Test method of laser radar, vehicle, electronic equipment and readable storage medium

Abstract

The application discloses a kind of test method of laser radar, vehicle, electronic equipment and readable storage medium.The method comprises: in response to the test request of laser radar, at least one performance index to be tested of laser radar is determined, wherein the performance index to be tested is requested to test the performance of performance;Determine the test strategy matched with the performance index to be tested in the test strategy set of laser radar, wherein the test strategy set includes the test strategy corresponding to multiple performance indexes to be tested respectively;According to test strategy, adjust the mobile test platform of laser radar, to build the test scene of the performance index to be tested of laser radar;In test scene, the performance index to be tested is tested, and test result is obtained, wherein test result is used to characterize the performance of performance index to be tested.The application solves the technical problem that the test accuracy of performance index of laser radar is low.

Description

Technical Field

[0001] This application relates to the field of sensor testing technology, and more specifically, to a test method for lidar, a vehicle, an electronic device, and a readable storage medium. Background Technology

[0002] With the rapid popularization and application of unmanned equipment, LiDAR is attracting increasing attention. LiDAR uses its high-speed emitted laser beam for distance detection and point cloud imaging, featuring high precision, strong penetration, long operating range, and real-time performance. Currently, it has high development prospects in multiple fields such as autonomous driving, robot navigation, road monitoring, and terrain mapping, and market demand is increasing daily.

[0003] In related technologies, when testing the performance indicators of lidar, the main method relies on manual handheld reflectivity plates to perform rough positioning at a specified distance. This method suffers from problems such as large human error, inaccurate distance positioning, low testing efficiency, high manpower input, unstable reflectivity plate fixation, and poor repeatability. In particular, when testing multiple reflectivity and multiple distance combinations, frequent handling and repositioning lead to long testing cycles and poor data consistency, resulting in low accuracy of lidar performance indicator testing.

[0004] There is currently no effective solution to the technical problem of low accuracy in testing the performance indicators of the aforementioned lidar. Summary of the Invention

[0005] This application provides a testing method, vehicle, electronic device, and readable storage medium for lidar, to at least solve the technical problem of low accuracy in testing lidar performance indicators.

[0006] According to one aspect of the embodiments of this application, a testing method for a lidar is provided. The method includes: responding to a test request from the lidar, determining at least one performance indicator to be tested for the lidar, wherein the test request is used to request testing the performance of the performance indicator to be tested; determining a test strategy matching the performance indicator to be tested from a set of test strategies for the lidar, wherein the set of test strategies includes test strategies corresponding to various performance indicators to be tested; adjusting a mobile test platform of the lidar according to the test strategy to construct a test scenario for the performance indicator to be tested; and testing the performance indicator to be tested in the test scenario to obtain test results, wherein the test results are used to characterize the performance of the performance indicator to be tested.

[0007] Optionally, the performance index to be tested shall be at least one of the following: ranging accuracy index, maximum distance detection index, echo intensity response characteristic index, and echo intensity distance attenuation characteristic index. Among them, the ranging accuracy index is used to characterize the distance detection accuracy of the lidar, the maximum distance detection index is used to characterize the maximum distance that the lidar is allowed to detect, the echo intensity response characteristic index is used to characterize the lidar's ability to identify objects with different reflectivities, and the echo intensity distance attenuation characteristic index is used to characterize the lidar's stability in identifying objects with the same reflectivity at different detection distances.

[0008] Optionally, a test strategy matching the performance indicator to be tested is determined from the test strategy set of the lidar, including: responding to the performance indicator to be tested being a ranging accuracy indicator, a ranging test strategy matching the ranging accuracy indicator is determined from the test strategy set, wherein the ranging test strategy is used to characterize the testing of the distance detection accuracy of the lidar at multiple detection distance points; responding to the performance indicator to be tested being a maximum distance detection indicator, a maximum distance test strategy matching the maximum distance detection indicator is determined from the test strategy set, wherein the maximum distance test strategy is used to characterize the testing of the actual maximum detection range of the lidar by adjusting the distance between the mobile test platform and the lidar. Distance; Responding to the performance index to be tested as the echo intensity response characteristic index, an echo intensity test strategy matching the echo intensity response characteristic index is determined from the test strategy set. The echo intensity test strategy is used to characterize the response value of the test lidar to the echo intensity of reflectors with different reflectivities at a preset detection distance point. Responding to the performance index to be tested as the echo intensity distance attenuation characteristic index, an echo intensity distance attenuation test strategy matching the echo intensity distance attenuation characteristic index is determined from the test strategy set. The echo intensity distance attenuation strategy is used to characterize the response value of the test lidar to the echo intensity of reflectors with the same reflectivity at multiple detection distance points.

[0009] Optionally, the mobile test platform carries reflectors with different reflectivities and a positioning module. When the performance index to be tested is the ranging accuracy index and the test strategy is the ranging test strategy, the mobile test platform of the lidar is adjusted according to the test strategy to construct a test scenario for the performance index to be tested of the lidar. This includes: according to the ranging test strategy, controlling the positioning module on the mobile test platform to move the mobile test platform to the locations of multiple detection distance points in sequence from near to far from the lidar, thereby constructing test scenarios corresponding to multiple detection distance points respectively. The ambient temperature and ambient humidity in the test scenarios meet preset requirements.

[0010] Optionally, in the test scenario, the performance index to be tested is tested to obtain test results, including: in test scenarios corresponding to multiple detection distance points, controlling the lidar to collect point cloud data of the target reflector on the mobile test platform to obtain multiple sets of point cloud data, wherein the target reflector is any one of the reflectors with different reflectivities; based on the multiple sets of point cloud data, determining the distance measurement value between the center point of the target reflector and the lidar in multiple test scenarios; based on the distance measurement value and distance reference value in multiple test scenarios, determining the test result of the ranging accuracy index, wherein the distance reference value is the distance between the center point of the target reflector and the lidar measured by a reference ranging device in the test scenario, and the test accuracy of the reference ranging device is higher than that of the lidar.

[0011] Optionally, the mobile test platform carries reflectors with different reflectivities and a positioning module. Responding to the performance indicator under test being the maximum distance detection indicator and the test strategy being the maximum distance test strategy, the mobile test platform of the LiDAR is adjusted according to the test strategy to construct a test scenario for the LiDAR's performance indicator under test. This includes: determining the nominal maximum test distance of the LiDAR according to the ranging test strategy, where the nominal maximum test distance is the theoretical maximum detection distance corresponding to the LiDAR; determining the initial position of the mobile test platform based on the nominal maximum test distance; controlling the positioning module on the mobile test platform to move the mobile test platform away from the LiDAR to a new distance point with a preset distance step size, starting from the initial position, to construct a test scenario for the LiDAR's performance indicator under test; and testing the performance indicator under test within the test scenario to obtain the test results, including... The process includes: In the test scenario, controlling the LiDAR to collect multi-frame point cloud data of a target reflector on a mobile test platform, where the target reflector is any one of reflectors with different reflectivities; based on the multi-frame point cloud data, determining the probability that the LiDAR detects the target reflector in the test scenario; in response to the probability being greater than a preset probability value, determining a new distance point as the initial position, and returning to the following steps to begin execution: controlling the positioning module on the mobile test platform to move the mobile test platform away from the LiDAR from the initial position at a preset distance step size to the new distance point, thereby constructing a test scenario for the LiDAR's performance indicators to be tested, until the probability is less than or equal to the preset probability value, and determining the distance between the new distance point and the LiDAR as the actual maximum detection distance of the LiDAR; based on the actual maximum detection distance, determining the test result of the maximum distance detection indicator.

[0012] Optionally, the mobile testing platform carries reflectors with different reflectivities and a positioning module. When the performance indicator to be tested is the echo intensity response characteristic indicator, and the testing strategy is the echo intensity testing strategy, the mobile testing platform of the LiDAR is adjusted according to the testing strategy to construct a test scenario for the LiDAR's performance indicator to be tested, and the test results are obtained. This includes: determining a preset detection distance point for testing the echo intensity response characteristic indicator according to the echo intensity testing strategy; controlling the positioning module on the mobile testing platform to move the mobile testing platform to the preset detection distance point to construct a test scenario for the LiDAR's performance indicator to be tested; and testing the performance indicator to be tested in the test scenario to obtain test results. This includes: controlling the LiDAR to collect point cloud data of reflectors with different reflectivities carried by the mobile testing platform in the test scenario, obtaining point cloud data corresponding to reflectors with different reflectivities; determining the echo intensity response value of the LiDAR to reflectors with different reflectivities based on the point cloud data corresponding to reflectors with different reflectivities; and determining the test results of the LiDAR's echo intensity response characteristic indicator based on the echo intensity response value of the LiDAR to reflectors with different reflectivities.

[0013] Optionally, based on the echo intensity response values ​​of the lidar to reflectors with different reflectivities, the test results of the lidar's echo intensity response characteristics are determined, including: determining the lidar's reflection intensity discrimination based on the echo intensity response values ​​of the lidar to reflectors with different reflectivities, wherein the reflection intensity discrimination is used to characterize the lidar's ability to identify reflectors with different reflectivities; and determining the test results of the lidar's echo intensity response characteristics based on the reflection intensity discrimination.

[0014] Optionally, the mobile test platform carries reflectors with different reflectivities and a positioning module. Responding to the performance indicator under test being the echo intensity distance attenuation characteristic indicator, and the test strategy being the echo intensity distance attenuation test strategy, the mobile test platform of the lidar is adjusted according to the test strategy to construct a test scenario for the lidar's performance indicator under test. This includes: determining multiple detection distance points for testing the echo intensity distance attenuation characteristic indicator according to the echo intensity distance attenuation test strategy; controlling the positioning module on the mobile test platform to sequentially move the mobile test platform to the multiple detection distance points to construct test scenarios corresponding to each of the multiple detection distance points; and then, within the test scenario... In this process, the performance indicators to be tested are tested, and the test results are obtained, including: in the test scenarios corresponding to multiple detection distance points, the lidar is controlled to collect point cloud data of the target reflector on the mobile test platform, and the point cloud data corresponding to multiple detection distance points are obtained, wherein the target reflector is any one of the reflectors with different reflectivities; based on the point cloud data corresponding to multiple detection distance points, the echo intensity response value of the lidar to the target reflector at multiple detection distance points is determined; based on the echo intensity response value of the lidar to the target reflector at multiple detection distance points, the test results of the lidar's echo intensity distance attenuation characteristic index are determined.

[0015] Optionally, based on the echo intensity response values ​​of the lidar to the target reflector at multiple detection distance points, the test results of the lidar's echo intensity distance attenuation characteristic index are determined, including: based on the echo intensity response values ​​of the lidar to the target reflector at multiple detection distance points, determining the attenuation curve of the lidar's echo intensity response value; and based on the attenuation curve, determining the test results of the lidar's echo intensity distance attenuation characteristic index.

[0016] According to another aspect of the embodiments of this application, a testing apparatus for a lidar is also provided. The apparatus includes: a first determining unit, configured to determine at least one performance indicator to be tested for the lidar in response to a test request from the lidar, wherein the test request is used to request testing the performance of the performance indicator to be tested; a second determining unit, configured to determine a test strategy matching the performance indicator to be tested from a set of test strategies for the lidar, wherein the set of test strategies includes test strategies corresponding to various performance indicators to be tested; an adjusting unit, configured to adjust a mobile test platform of the lidar according to the test strategy to construct a test scenario for the performance indicator to be tested of the lidar; and a testing unit, configured to test the performance indicator to be tested in the test scenario and obtain test results, wherein the test results are used to characterize the performance of the performance indicator to be tested.

[0017] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the lidar testing methods of various embodiments of this application when it runs.

[0018] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to execute the lidar testing method of various embodiments of this application.

[0019] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the lidar testing method in various embodiments of this application.

[0020] According to another aspect of the embodiments of this application, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the lidar testing method in various embodiments of this application.

[0021] According to another aspect of the embodiments of this application, a computer program is also provided, which, when executed by a processor, implements the lidar testing method in various embodiments of this application.

[0022] According to another aspect of the embodiments of this application, a vehicle is also provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the lidar testing methods of various embodiments of this application when it runs.

[0023] In this embodiment, in response to a test request from a LiDAR, at least one performance indicator to be tested for the LiDAR is determined, wherein the test request is used to request testing the performance of the performance indicator to be tested; a test strategy matching the performance indicator to be tested is determined from a set of test strategies for the LiDAR, wherein the set of test strategies includes test strategies corresponding to various performance indicators to be tested; according to the test strategy, the mobile test platform of the LiDAR is adjusted to construct a test scenario for the performance indicator to be tested; in the test scenario, the performance indicator to be tested is tested to obtain test results, wherein the test results are used to characterize the performance of the performance indicator to be tested. In other words, in this embodiment, a test strategy matching the performance indicator to be tested can be determined based on the performance indicator to be tested for the LiDAR, and then the mobile test platform of the LiDAR can be adjusted according to the test strategy to construct a test scenario for testing the performance indicator to be tested for the LiDAR, and then the performance indicator to be tested can be tested using the test scenario to obtain test results. In other words, the entire testing process requires no human intervention. The corresponding test scenario can be constructed according to the test strategy of the performance index to be tested, and the performance index to be tested can be tested. This significantly improves the testing efficiency and accuracy of the performance index of LiDAR, avoids the drawbacks of inaccurate positioning and time-consuming and labor-intensive traditional manual testing, and thus solves the technical problem of low test accuracy of LiDAR performance index. Attached Figure Description

[0024] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0025] Figure 1 This is a flowchart of a test method for a lidar according to an embodiment of this application;

[0026] Figure 2 This is a test diagram illustrating the performance indicators of a lidar according to an embodiment of this application;

[0027] Figure 3 This is a flowchart of a method for testing the ranging accuracy index of a lidar according to an embodiment of this application;

[0028] Figure 4 This is a flowchart of a test method for the maximum range detection index of a lidar according to an embodiment of this application;

[0029] Figure 5 This is a flowchart of a test method for the echo intensity response characteristics of a lidar according to an embodiment of this application;

[0030] Figure 6This is a flowchart of a test method for the echo intensity distance attenuation characteristic index of a lidar according to an embodiment of this application;

[0031] Figure 7 This is a schematic diagram of a lidar testing device according to an embodiment of this application. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0033] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0034] According to an embodiment of this application, an embodiment of a testing method for a lidar is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0035] Figure 1 This is a flowchart of a test method for a lidar according to an embodiment of this application, as shown below. Figure 1 As shown, the method includes the following steps.

[0036] Step S101: In response to the test request of the lidar, determine at least one performance indicator to be tested for the lidar.

[0037] In the technical solution provided in step S101 of this application, the test request for the aforementioned lidar can come from the user or the upper control platform. The lidar can be a vehicle-mounted lidar, and the test request is used to request the performance of the performance index to be tested.

[0038] In this embodiment, upon receiving a test request from the lidar, at least one performance index to be tested for the lidar can be determined based on the test request. The performance index to be tested is at least one of the following: ranging accuracy index, maximum distance detection index, echo intensity response characteristic index, and echo intensity distance attenuation characteristic index. The ranging accuracy index characterizes the distance detection accuracy of the lidar, the maximum distance detection index characterizes the maximum allowable detection distance of the lidar, the echo intensity response characteristic index characterizes the lidar's ability to identify objects with different reflectivities, and the echo intensity distance attenuation characteristic index characterizes the lidar's stability in identifying objects with the same reflectivity at different detection distances.

[0039] For example, upon receiving a test request from a lidar system, the system can automatically parse the request content and identify the performance indicator to be tested. For instance, if the test request is "Please evaluate the lidar's intensity discrimination capability at 10%~100% reflectivity," then the performance indicator to be tested for the lidar is the echo intensity response characteristic. If the test request is "Verify whether the lidar can still detect stably at 300 meters," then the performance indicator to be tested for the lidar is the maximum detection range. In some cases, a test request may contain multiple performance indicators to be tested simultaneously. For example, if the test request is "Please complete the joint test of distance accuracy and multi-reflectivity intensity," then the performance indicators to be tested for the lidar can be determined to be both the ranging accuracy indicator and the echo intensity response characteristic. This is merely an example.

[0040] Step S102: Determine the test strategy that matches the performance index to be tested from the set of test strategies for the lidar.

[0041] In the technical solution provided in step S102 of this application, the test strategy set includes test strategies corresponding to various performance indicators to be tested. The test strategy is used to characterize the test execution plan for testing the performance indicators to be tested of the lidar, and can be jointly constructed by industry standards, enterprise test specifications and long-term practical experience.

[0042] In this embodiment, after determining the performance index to be tested for the lidar, a test strategy matching the performance index can be retrieved from the test strategy set to provide technical guidance for subsequent testing of the performance index.

[0043] Optionally, if the performance indicator to be tested is a ranging accuracy indicator, a ranging test strategy matching the ranging accuracy indicator can be retrieved from the test strategy set. This ranging test strategy guides how to test the range detection accuracy of the lidar at multiple detection distance points. If the performance indicator to be tested is a maximum range detection indicator, a maximum range test strategy matching the maximum range detection indicator can be retrieved from the test strategy set. This maximum range test strategy guides how to test the actual maximum detection range of the lidar.

[0044] In this step, the test strategy set includes test strategies corresponding to various performance indicators to be tested. After determining the performance indicators to be tested for the LiDAR, the test strategy set can be directly retrieved to find the test strategy that matches the performance indicators to be tested, so as to ensure the standardization and normalization of the subsequent test process for the performance indicators to be tested.

[0045] Step S103: According to the testing strategy, adjust the mobile testing platform of the lidar to construct the test scenario for the lidar's performance indicators to be tested.

[0046] In the technical solution provided in step S103 of this application, the mobile testing platform can be a highly integrated intelligent mobile vehicle. This vehicle adopts a closed structure design, with four standard reflectivity plates (e.g., 10%, 30%, 50%, and 100% reflectivity) fixedly installed on its four outer walls. Each reflector undergoes traceability calibration to ensure reflectivity stability and consistency. The bottom of the mobile vehicle is equipped with high-precision servo drive wheels and an encoder positioning system. Combined with a laser rangefinder and inertial navigation module, it can achieve centimeter-level autonomous movement and precise positioning. Simultaneously, the mobile vehicle has a 360° rotation function, automatically adjusting the normal direction of the reflector to ensure that the reflector is always strictly perpendicular to the laser radar's output direction, eliminating the influence of incident angle deviation on the test results. Furthermore, the mobile vehicle supports remote wireless control and can be scheduled in real-time by the test host via a local area network or remote commands, achieving fully unmanned operation with "one-click positioning, automatic calibration, and contactless switching."

[0047] In this embodiment, after determining the test strategy that matches the performance indicators to be tested of the lidar, the mobile test platform (e.g., a mobile vehicle) of the lidar can be adjusted according to the test strategy to construct the test scenario of the performance indicators to be tested of the lidar.

[0048] Optionally, if the performance indicator to be tested for the lidar is the echo intensity response characteristic, then the mobile test platform (e.g., a mobile vehicle) of the lidar can be adjusted according to the echo intensity test strategy corresponding to this echo intensity response characteristic to construct a test scenario for the lidar's performance indicator to be tested. This echo intensity test strategy guides how to test the response value of the lidar to the echo intensity of reflectors with different reflectivities at a preset detection distance point.

[0049] For example, a precise displacement command can be sent to a mobile testing platform (e.g., a mobile trolley) based on a preset detection distance point (e.g., 10m) in the echo intensity testing strategy. This drives the mobile testing platform to move smoothly along a guide rail or a flat testing area to the preset detection distance point. The actual distance of the mobile testing platform is fed back in real time through built-in sensors, ensuring that the positioning error is always controlled within the allowable range. Then, based on the reflectivity required by the echo intensity testing strategy (e.g., 10%, 30%, 50%, 100%), the rotating mechanism built into the mobile testing platform is controlled to flip it to the corresponding reflector, achieving rapid, contactless switching between different reflectivities. If the echo intensity testing strategy requires multi-angle testing (e.g., nine-grid sampling), the azimuth angle of the mobile testing platform can be adjusted synchronously, or a rotating radar can be used in conjunction with a turntable, so that the lidar beam sequentially covers different incident angle areas, ensuring sampling integrity. This is merely an example.

[0050] In this step, the mobile test platform of the lidar can be adjusted according to the test strategy to construct the test scenario of the lidar's performance indicators. The whole process does not require manual intervention, which not only eliminates the systematic errors such as position drift, incorrect posture and poor repeatability caused by traditional manual board pushing, but also realizes the parameterized reproduction of the test scenario with the same strategy, thus providing solid physical support for the comparability and traceability of multi-round test data.

[0051] Step S104: In the test scenario, test the performance index to be tested and obtain the test results.

[0052] In the technical solution provided by step S104 of this application, the test results are used to characterize the performance of the performance index to be tested.

[0053] In this embodiment, after constructing the test scenario, the performance indicators of the lidar to be tested can be tested in the test scenario, thereby generating objective and quantitative test results to truly and comprehensively characterize the performance of the lidar's performance indicators to be tested.

[0054] Optionally, in the test scenario, the lidar can be triggered to enter working mode, rotated so that the points to be measured are sequentially incident on the reflector, and high frame rate point cloud data acquisition can be initiated to ensure that no less than 100 frames of raw measurement data are continuously recorded for each test point, covering key parameters such as distance value, echo intensity, incident angle, and signal-to-noise ratio. During the acquisition process, a high-precision reference rangefinder or total station performs true value calibration to ensure the reliability of the benchmark for subsequent evaluation.

[0055] In this step, by testing the performance indicators of the LiDAR in the test scenario, the test results of the performance indicators can be obtained, which can improve the test accuracy of the performance indicators and make the test results no longer dependent on subjective experience, thus providing solid data support for the performance certification and functional safety assessment of LiDAR.

[0056] In steps S101 to S104 above, a test strategy matching the performance index to be tested of the LiDAR can be determined based on the performance index to be tested. Then, according to the test strategy, the mobile test platform of the LiDAR is adjusted to construct a test scenario for testing the performance index to be tested. The performance index to be tested is then tested within this test scenario to obtain the test results. That is, the entire testing process requires no human intervention. The corresponding test scenario can be constructed based on the test strategy for the performance index to be tested, significantly improving the testing efficiency and accuracy of the LiDAR's performance index. This avoids the drawbacks of inaccurate positioning and time-consuming processes in traditional manual testing, thus solving the technical problem of low accuracy in testing LiDAR performance indexes.

[0057] The method described in this embodiment will be further described below.

[0058] As an optional implementation method, the performance index to be tested is at least one of the following: ranging accuracy index, maximum distance detection index, echo intensity response characteristic index, and echo intensity distance attenuation characteristic index. Among them, the ranging accuracy index is used to characterize the distance detection accuracy of the lidar, the maximum distance detection index is used to characterize the maximum distance that the lidar is allowed to detect, the echo intensity response characteristic index is used to characterize the lidar's ability to identify objects with different reflectivities, and the echo intensity distance attenuation characteristic index is used to characterize the lidar's stability in identifying objects with the same reflectivity at different detection distances.

[0059] In this embodiment, the aforementioned ranging accuracy index is used to quantify the distance detection accuracy of the lidar. Its core function is to reflect the measurement deviation and fluctuation range of the lidar on a known distance reflective target (e.g., a 100% reflectivity plate) in a standard environment (e.g., 25°C, moderate humidity). This ranging accuracy index directly relates to the reliability of the autonomous driving system's obstacle localization. Insufficient ranging accuracy will lead to safety risks such as path planning misjudgments and emergency braking delays.

[0060] Optionally, the aforementioned maximum detection range metric is used to characterize the farthest distance at which a lidar can stably identify a target while maintaining an effective detection probability (not less than 50%). This maximum detection range metric is a hard indicator that measures the boundary of lidar's detection capability, and is particularly crucial for forward perception in high-speed driving scenarios.

[0061] Optionally, the aforementioned echo intensity response characteristic index is used to evaluate the LiDAR's perception sensitivity and discrimination ability for targets with different reflectivities. Its essence is to measure whether the LiDAR can effectively identify diverse objects ranging from low reflectivity (e.g., 10% black tires, dark clothing) to high reflectivity (e.g., 100% white board).

[0062] Optionally, the aforementioned echo intensity distance attenuation characteristic index is used to characterize the stability and consistency of the signal intensity returned by the lidar to the same reflectivity target at different detection distances. This echo intensity distance attenuation characteristic index reveals whether the lidar has problems such as abnormal signal attenuation, inaccurate gain control, or nonlinearity of the optical system in long-distance scenarios.

[0063] The above four performance indicators together constitute a complete evaluation system for the sensing capabilities of LiDAR: ranging accuracy ensures "accurate perception," maximum detection distance ensures "long-range perception," echo intensity response ensures "clear recognition," and echo intensity attenuation ensures "stable performance." Through structured strategies and automated execution, this system has for the first time achieved collaborative testing of these four indicators on a unified platform and in the same process. It comprehensively covers the full-scenario performance verification requirements of LiDAR, from close-range recognition to long-range stabilization, and from static reflection to dynamic attenuation, significantly improving the systematic nature, completeness, and engineering practicality of the testing.

[0064] As an optional embodiment, step S102, determining a test strategy matching the performance index to be tested from the test strategy set of the lidar, includes: in response to the performance index to be tested being a ranging accuracy index, determining a ranging test strategy matching the ranging accuracy index from the test strategy set, wherein the ranging test strategy is used to characterize the testing of the distance detection accuracy of the lidar at multiple detection distance points; in response to the performance index to be tested being a maximum distance detection index, determining a maximum distance test strategy matching the maximum distance detection index from the test strategy set, wherein the maximum distance test strategy is used to characterize the testing of the lidar's distance detection accuracy by adjusting the distance between the mobile test platform and the lidar. The actual maximum detection range is determined; in response to the performance index to be tested being the echo intensity response characteristic index, an echo intensity test strategy matching the echo intensity response characteristic index is determined from the test strategy set, wherein the echo intensity test strategy is used to characterize the response value of the test lidar to the echo intensity of reflectors with different reflectivities at a preset detection distance point; in response to the performance index to be tested being the echo intensity distance attenuation characteristic index, an echo intensity distance attenuation test strategy matching the echo intensity distance attenuation characteristic index is determined from the test strategy set, wherein the echo intensity distance attenuation strategy is used to characterize the response value of the test lidar to the echo intensity of reflectors with the same reflectivity at multiple detection distance points.

[0065] In this embodiment, when determining the test strategy that matches the performance indicators to be tested of the LiDAR, the corresponding test strategy can be matched from a pre-set set of LiDAR test strategies based on the performance indicators to be tested, ensuring that each performance evaluation has a clear, executable, and industry-compliant execution path. This matching process is not a simple table lookup, but a precise mapping based on the semantics and technical connotations of the indicators.

[0066] Optionally, when the performance indicator to be tested is the ranging accuracy indicator, a ranging test strategy can be determined from the test strategy set based on this ranging accuracy indicator, and this ranging test strategy can be identified as the test strategy that matches the ranging accuracy indicator. This ranging test strategy is used to characterize the distance detection accuracy of the lidar at multiple detection distance points. For example, this ranging test strategy requires sequentially driving a mobile test platform (e.g., a mobile vehicle) to position itself at multiple typical detection distance points such as 5m, 10m, 20m, 50m, 80m, and 100m, and ensuring that at each detection distance point, the lidar collects no less than 100 frames of point cloud data from the reflectivity panel. When acquiring point cloud data from a reflector, the lidar must be aligned with the reflector's normal, the ambient temperature must be between 25℃ and 5℃, and the ambient humidity between 20%RH and 80%RH. The ambient temperature and humidity can be determined using a temperature measuring instrument and a humidity measuring instrument, respectively. Values ​​measured by a reference ranging device (e.g., a total station or a reference rangefinder) are used as the true reference to calculate the lidar's average ranging deviation μ and standard deviation σ. The core of this ranging test strategy lies in comprehensively evaluating the lidar's ranging stability and systematic errors within its normal operating range through multi-distance, multi-frame, and highly repeatable measurements, replacing the traditional, laborious method of manually holding a reflector for measurement.

[0067] Optionally, when the performance indicator to be tested is the maximum distance detection indicator, a maximum distance test strategy can be determined from the test strategy set based on this maximum distance detection indicator, and this maximum distance test strategy can be identified as the test strategy matching the maximum distance detection indicator. This maximum distance test strategy is used to characterize the test of the actual maximum detection range of the lidar by adjusting the distance between the mobile test platform and the lidar. For example, this maximum distance test strategy requires setting the starting point to 3 / 4 of the nominal maximum detection range (e.g., starting from 225m when the nominal distance is 300m), gradually increasing the distance between the lidar and the reflector in small steps (e.g., 0.5m), and continuously recording the point cloud detection probability at each point. This maximum distance test strategy also stipulates that when the detection probability is continuously lower than 50% for multiple consecutive frames, the distance between the location of the distance detection point and the lidar is automatically locked as the actual maximum detection range of the lidar, and can be combined with a light intensity attenuator to simulate an ultra-long-distance environment to expand the test boundary. The core of this maximum distance testing strategy lies in transforming the fuzzy judgment of "whether it can be detected" into an objective threshold judgment based on statistical significance, ensuring that the assessment of the maximum distance has engineering repeatability and safety redundancy.

[0068] Optionally, when the performance index to be tested is the echo intensity response characteristic index, an echo intensity test strategy can be determined from the test strategy set based on this echo intensity response characteristic index, and this echo intensity test strategy can be determined as the test strategy matching the echo intensity response characteristic index. This echo intensity test strategy is used to characterize the response value of the lidar to the echo intensity of reflectors with different reflectivities at a preset detection distance. For example, this echo intensity test strategy focuses on the lidar's recognition sensitivity to reflectors with different reflectivities, strictly adhering to the four reflectivity reflectors (10%, 30%, 50%, 100%) and a fixed distance of 10m ± 0.02m. This echo intensity test strategy stipulates that, at a preset reference distance of 10m, the four reflectors of the vehicle are switched sequentially (10%, 30%, 50%, 100%), and no less than 100 frames of data are collected each time the reflector is changed, with the incident angle limited to within 0° ± 5° to eliminate angular interference. Subsequently, the average echo intensity I_E and the discrimination C_I under each reflectivity are calculated, thereby quantifying the LiDAR's ability to distinguish between low reflectivity (e.g., black tires) and high reflectivity (e.g., white signs), providing key material property identification basis for perception algorithms.

[0069] Optionally, when the performance index to be tested is the echo intensity distance attenuation characteristic index, an echo intensity distance attenuation test strategy can be determined from the test strategy set based on this index, and this strategy can be identified as the test strategy matching the echo intensity distance attenuation characteristic index. This echo intensity distance attenuation strategy is used to characterize the response value of the lidar to the echo intensity of a reflector with the same reflectivity at multiple detection distance points. For example, addressing the shortcoming of traditional testing that neglects the "intensity variation trend with distance," this strategy explicitly requires controlling a mobile test platform (e.g., a mobile trolley) to continuously sample multiple distance points within a range of 5m to 300m under a single reflectivity (e.g., 50%), recording the echo intensity value point by point, and constructing an intensity-distance curve. This strategy emphasizes excluding the influence of incident angle deviation, retaining only valid data within the range of 0°±5°, and calculating the deviation rate between the actual attenuation and the theoretical model by fitting an inverse square attenuation model. This echo intensity distance attenuation strategy can not only identify nonlinear responses or automatic gain control anomalies in the radar receiver module, but also provide a predictive basis for the long-term performance stability of lidar in complex lighting, rain and fog environments.

[0070] In this step, the abstract performance testing requirements are transformed into specific, executable automated operation instructions by mapping the performance indicators to be tested to the testing strategies. Each testing strategy covers all dimensions of specifications, including environmental constraints, equipment actions, sampling requirements, and data processing methods, completely transforming the entire testing process from "experience-driven" to "strategy-driven." This achieves standardization, traceability, and engineering reproducibility of testing behavior, laying a solid execution foundation for the reliable verification of LiDAR.

[0071] As an optional embodiment, the mobile test platform carries reflectors with different reflectivities and a positioning module. When the performance index to be tested is the ranging accuracy index and the test strategy is the ranging test strategy, step S103 involves adjusting the mobile test platform of the lidar according to the test strategy to construct a test scenario for the performance index to be tested of the lidar. This includes controlling the positioning module on the mobile test platform according to the ranging test strategy to move the mobile test platform to the locations of multiple detection distance points in sequence from near to far from the lidar, thereby constructing test scenarios corresponding to multiple detection distance points. The ambient temperature and humidity in the test scenarios meet preset requirements.

[0072] In this embodiment, when the performance indicator to be tested is the ranging accuracy indicator and has been matched with the corresponding ranging test strategy, the mobile test platform will be automatically controlled to dynamically construct the test scenario according to the precise parameter requirements of the ranging test strategy.

[0073] Optionally, as described above, the mobile testing platform integrates a high-precision positioning module and four standardized reflectors (e.g., 10%, 30%, 50%, 100%). The positioning module consists of a high-resolution encoder, servo drive wheels, and a laser rangefinder. Combined with an inertial navigation unit, it achieves centimeter-level (±0.02m) autonomous positioning and closed-loop control capabilities. Driven by the ranging test strategy, the environmental conditions are first confirmed to meet preset requirements: real-time monitoring via built-in temperature and humidity sensors ensures the test environment temperature remains stable at 25℃±5℃ and the relative humidity is within the range of 20%RH~80%RH. If the environment deviates from the threshold, movement will be paused and an environmental adjustment command will be triggered until the conditions are met.

[0074] Optionally, provided that the ambient temperature and humidity in the test scenario meet the preset requirements, the mobile test platform is controlled to start from the point closest to the lidar (e.g., 5m) and move sequentially to multiple preset detection distance points such as 10m, 20m, 50m, 80m, and 100m, according to the order specified in the ranging test strategy. Each movement is not a simple displacement, but includes a four-step closed-loop process of "precise path planning - real-time distance calibration - stable parking - attitude self-check": During the movement, the mobile test platform continuously feeds back the real-time deviation from the target distance through the laser ranging sensor, and the servo system dynamically fine-tunes the position to ensure that the final positioning error when reaching the target point is less than ±0.02m; after reaching the target point, the mobile test platform automatically activates the horizontal and vertical attitude correction mechanism to ensure that the reflector plane is strictly perpendicular to the lidar's light output direction, eliminating the interference of the incident angle deviation on the ranging results.

[0075] Optionally, the arrival of each detection distance point corresponds to an independent and stable test scenario. In this test scenario, not only is spatial positioning completed, but key variables such as environmental parameters, reflector reflectivity (a 100% reflector is used uniformly in the ranging accuracy test to eliminate variables), and radar orientation are also simultaneously solidified, providing a highly consistent physical basis for the point cloud acquisition in the subsequent S104 step. The entire process is fully automated, requiring no manual intervention, and realizing a closed-loop process where the test strategy drives the location, the location constructs the scenario, and the scenario ensures accuracy.

[0076] In this step, following the ranging test strategy, the positioning module on the mobile test platform is controlled to move the platform sequentially to the locations of multiple detection distance points in order of distance from the lidar, from near to far. This constructs test scenarios corresponding to multiple detection distance points, solving the systematic errors caused by inaccurate positioning, unstable posture, and poor repeatability in the traditional manual push-plate method. It transforms ranging accuracy testing from an experience-dependent model to a parameter-controlled engineering verification model, providing a standardized test prerequisite for the real ranging capability of lidar in complex vehicle environments that is repeatable, verifiable, and traceable.

[0077] As an optional embodiment, step S104 involves testing the performance index to be tested in a test scenario to obtain test results, including: controlling the lidar to collect point cloud data of the target reflector on the mobile test platform in test scenarios corresponding to multiple detection distance points, obtaining multiple sets of point cloud data, wherein the target reflector is any one of reflectors with different reflectivities; based on the multiple sets of point cloud data, determining the distance measurement value between the center point of the target reflector and the lidar in multiple test scenarios; and determining the test result of the ranging accuracy index based on the distance measurement value and the distance reference value in multiple test scenarios, wherein the distance reference value is the distance between the center point of the target reflector and the lidar measured by a reference ranging device in the test scenario, and the test accuracy of the reference ranging device is higher than that of the lidar.

[0078] In this embodiment, after constructing multiple test scenarios through the above steps, the ranging accuracy indicators of the lidar can be automatically and accurately collected and analyzed in each of the multiple test scenarios, and finally quantitative and verifiable test results can be output.

[0079] Optionally, the lidar can be controlled to collect point cloud data of a target reflector on a mobile test platform in multiple test scenarios corresponding to different detection distances (such as 5m, 10m, 20m, 50m, 80m, 100m, etc.). To avoid interference with the lidar's ranging accuracy due to differences in reflectivity, the target reflector can be any reflector with different reflectivities; for example, it can be a reflector with 100% reflectivity. No specific limitation is made here. When the lidar collects point cloud data of the target reflector in each test scenario, it must ensure that the plane of the target reflector is perpendicular to the lidar's light emission direction to achieve normal incidence.

[0080] Optionally, the lidar can continuously acquire multiple frames (e.g., no less than 100 frames) of point cloud data from the target reflector in each test scenario, ensuring that the point cloud data covers a sufficient number of sampling periods to effectively suppress transient noise and random errors. The multiple frames of point cloud data acquired in each test scenario constitute a set of point cloud data corresponding to that test scenario. In this way, after acquiring point cloud data from the target reflector in multiple test scenarios, multiple sets of point cloud data can be obtained.

[0081] Optionally, after obtaining multiple sets of point cloud data, the distance measurement values ​​between the center point of the target reflector and the lidar in multiple test scenarios can be determined based on these multiple sets of point cloud data. For example, taking a set of point cloud data corresponding to a test scenario as an example, for each frame of point cloud data obtained in the set of point cloud data in the test scenario, the center point position of the target reflector can be automatically identified and extracted through image processing and spatial clustering algorithms. Combined with the radar coordinate system and the vehicle motion compensation algorithm, the single distance measurement value between the lidar's own light emission center and the center point of the target reflector can be calculated. In this way, multiple distance measurements corresponding to a test scene can be calculated based on multiple frames of point cloud data. By averaging these multiple distance measurements, the distance measurement value corresponding to the test scene can be obtained. .

[0082] Optionally, after obtaining the distance measurement value corresponding to the test scenario, the distance measurement value can be used as a basis for further analysis. The distance reference value d corresponding to the test scenario is used to determine the test results of the lidar's ranging accuracy in that test scenario. This distance reference value is the distance between the center point of the target reflector and the lidar, measured by a reference ranging device (e.g., a total station or laser interferometer) in the test scenario. The test accuracy of the reference ranging device is higher than that of the lidar.

[0083] For example, the test accuracy of the lidar in this test scenario can be determined using the following formulas (1) and (2). and test accuracy .

[0084] (1)

[0085] (2)

[0086] Among them, the above This can represent the distance measurement value corresponding to the test scenario, that is, the average distance measurement value, and d can represent the distance reference value corresponding to the test scenario. N can represent the single distance measurement value in the test scenario, and N can represent the number of times the point cloud data of the target reflector is collected in the test scenario, that is, the number of tests.

[0087] Optionally, after determining the test accuracy and test precision corresponding to multiple test scenarios using the above method, the test results of the ranging accuracy index of the lidar can be determined based on the test accuracy and test precision corresponding to these multiple test scenarios. For example, the test accuracy and test precision data of the lidar in each test scenario can be summarized to generate a complete ranging accuracy test report. This test report may include accuracy curves at different detection distances, accuracy fluctuation ranges, and judgment conclusions on whether it meets preset requirements. For example, if the test accuracy and test precision of the lidar in multiple test scenarios all reach preset thresholds, it indicates that the performance level of the ranging accuracy index of the lidar is high; conversely, it indicates that the performance level of the ranging accuracy index of the lidar is insufficient. This is only an example.

[0088] In this step, the traditional, crude ranging verification method relying on "human eyes and measuring tape" is upgraded to an engineering-grade quantitative test based on high-precision benchmarks, automated data acquisition, intelligent algorithm extraction, and statistical model evaluation. The entire process not only eliminates human error but also ensures the objectivity, scientific rigor, and reproducibility of the test results through a data closed loop, providing solid data support for the safe application of LiDAR in ADAS and autonomous driving systems.

[0089] As an optional embodiment, the mobile test platform carries reflectors with different reflectivities and a positioning module. In response to the performance indicator to be tested being the maximum distance detection indicator and the test strategy being the maximum distance test strategy, step S103 involves adjusting the mobile test platform of the lidar according to the test strategy to construct a test scenario for the lidar's performance indicator to be tested. This includes: determining the nominal maximum test distance of the lidar according to the ranging test strategy, where the nominal maximum test distance is the theoretical maximum detection distance corresponding to the lidar; determining the initial position of the mobile test platform based on the nominal maximum test distance; controlling the positioning module on the mobile test platform to move the mobile test platform away from the lidar to a new distance point with a preset distance step size, starting from the initial position, to construct a test scenario for the lidar's performance indicator to be tested; step S104 involves performing the test on the performance indicator to be tested within the test scenario. The test is conducted to obtain test results, including: in the test scenario, controlling the LiDAR to collect multi-frame point cloud data of a target reflector on a mobile test platform, where the target reflector is any one of reflectors with different reflectivities; based on the multi-frame point cloud data, determining the probability that the LiDAR detects the target reflector in the test scenario; in response to the probability being greater than a preset probability value, determining a new distance point as the initial position, and returning to the following steps to begin execution: controlling the positioning module on the mobile test platform to move the mobile test platform away from the LiDAR from the initial position at a preset distance step size to the new distance point, thereby constructing a test scenario for the LiDAR's performance indicators to be tested, until the probability is less than or equal to the preset probability value, and determining the distance between the new distance point and the LiDAR as the actual maximum detection distance of the LiDAR; based on the actual maximum detection distance, determining the test result of the maximum distance detection indicator.

[0090] In this embodiment, when the performance indicator to be tested is the maximum distance detection indicator, and it is matched with the maximum distance test strategy, the nominal maximum test distance is first read according to the product specifications of the LiDAR. This nominal maximum test distance refers to the theoretical maximum detection distance declared by the manufacturer in the design or specifications. This nominal maximum test distance serves as the starting reference benchmark for the test, used to determine the starting exploration point of the test.

[0091] Optionally, after determining the nominal maximum test range of the lidar, the initial position of the mobile test platform can be determined based on this nominal maximum test range. For example, the initial position of the mobile test platform can be determined at 3 / 4 of the nominal maximum distance. For instance, when the nominal maximum test range is 300m, the initial position is set to 225m. This initial setting aims to avoid the near-field saturation zone and focus on the critical edge of the detection capability, avoiding the inability to effectively capture the attenuation inflection point due to excessively high detection probability caused by too close a distance.

[0092] Optionally, after determining the initial position of the mobile test platform, the positioning module on the platform can be controlled to move the platform away from the lidar to a new distance point with a preset distance step size, thus constructing a test scenario for the lidar's performance indicators. For example, the high-precision positioning module on the mobile test platform can be controlled to move point by point away from the lidar from the initial position with a preset small step size (e.g., 0.5m or 1m), each movement constituting a new test scenario. At each new distance point, the mobile test platform automatically completes positioning locking, attitude correction (ensuring the reflector normal is aligned with the lidar), and maintains the ambient temperature and humidity within a stable range of 25℃±5℃ and 20%RH~80%RH to ensure consistent test conditions.

[0093] Optionally, after moving the mobile test platform to a new distance point and constructing a test scenario, the LiDAR can be triggered to collect multi-frame point cloud data of the target reflector on the mobile test platform within that test scenario. The target reflector can be a high-reflectivity surface (e.g., a 100% reflectivity plate) to maximize signal strength and eliminate interference from reflectivity variables on the detection probability. After obtaining the multi-frame point cloud data, each frame can be intelligently analyzed to identify whether the echo point of the target reflector was successfully detected in the test scenario. The number of frames in the multi-frame point cloud data that effectively detected the target reflector is then counted, and the detection probability of detecting the target reflector in the test scenario is calculated: Detection probability = (Number of successfully detected frames) / (Total number of frames) × 100%.

[0094] Optionally, after obtaining the detection probability corresponding to the test scenario, this detection probability can be compared with a preset probability threshold (e.g., 50%). If the detection probability is greater than the preset probability threshold, it indicates that the new distance point is still within the effective working range of the LiDAR. In this case, the location of the new distance point can be used as the new initial position, and the movement process of the mobile test platform can be repeated. For example, the mobile test platform can continue to move further away from the LiDAR at the same pace to construct the next test scenario and continuously explore the detection range boundary of the LiDAR. The above process forms a closed-loop progressive search mechanism, that is, it continuously approaches the critical point where the detection probability decreases until, after a certain movement of the test platform, the detection probability is less than or equal to the preset probability threshold. At this point, the distance point is immediately locked as the actual maximum detection range of the LiDAR. This actual maximum detection range is used to characterize the farthest physical distance at which the LiDAR can stably identify a target while ensuring that the detection probability is not lower than the preset probability threshold.

[0095] Optionally, after determining the actual maximum detection range of the lidar, this actual maximum detection range can be used as the test result of the lidar's maximum distance detection index, which can be used to analyze the lidar's stability and redundancy under extreme conditions.

[0096] In this step, the test results of the maximum range detection index of the lidar are tested through an automated detection process, which abandons the traditional rough mode of manually pushing the reflector and subjective judgment, thus improving the accuracy of the test results of the maximum range detection index of the lidar.

[0097] As an optional embodiment, the mobile test platform carries reflectors with different reflectivities and a positioning module. When the performance indicator to be tested is the echo intensity response characteristic indicator, and the test strategy is the echo intensity test strategy, step S103 involves adjusting the mobile test platform of the lidar according to the test strategy to construct a test scenario for the lidar's performance indicator to be tested and obtaining test results. This includes: determining a preset detection distance point for testing the echo intensity response characteristic indicator according to the echo intensity test strategy; controlling the positioning module on the mobile test platform to move the mobile test platform to the preset detection distance point to construct the test scenario. Establish a test scenario for the performance indicators to be tested of the lidar; Step S104: In the test scenario, test the performance indicators to be tested and obtain test results, including: In the test scenario, control the lidar to collect point cloud data of reflectors with different reflectivities carried by the mobile test platform, and obtain point cloud data corresponding to reflectors with different reflectivities; Based on the point cloud data corresponding to reflectors with different reflectivities, determine the echo intensity response value of the lidar to reflectors with different reflectivities; Based on the echo intensity response value of the lidar to reflectors with different reflectivities, determine the test results of the lidar's echo intensity response characteristic indicators.

[0098] In this embodiment, when the performance index to be measured is the echo intensity response characteristic index, and it is matched with the echo intensity test strategy, a unique preset distance detection point (e.g., 10m ± 0.02m) is first set according to the echo intensity test strategy. This preset distance detection point is strictly selected to eliminate the interference of natural signal attenuation caused by distance changes on the measurement results, ensuring that all echo intensity data only reflect the response characteristics of the lidar to the target reflectivity itself, rather than the coupling effect of distance or environmental factors.

[0099] Optionally, after determining the preset detection distance point for testing the echo intensity response characteristics, the high-precision positioning module on the mobile test platform can be controlled to precisely move the platform to the 10m reference position, and simultaneously activate the attitude calibration mechanism: ensuring that the reflector planes with different reflectivities are completely perpendicular to the laser radar's output direction, and the incident angle is controlled within the range of 0°±5°, thus physically eliminating nonlinear errors introduced by angular deviations. Simultaneously, the built-in sensors monitor the ambient temperature and humidity in real time, ensuring that the ambient temperature remains stable within the preset range of 25℃±5℃ and the ambient humidity remains stable within the preset range of 20%RH~80%RH, guaranteeing the consistency and reproducibility of the test environment.

[0100] Optionally, after the test scenario is constructed, the data acquisition phase begins. As described above, the mobile test platform integrates standard reflectivity plates (e.g., 10%, 30%, 50%, 100%) on each of its four sides. Following the echo intensity test strategy, the four platforms on the mobile side are automatically rotated, sequentially aligning the reflective plates with different reflectivities precisely with the optical axis center of the lidar. After each reflective plate is in place, the lidar is triggered, continuously acquiring multiple frames of point cloud data from the reflective plates, obtaining multiple frames of point cloud data corresponding to different interfering reflective plates. Then, each frame of point cloud data undergoes intelligent filtering, retaining valid echo points within the incident angle range of 0°±5°, and removing edge points, clutter, and multipath interference signals to ensure that all point cloud data used originates from the normal incident response of the main reflecting surface.

[0101] Optionally, for each reflector, the echo intensity response value, i.e., the average echo intensity, is calculated. This echo intensity response value characterizes the typical response capability of the lidar to that reflector. Simultaneously, the measurement standard deviation of multi-frame point cloud data corresponding to each reflector is calculated to evaluate repeatability and system noise level. Based on the echo intensity response values ​​of the lidar to reflectors with different reflectivities, the test results of the lidar's echo intensity response characteristic indicators are determined.

[0102] For example, the echo intensity response value of each reflector can be calculated using the following formula (3). The measurement standard deviation corresponding to each reflector is calculated using the following formula (4). .

[0103] (3)

[0104] (4)

[0105] Among them, the above It can represent the reflection intensity obtained by a certain reflector in the i-th measurement, and N can represent the number of measurements of a certain reflector.

[0106] Optionally, the echo intensity response values ​​corresponding to each reflector are determined according to the above formulas (3) and (4). and measurement standard deviation Then, the reflectivity of each reflector can be further calculated using the following formula (5). .

[0107] (5)

[0108] Optionally, after determining the reflectivity of each reflector, the average echo intensity difference between reflectors with different reflectivities can be combined to comprehensively evaluate the lidar's ability to distinguish reflectors with different reflectivities. For example, if the average echo intensity difference between reflectors with 10% and 30% reflectivity is significantly greater than their respective measurement fluctuation ranges (e.g., measurement standard deviation), and the reflectivity of these two reflectors is not significantly different... If all values ​​are below a preset threshold (e.g., 15%), the lidar is deemed to possess good reflectivity discrimination capability. This means the lidar's echo intensity response characteristic performance is high, indicating high sensitivity and classification ability for target materials in real-world road environments. For example, this lidar can effectively identify low-reflectivity black tires, dark road barriers, and highly reflective traffic signs and white lane lines in real-world scenarios. This is merely an example; different judgment criteria can be designed based on the above parameters to determine the test results of the lidar's echo intensity response characteristic.

[0109] In this step, the echo intensity response characteristics of the lidar are tested through a fully automated, environmentally controllable, data-closed-loop, and quantifiable engineering verification process. Compared with the crude testing method that relies on manual replacement of reflectors and subjective judgment, this method significantly improves the testing efficiency and accuracy of the lidar's echo intensity response characteristics.

[0110] As an optional embodiment, the test results of the echo intensity response characteristics of the lidar are determined based on the echo intensity response values ​​of the lidar to reflectors with different reflectivities. This includes: determining the reflection intensity discrimination of the lidar based on the echo intensity response values ​​of the lidar to reflectors with different reflectivities, wherein the reflection intensity discrimination is used to characterize the lidar's ability to identify reflectors with different reflectivities; and determining the test results of the echo intensity response characteristics of the lidar based on the reflection intensity discrimination.

[0111] In this embodiment, based on the echo intensity response values ​​of the lidar to reflectors with different reflectivities, the test results of the lidar's echo intensity response characteristic index are determined. The lidar's reflection intensity discrimination can be determined based on the echo intensity response values ​​of the lidar to reflectors with different reflectivities, thereby evaluating the lidar's ability to identify reflectors with different reflectivities, and finally generating the test results of the echo intensity response characteristic index.

[0112] For example, for each reflector, calculate its echo intensity response value. That is, the average echo intensity is used as the typical response intensity of the reflector; at the same time, the measurement standard deviation of the reflector is calculated. This is to quantify random fluctuations or system noise during the measurement process. Based on this, a reflection intensity discrimination metric is introduced. This is used as a core evaluation parameter. The reflectance intensity discrimination... It does not measure the accuracy of a single reflectivity, but rather reflects the relative stability of the measurement results at that intensity level. The lower the reflectivity discrimination, the more consistent the lidar's response to the reflector with that reflectivity, the lower the noise, and the more reliable its sensing output.

[0113] Optionally, the echo intensity response characteristics of the lidar are comprehensively judged by combining the reflectivity distribution of all reflectivity plates, the distinguishability of adjacent layers, and the linearity and step clarity of the overall echo response curve. For example, if the reflectivity of all reflectivity plates is lower than a preset threshold (e.g., 15%), and the difference in echo intensity response values ​​between reflectivity plates of adjacent reflectivity is significantly higher than the noise fluctuation, then the lidar is judged to have excellent reflectivity resolution capability and can stably identify real road targets such as low-reflectivity black plastic, dark vehicles, and high-reflectivity traffic markings and metal guardrails.

[0114] In this step, the test results not only verified the consistency of LiDAR performance at the signal response level, but also directly supported the autonomous driving system's semantic classification ability of obstacle materials. Through this automated and standardized process, the traditional evaluation method relying on human experience is upgraded to quantifiable, reproducible, and traceable engineering indicators, providing a solid data basis for LiDAR's perception safety in complex lighting and material environments.

[0115] As an optional embodiment, the mobile test platform carries reflectors with different reflectivities and a positioning module. Responding to the performance indicator to be tested being the echo intensity distance attenuation characteristic indicator, and the test strategy being the echo intensity distance attenuation test strategy, step S103 involves adjusting the mobile test platform of the lidar according to the test strategy to construct a test scenario for the lidar's performance indicator to be tested. This includes: determining multiple detection distance points for testing the echo intensity distance attenuation characteristic indicator according to the echo intensity distance attenuation test strategy; controlling the positioning module on the mobile test platform to sequentially move the mobile test platform to the multiple detection distance points to construct test scenarios corresponding to each of the multiple detection distance points; step... Step S104: In the test scenario, the performance indicators to be tested are tested, and the test results are obtained, including: in the test scenarios corresponding to multiple detection distance points, the lidar is controlled to collect point cloud data of the target reflector on the mobile test platform, and point cloud data corresponding to multiple detection distance points are obtained, wherein the target reflector is any one of the reflectors with different reflectivities; based on the point cloud data corresponding to multiple detection distance points, the echo intensity response value of the lidar to the target reflector at multiple detection distance points is determined; based on the echo intensity response value of the lidar to the target reflector at multiple detection distance points, the test results of the lidar's echo intensity distance attenuation characteristic index are determined.

[0116] In this embodiment, when the performance index to be tested is the echo intensity distance attenuation characteristic index, firstly, according to the echo intensity distance attenuation test strategy corresponding to this index, a single, high-reflectivity standard reflector (e.g., a 100% reflectivity plate) is clearly selected as the detection target to eliminate the interference of reflectivity variables on the attenuation curve and ensure that the measured data only reflects the signal attenuation law caused by the distance itself. Then, a set of multiple detection distance points covering the working range of the lidar are determined, including key locations at close, medium, and long distances (e.g., 5m, 10m, 20m, 50m, 80m, 100m, 150m, 200m, 250m, 300m). These detection distance points are evenly distributed and extend to the nominal maximum detection distance to comprehensively capture the complete trend of echo intensity changing with distance.

[0117] Optionally, after determining multiple detection distance points for testing the echo intensity distance attenuation characteristics, the positioning module on the mobile test platform can be controlled to sequentially move the mobile test platform to each of the multiple detection distance points, thereby constructing test scenarios corresponding to each detection distance point. For example, the high-precision positioning module on the mobile test platform can be controlled to precisely move the mobile test platform to each detection distance point sequentially. At each detection distance point, the mobile test platform automatically completes attitude calibration to ensure that the target reflector plane is strictly perpendicular to the laser radar's output direction, the incident angle is controlled within the range of 0°±5°, and the ambient temperature and humidity are monitored in real time to ensure that they are stable within the specified range of 25℃±5℃ and 20%RH~80%RH, thus constructing an independent, stable, and reproducible test scenario for each distance point.

[0118] Optionally, after constructing the test scenario corresponding to each detection range point using the above method, the lidar is activated in each test scenario to continuously acquire multiple frames of point cloud data of the target reflector. Then, each frame of point cloud data is intelligently analyzed to filter out the main reflection echo points that meet the incident angle conditions, eliminating edge noise and multipath interference, and retaining only the effective signal under normal incidence. The effective echo intensity values ​​at each detection range point are statistically analyzed, and the echo intensity response value corresponding to each detection range point is calculated as the lidar's echo intensity response value to the target reflector at that detection range, thus obtaining a complete response sequence between "detection range point – echo intensity response value".

[0119] Optionally, after determining the echo intensity response values ​​of the lidar to the target reflector at multiple detection distance points, the test results of the lidar's echo intensity distance attenuation characteristic index can be determined based on the echo intensity response values ​​of the lidar to the target reflector at multiple detection distance points.

[0120] The following section further describes the process of determining the test results of the lidar's echo intensity distance attenuation characteristics based on the lidar's echo intensity response values ​​at multiple detection distance points to the target reflector.

[0121] As an optional embodiment, the test results of the laser radar's echo intensity distance attenuation characteristic index are determined based on the echo intensity response values ​​of the laser radar to the target reflector at multiple detection distance points. This includes: determining the attenuation curve of the laser radar's echo intensity response value to the target reflector based on the echo intensity response values ​​of the laser radar to the target reflector at multiple detection distance points; and determining the test results of the laser radar's echo intensity distance attenuation characteristic index based on the attenuation curve.

[0122] In this embodiment, after determining the echo intensity response values ​​of the lidar to the target reflector at multiple detection distance points, the attenuation curve of the lidar's echo intensity response value can be determined based on the echo intensity response values ​​of the lidar to the target reflector at multiple detection distance points.

[0123] For example, the echo intensity response values ​​corresponding to each detection distance point are arranged sequentially according to the detection distance, forming an attenuation curve with distance as the horizontal axis and echo intensity as the vertical axis. Then, based on this attenuation curve, the test results of the lidar's echo intensity distance attenuation characteristics are determined.

[0124] Optionally, the attenuation curve can be fitted and compared with the theoretical inverse square attenuation model to calculate the average deviation rate between the actual and theoretical values, and analyze its standard deviation to quantify the signal attenuation consistency and system stability of the lidar at long distances. If the measured echo intensity response value deviates from the theoretical model by more than ±10% within the range of 150m to 300m, or the intensity fluctuation increases significantly, it is determined that the lidar has a risk of response distortion in long-distance detection.

[0125] Optionally, the test results of the echo intensity distance attenuation characteristics index are comprehensively output based on the goodness of fit of the attenuation curve, the overall fluctuation level, and the intensity retention rate at key distance points. These test results clearly reveal whether the LiDAR can maintain a stable echo signal output and whether it possesses reliable long-range perception capabilities in real-world long-range application scenarios. This is a core basis for evaluating its safety and robustness under harsh conditions such as high-speed autonomous driving and severe weather.

[0126] The above testing process replaces the traditional crude mode of only measuring the farthest distance or manually moving the reflector. It realizes the engineering verification of the echo intensity attenuation characteristics across the entire distance, multiple points, controlled environment, and closed data loop, providing quantifiable key performance evidence for the optimization of LiDAR signal processing algorithms, hardware design verification, and functional safety certification.

[0127] The technical solutions of the embodiments of this application will be illustrated below with reference to preferred embodiments.

[0128] Because LiDAR requires extremely high ranging accuracy in autonomous driving and advanced driver assistance systems, its performance evaluation must achieve sub-centimeter-level positioning repeatability and traceability at various test distances (e.g., 10m, 30m, 50m, 100m, 300m, etc.). Traditional testing methods rely on manual movement of the reflectivity plate to a preset position, followed by another operator starting the radar to collect data. This method has two significant drawbacks: First, it is highly dependent on manpower and inefficient. Each test requires at least two people: one to hold the plate for positioning and the other to monitor the equipment. This not only increases labor costs but also leads to lengthy testing cycles and unreproducible data acquisition due to inconsistent human operation rhythms. Second, positioning errors are uncontrollable. When manually placing the reflectivity plate, using a measuring tape or laser rangefinder for rough alignment makes it difficult to avoid millimeter-level lateral offsets, angular tilts, or height deviations caused by uneven ground. These minute errors directly introduce deviations into the ranging system, severely interfering with the accuracy evaluation results, especially in high-precision scenarios (e.g., within ±2cm), which can easily lead to misjudgments.

[0129] However, this application provides a testing method for lidar, which introduces a mobile testing platform (e.g., an intelligent mobile vehicle) integrating multiple standard reflectivity targets and a high-precision real-time positioning module to achieve automated, standardized scenario construction and unmanned testing of various performance indicators of lidar. The mobile testing platform adopts an integrated structural design, with calibrated standard reflectivity targets (e.g., 10%, 30%, 50%, 100%) integrated on each of its four sides, ensuring stable and traceable reflection characteristics. Simultaneously, the mobile testing platform is equipped with a high-precision differential positioning module, which can monitor and provide feedback on the absolute position of the mobile testing platform on the test path in real time, meeting the stringent requirements for lidar ranging accuracy evaluation. During testing, the system automatically invokes corresponding testing strategies based on the performance indicators to be tested (e.g., distance accuracy, maximum detection distance, echo intensity response characteristics, distance attenuation characteristics, etc.). The mobile testing platform is remotely and precisely controlled to autonomously move to preset testing distance points (e.g., 5m, 10m, 20m, 50m, 80m, 100m, etc.), and automatically completes attitude correction upon reaching the target position, ensuring that the reflector plane is strictly perpendicular to the lidar optical axis and the incident angle is controlled within ±0.5°. The entire process requires no manual intervention in positioning, handling, or adjustment, achieving "one-click start, automatic docking, and data linkage acquisition." This solution eliminates the inefficient traditional model of manual plate pushing, visual alignment, and multi-person coordination, fundamentally solving core pain points such as large positioning errors, poor test repeatability, and high data fluctuations caused by human operation. Simultaneously, by integrating four sets of reflectivity targets within a single platform, it achieves highly efficient integration of "multi-reflectivity, multi-distance, and continuous testing," significantly improving testing efficiency and accuracy.

[0130] Figure 2This is a schematic diagram illustrating the performance indicators of a lidar according to an embodiment of this application. As described above, the mobile vehicle has a tetrahedral structure, with standard reflective plates of calibrated reflectivity fixed on its four sides. The reflectivity values ​​are 10%, 30%, 50%, and 100%, covering typical scene reflection characteristics from low-reflectivity road markings to high-reflectivity traffic signs. This design enables parallel testing under multiple reflectivity conditions with a single deployment, significantly improving testing efficiency and avoiding operational errors and time waste caused by repeatedly replacing reflective plates in traditional methods. Because the mobile vehicle integrates a high-precision displacement sensor and positioning module at its bottom, it can accurately move along a linear guide rail to six preset detection distance points (e.g., 5m, 10m, 20m, 50m, 80m, 100m), with the positioning error at each position controlled within ±5mm, ensuring the repeatability and accuracy of the test distance. The lidar is fixed on a high-precision turntable, and its light emission center is strictly aligned with the center of the reflector of the moving trolley to ensure that each measurement meets the normal incidence condition (e.g., incident angle ≤ ±0.5°), minimizing the interference of angle error on point cloud distribution and intensity acquisition.

[0131] Optionally, at each test distance point, the lidar continuously collects no less than 100 frames of point cloud data, automatically selecting valid echo points located in the center area of ​​the reflector with compliant incident angles. Combined with the true values ​​measured by a total station or reference rangefinder, the average distance measurement, accuracy (difference between the average and the true value), and precision (root mean square error, i.e., standard deviation) are calculated for each distance. This allows for the evaluation of the lidar's comprehensive performance under different distances and reflectivity scenarios. This method optimizes the traditional testing process, which required multiple people, multiple relocations, and repeated alignment, into a fully automated mode with automatic positioning, multi-target simultaneous measurement, and data closed-loop. This not only significantly reduces human error and saves manpower and time but also achieves high consistency, traceability, and engineering comparability of test data, providing a scientific, efficient, and standardized testing basis for lidar mass production screening, performance grading, and functional safety certification.

[0132] The testing process for various performance indicators of the lidar will be described in more detail below.

[0133] Figure 3 This is a flowchart of a method for testing the ranging accuracy of a lidar according to an embodiment of this application, as shown below. Figure 3 As shown, the method includes the following steps.

[0134] Step S301: Construct a standardized test scenario and calibrate the benchmark reference system.

[0135] In this embodiment, in a stable environment with a temperature of 25℃±5℃ and a humidity of 20%RH~80%RH, the lidar under test is fixed on a high-precision turntable to ensure that the light output center of the lidar coincides with the rotation axis of the turntable. Simultaneously, a reference rangefinder or total station is set up with an accuracy of not less than one-third of the nominal accuracy of the lidar. Using the total station as the coordinate origin, the three-dimensional spatial relative coordinates between the light output center of the lidar and the center point of the reflector under test are accurately measured. The true values ​​at each test distance are calculated and recorded as the benchmark for subsequent accuracy evaluation.

[0136] Step S302: Control the mobile test platform to automatically position itself at the preset test distance point.

[0137] In this embodiment, a remote control terminal sends commands to drive a mobile testing platform integrating multiple reflectivity plates (e.g., 10%, 30%, 50%, 100%) and a high-precision positioning module to automatically move along a linear guide rail to a preset ranging point (e.g., 5m, 10m, 20m, 50m, 80m, 100m). After reaching the target position, the platform automatically locks in position to ensure that the mobile testing platform is stable, the reflector plane is perpendicular to the normal direction of the lidar, the incident angle is controlled within ±0.5°, and the current actual distance value is fed back in real time, achieving millimeter-level precise alignment of the test distance.

[0138] Step S303: Collect multiple frames of point cloud data.

[0139] In this embodiment, the lidar under test is started, and after the lidar under test has completed its warm-up and reached a stable operating temperature, point cloud data of no less than 100 frames are continuously collected at each test distance point. Each frame of data is intelligently filtered to remove noise, multipath interference and non-normal incident points, and only valid echo points with an incident angle within the range of 0°±5° and located in the center area of ​​the reflector are retained to ensure that the data is representative and consistent, and to provide high-quality original samples for subsequent statistical analysis.

[0140] Step S304: Calculate the ranging accuracy and precision and output the test results.

[0141] In this embodiment, based on the collected multi-frame effective ranging data, the test accuracy and test precision of the lidar at multiple preset ranging points are calculated. The method for calculating the test accuracy and test precision can refer to the aforementioned formulas (1) and (2), which will not be repeated here. Then, based on the test accuracy and test precision, the test results of the lidar's ranging precision index are determined.

[0142] In steps S301 to S304 above, a four-step closed loop of "environmental calibration - precise positioning - data acquisition - intelligent analysis" replaces the inefficient mode of traditional manual plate pushing and visual distance estimation, realizing the automation, high repeatability and traceability of distance measurement accuracy testing, and significantly improving testing efficiency and data reliability.

[0143] Figure 4 This is a flowchart of a test method for the maximum range detection index of a lidar according to an embodiment of this application, as shown below. Figure 4 As shown, the method includes the following steps.

[0144] Step S401: Construct a high-precision long-distance testing environment and calibrate the benchmark system.

[0145] In this embodiment, to ensure the authority and repeatability of the maximum detection distance test results, a standardized test environment is established in an open area free from obstructions and strong light interference. The ambient temperature is controlled at 25℃±5℃, and the relative humidity is within the range of 20%RH~80%RH. High-precision temperature and humidity sensors monitor and record the data in real time to ensure that the test conditions meet industry standards. The lidar under test is rigidly fixed to a high-precision turntable, ensuring that its light emission center is perfectly aligned with the turntable's vertical axis, avoiding angular errors caused by pitch or yaw. Simultaneously, a reference ranging device (such as a total station or high-precision laser rangefinder) with an accuracy one-third better than the lidar's nominal accuracy is used as the true reference source. Through three-dimensional coordinate measurement, the true spatial distance between the lidar's light emission center and the test reflector center is accurately obtained, providing an indisputable benchmark for all subsequent detection probability judgments.

[0146] Step S402: Control the mobile test platform to move gradually from the preset starting point to a distant location, and collect point cloud data synchronously.

[0147] In this embodiment, a mobile test platform is activated, equipped with a calibrated 100% high-reflectivity standard target and an integrated high-precision real-time positioning module. The test begins at 3 / 4 of the lidar's nominal maximum detection range (e.g., starting at 225m if nominally 300m). The small mobile test platform moves uniformly along a straight track at a fixed step size (5m or 10m) towards the distant end. At each target distance point, the mobile test platform is automatically controlled to precisely stop, ensuring the reflector plane is strictly perpendicular to the lidar's optical axis and the incident angle is controlled within ±0.5°. Then, the lidar is triggered to continuously acquire no fewer than 100 frames of point cloud data, while simultaneously recording the actual distance value fed back in real-time by the reference system. This ensures that each set of data has an accurate distance label, achieving precise binding and dynamic tracking of "distance-point cloud" data.

[0148] Step S403: Calculate the detection probability of each distance point and determine the critical point of detection capability.

[0149] In this embodiment, for the point cloud data collected at each distance point, effective echo points that meet the normal incidence condition (incident angle 0°±5°) are automatically selected, and noise, clutter, and multipath interference are eliminated. The detection probability (effective points / theoretical points) of that distance point is calculated by comparing the theoretical point density model of the lidar (e.g., expected number of points per unit area) with the actual number of effective points collected. As the mobile test platform moves further away, the curve of the detection probability changing with distance is continuously plotted. When the detection probability first stably drops below 50% at multiple consecutive distance points, this location is determined to be the "critical point of detection capability," and the movement is automatically stopped. If it is still above 50% at the nominal maximum distance, a standard-compliant light intensity attenuator is introduced to simulate signal attenuation at a greater distance, and the test range is further expanded until the actual physical distance where the detection probability stably drops below 50% is accurately captured, ensuring that the results are not artificially inflated due to equipment performance redundancy.

[0150] Step S404: Combining the true value and the detection probability curve, output the maximum effective detection distance and confidence assessment results.

[0151] In this embodiment, a complete detection probability decay curve is fitted using the actual distance measured by the benchmark system as the x-axis and the detection probability as the y-axis. The actual distance value corresponding to the first drop in detection probability to 50% is precisely located on the curve and defined as the "maximum effective detection distance" of the LiDAR. To evaluate the reliability of this result, the standard deviation of the detection probability at each distance point before and after this critical point is further calculated to analyze the system's stability and noise resistance in the edge detection area. Finally, the system outputs the measured value of the maximum detection distance and the confidence interval (e.g., distance ±0.5m corresponding to 50% detection probability). Combined with the curve smoothness, fluctuation amplitude, and environmental consistency, a comprehensive judgment is made on whether the LiDAR's long-distance perception capability in real complex scenarios meets the standards, providing core data support for the safety redundancy design and functional certification of autonomous driving systems.

[0152] In steps S401 to S404 above, the four-step closed loop of "environmental control - precise progression - probability quantification - critical judgment" realizes the automated, continuous and quantitative evaluation of the maximum detection capability of lidar. This overcomes the subjective judgment defects of the traditional "manual visual inspection of whether the target is captured". The maximum detection distance is upgraded from an empirical description to a reproducible, comparable and verifiable engineering indicator, providing solid data support for the safety redundancy design of autonomous driving systems in high-risk scenarios such as night, rain and fog, and long-distance low reflectivity target recognition.

[0153] Figure 5 This is a flowchart illustrating a method for testing the echo intensity response characteristics of a lidar according to an embodiment of this application. Figure 5 As shown, the method includes the following steps.

[0154] Step S501: Construct a standardized test environment and select a high-reflectivity reflector.

[0155] In this embodiment, the ambient temperature in the test environment is maintained at 25℃±5℃, and the relative humidity is kept within the range of 20%RH~80%RH, and monitored in real time by a high-precision temperature and humidity sensor. Simultaneously, the test area must be far from direct sunlight and sources of reflective interference, and the ambient illuminance must be kept stable and recorded. The lidar under test is rigidly fixed to a high-precision turntable, ensuring that its optical axis is completely aligned with the turntable's central axis. A 100% high-reflectivity reflector is selected as the test target. This reflector has uniform, stable, and traceable optical reflection characteristics, and its surface flatness meets the requirements for laser ranging. The purpose of selecting a 100% reflectivity reflector is to eliminate the influence of reflectivity variables on the echo intensity, ensuring that the test results only reflect the lidar's own signal response capability at different distances, and ensuring that the evaluation focuses on the sensing performance of the device rather than the characteristics of external targets.

[0156] Step S502: Control the mobile test platform to precisely stop at multiple preset distance points and collect multiple frames of echo intensity data.

[0157] In this embodiment, a remote control system drives a mobile testing platform carrying a high-reflectivity target plate to automatically move along a linear guide rail to a series of preset testing distance points (e.g., 5m, 10m, 20m, 50m, 80m, 100m, 150m, 200m, etc.). At each target location, the system automatically triggers the trolley positioning and locking function, combined with a built-in high-precision encoder or positioning module, to ensure that the actual distance deviates from the set value by no more than ±5mm. Simultaneously, the mobile testing platform dynamically adjusts its attitude to ensure that the target plate plane is strictly perpendicular to the laser radar's light emission direction, with the incident angle controlled within ±0.5°, minimizing the impact of angle errors on echo intensity. After the laser radar starts up and completes its warm-up and stabilization, it continuously collects no less than 100 frames of point cloud data. In each frame, the intensity values ​​corresponding to all valid echo points that meet the normal incidence condition are extracted to form a high-density, low-noise original intensity sample set, achieving refined sampling of the distance-intensity relationship.

[0158] Step S503: Calculate the average echo intensity at each distance point and construct the echo intensity attenuation curve.

[0159] In this embodiment, over 100 frames of intensity data collected at each distance point are automatically filtered and statistically processed to remove outliers and exceptions, retaining only valid data that meet the incident angle and position constraints. The average value is calculated as the representative echo intensity at that distance. The average intensity values ​​of all distance points are arranged in order of distance, and an echo intensity attenuation curve of the measured echo intensity as a function of distance is plotted. To evaluate system performance, a theoretical attenuation curve, i.e., a theoretical model based on the laws of optical propagation, is simultaneously fitted as an ideal response benchmark. Subsequently, the relative deviation between the measured curve and the theoretical curve at each distance point is calculated, and the overall average deviation, maximum deviation, and standard deviation of intensity fluctuation are statistically analyzed. This comprehensively characterizes the lidar's ability to compensate for signal attenuation and its output stability at different distances, providing a quantitative basis for subsequent performance evaluation.

[0160] Step S504: Evaluate the echo intensity response characteristics and output quantitative test conclusions.

[0161] In this embodiment, based on the comparison between the measured attenuation curve and the theoretical model, the system comprehensively judges whether the echo intensity response characteristics of the lidar meet the engineering requirements. If the overall trend of the echo intensity attenuation curve is smooth, the average deviation from the theoretical model is controlled within ±10%, and the standard deviation of intensity fluctuation in the long-distance region (e.g., above 200m) is less than 8% of the average intensity, then it is determined to have excellent echo intensity response characteristics, indicating that its internal signal processing algorithm is working normally and can effectively maintain stable echo output of long-distance targets, supporting the reliable identification of weak signal targets by the autonomous driving system. If the echo intensity attenuation curve shows an obvious nonlinear inflection point, a sudden drop in long-distance intensity, or violent fluctuations, then the response characteristics are determined to be substandard, possibly due to hardware or software defects such as laser power attenuation, receiver sensitivity reduction, or algorithm compensation inaccuracy. Finally, a standardized test report containing intensity curve graphs, deviation statistics tables, judgment conclusions, and improvement suggestions is automatically generated, providing traceable and quantifiable key performance data for lidar production line screening, algorithm optimization, and functional safety certification, realizing an upgrade from "detecting targets" to "stable perceived intensity."

[0162] In steps S501 to S504 above, the automated testing process enables automated, continuous, and quantitative evaluation of the echo intensity response characteristics of the lidar, overcoming the subjective judgment defects of the traditional method of "manual visual inspection to see if the target is captured", and improving the testing accuracy and efficiency of the lidar echo intensity response characteristics.

[0163] Figure 6 This is a flowchart illustrating a method for testing the echo intensity distance attenuation characteristics of a lidar according to an embodiment of this application. Figure 6 As shown, the method includes the following steps.

[0164] Step S601: Construct a standardized test environment and select a high-reflectivity reflector.

[0165] In this embodiment, to ensure accurate and reliable test results, the test is conducted in an environment with constant temperature (e.g., 25℃±5℃), constant humidity (e.g., 20%RH~80%RH), and no strong light interference. High-precision sensors are used to monitor environmental parameters in real time. The lidar under test is fixed on a high-precision turntable to ensure optical axis stability. The test target uses a calibrated 100% high-reflectivity standard target plate, whose reflection characteristics are stable and traceable. This aims to eliminate the influence of reflectivity variations on echo intensity, ensuring that the test only reflects the lidar's own signal attenuation characteristics as a function of distance.

[0166] Step S602: Control the mobile test platform to precisely stop at multiple preset distance points and collect multiple frames of echo intensity data.

[0167] In this embodiment, the mobile testing platform is equipped with a 100% reflective reflector and a high-precision positioning module, enabling it to automatically move to preset distance points such as 5m, 10m, 20m, 50m, 80m, 100m, 150m, and 200m. At each location, the mobile testing platform automatically corrects its attitude to ensure that the reflector is perpendicular to the laser optical axis, the incident angle is controlled within ±0.5°, and the actual distance is accurately fed back. After the lidar is activated, it continuously acquires no less than 100 frames of point cloud data, retaining only the valid echo points that meet the normal incidence conditions, extracting their intensity values, and forming a high-precision, highly consistent raw dataset.

[0168] Step S603: Calculate the average echo intensity at each distance point and construct the echo intensity attenuation curve.

[0169] In this embodiment, statistical processing is performed on more than 100 frames of echo intensity data for each distance point. After removing outliers, the average intensity is calculated as the representative response value for that distance point. The measured echo intensity attenuation curve of the lidar is plotted with distance as the horizontal axis and average intensity as the vertical axis. This curve is then compared with the theoretical inverse square attenuation model to analyze whether the measured curve is smooth and conforms to physical laws, thereby determining whether its signal compensation capability is normal.

[0170] Step S604: Evaluate the echo intensity response characteristics and output the quantitative test results.

[0171] In this embodiment, the echo intensity response characteristics of the lidar are comprehensively judged based on the degree of deviation between the measured curve and the theoretical model, the stability of long-distance intensity, and the fluctuation range. If the overall trend of the measured curve is highly consistent with the theoretical model, and the standard deviation of intensity fluctuation in the long-distance region above 100m is within a reasonable range, it is determined that it has excellent distance attenuation characteristics, indicating that its internal signal processing mechanism is working normally and can effectively offset optical path attenuation, ensuring that weak targets at long distances can still output stable and identifiable echo signals, meeting the stringent requirements of autonomous driving systems for consistent perception of long-distance targets. If the curve shows obvious nonlinear inflection points, violent fluctuations, or a significant deviation of the attenuation slope from the theoretical value, its attenuation characteristics are determined to be abnormal, requiring targeted debugging or rework.

[0172] In steps S601 to S604 above, the above test process replaces the traditional crude mode of only measuring the farthest distance or manually moving the reflector. It realizes the engineering verification of the echo intensity attenuation characteristics across the entire distance, multiple points, controllable environment, and closed data loop, and provides quantifiable key performance evidence for the optimization of LiDAR signal processing algorithms, hardware design verification and functional safety certification.

[0173] According to an embodiment of this application, a testing device for lidar is also provided. It should be noted that this lidar testing device can be used to execute the lidar testing method described in the embodiments.

[0174] Figure 7 This is a schematic diagram of a lidar testing device according to an embodiment of this application. Figure 7 As shown, the testing device 700 for the lidar may include: a first determining unit 701, a second determining unit 702, an adjustment unit 703, and a testing unit 704.

[0175] The first determining unit 701 is configured to determine at least one performance index to be tested for the lidar in response to a test request from the lidar, wherein the test request is used to request the performance of the performance index to be tested.

[0176] The second determining unit 702 is used to determine a test strategy that matches the performance index to be tested from the test strategy set of the lidar, wherein the test strategy set includes test strategies corresponding to various performance indices to be tested.

[0177] The adjustment unit 703 is used to adjust the mobile test platform of the lidar according to the test strategy to construct the test scenario of the lidar's performance indicators to be tested.

[0178] Test unit 704 is used to test the performance index under test in a test scenario and obtain test results, wherein the test results are used to characterize the performance index under test.

[0179] Optionally, the second determining unit 702 is further configured to: in response to the performance index to be tested being a ranging accuracy index, determine a ranging test strategy matching the ranging accuracy index from a set of test strategies, wherein the ranging test strategy is used to characterize the testing of the range detection accuracy of the lidar at multiple detection distance points; in response to the performance index to be tested being a maximum range detection index, determine a maximum distance test strategy matching the maximum range detection index from a set of test strategies, wherein the maximum distance test strategy is used to characterize the testing of the actual maximum detection range of the lidar by adjusting the distance between the mobile test platform and the lidar ... The test index is the echo intensity response characteristic index. An echo intensity test strategy matching the echo intensity response characteristic index is determined from the test strategy set. This echo intensity test strategy characterizes the response value of the LiDAR to the echo intensity of reflectors with different reflectivities at a preset detection distance. The test index is the echo intensity distance attenuation characteristic index. An echo intensity distance attenuation test strategy matching the echo intensity distance attenuation characteristic index is determined from the test strategy set. This echo intensity distance attenuation strategy characterizes the response value of the LiDAR to the echo intensity of reflectors with the same reflectivity at multiple detection distances.

[0180] Optionally, the adjustment unit 703 is also used to: control the positioning module on the mobile test platform according to the ranging test strategy, and move the mobile test platform to the locations of multiple detection distance points in sequence according to the order of distance from the lidar from near to far, so as to construct test scenarios corresponding to multiple detection distance points respectively, wherein the ambient temperature and ambient humidity in the test scenarios meet preset requirements.

[0181] Optionally, the test unit 704 is further configured to: control the lidar to collect point cloud data of the target reflector on the mobile test platform in test scenarios corresponding to multiple detection distance points, obtaining multiple sets of point cloud data, wherein the target reflector is any one of the reflectors with different reflectivities; based on the multiple sets of point cloud data, determine the distance measurement value between the center point of the target reflector and the lidar in multiple test scenarios; and based on the distance measurement value and distance reference value in multiple test scenarios, determine the test result of the ranging accuracy index, wherein the distance reference value is the distance between the center point of the target reflector and the lidar measured by a reference ranging device in the test scenario, and the test accuracy of the reference ranging device is higher than that of the lidar.

[0182] Optionally, the adjustment unit 703 is further configured to: determine the nominal maximum test distance of the lidar according to the ranging test strategy, wherein the nominal maximum test distance is the theoretical maximum detection distance corresponding to the lidar; determine the initial position of the mobile test platform based on the nominal maximum test distance; control the positioning module on the mobile test platform to move the mobile test platform away from the lidar to a new distance point from the initial position with a preset distance step size, thereby constructing a test scenario for the lidar's performance indicators to be tested; the test unit 704 is further configured to: in the test scenario, control the lidar to collect multi-frame point cloud data of the target reflector on the mobile test platform, wherein the target reflector is a reflector with different reflectivities. Any one of the target reflectors; based on multi-frame point cloud data, determine the probability that the LiDAR detects the target reflector in the test scenario; in response to the probability being greater than a preset probability value, determine the new distance point as the initial position, and return to the following steps to begin execution: control the positioning module on the mobile test platform to move from the initial position to the new distance point in a preset distance step size, in order to construct a test scenario for the LiDAR's performance indicators to be tested, until the probability is less than or equal to the preset probability value, and determine the distance between the new distance point and the LiDAR as the actual maximum detection distance of the LiDAR; based on the actual maximum detection distance, determine the test result of the maximum distance detection indicator.

[0183] Optionally, the adjustment unit 703 is further configured to: determine a preset detection distance point for testing the echo intensity response characteristic index according to the echo intensity testing strategy; control the positioning module on the mobile test platform to move the mobile test platform to the preset detection distance point to construct a test scenario for the performance index to be tested of the lidar; the test unit 704 is further configured to: in the test scenario, control the lidar to collect point cloud data of reflectors with different reflectivities carried by the mobile test platform to obtain point cloud data corresponding to reflectors with different reflectivities; determine the echo intensity response value of the lidar to reflectors with different reflectivities based on the point cloud data corresponding to reflectors with different reflectivities; and determine the test result of the echo intensity response characteristic index of the lidar based on the echo intensity response value of the lidar to reflectors with different reflectivities.

[0184] Optionally, the test unit 704 is also used to: determine the reflectance intensity discrimination of the lidar based on the echo intensity response values ​​of the lidar to reflectors with different reflectivities, wherein the reflectance intensity discrimination is used to characterize the lidar's ability to identify reflectors with different reflectivities; and determine the test results of the lidar's echo intensity response characteristic index based on the reflectance intensity discrimination.

[0185] Optionally, the adjustment unit 703 is further configured to: determine multiple detection range points for testing the echo intensity distance attenuation characteristic index according to the echo intensity distance attenuation test strategy; control the positioning module on the mobile test platform to move the mobile test platform to the multiple detection range points in sequence to construct test scenarios corresponding to the multiple detection range points respectively; the test unit 704 is further configured to: control the lidar to collect point cloud data of the target reflector on the mobile test platform in the test scenarios corresponding to the multiple detection range points respectively, to obtain point cloud data corresponding to the multiple detection range points respectively, wherein the target reflector is any one of the reflectors with different reflectivities; determine the echo intensity response value of the lidar to the target reflector at the multiple detection range points based on the point cloud data corresponding to the multiple detection range points respectively; and determine the test result of the lidar's echo intensity distance attenuation characteristic index based on the echo intensity response value of the lidar to the target reflector at the multiple detection range points.

[0186] Optionally, the test unit 704 is also used to: determine the attenuation curve of the echo intensity response value of the lidar based on the echo intensity response value of the lidar to the target reflector at multiple detection distance points; and determine the test results of the lidar's echo intensity distance attenuation characteristic index based on the attenuation curve.

[0187] In the testing apparatus for the lidar described in this application, a testing strategy matching the performance index to be tested can be determined based on the lidar's performance index. Then, according to this testing strategy, the mobile testing platform of the lidar is adjusted to construct a testing scenario for testing the lidar's performance index. The performance index is then tested within this testing scenario to obtain the test results. In other words, the entire testing process requires no human intervention. A corresponding testing scenario can be constructed based on the testing strategy for the performance index to be tested, significantly improving the testing efficiency and accuracy of the lidar's performance index. This avoids the drawbacks of inaccurate positioning and time-consuming processes in traditional manual testing, thus solving the technical problem of low accuracy in lidar performance index testing.

[0188] Embodiments of this application also provide an electronic device, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the lidar testing methods of various embodiments of this application when it runs.

[0189] Embodiments of this application also provide a computer-readable storage medium, which includes a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to execute the lidar testing method of various embodiments of this application.

[0190] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the lidar testing methods of various embodiments of this application.

[0191] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the lidar testing method in various embodiments of this application.

[0192] The embodiments of this application also provide a computer program that, when executed by a processor, implements the lidar testing methods described in the various embodiments of this application.

[0193] Embodiments of this application also provide a vehicle for performing the lidar testing methods in various embodiments of this application.

[0194] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0195] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0196] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0197] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0198] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0199] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0200] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A testing method for lidar, characterized in that, include: In response to a test request from the lidar, at least one performance indicator to be tested for the lidar is determined, wherein the test request is used to request testing of the performance of the performance indicator to be tested. A test strategy matching the performance index to be tested is determined from the test strategy set of the lidar, wherein the test strategy set includes test strategies corresponding to various performance indices to be tested. According to the test strategy, the mobile test platform of the lidar is adjusted to construct the test scenario for the lidar's performance indicators to be tested. In the test scenario, the performance index to be tested is tested to obtain test results, wherein the test results are used to characterize the performance of the performance index to be tested.

2. The method according to claim 1, characterized in that, The performance index to be tested is at least one of the following: ranging accuracy index, maximum distance detection index, echo intensity response characteristic index, and echo intensity distance attenuation characteristic index. The ranging accuracy index is used to characterize the distance detection accuracy of the lidar, the maximum distance detection index is used to characterize the maximum distance that the lidar is allowed to detect, the echo intensity response characteristic index is used to characterize the lidar's ability to identify objects with different reflectivities, and the echo intensity distance attenuation characteristic index is used to characterize the lidar's stability in identifying objects with the same reflectivity at different detection distances.

3. The method according to claim 2, characterized in that, Determining a test strategy from the set of test strategies for the lidar that matches the performance index to be tested includes: In response to the performance index to be tested being the ranging accuracy index, a ranging test strategy matching the ranging accuracy index is determined from the test strategy set, wherein the ranging test strategy is used to characterize the distance detection accuracy of the lidar at multiple detection distance points; In response to the performance index to be tested being the maximum distance detection index, a maximum distance test strategy matching the maximum distance detection index is determined from the test strategy set, wherein the maximum distance test strategy is used to characterize the test of the actual maximum detection range of the lidar by adjusting the distance between the mobile test platform and the lidar; In response to the fact that the performance index to be tested is the echo intensity response characteristic index, an echo intensity test strategy matching the echo intensity response characteristic index is determined from the test strategy set, wherein the echo intensity test strategy is used to characterize the response value of the lidar to the echo intensity of reflectors with different reflectivities at a preset detection distance point. In response to the performance index to be tested being the echo intensity distance attenuation characteristic index, an echo intensity distance attenuation test strategy matching the echo intensity distance attenuation characteristic index is determined from the test strategy set. The echo intensity distance attenuation strategy is used to characterize the response value of the lidar to the echo intensity of a reflector with the same reflectivity at the plurality of detection distance points.

4. The method according to claim 3, characterized in that, The mobile testing platform carries reflectors with different reflectivities and a positioning module. In response to the performance indicator to be tested being the ranging accuracy indicator and the testing strategy being the ranging testing strategy, the mobile testing platform of the lidar is adjusted according to the testing strategy to construct a test scenario for the lidar's performance indicator to be tested, including: According to the ranging test strategy, the positioning module on the mobile test platform is controlled to move the mobile test platform to the locations of the multiple detection distance points in order of distance from the lidar from near to far, so as to construct test scenarios corresponding to the multiple detection distance points respectively, wherein the ambient temperature and ambient humidity in the test scenarios meet preset requirements.

5. The method according to claim 4, characterized in that, In the test scenario, the performance index to be tested is tested, and the test results are obtained, including: In the test scenarios corresponding to the multiple detection distance points, the lidar is controlled to collect point cloud data of the target reflector on the mobile test platform to obtain multiple sets of point cloud data, wherein the target reflector is any one of the reflectors with different reflectivities; Based on the multiple sets of point cloud data, the distance measurement values ​​between the center point of the target reflector and the lidar are determined in multiple test scenarios respectively. Based on the distance measurement values ​​and distance reference values ​​in multiple test scenarios, the test result of the ranging accuracy index is determined. The distance reference value is the distance between the center point of the target reflector and the lidar, measured by a reference ranging device in the test scenario. The test accuracy of the reference ranging device is higher than that of the lidar.

6. The method according to claim 3, characterized in that, The mobile testing platform carries reflectors with different reflectivities and a positioning module. In response to the performance indicator to be tested being the maximum distance detection indicator, and the testing strategy being the maximum distance testing strategy, the mobile testing platform of the lidar is adjusted according to the testing strategy to construct a test scenario for the lidar's performance indicator to be tested, including: According to the ranging test strategy, the nominal maximum test distance of the lidar is determined, wherein the nominal maximum test distance is the theoretical maximum detection distance of the lidar. Based on the nominal maximum test distance, determine the initial position of the mobile test platform; The positioning module on the mobile test platform is controlled to move the mobile test platform away from the lidar to a new distance point from the initial position with a preset distance step size, thereby constructing a test scenario for the lidar's performance index to be tested. In the test scenario, the performance index to be tested is tested, and the test results are obtained, including: In the test scenario, the lidar is controlled to acquire multi-frame point cloud data of the target reflector on the mobile test platform, wherein the target reflector is any one of the reflectors with different reflectivities; Based on the multi-frame point cloud data, the probability that the lidar detects the target reflector in the test scenario is determined. In response to the probability being greater than a preset probability value, the new distance point is determined as the initial position, and the following steps are executed: the positioning module on the mobile test platform is controlled to move the mobile test platform away from the lidar to the new distance point from the initial position with a preset distance step size, so as to construct a test scenario for the lidar's performance index to be tested, until the probability is less than or equal to the preset probability value, and the distance between the new distance point and the lidar is determined as the actual maximum detection distance of the lidar; Based on the actual maximum detection distance, the test results of the maximum distance detection index are determined.

7. The method according to claim 3, characterized in that, The mobile testing platform carries reflectors with different reflectivities and a positioning module. In response to the performance indicator to be tested being the echo intensity response characteristic indicator, and the testing strategy being the echo intensity testing strategy, the mobile testing platform of the lidar is adjusted according to the testing strategy to construct a test scenario for the lidar's performance indicator to be tested, and test results are obtained, including: According to the echo intensity testing strategy, a preset detection distance point is determined to test the echo intensity response characteristic index; The positioning module on the mobile test platform is controlled to move the mobile test platform to the preset detection distance point to construct a test scenario for the performance index to be tested of the lidar. In the test scenario, the performance index to be tested is tested, and the test results are obtained, including: In the test scenario, the lidar is controlled to collect point cloud data of reflectors with different reflectivities carried by the mobile test platform, so as to obtain point cloud data corresponding to reflectors with different reflectivities. Based on the point cloud data corresponding to reflectors with different reflectivities, the echo intensity response value of the lidar to reflectors with different reflectivities is determined. Based on the echo intensity response values ​​of the lidar to reflectors with different reflectivities, the test results of the echo intensity response characteristic index of the lidar are determined.

8. The method according to claim 7, characterized in that, Based on the echo intensity response values ​​of the lidar to reflectors with different reflectivities, the test results of the echo intensity response characteristic index of the lidar are determined, including: Based on the echo intensity response values ​​of the lidar to reflectors with different reflectivities, the reflection intensity discrimination of the lidar is determined, wherein the reflection intensity discrimination is used to characterize the lidar's ability to identify reflectors with different reflectivities. Based on the reflection intensity discrimination, the test results of the echo intensity response characteristic index of the lidar are determined.

9. The method according to claim 3, characterized in that, The mobile testing platform carries reflectors with different reflectivities and a positioning module. In response to the performance indicator to be tested being the echo intensity distance attenuation characteristic indicator, and the testing strategy being the echo intensity distance attenuation testing strategy, the mobile testing platform of the lidar is adjusted according to the testing strategy to construct a test scenario for the lidar's performance indicator to be tested, including: According to the echo intensity distance attenuation test strategy, determine the plurality of detection distance points for testing the echo intensity distance attenuation characteristic index; The positioning module on the mobile test platform is controlled to move the mobile test platform to the plurality of detection distance points in sequence, so as to construct the test scenarios corresponding to the plurality of detection distance points respectively; In the test scenario, the performance index to be tested is tested, and the test results are obtained, including: In the test scenarios corresponding to the multiple detection distance points, the lidar is controlled to collect point cloud data of the target reflector on the mobile test platform to obtain the point cloud data corresponding to the multiple detection distance points, wherein the target reflector is any one of the reflectors with different reflectivities; Based on the point cloud data corresponding to the plurality of detection distance points, the echo intensity response value of the lidar to the target reflector at the plurality of detection distance points is determined; Based on the echo intensity response values ​​of the lidar to the target reflector at multiple detection distance points, the test results of the lidar's echo intensity distance attenuation characteristic index are determined.

10. The method according to claim 9, characterized in that, Based on the echo intensity response values ​​of the lidar to the target reflector at multiple detection distance points, the test results of the lidar's echo intensity distance attenuation characteristic index are determined, including: Based on the echo intensity response values ​​of the lidar to the target reflector at the multiple detection distance points, the attenuation curve of the lidar's echo intensity response value is determined. Based on the attenuation curve, the test results of the echo intensity distance attenuation characteristic index of the lidar are determined.

11. A vehicle, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 10.

12. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the method according to any one of claims 1 to 10.

14. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 10.

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