Foggy weather simulation control method and system for vehicle expected function safety test

Abstract

The invention relates to the technical field of intelligent network connection automobile testing, in particular to a foggy day simulation control method and system for a vehicle expected function safety test, and the method comprises the following steps: S1, setting a target visibility value and a visibility deviation threshold value; s2, acquiring measurement values of a plurality of visibility sensors arranged in the test tunnel along the driving direction in real time, and acquiring a real-time water pressure value of a pressure sensor in a water supply main pipeline of the atomization unit; s3, comprehensively analyzing the measured values of the plurality of visibility sensors, and determining the current average visibility; s4, comparing the target visibility value with the current average visibility, and determining the visibility deviation; s5, executing a hierarchical control strategy according to a comparison result of the visibility deviation and a threshold value; and S6, circularly executing the steps S2 to S5 until the simulation test task is finished. According to the invention, a control logic of closed-loop feedback and hierarchical decision is constructed, and a reliable environment reference is provided for SOTIF testing.

Description

Technical Field

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

[0002] Safety of the Intended Functionality (SOTIF) testing for intelligent connected vehicles requires systematically verifying their functional performance in their intended operating scenarios, particularly in "long-tail scenarios" where sensor performance is affected by environmental interference. Reduced visibility due to fog is a typical scenario that severely impacts the perception capabilities of sensors such as vision and lidar. Therefore, realistically, reliably, and repeatedly simulating different levels of fog conditions in a controlled testing environment has become a crucial step in verifying the functional safety of intelligent driving systems.

[0003] However, the control modes of fog simulation facilities currently deployed in the industry have fundamental flaws. Existing technologies primarily employ an open-loop control mode: at the start of the test, operators set the operating parameters of the spray system (such as water pump pressure and spray duration) based on experience, and the system then runs continuously at a fixed power until manually stopped or a preset time is reached. This "set-and-forget" mode leads to the following serious consequences:

[0004] Uncontrollable fog concentration leads to unstable visibility. Because the system lacks real-time visibility (i.e., fog concentration) perception and feedback within the test area, it cannot dynamically adjust the spray volume according to actual conditions. Continuous spraying causes water mist to continuously generate and accumulate in the test space, while the rates of natural settling and diffusion are relatively fixed, resulting in a continuous unidirectional increase in fog concentration and a monotonous decrease in visibility. Test personnel cannot precisely maintain visibility at a target value (e.g., a stable 100-meter moderate fog or 50-meter dense fog), nor can they achieve a controllable and smooth transition from high to low visibility.

[0005] This fundamental flaw directly leads to the problem of unreproducible test conditions. The visibility environment faced by the vehicle sensors is constantly changing and unpredictable during each test, and even at different times within the same test. These uncontrollable and unreproducible test conditions severely limit the effectiveness and reliability of SOTIF testing. One of the core requirements of SOTIF standards (such as ISO 21448) is to expose functional deficiencies in a controlled and reproducible environment through a systematic approach. Open-loop fog simulation cannot provide a stable and accurate controlled environment, introducing significant uncertainty into the testing process itself. The test results fail to accurately reflect the performance and limitations of the vehicle system, and cannot provide a reliable basis for functional improvement and safety certification.

[0006] Therefore, overcoming the limitations of open-loop control and achieving accurate, stable, and reproducible closed-loop dynamic control of visibility in foggy simulated environments has become an urgent technical requirement for improving the maturity of SOTIF testing technology for intelligent connected vehicles. Summary of the Invention

[0007] The purpose of this invention is to provide a fog simulation control method and system for vehicle expected functional safety testing, in order to solve the problem that existing open-loop fog simulation cannot provide a stable and accurate controlled environment.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A fog simulation control method for testing the intended functional safety of vehicles includes the following steps: S1. Set the target visibility value and visibility deviation threshold This is to initialize the test environment parameters; S2. Real-time acquisition of the measurement values ​​of multiple visibility sensors arranged along the driving direction in the test tunnel, and real-time water pressure value P of the pressure sensor in the water supply main pipeline of the atomizing unit. S3. Perform comprehensive analysis on the measurement values ​​of multiple visibility sensors to determine the current average visibility. To characterize the overall fog concentration distribution within the test tunnel; S4. The target visibility value With the current average visibility Compare and determine the visibility deviation. To quantify the differences between the current environment and the target environment; S5. Based on the aforementioned visibility deviation With the threshold Based on the comparison results, a hierarchical control strategy is implemented: when At that time, based on visibility deviation If there is a water pressure deviation, adjust the high-pressure water pump and control the exhaust unit to stop; when At that time, based on the aforementioned visibility deviation Determine the exhaust intensity, adjust the exhaust unit, and maintain the current state of the high-pressure water pump; S6: Repeat steps S2 to S5 to continuously perform closed-loop control of the foggy environment until the simulation test task ends.

[0009] Based on the aforementioned technical means, the core of this invention lies in constructing a closed-loop feedback and hierarchical decision-making control logic. Firstly, by setting the target visibility... and tolerance threshold First, the desired control state and accuracy requirements were clearly defined. Second, multiple spatially distributed visibility sensors were used to acquire environmental conditions, overcoming the limitations of single-point measurements. The average value was calculated. and visibility deviation This quantifies environmental conditions into controllable decision-making criteria. Most importantly, this invention quantifies the severity of deviations (and...). (Comparison) Dynamic switching control mode: Finely adjusts the atomization source (high-pressure water pump) for small deviations, and prioritizes the activation of the rapid adjustment mechanism (exhaust unit) for large deviations, forming a collaborative mechanism of joint control by multiple execution structures. The perception, decision-making, and execution processes are executed cyclically, forming a closed-loop system that dynamically adapts to environmental changes.

[0010] This invention, through real-time feedback and adjustment, can maintain the visibility within the test tunnel at a long-term level near the target value, solving the core problem of unreproducible test conditions and providing a reliable environmental benchmark for SOTIF testing. Simultaneously, the hierarchical strategy enables the system to distinguish the nature of deviations and take optimal measures, ensuring both steady-state accuracy and rapid suppression of large disturbances.

[0011] Furthermore, in step S3, the current average visibility is determined by performing an arithmetic average of the measurements from the multiple visibility sensors. .

[0012] Based on the aforementioned technical methods, the fog concentration distribution within the tunnel may be uneven, and the readings of individual sensors are easily affected by localized airflow or uneven spray patterns. Averaging the measurements from multiple spatial points can effectively estimate the overall fog concentration level across the entire test area, significantly improving the accuracy and interference resistance of the system's environmental state information.

[0013] Furthermore, in S5, when Adjusting the high-pressure water pump specifically includes the following steps: S51, Based on target visibility value Determine the corresponding target water pressure ; S52, Target water pressure Compare the water pressure with the real-time water pressure value P to determine the water pressure deviation; S53. Determine the feedforward control quantity based on the water pressure deviation. And based on visibility deviation Perform analysis to determine the feedback control quantity. ; S54, Combine the aforementioned feedforward control quantities With the feedback control quantity Determine the water pump control quantity This generates control commands for the high-pressure water pump.

[0014] Based on the above-mentioned technical means, the present invention improves target visibility. Mapping the target water pressure Feedforward control rapidly approaches the target state by eliminating steady-state deviations in water pressure; the feedback channel directly adjusts based on visibility deviations, compensating in real time for the effects of inaccurate feedforward models, equipment performance drift (such as nozzle blockage), or unknown environmental disturbances. The combination of these two mechanisms ensures the reliability of long-term testing.

[0015] Furthermore, the feedforward control quantity Calculated using the following feedforward control formula: , in, and The gain coefficient is a constant. For target visibility The corresponding target water pressure value, where P is the real-time water pressure value.

[0016] Based on the above technical means, this invention employs a proportional-integral control algorithm to calculate the feedforward control quantity. Among them, the proportional term... It is a real-time response to the current water pressure deviation, propelling the system rapidly toward the target; integral term This involves accumulating historical deviations to eliminate steady-state errors and ensure that the water pressure accurately tracks the set value during long-term operation. This ensures the accuracy of the reference atomization intensity.

[0017] Furthermore, the feedback control quantity Calculated using the following feedback control formula: , in, and The gain coefficient is a constant. This refers to visibility deviation.

[0018] Based on the aforementioned technical means, this invention also employs a proportional-integral (PI) control algorithm to calculate the feedback control quantity. The proportional term rapidly responds to changes in visibility, while the integral term accumulates and eliminates steady-state deviations in visibility. This ensures that even with persistent minor disturbances, the system can continuously fine-tune the pump output through integral action, ultimately precisely locking the visibility at the target value. superior.

[0019] Furthermore, in S5, when At that time, the exhaust control amount used to adjust the exhaust unit This is determined through the following relationship: , in, The gain coefficient is a constant. This is the maximum permissible drive value for the exhaust unit.

[0020] Based on the above technical means, the present invention uses the gain coefficient Control the amount of exhaust ventilation Linear mapping to The deviation is directly proportional to the exhaust fan's strength; the greater the deviation, the stronger the exhaust unit's drive, and vice versa. Simultaneously, this invention sets a control upper limit to prevent equipment damage or loss of control due to the calculated control quantity exceeding the exhaust fan's physical limits, thus ensuring the system's safe operation.

[0021] Furthermore, S2 also includes: acquiring the illumination simulation parameters and traffic light status in the test tunnel; S6 also includes: while maintaining the target visibility, coordinating to maintain or switch the illumination simulation parameters and traffic light status to construct a composite test scenario.

[0022] Based on the aforementioned technical means, this invention adds synchronous control of illumination simulation parameters and traffic light status to the closed-loop control of foggy environments. During the fog environment adjustment cycle, the system simultaneously and in parallel adjusts the illumination intensity and color temperature to simulate specific weather or time periods (such as dusk or overcast skies), and controls the color and timing of traffic lights to simulate intersection scenarios, together forming a complete and high-fidelity driving environment.

[0023] The present invention also provides a fog simulation control system for vehicle expected functional safety testing, for implementing the above-described method, the system comprising: Test tunnel; The atomizing unit includes a high-pressure water pump connected via a water supply line and multiple spray heads; The ventilation unit includes multiple variable frequency exhaust fans installed on the side wall of the test tunnel; The sensing unit includes multiple visibility sensors arranged longitudinally along the test tunnel, and a pressure sensor installed on the water supply pipeline. The control unit, which is signal-connected to the sensing unit, the atomizing unit, and the exhaust unit, is configured as follows: Acquire the measurement values ​​of the multiple visibility sensors and the water pressure value of the pressure sensor; The average visibility was determined based on multiple visibility measurements. ; Calculate target visibility and deviation ; when At that time, based on visibility deviation In response to water pressure deviation, adjust the high-pressure water pump and control the exhaust unit to stop; when At that time, based on the aforementioned visibility deviation Determine the exhaust intensity, adjust the exhaust unit, and maintain the current state of the high-pressure water pump.

[0024] Furthermore, the inner wall of the test tunnel is covered with radar-absorbing material.

[0025] Based on the aforementioned technical means, this invention applies electromagnetic wave absorption technology to actively and significantly attenuate the reflection intensity of vehicle-mounted millimeter-wave radar signals from the tunnel walls. In tunnels with ordinary walls, radar waves are strongly reflected, generating a large number of fixed, strong false echoes (clutter) that do not exist in open roads. After laying absorbing materials, wall reflection is greatly suppressed, making the electromagnetic environment inside the tunnel closer to the state of natural attenuation and diffusion of radar waves in open roads.

[0026] Furthermore, it also includes a traffic signal simulation system and a multi-scene lighting simulation system installed in the test tunnel. The control unit is also connected to the traffic signal simulation system and the multi-scene lighting simulation system to synchronously control the lighting environment and traffic signals during fog simulation.

[0027] The beneficial effects achieved by this invention are as follows: This invention, through real-time feedback and adjustment, can maintain the visibility within the test tunnel at a long-term level near the target value, solving the core problem of unreproducible test conditions and providing a reliable environmental benchmark for SOTIF testing. Simultaneously, the hierarchical strategy enables the system to distinguish the nature of deviations and take optimal measures, ensuring both steady-state accuracy and rapid suppression of large disturbances. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of the overall process of the present invention.

[0029] 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

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] The technical solution of this embodiment will be described in detail below with reference to the accompanying drawings.

[0036] This embodiment provides a fog simulation closed-loop control method and system for vehicle expected functional safety testing. The system structure mainly includes: a closed test tunnel approximately 50 meters long with radar-absorbing material lining its inner walls; an atomization unit consisting of a high-pressure water pump, pipelines, and an array of spray nozzles; an exhaust unit consisting of variable frequency exhaust fans installed on both sides of the tunnel; a sensing unit consisting of five visibility sensors evenly arranged along the longitudinal direction of the tunnel and a pipeline pressure sensor; a traffic signal simulation system conforming to GB 14886-2016; a multi-scenario LED lighting simulation system conforming to the illumination requirements of GB / T 38418-2019; and a central control unit integrating all control logic.

[0037] like Figure 1 As shown, the specific steps of the control method in this embodiment are as follows: S1. Set the target visibility value and visibility deviation threshold This is used to initialize the test environment parameters.

[0038] Set the target visibility for this test using the human-computer interface. =100m (simulated fog), deviation threshold =10m ( The 10% needs to be explained. The appropriate value should be selected based on the system's operating conditions and the target's visibility; for example, it could be set to... (5% to 15% to meet the requirements of response speed and control accuracy).

[0039] At the same time, the lighting system is set to "cloudy" mode (illuminance 3000lx, color temperature 6500K), and the traffic lights enter normal operation cycle.

[0040] S2. Real-time acquisition of the measurement values ​​of multiple visibility sensors arranged along the driving direction in the test tunnel, and real-time water pressure value P of the pressure sensor in the water supply main pipeline of the atomizing unit.

[0041] When the system starts up, the central control unit reads the values ​​of the five visibility sensors in real time (e.g., V1=50m, V2=55m, V3=60m, V4=58m, V5=52m) and the water supply main pipeline pressure value P=4.0MPa.

[0042] S3. Perform comprehensive analysis on the measurement values ​​of multiple visibility sensors to determine the current average visibility. .

[0043] Current average visibility =(50+55+60+58+52) / 5=55m.

[0044] S5. Based on the aforementioned visibility deviation With the threshold Based on the comparison results, a hierarchical control strategy is implemented.

[0045] Calculate visibility deviation =100-55=45m.

[0046] =45m> .

[0047] because The control unit maintains the current state of the high-pressure water pump while simultaneously calculating the exhaust control quantity. Assume the system parameters... =2.2, =100%.

[0048] =99%. The control unit drives the dual exhaust fans to operate at 99% power, generating a horizontal airflow to quickly disperse excess fog.

[0049] With the help of ventilation, visibility decreased rapidly. After about one minute, the sensor readings became: V1=100m, V2=90m, V3=98m, V4=92m, V5=95m. =95m, 5m, =5m< .

[0050] At this point, the control unit shuts down the exhaust unit.

[0051] Based on the calibration curve, the query was found =100m corresponding target water pressure =3.8MPa.

[0052] The water pressure deviation is 3.8-3.5 (current P) = 0.3 MPa.

[0053] The feedforward controller calculates the feedforward control quantity based on the water pressure deviation. The purpose is to increase water pressure.

[0054] Feedback controller based on visibility deviation Calculate the feedback control quantity using 5m. This is designed to fine-tune the amount of atomization.

[0055] Integrated feedforward control quantity With feedback control quantity To obtain the water pump control quantity Send a command to increase the speed of the high-pressure water pump.

[0056] The system operated continuously, and visibility was stably maintained within the range of 100m ± 5m. During this period, feedback control variables... It continuously makes minor adjustments and automatically compensates for changes in atomization efficiency caused by slight increases in water temperature or slight buildup of scale in the nozzle, without requiring manual intervention.

[0057] The above steps 2 to 5 are repeated continuously until the test task time ends, at which point the system automatically shuts down all units such as atomization, lighting, and signaling.

[0058] 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 fog simulation control method for testing the expected functional safety of vehicles, characterized in that, Includes the following steps: S1. Set the target visibility value and visibility deviation threshold This is to initialize the test environment parameters; S2. Real-time acquisition of the measurement values ​​of multiple visibility sensors arranged along the driving direction in the test tunnel, and real-time water pressure value P of the pressure sensor in the water supply main pipeline of the atomizing unit. S3. Perform comprehensive analysis on the measurement values ​​of multiple visibility sensors to determine the current average visibility. To characterize the overall fog concentration distribution within the test tunnel; S4. The target visibility value With the current average visibility Compare and determine the visibility deviation. , ; S5. Based on the aforementioned visibility deviation With the threshold Based on the comparison results, a hierarchical control strategy is implemented: when At that time, based on visibility deviation If there is a water pressure deviation, adjust the high-pressure water pump and control the exhaust unit to stop; when At that time, based on the aforementioned visibility deviation Determine the exhaust intensity, adjust the exhaust unit, and maintain the current state of the high-pressure water pump; S6: Repeat steps S2 to S5 to continuously perform closed-loop control of the foggy environment until the simulation test task ends.

2. The fog simulation control method for vehicle expected functional safety testing according to claim 1, characterized in that, In step S3, the current average visibility is determined by performing an arithmetic average of the measurements from the multiple visibility sensors. .

3. The fog simulation control method for vehicle expected functional safety testing according to claim 1, characterized in that, In S5, when Adjusting the high-pressure water pump specifically includes the following steps: S51, Based on target visibility value Determine the corresponding target water pressure ; S52, Target water pressure Compare the water pressure with the real-time water pressure value P to determine the water pressure deviation; S53. Determine the feedforward control quantity based on the water pressure deviation. And based on visibility deviation Perform analysis to determine the feedback control quantity. ; S54, Combine the aforementioned feedforward control quantities With the feedback control quantity Determine the water pump control quantity This generates control commands for the high-pressure water pump.

4. The fog simulation control method for vehicle expected functional safety testing according to claim 3, characterized in that, The feedforward control quantity Calculated using the following feedforward control formula: ， in, and The gain coefficient is a constant. For target visibility The corresponding target water pressure value, where P is the real-time water pressure value.

5. The fog simulation control method for vehicle expected functional safety testing according to claim 3, characterized in that, The feedback control quantity Calculated using the following feedback control formula: ， in, and The gain coefficient is a constant. This refers to visibility deviation.

6. The fog simulation control method for vehicle expected functional safety testing according to claim 1, characterized in that, In S5, when At that time, the exhaust control amount used to adjust the exhaust unit This is determined through the following relationship: ， in, The gain coefficient is a constant. This is the maximum permissible drive value for the exhaust unit.

7. The fog simulation control method for vehicle expected functional safety testing according to any one of claims 1-6, characterized in that, S2 further includes: acquiring simulated lighting parameters and traffic light status within the test tunnel; S6 further includes: while maintaining the target visibility, coordinating the maintenance or switching of the illumination simulation parameters and traffic light status to construct a composite test scenario.

8. A fog simulation control system for testing the intended functional safety of vehicles, characterized in that, The system for implementing the method of claim 7 comprises: Test tunnel; The atomizing unit includes a high-pressure water pump connected via a water supply line and multiple spray heads; The ventilation unit includes multiple variable frequency exhaust fans installed on the side wall of the test tunnel; The sensing unit includes multiple visibility sensors arranged longitudinally along the test tunnel, and a pressure sensor installed on the water supply pipeline. The control unit, which is signal-connected to the sensing unit, the atomizing unit, and the exhaust unit, is configured as follows: Acquire the measurement values ​​of the multiple visibility sensors and the water pressure value of the pressure sensor; The average visibility was determined based on multiple visibility measurements. ; Calculate target visibility and deviation ; when At that time, based on visibility deviation In response to water pressure deviation, adjust the high-pressure water pump and control the exhaust unit to stop; when At that time, based on the aforementioned visibility deviation Determine the exhaust intensity, adjust the exhaust unit, and maintain the current state of the high-pressure water pump.

9. The fog simulation control system for vehicle expected functional safety testing according to claim 8, characterized in that, The inner wall of the test tunnel is covered with radar-absorbing material.

10. The fog simulation control system for vehicle expected functional safety testing according to claim 8, characterized in that, It also includes a traffic signal simulation system and a multi-scene lighting simulation system installed in the test tunnel. The control unit is also connected to the traffic signal simulation system and the multi-scene lighting simulation system to synchronously control the lighting environment and traffic signals during fog simulation.

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