Dynamic target simulation and automatic analysis method, device, equipment, medium and program product

Abstract

The embodiment of the invention provides a dynamic target simulation and automatic analysis method and device, equipment, a medium and a program product, and the method comprises the steps: configuring simulation parameters of a dynamic target according to a test demand; controlling the electric platform bearing the target model to move according to the preset movement track and the preset speed curve; tracking the position of the electric platform in real time by adopting an ultra wide band positioning technology, recording a first timestamp, synchronously monitoring a vehicle CAN bus to record a second timestamp when an alarm signal is captured, and calculating alarm delay based on the synchronized first timestamp and second timestamp; generating a risk area detection thermodynamic diagram based on the corresponding relation between the track position of the electric platform tested for multiple times and the alarm signal; a high-precision positioning and CAN signal timestamp synchronization technology is adopted, a test conclusion is obtained through automatic analysis, a single test period is compressed, a quantifiable unified evaluation framework is established, and the efficiency is greatly improved.

Description

Technical Field

[0001] This application relates to the field of target simulation and analysis technology, and more specifically, to a dynamic target simulation and automated analysis method, apparatus, equipment, medium, and program product. Background Technology

[0002] Currently, testing of vehicle door opening warning (DOW) functions mainly relies on three technical solutions, but all have significant limitations: The static obstacle testing method, as the industry's basic solution, uses fixed cones or dummy models to simulate obstacles behind and to the side of the vehicle, verifying the basic detection capabilities of the DOW system. However, this method completely fails to reproduce the motion characteristics of dynamic targets in real traffic (such as vehicles accelerating towards the vehicle or motorcycles braking to avoid them), leading to test results that are out of sync with actual operating conditions. For example, when a vehicle approaches from behind at a high speed of 60 km / h, the static model cannot reflect the impact of speed changes on the system's response delay, causing a calibration deviation of over 30% in the alarm trigger distance.

[0003] While real-vehicle road testing can partially reflect dynamic scenarios, it requires coordinating real vehicles, drivers, and test site resources, resulting in high costs per test (including manpower, fuel and electricity consumption, and equipment wear and tear). More importantly, due to environmental interference (such as other road users intruding) and the inconsistency of human operation, the repeatability of test data is less than 60%. In particular, the measurement of millisecond-level indicators such as alarm delay relies on manual timing or visual judgment of target location, with an error range of ±0.5 seconds, which cannot meet the requirements for accurate functional verification.

[0004] Pure software simulation testing reduces hardware costs by simulating target motion using virtual sensor models, but it ignores the inherent limitations of physical sensors. Hardware characteristics such as millimeter-wave radar installation errors and camera calibration deviations are idealized in the simulation. The simulation results do not match real-vehicle testing sufficiently, making them unsuitable as acceptance criteria for mass-production systems.

[0005] Furthermore, existing solutions employ varying definitions and measurement methods for alarm delay and false alarm rate (e.g., using audible and visual alerts as alarm nodes or CAN signal triggering as the standard), making cross-sectional comparison of test results impossible. Most publicly available technical solutions focus on static target detection or forward collision warning in driving scenarios, lacking a systematic solution for dynamic trajectory control in scenarios where vehicles are approaching longitudinally from behind. This makes it difficult to accurately define the performance boundaries of the DOW system. Consequently, dynamic realism is lacking: static models cannot simulate vehicle acceleration / braking behaviors, resulting in a disconnect between test scenarios and actual traffic; testing efficiency is low: real-vehicle road tests consume significant resources, are lengthy, and have poor data repeatability.

[0006] Invention application content The purpose of this application is to provide a dynamic target simulation and automated analysis method, apparatus, equipment, medium, and program product to solve the problems of lack of dynamic realism and low efficiency in the current vehicle door opening warning function test.

[0007] In a first aspect, embodiments of this application provide a method for dynamic target simulation and automated analysis, the method comprising: Configure simulation parameters for the dynamic target according to the testing requirements; wherein, the simulation parameters include at least the target type, preset velocity curve, and preset motion trajectory; The electric platform carrying the target model is controlled to move according to the preset motion trajectory and preset speed curve; Ultra-wideband positioning technology is used to track the position of the electric platform in real time and record the first timestamp, while simultaneously monitoring the vehicle's CAN bus to record the second timestamp when an alarm signal is captured; Calculate the alarm delay based on the synchronized first and second timestamps; Based on the correspondence between the trajectory position of the electric platform and the alarm signal obtained from multiple tests, a heat map of the risk area is generated.

[0008] In the above implementation process, simulation parameters of the dynamic target are configured according to the test requirements; wherein, the simulation parameters include at least the target type, preset speed curve, and preset motion trajectory; the electric platform carrying the target model is controlled to move according to the preset motion trajectory and preset speed curve; ultra-wideband positioning technology is used to track the position of the electric platform in real time and record the first timestamp, and simultaneously monitor the vehicle CAN bus to record the second timestamp when an alarm signal is captured; the alarm delay is calculated based on the synchronized first timestamp and second timestamp; based on the correspondence between the electric platform trajectory position and the alarm signal in multiple tests, a risk area detection heat map is generated; high-precision positioning and CAN signal timestamp synchronization technology are used to obtain test conclusions through automated analysis, compressing the single test cycle and establishing a quantifiable unified evaluation framework, which greatly improves efficiency.

[0009] Furthermore, after calculating the alarm delay based on the synchronized first and second timestamps, the method further includes: It also records the raw data from the millimeter-wave radar of the vehicle under test simultaneously; The ultra-wideband positioning and CAN bus monitoring times are synchronized using the PTP time synchronization protocol.

[0010] In the above implementation process, automated timing synchronization replaces human error with millisecond-level measurement accuracy.

[0011] Furthermore, it also includes: When the test does not trigger an alarm, the raw data from the millimeter-wave radar and the ultra-wideband positioning data are analyzed to diagnose the cause of the missed alarm.

[0012] The above implementation process can be used to analyze the reasons for missed reports.

[0013] Furthermore, the electric platform controlling the target model moves according to the preset motion trajectory and preset speed curve, including: The electric platform carrying the target model drives the car to simulate the longitudinal approach of vehicles behind at different speeds, verifying the detection capability of the DOW door opening warning system under various conditions; among which, the behavior includes key scenarios such as acceleration, constant speed, and emergency braking.

[0014] In the above implementation process, the problem of dynamic distortion is avoided by restoring the real motion behavior.

[0015] Furthermore, the ultra-wideband positioning and CAN bus monitoring time are synchronized via the PTP time synchronization protocol, including: When a target enters the risk zone at a set distance to the side of the vehicle door, the system automatically aligns the ultra-wideband positioning time with the CAN bus monitoring time via PTP to ensure that the error between the first and second timestamps does not exceed the set time.

[0016] In the above implementation process, test conclusions are drawn through automated analysis, which compresses the single test cycle and greatly improves efficiency.

[0017] Furthermore, before generating the risk area detection heatmap based on the correspondence between the electric platform trajectory position and alarm signals from multiple tests, the process also includes: Configure the scenario; the scenario configuration includes setting parameters such as the target vehicle model, speed curve, and number of tests.

[0018] In the above implementation process, the accuracy of dynamic scene reproduction is improved, and the problem of test distortion is solved.

[0019] Furthermore, before generating the risk area detection heatmap based on the correspondence between the electric platform trajectory position and alarm signals from multiple tests, the process also includes: The system automatically calculates the average alarm delay and false alarm rate for a set number of tests and generates two types of visualization results.

[0020] In the above implementation process, intelligent data analysis enables fault tracing and promotes functional optimization.

[0021] Furthermore, after generating the risk area detection heatmap based on the correspondence between the electric platform trajectory position and alarm signals from multiple tests, the method further includes: Generate an alarm delay distribution map to show the delay variation trend at different speeds.

[0022] In the above implementation process, the results are intuitive and clear.

[0023] Secondly, embodiments of this application provide a dynamic target simulation and automated analysis device, the device comprising: The parameter configuration module is used to configure the simulation parameters of the dynamic target according to the test requirements; wherein, the simulation parameters include at least the target type, the preset velocity curve and the preset motion trajectory; The motion control module is used to control the electric platform carrying the target model to move according to the preset motion trajectory and preset speed curve. The time recording module is used to track the position of the electric platform in real time using ultra-wideband positioning technology and record a first timestamp, and simultaneously monitor the vehicle CAN bus to record a second timestamp when an alarm signal is captured. The ultra-wideband positioning and CAN bus monitoring times are synchronized through the PTP time synchronization protocol. The delay synchronization module is used to calculate the alarm delay based on the synchronized first timestamp and second timestamp; The graphics generation module is used to generate a heat map of the risk area based on the correspondence between the trajectory position of the electric platform and the alarm signal from multiple tests.

[0024] Thirdly, embodiments of this application provide an electronic device, including: The system includes a processor, a memory, and a bus. The processor is connected to the memory via the bus. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, they are used to implement the dynamic target simulation and automated analysis method described above.

[0025] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a server, implements the dynamic target simulation and automated analysis method described above.

[0026] Fifthly, embodiments of this application provide a computer program product, the computer program product including instructions, which, when executed by a computer, cause the computer to perform the method described above. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart illustrating a dynamic target simulation and automated analysis method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a dynamic target simulation and automated analysis device provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0029] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0030] It should be noted that in the description of this application, the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance. Furthermore, the step numbers in the text are only for the convenience of explaining the embodiments of this application and are not intended to limit the order in which the steps are performed. The methods provided in the embodiments of this application can be executed by related terminal devices, and the following description uses an integrated range extender as the execution subject.

[0031] Currently, testing of vehicle door opening warning (DOW) functions mainly relies on three types of technical solutions, but all of them have significant limitations: Static obstacle testing, a fundamental industry solution, uses fixed cones or dummy models to simulate obstacles behind and to the side of a vehicle, verifying the basic detection capabilities of the DOW system. However, this method completely fails to replicate the motion characteristics of dynamic targets in real traffic (such as vehicles accelerating to approach or motorcycles braking to avoid them), leading to test results that are out of sync with actual operating conditions. For example, when a vehicle approaches from behind at a high speed of 60 km / h, the static model cannot reflect the impact of speed changes on the system's response delay, causing a calibration deviation of over 30% in the alarm trigger distance.

[0032] While real-vehicle road testing can partially reflect dynamic scenarios, it requires coordinating real vehicles, drivers, and test site resources, resulting in high costs per test (including manpower, fuel and electricity consumption, and equipment wear and tear). More importantly, due to environmental interference (such as other road users intruding) and the inconsistency of human operation, the repeatability of test data is less than 60%. In particular, the measurement of millisecond-level indicators such as alarm delay relies on manual timing or visual judgment of target location, with an error range of ±0.5 seconds, which cannot meet the requirements for accurate functional verification.

[0033] Pure software simulation testing reduces hardware costs by simulating target motion using virtual sensor models, but it ignores the inherent limitations of physical sensors. Hardware characteristics such as millimeter-wave radar installation errors and camera calibration deviations are idealized in the simulation. The simulation results do not match real-vehicle testing sufficiently, making them unsuitable as acceptance criteria for mass-production systems.

[0034] Furthermore, existing solutions employ varying definitions and measurement methods for alarm delay and false alarm rate (e.g., using audible and visual alerts as alarm nodes or CAN signal triggering as the standard), making cross-sectional comparisons of test results impossible. Most publicly available technical solutions focus on static target detection or forward collision warning in driving scenarios, lacking a systematic solution for dynamic trajectory control in scenarios where vehicles are approaching longitudinally from behind. This makes it consistently difficult to accurately define the performance boundaries of the DOW system.

[0035] Key pain points summarized: 1. Lack of dynamic realism: Static models cannot simulate vehicle acceleration / braking behaviors, and the test scenarios are disconnected from actual traffic; 2. Low testing efficiency: Real vehicle road tests consume a lot of resources, have long cycles, and have poor data repeatability; 3. Ignoring hardware characteristics: Simulation schemes over-idealize sensor performance, resulting in significant deviations from real vehicle performance; 4. Fragmented evaluation standards: Core indicators lack unified measurement standards, hindering technological iteration and industry benchmarking.

[0036] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a dynamic target simulation and automated analysis method provided in an embodiment of this application. The dynamic target simulation and automated analysis method includes: 100. Configure simulation parameters for the dynamic target according to the test requirements; wherein the simulation parameters include at least the target type, preset velocity curve and preset motion trajectory.

[0037] Optionally, the target type is a vehicle, and the vehicle's preset speed curve and preset trajectory are controlled according to the test requirements. For example, the initial position of the vehicle is obtained, the vehicle is controlled to accelerate uniformly to a first speed within a first set time period, the vehicle is controlled to maintain the first speed within a second set time period, and the vehicle is controlled to decelerate to a stop within a third set time period. Alternatively, the initial position of the vehicle is obtained, and the vehicle is controlled to travel along a set path.

[0038] 200. Control the electric platform carrying the target model to move according to the preset motion trajectory and preset speed curve.

[0039] For example, the electric platform carrying the target model drives the car to simulate the longitudinal approach of a vehicle from behind at different speeds, verifying the detection capability of the DOW door opening warning system under various conditions; among which, the behavior includes key scenarios such as acceleration, constant speed, and emergency braking.

[0040] Understandably, a programmable electric vehicle platform is used to address the issue of static testing's inability to accurately reproduce the motion of real vehicles. This platform carries interchangeable vehicle models (cars, motorcycles) and runs along a track parallel to the vehicle being tested. Driven by a high-precision motor, the vehicle can precisely simulate the longitudinal approach of vehicles from behind at different speeds, including key scenarios such as acceleration, constant speed, and emergency braking. For example, when a motorcycle approaches at 30 km / h, the system controls the vehicle to move along a preset speed curve, ensuring a speed error of less than ±0.1 m / s. To handle complex traffic scenarios, the platform allows the vehicle to move along a set trajectory, simulating various motion trajectories of the target vehicle in reality, thus verifying the DOW system's detection capabilities under complex conditions.

[0041] 300. The electric platform is tracked in real time using ultra-wideband positioning technology and a first timestamp is recorded. Simultaneously, the vehicle's CAN bus is monitored to record a second timestamp when an alarm signal is captured.

[0042] The ultra-wideband positioning and CAN bus monitoring times are synchronized using the PTP time synchronization protocol.

[0043] Specifically, when a target enters the risk zone at a set distance to the side of the vehicle door, the system automatically aligns the ultra-wideband positioning time with the CAN bus monitoring time via PTP to ensure that the error between the first and second timestamps does not exceed the set time.

[0044] For example, a UWB positioning system, a CAN bus monitoring module, a time synchronization server, and a data recording and synchronization module are configured. The UWB positioning system includes: tags: installed on the electric platform to transmit position signals; base stations: fixed in the environment to receive tag signals and calculate position; and a positioning engine: running a positioning algorithm and outputting the platform's three-dimensional coordinates. The CAN bus monitoring module includes: a CAN interface: connecting to the vehicle bus via a CAN transceiver; and alarm signal capture: monitoring alarm frames for specific CAN IDs. The time synchronization server includes: a PTP master clock: such as a GPS-tamed rubidium atomic clock or a switch supporting IEEE 1588-2008; and a PTP slave clock: the UWB positioning engine and the CAN monitoring device act as slave clock nodes. The data recording and synchronization module includes: a timestamp recording unit: a high-precision timer; and data storage: local SD card or real-time upload to the server.

[0045] For example, PTP time synchronization: All devices, including UWB base stations, CAN listeners, and recording terminals, synchronize with the master clock via the PTP protocol, achieving sub-microsecond accuracy. Real-time positioning and CAN listening: The UWB positioning engine outputs the platform position at a fixed frequency, appending a PTP synchronization timestamp. When the CAN listening module captures an alarm signal, it records the current PTP timestamp. Data alignment and storage: UWB location data and CAN alarm events are correlated via timestamps and stored as a structured log.

[0046] 400. Calculate the alarm delay based on the synchronized first timestamp and second timestamp.

[0047] Optionally, the raw data of the millimeter-wave radar of the vehicle under test is also recorded simultaneously; when the test does not trigger an alarm, the raw data of the millimeter-wave radar and the ultra-wideband positioning data are analyzed to diagnose the cause of the missed alarm.

[0048] Understandably, alarm delay refers to the time difference between when the electric platform actually arrives at a certain location (the first timestamp recorded by UWB) and when the vehicle's CAN bus triggers an alarm signal (the second timestamp).

[0049] For example, before calculating the delay, it is necessary to ensure that the time of the UWB and CAN devices has been synchronized to the same clock source via PTP and that the synchronization error is negligible; sort the UWB location log and CAN alarm log by timestamp and match the data of the same event period; directly calculate the timestamp difference; calculate the mean and standard deviation of the delay of multiple alarm events to eliminate random errors.

[0050] Understandably, to eliminate human measurement errors, UWB (Ultra-Wideband) positioning technology is used to track the target vehicle's position in real time, achieving a positioning accuracy of ±1 cm. When the target enters the 2-meter risk zone to the side of the door, the system automatically aligns the DOW (Door-to-Wire) CAN bus alarm signal via Time Synchronization Protocol (PTP) to ensure that the timestamp error does not exceed 1 millisecond. For example, if the moment the target enters the risk zone is recorded by UWB as T1, and the moment the DOW triggers the CAN alarm signal is T2, the system directly calculates the alarm delay (T2-T1), replacing the traditional timer. Simultaneously, vehicle millimeter-wave radar data is recorded, which can be used to analyze the causes of missed alarms.

[0051] 500. Based on the correspondence between the trajectory position of the electric platform and the alarm signal obtained from multiple tests, a heat map of the risk area is generated.

[0052] For example, trajectory data acquisition: The dual-mode positioning module acquires the electric platform's location information in real time, including longitude, latitude, speed, and direction, and uploads the data to the cloud server in conjunction with the communication module. Alarm signal association: Alarm events such as vibration alarms, electronic fence intrusions, and abnormal power outages are bound to the timestamps and location coordinates of trajectory points to form an event-location association dataset. Data cleaning: The Douglas-Peucker algorithm is used to compress the trajectory and remove redundant points; a heuristic anomaly detection algorithm is used to filter out noise data such as positioning drift and sudden speed changes to ensure the trajectory matches the actual road conditions.

[0053] For example, the raw trajectory data is stored using the Hadoop Distributed File System, and the clustering results are stored in an HBase database. The DBSCAN or OPTICS algorithm is used to perform density clustering of the trajectory points to identify high-frequency stopping areas (such as charging stations and accident-prone road sections). Weights are assigned to different alarm types, such as a theft alarm weight of 0.8 and a boundary violation alarm weight of 0.5. A time decay factor, such as a 50% weight decay over 24 hours, is used to calculate the risk value of each clustered region. The risk values ​​are mapped to color gradients, such as low risk = green and high risk = red, and a hierarchical color-coded heatmap is generated using tools such as ECharts or FineBI.

[0054] Optionally, multi-source data fusion optimization can be employed. Static risk overlay: Integrating static information such as road grade, traffic flow, and historical accident data, a comprehensive risk heatmap is generated through weighted overlay. For example, due to high vehicle speeds and accident rates, the entrance area of ​​an elevated bridge can be overlaid with a static risk coefficient of 1.2. Real-time dynamic updates: New alarm data is processed in real time using a streaming computing framework, updating the heatmap every 5 minutes to ensure the timeliness of risk area detection.

[0055] Furthermore, before generating the risk area detection heatmap based on the correspondence between the electric platform trajectory position and alarm signals from multiple tests, the process also includes: Configure the scenario; the scenario configuration includes setting parameters such as the target vehicle model, speed curve, and number of tests.

[0056] The system automatically calculates the average alarm delay and false alarm rate for a set number of tests and generates two types of visualization results.

[0057] Furthermore, after generating the risk area detection heatmap based on the correspondence between the electric platform trajectory position and alarm signals from multiple tests, the method further includes: Generate an alarm delay distribution map to show the delay variation trend at different speeds.

[0058] For example, scenario configuration: set parameters such as target vehicle model, speed curve (e.g., "car at a constant speed of 40km / h"), and number of tests; automatic execution: after startup, the electric car runs along the planned trajectory, and the data acquisition system synchronously records UWB position, CAN signal, and radar data; intelligent report generation: the system automatically calculates the average alarm delay and false alarm rate of 20 tests and generates two types of visualization results: alarm delay distribution map: showing the delay change trend at different speeds; risk area heat map: using color depth to mark the detection blind spots of the DOW system (e.g., weak coverage 1.8-2.2 meters behind the car door).

[0059] As described above, this embodiment configures simulation parameters for a dynamic target based on testing requirements. These simulation parameters include at least the target type, a preset speed curve, and a preset motion trajectory. An electric platform carrying the target model is controlled to move according to the preset motion trajectory and speed curve. Ultra-wideband positioning technology is used to track the electric platform's position in real time and record a first timestamp, while simultaneously monitoring the vehicle's CAN bus to record a second timestamp upon capturing an alarm signal. An alarm delay is calculated based on the synchronized first and second timestamps. A risk area detection heatmap is generated based on the correspondence between the electric platform's trajectory position and the alarm signal from multiple tests. High-precision positioning and CAN signal timestamp synchronization technology are used to automatically analyze and derive test conclusions, compressing the single test cycle and establishing a quantifiable unified evaluation framework, significantly improving efficiency.

[0060] The technical effects of the application embodiments are achieved through the following mechanisms: In terms of the realism of dynamic scenes, the electric car accurately reproduces the acceleration, constant speed, and braking behavior of real vehicles through high-precision motor control (e.g., deceleration of -3m / s when braking at 30km / h). This solution addresses the limitation of static models in simulating speed changes. Improved measurement accuracy relies on millisecond-level time synchronization between UWB positioning and CAN signals, reducing alarm delay and measurement errors. Optimized testing efficiency is achieved through fully automated execution and batch analysis, with a closed-loop process controlling target movement, data acquisition, and report generation, compressing single-test time. Intelligent problem diagnosis, based on synchronously recorded data, quickly pinpoints the root cause of missed alarms, providing direct evidence for algorithm improvement. The dynamic target simulation platform recreates dynamic behavior, eliminating scene distortion; electrical synchronization mechanisms ensure accuracy, replacing manual measurement; automated software processes improve efficiency and shorten test cycles; intelligent data analysis enables fault tracing and drives functional optimization. Ultimately, a complete technology chain of "motion control - precise measurement - efficient diagnosis" is formed, establishing a high-reliability verification benchmark for DOW testing. This solution optimizes traditional testing pain points through a three-layer design: hardware motion control (recreating real-world scenarios), timing synchronization technology (precise measurement), and automated analysis (efficient diagnosis), ensuring reliable and quantifiable DOW functional test data is obtained at the lowest cost.

[0061] As mentioned above, in terms of dynamic scene reproduction, traditional static obstacle testing methods cannot simulate real vehicle movement behavior (such as emergency braking of a motorcycle at 60km / h), resulting in excessive deviations in alarm trigger distance calibration. This application's embodiment uses a programmable target motion platform to accurately reproduce complex trajectories such as acceleration, constant speed, and braking, improving the accuracy of dynamic scene reproduction and solving the problem of test distortion. Regarding testing efficiency and standardization, real-vehicle road tests rely on manual operation, with a single test taking over 15 minutes. Furthermore, inconsistent measurement methods (mixed use of audible and visual prompts and CAN signal triggers) prevent industry data from being benchmarked. This application's embodiment employs high-precision UWB positioning (±1cm) and CAN signal timestamp synchronization technology (error ≤1ms), deriving test conclusions through automated analysis, compressing the single test cycle to within 3 minutes, significantly improving efficiency, and establishing a quantifiable and unified evaluation framework.

[0062] The aforementioned breakthroughs bring three significant benefits: 1. Improved testing accuracy, with dynamic target trajectory control accuracy reaching ±0.1m / s, and alarm delay measurement error reduced from the traditional ±0.5s to ±0.05s, meeting functional requirements; 2. Unified use of CAN signal triggering as the alarm node benchmark, and generation of standard reports through automated tools, standardizing the definition of DOW test indicators; 3. Optimized cost efficiency, reducing the need for real-vehicle road testing by 90%, lowering the cost and cycle of a single test, and significantly accelerating the speed of functional development and iteration.

[0063] These achievements are directly driven by core technological innovations: 1. Programmable target platforms avoid dynamic distortion by reproducing real motion behavior; 2. Automated time synchronization replaces human error with millisecond-level measurement accuracy; 3. Standardized analysis processes output comparable data, which is more efficient than fragmented evaluation.

[0064] The embodiments of this application avoid to some extent the "static" and "empirical" defects of the prior art, and establish a high-confidence verification benchmark for DOW functionality.

[0065] Example 1: High-Speed ​​Car Approach Test: This example verifies the response performance of the DOW system to a high-speed approach scenario of a car approaching from behind. Before the test, the electric car was switched to a car model, and the motion parameters were set in the control software: the initial position was 20 meters behind the test vehicle, approaching longitudinally at a constant speed of 60 km / h, with a lateral distance of 1.5 meters. After the test started, the target car moved at a constant speed along the track, and the UWB positioning system (accuracy ±1 cm) tracked its position in real time. When the target entered the preset 2-meter risk zone (UWB coordinate trigger threshold), the DOW system issued an alarm signal via the CAN bus after 0.18 seconds. The test was repeated 20 times, with an average alarm delay of 0.18 ± 0.03 seconds and a false alarm rate of 0%. Through heat map analysis, the detection range of the DOW system completely covered the risk zone. This example demonstrates that the present invention can accurately quantify the alarm delay of high-speed moving targets, and the repeatability error is controlled at the millisecond level.

[0066] Example 2: Motorcycle Small Target Detection: This example tests the DOW system's ability to identify small targets. The target was switched to a motorcycle model, with motion parameters set to a constant speed of 40 km / h approaching at a lateral distance of 1.2 meters. Test results showed that in 5% of the tests (1 / 20 times), the target entered the risk zone without triggering an alarm. Analysis of the original point cloud data from the synchronously recorded millimeter-wave radar revealed that due to the motorcycle's small outline, the number of reflection points was only 30% of that of a car target, causing the detection algorithm to miss the target in some frames. This result reveals the system's bottleneck in detecting small targets and provides direct evidence for algorithm optimization.

[0067] The steps above are not strictly performed in the order described by the numbers; they should be understood as a whole.

[0068] Secondly, based on the above embodiments, Figure 2 This is a schematic diagram of a dynamic target simulation and automated analysis device provided in an embodiment of this application. (Reference) Figure 2 The dynamic target simulation and automated analysis device provided in this embodiment includes: a parameter configuration module 201, a motion control module 202, a time recording module 203, a delay synchronization module 204, and a graphics generation module 205.

[0069] The parameter configuration module 201 is used to configure the simulation parameters of the dynamic target according to the test requirements; wherein the simulation parameters include at least the target type, preset speed curve, and preset motion trajectory; the motion control module 202 is used to control the electric platform carrying the target model to move according to the preset motion trajectory and preset speed curve; the time recording module 203 is used to track the position of the electric platform in real time using ultra-wideband positioning technology and record a first timestamp, and simultaneously listen to the vehicle CAN bus to record a second timestamp when an alarm signal is captured, wherein the ultra-wideband positioning and CAN bus listening time are synchronized through the PTP time synchronization protocol; the delay synchronization module 204 is used to calculate the alarm delay based on the synchronized first timestamp and second timestamp; the graphics generation module 205 is used to generate a risk area detection heat map based on the correspondence between the electric platform trajectory position and the alarm signal from multiple tests.

[0070] As described above, this embodiment configures simulation parameters for a dynamic target based on testing requirements. These simulation parameters include at least the target type, a preset speed curve, and a preset motion trajectory. An electric platform carrying the target model is controlled to move according to the preset motion trajectory and speed curve. Ultra-wideband positioning technology is used to track the electric platform's position in real time and record a first timestamp, while simultaneously monitoring the vehicle's CAN bus to record a second timestamp upon capturing an alarm signal. The ultra-wideband positioning and CAN bus monitoring times are synchronized using the PTP time synchronization protocol. An alarm delay is calculated based on the synchronized first and second timestamps. A risk area detection heatmap is generated based on the correspondence between the electric platform's trajectory position and alarm signals from multiple tests. High-precision positioning and CAN signal timestamp synchronization technology are used to automatically analyze and derive test conclusions, compressing the single test cycle and establishing a quantifiable unified evaluation framework, significantly improving efficiency.

[0071] The dynamic target simulation and automated analysis device provided in this application embodiment can be used to execute the dynamic target simulation and automated analysis method provided in the above embodiment, and has corresponding functions and beneficial effects.

[0072] Thirdly, embodiments of this application also provide an electronic device that can integrate the dynamic target simulation and automated analysis device provided in embodiments of this application. Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. (Reference) Figure 3The electronic device includes: an input device 33, an output device 34, a memory 32, and one or more processors 31; the memory 32 is used to store one or more programs; when the one or more programs are executed by the one or more processors 31, the one or more processors 31 implement the dynamic target simulation and automated analysis method provided in the above embodiments. The input device 33, output device 34, memory 32, and processor 31 can be connected via a bus or other means. Figure 3 Taking the example of a connection between China and Israel via a bus.

[0073] The processor 31 executes various functional applications and data processing of the device by running software programs, instructions and modules stored in the memory 32, thereby realizing the above-mentioned dynamic target simulation and automated analysis method.

[0074] The electronic device provided above can be used to execute the dynamic target simulation and automated analysis method provided in the above embodiments, and has corresponding functions and beneficial effects.

[0075] Fourthly, embodiments of this application also provide a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program is running, it controls the device where the computer-readable storage medium is located to execute the dynamic target simulation and automated analysis method as described above, and can achieve the same beneficial effects.

[0076] Of course, the computer-executable instructions provided in the embodiments of this application are not limited to the dynamic target simulation and automated analysis method described above, but can also execute related operations in the dynamic target simulation and automated analysis method provided in any embodiment of this application.

[0077] Fifthly, embodiments of this application also provide a computer program product. The methods described in the various embodiments of this application can be implemented entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the various embodiments of this application are executed entirely or partially. The computer can be a general-purpose computer, a special-purpose computer, a computer network, network equipment, user equipment, core network equipment, OAM (Open Application Model), or other programmable devices.

[0078] The computer program or instructions may be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions may be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium may be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; or an optical medium, such as a digital video optical disc; or a semiconductor medium, such as a solid-state drive. The computer-readable storage medium may be a volatile or non-volatile storage medium, or may include both volatile and non-volatile types of storage media.

[0079] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0080] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0081] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they 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 a portion 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 an electronic 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 USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0082] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0083] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0084] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for dynamic target simulation and automated analysis, characterized in that, The method includes: Configure simulation parameters for the dynamic target according to the testing requirements; wherein, the simulation parameters include at least the target type, preset velocity curve, and preset motion trajectory; The electric platform carrying the target model is controlled to move according to the preset motion trajectory and preset speed curve; Ultra-wideband positioning technology is used to track the position of the electric platform in real time and record the first timestamp, while simultaneously monitoring the vehicle's CAN bus to record the second timestamp when an alarm signal is captured; Calculate the alarm delay based on the synchronized first and second timestamps; Based on the correspondence between the trajectory position of the electric platform and the alarm signal obtained from multiple tests, a heat map of the risk area is generated.

2. The dynamic target simulation and automated analysis method according to claim 1, characterized in that, After calculating the alarm delay based on the synchronized first and second timestamps, the method further includes: It also records the raw data from the millimeter-wave radar of the vehicle under test simultaneously; The ultra-wideband positioning and CAN bus monitoring times are synchronized using the PTP time synchronization protocol.

3. The dynamic target simulation and automated analysis method according to claim 2, characterized in that, Also includes: When the test does not trigger an alarm, the raw data from the millimeter-wave radar and the ultra-wideband positioning data are analyzed to diagnose the cause of the missed alarm.

4. The dynamic target simulation and automated analysis method according to claim 1, characterized in that, The electric platform controlling the target model moves according to the preset motion trajectory and preset speed curve, including: The electric platform carrying the target model drives the car to simulate the longitudinal approach of vehicles behind at different speeds, verifying the detection capability of the DOW door opening warning system under various conditions; among which, the behavior includes key scenarios such as acceleration, constant speed, and emergency braking.

5. The dynamic target simulation and automated analysis method according to claim 2, characterized in that, The ultra-wideband positioning and CAN bus monitoring time are synchronized via the PTP time synchronization protocol, including: When a target enters the risk zone at a set distance to the side of the vehicle door, the system automatically aligns the ultra-wideband positioning time with the CAN bus monitoring time via PTP to ensure that the error between the first and second timestamps does not exceed the set time.

6. The dynamic target simulation and automated analysis method according to claim 1, characterized in that, Before generating the risk area detection heatmap based on the correspondence between the electric platform trajectory position and alarm signals obtained from multiple tests, the following steps are also included: Configure the scenario; the scenario configuration includes setting parameters such as the target vehicle model, speed curve, and number of tests.

7. The dynamic target simulation and automated analysis method according to claim 1, characterized in that, Before generating the risk area detection heatmap based on the correspondence between the electric platform trajectory position and alarm signals obtained from multiple tests, the following steps are also included: The system automatically calculates the average alarm delay and false alarm rate for a set number of tests and generates two types of visualization results.

8. The dynamic target simulation and automated analysis method according to claim 1, characterized in that, After generating a risk area detection heatmap based on the correspondence between the electric platform trajectory position and alarm signals obtained from multiple tests, the method further includes: Generate an alarm delay distribution map to show the delay variation trend at different speeds.

9. A dynamic target simulation and automated analysis device, characterized in that, The device includes: The parameter configuration module is used to configure the simulation parameters of the dynamic target according to the test requirements; wherein, the simulation parameters include at least the target type, the preset velocity curve and the preset motion trajectory; The motion control module is used to control the electric platform carrying the target model to move according to the preset motion trajectory and preset speed curve. The time recording module is used to track the position of the electric platform in real time using ultra-wideband positioning technology and record a first timestamp, and simultaneously monitor the vehicle CAN bus to record a second timestamp when an alarm signal is captured. The ultra-wideband positioning and CAN bus monitoring times are synchronized through the PTP time synchronization protocol. The delay synchronization module is used to calculate the alarm delay based on the synchronized first timestamp and second timestamp; The graphics generation module is used to generate a heat map of the risk area based on the correspondence between the trajectory position of the electric platform and the alarm signal from multiple tests.

10. An electronic device, characterized in that, include: The processor, memory, and bus are provided, wherein the processor is connected to the memory via the bus, and the memory stores computer-readable instructions that, when executed by the processor, are used to implement the dynamic target simulation and automated analysis method as described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by the server, implements the dynamic target simulation and automated analysis method as described in any one of claims 1-8.

12. A computer program product, characterized in that, The computer program product includes instructions that, when executed by a computer, cause the computer to implement the dynamic target simulation and automated analysis method according to any one of claims 1-8.

Images