Vehicle acc simulation test system based on traffic flow characteristics

Abstract

This application relates to the field of vehicle simulation test control technology, specifically to a vehicle ACC simulation test system based on traffic flow characteristics. The system includes: a traffic flow simulation module and a sensor simulation module for acquiring the simulated vehicle speed and the simulated speed of the vehicle ahead in the same lane; the ACC control module is divided into a variation analysis unit, a proportional control unit, and a PID control unit. The variation analysis unit is used to acquire high-frequency complexity and obtain the cooperative variation degree at each acquisition time; the proportional control unit is used to adjust the preset proportional parameters of the PID controller according to the changing trend of the cooperative variation degree; the PID control unit is used to control and adjust the vehicle speed of the simulated vehicle in the ACC simulation test. This application improves the control accuracy of the vehicle during the simulation test.

Description

Technical Field

[0001] This application relates to the field of vehicle simulation test control technology, specifically to a vehicle ACC simulation test system based on traffic flow characteristics. Background Technology

[0002] Adaptive Cruise Control (ACC) primarily relies on lidar or visual sensors to perceive vehicles ahead in the lane and uses the perceived data to control the vehicle's braking or accelerator in real time, automatically adjusting speed to maintain a safe following distance. Currently, most ACC tests utilize six-axis motion platforms for vehicle motion simulation and simulation software. This software creates a virtual traffic scenario with traffic flow characteristics, establishes a vehicle dynamics model, simulates the operation of onboard sensors, and imports control algorithms to simulate and test the vehicle's ACC.

[0003] In vehicle ACC simulation testing, existing technologies generally employ PID (Proportion Integration Differential) controllers to control and adjust vehicle speed in real time, ensuring the safety and stability of following other vehicles during the simulation. However, existing PID controllers with fixed proportional parameters cannot respond promptly to complex changes in vehicle speed and have poor adaptability to complex following scenarios, easily leading to lag in vehicle speed control results, thus affecting the safety and stability of vehicle driving during ACC simulation testing. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a vehicle ACC simulation test system based on traffic flow characteristics. The specific technical solution adopted is as follows: This application proposes a vehicle ACC simulation test system based on traffic flow characteristics, the system comprising: The traffic flow simulation module is used to simulate road environments with traffic flow characteristics using simulation software. The sensor simulation module is used to simulate the on-board sensors of the simulated vehicle using simulation software, so as to obtain the simulated vehicle speed and the simulated speed of the vehicle in front in the same lane. The ACC control module is divided into a variation analysis unit, a proportional control unit, and a PID control unit. The specific implementation process of each unit is as follows: The variation analysis unit is used to analyze the degree of relative speed change between the vehicle and the preceding vehicle in a local short time at each acquisition moment, as well as the high-frequency peak fluctuation, in order to obtain the high-frequency complexity. Combined with the variation distribution of the high-frequency complexity, the degree of coordinated variation at each acquisition moment is obtained. The proportional adjustment unit is used to adjust the preset proportional parameters of the PID controller according to the changing trend characteristics of the cooperative variability in the vehicle ACC simulation test. The PID control unit is used to control and adjust the vehicle speed of the simulated vehicle in the ACC simulation test using a PID controller.

[0005] Preferably, the simulated vehicle speed of the vehicle and the simulated speed of the vehicle in front in the same lane within a preset time period before each acquisition time are arranged in time sequence to form the vehicle speed sequence and the vehicle speed sequence of the vehicle in front at each acquisition time. The difference sequence between the vehicle speed sequence and the vehicle speed sequence of the vehicle in front is used as the relative speed sequence at each acquisition time. The modal components of the relative speed sequence are obtained by modal decomposition.

[0006] Preferably, the process for obtaining the high-frequency complexity is as follows: In the formula, Let be the high-frequency complexity of the j-th modal component corresponding to the t-th acquisition time. Let z be the Shannon entropy of all elements in the first-order difference sequence of the j-th modal component corresponding to the t-th acquisition time, and z be a constant to avoid the denominator being zero. It is the mean of all first-order difference values ​​in the peak position sequence of the j-th modal component corresponding to the t-th acquisition time.

[0007] Preferably, peak detection is performed on the modal components, and the peak positions of all the modal components are arranged in ascending order to form the peak position sequence of each modal component.

[0008] Preferably, the process for obtaining the degree of coordinated variation at each acquisition time is as follows: In the formula, Let be the degree of coordinated variation at the t-th data collection time. Let be the proportion of high-frequency complex data at the t-th acquisition time. It is the average high-frequency complexity of all modal components corresponding to the t-th acquisition time that is greater than or equal to the segmentation threshold.

[0009] Preferably, the process of obtaining the high-frequency complexity ratio is as follows: the average high-frequency complexity of all modal components corresponding to each acquisition time is used as the segmentation threshold, and the proportion of the number of high-frequency complexities greater than or equal to the segmentation threshold among the number of high-frequency complexities of all modal components corresponding to each acquisition time is used as the high-frequency complexity ratio corresponding to each acquisition time.

[0010] Preferably, the normalized results of all coordinated variation degrees within a preset time period prior to the current acquisition time are arranged in chronological order to form the coordinated variation sequence at the current acquisition time, and the trend change components of the coordinated variation sequence are extracted using a time series decomposition algorithm.

[0011] Preferably, the process of adjusting the preset proportional parameters of the PID controller is as follows: In the formula, This represents the desired scale parameter at the current acquisition time. Preset the proportional parameter in the PID controller. () is a sign function. This represents the fitting slope of the trend change component corresponding to the current data acquisition moment. This is the cumulative trend value at the current data collection moment.

[0012] Preferably, the sum of all first-order difference values ​​in the trend change component corresponding to the current acquisition time is used as the trend accumulation value at the current acquisition time.

[0013] Preferably, the desired proportional parameter, preset integral parameter, and derivative parameter are used as the controller parameters of the PID controller, and the speed error between the simulated vehicle speed and the simulated speed of the vehicle in the same lane is combined to adjust the simulated vehicle speed in the ACC simulation test.

[0014] This application has the following beneficial effects: This application analyzes the modal characteristics of the relative speed between the vehicle and the vehicle in front, extracts the high-frequency peak fluctuations and the degree of disorder of modal changes in each modal component, and thus more accurately measures the high-frequency modal complexity of the relative speed change between the vehicle and the vehicle in front, which is beneficial to improving the safety and stability of following vehicle driving simulation in subsequent vehicle ACC simulation tests. Furthermore, this application accurately measures the multimodal cooperative variability of the relative speed change between the vehicle and the preceding vehicle, thereby more clearly reflecting the complex components of the relative speed change between the vehicle and the preceding vehicle. This is beneficial for accurately fine-tuning the proportional parameters of the PID controller and avoiding the problem of high lag in the vehicle speed control results. This application uses the change characteristics of the degree of coordinated variation in a short period of time before the current acquisition time to accurately fine-tune the preset proportional parameter of the PID controller, so that the adjusted PID controller can respond more promptly to complex changes in relative vehicle speed, and avoid oscillations caused by excessively large proportional parameters during vehicle speed control, thereby improving the safety and stability of following the vehicle during ACC simulation testing. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A block diagram of a vehicle ACC simulation test system based on traffic flow characteristics provided in one embodiment of this application; Figure 2 The flowchart illustrates the implementation process of each unit of the ACC control module provided in one embodiment of this application. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the vehicle ACC simulation test system based on traffic flow characteristics proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of the vehicle ACC simulation test system based on traffic flow characteristics provided in this application.

[0020] Please see Figure 1 The diagram illustrates a block diagram of a vehicle ACC simulation test system based on traffic flow characteristics according to an embodiment of this application. The system includes: In this embodiment, the vehicle ACC simulation test system based on traffic flow characteristics includes a traffic flow simulation module, a sensor simulation module, and an ACC control module. Specifically, the hardware of the vehicle ACC simulation test system based on traffic flow characteristics in this embodiment is a six-axis motion platform supporting the simulated vehicle, supported by six electric cylinders. The load-bearing platform and the piston rod ends of the electric cylinders are connected by six sets of Hooke hinges, and the cylinder body ends of the electric cylinders are also connected to the lower platform by six sets of Hooke hinges. The six electric cylinders are driven by servo motors.

[0021] The traffic flow simulation module is used to simulate road environments with traffic flow characteristics using simulation software.

[0022] In this embodiment, PreScan simulation software is used to simulate a road environment with traffic flow characteristics. The dynamic elements of traffic flow mainly consist of vehicles, pedestrians, and non-motorized vehicles. A multi-lane traffic flow simulation model is constructed for vehicle ACC simulation testing.

[0023] The sensor simulation module is used to simulate the on-board sensors of a simulated vehicle using simulation software, in order to obtain the simulated vehicle speed and the simulated speed of the vehicle in front in the same lane.

[0024] In this embodiment, the sensor simulation module uses PreScan simulation software to perform target-level simulation of the vehicle's onboard sensors, such as lidar, millimeter-wave radar, and camera sensors. It then performs vehicle ACC (Adaptive Cruise Control) simulation within a traffic flow simulation model. The target-level simulated onboard sensors collect the simulated vehicle's own speed and the simulated speed of the vehicle ahead in the same lane, providing vehicle speed information to the ACC control module. The data collection time interval is specified in the model. It takes 0.05 seconds.

[0025] The ACC control module is divided into a variation analysis unit, a proportional control unit, and a PID control unit. The flowchart of each unit's implementation process is shown below. Figure 2 As shown. The implementation process of each unit is as follows: Variation Analysis Unit: Used to analyze the degree of relative speed change between the vehicle and the preceding vehicle in a local short time at each acquisition moment, as well as the high-frequency peak fluctuation, in order to obtain the high-frequency complexity. Combined with the variation distribution of the high-frequency complexity, the degree of coordinated variation at each acquisition moment is obtained.

[0026] Due to the inherent complexity of following scenarios with traffic flow characteristics, fixed-proportional PID controllers cannot respond promptly to complex changes in relative vehicle speed and have poor adaptability to complex following scenarios. This can easily lead to significant lag in vehicle speed control results, resulting in a higher collision risk during vehicle ACC simulation testing. Therefore, to improve the safety and stability of following vehicles during ACC simulation testing, it is necessary to analyze the characteristics of relative vehicle speed changes in complex following scenarios.

[0027] Therefore, to analyze the short-term relative speed during the vehicle ACC simulation test, the simulated speed of the vehicle itself and the simulated speed of the vehicle in front in the same lane within a preset time period before each acquisition moment are arranged in chronological order to obtain the vehicle speed sequence and the vehicle speed sequence of the vehicle in front at each acquisition moment. The difference sequence between the vehicle speed sequence and the vehicle speed sequence of the vehicle in front is calculated and recorded as the relative speed sequence at each acquisition moment, reflecting the change in relative speed between the vehicle and the vehicle in front in a short period of time. In this embodiment, the preset time period is 1 second.

[0028] Furthermore, the modal characteristics of the relative speed between the vehicle and the preceding vehicle are analyzed. The relative speed sequence at each acquisition time is used as the input to the mode decomposition algorithm. The mode decomposition algorithm can be an empirical mode decomposition algorithm or a variational mode decomposition algorithm. In this embodiment, the empirical mode decomposition algorithm is used to obtain each modal component of the relative speed sequence at each acquisition time. The empirical mode decomposition algorithm is a well-known technology, and the specific process will not be described in detail.

[0029] Generally, if the high-frequency peak fluctuations in each modal component of the relative speed change between the vehicle and the vehicle in front are more significant and the degree of disorder in the modal changes in each modal component is higher, it can more clearly illustrate the high-frequency modal complexity of the relative speed change between the vehicle and the vehicle in front, which will significantly affect the safety and stability of the following vehicle simulation.

[0030] Therefore, to extract the high-frequency modal variation features of each modal component, each modal component at each acquisition time is used as input to the Automatic Multiscale Peak Detection algorithm. The algorithm obtains the peak position sequence of each modal component and arranges them in ascending order, resulting in the peak position sequence of each modal component at each acquisition time. The smaller the mean of all first-order differences in the peak position sequence, the more significant the high-frequency peak fluctuations in the modal component. The peak detection algorithm is a well-known technique, and its specific process will not be elaborated further.

[0031] Simultaneously, the first-order difference sequence of each modal component corresponding to each acquisition time is calculated. The first-order difference sequence reflects the modal change characteristics in the modal component. The larger the Shannon entropy of all elements in the first-order difference sequence, the higher the degree of disorder of the modal change in the modal component.

[0032] Based on the above analysis, the high-frequency complexity of each modal component at each acquisition time is calculated: In the formula, Let be the high-frequency complexity of the j-th modal component corresponding to the t-th acquisition time. Let be the Shannon entropy of all elements in the first-order difference sequence of the j-th modal component corresponding to the t-th acquisition time. Let z be the mean of all first-order differences in the peak position sequence of the j-th modal component corresponding to the t-th acquisition time, and z be a constant to avoid the denominator being zero, with a value ranging from 0 to 0.005, and a value of 0.001 in this embodiment. The calculation of Shannon entropy is a well-known technique, and the specific process will not be described in detail.

[0033] It is understandable that high-frequency complexity reflects the high-frequency modal complexity of the relative speed change between the vehicle and the vehicle in front. The higher the high-frequency complexity, the more prominent the high-frequency complexity within that modal component. In this case, the difficulty of controlling the vehicle speed is greater, which can easily affect the safety and stability of the following vehicle simulation.

[0034] Furthermore, the mean of the high-frequency complexity of all modal components at each acquisition time is used as the segmentation threshold. The number of high-frequency complexities greater than or equal to the segmentation threshold is counted. The proportion of the number of high-frequency complexities greater than or equal to the segmentation threshold in the total number of high-frequency complexities of all modal components at each acquisition time is recorded as the high-frequency complexity proportion at each acquisition time. The larger the high-frequency complexity proportion, the more modal components in the corresponding time exhibit high-frequency complexity characteristics, reflecting that the relative speed change between the vehicle and the vehicle in front is more complex at this time.

[0035] Generally, if the proportion of high-frequency complexity at a certain acquisition moment is larger, and the average high-frequency complexity at that acquisition moment is greater than or equal to the segmentation threshold, it can better reflect the complex changes in the relative speed between the vehicle and the vehicle in front. In this case, the PID controller needs to respond more promptly to the complex changes in relative speed to avoid causing a high lag in the control results of the vehicle speed.

[0036] Based on the above analysis, the degree of coordinated variation at each acquisition time is calculated: In the formula, Let be the degree of coordinated variation at the t-th data collection time. Let be the proportion of high-frequency complex data at the t-th acquisition time. It is the average high-frequency complexity of all modal components corresponding to the t-th acquisition time that is greater than or equal to the segmentation threshold.

[0037] Understandably, the degree of cooperative variation reflects the multimodal cooperative variation of the relative speed change between the vehicle and the vehicle in front. The greater the degree of cooperative variation, the stronger the cooperative variation among the multiple high-frequency complex modes of relative speed change. This more clearly reflects the complex components of the relative speed change between the vehicle and the vehicle in front. At this time, the PID controller needs to respond to the complex changes in relative speed more promptly to avoid the problem of high lag in the vehicle speed control results.

[0038] Proportional adjustment unit: used to adjust the preset proportional parameters of the PID controller according to the changing trend characteristics of the cooperative variability in the vehicle ACC simulation test.

[0039] To improve the adaptability of the ACC control module to complex following scenarios, it is necessary to analyze the changes in the degree of coordination during the vehicle ACC simulation test in real time. This allows for accurate fine-tuning of the proportional parameters of the PID controller, enabling the PID controller to respond more promptly to complex changes in relative vehicle speed and thus avoiding the problem of high lag in the vehicle speed control results.

[0040] Therefore, the normalized results of the degree of coordinated variation of all acquisition times within one second prior to the current acquisition time are arranged in chronological order to form the sequence of coordinated variation at the current acquisition time, reflecting the changing characteristics of the degree of coordinated variation in the short period prior to the current acquisition time. This embodiment uses the range normalization method; however, in practical applications, implementers can choose other existing normalization methods, and this embodiment does not impose any special restrictions on this.

[0041] Generally, if the overall trend of all cooperative variation degrees in the cooperative variation sequence is upward, it indicates that the complex changes in the relative speed between the vehicle and the vehicle in front are becoming more and more prominent. At this time, the proportional parameter of the PID controller should be increased. The greater the cumulative difference in the upward trend, the higher the degree of complex changes in the relative speed between the vehicle and the vehicle in front. In this case, the greater the increase in the proportional parameter of the PID controller, the more timely the PID controller can respond to the complex changes in relative speed, thus improving the safety of following the vehicle during the ACC simulation test.

[0042] Conversely, if the overall trend of all cooperative variation degrees in the cooperative variation sequence is downward, it indicates that the complex changes in relative speed between the vehicle and the vehicle in front are constantly weakening. At this time, the proportional parameter of the PID controller should be lowered. The greater the cumulative difference in the downward trend, the lower the degree of complex changes in relative speed between the vehicle and the vehicle in front. In this case, the greater the reduction in the proportional parameter of the PID controller, the better to avoid excessive proportional parameter causing oscillations in the vehicle speed control process and to improve the stability of following the vehicle during the ACC simulation test.

[0043] Therefore, the co-variation sequence at the current acquisition time is used as the input of the STL time series decomposition algorithm (Seasonal-Trend Decomposition Procedure based on Loess). The trend change component of the co-variation sequence at the current acquisition time is obtained through the STL time series decomposition algorithm. The trend change component corresponding to the current acquisition time is used as the input of the least squares linear fitting algorithm. The fitting slope of the trend change component corresponding to the current acquisition time is obtained through the least squares linear fitting algorithm. The STL time series decomposition algorithm and the least squares linear fitting algorithm are both well-known techniques, and the specific process will not be described in detail.

[0044] Simultaneously, the sum of all first-order difference values ​​in the trend change component corresponding to the current acquisition time is calculated and recorded as the trend accumulation value at the current acquisition time. The larger the trend accumulation value, the greater the cumulative difference in the trend of change in the coordinated change sequence. At this time, the greater the adjustment of the proportional parameter of the PID controller, the better the safety and stability of following the vehicle during the ACC simulation test.

[0045] Therefore, based on the above analysis, the expected scale parameter at the current acquisition time is calculated: In the formula, This represents the desired scale parameter at the current acquisition time. A preset proportional parameter is used in the PID controller. The preset proportional parameter has a value range of (4.16, 8). In this embodiment, the preset proportional parameter is 5.5. () is a sign function. This represents the fitting slope of the trend change component corresponding to the current data acquisition moment. This is the cumulative trend value at the current acquisition time. Additionally, the PID controller has preset integral parameters. The value range is (5,7), and the preset differential parameter is... The value range is (0.28, 1). In this embodiment, the preset integral parameter and the preset differential parameter are 6.5 and 0.7, respectively.

[0046] By accurately fine-tuning the preset proportional parameters of the PID controller through the change characteristics of all cooperative variation degrees in the cooperative variation sequence at the current acquisition time, the adjusted PID controller can respond more promptly to complex changes in relative vehicle speed, and avoid oscillations during vehicle speed control caused by excessively large proportional parameters, thereby improving the safety and stability of following the vehicle during ACC simulation testing.

[0047] PID control unit: Used to control and adjust the speed of the simulated vehicle in the ACC simulation test using a PID controller.

[0048] A PID controller is used to control and adjust the speed of the simulated vehicle in the ACC simulation test, so as to achieve the desired proportional parameter. Preset integration parameters Preset differential parameters As the controller parameter of the PID controller, the speed error between the simulated vehicle speed and the simulated speed of the vehicle in front in the same lane is calculated, and the calculated speed error is used as the input variable of the PID controller. The PID controller outputs a control signal to the vehicle dynamics model, and the vehicle dynamics model updates the execution speed information of the simulated vehicle in real time to control and adjust the vehicle speed in the ACC simulation test.

[0049] Preferably, the vehicle dynamics module uses CarSim software to perform vehicle dynamics modeling, obtains a vehicle dynamics model, and calculates the motion state of the vehicle during the ACC simulation test, and updates the pose information and execution speed information of the simulated vehicle in real time.

[0050] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0051] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0052] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A vehicle ACC simulation test system based on traffic flow characteristics, characterized in that, The system includes: The traffic flow simulation module is used to simulate road environments with traffic flow characteristics using simulation software. The sensor simulation module is used to simulate the on-board sensors of the simulated vehicle using simulation software, so as to obtain the simulated vehicle speed and the simulated speed of the vehicle in front in the same lane. The ACC control module is divided into a variation analysis unit, a proportional control unit, and a PID control unit. The specific implementation process of each unit is as follows: The variation analysis unit is used to analyze the degree of relative speed change between the vehicle and the preceding vehicle in a local short time at each acquisition moment, as well as the high-frequency peak fluctuation, in order to obtain the high-frequency complexity. Combined with the variation distribution of the high-frequency complexity, the degree of coordinated variation at each acquisition moment is obtained. The proportional adjustment unit is used to adjust the preset proportional parameters of the PID controller according to the changing trend characteristics of the cooperative variability in the vehicle ACC simulation test. The PID control unit is used to control and adjust the vehicle speed of the simulated vehicle in the ACC simulation test using a PID controller.

2. The vehicle ACC simulation test system based on traffic flow characteristics as described in claim 1, characterized in that, The simulated vehicle speed of the vehicle and the simulated speed of the vehicle in front in the same lane within a preset time period before each acquisition time are arranged in time sequence to form the vehicle speed sequence and the vehicle speed sequence of the vehicle in front at each acquisition time. The difference sequence between the vehicle speed sequence and the vehicle speed sequence of the vehicle in front is used as the relative speed sequence at each acquisition time. The modal components of the relative speed sequence are obtained by modal decomposition.

3. The vehicle ACC simulation test system based on traffic flow characteristics as described in claim 2, characterized in that, The process of obtaining the high-frequency complexity is as follows: In the formula, Let be the high-frequency complexity of the j-th modal component corresponding to the t-th acquisition time. Let z be the Shannon entropy of all elements in the first-order difference sequence of the j-th modal component corresponding to the t-th acquisition time, and z be a constant to avoid the denominator being zero. It is the mean of all first-order difference values ​​in the peak position sequence of the j-th modal component corresponding to the t-th acquisition time.

4. The vehicle ACC simulation test system based on traffic flow characteristics as described in claim 2, characterized in that, Peak detection is performed on the modal components, and the peak positions of all peaks in each modal component are arranged in ascending order to form the peak position sequence of each modal component.

5. The vehicle ACC simulation test system based on traffic flow characteristics as described in claim 2, characterized in that, The process for obtaining the degree of coordinated change at each acquisition time is as follows: In the formula, Let be the degree of coordinated variation at the t-th data collection time. Let be the proportion of high-frequency complex data at the t-th acquisition time. It is the average high-frequency complexity of all modal components corresponding to the t-th acquisition time that is greater than or equal to the segmentation threshold.

6. The vehicle ACC simulation test system based on traffic flow characteristics as described in claim 5, characterized in that, The process of obtaining the high-frequency complexity ratio is as follows: the average high-frequency complexity of all modal components corresponding to each acquisition time is used as the segmentation threshold, and the proportion of the number of high-frequency complexities greater than or equal to the segmentation threshold among the number of high-frequency complexities of all modal components corresponding to each acquisition time is used as the high-frequency complexity ratio corresponding to each acquisition time.

7. The vehicle ACC simulation test system based on traffic flow characteristics as described in claim 1, characterized in that, The normalized results of all the cooperative variation degrees within the preset time period before the current acquisition time are arranged in chronological order to form the cooperative variation sequence at the current acquisition time. The trend change component of the cooperative variation sequence is extracted by using a time series decomposition algorithm.

8. The vehicle ACC simulation test system based on traffic flow characteristics as described in claim 1, characterized in that, The process of adjusting the preset proportional parameters of the PID controller is as follows: In the formula, This represents the desired scale parameter at the current acquisition time. Preset the proportional parameter in the PID controller. () is a sign function. This represents the fitting slope of the trend change component corresponding to the current data acquisition moment. This is the cumulative trend value at the current data collection moment.

9. The vehicle ACC simulation test system based on traffic flow characteristics as described in claim 8, characterized in that, The sum of all first-order difference values ​​in the trend change component corresponding to the current acquisition time is used as the trend accumulation value at the current acquisition time.

10. The vehicle ACC simulation test system based on traffic flow characteristics as described in claim 8, characterized in that, The desired proportional parameter, preset integral parameter, and derivative parameter are used as the controller parameters of the PID controller. Combined with the speed error between the simulated vehicle speed and the simulated speed of the vehicle ahead in the same lane, the vehicle speed of the simulated vehicle in the ACC simulation test is adjusted.

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