A highway ramp speed measurement error compensation method based on a binary polynomial fitting

Abstract

The application discloses a highway ramp speed measurement error compensation method based on a binary polynomial fitting. First, the measured ramp speed measurement environment is deployed, and time is matched. Second, the standard speed of the test vehicle, the speed measurement value and the distance measured by the radar speed measurement instrument at the same time are collected, and the measured ramp speed measurement data sample set is made. Then, the ramp speed fitting model is established, the training sample set is input into the ramp speed fitting model for data fitting and calibration. Finally, the calibrated binary polynomial is obtained, the ramp vehicle speed measurement value of the radar speed measurement instrument which needs to be corrected and the captured setting distance are input into the obtained binary polynomial for calculation, and the standard speed after error compensation is output. The application captures the ramp road environment characteristics through data fitting and calibration, realizes adaptive error compensation and measurement value correction of the ramp vehicle speed measurement of the radar speed measurement instrument, and avoids complex mathematical modeling.

Description

Technical Field

[0001] This invention belongs to the field of road traffic detection technology and relates to a method for compensating for speed measurement errors on highway ramps based on bivariate polynomial fitting. Background Technology

[0002] Ramps are bottlenecks in highway traffic. Ramp accidents often involve hazardous material transport vehicles, freight trucks, and large buses. The large amounts of spilled cargo slow down rescue efforts, frequently necessitating road closures and severely impacting traffic. This can easily trigger secondary accidents, further causing casualties and property damage. As a key traffic safety hazard, highway ramps are a crucial daily task for traffic management departments in accident prevention and a vital tool for effective traffic safety management. Therefore, effectively controlling speeds on ramps, identifying relevant accident prevention points, and substantially reducing the probability of traffic accidents are imperative.

[0003] Speed ​​measurement on ramps is a core issue in ramp speed control systems. However, existing radar speed detectors on the market can only accurately measure the speed of vehicles traveling on straight, flat roads, and cannot effectively measure the speed of vehicles on ramps with varying slopes and curvatures. Therefore, how to implement adaptive error compensation and measurement correction for ramp speed measurements based on existing radar speed detectors to achieve accurate speed measurement on ramps with different slopes and curvatures is a pressing problem in current road traffic detection technology. Summary of the Invention

[0004] To address the aforementioned problems, this invention provides a method for compensating for speed measurement errors on highway ramps based on bivariate polynomial fitting. By using data fitting and calibration, the method captures the environmental characteristics of the ramp road, enabling adaptive error compensation and measurement value correction for vehicle speed measurement on ramps by radar speed measuring instruments. This overcomes the technical deficiency of existing radar speed measuring instruments in being unable to effectively measure the speed of vehicles on ramps.

[0005] The technical solution of this invention is a method for compensating for speed measurement errors on highway ramps based on bivariate polynomial fitting. This method involves calibrating the radar speedometers at ramp speed measurement points using a high-precision roadside millimeter-wave radar, establishing a ramp speed measurement fitting model, and outputting the corrected ramp speed measurement value from the radar speedometer after error compensation. The method includes the following steps:

[0006] Step 1, Deploy the speed measurement calibration environment for the ramp to be tested:

[0007] The millimeter-wave radar is fixed at a speed measurement point on a ramp to be calibrated in order to obtain the speed measurement value of vehicles traveling on the ramp and their distance from the speed measurement point.

[0008] A non-contact speedometer is installed on the test vehicle to obtain the standard speed of the test vehicle at the current moment; and GPS and Beidou dual-satellite time synchronization is used.

[0009] Step 2: Register the system time of the millimeter-wave radar with the standard time of the GPS and Beidou dual-satellite time synchronization on the test vehicle;

[0010] Step 3: Perform the speed measurement calibration test on the ramp under test.

[0011] The test vehicle traveled through the test ramp at different speeds, and the standard speed of the test vehicle and the speed measurement value and distance measured by the millimeter-wave radar were collected at the same time.

[0012] Step 4, create the speed measurement calibration data sample set S for the tested ramp:

[0013] Define the vehicle speed measured by the millimeter-wave radar at the same moment as x, the distance as y, and the standard vehicle speed as z;

[0014] Definition (x) i, y i ,z i A set of collected samples is used to create a speed calibration data sample set S = {(x1,y1,z1),(x2,y2,z2),…(x...}. n ,y n ,z n )}, where i = 1, 2, 3, ..., n, and n is the number of samples collected;

[0015] Step 5, construct the training sample set S0 and the test sample set S1:

[0016] The collected samples from the speed measurement calibration data sample set S of the ramp to be tested are randomly assigned to the training sample set S0 and the test sample set S1;

[0017] Step 6: Establish a ramp speed measurement fitting model and input the training sample set S0 into the ramp speed measurement fitting model for data fitting and calibration; the ramp speed measurement fitting model consists of two parts: a fitting polynomial and an error function.

[0018] Step 7: Substitute the test sample set S1 into the calibrated fitting polynomial for testing, and calculate the average error E output by the fitting polynomial; set the error threshold h. When E>h, it means that the fitting effect is not ideal, so return to step 4; otherwise, proceed to step 8.

[0019] Step 8: Obtain the calibrated fitting polynomial;

[0020] Step 9: Input the radar speedometer ramp speed measurement value and the capture setting distance that need to be corrected into the acquired fitting polynomial for calculation, and output the standard speed after error compensation.

[0021] The beneficial effects of this invention are:

[0022] This invention provides a method for compensating for speed measurement errors on highway ramps based on bivariate polynomial fitting. By capturing the environmental characteristics of the ramp road through data fitting and calibration, it achieves adaptive error compensation and measurement correction for vehicle speed measurements on ramps using radar speedometers, avoiding complex mathematical modeling. This method is simple, practical, and highly flexible, unaffected by physical factors such as ramp slope and curvature, and can be widely applied in practical engineering applications. Attached Figure Description

[0023] Figure 1 This is a flowchart of the present invention.

[0024] Figure 2 This is a schematic diagram of the deployment environment for the ramp speed measurement calibration of the present invention. Detailed Implementation

[0025] The present invention will be further described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0026] Reference Figures 1-2 The technical solution in this embodiment is a method for compensating for speed measurement errors on highway ramps based on bivariate polynomial fitting. The implementation process is as follows: Figure 1 As shown:

[0027] First, the speed measurement calibration environment of the ramp to be measured is deployed and time registration is performed.

[0028] Secondly, a speed calibration test is performed on the ramp under test. The standard vehicle speed of the test vehicle and the speed measurement value and distance measured by the radar speed measuring instrument are collected at the same time. A speed calibration data sample set of the ramp under test is then created, including the construction of a training sample set and a test sample set.

[0029] Then, a ramp speed measurement fitting model is established, and the constructed training sample set is input into the ramp speed measurement fitting model for data fitting and calibration. The fitting effect of the calibrated ramp speed measurement fitting model is tested using the test sample set.

[0030] Finally, the bivariate polynomial in the calibrated ramp speed measurement fitting model is obtained. The ramp speed measurement value of the radar speedometer that needs to be corrected and the capture setting distance are input into the obtained bivariate polynomial for calculation, and the standard speed after error compensation is output.

[0031] The specific technical solution of this embodiment is as follows:

[0032] Step 1, Deploy the speed measurement calibration environment for the ramp to be tested:

[0033] like Figure 2 As shown, the deployment of the ramp speed measurement calibration environment includes two parts: high-precision roadside millimeter-wave radar deployment and test vehicle deployment.

[0034] First, a millimeter-wave radar is fixed at a designated speed measurement point on the ramp to obtain the speed measurement value of vehicles traveling on the ramp and their distance from the speed measurement point. Then, a non-contact speedometer is installed on the test vehicle, and GPS and Beidou dual-satellite timing is used. An external large screen is placed on the windshield of the test vehicle to obtain the standard speed of the test vehicle at the current moment.

[0035] Step 2: Register the system time of the millimeter-wave radar equipment with the standard time of the test vehicle's GPS and Beidou dual-satellite time synchronization.

[0036] Step 3: Perform the speed measurement calibration test on the ramp under test.

[0037] The test vehicle traveled through the test ramp at different speeds, and the standard speed of the test vehicle and the speed measurement value and distance measured by the millimeter-wave radar were collected at the same time.

[0038] Step 4, create the speed measurement calibration data sample set S for the tested ramp:

[0039] The collected data is preprocessed to remove duplicate and outlier data. The vehicle speed measured by the millimeter-wave radar at the same time is defined as x, the distance as y, and the standard speed of the test vehicle as z.

[0040] Definition (x) i, y i ,z i Let (i = 1, 2, 3, ..., n) be a set of collected samples, and construct a speed measurement calibration data sample set S = {(x1, y1, z1), (x2, y2, z2), ..., (x n ,y n ,z n )}, where n is the number of samples collected.

[0041] Step 5, construct the training sample set S0 and the test sample set S1:

[0042] The collected samples from the speed measurement calibration data sample set S of the ramp to be tested are randomly assigned to the training sample set S0 and the test sample set S1, such that the ratio of the number of collected samples in the training sample set S0 to the number of collected samples in the test sample set S1 is 7:3.

[0043] Step 6: Establish a ramp speed measurement fitting model and input the training sample set S0 into the ramp speed measurement fitting model for data fitting and calibration.

[0044] The ramp speed measurement fitting model mainly consists of two parts: a fitting polynomial and an error function. The constructed bivariate polynomial is as follows:

[0045]

[0046] in The test vehicle standard speed is used for fitting, and a0, a1, a2, a3, a4 and a5 are the calibration coefficients of the bivariate polynomial.

[0047] The sum of squared residuals from the polynomial output is selected as the error function as follows:

[0048]

[0049] Where ε is the sum of squared residuals of the polynomial output, and m is the number of samples collected in the training sample set S0.

[0050] With the objective function of minimizing the sum of squared residuals ε, the least squares method is used to perform polynomial fitting on the collected sample data in the training sample set, and the coefficients of the fitted polynomial are obtained to complete the calibration of the bivariate polynomial.

[0051] Step 7: Substitute the test sample set S1 into the calibrated bivariate polynomial for testing, and calculate the average error E of the polynomial output as follows:

[0052]

[0053] Where k is the number of samples collected from the test sample set S1.

[0054] Set an error threshold h. If E>h, it means that the polynomial fitting effect is not ideal, then return to step 4; otherwise, proceed to step 8.

[0055] Step 8: Obtain the calibrated bivariate polynomial.

[0056] Step 9: Input the radar speedometer ramp speed measurement value and the capture setting distance that need to be corrected into the acquired binary polynomial for calculation, and output the standard speed after error compensation.

[0057] The above description is merely a specific implementation of the embodiments of this specification. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principles of the embodiments of this specification, and these improvements and modifications should also be considered within the protection scope of the embodiments of this specification.

Claims

1. A method for compensating for speed measurement errors on highway ramps based on bivariate polynomial fitting, characterized in that... The method includes the following steps: Step 1, Deploy the speed measurement calibration environment for the ramp to be tested: The millimeter-wave radar is fixed at a speed measurement point on a ramp to be calibrated in order to obtain the speed measurement value of vehicles traveling on the ramp and their distance from the speed measurement point. A non-contact speedometer is installed on the test vehicle to obtain the standard speed of the test vehicle at the current moment; and GPS and Beidou dual-satellite time synchronization is used. Step 2: Register the system time of the millimeter-wave radar with the standard time of GPS and Beidou dual-satellite time synchronization on the test vehicle; Step 3: Perform the speed measurement calibration test on the ramp under test. The test vehicle traveled through the test ramp at different speeds, and the standard speed of the test vehicle and the speed measurement value and distance measured by the millimeter-wave radar were collected at the same time. Step 4, create the speed measurement calibration data sample set S for the tested ramp: Define the vehicle speed measured by the millimeter-wave radar at the same moment as x, the distance as y, and the standard vehicle speed as z; Definition (x) i, y i ,z i A set of collected samples is used to create a speed calibration data sample set S = {(x1,y1,z1),(x2,y2,z2),…(x...}. n ,y n ,z n )}, where i = 1, 2, 3, ..., n, and n is the number of samples collected; Step 5, construct the training sample set S0 and the test sample set S1: The collected samples from the speed measurement calibration data sample set S of the ramp to be tested are randomly assigned to the training sample set S0 and the test sample set S1; Step 6: Establish a ramp speed measurement fitting model and input the training sample set S0 into the ramp speed measurement fitting model for data fitting and calibration; the ramp speed measurement fitting model consists of two parts: a fitting polynomial and an error function. Step 7: Substitute the test sample set S1 into the calibrated fitting polynomial for testing, and calculate the average error E output by the fitting polynomial; set the error threshold h. When E>h, it means that the fitting effect is not ideal, so return to step 4; otherwise, proceed to step 8. Step 8: Obtain the calibrated fitting polynomial; Step 9: Input the radar speedometer ramp speed measurement value and the capture setting distance that need to be corrected into the acquired fitting polynomial for calculation, and output the standard speed after error compensation.

2. The method for compensating for speed measurement errors on highway ramps based on bivariate polynomial fitting according to claim 1, characterized in that: Before creating the speed measurement calibration data sample set S for the tested ramp, the process also includes preprocessing the collected data to remove duplicate and abnormal data.

3. The method for compensating for speed measurement errors on highway ramps based on bivariate polynomial fitting according to claim 2, characterized in that: The ratio of the number of samples collected in the training sample set S0 to the number of samples collected in the test sample set S1 is 7:

3.

4. A method for compensating for speed measurement errors on highway ramps based on bivariate polynomial fitting according to any one of claims 1 to 3, characterized in that: The fitting polynomial is a bivariate polynomial, where the inputs of the polynomial are the vehicle speed measurement value x and the distance y measured by the radar speedometer, and the output is the standard vehicle speed z of the test vehicle.

5. The method for compensating for speed measurement errors on highway ramps based on bivariate polynomial fitting according to claim 4, characterized in that: The sum of squared residuals of the polynomial output is selected as the error function. The least squares method is used to fit the collected sample data in the training sample set to a polynomial. The coefficients of the fitted polynomial are then obtained, and the calibration of the bivariate polynomial is completed.

Images