A traffic speed monitoring method based on doppler shift enhancement algorithm

Abstract

The application discloses a traffic speed monitoring method based on a Doppler frequency shift enhancement algorithm and belongs to the technical field of traffic control, and specifically comprises the following steps: receiving a Doppler signal stream reflected by a tire of a vehicle in a target traffic direction lane, and synchronously collecting fluctuation deformation data of a lane surface layer material; calculating a frequency shift deviation amount caused by non-vehicle motion in the Doppler signal stream according to the fluctuation deformation data, and forming a corrected signal sequence after compensation; identifying a continuous section with a zero frequency shift rate of change in the corrected sequence as a stable driving trajectory section, and extracting an arithmetic average value of the frequency shift amount; converting the average value into a vehicle driving speed value in combination with a radar beam incidence angle, and statistically determining a distribution range of all vehicle speeds in a current signal control period; and when a difference between upper and lower limits of the distribution range is continuously greater than a lane design speed ratio, triggering a traffic direction green light extension instruction and sending the instruction to a signal control machine; and the application enhances the accuracy of traffic speed monitoring and the adaptability of signal control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of traffic control, in particular to a traffic speed monitoring method based on a Doppler frequency shift enhancement algorithm. BACKGROUND

[0002] With the acceleration of urbanization, road traffic flow continues to rise, and intelligent transportation systems have higher requirements for real-time and accurate traffic speed monitoring. Accurate vehicle speed data is an important basis for optimizing traffic signal control, relieving congestion, and improving road traffic safety, and plays an indispensable role in urban traffic management.

[0003] Currently, traffic speed monitoring technology based on the principle of Doppler frequency shift has been applied in practical scenarios. This kind of method realizes vehicle speed measurement through the frequency change of electromagnetic wave reflection signals, and becomes an important means of dynamic traffic monitoring with the advantages of non-contact detection and strong environmental adaptability, and is widely used in traffic state perception of urban trunk roads and intersections.

[0004] However, the existing traffic speed monitoring technology based on Doppler frequency shift still has many limitations in practical application. On the one hand, its measurement results are easily disturbed by complex road environmental factors, such as changes in road conditions, reflections of surrounding buildings, etc., which may cause deviation of the frequency shift signal, thereby affecting the accuracy of speed measurement; on the other hand, the analysis and application of the existing technology to the monitored speed data are not deep enough, and only a single speed value can be provided, which is difficult to fully reflect the overall state of traffic flow, resulting in difficulty in achieving precise and dynamic timing optimization when adjusting traffic signal control based on speed data, especially in the case of complex and variable traffic flow state, its adaptability and effectiveness need to be further improved. SUMMARY

[0005] The purpose of the present application is to provide a traffic speed monitoring method based on a Doppler frequency shift enhancement algorithm, which solves the following technical problems:

[0006] The existing traffic speed monitoring technology based on Doppler frequency shift is easily disturbed by complex road environmental factors, which causes deviation of the frequency shift signal and affects the measurement accuracy, and the analysis and application of the speed data are not deep enough, only a single speed value can be provided, which is difficult to fully reflect the overall state of traffic flow.

[0007] The purpose of the present application can be achieved by the following technical solutions:

[0008] A traffic speed monitoring method based on a Doppler frequency shift enhancement algorithm, comprising the following steps:

[0009] S1, receiving the Doppler signal stream generated by the tire reflection point of the vehicle in the target traffic direction lane, and synchronously collecting the fluctuation deformation data of the lane surface layer material;

[0010] S2, calculate the frequency shift deviation caused by non-vehicle motion in the Doppler signal stream according to the relief deformation data, compensate the frequency shift deviation in the Doppler signal stream, and form a corrected signal sequence;

[0011] S3, identify the continuous section with zero frequency shift rate in the corrected signal sequence, mark it as a stable driving track section, and extract the arithmetic mean value of the frequency shift amount in the stable driving track section;

[0012] S4, convert the arithmetic mean value into a vehicle driving speed value in combination with the radar beam incidence angle, and count the distribution range of all vehicle driving speed values in the current signal control period;

[0013] S5, when the difference between the upper and lower limits of the distribution range is continuously greater than the proportion of the design speed of the lane, trigger the generation of a green light time extension instruction for the passing direction, and send the extension instruction to the signal control machine to execute the passing period adjustment.

[0014] As a further scheme of the application: in the S1, the Doppler signal stream generated by the tire reflection points in the target passing direction lane is specifically:

[0015] Set a signal intensity detection threshold to filter reflection signals below the background noise level, establish a correspondence table of reflection signals and spatial coordinates at different lane positions, and select reflection signal components corresponding to the spatial coordinates of the lane according to the current signal release direction.

[0016] As a further scheme of the application: in the S1, the process of collecting the relief deformation data of the lane surface layer material is:

[0017] Deploy a pressure wave conduction medium array inside the lane asphalt concrete structure layer, which is composed of rigid sensing units distributed at equal intervals, and record the compression deformation waveform data generated by the compression of the sensing units when the vehicle tire passes through the area where the sensing units are located;

[0018] Remove the short-time pulse components corresponding to the instantaneous impact of the tire in the compression deformation waveform using a high-frequency filter, extract the oscillation signals with a period longer than the passing time of a single vehicle in the remaining waveform through a low-frequency band-pass filter, and measure the time distance between adjacent trough points of the oscillation signal as the road surface relief change period.

[0019] Calculate the vertical height difference of the peak vertex position in each relief change period relative to the road surface static reference surface, arrange all the vertical height difference data of the relief change periods in chronological order, and form a complete lane surface relief feature sequence.

[0020] As a further scheme of the application: in the S2, the calculation process of the frequency shift deviation is:

[0021] Map the time of occurrence of the crest top of the lane surface fluctuation feature sequence record to the time coordinate axis of the Doppler signal stream, and insert a marker point at the sampling point position of the Doppler signal stream corresponding to the mapping time;

[0022] Extend an analysis window of a fixed time length to both sides with the marker point as the center, calculate the offset square sum of the original frequency shift amount data points in the analysis window relative to the linear change trend, and take the maximum value of the offset square sum as the basic deviation reference value;

[0023] Construct a basic deviation reference value distribution function with the marker point position as the maximum center, the distribution function value decreases linearly to zero from the marker point to both sides with the increase of distance, and assign a corresponding distribution function calculation value to each sampling point position of the Doppler signal stream to form a continuous curve of the frequency shift deviation amount.

[0024] As a further scheme of the application: in S3, the process of identifying the stable driving trajectory segment is:

[0025] Perform a difference operation on the frequency shift amount values of adjacent sampling point positions in the corrected signal sequence to obtain a difference result sequence describing the change amplitude of the frequency shift amount per unit time;

[0026] Set the continuous interval in the difference result sequence whose absolute value continuously falls below the fluctuation tolerance threshold as a stable sub-section, and check the difference result jump amplitude at the connection point position of adjacent stable sub-sections; when the jump amplitude is less than the trajectory continuity threshold, merge the adjacent stable sub-sections into an extended stable section;

[0027] Measure the number of sampling points contained in the extended stable section and convert it into a time length value; only when the time length value exceeds the minimum stable driving time requirement, the extended stable section is retained as a candidate stable section;

[0028] Cumulatively count the number of times of change direction reversal of the frequency shift amount in the candidate stable section, and determine the candidate stable section with a reversal number less than the trajectory stability index as the final stable driving trajectory segment.

[0029] As a further scheme of the application: in S4, the process of converting the arithmetic mean value into a vehicle driving speed value in combination with the radar beam incidence angle is:

[0030] Obtain the vertical height measurement value of the radar transmitter installation position and the lane plane, measure the horizontal projection distance of the radar transmitter center point to the lane center line, calculate the tangent function value of the radar beam incidence angle according to the ratio of the vertical height measurement value to the horizontal projection distance, and obtain the specific value of the radar beam incidence angle through inverse trigonometric function solution;

[0031] The arithmetic mean of the frequency shift amount in the stable driving trajectory section is read, the relative speed component in the radar beam direction is obtained by substituting the arithmetic mean into the Doppler frequency shift basic relationship, a geometric projection relationship model of the vehicle driving direction and the radar beam direction is established, and the vehicle driving speed is defined as the projection of the relative speed component on the horizontal lane plane in the model;

[0032] According to the direction angle corresponding relationship in the geometric projection relationship model, the relative speed component is divided by the cosine function value of the radar beam incidence angle, and the calculation result is output as the vehicle driving speed value.

[0033] As a further scheme of the present application: in the S5, the specific determination process of the upper and lower limit difference value of the distribution range is:

[0034] The signal control period is uniformly divided into statistical units with equal time span, and the total number of vehicles entering the stable driving trajectory section is detected in each statistical unit;

[0035] When the total number of vehicles reaches the unit effective statistical basis, the set of vehicle driving speed values is read, the maximum speed value and the minimum speed value in the speed set are found, and the arithmetic difference between the maximum speed value and the minimum speed value is calculated as the unit speed dispersion;

[0036] The position of the statistical unit whose unit speed dispersion exceeds the lane dispersion reference value is recorded, the number of adjacent statistical unit groups that continuously exceed the lane dispersion reference value is counted, and the proportion value of the total time length of the statistical unit group to the total time length of the signal control period is calculated. When the proportion value is greater than the dispersion activity coefficient, it is determined that the upper and lower limit difference value of the distribution range is continuously greater than the lane design speed proportion.

[0037] As a further scheme of the present application: in the S5, the process of generating the green time extension instruction for the passing direction is:

[0038] The starting time of the statistical unit of the trigger instruction is located, the remaining green light time length of the signal phase at the starting time is determined, and the road length occupied by the queuing vehicles behind the stop line of the downstream intersection in the passing direction is obtained through the video detection device;

[0039] The database is queried to obtain the average value of the historical same-period vehicle fleet dissipation rate under the same weather condition, and the road length of the queuing vehicles is divided by the average value of the vehicle fleet dissipation rate to obtain the basic extension time amount;

[0040] The ratio of the number of statistical units of the trigger instruction to the total number of statistical units is calculated, the basic extension time amount is multiplied by the ratio to obtain the dynamically adjusted extension value, and the binary control instruction containing the starting time position and the extension value is generated.

[0041] As a further scheme of the present application: the S5 further comprises, when multiple traffic directions simultaneously trigger the generation of a traffic direction green light time extension instruction, executing a cooperative control process:

[0042] From all the traffic directions that generate the green light time extension instruction, a first traffic direction with the largest traffic volume and a second traffic direction with the second largest traffic volume are identified, and the extension value in the first traffic direction control instruction is extracted as a reference extension time amount;

[0043] The number of times of green light extension performed by the second traffic direction in the current signal cycle is obtained, a priority decay coefficient of the second traffic direction is calculated according to the number of times of extension, and the reference extension time amount is multiplied by the priority decay coefficient to obtain an actual extension time of the second traffic direction;

[0044] The time interval between the first traffic direction green light planned end time and the second traffic direction green light planned start time is compared, when the time interval is less than the phase conversion buffer time, the first traffic direction green light end time is kept unchanged, and the second traffic direction green light start time is advanced to before the first traffic direction green light end time, and the length of the second traffic direction green light advanced start is equal to the phase conversion buffer time minus the time interval.

[0045] The beneficial effects of the present application are:

[0046] The present application effectively eliminates the frequency shift interference caused by non-vehicle motion by synchronously collecting the surface layer material fluctuation deformation data of the lane and calculating the frequency shift deviation amount for compensation, solving the problem of low measurement accuracy caused by the influence of complex road environment in the prior art; the stability and reliability of the speed data are ensured by identifying the continuous section with a frequency shift rate of zero in the corrected signal sequence and extracting the arithmetic mean value of the frequency shift amount in the stable driving trajectory section, overcoming the defects of the prior art that can only provide a single speed value and is difficult to reflect the overall state of traffic flow; the accurate conversion of the frequency shift amount to the driving speed is realized by combining the radar beam incidence angle, improving the accuracy of speed measurement; the distribution range of the vehicle driving speed value is counted, and the green light time extension instruction is triggered when the difference continuously exceeds the limit, realizing the dynamic optimization of traffic signal timing, solving the problem of inaccurate signal control adjustment in the prior art; at the same time, when multiple traffic directions need to extend the green light time, cooperative control is executed, taking into account the multi-direction traffic demand, further improving the road traffic efficiency, and the overall technical scheme enhances the accuracy of traffic speed monitoring and the adaptability of signal control through multi-link cooperation. BRIEF DESCRIPTION OF DRAWINGS

[0047] The present application will be further described below with reference to the accompanying drawings.

[0048] Figure 1 is a flowchart of the present application. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of the present application.

[0050] Please refer to Figure 1 As shown in the drawings, the present application is a traffic speed monitoring method based on a Doppler frequency shift enhancement algorithm, comprising the following steps:

[0051] S1, receiving the Doppler signal stream reflected by the tires of vehicles in the target lane, and simultaneously collecting the fluctuation deformation data of the lane surface material. When receiving the signal, a signal strength detection threshold is first set to filter out the reflected signals that are weaker than the background noise to avoid invalid signal interference; then, according to the direction of the current signal light release, the effective signal components of the corresponding lane are selected from the reflected signals of multiple lanes. The road surface deformation data is collected through a pressure wave transmission medium array buried in the lane asphalt concrete layer, which is composed of multiple rigid sensing units distributed at equal intervals. When the vehicle tires pass above these sensing units, the compression deformation waveform generated by the compression of the sensing units can be recorded.

[0052] S2, according to the collected road surface fluctuation deformation data, the frequency shift deviation amount in the Doppler signal stream that is not caused by vehicle movement is calculated, and then this deviation amount is used to compensate and correct the original Doppler signal stream, and finally the corrected signal sequence is formed to make the signal more consistent with the actual movement of the vehicle.

[0053] S3, in the corrected signal sequence, the continuous sections in which the frequency shift rate remains zero are found by performing difference operation on the frequency shift amounts of adjacent sampling points, and these sections are marked as trajectory sections of stable vehicle driving; then, the arithmetic mean value of all frequency shift amounts in these stable trajectory sections is extracted as the basic data for subsequent calculation.

[0054] S4, combining the incident angle of the radar beam, the frequency shift average value calculated above is converted into the actual driving speed of the vehicle. The angle is calculated according to the installation height of the radar transmitter and its horizontal distance to the lane center line; then, the distribution range of the driving speed of all vehicles in the current traffic signal control period is calculated, including the interval between the fastest speed and the slowest speed.

[0055] S5, if the upper and lower limits of the speed distribution range continue to exceed a certain proportion of the design speed of the lane, a command will be triggered to extend the green light time of the traffic direction. The command is generated by referring to the queuing vehicle length and historical dissipation speed of the downstream intersection, and then sent to the signal control machine for specific adjustment of the traffic period. If multiple directions need to extend the green light at the same time, priority coordination will be performed to ensure overall traffic efficiency.

[0056] In S1, the received Doppler signal stream generated by the tire reflection points in the target traffic direction lane is specifically:

[0057] First, a reasonable signal intensity detection threshold is set. The determination of this threshold needs to refer to the background noise level in the current environment. By continuously monitoring the signal fluctuation range in the environment without vehicle traffic, the reflection signals below the lower limit of the range are judged as invalid interference signals and are filtered. This step can effectively eliminate weak signals generated by non-vehicle targets such as surrounding building reflections and atmospheric clutter, reducing the interference sources in subsequent data processing.

[0058] Then, a correspondence table of reflection signals in different lane positions and spatial coordinates needs to be established. Specifically, multiple spatial grid units are divided within the radar coverage range, each unit corresponds to a specific lane area and coordinate range. By testing multiple round trips of calibration vehicles with known positions, the characteristic parameters of reflection signals in different grid units are recorded, including signal arrival time, intensity distribution, etc. Then these parameters are associated and stored with the corresponding physical coordinates to form a complete mapping relationship database. When the system is running, according to the release direction of the current signal light, for example, the west-east green light is on, the spatial coordinate parameters corresponding to the east-west lane are called from the database, and only the reflection signal components falling within the coordinate range are extracted to ensure that the obtained signals strictly come from the target traffic direction lane, avoiding the crosstalk of adjacent lane vehicles.

[0059] In S1, the process of collecting the fluctuation deformation data of the lane surface layer material is:

[0060] A pressure wave conduction medium array is deployed inside the asphalt concrete structure layer of the lane. The array is composed of multiple rigid sensing units, which are distributed along the longitudinal direction of the lane at equal intervals and are set at the joint between the asphalt layer and the base layer. They can sense the deformation transmission of the road surface layer and avoid damage caused by direct vehicle crushing. When the vehicle tire passes through the area where the sensing unit is located, the pressure of the tire on the road surface will be transmitted to the sensing unit through the asphalt layer, causing the unit to produce a small compression deformation. The sensing unit will record the waveform data of this deformation changing with time in real time to form the original compression deformation waveform.

[0061] Next, the original waveform is layered and filtered. First, a high-frequency filter is used to process the compressed deformation waveform, which removes short-time pulse components corresponding to instantaneous impacts on the tire. These pulses are usually generated by the tire's instantaneous contact with the road surface, and have a very short duration and high frequency, which is unrelated to the road surface's undulating deformation. After filtering, the waveform components related to road surface structure vibrations can be retained. Subsequently, a low-frequency band-pass filter is used to extract the remaining waveform oscillation signals with a period longer than the duration of a single vehicle passing. Because the duration of a single vehicle passing is short, the instantaneous deformation caused by the vehicle will not form a long-period oscillation. However, the undulating deformation of the road surface due to material properties or foundation settlement will exhibit longer-period vibrations.

[0062] After obtaining the effective oscillation signal, the time distance between adjacent wave trough points is measured, which is used as the road surface undulation period. The determination of the wave trough points requires waveform feature analysis, and the point with the lowest signal value in each period is selected as the reference to ensure that the time interval between adjacent wave trough points accurately reflects the periodic changes of the road surface undulation. Then, the vertical height difference of the wave peak point position relative to the road surface static reference surface is calculated for each undulation period. The road surface static reference surface is determined by long-term monitoring, i.e., the stable position of the road surface when no vehicles are passing. Arranging all the vertical height differences of the undulation periods in chronological order forms a complete sequence of lane surface undulation characteristics, providing basic data for subsequent frequency shift deviation calculation.

[0063] The calculation process of the frequency shift deviation is as follows:

[0064] The calculation process of the frequency shift deviation needs to accurately associate the influence of the road surface undulation on the signal with the Doppler signal stream. First, the wave peak point occurrence time recorded in the lane surface undulation characteristic sequence is mapped to the time coordinate axis of the Doppler signal stream. This step requires establishing a time synchronization mechanism for the two sequences to ensure that the key time points of the road surface undulation can be accurately matched to the corresponding time of the Doppler signal stream. Then, marker points are inserted at the sampling points of the Doppler signal stream corresponding to these mapped time points, marking the time positions where the road surface undulation may have a significant impact on the frequency shift.

[0065] Next, an analysis window with a fixed time length is extended to both sides of each marker point. The length of this window needs to be determined based on the influence range of the road surface undulation and the signal sampling frequency to ensure that it can completely cover the signal segment affected by the road surface undulation around the marker point. Within the analysis window, the sum of the squares of the deviations of the original frequency shift data points from the linear trend is calculated. The linear trend reflects the natural variation of the frequency shift when the vehicle is driving normally, while the deviations represent the deviations caused by the road surface undulation. The maximum value of the sum of the squares of these deviations is taken as the reference value of the basic deviation, which quantifies the maximum impact of the road surface undulation on the frequency shift at the marker point.

[0066] Then a reference value distribution function of the basic deviation is constructed with the maximum value of the marker point position as the center. The function is characterized by linearly decreasing to zero from both sides of the marker point with the increase of the distance, which means the closer to the marker point, the greater the influence of the road surface undulation on the frequency shift, and vice versa. According to this distribution function, a corresponding calculation value is assigned to each sampling point position of the Doppler signal stream, and finally a continuous frequency shift deviation curve is formed, which fully presents the deviation distribution caused by the road surface undulation in the entire signal stream, and provides accurate basis for subsequent signal correction.

[0067] In S3, the process of identifying the stable driving trajectory segment is:

[0068] Firstly, difference operation is performed on the frequency shift values of adjacent sampling points in the corrected signal sequence, and the difference result sequence obtained can intuitively reflect the change amplitude of the frequency shift value per unit time. The smaller the change amplitude, the more stable the vehicle driving state, and vice versa.

[0069] Then, the continuous interval in the difference result sequence whose absolute value is continuously lower than the fluctuation tolerance threshold is set as a stable sub-section. The fluctuation tolerance threshold needs to be set according to the road type and vehicle driving characteristics to ensure that it can effectively distinguish between normal small fluctuations and obvious speed changes. Then the difference result jump amplitude of the connection points of adjacent stable sub-sections is checked. When the jump amplitude is less than the trajectory continuity threshold, it means that these sub-sections belong to the continuous state in the same stable driving process, and they can be merged into an extended stable section to solve the segmentation problem caused by the possible short-term fluctuations in the signal sampling process.

[0070] Then the number of sampling points contained in the extended stable section is measured, and the time length value is converted according to the sampling frequency. Only when this time length value exceeds the minimum stable driving time requirement, it is retained as a candidate stable section. This step can exclude the short-term stable state of the vehicle starting and braking, and ensure that the selected stable driving period has practical significance.

[0071] Finally, the number of times of reversing the change direction of the frequency shift value in the candidate stable section is accumulated, such as the number of times from increasing to decreasing or vice versa. The candidate stable section with the number of times of reversing lower than the trajectory stability index is determined as the final stable driving trajectory section, so as to ensure that the vehicle driving state is stable and the frequency shift value change rule is consistent in this period, and to provide reliable basic data for subsequent speed calculation.

[0072] In S4, the process of converting the arithmetic mean value into the vehicle driving speed value combined with the radar beam incidence angle is:

[0073] The process of converting the arithmetic mean value into the vehicle driving speed value in combination with the radar beam incidence angle needs to go through two core links of geometric parameter measurement and speed component conversion. First, the installation parameters of the radar transmitter need to be obtained, specifically including measuring the vertical height of the radar transmitter installation position and the lane plane, which can be selected by a laser range finder on the lane surface directly below the transmitter to take multiple measurement points and take their average as the final vertical height measurement value. At the same time, the horizontal projection distance of the radar transmitter center point to the center line of the lane is measured, which needs to be measured along the horizontal direction to avoid the inclined area of the lane edge to ensure data accuracy.

[0074] According to the ratio of the above two parameters, the tangent function value of the radar beam incidence angle can be obtained, and the specific value of the incidence angle can be obtained by inverse trigonometric function operation. This angle reflects the inclination between the radar beam and the lane plane, which is a key geometric parameter for subsequent speed conversion.

[0075] Next, the arithmetic mean value of the frequency shift amount in the stable driving trajectory section is read and substituted into the basic relationship of the Doppler frequency shift to calculate the relative speed component in the direction of the radar beam. Since the radar beam is not transmitted along the horizontal direction, the relative speed component is actually the projection of the vehicle driving speed in the beam direction, so it is necessary to establish a geometric projection relationship model between the vehicle driving direction and the radar beam direction. The model clearly specifies that the actual driving speed of the vehicle is the projection value of the relative speed component on the horizontal lane plane.

[0076] According to this geometric relationship, combined with the radar beam incidence angle that has been obtained, the relative speed component is divided by the cosine function value of the angle to obtain the actual driving speed of the vehicle on the horizontal lane, which can more truly reflect the motion state of the vehicle.

[0077] In S5, the specific determination process of the upper and lower limit difference value of the distribution range is:

[0078] The specific determination process of the upper and lower limit difference value of the distribution range needs to be realized through multi-stage statistics and threshold comparison. First, the signal control period is evenly divided into multiple time span equal statistical units, the time length of each unit needs to be set according to the characteristics of the intersection traffic flow to ensure that both short-term speed fluctuations and overall trends can be captured. In each statistical unit, the system automatically detects the total number of vehicles entering the stable driving trajectory section to determine whether the data in the unit has statistical significance.

[0079] When the total number of vehicles in a statistical unit reaches the preset unit effective statistical base, the system extracts the driving speed values of these vehicles to form a set, from which the maximum speed value and the minimum speed value are selected, and the arithmetic difference between the two is calculated as the speed dispersion of the unit, which directly reflects the distribution difference of the vehicle speed in the unit.

[0080] Then record the position of all statistical units whose speed dispersion exceeds the lane dispersion reference value, which is pre-set according to the road design speed and traffic management requirements. Count the number of adjacent statistical unit groups that continuously exceed the reference value, and calculate the proportion of the total time length of these unit groups in the total time length of the entire signal control cycle. When the proportion value is greater than the pre-set dispersion activity coefficient, the system determines that the upper and lower limits of the current vehicle speed distribution range continue to be greater than the proportion of the lane design speed, which provides a basis for subsequent signal adjustment.

[0081] In S5, the process of generating the green light time extension instruction for the passing direction is as follows:

[0082] The process of generating the green light time extension instruction for the passing direction needs to go through multiple links of accurate calculation and instruction construction. First, the starting time of the statistical unit triggering the instruction needs to be located. This time is the time point when the statistical unit first detects that the speed distribution range exceeds the threshold value. After determining the starting time, the system queries the currently executing signal phase to obtain the remaining green light time length of the phase as the reference for time adjustment. At the same time, the video detection device deployed above the road captures the vehicle queue situation behind the stop line of the downstream intersection of the passing direction in real time, and measures the road length occupied by the queue vehicles using image recognition technology, including the total length of all waiting vehicles extending from the stop line.

[0083] Next, the historical database needs to be queried to filter out the historical same period data with the same meteorological conditions as the current ones, from which the average value of the vehicle fleet dissipation rate is extracted. The meteorological conditions here include sunny, rainy, snowy, foggy and other weather conditions to ensure the environmental consistency of the reference data. Divide the measured road length of the queue vehicles by the average value of the vehicle fleet dissipation rate to obtain the basic extension time amount, which reflects the time required to theoretically dissipate the current queue vehicles.

[0084] Then calculate the ratio of the number of statistical units triggering the instruction to the total number of statistical units, which reflects the proportion of the speed distribution anomaly in the entire signal control cycle. Multiply this ratio by the basic extension time amount to obtain the dynamically adjusted extension value, so that the extension time can be flexibly changed according to the duration of the abnormal situation. Finally, a binary control instruction containing the starting time position and the extension value is generated, which is convenient for the signal control machine to quickly analyze and execute.

[0085] Also included is a cooperative control process to avoid conflicts when multiple traffic directions simultaneously trigger the generation of green light time extension instructions. First, from all the traffic directions that generate instructions, the first traffic direction with the largest traffic volume and the second traffic direction with the second largest traffic volume are identified through real-time traffic volume monitoring data, to prioritize the traffic efficiency of the main traffic flow. The extension value in the control instruction of the first traffic direction is extracted as the reference extension time amount, and this is used as the basis for coordinating the time allocation of other directions.

[0086] The number of times the second traffic direction has executed green light extension in the current signal cycle is obtained, and the more times the direction has obtained more recent traffic opportunities. According to the preset rules, the priority decay coefficient is calculated, and the number of times increases, and the coefficient decreases accordingly. Multiply the reference extension time amount by the priority decay coefficient to get the actual extension time of the second traffic direction, and realize the differentiated adjustment of different directions.

[0087] Finally, compare the time interval between the first traffic direction green light plan end time and the second traffic direction green light plan start time. When this interval is less than the preset phase transition buffer time, to avoid too hasty signal switching, keep the first traffic direction green light end time unchanged, and advance the second traffic direction green light start time to before the first traffic direction green light end time. The length of the advance start time is equal to the phase transition buffer time minus the above time interval, to ensure that the signal transition of the two directions has enough buffer time, and to reduce traffic conflicts.

[0088] The above describes one embodiment of the present application in detail, but the content described is only the preferred embodiment of the present application, and cannot be considered to limit the scope of the present application. Any equivalent changes and improvements made in accordance with the scope of the present application should still be included in the scope of the present application.

Claims

1. A method for traffic speed monitoring based on Doppler shift enhancement algorithm, characterized in that, The method comprises the following steps: S1, receiving a Doppler signal stream generated by a tire reflection point of a vehicle in a target traffic direction lane, and synchronously collecting data of a surface layer material fluctuation of the lane; S2, calculating a frequency shift deviation amount caused by non-vehicle motion in the Doppler signal stream according to the fluctuation data, compensating the frequency shift deviation amount in the Doppler signal stream, and forming a corrected signal sequence; S3, identifying a continuous section with a frequency shift rate of zero in the corrected signal sequence, marking the continuous section as a stable driving track section, and extracting an arithmetic mean value of the frequency shift amount in the stable driving track section; S4, converting the arithmetic mean value into a vehicle driving speed value in combination with a radar beam incidence angle, and counting a distribution range of all vehicle driving speed values in a current signal control period; S5, when a difference between upper and lower limits of the distribution range continuously exceeds a lane design speed ratio, triggering a generation of a green light time extension instruction for the traffic direction, and sending the extension instruction to a signal controller to execute traffic period adjustment. In the S3, the process of identifying the stable driving track section is as follows: performing a difference operation on the frequency shift amount values of adjacent sampling point positions in the corrected signal sequence to obtain a difference result sequence describing a change amplitude of the frequency shift amount in a unit time; setting a continuous interval with an absolute value continuously below a fluctuation tolerance threshold in the difference result sequence as a stable sub-section, checking a difference result jump amplitude of a connection point position of adjacent stable sub-sections, and merging the adjacent stable sub-sections into an extended stable section when the jump amplitude is less than a track continuity threshold; measuring a sampling point quantity contained in the extended stable section and converting the sampling point quantity into a time length value, and reserving the time length value as a candidate stable section only when the time length value exceeds a minimum stable driving time requirement; counting a frequency shift amount change direction reversal number in the candidate stable section, and determining a candidate stable section with a reversal number below a track stability index as a final stable driving track section. In the S4, the process of converting the arithmetic mean value into the vehicle driving speed value in combination with the radar beam incidence angle is as follows: obtaining a vertical height measurement value of a radar transmitter installation position and a lane plane, measuring a horizontal projection distance of a radar transmitter center point to a lane center line, calculating a tangent function value of the radar beam incidence angle according to a ratio of the vertical height measurement value to the horizontal projection distance, and obtaining a specific value of the radar beam incidence angle through an inverse trigonometric function solution; reading the arithmetic mean value of the frequency shift amount in the stable driving track section, substituting the arithmetic mean value into a Doppler frequency shift basic relationship to obtain a relative speed component in the radar beam direction, establishing a geometric projection relationship model of a vehicle driving direction and the radar beam direction, and defining a vehicle driving speed as a projection of the relative speed component on a horizontal lane plane in the model; dividing the relative speed component by a cosine function value of the radar beam incidence angle according to a direction angle corresponding relationship in the geometric projection relationship model, and outputting a calculation result as the vehicle driving speed value.

2. The method of claim 1, wherein, In the S1, the Doppler signal stream generated by the tire reflection point of the vehicle in the target traffic direction lane is specifically as follows: setting a signal intensity detection threshold to filter reflection signals below a background noise level, establishing a corresponding relationship table of reflection signals and spatial coordinates at different lane positions, and selecting reflection signal components of corresponding lane spatial coordinates according to a current signal release direction.

3. The method of claim 1, wherein the method is characterized by, The process of collecting the fluctuation deformation data of the lane surface layer material in S1 is as follows: A pressure wave conducting medium array is arranged in the lane asphalt concrete structural layer, and the array is composed of rigid sensing units distributed at equal intervals. When a vehicle tire passes through the area where the sensing units are located, the compression deformation waveform data generated by the compression of the sensing units is recorded; A high-frequency filter is used to remove the short-time pulse components corresponding to the instantaneous impact of the tire in the compression deformation waveform, and a low-frequency band-pass filter is used to extract the oscillation signals with a period longer than the duration of a single vehicle passing through in the remaining waveform. The time distance between adjacent trough points of the oscillation signal is measured as the road surface fluctuation period; The vertical height difference of the peak position in each fluctuation period relative to the static reference surface of the road is calculated, and the vertical height difference data of all fluctuation periods are arranged in chronological order to form a complete lane surface fluctuation feature sequence.

4. The method of claim 1, wherein, The calculation process of the frequency shift deviation in S2 is as follows: The time when the peak of the lane surface fluctuation feature sequence occurs is mapped to the time coordinate axis of the Doppler signal stream, and a marker point is inserted at the sampling point position of the Doppler signal stream corresponding to the mapping time; An analysis window with a fixed time length is extended to both sides of the marker point, and the sum of the deviations of the original frequency shift data points in the analysis window relative to the linear trend is calculated. The maximum value of the sum of the deviations is taken as the reference value of the basic deviation; A basic deviation reference value distribution function is constructed with the marker point position as the maximum center. The distribution function value decreases linearly to zero from the marker point to both sides with the increase of the distance. Each sampling point position of the Doppler signal stream is assigned a corresponding distribution function calculation value to form a continuous curve of the frequency shift deviation.

5. The method of claim 1, wherein, The specific determination process of the upper and lower limit difference value of the distribution range in S5 is as follows: The signal control period is evenly divided into statistical units with equal time span, and the total number of vehicles entering the stable driving track section is detected in each statistical unit; When the total number of vehicles reaches the effective statistical unit, the set of vehicle speed values is read, the maximum speed value and the minimum speed value in the speed set are found, and the arithmetic difference between the maximum speed value and the minimum speed value is calculated as the unit speed dispersion; The positions of the statistical units whose unit speed dispersion exceeds the lane dispersion reference value are recorded, the number of adjacent statistical unit groups that continuously exceed the lane dispersion reference value is counted, and the proportion of the total time length of the statistical unit group to the total time length of the signal control period is calculated. When the proportion value is greater than the dispersion activity coefficient, it is determined that the upper and lower limit difference value of the distribution range is continuously greater than the proportion of the lane design speed.

6. The method of claim 5, wherein the Doppler shift enhanced algorithm is based on the following equation: ###0001### where f is the Doppler shift, v is the vehicle speed, f0 is the carrier frequency, c is the speed of light, and θ is the angle between the vehicle and the radar. The process of generating the green light time extension instruction in the passing direction in S5 is as follows: The starting time of the statistical unit triggering the instruction is located, and the remaining green light time length of the signal phase at the starting time is determined. The road length occupied by the queuing vehicles behind the stop line of the downstream intersection in the passing direction is obtained through the video detection device; The average value of the historical same-period vehicle fleet dissipation rate under the same meteorological conditions is obtained by querying the database, and the basic extension time amount is obtained by dividing the road length occupied by the queuing vehicles by the average value of the vehicle fleet dissipation rate. The statistical unit quantity of the calculation trigger instruction is compared with the total statistical unit quantity, and a ratio value is obtained. The ratio value is multiplied by a basic extension time amount to obtain a dynamically adjusted extension value. A binary control instruction containing a starting time position and the extension value is generated.

7. The method of claim 1, wherein the method is characterized by, The S5 further includes when multiple traffic directions simultaneously trigger generation of a traffic direction green light time extension instruction, performing a cooperative control process: From all the traffic directions that generate the green light time extension instruction, a first traffic direction with the largest traffic flow and a second traffic direction with the second largest traffic flow are identified, and an extension value in the first traffic direction control instruction is extracted as a reference extension time amount; A green light extension number of the second traffic direction in a current signal cycle is obtained, a priority decay coefficient of the second traffic direction is calculated according to the extension number, and the reference extension time amount is multiplied by the priority decay coefficient to obtain an actual extension time of the second traffic direction; A time interval between a first traffic direction green light planned end time and a second traffic direction green light planned start time is compared, when the time interval is less than a phase conversion buffer time, the first traffic direction green light end time is kept unchanged, and the second traffic direction green light start time is advanced to before the first traffic direction green light end time, and a second traffic direction green light advance start time length is equal to the phase conversion buffer time minus the time interval.

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