtained according to the "Highway Technical Condition Evaluation Index System" (JTG5210-2018). For expressways, IRI ≤ 2.0 m / km is converted to a height difference between points ≤ 1.2 mm (sampling interval 0.5 meters), and for Class I highways, IRI ≤ 2.5 m / km is converted to a height difference between points ≤ 1.5 mm. Then, a 20% safety margin is added to the specification to obtain the standard difference value. For example, in the scenario of expressway smoothness testing, the theoretical maximum allowable height difference = 1.2 mm, and the standard difference value = 1.2 × (1 + 0.20) = 1.44 mm.

[0089] However, in reality, even if the fused target elevation data is more reliable, local contaminants covering the road surface (such as standing water or thin ice) can cause small, real changes in apparent height (non-structural deformation). Ignoring this could easily cause the differences between these points to exceed standard thresholds and be misjudged as unevenness. Therefore, a dynamic difference is calculated for each pair of adjacent points based on the correction coefficients (characterizing the probability of local disturbances) obtained in the previous steps.

[0090] The specific steps are as follows: First, determine an amplification factor (calculated in S451 and S452) for the pair of adjacent points to be evaluated (e.g., point k and point k+1). Then, multiply this amplification factor by the standard deviation. The amplification factor is a value ≥ 1 (1 for no interference, > 1 for interference). The resulting dynamic deviation is the adaptively relaxed flatness judgment threshold for the local environment (which may contain pollution) of this pair of adjacent points. For areas with interference risks, the judgment standard is appropriately raised (the threshold is more lenient).

[0091] In S46, this step performs the actual smoothness point pair determination and statistics. Based on the pavement difference of all adjacent measurement point pairs calculated in S44 (e.g., points 1 to 2, 2 to 3, ..., n-1 to n), and the dynamic difference threshold for each pair calculated in S45, a comparison is made one by one. For each pair of adjacent points, if the calculated pavement difference value is less than or equal to the dynamic difference threshold specific to that pair, the pavement between the adjacent points is judged to meet the relaxed smoothness requirements under this index, and the pair is marked as smooth. All adjacent point pairs on the measurement route are traversed, and the total number of point pairs marked as smooth is counted. This total number is named the smoothness number. It represents the number of consecutive road segments (each point pair represents a small road segment) that are evaluated as qualified after considering the influence of local interference.

[0092] In S47, obtain the number of leveling points (the number of adjacent point pairs that meet the conditions) counted in S46, and calculate the total number of all adjacent point pairs in the entire measurement route (i.e., the number of consecutive measurement points minus one).

[0093] Then, the number of smooth sections is divided by the total number of adjacent measurement points to obtain a ratio. This ratio is usually expressed as a percentage or a decimal and is defined as the road surface smoothness of the section to be measured. Its meaning is: within the area covered by the sampling points, after adaptive evaluation considering local interference, the proportion of continuous road sections that meet the relaxed judgment criteria out of the total number of road sections.

[0094] For example, a ratio of 0.95 (or 95%) means that 95% of the adjacent road sections (divided by measurement points) are considered smooth under relaxed criteria. This indicator comprehensively reflects the macroscopic smoothness of the road surface while incorporating the specific characteristics of interference areas, making it more practical for engineering and more closely aligned with actual road conditions.

[0095] like Figure 6 As shown, in one embodiment, S45, obtaining the magnification factor of an adjacent measurement point based on the correction coefficients of each laser sensor corresponding to any one of the adjacent measurement points includes:

[0096] S451. Obtain the maximum value of the correction coefficients of each laser sensor corresponding to any one of the adjacent measurement points, and use it as the coefficient to be processed for the adjacent measurement points.

[0097] S452. Add half of the coefficient to be processed of the adjacent measurement points to 1 to obtain the expansion coefficient of the adjacent measurement points.

[0098] In this embodiment, it should be noted that in S451, a key value that best represents the intensity of pollution interference risk within the area covered by the adjacent point pair (one of the two measurement points or between them) is determined. The set of correction coefficients calculated by all laser sensors at any measurement point (such as point k or point k+1) that makes up the adjacent point pair is obtained. The correction coefficient reflects the magnitude of the interference risk to each sensor at that point (0 represents no risk, and a larger value indicates a higher risk).

[0099] The largest correction coefficient value is selected from this set of coefficients and used as the coefficient to be processed for that pair of adjacent points. The maximum value is chosen because it represents the upper limit of the most severe interference risk within the smallest road surface area involved in that adjacent point (at least one of the measurement points). If there is no interference in the area, all sensor correction coefficients are 0, and the maximum value is also 0; if local pollution affects the measurement position of a sensor, its larger coefficient will be selected to represent the overall degree of anomaly in that area, used for subsequent adjustment of the smoothness judgment tolerance.

[0100] In S452, based on the coefficient to be processed selected in S451 (a value representing the maximum interference risk of the adjacent point to the area, ranging from 0 to 1), this step calculates the expansion coefficient used to finally adjust the flatness judgment criteria. A calculation is performed: first, half of the coefficient to be processed is taken (i.e., multiplied by 0.5), and then the result is added to the value of 1. The result obtained in this way is the expansion coefficient for the adjacent point pair.

[0101] The meaning of this design is: when the coefficient to be processed is 0 (no interference risk), the amplification factor is equal to 1 (standard state). When the coefficient to be processed is greater than 0 (interference risk exists), the amplification factor varies between 1 and 1.5 (since the maximum coefficient is 1, the maximum amplification factor is 1.5). The larger the value of the coefficient to be processed, the higher the interference risk in that area, and the larger the calculated amplification factor will be.

[0102] In subsequent steps, this amplification factor is multiplied by the standard deviation to generate the dynamic deviation (i.e., the relaxed threshold). Therefore, it determines how much the tolerance range for judging whether the road surface between adjacent points is smooth is amplified in potentially polluted areas. For example, if the amplification factor is 1.2 and the standard deviation is 1 mm, the dynamic deviation is 1.2 mm. This means that in areas of risk of disturbance, a larger height difference between points is allowed before being judged as uneven, thereby reducing the probability of misjudgment caused by apparent changes due to localized pollution.

[0103] A road surface evenness measurement system based on multi-angle weighted correction is also provided, the system including:

[0104] The acquisition module is used to acquire the road segment to be measured and the measurement route located on the road segment to be measured, acquire multiple laser sensors that are all facing the measurement route and have different initial angles, move each laser sensor along the measurement route and synchronously collect elevation data of multiple measurement points in the measurement route;

[0105] The first measurement module is used to obtain the initial weight coefficients of each laser sensor, and to obtain the reflection intensity of each laser sensor corresponding to the i-th measurement point (road icing, water stains and other substances will affect the reflection intensity of the laser sensor), and to obtain the correction coefficients of each laser sensor corresponding to the i-th measurement point based on the reflection intensity of each laser sensor corresponding to the i-th measurement point.

[0106] The second measurement module is used to obtain the target weight coefficients of each laser sensor corresponding to the i-th measurement point based on the correction coefficients and initial weight coefficients of each laser sensor corresponding to the i-th measurement point.

[0107] The third measurement module is used to obtain the target elevation data corresponding to the i-th measurement point based on the target weight coefficients and elevation data of each laser sensor corresponding to the i-th measurement point, and to obtain the road surface smoothness of the road section to be measured based on the target elevation data of each measurement point in the measurement route.

[0108] In one embodiment, the first measurement module is further configured to: obtain a standard intensity limit and compare the reflection intensity of each laser sensor corresponding to the i-th measurement point with the standard intensity limit; if the reflection intensity of the j-th laser sensor corresponding to the i-th measurement point is lower than the standard intensity limit, subtract the reflection intensity of the j-th laser sensor from the standard intensity limit and divide by the standard intensity limit to obtain the correction coefficient of the j-th laser sensor corresponding to the i-th measurement point; if the reflection intensity of the j-th laser sensor corresponding to the i-th measurement point is not lower than the standard intensity limit, use 0 as the correction coefficient of the j-th laser sensor corresponding to the i-th measurement point.

[0109] In one embodiment, the second measurement module is further configured to: multiply the correction coefficient of the j-th laser sensor corresponding to the i-th measurement point by half of the initial weight coefficient to obtain the correction amount of the j-th laser sensor corresponding to the i-th measurement point; subtract the correction amount from the initial weight coefficient of the j-th laser sensor corresponding to the i-th measurement point to obtain the target weight coefficient of the j-th laser sensor corresponding to the i-th measurement point.

[0110] In one embodiment, the third measurement module is further configured to: obtain the sum of the target weight coefficients of all laser sensors corresponding to the i-th measurement point, and record it as the weight sum of the i-th measurement point; divide the target weight coefficients of each laser sensor corresponding to the i-th measurement point by the weight sum of the i-th measurement point, and obtain the dynamic weights of each laser sensor corresponding to the i-th measurement point; multiply the dynamic weights of each laser sensor corresponding to the i-th measurement point by the elevation data and obtain the sub-data of each laser sensor; and add the sub-data of all laser sensors corresponding to the i-th measurement point to obtain the target elevation data corresponding to the i-th measurement point.

[0111] In this embodiment, it should be noted that the specific method of performing the above-mentioned road surface smoothness measurement system based on multi-angle weighted correction has been described in detail in the embodiments of the road surface smoothness measurement method based on multi-angle weighted correction, and will not be elaborated here.

[0112] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the specific details of the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the protection scope of the present invention.

[0113] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the present invention will not describe the various possible combinations separately.

[0114] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed by the present invention.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for measuring road surface smoothness based on multi-angle weighted correction, characterized in that, include: The process involves acquiring the road segment to be measured and the measurement route located on the road segment, acquiring multiple laser sensors that are all facing the measurement route and have different initial angles, moving each laser sensor along the measurement route and synchronously collecting elevation data of multiple measurement points in the measurement route; Obtain the initial weight coefficients of each laser sensor, obtain the reflection intensity of each laser sensor corresponding to the i-th measurement point, and obtain the correction coefficients of each laser sensor corresponding to the i-th measurement point based on the reflection intensity of each laser sensor corresponding to the i-th measurement point. The target weight coefficients of each laser sensor corresponding to the i-th measurement point are obtained based on the correction coefficients and initial weight coefficients of each laser sensor corresponding to the i-th measurement point. The target elevation data corresponding to the i-th measurement point is obtained based on the target weight coefficients and elevation data of each laser sensor corresponding to the i-th measurement point, and the road surface smoothness of the road section to be measured is obtained based on the target elevation data of each measurement point in the measurement route.

2. The road surface smoothness measurement method based on multi-angle weighted correction according to claim 1, characterized in that, The step of obtaining the correction coefficients for each laser sensor corresponding to the i-th measurement point based on the reflection intensity of each laser sensor corresponding to the i-th measurement point includes: Obtain the standard intensity limit and compare the reflection intensity of each laser sensor corresponding to the i-th measurement point with the standard intensity limit; If the reflection intensity of the j-th laser sensor corresponding to the i-th measurement point is lower than the standard intensity limit, then the reflection intensity of the j-th laser sensor is subtracted from the standard intensity limit and divided by the standard intensity limit to obtain the correction coefficient of the j-th laser sensor corresponding to the i-th measurement point. If the reflection intensity of the j-th laser sensor corresponding to the i-th measurement point is not lower than the standard intensity limit, then 0 is used as the correction coefficient of the j-th laser sensor corresponding to the i-th measurement point.

3. The method for measuring road surface smoothness based on multi-angle weighted correction according to claim 1, characterized in that, The step of obtaining the target weight coefficients of each laser sensor corresponding to the i-th measurement point based on the correction coefficients and initial weight coefficients of each laser sensor corresponding to the i-th measurement point includes: Multiply the correction coefficient of the j-th laser sensor corresponding to the i-th measurement point by half of the initial weight coefficient to obtain the correction amount of the j-th laser sensor corresponding to the i-th measurement point. Subtract the correction amount from the initial weight coefficient of the j-th laser sensor corresponding to the i-th measurement point to obtain the target weight coefficient of the j-th laser sensor corresponding to the i-th measurement point.

4. The road surface smoothness measurement method based on multi-angle weighted correction according to claim 1, characterized in that, The step of obtaining the target elevation data corresponding to the i-th measurement point based on the target weight coefficients and elevation data of each laser sensor corresponding to the i-th measurement point includes: Obtain the sum of the target weight coefficients of all laser sensors corresponding to the i-th measurement point, and record it as the weight sum of the i-th measurement point; Divide the target weight coefficient of each laser sensor corresponding to the i-th measurement point by the sum of the weights of the i-th measurement point, and obtain the dynamic weight of each laser sensor corresponding to the i-th measurement point. Multiply the dynamic weights of each laser sensor corresponding to the i-th measurement point by the elevation data to obtain the sub-data of each laser sensor. Add the sub-data of all laser sensors corresponding to the i-th measurement point to obtain the target elevation data corresponding to the i-th measurement point.

5. The method for measuring road surface smoothness based on multi-angle weighted correction according to claim 1, characterized in that, The process of obtaining the road surface smoothness of the section to be measured based on the target elevation data of each measurement point in the measurement route includes: Obtain the absolute value of the difference between the target elevation data of adjacent measurement points and record it as the road surface difference between adjacent measurement points; Obtain the standard difference; Obtain the maximum value among the correction coefficients of each laser sensor corresponding to any one of the adjacent measurement points, and use it as the coefficient to be processed for the adjacent measurement points; Add half of the coefficient to be processed for adjacent measurement points to 1 to obtain the magnification coefficient for adjacent measurement points; Multiply the magnification factor of adjacent measurement points by the standard difference to obtain the dynamic difference between adjacent measurement points; Obtain the number of adjacent measurement points in the measurement route whose road surface difference is lower than the dynamic difference of the corresponding adjacent measurement points, and record it as the leveling quantity; Divide the leveling quantity by the number of adjacent measurement points in the measurement route to obtain the road surface smoothness of the section to be measured.

6. A road surface smoothness measurement system based on multi-angle weighted correction, characterized in that, The system includes: The acquisition module is used to acquire the road segment to be measured and the measurement route located on the road segment to be measured, acquire multiple laser sensors that are all facing the measurement route and have different initial angles, move each laser sensor along the measurement route and synchronously collect elevation data of multiple measurement points in the measurement route; The first measurement module is used to obtain the initial weight coefficients of each laser sensor, obtain the reflection intensity of each laser sensor corresponding to the i-th measurement point, and obtain the correction coefficients of each laser sensor corresponding to the i-th measurement point based on the reflection intensity of each laser sensor corresponding to the i-th measurement point. The second measurement module is used to obtain the target weight coefficients of each laser sensor corresponding to the i-th measurement point based on the correction coefficients and initial weight coefficients of each laser sensor corresponding to the i-th measurement point. The third measurement module is used to obtain the target elevation data corresponding to the i-th measurement point based on the target weight coefficients and elevation data of each laser sensor corresponding to the i-th measurement point, and to obtain the road surface smoothness of the road section to be measured based on the target elevation data of each measurement point in the measurement route.

7. The road surface smoothness measurement system based on multi-angle weighted correction according to claim 6, characterized in that, The first measurement module is also used for: Obtain the standard intensity limit and compare the reflection intensity of each laser sensor corresponding to the i-th measurement point with the standard intensity limit; If the reflection intensity of the j-th laser sensor corresponding to the i-th measurement point is lower than the standard intensity limit, then the reflection intensity of the j-th laser sensor is subtracted from the standard intensity limit and divided by the standard intensity limit to obtain the correction coefficient of the j-th laser sensor corresponding to the i-th measurement point. If the reflection intensity of the j-th laser sensor corresponding to the i-th measurement point is not lower than the standard intensity limit, then 0 is used as the correction coefficient of the j-th laser sensor corresponding to the i-th measurement point.

8. The road surface smoothness measurement system based on multi-angle weighted correction according to claim 6, characterized in that, The second measurement module is also used for: Multiply the correction coefficient of the j-th laser sensor corresponding to the i-th measurement point by half of the initial weight coefficient to obtain the correction amount of the j-th laser sensor corresponding to the i-th measurement point. Subtract the correction amount from the initial weight coefficient of the j-th laser sensor corresponding to the i-th measurement point to obtain the target weight coefficient of the j-th laser sensor corresponding to the i-th measurement point.

9. The road surface smoothness measurement system based on multi-angle weighted correction according to claim 6, characterized in that, The third measurement module is also used for: Obtain the sum of the target weight coefficients of all laser sensors corresponding to the i-th measurement point, and record it as the weight sum of the i-th measurement point; Divide the target weight coefficient of each laser sensor corresponding to the i-th measurement point by the sum of the weights of the i-th measurement point, and obtain the dynamic weight of each laser sensor corresponding to the i-th measurement point. Multiply the dynamic weights of each laser sensor corresponding to the i-th measurement point by the elevation data to obtain the sub-data of each laser sensor. Add the sub-data of all laser sensors corresponding to the i-th measurement point to obtain the target elevation data corresponding to the i-th measurement point.

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