A vehicle collision warning test method and system based on UWB

Abstract

This invention relates to the field of intelligent connected vehicle testing and verification technology, specifically to a vehicle collision warning testing method and system based on UWB. The method comprises the following steps: installing a UWB base station, a warning device under test, and a UWB tag on both the driver vehicle and the target vehicle; initializing the testing subsystem and starting the test to acquire measured UWB data and data from the warning device under test; preprocessing and merging the data to establish a fused dataset at the same time scale; using the measured UWB data as a benchmark, and based on the fused dataset, performing a comparative and quantitative evaluation of the performance of the warning device under test; iteratively completing the test and outputting the evaluation results. The system includes a driver vehicle subsystem, a target vehicle subsystem, and a testing subsystem, executing the steps of the method. This invention reduces the testing and maintenance costs of collision warning systems, improves the testing efficiency and accuracy of warning device performance, and provides reliable data support for the optimization of warning devices.

Description

Technical Field

[0001] This invention relates to the field of intelligent connected vehicle testing and verification technology, and in particular to a vehicle collision warning testing method and system based on UWB. Background Technology

[0002] In the field of intelligent driving, quantitatively acquiring distance measurement information for collision warning systems is a necessary step. How to quickly and accurately obtain distance measurement information is a key problem that collision warning systems need to solve. Common methods for acquiring distance measurement information include LiDAR, millimeter-wave radar, and visual cameras.

[0003] Millimeter-wave radar, used as a ground truth acquisition or evaluation input device, is deployed collaboratively by vehicle-mounted and roadside equipment to detect, measure speed, and track targets, and generates evaluation ground truth values ​​using fusion algorithms. The advantages of this approach are that millimeter-wave radar is relatively less affected by environmental factors such as rain, snow, fog, nighttime, and strong light, exhibiting strong anti-interference capabilities. Simultaneously, millimeter-wave radar has a long detection range, covering a wide area, making it suitable for dynamic target detection and evaluation in complex road environments. However, this approach typically requires the simultaneous deployment of vehicle-mounted and roadside equipment, resulting in a complex system structure and high costs for hardware investment, installation, debugging, and subsequent maintenance, hindering low-cost, rapid deployment and widespread application. Furthermore, in complex road environments, millimeter-wave signals may be interfered with by reflections from road surfaces, metal guardrails, and buildings, leading to false targets and target position drift, thus affecting the accuracy and reliability of the ground truth data.

[0004] Another existing approach is based on visual markers for testing, such as capturing images of pre-placed QR codes or other markings with a camera and using the visual recognition results as a reference value. This type of approach typically has advantages such as low cost, simple marker fabrication, readily available equipment, easy setup, and minimal modification to the testing environment. However, the range of truth values ​​obtained by this approach usually depends on the deployment range of the visual markers, making it difficult to meet the needs of long-distance, large-scale testing scenarios. Furthermore, visual markers are susceptible to occlusion, damage, changes in lighting, and inclement weather, leading to decreased stability and reliability in complex environments. Therefore, a low-cost, high-efficiency, and high-precision testing method is urgently needed for collision warning systems. Summary of the Invention

[0005] The purpose of this invention is to overcome the problems of system complexity, high cost, insufficient accuracy, and low efficiency in existing quantitative ranging and testing methods. It provides a vehicle collision warning testing method and system based on UWB (Ultra-Wideband), achieving ranging accuracy typically down to the centimeter level using UWB. Compared to millimeter-wave radar solutions, this invention has advantages such as lower system development difficulty, simpler algorithm implementation, and lower computational requirements. Furthermore, it offers advantages over visual markers, including stronger anti-interference capabilities and greater environmental adaptability.

[0006] In a first aspect, the present invention provides a vehicle collision warning test method based on UWB, comprising the following steps:

[0007] S1. Install a UWB base station and a test warning device on the vehicle, and install a UWB tag on the target vehicle; connect the UWB base station and the test warning device to the test subsystem through the communication interface; initialize the test subsystem; set the scenario parameters according to the test requirements.

[0008] Preferably, the test subsystem transmits data between the UWB base station and the early warning device under test through the communication interface; The initialization of the test subsystem includes: Configure the communication protocol parameters and data verification rules of the data receiving module; Configure the filtering algorithm parameters, time synchronization rules, and collision time calculation parameters of the data processing module.

[0009] The scenario parameters include at least the initial distance between the vehicle and the target vehicle, the initial speed, the trajectory, and acceleration / deceleration parameters.

[0010] S2. The vehicle and the target vehicle start the test according to the scenario parameters. The test subsystem simultaneously collects the measured UWB data obtained through the UWB base station and the warning device data generated by the operation of the warning device under test.

[0011] S3. The test subsystem parses and preprocesses the measured UWB data and the early warning device data based on the communication protocol, and aligns them after time synchronization to obtain a fused dataset.

[0012] Preferably, the parsing is based on a communication protocol, wherein the parsing process for the measured UWB data is as follows: The distance between the vehicle and the target vehicle at adjacent sampling times is determined using the measured UWB data. The real-time relative velocity is calculated based on the ratio of the distance difference to the time difference between adjacent sampling times. The approach speed is defined as the opposite of the real-time relative speed, based on the real-time relative speed. The collision time is calculated based on the distance and the approach speed.

[0013] Preferably, the preprocessing includes filtering and denoising the measured UWB data, smoothing the data, and performing CRC verification on the data from the early warning device.

[0014] Preferably, the alignment step includes: S31. Using the timestamp of the measured UWB data as a reference, the timestamp of the early warning device data is corrected by time offset; S32. Using the nearest neighbor matching method, for each time of the measured UWB data, find the output frame of the early warning device data that is closest in time distance; S33. When the time difference between the measured UWB data and the output frame of the closest early warning device data does not exceed the preset maximum error, it is determined that the measured UWB data and the early warning device data are effectively matched, and time alignment is completed; S34. Normalize the measured UWB data and the early warning device data to obtain a fused dataset.

[0015] S4. Using the measured UWB data as a benchmark, the data of the early warning device is compared and evaluated based on the fused dataset; after all the test requirements are completed iteratively, data collection is stopped; the test subsystem statistically analyzes and outputs the evaluation results.

[0016] Preferably, the comparative evaluation includes: The distance and velocity values ​​are compared with the measured UWB data and the data from the early warning device, and the error is calculated to evaluate the basic measurement accuracy of the early warning device under test. The collision risk obtained based on the measured UWB data is compared with the warning information output by the warning device to determine the accuracy of the warning from the device under test; the collision risk is calculated based on the collision time. Based on the warning signal from the warning device, the measured UWB data at the current moment are examined to verify the accuracy of the warning.

[0017] Preferably, the evaluation results are classified based on the scenario parameters and include at least data error statistics, early warning accuracy, missed alarm rate, and false alarm rate.

[0018] In a second aspect, the present invention provides a UWB-based vehicle collision warning testing system, comprising: The self-driving vehicle subsystem, mounted on the self-driving vehicle, includes a UWB base station, a device under test and warning, and a power supply module; the power supply module provides power to the device under test and warning. The target vehicle subsystem, mounted on the target vehicle, includes a UWB tag; The testing subsystem includes: The data receiving module is connected to the UWB base station and the early warning device under test through a communication interface, respectively, and is used to receive the measured UWB data output by the UWB base station and synchronously receive the early warning device data output by the early warning device under test. A data processing module, connected to the data receiving module, is used to process and fuse the received measured UWB data and the early warning device data to generate a fused dataset for evaluating the performance of the early warning device under test. The data statistical analysis module is connected to the data processing module, performs statistical analysis based on the fused dataset, and outputs evaluation results.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a UWB-based vehicle collision warning testing method and system. High-precision ranging and speed measurement is achieved by installing UWB base stations and UWB tags on both the driver and target vehicles. A warning device under test is installed on the driver vehicle, and data from the device and environmental target information are simultaneously collected during the test. A testing subsystem receives and processes the measured UWB data and the warning device data generated by the device under test, performing fusion and comparative analysis to evaluate the performance of the device. This method and system eliminate the need for complex joint calibration, reducing the implementation and maintenance costs of collision warning testing, and offering simple and convenient operation. The high-precision ranging and speed measurement based on UWB improves the testing accuracy of the warning device, providing reliable data support for its optimization. Real-time test result generation improves testing efficiency and shortens the testing cycle, making it applicable to product testing solutions for warning systems. Attached Figure Description

[0020] Figure 1 This is a flowchart of the testing method for an embodiment; Figure 2 This is a flowchart illustrating the data alignment process in an example. Figure 3 This is a schematic diagram of the test system configuration for an embodiment. Detailed Implementation

[0021] The present invention will be further described in detail below with reference to experimental examples and specific embodiments. However, this should not be construed as limiting the scope of the above-mentioned subject matter of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0022] Unless otherwise specified, the use of terms such as "upper," "lower," "left," "right," "center," "inner," and "outer" to indicate orientation or positional relationships in the description of specific embodiments of the present invention is based on the orientation or positional relationships shown in the accompanying drawings, or the orientation or positional relationship in which the product / equipment / device is typically placed during use. These terms are merely for the purpose of facilitating the description of the present invention or simplifying the description in specific embodiments, enabling those skilled in the art to quickly understand the solution, and do not indicate or imply that a particular device / component / element must have a specific orientation, or be constructed and operated in a specific positional relationship. Therefore, they should not be construed as limitations on the present invention.

[0023] Furthermore, the use of terms such as "horizontal," "vertical," "suspended," and "parallel" does not imply that the corresponding device / component / element must be absolutely horizontal, vertical, suspended, or parallel, but rather that it can be slightly tilted or have a deviation. For example, "horizontal" merely means that its direction is more horizontal relative to "vertical," not that the structure must be completely horizontal, but that it can be slightly tilted. Alternatively, it can be simplified to mean that the corresponding device / component / element, when set in a "horizontal," "vertical," "suspended," or "parallel" direction, can have an error / deviation of ±10% relative to the corresponding direction, more preferably within ±8%, more preferably within ±6%, more preferably within ±5%, and more preferably within ±4%. As long as the corresponding device / component / element is within the error / deviation range, it can still achieve its function in the present invention.

[0024] Furthermore, the use of terms such as "first," "second," and "third" in terminology is merely for distinguishing descriptions of identical or similar components and should not be interpreted as emphasizing or implying the relative importance of a particular component.

[0025] Furthermore, in the description of the embodiments of the present invention, "several", "more than", and "a number of" represent at least two. The number can be any number, such as 2, 3, 4, 5, 6, 7, 8, or 9, and can even exceed nine.

[0026] Furthermore, in the description of the technical solution of this invention, unless otherwise explicitly specified / limited / restricted, the terms "set up," "install," "connect," "link," "provided with," "laid out," and "arranged" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to common connection methods in the art, such as welding, riveting, bolting, and threaded connections. Such connections can be mechanical, electrical, or communication connections; they can be direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components.

[0027] Example 1 This embodiment provides a vehicle collision warning test method based on UWB, the flowchart of which is as follows: Figure 1 As shown, it includes the following steps: S1. Install a UWB base station and a test warning device on the vehicle, and install a UWB tag on the target vehicle; connect the UWB base station and the test warning device to the test subsystem through the communication interface; initialize the test subsystem; set the scenario parameters according to the test requirements.

[0028] (1) Equipment installation and commissioning Fix the UWB base station and the early warning device under test at the designated location on the vehicle, and install the UWB tag at the corresponding location on the target vehicle, ensuring unobstructed signal transmission between the UWB base station and the UWB tag, with no large metal obstacles blocking the signal. Connect a stable power supply module to the UWB devices and the early warning device under test on both the vehicle and the target vehicle, and complete the device power-on self-test. Both the vehicle and the target vehicle are powered by on-board power supply or a regulated power adapter. The operating voltage fluctuation of the UWB device is ≤±5%, ensuring the continuity of ranging.

[0029] In a single test, only the UWB device of this test system is enabled to avoid channel conflicts caused by other UWB devices in the same frequency band.

[0030] (2) Test subsystem parameter configuration Configure the communication protocol parameters (baud rate, data format) between UWB data and the data from the early warning device under test in the data receiving module, and set the data CRC (Cyclic Redundancy Check) verification parameter rules. In the data processing module, preset the filtering algorithm parameters (process noise covariance of Kalman filtering, observation noise covariance, sliding window size), spatiotemporal alignment time threshold (≤10ms), and collision time calculation parameters. Configure data recording trigger conditions (effective warning signal threshold, abnormal warning judgment criteria) in the vehicle under test warning device.

[0031] (3) Setting of test site and scene parameters The test site is a closed test field (such as a standard test track or intelligent connected vehicle test base), with a flat ground and no strong electromagnetic interference sources (such as high-voltage lines or high-power radar base stations). The ambient light, temperature and humidity are stable, avoiding significant attenuation of UWB signal propagation caused by extreme weather.

[0032] Based on the test requirements (such as straight-going rear-end collision, crossing intersections, etc.), set the initial distance, initial speed, trajectory and acceleration / deceleration parameters between the vehicle and the target vehicle, and mark the corresponding initial position in the closed test track.

[0033] S2. The vehicle and the target vehicle start the test according to the scenario parameters. The test subsystem simultaneously collects the measured UWB data obtained through the UWB base station and the warning device data generated by the operation of the warning device under test.

[0034] (1) System startup: The target vehicle's power supply system and the self-vehicle's power supply system are started sequentially to ensure that the UWB tag, UWB base station, and the warning device under test are started normally; the data receiving and recording function of the test subsystem is started, and the synchronization timing is started in the data receiving module of the test subsystem to ensure that the self-vehicle and the target vehicle's equipment are synchronized; the drivers of the self-vehicle and the target vehicle are notified to start vehicle movement according to the preset scenario parameters to start the test.

[0035] (2) Real-time data acquisition: The data receiving module obtains measured UWB data between UWB tags collected in real time by the UWB base station through the communication interface, including at least metadata such as timestamp, distance, and signal strength; Meanwhile, the data receiving module collects data from the on-board warning device in real time via a serial port. The warning device runs a warning algorithm in real time to generate warning information, which includes at least a timestamp, distance, speed, collision time, warning level, and warning status.

[0036] S3. The test subsystem parses and preprocesses the measured UWB data and the early warning device data based on the communication protocol, and aligns them after time synchronization to obtain a fused dataset.

[0037] (1) Data analysis and solution The testing subsystem collects measured UWB data and early warning device data through the communication interface, and performs protocol parsing on the collected data according to the data frame format, field definition and verification rules corresponding to each device.

[0038] Obtain the timestamp, the distance and speed corresponding to the sampling time of the timestamp, the collision time, the warning level, and the warning status from the data of the warning equipment.

[0039] The distance is obtained from the timestamp and the sampling time corresponding to that timestamp in the measured UWB data. To address potential random noise, multipath errors, and transient jump interference in the UWB ranging process, Kalman filtering is used to estimate the state of the distance sequence. A sliding window algorithm is then used for secondary smoothing to remove jump interference values, thus obtaining continuous and stable relative distance values. Further calculations are then performed based on these distance values. Specifically, the process and steps include the following: Obtain the raw data measured by the UWB base station at the k-th and the adjacent (k-1)-th sampling times, at the k-th sampling time. The distance between the vehicle and the target vehicle is The (k-1)th sampling time The distance between the vehicle and the target vehicle is Then the real-time relative velocity can be expressed as:

[0040] when When the distance between the two vehicles is decreasing, it indicates that they are approaching each other; when At this time, it indicates that the two vehicles are not approaching each other or are moving away from each other.

[0041] To calculate the collision time, the approach velocity is defined as the inverse of the real-time relative velocity:

[0042] The collision time at time k can be expressed as:

[0043] When the approach speed is 0 or less than 0, it indicates that there is no tendency for the target vehicle and the vehicle to approach further, and the test subsystem marks it as having no risk of collision.

[0044] Finally, the system outputs the relative velocity and collision time data at the k-th moment after filtering and smoothing, providing true data for the performance evaluation of the early warning device under test.

[0045] (2) Alignment after data time synchronization Based on timestamps, UWB processed data and early warning device data are spatiotemporally aligned to establish a fused dataset with a correspondence between "measured UWB data" and "early warning device data" at the same moment. The flowchart of the alignment after time synchronization is as follows: Figure 2 The steps include: S31. Using the timestamp of the measured UWB data as a reference, the timestamp of the early warning device data is corrected by time offset.

[0046] The testing subsystem performs spatiotemporal alignment of measured UWB data and early warning device data based on a unified timestamp. The measured UWB data obtained after Kalman filtering and smoothing is as follows:

[0047] in, This is the original timestamp corresponding to the i-th data item output by UWB. For the first Distance measured at any time For the first The relative velocity obtained after Kalman filtering and smoothing at each moment. For the first The collision time is calculated at each moment.

[0048] The processed data from the early warning equipment is as follows:

[0049] in, This is the original timestamp corresponding to the j-th data output by the early warning device under test. For the early warning equipment under test in the first Distance measured at any time For the early warning equipment under test in the first The speed measured at any time For the first The collision time is calculated at each moment. For the early warning equipment under test in the first The warning status information measured at all times, For the early warning equipment under test in the first The warning level information is measured at all times.

[0050] The warning status obtained from the warning equipment can be defined as:

[0051] The warning level obtained from the warning equipment can be defined as:

[0052] S32. Using the nearest neighbor matching method, for each measured UWB data moment, find the output frame of the early warning device data that is closest in time distance.

[0053] Using the timestamp of UWB data as a reference, the nearest neighbor matching method is used to find the corresponding data frame of the early warning device. For each measured UWB data moment, the frame with the closest time distance is found in the early warning device data.

[0054] in, This represents the timestamp corresponding to the i-th data item output by UWB. This represents the timestamp corresponding to the j-th data point output by the early warning device under test. Indicates the relationship with the first The index of the data point of the early warning device that is closest in time to the measured UWB data point.

[0055] S33. When the time difference between the measured UWB data and the output frame of the closest early warning device data does not exceed the preset maximum error, it is determined that the measured UWB data and the early warning device data are effectively matched, and time alignment is completed.

[0056] The UWB data is considered to be effectively matched with the early warning device data in time if the following conditions are met:

[0057] in, This represents the timestamp corresponding to the i-th data item output by UWB. Indicates the first output of UWB The timestamp corresponding to each data item This represents the index of the nearest warning data in the i-th frame of UWB data. Indicates the maximum permissible time alignment error. The sampling frequency can be set, for example, 0.05s or 0.1s.

[0058] S34. Normalize the measured UWB data and the early warning device data to obtain a fused dataset.

[0059] After completing time alignment, the system further unifies the units of measurement for the two types of data, establishing a fused dataset that maps "measured UWB data - early warning device data" at the same time point:

[0060] in, Let be the timestamp corresponding to the i-th data item after the given time. In the first Distance measured from UWB at any time For the first The relative velocity obtained after Kalman filtering and smoothing at each moment. For the first The collision time is calculated at each moment. This is a reference warning label generated based on the collision time threshold of measured UWB data. For the early warning equipment under test in the first Distance measured at any time For the early warning equipment under test in the first The speed measured at any time The actual warning level output by the device under test. This represents the actual warning status output by the device under test. For the first The collision time calculated by the monitoring and early warning device at each moment.

[0061] S4. Using the measured UWB data as a benchmark, the data of the early warning device is compared and evaluated based on the fused dataset; after all the test requirements are completed iteratively, data collection is stopped; the test subsystem statistically analyzes and outputs the evaluation results.

[0062] After acquiring the fused dataset Si, the testing subsystem performs comparative analysis on the data through the data analysis and statistics module. The specific comparison parameters and content include: (1) Extract the distance, relative speed, and approach speed obtained from the measured UWB data in the fusion dataset Si and the distance and speed output by the early warning device under test. Then, calculate the absolute and relative errors of the two in real time through the preset error calculation model to quantitatively evaluate the basic measurement accuracy of the early warning device under test.

[0063] (2) A dynamic collision risk benchmark is constructed based on the collision time derived from the measured UWB data, and it is logically compared with the warning information output by the warning device under test. The algorithm automatically traverses the full amount of data to accurately identify and mark abnormal events such as missed warnings (risk exists but not triggered), false warnings (no risk is falsely triggered), and warning timing deviations (time difference between the trigger time and the UWB time).

[0064] (3) Based on the collision warning signal of the device under test, reversely detect the distance, relative speed, approach speed and collision time of the measured UWB data volume at the current moment, and judge the accuracy of the warning of the device under test.

[0065] Test End and Result Output: After the vehicle and the target vehicle complete the preset scenario movement, the vehicle stops and the test warning device, UWB device and power supply system are turned off in sequence; the test subsystem stops data acquisition, and the data statistics and analysis module performs statistics on the data throughout the test, including key indicators such as test scenario parameters, data error statistics, warning accuracy, missed alarm rate and false alarm rate. Based on all test scenarios and requirements, multiple rounds of testing were completed iteratively. The statistical content shown in Tables 1 and 2 are exemplary results after anonymization, and are only used to illustrate that the present invention can output key evaluation indicators after the test, and do not constitute a limitation on the scope of protection of the present invention.

[0066] Table 1 Statistical Output Results of Key Indicators Scene Number Scene Model Data collection Time alignment Distance measurement error Speed ​​measurement error Early warning accuracy false alarm rate False alarm rate Overall Results S1 Approaching at a constant speed Finish efficient qualified qualified qualified Low Low pass S2 Accelerate approach Finish efficient qualified qualified qualified Low Low pass S3 Moving away at a constant speed Finish efficient qualified qualified qualified Low Low pass Table 2. Verification results of universality across different test scenarios Scene type Does it support data collection? Does it support error statistics? Does it support early warning statistics? Does it support report output? Generality evaluation Approaching at a constant speed yes yes yes yes Applicable Accelerate approach yes yes yes yes Applicable Moving away at a constant speed yes yes yes yes Applicable The actual test scenario, sample size, threshold parameters, statistical results, and evaluation level can be adjusted according to the type of early warning device under test, test standards, and application environment.

[0067] Example 2 This embodiment provides a vehicle collision warning test system based on UWB, as shown in the schematic diagram below. Figure 3 As shown, it includes: The self-driving vehicle subsystem, mounted on the self-driving vehicle, includes a UWB base station, a device under test and warning, and a power supply module; the power supply module provides power to the device under test and warning. The target vehicle subsystem, mounted on the target vehicle, includes a UWB tag; The testing subsystem includes: The data receiving module is connected to the UWB base station and the early warning device under test through a communication interface, respectively, and is used to receive the measured UWB data output by the UWB base station and synchronously receive the early warning device data output by the early warning device under test. A data processing module, connected to the data receiving module, is used to process and fuse the received measured UWB data and the early warning device data to generate a fused dataset for evaluating the performance of the early warning device under test. The data statistical analysis module is connected to the data processing module, performs statistical analysis based on the fused dataset, and outputs evaluation results.

[0068] The embodiments can be applied to the following scenarios: As a truth system for the intelligent driving R&D team, it is used for internal testing and data collection to optimize and iterate algorithms. During the R&D phase, closed-loop tests with multiple parameters and operating conditions are conducted on the algorithm prototype. By utilizing the high-precision ranging and speed measurement truth provided by UWB, the system accurately identifies the sources of error in the algorithm in target recognition, distance estimation, and risk prediction, providing quantitative data support for adjusting warning thresholds and optimizing collision time calculation models.

[0069] As an acceptance testing device for OEMs, it is used to evaluate and accept product indicators for intelligent driving. OEMs use this system to formulate unified acceptance testing specifications and conduct comparative tests on products from different suppliers under the same test scenarios. Through quantitative indicators such as warning accuracy, missed warning rate, false alarm rate, and data error output by the system, the system objectively evaluates whether the supplier's products meet the vehicle integration requirements.

[0070] As a testing device used by professional evaluation organizations, it is used to assess and compare the performance of different intelligent driving products. The accurate evaluation data output by the system can intuitively reflect the differences between different products in terms of ranging accuracy, warning timeliness, and environmental adaptability.

[0071] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A vehicle collision warning test method based on UWB, characterized in that, Includes the following steps: S1. Install UWB base stations and early warning devices on your own vehicle, and install UWB tags on the target vehicle; The UWB base station and the early warning device under test are connected to the test subsystem via a communication interface; the test subsystem is initialized; and scenario parameters are set according to test requirements. S2. The vehicle and the target vehicle start the test according to the scenario parameters. The test subsystem simultaneously collects the measured UWB data obtained through the UWB base station and the warning device data generated by the operation of the warning device under test. S3. The test subsystem parses and preprocesses the measured UWB data and the early warning device data based on the communication protocol, and aligns them after time synchronization to obtain a fused dataset. S4. Using the measured UWB data as a benchmark, compare and evaluate the data of the early warning device based on the fused dataset; After all the test requirements have been iterated through, data collection stops; the test subsystem then compiles and outputs the evaluation results.

2. The vehicle collision warning test method based on UWB according to claim 1, characterized in that, The test subsystem transmits data between the UWB base station and the early warning device under test through the communication interface.

3. The vehicle collision warning test method based on UWB according to claim 1, characterized in that, The initialization of the test subsystem in step S1 includes: Configure the communication protocol parameters and data verification rules of the data receiving module; Configure the filtering algorithm parameters, time synchronization rules, and collision time calculation parameters of the data processing module.

4. The vehicle collision warning test method based on UWB according to claim 1, characterized in that, The scene parameters mentioned in step S1 include at least the initial distance between the vehicle and the target vehicle, the initial speed, the trajectory, and the acceleration / deceleration parameters.

5. The vehicle collision warning test method based on UWB according to claim 1, characterized in that, The parsing described in step S3 is based on the communication protocol, and the parsing process for the measured UWB data is as follows: The distance between the vehicle and the target vehicle at adjacent sampling times is determined using the measured UWB data. The real-time relative velocity is calculated based on the ratio of the distance difference to the time difference between adjacent sampling times. According to the real-time relative speed, the approach speed is defined as the opposite of the real-time relative speed; The collision time is calculated based on the distance and the approach speed.

6. The vehicle collision warning test method based on UWB according to claim 1, characterized in that, The preprocessing in step S3 includes filtering and denoising the measured UWB data, smoothing the data, and performing CRC verification on the data from the early warning device.

7. The vehicle collision warning test method based on UWB according to claim 1, characterized in that, The alignment step described in step S3 includes: S31. Using the timestamp of the measured UWB data as a reference, the timestamp of the early warning device data is corrected by time offset; S32. Using the nearest neighbor matching method, for each time of the measured UWB data, find the output frame of the early warning device data that is closest in time distance; S33. When the time difference between the measured UWB data and the output frame of the closest early warning device data does not exceed the preset maximum error, it is determined that the measured UWB data and the early warning device data are effectively matched, and time alignment is completed; S34. Normalize the measured UWB data and the early warning device data to obtain a fused dataset.

8. The vehicle collision warning test method based on UWB according to claim 5, characterized in that, The comparative evaluation described in step S4 includes: The distance and velocity values ​​are compared with the measured UWB data and the data from the early warning device, and the error is calculated to evaluate the basic measurement accuracy of the early warning device under test. The collision risk obtained based on the measured UWB data is compared with the warning information output by the warning device to determine the accuracy of the warning from the device under test; the collision risk is calculated based on the collision time. Based on the warning signal from the warning device, the measured UWB data at the current moment are examined to verify the accuracy of the warning.

9. A vehicle collision warning test method based on UWB according to claim 1, characterized in that, The evaluation results in step S4 are classified based on the scenario parameters and include at least data error statistics, early warning accuracy, missed alarm rate, and false alarm rate.

10. A UWB-based vehicle collision warning test system, comprising executing the UWB-based vehicle collision warning test method according to any one of claims 1-9, characterized in that, include: The autonomous vehicle subsystem, installed on the autonomous vehicle, includes a UWB base station, a test and warning device, and a power supply module; The power supply module provides power to the early warning device under test; The target vehicle subsystem, mounted on the target vehicle, includes a UWB tag; The testing subsystem includes: The data receiving module is connected to the UWB base station and the early warning device under test through a communication interface, respectively, and is used to receive the measured UWB data output by the UWB base station and synchronously receive the early warning device data output by the early warning device under test. A data processing module, connected to the data receiving module, is used to process and fuse the received measured UWB data and the early warning device data to generate a fused dataset for evaluating the performance of the early warning device under test. The data statistical analysis module is connected to the data processing module, performs statistical analysis based on the fused dataset, and outputs evaluation results.

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