Rainy day simulation control method and system for vehicle intended function safety testing

Abstract

This invention relates to the field of intelligent connected vehicle testing technology, and more particularly to a rain simulation control method and system for testing the expected functional safety of vehicles. The method includes the following steps: S1, setting the target rainfall intensity and system control cycle; S2, acquiring rainfall data using multiple rain sensors arranged along the test tunnel. k S2: Calculate multiple measured values ​​within a control cycle, and obtain real-time water pressure values ​​through a pressure sensor installed in the rainfall unit; S3: Determine the current average rainfall intensity based on the measured values ​​from multiple rain sensors; S4: Based on the difference between the target rainfall intensity and the current average rainfall intensity, and by integrating the real-time water pressure values, generate a drive control signal for the high-pressure water pump in the rainfall unit using a composite control strategy combining feedforward and feedback control; S5: Repeat steps S2 to S4 until the rain simulation task ends. This invention provides accurately quantifiable and stable environmental conditions for SOTIF testing.

Description

Technical Field

[0001] This invention relates to the field of intelligent connected vehicle testing technology, and in particular to a rain simulation control method and system for testing the expected functional safety of vehicles. Background Technology

[0002] With the development of intelligent connected vehicle technology, higher requirements have been placed on the testing and verification of its expected functional safety. Rainy environments are typical and complex scenarios that affect the performance of vehicle perception systems (such as cameras and millimeter-wave radar) and execution systems (such as braking and steering), and have been included in the mandatory testing items of relevant national standards. Therefore, simulating rainy environments of different intensities with high precision and high reproducibility in a controlled environment is crucial for verifying the safety and reliability of intelligent driving systems in rainy scenarios.

[0003] However, existing rain simulation devices generally employ open-loop control. For example, they spray water by preset pump power or valve opening, lacking real-time monitoring and feedback adjustment of actual rainfall intensity. This mode has significant drawbacks: first, it has low control precision, failing to accurately stabilize rainfall intensity at the target value; second, it has poor robustness, unable to adaptively compensate for performance drift caused by factors such as nozzle blockage, water pressure fluctuations, environmental wind disturbances, or equipment aging; and third, it has poor repeatability, leading to inconsistent test conditions, seriously affecting the validity and credibility of SOTIF (Safety of the Intended Functionality) tests for intelligent connected vehicles.

[0004] Therefore, there is an urgent need for a rain simulation method and system that can achieve accurate, stable, and adaptive closed-loop control of rainfall intensity. Summary of the Invention

[0005] The purpose of this invention is to provide a rain simulation control method and system for testing the expected functional safety of vehicles, in order to solve the problem that existing rain simulation devices cannot provide a stable and accurate controlled environment.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A rain simulation control method for testing the intended functional safety of vehicles includes the following steps:

[0008] S1, Set target rainfall intensity and system control cycle ;

[0009] S2, Obtain the first rainfall data by using multiple rain sensors arranged along the test tunnel. k Multiple measurements within a control cycle, and real-time water pressure values ​​obtained through a pressure sensor installed in the rainfall unit. ;

[0010] S3. Determine the current average rainfall intensity based on the measurements from the multiple rain sensors. ;

[0011] S4. Based on the target rainfall intensity With the current average rainfall intensity The differences, and integrate the real-time water pressure values A composite control strategy combining feedforward control and feedback control is adopted to generate drive control signals for the high-pressure water pump in the rainfall unit.

[0012] The feedback control is a model-free adaptive sliding mode control, which adjusts the control parameters online based on the historical data and current measurement values ​​fed back by the rain sensor.

[0013] S5: Repeat steps S2 to S4 until the rain simulation task ends.

[0014] Based on the aforementioned technical means, this invention acquires measurements from multiple rain sensors in real time during each control cycle and determines the current average rainfall intensity, enabling the system to accurately perceive the overall rainfall status of the test environment. Based on this, the deviation between the target rainfall intensity and the current average rainfall intensity is used as the core control criterion, combined with real-time water pressure values ​​for composite control, allowing the system to dynamically and precisely adjust the rainfall intensity. This fundamentally overcomes the shortcomings of existing open-loop control systems that rely on preset values ​​and cannot respond to actual working conditions in real time. It can maintain the rainfall intensity within the test tunnel consistently near the target value, providing a precisely quantifiable and stable environmental condition for SOTIF testing.

[0015] This invention employs model-free adaptive sliding mode control for feedback control. This algorithm adjusts control parameters online based on historical data and current measurements, without relying on a precise mathematical model of the controlled object (i.e., the rainfall system). This gives the system strong self-learning and adaptive capabilities, effectively compensating for changes in system characteristics or external disturbances caused by equipment aging (such as nozzle blockage and pump performance degradation) and environmental disturbances (such as airflow in tunnels and wind disturbances caused by passing vehicles). This solves the problem of poor robustness of traditional simple controllers in nonlinear and time-varying systems, ensuring consistent control performance over long-term, repeated use.

[0016] This invention effectively overcomes the localized rainfall intensity differences caused by uneven airflow and spray within tunnels by limiting the use of five rain gauges evenly distributed along the driving direction and arithmetically averaging their measurements. The average value obtained is... It can more realistically and comprehensively characterize the overall rainfall status of the test area, avoid the misleading effect of random errors in single-point measurement on system perception, provide more reliable and representative feedback signals for subsequent control decisions, and fundamentally improve the basic measurement accuracy of the entire closed-loop control system.

[0017] Furthermore, the execution process of the model-free adaptive sliding mode control includes the following steps:

[0018] Based on the increase in pump control quantity and the change in rainfall intensity from the previous control cycle, update the [number]th [cycle / time]. k Pseudo-partial derivatives used to characterize dynamic properties within each control cycle ;

[0019] Based on the pseudo-partial derivative and the target rainfall intensity Compared with the current average rainfall intensity To determine the deviation, construct an equivalent control law. ;

[0020] Combined with sliding mode functions that include deviations in current and historical rainfall intensity With the equivalent control law The feedback control component is calculated. .

[0021] Based on the above-mentioned technical means, the present invention introduces pseudo-partial derivatives. It can estimate system dynamics online using real-time data, enabling the control unit to automatically track and compensate for characteristic changes caused by equipment aging (such as nozzle clogging) or environmental disturbances. Simultaneously, an equivalent control law is constructed based on the online estimated system dynamics and current deviations. It can calculate the ideal reference control quantity for achieving accurate tracking. By designing sliding mode functions and robust terms, it suppresses external disturbances such as traffic flow and wind disturbances, fundamentally solving the core problems of insufficient accuracy, poor adaptability, and weak anti-interference ability in traditional open-loop or simple feedback control, and providing a highly reliable and repeatable environmental reference for rainy weather simulation testing.

[0022] Furthermore, the pseudo-partial derivatives This is determined through the following relationship:

[0023] , ,

[0024] in, Step size factor Here is the regularization constant. This represents the change in rainfall intensity at the previous moment. This is the increment of the control quantity from the previous moment.

[0025] Based on the above technical means, by giving pseudo-partial derivatives The specific update formula enables online real-time estimation of the system's dynamic characteristics. The algorithm utilizes the incremental input and output data from the previous cycle to compensate for system characteristic drift caused by variations in nozzle efficiency and water pressure pulsations, thus solving the problem of open-loop preset failure due to equipment aging or environmental disturbances in existing technologies.

[0026] Furthermore, the equivalent control law This is constructed through the following relationship:

[0027] ,

[0028] in, This is the control quantity for the high-pressure water pump in the previous control cycle. This represents the average rainfall intensity of the previous control period.

[0029] Based on the above-mentioned technical means, the present invention utilizes online estimated pseudo-partial derivatives. Dynamically correcting the control inputs enables the control unit to determine the basic control inputs required to approximate the target state based on the current dynamic response characteristics of the system, thereby improving the accuracy and efficiency of target tracking.

[0030] Furthermore, the sliding mode function Based on the following relationship:

[0031] ,

[0032] in, These are the integral weighting coefficients. This represents the deviation in rainfall intensity.

[0033] Based on the above technical means, the present invention specifically constructs a structure containing integral terms. sliding mode function The control law incorporates a cumulative memory and compensation mechanism for historical deviations, which continuously eliminates steady-state errors in the system, ensuring that even small but persistent disturbances can ultimately reduce the average rainfall intensity. Locked at the target value without steady-state error This allows for long-term, precise, and stable control.

[0034] Furthermore, the feedback control component This is determined based on the following relationship:

[0035] ,

[0036] in, For switching gain constant, For boundary layer thickness, It is a saturation function.

[0037] Based on the above technical means, the feedback control component is limited. The calculation employs a modified form with a saturation function `sat()` and boundary layer thickness `δ`, effectively suppressing the inherent high-frequency "chattering" phenomenon of traditional sliding mode control. While retaining the strong robustness of sliding mode control to disturbances, it significantly improves the smoothness and stability of the control signal.

[0038] Furthermore, the execution process of the feedforward control includes the following steps:

[0039] Based on the target rainfall intensity Mapping to obtain target water pressure ;

[0040] According to the target water pressure With the k Real-time water pressure value for each control cycle The deviation is used to generate feedforward control components through proportional-integral calculations. .

[0041] Based on the aforementioned technical means, and considering the target water pressure Compared with actual water pressure The deviation is calculated using proportional-integral operations, enabling the feedforward loop to quickly respond to and compensate for major pressure disturbances in the water supply pipeline. Before the feedback loop makes fine adjustments, the system operating point is quickly brought closer to the target area, significantly shortening the system adjustment time.

[0042] Furthermore, the final control quantity of the high-pressure water pump in the composite control strategy... This is determined through the following relationship:

[0043] ,

[0044] in, For feedforward control components, This is the feedback control component.

[0045] Based on the above technical means, the final control quantity For feedforward components With feedback components The linear superposition of these elements enables the system to possess both fast dynamic response and high steady-state accuracy.

[0046] Furthermore, S2 also includes: synchronously acquiring the status signal of the traffic signal simulation system in the test tunnel and the illumination parameters of the multi-scene lighting simulation system;

[0047] The S5 further includes: during the cyclic execution process, coordinating the control of the rainfall intensity, traffic light status and illumination parameters to construct a composite test scenario.

[0048] Based on the above technical means, the traffic light status and illumination parameters are acquired and controlled synchronously in the control loop, expanding the single rainfall intensity control into an integrated control of multiple environmental factors, enabling the test system to reproduce complex driving scenarios and more comprehensively evaluate the overall performance of the intelligent driving system.

[0049] The present invention also provides a rain simulation control system for testing the expected functional safety of vehicles, for implementing the above-described control method, comprising: a test tunnel, the inner wall of which is covered with radar-band absorbing material; a rainfall unit, including a high-pressure water pump, a water supply pipeline, a pressure sensor disposed on the water supply pipeline, and multiple high-pressure spray nozzles; a rainfall intensity sensing unit, including multiple rainfall sensors arranged longitudinally along the test tunnel; and a control unit, which is signal-connected to the rainfall intensity sensing unit, the pressure sensors, and the high-pressure water pump, and configured to execute the above-described control method.

[0050] The beneficial effects achieved by this invention are as follows:

[0051] This invention acquires measurements from multiple rain sensors in real time during each control cycle and determines the current average rainfall intensity, enabling the system to accurately perceive the overall rainfall status of the test environment. Based on this, the deviation between the target rainfall intensity and the current average rainfall intensity is used as the core control criterion, combined with real-time water pressure values ​​for composite control, allowing the system to dynamically and precisely adjust the rainfall intensity. This fundamentally overcomes the shortcomings of existing open-loop control systems that rely on preset values ​​and cannot respond to actual working conditions in real time. It can maintain the rainfall intensity in the test tunnel stable near the target value over a long period, providing a precisely quantifiable and stable environmental condition for SOTIF testing.

[0052] This invention employs model-free adaptive sliding mode control for feedback control. This algorithm adjusts control parameters online based on historical data and current measurements, without relying on a precise mathematical model of the controlled object (i.e., the rainfall system). This gives the system strong self-learning and adaptive capabilities, effectively compensating for changes in system characteristics or external disturbances caused by equipment aging (such as nozzle blockage and pump performance degradation) and environmental disturbances (such as airflow in tunnels and wind disturbances caused by passing vehicles). This solves the problem of poor robustness of traditional simple controllers in nonlinear and time-varying systems, ensuring consistent control performance over long-term, repeated use. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of the overall process of the present invention.

[0054] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this application. To better illustrate the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual dimensions of the product. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings. The same or similar reference numerals correspond to the same or similar parts. The terms describing positional relationships in the drawings are for illustrative purposes only and should not be construed as limiting the scope of this application. Detailed Implementation

[0055] It should be noted that, unless otherwise specified, the embodiments and technical features in the embodiments of this application can be combined with each other, and the detailed descriptions in the specific embodiments should be understood as explanations of the purpose of this application and should not be regarded as undue limitations on this application.

[0056] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the specific technical solutions of this application will be further described in detail below with reference to the accompanying drawings of the embodiments of this application. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application.

[0057] In the embodiments of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0058] In the embodiments of this application, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral part; it can be a direct connection or an indirect connection through an intermediate medium.

[0059] In embodiments of this application, 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 limitation, 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 that element.

[0060] The technical solution of this embodiment will be described in detail below with reference to the accompanying drawings.

[0061] One embodiment of the present invention provides a rain simulation control system and method for testing the expected functional safety of vehicles.

[0062] The system mainly includes:

[0063] Test tunnel: An enclosed structure approximately 80 meters long, with vehicle entrances and exits and flexible windbreaks at both ends. The tunnel's inner walls are fully covered with radar-absorbing materials of a specific frequency band to simulate the electromagnetic environment of an open road and prevent strong wall reflections from interfering with vehicle-mounted millimeter-wave radar.

[0064] Rainfall unit: Includes a collection tank, multi-stage filtration system, high-pressure water pump, main water supply line, multiple branch lines, and arrayed high-pressure fine atomizing nozzles. High-precision pressure sensors are installed on the main water supply line.

[0065] Sensing unit: includes five tipping bucket rain gauges evenly arranged along the tunnel's travel direction to measure local rainfall intensity (unit: mm / h); and pressure sensors on the aforementioned water supply pipeline.

[0066] Control unit: It adopts an industrial-grade programmable logic controller (PLC) or industrial computer, which integrates data acquisition, control algorithm calculation, instruction output and human-machine interaction functions.

[0067] Optional components: Install traffic signal simulation devices conforming to GB 14886-2016 at key locations at the tunnel entrance and inside; deploy a high color rendering LED array lighting system on the tunnel ceiling to simulate various standard lighting conditions such as sunny days, cloudy days, and dusk (conforming to GB / T 38438-2019); and install microwave speed measuring radars at the tunnel entrance and exit.

[0068] Based on the above system, such as Figure 1 As shown, the specific implementation process of the rain simulation control method in this embodiment is as follows:

[0069] S1. The operator sets the target rainfall intensity value for this test through the local human-machine interface or remote control terminal. (For example: 30 mm / h, simulating moderate rain), and simultaneously set the system control cycle. It takes 100ms.

[0070] S2, Obtain the first rainfall data by using multiple rain sensors arranged along the test tunnel. k Multiple measurements within a control cycle, and real-time water pressure values ​​obtained through a pressure sensor installed in the rainfall unit. .

[0071] Specifically, after the system starts, the control unit performs each control cycle (let's call the current cycle the 1st cycle)... k Perform the following operations (per cycle):

[0072] Read real-time measurements from five rain gauges. , , , , .

[0073] Read the real-time water pressure value from the pressure sensor on the main water supply line. .

[0074] S3. Determine the current average rainfall intensity based on the measurements from the multiple rain sensors. .

[0075] Specifically, the control unit calculates the average rainfall intensity within the current period:

[0076] ,

[0077] And calculate the current rainfall intensity deviation:

[0078] .

[0079] S4. Based on the target rainfall intensity With the current average rainfall intensity The differences, and integrate the real-time water pressure values A composite control strategy combining feedforward control and feedback control is adopted to generate drive control signals for the high-pressure water pump in the rainfall unit.

[0080] The feedback control is a model-free adaptive sliding mode control, which adjusts the control parameters online based on historical data and current measurements from the rain sensor.

[0081] Specifically, the control unit generates control signals for the high-pressure water pump frequency converter. The control quantity is generated by the feedforward component. and feedback components Composed of multiple layers:

[0082] ,

[0083] (1) Calculation of feedforward control components:

[0084] The system internally stores a target rainfall intensity-target water pressure mapping table obtained through pre-calibration. Based on the current... The target water pressure is obtained by referring to the table. .

[0085] The feedforward controller uses a PI algorithm to calculate the control quantity based on the water pressure deviation.

[0086] ,

[0087] in, and The proportional and integral gain coefficients of the feedforward PI controller are used to quickly adjust the system water pressure to a reference state that matches the target rainfall intensity.

[0088] (2) Calculation of feedback control components (model-free adaptive sliding mode control):

[0089] This section is used to eliminate steady-state errors and suppress disturbances. The specific steps are as follows:

[0090] 1. Online estimation system dynamics: updating pseudo-partial derivatives It reflects the sensitivity of the system output (rainfall intensity) to changes in the control input (pump control quantity).

[0091] ,

[0092] in, Step size factor This is a regularization constant used to ensure the numerical stability of the algorithm.

[0093] 2. Constructing the equivalent control law:

[0094] ,

[0095] in, This is the control quantity for the high-pressure water pump in the previous control cycle. This represents the average rainfall intensity of the previous control period.

[0096] 3. Design a sliding mode function (introduce an integral term to eliminate steady-state error):

[0097] ,

[0098] in, These are the integral weighting coefficients. This represents the deviation in rainfall intensity.

[0099] 4. Calculate the feedback control components (using a saturation function to reduce chattering):

[0100] ,

[0101] in, For switching gain constant, For boundary layer thickness, It is a saturation function.

[0102] 5. The control unit will calculate the final control quantity. The frequency commands are converted into those of the variable frequency drive and sent to the high-pressure water pump, which adjusts its speed in real time, thereby changing the spray volume. Simultaneously, all sensor data, equipment status, and control commands are displayed and recorded in real time on the remote control terminal.

[0103] S5: Repeat steps S2 to S4 to form a closed-loop control loop of "perception, decision-making, and execution," continuously transmitting the measured average rainfall intensity within the tunnel. Dynamically adjust and stabilize at the target value Nearby, until the test mission ends.

[0104] Throughout the process, the control unit can simultaneously control the lighting system to switch to "cloudy" mode (illuminance 5000 lx, color temperature 7000K) and control the traffic lights to switch according to a predetermined sequence, thereby constructing a composite scenario for comprehensive testing of the vehicle's perception and decision-making system performance.

[0105] Applying this embodiment, the system can stably control the rainfall intensity inside the tunnel from its initial state within an error band of ±1.5 mm / h of the target value in a short time. When faced with simulated lateral wind disturbances, the model-free adaptive sliding mode controller can quickly adjust and suppress rainfall intensity fluctuations caused by disturbances to within ±2 mm / h, significantly outperforming traditional PID control. Throughout the test, the rainfall intensity curve remained stable, providing a highly consistent and reliable testing environment for the vehicle.

[0106] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made based on the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A rain simulation control method for testing the expected functional safety of vehicles, characterized in that, Includes the following steps: S1, Set target rainfall intensity and system control cycle ; S2. Obtain the first rainfall data by using multiple rain sensors arranged along the test tunnel. k Multiple measurements within a control cycle, and real-time water pressure values ​​obtained through a pressure sensor installed in the rainfall unit. ; S3. Determine the current average rainfall intensity based on the measurements from the multiple rain sensors. ; S4. Based on the target rainfall intensity With the current average rainfall intensity The differences, and integrate the real-time water pressure values A composite control strategy combining feedforward control and feedback control is adopted to generate drive control signals for the high-pressure water pump in the rainfall unit. The execution process of the feedforward control includes the following steps: based on the target rainfall intensity Mapping to obtain target water pressure According to the target water pressure With the k Real-time water pressure value for each control cycle The deviation is used to generate feedforward control components through proportional-integral calculations. ; The feedback control is a model-free adaptive sliding mode control. The execution process of the model-free adaptive sliding mode control includes the following steps: based on the increment of the water pump control quantity and the change in rainfall intensity in the previous control cycle, update the... k Pseudo-partial derivatives used to characterize dynamic properties within each control cycle , , , Step size factor Here is the regularization constant. This represents the change in rainfall intensity at the previous moment. This represents the increment of the control quantity from the previous moment; Based on the pseudo-partial derivative and the target rainfall intensity Compared with the current average rainfall intensity To determine the deviation, construct an equivalent control law. , , This is the control quantity for the high-pressure water pump in the previous control cycle. The average rainfall intensity of the previous control period; Combined with sliding mode functions that include deviations in current and historical rainfall intensity With the equivalent control law The feedback control component is calculated. , , For switching gain constant, For boundary layer thickness, It is a saturation function. , These are the integral weighting coefficients. This is due to the deviation in rainfall intensity; S5: Repeat steps S2 to S4 until the rain simulation task ends.

2. The rain simulation control method for vehicle expected functional safety testing according to claim 1, characterized in that, The final control quantity of the high-pressure water pump in the composite control strategy This is determined through the following relationship: ， in, For feedforward control components, This is the feedback control component.

3. The rain simulation control method for vehicle expected functional safety testing according to claim 1, characterized in that, S2 further includes: synchronously acquiring the status signals of the traffic signal light simulation system in the test tunnel and the illumination parameters of the multi-scene lighting simulation system; The S5 further includes: during the cyclic execution process, coordinating the control of the rainfall intensity, traffic light status and illumination parameters to construct a composite test scenario.

4. A rain simulation control system for testing the expected functional safety of a vehicle, used to implement the control method as described in any one of claims 1 to 3, characterized in that, include: A test tunnel, the inner wall of which is covered with radar-absorbing material; The rainfall unit includes a high-pressure water pump, a water supply pipeline, a pressure sensor installed on the water supply pipeline, and multiple high-pressure spray heads; The rainfall intensity sensing unit includes multiple rainfall sensors arranged longitudinally along the test tunnel; The control unit is connected to the rainfall intensity sensing unit, the pressure sensor, and the high-pressure water pump signal respectively, and is configured to perform the control method as described in any one of claims 1 to 3.

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