A vehicle data processing method and a computer storage medium
By constructing a kinematic model of the target obstacle and triggering the automatic emergency braking system, the problems of low efficiency, high cost, and high risk in real vehicle road testing are solved, and safe and efficient simulation testing is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GREAT WALL MOTOR CO LTD
- Filing Date
- 2026-02-06
- Publication Date
- 2026-06-05
AI Technical Summary
The functional testing and parameter calibration of existing automatic emergency braking systems mainly rely on real vehicle road testing, which has problems such as low testing efficiency, high cost, high risk and poor controllability.
By acquiring the test condition configuration parameters of the automatic emergency braking system, a kinematic model of the target obstacle is constructed, its motion state is calculated, and the automatic emergency braking system is triggered based on this state. The response is recorded and analyzed to achieve simulation testing.
It eliminates dependence on weather and location, improves testing safety and efficiency, ensures consistency of testing conditions, and lays the foundation for automated testing.
Smart Images
Figure CN122151806A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of intelligent driving technology for vehicles, and in particular to a vehicle data processing method and a computer storage medium. Background Technology
[0002] Currently, the functional testing and parameter calibration of automatic emergency braking systems mainly rely on real vehicle road testing.
[0003] Real-world road testing requires setting up real-world obstacles such as dummies and moving target vehicles in a specific location, and then operating the vehicle under test to complete the trigger verification. This traditional method has the following significant drawbacks: testing efficiency is low due to weather and location constraints; it requires expensive specialized equipment and manpower teams, resulting in high costs; high-risk scenarios are difficult to reproduce safely and stably; and the testing process has poor controllability, leading to low repeatability of results. Summary of the Invention
[0004] To address the aforementioned technical problems, this disclosure provides a vehicle data processing method and a computer storage medium.
[0005] A first aspect of this disclosure provides a vehicle data processing method, including: Obtain the configuration parameters of the test conditions for the automatic emergency braking system, including the type of test conditions and the initial state of the target obstacle; Based on the initial state of the target obstacle and the target kinematic model corresponding to the type of the test condition, the motion state of the target obstacle under the test condition is calculated. Based on the motion state of the target obstacle, the automatic emergency braking system is triggered to respond, and the response status of the automatic emergency braking system is recorded. The response was analyzed to obtain the simulation test results of the automatic emergency braking system.
[0006] In some embodiments of this disclosure, obtaining the configuration parameters for the test conditions of the automatic emergency braking system includes: In response to the tester's selection of test conditions on the configuration interface, the type of the test condition is determined; In response to the tester's configuration operation on the configuration interface for the initial state, the initial state of the target obstacle is obtained.
[0007] In some embodiments of this disclosure, calculating the motion state of the target obstacle under the test condition based on the initial state of the target obstacle and a target kinematic model corresponding to the type of the test condition includes: Construct kinematic models for various test conditions; Based on the type of test conditions, the target kinematic model is determined from the kinematic models corresponding to each test condition; Based on the initial state of the target obstacle and the target kinematic model, the state is updated according to a preset time interval to obtain the motion state of the target obstacle.
[0008] In some embodiments of this disclosure, the step of updating the state based on the initial state of the target obstacle and the target kinematic model according to a preset time interval to obtain the motion state of the target obstacle includes: When the target kinematic model is a preset static model, the longitudinal position of the target obstacle is maintained at the initial longitudinal distance, and the current velocity and acceleration of the target obstacle are set to zero; When the target kinematic model is a preset deceleration model, the current velocity and longitudinal position of the target obstacle as a function of time are calculated based on the initial velocity and constant deceleration of the target obstacle. When the target kinematic model is a preset traversal model, the longitudinal position of the target obstacle is updated based on the vehicle speed, and the lateral offset increases linearly with time. When the target kinematic model is a preset ghost-peek model, after a preset delay time, the lateral offset of the target obstacle is increased by a set speed, the longitudinal position of the target obstacle is updated based on the vehicle speed, and the moving speed after the target obstacle appears is set.
[0009] In some embodiments of this disclosure, triggering the automatic emergency braking system to respond based on the motion state of the target obstacle and recording the response status of the automatic emergency braking system includes: The motion state of the target obstacle is encapsulated into sensor messages according to a preset vehicle communication protocol format; The sensor message is sent to the automatic emergency braking system, and the automatic emergency braking system responds to the sensor message to obtain the response status of the automatic emergency braking system.
[0010] In some embodiments of this disclosure, the step of responding to the sensor message through the automatic emergency braking system to obtain the response status of the automatic emergency braking system includes: Based on the automatic emergency braking system's response to the sensor messages, a status message fed back by the automatic emergency braking system is obtained; The status message is parsed to obtain the response status of the automatic emergency braking system.
[0011] In some embodiments of this disclosure, the analysis of the response to obtain the simulation test results of the automatic emergency braking system includes: Based on the response, it is determined whether the response of the automatic emergency braking system meets the expected results; If the expected results are met, the test ends, test results are generated, or the motion state of the target obstacle is changed for the next test.
[0012] In some embodiments of this disclosure, determining whether the response of the automatic emergency braking system meets the expected result based on the response situation includes: If the response indicates that the automatic braking function is activated, then the automatic emergency braking system response is determined to be in line with expectations; or, Based on the motion state of the target obstacle and the response, the warning trigger distance is calculated. If the warning trigger distance is greater than a preset threshold, the automatic emergency braking system response is determined to meet the expected result.
[0013] In some embodiments of this disclosure, the method further includes: During simulation testing, at least one trigger threshold parameter of the automatic emergency braking system is adjusted each time. Based on the response data recorded from multiple simulation tests, the performance index scores of the automatic emergency braking system under different parameter combinations are calculated, and the parameter combination corresponding to the highest score is determined as the target parameter combination. The automatic emergency braking system is calibrated based on the target parameter combination.
[0014] A second aspect of this disclosure provides a vehicle data processing apparatus, comprising: The acquisition module is used to acquire the configuration parameters of the test conditions of the automatic emergency braking system. The configuration parameters include the type of test conditions and the initial state of the target obstacle. The calculation module is used to calculate the motion state of the target obstacle under the test condition based on the initial state of the target obstacle and the target kinematic model corresponding to the type of the test condition. The triggering module is used to trigger the automatic emergency braking system to respond based on the motion state of the target obstacle, and to record the response status of the automatic emergency braking system. The module is used to analyze the response and obtain the simulation test results of the automatic emergency braking system.
[0015] In some embodiments of this disclosure, when the acquisition module acquires the configuration parameters of the test conditions of the automatic emergency braking system, it is specifically used for: In response to the tester's selection of test conditions on the configuration interface, the type of the test condition is determined; In response to the tester's configuration operation on the configuration interface for the initial state, the initial state of the target obstacle is obtained.
[0016] In some embodiments of this disclosure, when the calculation module calculates the motion state of the target obstacle under the test condition based on the initial state of the target obstacle and the target kinematic model corresponding to the type of the test condition, it is specifically used for: Construct kinematic models for various test conditions; Based on the type of test conditions, the target kinematic model is determined from the kinematic models corresponding to each test condition; Based on the initial state of the target obstacle and the target kinematic model, the state is updated according to a preset time interval to obtain the motion state of the target obstacle.
[0017] In some embodiments of this disclosure, when the calculation module updates the state of the target obstacle according to a preset time interval based on the initial state of the target obstacle and the target kinematic model, it is specifically used for: When the target kinematic model is a preset static model, the longitudinal position of the target obstacle is maintained at the initial longitudinal distance, and the current velocity and acceleration of the target obstacle are set to zero; When the target kinematic model is a preset deceleration model, the current velocity and longitudinal position of the target obstacle as a function of time are calculated based on the initial velocity and constant deceleration of the target obstacle. When the target kinematic model is a preset traversal model, the longitudinal position of the target obstacle is updated based on the vehicle speed, and the lateral offset increases linearly with time. When the target kinematic model is a preset ghost-peek model, after a preset delay time, the lateral offset of the target obstacle is increased by a set speed, the longitudinal position of the target obstacle is updated based on the vehicle speed, and the moving speed after the target obstacle appears is set.
[0018] In some embodiments of this disclosure, when the triggering module triggers the automatic emergency braking system to respond based on the motion state of the target obstacle, and records the response of the automatic emergency braking system, it is specifically used for: The motion state of the target obstacle is encapsulated into sensor messages according to a preset vehicle communication protocol format; The sensor message is sent to the automatic emergency braking system, and the automatic emergency braking system responds to the sensor message to obtain the response status of the automatic emergency braking system.
[0019] In some embodiments of this disclosure, when the triggering module responds to the sensor message through the automatic emergency braking system and obtains the response status of the automatic emergency braking system, it is specifically used for: Based on the automatic emergency braking system's response to the sensor messages, a status message fed back by the automatic emergency braking system is obtained; The status message is parsed to obtain the response status of the automatic emergency braking system.
[0020] In some embodiments of this disclosure, when the obtaining module analyzes the response and obtains the simulation test results of the automatic emergency braking system, it is specifically used for: Based on the response, it is determined whether the response of the automatic emergency braking system meets the expected results; If the expected results are met, the test ends, test results are generated, or the motion state of the target obstacle is changed for the next test.
[0021] In some embodiments of this disclosure, when the obtaining module determines whether the response of the automatic emergency braking system meets the expected result based on the response situation, it is specifically used for: If the response indicates that the automatic braking function is activated, then the automatic emergency braking system response is determined to be in line with expectations; or, Based on the motion state of the target obstacle and the response, the warning trigger distance is calculated. If the warning trigger distance is greater than a preset threshold, the automatic emergency braking system response is determined to meet the expected result.
[0022] In some embodiments of this disclosure, the apparatus further includes: The calibration module is used to adjust at least one trigger threshold parameter of the automatic emergency braking system each time during simulation testing; calculate the performance index score of the automatic emergency braking system under different parameter combinations based on the response records of multiple simulation tests, determine the parameter combination corresponding to the highest score as the target parameter combination; and calibrate the automatic emergency braking system based on the target parameter combination.
[0023] A third aspect of this disclosure provides an electronic device, including: processor; Memory, used to store executable instructions; The processor is used to read executable instructions from memory and execute the executable instructions to implement the vehicle data processing method provided in the first aspect above.
[0024] A fourth aspect of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the vehicle data processing method provided in the first aspect.
[0025] A fifth aspect of this disclosure provides a computer program product comprising a computer program or instructions that, when executed by a processor, implement the vehicle data processing method of the first aspect described above.
[0026] A sixth aspect of this disclosure provides a vehicle that includes electronic equipment provided in the third aspect.
[0027] The technical solution provided in this disclosure has the following advantages: The vehicle data processing method and computer storage medium provided in this disclosure can acquire configuration parameters for the test conditions of an automatic emergency braking system. Based on the initial state of the target obstacle and the target kinematic model corresponding to the type of the test condition, the motion state of the target obstacle under the test condition is calculated. Further, based on the motion state of the target obstacle, the automatic emergency braking system is triggered to respond, and the response of the automatic emergency braking system is recorded. The response is then analyzed to obtain the simulation test results of the automatic emergency braking system. Therefore, by driving the motion of a virtual target obstacle through configuration parameters and triggering the automatic emergency braking system response based on the motion state of the target obstacle, simulation testing can eliminate dependence on weather, location, and expensive physical equipment, greatly improving the safety and efficiency of testing, ensuring a high degree of consistency of test conditions, and laying the foundation for automated testing. Attached Figure Description
[0028] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.
[0029] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0030] Figure 1 This is a flowchart of a vehicle data processing method provided in an embodiment of this disclosure; Figure 2 This is a flowchart of another vehicle data processing method provided in this embodiment of the disclosure; Figure 3 This is a flowchart of another vehicle data processing method provided in this disclosure embodiment; Figure 4 This is a schematic diagram of the structure of a vehicle data processing device provided in an embodiment of this disclosure; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation
[0031] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.
[0032] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.
[0033] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.
[0034] It should be noted that, in this document, relational terms such as "first" and "second" are used merely 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. Unless otherwise specified, 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 the element.
[0035] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".
[0036] Currently, the functional testing and parameter calibration of the Automatic Emergency Braking (AEB) system mainly rely on real vehicle road testing.
[0037] Real-world road testing requires setting up real-world obstacles such as dummies and moving target vehicles in a specific location, and then operating the vehicle under test to complete the trigger verification. This traditional method has the following significant drawbacks: testing efficiency is low due to weather and location constraints; it requires expensive specialized equipment and manpower, resulting in high costs; high-risk scenarios are difficult to reproduce safely and stably; and the testing process has poor controllability, leading to low repeatability of results. Therefore, this disclosure provides a vehicle data processing method, which will be described below with reference to specific embodiments.
[0038] Figure 1 This is a flowchart of a vehicle data processing method provided in an embodiment of the present disclosure. The method can be executed by a vehicle data processing device, which can be implemented in software and / or hardware. The vehicle data processing device can be configured in an electronic device, such as a server or terminal, wherein the terminal specifically includes an in-vehicle terminal, a computer, or a tablet computer, etc.
[0039] like Figure 1 As shown, the vehicle data processing method provided in this disclosure can be applied to the field of intelligent driving technology. For example, it can be used to process message content. The vehicle data processing method may include the following steps: S110. Obtain the configuration parameters of the test condition of the automatic emergency braking system. The configuration parameters include the type of test condition and the initial state of the target obstacle.
[0040] In this embodiment of the disclosure, the electronic device can acquire configuration parameters for the test conditions of the automatic emergency braking system. This is the initialization phase of the test, the purpose of which is to set clear input conditions for the simulation. Optionally, the configuration parameters include the type of test condition and the initial state of the target obstacle. The type of test condition may include a stationary target condition, a decelerating target condition, a target crossing condition, and a "ghosting" obstacle condition, and is not limited thereto. The initial state of the target obstacle may include initial longitudinal distance, initial velocity, acceleration, etc., and is not limited thereto.
[0041] In some embodiments, S110 includes, but is not limited to, S1101 and S1102: S1101. In response to the tester's selection of test conditions on the configuration interface, determine the type of test condition.
[0042] In this step, the tester can select a test condition on the configuration interface, such as a stationary target condition. The electronic device responds to the tester's selection of the test condition on the configuration interface and determines the type of test condition. Optionally, the configuration interface can be a graphical interface (Panel). Controls on the interface (such as radio buttons and drop-down menus) are bound to variables in the script. For example, the tester sees a radio button group called "Select Test Condition" on the Panel, which contains options such as "0 - Stationary Condition" and "1 - Deceleration Condition". When the tester clicks "0 - Stationary Condition", this operation triggers an event that writes the value (0) selected by the graphical control to the global variable "Test Condition Type" in the script, thus completing the determination of the test condition type.
[0043] In some embodiments, the Panel includes operating condition selection controls, numerical input controls, control buttons, etc.
[0044] The operating condition selection control is set as a radio button group to ensure that only one operating condition can be selected at a time, preventing conflicts. The displayed content clearly indicates the correspondence between the number and name, facilitating the matching of the operating condition type with the CAPL script during debugging. The operating condition type (scenarioType) is bound to a global variable to achieve bidirectional synchronization between the interface and script data. For example, 0 - stationary operating condition, 1 - deceleration operating condition, 2 - crossing operating condition, 3 - ghost-protruding model. The numerical input control allows setting the initial longitudinal distance, the initial velocity of the preceding vehicle, as well as acceleration and overlap rate. Control buttons include a start simulation button and a stop simulation button.
[0045] S1102. In response to the tester's configuration operation on the configuration interface for the initial state, the initial state of the target obstacle is obtained.
[0046] In this step, the tester can configure the initial state on the configuration interface. The electronic device responds to the tester's configuration operation on the configuration interface to obtain the initial state of the target obstacle.
[0047] The graphical configuration interface makes parameter acquisition intuitive and easy to operate, allowing test scenario construction. Testers can quickly select and configure different operating conditions without writing code, significantly reducing the technical threshold and time cost of test preparation, and improving the flexibility and ease of use of testing.
[0048] S120. Based on the initial state of the target obstacle and the target kinematic model corresponding to the type of test condition, calculate the motion state of the target obstacle under the test condition.
[0049] In this embodiment of the disclosure, after obtaining the initial state of the target obstacle and the type of test condition, the electronic device can calculate the motion state of the target obstacle under the test condition based on the initial state of the target obstacle and the target kinematic model corresponding to the type of test condition. That is, according to the physical laws defined by the target kinematic model, the state of the target obstacle at each moment can be deduced.
[0050] S130. Based on the motion state of the target obstacle, trigger the automatic emergency braking system to respond and record the response status of the automatic emergency braking system.
[0051] In this embodiment of the disclosure, the electronic device can trigger an automatic emergency braking system to respond based on the movement state of the target obstacle, and record the response status of the automatic emergency braking system. Optionally, the response status may include whether the response was successful, the speed of the response, etc. For example, the status may change from "0 - no warning" to "2 - braking preparation" and finally to "3 - automatic braking activated," and these state change sequences may be recorded.
[0052] S140. Analyze the response to obtain the simulation test results of the automatic emergency braking system.
[0053] In this step, the electronic device can analyze the response to obtain the simulation test results of the automatic emergency braking system. Specifically, by analyzing the recorded response, when the braking function is detected to be correctly triggered, the actual distance between the vehicle and the virtual target from the start of the simulation to the moment of braking activation can be calculated and recorded as the warning trigger distance. Finally, the simulation test results are obtained, which may include: test conditions, parameter configuration, whether braking was successfully triggered, and the distance and speed at the time of triggering.
[0054] Therefore, in this embodiment, configuration parameters for the test conditions of the automatic emergency braking system can be obtained. Based on the initial state of the target obstacle and the target kinematic model corresponding to the type of test condition, the motion state of the target obstacle under the test condition can be calculated. Furthermore, based on the motion state of the target obstacle, the automatic emergency braking system is triggered to respond, and the response of the automatic emergency braking system is recorded. The response is then analyzed to obtain the simulation test results of the automatic emergency braking system. Thus, by driving the motion of a virtual target obstacle through configuration parameters and triggering the automatic emergency braking system response based on the motion state of the target obstacle, simulation testing can eliminate dependence on weather, location, and expensive physical equipment, greatly improving the safety and efficiency of testing, and ensuring a high degree of consistency in test conditions, laying the foundation for automated testing.
[0055] Figure 2 This is a flowchart of another vehicle data processing method provided in this embodiment.
[0056] like Figure 2 As shown, the vehicle data processing method may include the following steps: S310. Obtain the configuration parameters of the test condition of the automatic emergency braking system. The configuration parameters include the type of test condition and the initial state of the target obstacle.
[0057] Specifically, the implementation process and principle of S310 and S110 are the same, and will not be repeated here.
[0058] S320. Construct kinematic models corresponding to various test conditions.
[0059] In this step, the electronic device can construct kinematic models corresponding to various test conditions. Specifically, testers can use CAPL scripts to write high-precision vehicle dynamics models, and the electronic device responds to the testers' writing operations by constructing kinematic models corresponding to various test conditions. Specifically, the logic for different models is defined in the script header or function library. For example, the core algorithm of the "deceleration model" is: current speed = target initial speed + target acceleration * elapsed time; current longitudinal position = initial longitudinal position - integral over current speed. These mathematical relationships are encoded in the corresponding functions or conditional branches.
[0060] S330. Based on the type of test condition, determine the target kinematic model from the kinematic models corresponding to various test conditions.
[0061] In this step, after determining the type of test condition, the electronic device can determine the target kinematic model from the kinematic models corresponding to various test conditions. For example, if the test condition is a traverse condition, the corresponding target kinematic model is a preset traverse model.
[0062] S340. Based on the initial state of the target obstacle and the target kinematic model, the state is updated according to a preset time period to obtain the motion state of the target obstacle.
[0063] In this step, the electronic device can update the state of the target obstacle according to a preset time interval based on the initial state of the target obstacle and the target kinematic model. The motion state includes the current velocity, current acceleration, overlap rate, etc.
[0064] In some embodiments, S340 may include S3401, S3402, S3403, and S3404: S3401. When the target kinematic model is a preset static model, the longitudinal position of the target obstacle is maintained at the initial longitudinal distance, and the current velocity and acceleration of the target obstacle are set to zero. In this step, when the target kinematic model is a preset static model, the velocity and acceleration of the target obstacle are both 0, and its position remains unchanged. Specifically, the target kinematic model is determined to be a "preset static model". According to the logic of this model, at each preset time interval (e.g., 10 milliseconds) after the simulation starts, the motion state of the target obstacle is calculated: the longitudinal position is always 50 meters, the velocity is always 0 m / s, and the acceleration is always 0 m / s², allowing for continuous periodic state updates.
[0065] The modeling strategy for the pre-defined static model is as follows: the longitudinal position is fixed at an initial distance (e.g., 50 meters) and will not change over time; the target velocity is always 0 m / s and the acceleration is also 0 m / s², indicating no power or braking action. Applicable scenario: Simulating a stationary obstacle (such as a parked vehicle or roadblock) suddenly appearing in front of a vehicle traveling at a constant speed, used to test whether AEB can identify and brake in time.
[0066] Optionally, the preset static model can be represented as: current longitudinal position = initial longitudinal distance, current velocity = 0, current acceleration = 0.
[0067] S3402. When the target kinematic model is a preset deceleration model, calculate the current velocity and longitudinal position of the target obstacle over time based on the initial velocity and constant deceleration of the target obstacle. This step refines the calculation logic of the "deceleration model". For example, the configuration parameters are: initial longitudinal distance = 100 meters, initial target speed = 20 m / s, target acceleration = -4 m / s². In the first 10-millisecond cycle, the following are calculated: running time = 0.01 seconds; current speed = 20 + (-4) * 0.01 = 19.96 m / s; current longitudinal position = 100 - 19.96 * 0.01 ≈ 99.8004 meters. In the next cycle, the running time = 0.02 seconds, and so on, thus achieving a smooth and continuous change in the target's position and speed. For example, if the vehicle in front decelerates from 20 m / s to -4 m / s², and the vehicle follows with the same initial speed, it verifies whether the AEB can trigger a warning 2 seconds before a collision.
[0068] The modeling strategy for the preset deceleration model is as follows: the initial target velocity is given by the target initial velocity targetInitSpeed; uniform deceleration is performed using the target acceleration targetAccel (negative value); the current velocity is calculated as: v(t) = v0 + a·t; the longitudinal position is updated by integrating the velocity over time (discretized to update the displacement every 10ms); the acceleration output remains constant. Applicable scenario: Simulating sudden deceleration of the vehicle in front to verify whether the AEB system can determine the collision risk and intervene with braking based on relative velocity.
[0069] Optionally, the preset deceleration model can be expressed as: Current velocity = target initial velocity + target acceleration * (time / 1000), = current longitudinal position = current velocity * time integral, current acceleration = target acceleration.
[0070] S3403. When the target kinematic model is a preset traversal model, the longitudinal position of the target obstacle is updated based on the vehicle speed, and the lateral offset increases linearly with time. In this step, if the target kinematic model is a preset traversing model, the longitudinal position of the target obstacle is updated based on the vehicle's speed, and the lateral offset increases linearly with time. The lateral offset of the target obstacle increases linearly with time (e.g., 0.5 m / s), and the longitudinal position is calculated based on the vehicle's speed. For example, if the vehicle is traveling at 15 m / s and a virtual pedestrian cuts laterally from 2 meters to the left at 0.5 m / s, the AEB's ability to detect laterally moving targets is tested.
[0071] The default modeling strategy for the traversing model is: the longitudinal position moves backward relative to the vehicle's forward movement: current longitudinal position = initial distance. The vehicle's velocity *t* represents the target's apparent backward movement due to its forward motion, even though it appears to be stationary in the longitudinal direction relative to the ground coordinate system. Lateral offset increases linearly with time (simulating a constant lateral velocity of 0.5 m / s). The target's composite velocity considers the vector combination of the vehicle's longitudinal and lateral velocities. Acceleration is set to 0, simulating only uniform lateral movement. Applicable scenarios: Simulating dangerous "side intrusion" scenarios to test the system's detection and response capabilities to laterally moving targets.
[0072] Optionally, the preset crossover model can be represented as: current longitudinal position = initial longitudinal distance - vehicle speed * (time / 1000), lateral offset = 0.5 * time integral, current speed = square root of (vehicle speed squared + (lateral offset > 1.0? If so, take 0.5; otherwise, take 0) squared), current acceleration = 0.
[0073] S3404. When the target kinematic model is the preset ghost-peek model, after a preset delay time, the lateral offset of the target obstacle is increased by a set speed, the longitudinal position of the target obstacle is updated based on the vehicle speed, and the moving speed after the target obstacle appears is set.
[0074] The pre-set "ghost peek" model needs to simulate two characteristics: "delayed appearance" and "sudden rapid movement," involving conditional judgment and time control. In the first two seconds of the simulation, the target's lateral offset and speed are both zero, and the simulation is completely obscured. From the second second onwards, the lateral offset increases at a rate of 1 meter per second, while the longitudinal position is updated according to the vehicle's speed (30 meters - 8 meters / second * time), with the target speed set at 5 meters / second. The simulation then simulates a pedestrian suddenly rushing out from the roadside and quickly crossing. For example, the vehicle is traveling at 20 m / s, and two seconds later, a pedestrian 20 meters ahead rushes out laterally at 1 m / s, verifying the performance of AEB (Automatic Emergency Braking) in triggering braking within a very short time.
[0075] The default modeling strategy for the "ghost peek" model is as follows: For the first 2 seconds, the lateral offset remains unchanged (i.e., the target has not yet appeared); after 2 seconds, it begins to rapidly cut laterally at a relatively high speed of 1.0 m / s (faster than traversing the scene); the vertical position is still calculated based on the vehicle's motion: current vertical position = initial distance. Vehicle speed t; after appearing, the target speed is set to a fixed value of 5m / s (approximately 18km / h, equivalent to a running pedestrian), with zero acceleration; no valid data is sent in the initial stage to create the effect of "sudden appearance". Applicable scenario: extreme emergency situation testing to verify whether AEB can complete the perception-decision-execution closed loop in a very short time.
[0076] Optionally, the preset ghost peek model can be expressed as follows: If (time / 1000) > 2.0, then the lateral offset = 1.0 * time integral; the current longitudinal position = initial longitudinal distance - vehicle speed * (time / 1000); the current speed = lateral offset > 0? If yes, take 5, otherwise take 0; the current acceleration = 0.
[0077] This embodiment defines the state update logic for four typical hazardous operating conditions (stationary, deceleration, crossing, and ghosting). This explicit modeling strategy enables high-risk scenarios to be safely, stably, and reproducibly reproduced in the laboratory. For example, the "ghosting" model accurately simulates sudden situations through delayed triggering, effectively testing the response limits and robustness of the AEB system under extreme conditions, which is difficult or extremely risky to achieve in real-vehicle testing.
[0078] S350 encapsulates the motion state of the target obstacle into sensor messages according to a preset vehicle communication protocol format.
[0079] In this step, the electronic device encapsulates the motion state of the target obstacle into a sensor message according to a preset vehicle communication protocol format. Specifically, the calculated motion state data must be converted into a language that the tested AEB controller can recognize, i.e., a sensor message conforming to a specific format (such as a CAN message). This step is the bridge connecting virtual simulation and real hardware. For example, a CAN message object is defined, such as radar target_01. At the end of the update function triggered by the timer, an assignment operation is performed: radar target_01.byte(0) = 1 (target valid); radar target_01.floating-point(1) = current longitudinal position (50.0); radar target_01.floating-point(9) = current speed (5.0). Other fields of the message (such as lateral distance and target type) are also filled according to the rules. This completes the encapsulation of a frame of virtual radar message. When the virtual obstacle has a longitudinal distance of 50 meters, a lateral offset of 0.5 meters, and a speed of 5 m / s, the message is encapsulated and sent to the CAN network.
[0080] S360: Send the sensor message to the automatic emergency braking system, and obtain the response status of the automatic emergency braking system by responding to the sensor message.
[0081] In this step, the electronic device sends sensor messages to the automatic emergency braking system (AEB). The AEB responds to these messages to determine its status. Specifically, the encapsulated message is injected into the vehicle's CAN network or the input port of the AEB controller via a software or hardware interface, allowing it to be received and processed by the controller.
[0082] In some embodiments, S360 responds to the sensor message through the automatic emergency braking system to obtain the response status of the automatic emergency braking system, including S3601 and S3602: S3601. Based on the automatic emergency braking system, respond to the sensor messages to obtain the status message fed back by the automatic emergency braking system.
[0083] In this embodiment, the electronic device can respond to sensor messages based on the automatic emergency braking system (AEB) to obtain the status message fed back by the AEB system. Specifically, the AEB controller can respond based on the virtual target information in the sensor message, generate a feedback status message, and output the status message via the CAN bus. The electronic device can listen to the messages to obtain the status message fed back by the automatic emergency braking system.
[0084] S3602. Parse the status message to obtain the response status of the automatic emergency braking system.
[0085] After receiving the status message, the electronic device can parse it to obtain the response status of the automatic emergency braking system. The feedback status message contains multiple pieces of information, and it is necessary to parse out the specific fields that are useful for the assessment.
[0086] This embodiment achieves real-time capture and digital recording of system response behavior by listening to and parsing status messages (such as warning level and braking status) fed back by the AEB system. This forms a complete "stimulus-response" closed loop, making the testing process monitorable and analyzable, and providing an accurate data foundation for subsequent performance evaluation and problem localization.
[0087] S370. Analyze the response to obtain the simulation test results of the automatic emergency braking system.
[0088] Specifically, the implementation process and principle of S370 and S140 are the same, and will not be repeated here.
[0089] This embodiment of the disclosure acquires configuration parameters for the test conditions of the automatic emergency braking system, including the type of test condition and the initial state of the target obstacle, and constructs kinematic models corresponding to various test conditions. Then, based on the type of test condition, a target kinematic model is determined from the kinematic models corresponding to various test conditions. Based on the initial state of the target obstacle and the target kinematic model, the state is updated according to a preset time interval to obtain the motion state of the target obstacle. Further, the motion state of the target obstacle is encapsulated into sensor messages according to a preset vehicle communication protocol format and sent to the automatic emergency braking system. The automatic emergency braking system responds to the sensor messages to obtain the response status of the automatic emergency braking system. The response status is then analyzed to obtain the simulation test results of the automatic emergency braking system. Thus, by pre-building accurate kinematic models for different conditions and updating the state according to a time interval, high-precision, real-time simulation of the target obstacle's trajectory is achieved. This modeling and periodic updating method ensures the continuity and realism of the simulation signal, accurately simulating the sampling behavior of sensors in the real world, thereby reliably verifying the perception and decision-making logic of the AEB system. By encapsulating virtual motion states into sensor messages conforming to vehicle network standards and injecting them, the signal output of real sensors such as radar and cameras is fully simulated at the software level. This step establishes a communication link between the virtual environment and the real AEB controller, enabling the controller to process simulated signals as if they were real signals, thereby achieving a true closed-loop test of the controller's hardware and software algorithms.
[0090] Figure 3 This is a flowchart of another vehicle data processing method provided in this disclosure.
[0091] like Figure 3 As shown, the vehicle data processing method may include the following steps: S410. Obtain the configuration parameters of the test condition of the automatic emergency braking system. The configuration parameters include the type of test condition and the initial state of the target obstacle.
[0092] Specifically, the implementation process and principle of S410 and S110 are the same, and will not be repeated here.
[0093] S420. Based on the initial state of the target obstacle and the target kinematic model corresponding to the type of test condition, calculate the motion state of the target obstacle under the test condition.
[0094] Specifically, the implementation process and principle of S420 and S120 are the same, and will not be repeated here.
[0095] S430. Based on the motion state of the target obstacle, trigger the automatic emergency braking system to respond and record the response status of the automatic emergency braking system.
[0096] Specifically, the implementation process and principle of S430 and S130 are the same, and will not be repeated here.
[0097] S440. Based on the response, determine whether the response of the automatic emergency braking system meets the expected results.
[0098] In this step, the electronic device can determine whether the automatic emergency braking system's response meets expectations based on the response status. This is the evaluation phase of the test. The monitored system response behavior needs to be compared with preset pass / fail criteria.
[0099] In some embodiments, S440 may include, but is not limited to, S4401 and S4402: S4401. If the response status indicates that the automatic braking function is activated, then the automatic emergency braking system response is determined to be in line with expectations.
[0100] In this step, if the response indicates that the automatic braking function is activated, the automatic emergency braking system response is determined to be in line with expectations.
[0101] S4402. Calculate the warning trigger distance based on the movement state and response of the target obstacle. If the warning trigger distance is greater than the preset threshold, it is determined that the automatic emergency braking system response meets the expected results.
[0102] In this step, the electronic device can calculate the warning trigger distance based on the movement and response of the target obstacle. If the warning trigger distance is greater than a preset threshold, the automatic emergency braking system response is deemed to meet expectations. This is a more refined performance evaluation standard, not only checking whether the function is triggered but also evaluating whether the triggering timing is sufficiently "early" or "safe." Specifically, the relative distance between the vehicle and the virtual target can be continuously recorded. When the status message is parsed to "1-Warning" for the first time, the relative distance recorded at that moment is immediately read and denoted as "warning trigger distance D_warn." Simultaneously, a safety threshold is preset in the test standard, such as "for the current vehicle speed, the warning distance should be greater than 30 meters." The judgment logic is: if D_warn > 30, the warning timing is evaluated as "excellent." This achieves a quantitative scoring of system performance.
[0103] By providing two specific compliance criteria: one based on functional triggering (braking activation) and the other based on performance quantification (warning distance), the testing and evaluation process extends from a simple yes / no judgment to quantifiable performance metrics. For example, by calculating the warning trigger distance and comparing it with a threshold, the sensitivity and safety balance of the AEB system under different parameters can be accurately assessed, providing data support for parameter optimization.
[0104] S450. If the expected results are met, the test ends, test results are generated, or the motion state of the target obstacle is changed for the next test.
[0105] In this step, if the automatic emergency braking system's response meets expectations, the test ends, test results are generated, or the target obstacle's motion state is changed for the next test. This demonstrates the automation of the test sequence. After one test is completed, scene parameters can be automatically modified to continuously execute the next test case. For example, after the "ghost peek" test, it is considered a pass. Subsequently, configuration parameters can be automatically modified, such as changing the initial longitudinal distance from 30 meters to 25 meters, and then the timer is restarted to begin the next more stringent test. This achieves batch automatic execution of regression test cases.
[0106] This embodiment automatically determines the test results and subsequent operations (end or continue) based on the response status, realizing automated execution and decision-making of the testing process. This reduces human intervention, not only improving testing efficiency but also eliminating errors caused by human judgment and ensuring the objectivity of test results.
[0107] S460. During simulation testing, at least one trigger threshold parameter of the automatic emergency braking system is adjusted each time.
[0108] In this step, during simulation testing, the electronic device will adjust at least one trigger threshold parameter of the automatic emergency braking system each time. The trigger threshold parameter may include the collision time threshold and the warning time threshold.
[0109] S470. Based on the response records of multiple simulation tests, calculate the performance index score of the automatic emergency braking system under different parameter combinations, and determine the parameter combination corresponding to the highest score as the target parameter combination.
[0110] In this step, the electronic device calculates the performance index score of the automatic emergency braking system under different parameter combinations based on the response records of multiple simulation tests, compares the performance index score of the automatic emergency braking system under different parameter combinations, and determines the parameter combination corresponding to the highest score as the target parameter combination.
[0111] S480. Calibrate the automatic emergency braking system based on the target parameter combination.
[0112] In this step, after determining the target parameter combination, the electronic device will calibrate the automatic emergency braking system based on the target parameter combination.
[0113] For example, it's necessary to optimize the "collision time threshold." This parameter can be modified within the AEB controller using an external calibration tool or a diagnostic service. Then, for the "deceleration model" scenario, a test is run, recording the "warning trigger distance" and "braking trigger distance." Based on a scoring rule (e.g., earlier triggering and smoother braking result in higher scores), the score for this parameter is calculated. Next, the threshold is automatically increased slightly, and the exact same test scenario is run again to obtain a new score. This process is repeated, iterating through a parameter range, and comparing all scores. The threshold parameter with the highest score is selected as the "target parameter combination," which can be automatically written to the controller to complete the calibration. The entire calibration process is automated, efficient, and objective.
[0114] This embodiment of the disclosure obtains the configuration parameters of the test conditions of the automatic emergency braking system. These parameters include the type of test condition, the initial state of the target obstacle, and the target kinematic model corresponding to the type of test condition. Based on the initial state of the target obstacle and the target kinematic model, the motion state of the target obstacle under the test condition is calculated. Then, based on the motion state of the target obstacle, the automatic emergency braking system is triggered to respond, and the response is recorded. Based on the response, it is determined whether the response of the automatic emergency braking system meets the expected result. If it meets the expected result, the test ends, a test result is generated, or the motion state of the target obstacle is changed for the next test. Next, during simulation testing, at least one trigger threshold parameter of the automatic emergency braking system is adjusted each time. Based on the response recorded from multiple simulation tests, the performance index score of the automatic emergency braking system under different parameter combinations is calculated, and the parameter combination corresponding to the highest score is determined as the target parameter combination. The automatic emergency braking system is then calibrated based on the target parameter combination. Thus, by systematically applying the simulation testing method to the parameter calibration of the AEB system, a data-driven automated calibration process is constructed by automatically adjusting parameters, batch executing tests, and selecting the optimal combination based on the score. It changes the traditional manual calibration mode that relies on engineers' experience, greatly improves calibration efficiency, accuracy and coverage, and can quickly find the globally or locally optimal system parameters.
[0115] Figure 4 This is a schematic diagram of the structure of a vehicle data processing device provided in an embodiment of this disclosure.
[0116] In this embodiment, the vehicle data processing device can be housed within an electronic device and is understood as a functional module within the aforementioned electronic device. Specifically, the electronic device can be a server or a terminal, wherein the terminal specifically includes an in-vehicle terminal, a computer, or a tablet computer, etc., without limitation.
[0117] like Figure 4 As shown, the vehicle data processing device 700 may include an acquisition module 710, a calculation module 720, a trigger module 730, and a result module 740.
[0118] The acquisition module 710 is used to acquire the configuration parameters of the test conditions of the automatic emergency braking system, the configuration parameters including the type of test conditions and the initial state of the target obstacle; The calculation module 720 is used to calculate the motion state of the target obstacle under the test condition based on the initial state of the target obstacle and the target kinematic model corresponding to the type of the test condition; The trigger module 730 is used to trigger the automatic emergency braking system to respond based on the motion state of the target obstacle, and to record the response status of the automatic emergency braking system. The module 740 is used to analyze the response and obtain the simulation test results of the automatic emergency braking system.
[0119] In this embodiment, configuration parameters for the test conditions of the automatic emergency braking system can be obtained. Based on the initial state of the target obstacle and the target kinematic model corresponding to the type of the test condition, the motion state of the target obstacle under the test condition is calculated. Further, based on the motion state of the target obstacle, the automatic emergency braking system is triggered to respond, and the response of the automatic emergency braking system is recorded. The response is then analyzed to obtain the simulation test results of the automatic emergency braking system. Therefore, by driving the motion of a virtual target obstacle through configuration parameters and triggering the automatic emergency braking system response based on the motion state of the target obstacle, simulation testing can eliminate dependence on weather, location, and expensive physical equipment, greatly improving the safety and efficiency of testing, ensuring a high degree of consistency of test conditions, and laying the foundation for automated testing.
[0120] In some embodiments of this disclosure, when the acquisition module 710 acquires the configuration parameters of the test conditions of the automatic emergency braking system, it is specifically used for: In response to the tester's selection of test conditions on the configuration interface, the type of the test condition is determined; In response to the tester's configuration operation on the configuration interface for the initial state, the initial state of the target obstacle is obtained.
[0121] In some embodiments of this disclosure, when the calculation module 720 calculates the motion state of the target obstacle under the test condition based on the initial state of the target obstacle and the target kinematic model corresponding to the type of the test condition, it is specifically used for: Construct kinematic models for various test conditions; Based on the type of test conditions, the target kinematic model is determined from the kinematic models corresponding to each test condition; Based on the initial state of the target obstacle and the target kinematic model, the state is updated according to a preset time interval to obtain the motion state of the target obstacle.
[0122] In some embodiments of this disclosure, when the calculation module 720 updates the state of the target obstacle according to a preset time interval based on the initial state of the target obstacle and the target kinematic model, it is specifically used for: When the target kinematic model is a preset static model, the longitudinal position of the target obstacle is maintained at the initial longitudinal distance, and the current velocity and acceleration of the target obstacle are set to zero; When the target kinematic model is a preset deceleration model, the current velocity and longitudinal position of the target obstacle as a function of time are calculated based on the initial velocity and constant deceleration of the target obstacle. When the target kinematic model is a preset traversal model, the longitudinal position of the target obstacle is updated based on the vehicle speed, and the lateral offset increases linearly with time. When the target kinematic model is a preset ghost-peek model, after a preset delay time, the lateral offset of the target obstacle is increased by a set speed, the longitudinal position of the target obstacle is updated based on the vehicle speed, and the moving speed after the target obstacle appears is set.
[0123] In some embodiments of this disclosure, when the triggering module 730 triggers the automatic emergency braking system to respond based on the motion state of the target obstacle, and records the response of the automatic emergency braking system, it is specifically used for: The motion state of the target obstacle is encapsulated into sensor messages according to a preset vehicle communication protocol format; The sensor message is sent to the automatic emergency braking system, and the automatic emergency braking system responds to the sensor message to obtain the response status of the automatic emergency braking system.
[0124] In some embodiments of this disclosure, when the trigger module 730 responds to the sensor message through the automatic emergency braking system and obtains the response status of the automatic emergency braking system, it is specifically used for: Based on the automatic emergency braking system's response to the sensor messages, a status message fed back by the automatic emergency braking system is obtained; The status message is parsed to obtain the response status of the automatic emergency braking system.
[0125] In some embodiments of this disclosure, when the obtaining module 740 analyzes the response and obtains the simulation test results of the automatic emergency braking system, it is specifically used for: Based on the response, it is determined whether the response of the automatic emergency braking system meets the expected results; If the expected results are met, the test ends, test results are generated, or the motion state of the target obstacle is changed for the next test.
[0126] In some embodiments of this disclosure, when the obtaining module 740 determines whether the response of the automatic emergency braking system meets the expected result based on the response situation, it is specifically used for: If the response indicates that the automatic braking function is activated, then the automatic emergency braking system response is determined to be in line with expectations; or, Based on the motion state of the target obstacle and the response, the warning trigger distance is calculated. If the warning trigger distance is greater than a preset threshold, the automatic emergency braking system response is determined to meet the expected result.
[0127] In some embodiments of this disclosure, the device 700 further includes: The calibration module 750 is used to adjust at least one trigger threshold parameter of the automatic emergency braking system each time during simulation testing; calculate the performance index score of the automatic emergency braking system under different parameter combinations based on the response records of multiple simulation tests, determine the parameter combination corresponding to the highest score value as the target parameter combination, and calibrate the automatic emergency braking system based on the target parameter combination.
[0128] It should be noted that, Figure 4 The vehicle data processing device 700 shown can execute the various steps in the above method embodiments and realize the various processes and effects in the above method embodiments, which will not be elaborated here.
[0129] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure.
[0130] In this embodiment of the disclosure, Figure 5 The electronic device shown can be a server or a terminal. Specifically, the terminal includes in-vehicle terminals, computers, or tablets, etc., without limitation.
[0131] like Figure 5 As shown, the electronic device may include a processor 810 and a memory 820 storing computer program instructions.
[0132] Specifically, the processor 810 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this disclosure.
[0133] Memory 820 may include mass storage for information or instructions. For example, and not limitingly, memory 820 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 820 may include removable or non-removable (or fixed) media. Where appropriate, memory 820 may be internal or external to the integrated gateway device. In a particular embodiment, memory 820 is non-volatile solid-state memory. In a particular embodiment, memory 820 includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (Electrically Programmable ROM, EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM), or flash memory, or a combination of two or more of these.
[0134] The processor 810 reads and executes computer program instructions stored in the memory 820 to perform the steps of the vehicle data processing method provided in this embodiment of the present disclosure.
[0135] In one example, the electronic device may also include a transceiver 830 and a bus 840. Wherein, as... Figure 5 As shown, the processor 810, memory 820 and transceiver 830 are connected via bus 840 and communicate with each other.
[0136] Bus 840 may include hardware, software, or both. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 840 may include one or more buses.
[0137] This disclosure also provides a computer-readable storage medium that can store a computer program, which, when executed by a processor, enables the processor to implement the vehicle data processing method provided in this disclosure.
[0138] When the computer program is executed by the processor, it can perform the following steps: acquiring the configuration parameters of the test condition of the automatic emergency braking system, the configuration parameters including the type of test condition and the initial state of the target obstacle; calculating the motion state of the target obstacle under the test condition based on the initial state of the target obstacle and the target kinematic model corresponding to the type of the test condition; triggering the automatic emergency braking system to respond based on the motion state of the target obstacle, and recording the response of the automatic emergency braking system; analyzing the response to obtain the simulation test results of the automatic emergency braking system.
[0139] The aforementioned storage medium may, for example, include a memory 820 containing computer program instructions, which can be executed by a processor 810 of an electronic device to complete the vehicle data processing method provided in this embodiment. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a read-only memory (ROM), random access memory (RAM), external cache memory, compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, flash memory, and optical data storage device. By way of illustration and not limitation, RAM is available in various forms, such as static random access memory (SRAM) and dynamic random access memory (DRAM).
[0140] This disclosure also provides a vehicle that includes electronic devices that can implement the various processes and effects described in the above embodiments of this disclosure, which will not be elaborated here.
[0141] This disclosure also provides a computer program product, which includes a computer program or instructions. When the computer program or instructions are executed by a processor, they implement the vehicle data processing method provided in this disclosure and can achieve the various processes and effects in the above embodiments of this disclosure, which will not be elaborated here.
[0142] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A vehicle data processing method, characterized in that, The method includes: Obtain the configuration parameters of the test conditions for the automatic emergency braking system, including the type of test conditions and the initial state of the target obstacle; Based on the initial state of the target obstacle and the target kinematic model corresponding to the type of the test condition, the motion state of the target obstacle under the test condition is calculated. Based on the motion state of the target obstacle, the automatic emergency braking system is triggered to respond, and the response status of the automatic emergency braking system is recorded. The response was analyzed to obtain the simulation test results of the automatic emergency braking system.
2. The method according to claim 1, characterized in that, The configuration parameters for obtaining the test conditions of the automatic emergency braking system include: In response to the tester's selection of test conditions on the configuration interface, the type of the test condition is determined; In response to the tester's configuration operation on the configuration interface for the initial state, the initial state of the target obstacle is obtained.
3. The method according to claim 1, characterized in that, The calculation of the motion state of the target obstacle under the test condition, based on the initial state of the target obstacle and the target kinematic model corresponding to the type of test condition, includes: Construct kinematic models for various test conditions; Based on the type of test conditions, the target kinematic model is determined from the kinematic models corresponding to each test condition; Based on the initial state of the target obstacle and the target kinematic model, the state is updated according to a preset time interval to obtain the motion state of the target obstacle.
4. The method according to claim 3, characterized in that, The process of updating the state of the target obstacle according to a preset time interval based on the initial state of the target obstacle and the target kinematic model to obtain the motion state of the target obstacle includes: When the target kinematic model is a preset static model, the longitudinal position of the target obstacle is maintained at the initial longitudinal distance, and the current velocity and acceleration of the target obstacle are set to zero; When the target kinematic model is a preset deceleration model, the current velocity and longitudinal position of the target obstacle as a function of time are calculated based on the initial velocity and constant deceleration of the target obstacle. When the target kinematic model is a preset traversal model, the longitudinal position of the target obstacle is updated based on the vehicle speed, and the lateral offset increases linearly with time. When the target kinematic model is a preset ghost-peek model, after a preset delay time, the lateral offset of the target obstacle is increased by a set speed, the longitudinal position of the target obstacle is updated based on the vehicle speed, and the moving speed after the target obstacle appears is set.
5. The method according to claim 1, characterized in that, The automatic emergency braking system is triggered to respond based on the motion state of the target obstacle, and the response status of the automatic emergency braking system is recorded, including: The motion state of the target obstacle is encapsulated into sensor messages according to a preset vehicle communication protocol format; The sensor message is sent to the automatic emergency braking system, and the automatic emergency braking system responds to the sensor message to obtain the response status of the automatic emergency braking system.
6. The method according to claim 5, characterized in that, The step of responding to the sensor message through the automatic emergency braking system to obtain the response status of the automatic emergency braking system includes: Based on the automatic emergency braking system's response to the sensor messages, a status message fed back by the automatic emergency braking system is obtained; The status message is parsed to obtain the response status of the automatic emergency braking system.
7. The method according to claim 1, characterized in that, The analysis of the response to obtain the simulation test results of the automatic emergency braking system includes: Based on the response, it is determined whether the response of the automatic emergency braking system meets the expected results; If the expected results are met, the test ends, test results are generated, or the motion state of the target obstacle is changed for the next test.
8. The method according to claim 7, characterized in that, The step of determining whether the response of the automatic emergency braking system meets the expected result based on the response status includes: If the response indicates that the automatic braking function is activated, then the automatic emergency braking system response is determined to be in line with expectations; or, Based on the motion state of the target obstacle and the response, the warning trigger distance is calculated. If the warning trigger distance is greater than a preset threshold, the automatic emergency braking system response is determined to meet the expected result.
9. The method according to claim 1, characterized in that, The method further includes: During simulation testing, at least one trigger threshold parameter of the automatic emergency braking system is adjusted each time. Based on the response data recorded from multiple simulation tests, the performance index scores of the automatic emergency braking system under different parameter combinations are calculated, and the parameter combination corresponding to the highest score is determined as the target parameter combination. The automatic emergency braking system is calibrated based on the target parameter combination.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-9.