Vehicle trajectory analysis method, apparatus, device, medium, and program product

Abstract

The application relates to a vehicle trajectory analysis method, device, equipment, medium and program product. The method is applied to a radar system, the radar system comprises bidirectional optical fibers and radar sensing devices connected with each other, the bidirectional optical fibers are arranged on two sides of a road respectively, the method comprises the following steps: a radar sensing device acquires original vibration signals collected by the bidirectional optical fibers respectively; the original vibration signals are vibration signals generated when a vehicle passes through the road; image conversion processing is performed on the original vibration signals, and an initial trajectory image is determined; the initial trajectory image comprises an initial trajectory formed by the vehicle on the road; trajectory tracking processing is performed on the initial trajectory in the initial trajectory image, and a target trajectory image is determined; the target trajectory image comprises a target trajectory corresponding to the vehicle. The method can solve the problems of traditional devices.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation technology, and in particular to a vehicle trajectory analysis method, apparatus, equipment, medium, and program product. Background Technology

[0002] Currently, with the rapid development of transportation, road traffic safety issues are becoming increasingly prominent. In terms of highway vehicle safety management, vehicle trajectory detection and tracking technology is of great significance for improving transportation safety, increasing transportation efficiency, and reducing environmental pollution.

[0003] In existing technologies, when detecting the trajectory of a vehicle, it is generally necessary to install a road monitoring camera and an infrared sensing device on one side of the road. The infrared sensing device and the road monitoring camera can measure the speed of vehicles traveling on the road and capture speeding vehicles.

[0004] However, in harsh environments such as strong winds, rain, snow, fog, sandstorms, and tunnel dust, traditional road monitoring cameras and infrared sensing devices may suffer from false alarms, missed alarms, and susceptibility to interference, and therefore cannot fully meet the needs of modern traffic management and safety management. Summary of the Invention

[0005] Therefore, it is necessary to provide a vehicle trajectory analysis method, device, equipment, medium, and program product that can solve the shortcomings of traditional equipment in order to address the above-mentioned technical problems.

[0006] In a first aspect, this application provides a vehicle trajectory analysis method applied to a radar system, the radar system including interconnected bidirectional optical fibers and radar sensing devices, the bidirectional optical fibers being respectively arranged on both sides of the road, the method comprising:

[0007] The radar sensing equipment acquires the raw vibration signals collected by each of the two-way optical fibers; the raw vibration signals are the vibration signals generated when vehicles pass over the road.

[0008] Image conversion processing is performed on each original vibration signal to determine the initial trajectory image; the initial trajectory image includes the initial trajectory formed by the vehicle on the road.

[0009] The initial trajectory in the initial trajectory image is processed by trajectory tracking to determine the target trajectory image; the target trajectory image includes the target trajectory corresponding to the vehicle.

[0010] In one embodiment, the above-described image conversion processing of each original vibration signal to determine the initial trajectory image includes:

[0011] Each raw vibration signal is preprocessed to determine the target vibration signal corresponding to each raw vibration signal; the preprocessing includes filtering and / or equalization.

[0012] Image conversion processing is performed on the vibration signals of each target to determine the initial trajectory image.

[0013] In one embodiment, the above-mentioned image conversion processing of each target vibration signal to determine the initial trajectory image includes:

[0014] Image conversion processing is performed on the vibration signals of each target to obtain grayscale images;

[0015] Denoising is performed on each grayscale image to determine the processed grayscale images;

[0016] The processed grayscale images are folded and merged to determine the initial trajectory image.

[0017] In one embodiment, the above-described trajectory tracking processing of the initial trajectory in the initial trajectory image to determine the target trajectory image includes:

[0018] The initial trajectory in the initial trajectory image is processed for trajectory tracking to determine the intermediate trajectory image; the intermediate trajectory image includes the intermediate trajectory corresponding to the vehicle.

[0019] Perform trajectory post-processing on the intermediate trajectory in the intermediate trajectory image to determine the target trajectory image.

[0020] In one embodiment, the above-described trajectory tracking processing of the initial trajectory in the initial trajectory image to determine the intermediate trajectory image includes:

[0021] A preset sliding window is used to perform threshold detection processing on the initial trajectory in the initial trajectory image to determine the trajectory starting point in the initial trajectory;

[0022] Obtain the candidate direction of movement for the vehicle and the vehicle information at the starting point of the trajectory; the vehicle information includes at least one of vehicle speed, lane, and vehicle type.

[0023] Based on the vehicle information, the sliding window is moved along the candidate movement direction by a preset step size on the initial trajectory to determine at least one intermediate trajectory point on the initial trajectory.

[0024] The intermediate trajectory image is determined based on the starting point and intermediate trajectory points.

[0025] In one embodiment, before determining the intermediate trajectory image based on the trajectory start point and intermediate trajectory points, the method further includes:

[0026] Weaken the other points between the starting point of the trajectory and the adjacent intermediate trajectory points, and weaken the other points between every two adjacent intermediate trajectory points.

[0027] In one embodiment, determining the intermediate trajectory image based on the trajectory start point and intermediate trajectory points includes:

[0028] Connect the starting point of the trajectory with the adjacent intermediate trajectory points to determine the first line segment;

[0029] Connect any two adjacent intermediate trajectory points to determine at least one second line segment;

[0030] The intermediate trajectory is determined based on the first and second line segments, and the intermediate trajectory image is obtained.

[0031] In one embodiment, the above-described post-processing of the intermediate trajectory in the intermediate trajectory image to determine the target trajectory image includes:

[0032] Perform trajectory connection processing on the intermediate trajectories in the intermediate trajectory image, and / or perform trajectory expansion processing on the intermediate trajectories in the intermediate trajectory image to determine the target trajectory image.

[0033] In one embodiment, the intermediate trajectory image includes multiple intermediate trajectories, and trajectory connection processing is performed on the intermediate trajectories in the intermediate trajectory image, including:

[0034] Traverse each intermediate trajectory in the intermediate trajectory image and determine the subsequent intermediate trajectory corresponding to the current intermediate trajectory.

[0035] Connect the current intermediate trajectory with the corresponding subsequent intermediate trajectory according to their relative positions.

[0036] In one embodiment, the above-described trajectory extension process for the intermediate trajectory in the intermediate trajectory image includes:

[0037] Determine the expansion direction corresponding to the current intermediate trajectory based on the preset road section;

[0038] Extend the current intermediate trajectory along the extension direction.

[0039] In one embodiment, the method further includes:

[0040] Traffic flow information is calculated and processed based on the target trajectory image to determine the traffic flow information; the traffic flow information includes at least one of traffic volume, average vehicle speed, and traffic density.

[0041] In one embodiment, the above-mentioned traffic flow information calculation and processing based on the target trajectory image to determine the traffic flow information includes:

[0042] Obtain the position of the point to be measured and the start and end positions of the target trajectory in the target trajectory image;

[0043] Based on the relative positional relationship between the starting and ending positions of the same target trajectory and the position to be measured, the number of vehicles passing through the point to be measured is accumulated to determine the traffic flow corresponding to the point to be measured and obtain traffic flow information.

[0044] In one embodiment, the average vehicle speed includes time-averaged vehicle speed and / or spatial-averaged vehicle speed. The above-mentioned traffic flow information calculation and processing based on the target trajectory image to determine the traffic flow information includes:

[0045] Based on the relative positional relationship between the starting and ending positions of the same target trajectory and the position to be measured, the speeds of vehicles passing through the point to be measured are accumulated to determine the time-averaged vehicle speed at the point to be measured, thereby obtaining traffic flow information.

[0046] And / or, obtain the time to be measured and the start and end times of the target trajectory; based on the relative temporal relationship between the start and end times of the same target trajectory and the time to be measured, accumulate the speeds of vehicles passing through the point to be measured, determine the spatial average vehicle speed corresponding to the point to be measured, and obtain traffic flow information.

[0047] In one embodiment, the above-mentioned traffic flow information calculation and processing based on the target trajectory image to determine the traffic flow information includes:

[0048] Obtain the time to be tested, as well as the start and end times of the target trajectory;

[0049] Based on the relative temporal relationship between the start and end times of the same target trajectory and the time to be measured, the number of vehicles passing through the point to be measured is accumulated to determine the traffic density corresponding to the point to be measured and obtain traffic flow information.

[0050] A vehicle trajectory analysis device is applied to a radar system, the radar system including interconnected bidirectional optical fibers and radar sensing equipment, the bidirectional optical fibers being respectively installed on both sides of the road; the device includes:

[0051] The acquisition module is used to acquire the raw vibration signals collected by each of the two-way optical fibers; the raw vibration signals are the vibration signals generated when vehicles pass over the road.

[0052] The determination module is used to perform image conversion processing on each original vibration signal to determine the initial trajectory image; the initial trajectory image includes the initial trajectory formed by the vehicle on the road.

[0053] The processing module is used to perform trajectory tracking processing on the initial trajectory in the initial trajectory image to determine the target trajectory image; the target trajectory image includes the target trajectory corresponding to the vehicle.

[0054] A radar sensing device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0055] The original vibration signals collected by each of the two-way optical fibers are obtained; the original vibration signals are the vibration signals generated when vehicles pass over the road.

[0056] Image conversion processing is performed on each original vibration signal to determine the initial trajectory image; the initial trajectory image includes the initial trajectory formed by the vehicle on the road.

[0057] The initial trajectory in the initial trajectory image is processed by trajectory tracking to determine the target trajectory image; the target trajectory image includes the target trajectory corresponding to the vehicle.

[0058] A radar sensing system includes bidirectional optical fibers and radar sensing devices. The bidirectional optical fibers are connected to the radar sensing devices and are respectively installed on both sides of the road, wherein:

[0059] Two-way optical fiber is used to collect raw vibration signals generated when vehicles pass over the road and send each raw vibration signal to radar sensing equipment.

[0060] Radar sensing equipment is used to acquire the raw vibration signals collected by each of the two-way optical fibers; the raw vibration signals are the vibration signals generated when vehicles pass over the road.

[0061] The radar sensing equipment is also used to perform image conversion processing on each original vibration signal to determine the initial trajectory image; the initial trajectory image includes the initial trajectory formed by the vehicle on the road;

[0062] The radar sensing equipment is also used to perform trajectory tracking processing on the initial trajectory in the initial trajectory image to determine the target trajectory image; the target trajectory image includes the target trajectory corresponding to the vehicle.

[0063] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0064] The original vibration signals collected by each of the two-way optical fibers are obtained; the original vibration signals are the vibration signals generated when vehicles pass over the road.

[0065] Image conversion processing is performed on each original vibration signal to determine the initial trajectory image; the initial trajectory image includes the initial trajectory formed by the vehicle on the road.

[0066] The initial trajectory in the initial trajectory image is processed by trajectory tracking to determine the target trajectory image; the target trajectory image includes the target trajectory corresponding to the vehicle.

[0067] A computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0068] The original vibration signals collected by each of the two-way optical fibers are obtained; the original vibration signals are the vibration signals generated when vehicles pass over the road.

[0069] Image conversion processing is performed on each original vibration signal to determine the initial trajectory image; the initial trajectory image includes the initial trajectory formed by the vehicle on the road.

[0070] The initial trajectory in the initial trajectory image is processed by trajectory tracking to determine the target trajectory image; the target trajectory image includes the target trajectory corresponding to the vehicle.

[0071] The aforementioned vehicle trajectory analysis method, apparatus, equipment, medium, and program products involve a radar sensing device first acquiring raw vibration signals collected by bidirectional optical fibers. These raw vibration signals are the vibration signals generated when a vehicle passes over a road. Next, image conversion processing is performed on each raw vibration signal to determine an initial trajectory image, which includes the initial trajectory formed by the vehicle on the road. Finally, trajectory tracking processing is performed on the initial trajectory in the initial trajectory image to determine a target trajectory image, which includes the target trajectory corresponding to the vehicle. In this method, the radar sensing device acquires the vibration signals generated by the vehicle passing over the road through bidirectional optical fibers, and then determines the target trajectory information for the entire road by performing image conversion processing and trajectory tracking processing on the acquired vibration signals. By analyzing and processing the information, the traffic conditions along the entire road can be determined. Because bidirectional fiber optic and radar sensing equipment are used to detect vehicles on the road, the problems of false alarms, missed alarms, and susceptibility to interference that may exist with existing road monitoring cameras and infrared sensing equipment in adverse weather conditions can be avoided, thus meeting the needs of modern traffic management and safety management. Furthermore, the use of bidirectional fiber optic radar sensing equipment enables full-range monitoring of the road, with a wide coverage area, and can promptly capture changes in the intensity of raw vibration signals, thereby effectively improving the level of transportation safety. Secondly, because light signals propagate quickly and are unaffected by external interference, high-precision detection results can be obtained. In addition, fiber optic radar technology also has advantages such as small size, low power consumption, and low maintenance costs, making it widely applicable in practical applications. Attached Figure Description

[0072] Figure 1This is a diagram illustrating the application environment of the vehicle trajectory analysis method in one embodiment;

[0073] Figure 2 This is a flowchart illustrating a vehicle trajectory analysis method in one embodiment;

[0074] Figure 3 This is the initial trajectory image of the left and right lanes in another embodiment;

[0075] Figure 4 This is an initial trajectory image obtained by splitting the initial trajectory images of the left and right lanes and then folding and merging them in another embodiment;

[0076] Figure 5 This is a flowchart illustrating the vehicle trajectory analysis method in another embodiment;

[0077] Figure 6 This is a flowchart illustrating the preprocessing steps for each raw vibration signal in another embodiment;

[0078] Figure 7 This is a flowchart illustrating the vehicle trajectory analysis method in another embodiment;

[0079] Figure 8 This is a diagram of intermediate trajectory connection processing in another embodiment;

[0080] Figure 9 This is a diagram of intermediate trajectory extension processing in another embodiment;

[0081] Figure 10 This is a flowchart illustrating the vehicle trajectory analysis method in another embodiment;

[0082] Figure 11 This is a flowchart illustrating the trajectory tracking process in another embodiment;

[0083] Figure 12 This is a flowchart illustrating the vehicle trajectory analysis method in another embodiment;

[0084] Figure 13 This is a flowchart illustrating the vehicle trajectory analysis method in another embodiment;

[0085] Figure 14 This is a flowchart illustrating the vehicle trajectory analysis method in another embodiment;

[0086] Figure 15 This is a flowchart illustrating the vehicle trajectory analysis method in another embodiment;

[0087] Figure 16 This is a schematic diagram of the overall process for calculating traffic flow information in another embodiment;

[0088] Figure 17This is a flowchart illustrating the vehicle trajectory analysis method in another embodiment;

[0089] Figure 18 This is a schematic diagram illustrating the calculation of traffic flow information in another embodiment;

[0090] Figure 19 This is a schematic diagram illustrating the calculation of traffic flow information in another embodiment;

[0091] Figure 20 This is a flowchart illustrating the vehicle trajectory analysis method in another embodiment;

[0092] Figure 21 This is a structural block diagram of a vehicle trajectory analysis device in one embodiment. Detailed Implementation

[0093] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0094] The vehicle trajectory analysis method provided in this application embodiment can be applied to, for example, Figure 1 The radar sensing system shown includes a radar sensing device 101 and a bidirectional optical fiber 102. The bidirectional optical fiber 102 is connected to the radar sensing device 101 and is respectively installed on both sides of the road. The bidirectional optical fiber 102 is used to collect the original vibration signals generated when vehicles pass through the road and send each original vibration signal to the radar sensing device 101. The radar sensing device 101 is used to acquire the original vibration signals returned by the bidirectional optical fiber 102 and process the original vibration information to obtain the target trajectory image of the vehicle.

[0095] In one embodiment, such as Figure 2 As shown, a vehicle trajectory analysis method is provided, which can be applied to... Figure 1 Taking radar sensing equipment as an example, the following steps are included:

[0096] S202, the radar sensing equipment acquires the raw vibration signals collected by each of the two-way optical fibers; the raw vibration signals are the vibration signals generated when vehicles pass over the road.

[0097] In this step, technicians can pre-lay two sections of optical fiber on the left and right sides of the road or tunnel, or bend a single section of optical fiber into a U-shape and then lay the U-shaped fiber on both sides of the road or tunnel, ensuring that optical fibers are laid on both sides of the road. The radar sensing equipment is connected to the ends of the optical fibers. Technicians can transmit fixed-frequency optical signals to the optical fibers through the laser modulator, optical amplifier, and optical looper in the radar sensing equipment. When a vehicle passes by on the road, it will generate vibrations. At this time, the optical path in the optical fiber laid on both sides of the road will be affected, thus changing the optical signal returned to the optical looper in the radar sensing equipment. According to the Rayleigh scattering principle, the returned optical signal contains the vibration intensity signal of the moving vehicle. Then, the photoelectric converter in the radar sensing equipment can convert the returned optical signal into an electrical signal, and then convert the converted electrical signal into a digital signal through analog-to-digital conversion. Finally, the converted digital signal is digitally acquired, thereby obtaining the original vibration signal of the vehicle traveling along the entire road. It should be noted that the acquired original vibration signal is floating-point data, and the value of the floating-point data represents the magnitude of the original vibration signal generated when the vehicle passes by the road.

[0098] S204, perform image conversion processing on each original vibration signal to determine the initial trajectory image; the initial trajectory image includes the initial trajectory formed by the vehicle on the road.

[0099] Since the optical fibers are installed on both sides of the road, the initial trajectory image can be the initial trajectory image of the left lane, the initial trajectory image of the right lane, or the initial trajectory image formed by merging the initial trajectory images of the left lane and the right lane. Unless otherwise specified, the following embodiments indicate that the initial trajectory image is the initial trajectory image formed by merging the initial trajectory images of the left lane and the right lane.

[0100] See Figure 3 , Figure 3 The initial trajectory image is for the left lane, and the initial trajectory image is for the right lane; see [link / reference]. Figure 4 , Figure 4 The initial trajectory image is formed by merging the initial trajectory images of the left lane and the right lane.

[0101] In this step, since the acquired raw vibration signal contains noise and interference from various frequency components, and the acquired raw vibration signal is floating-point data, which is not conducive to the analysis and processing of the signal by technicians, the radar sensing device needs to process the raw vibration signal after acquiring it to determine the initial trajectory image corresponding to the raw vibration signal. This initial trajectory image contains the initial trajectory formed by the vehicle on the road. This can convert the abstract floating-point data into intuitive image information, which is convenient for subsequent processing of the vehicle's initial trajectory image. Among them, the image conversion processing is to convert the raw vibration signal acquired by the radar sensing device into image information after processing. This image information can represent the initial trajectory information formed by the vehicle when it is driving on the road.

[0102] It should be noted that due to the influence of obstacles and other factors such as harsh environments, the initial trajectory determined by radar sensing equipment through the original vibration signal may be unclear, lost, or broken.

[0103] S206, Perform trajectory tracking processing on the initial trajectory in the initial trajectory image to determine the target trajectory image; the target trajectory image includes the target trajectory corresponding to the vehicle.

[0104] In this step, since the initial trajectory image may contain unclear vehicle trajectories, missing trajectories, or broken trajectories, the radar sensing device needs to perform tracking processing on the acquired initial trajectory image after acquisition to determine the target trajectory image. This trajectory tracking processing can be a vehicle trajectory tracking algorithm, a vehicle trajectory post-processing algorithm, or a combination of both. Technicians can choose the appropriate algorithm to process the initial trajectory in the initial trajectory image according to their needs. Specifically, the vehicle trajectory tracking algorithm can transform the relatively blurry initial trajectory in the initial trajectory image, which is difficult for technicians to analyze and process, into a clear target trajectory that is easy for technicians to analyze and process. The vehicle trajectory post-processing algorithm can transform the initial trajectory in the initial trajectory image, which is broken or has its upper and / or lower half missing due to obstacles or weather conditions, into a complete and accurate target trajectory.

[0105] In the above embodiment, the radar sensing device first acquires the original vibration signals collected by each of the two-way optical fibers. These original vibration signals are the vibration signals generated when a vehicle passes over the road. Then, image conversion processing is performed on each of the original vibration signals to determine an initial trajectory image, which includes the initial trajectory formed by the vehicle on the road. Finally, trajectory tracking processing is performed on the initial trajectory in the initial trajectory image to determine a target trajectory image, which includes the target trajectory corresponding to the vehicle. In this method, the radar sensing device acquires the vibration signals generated by the vehicle passing over the road through two-way optical fibers, and then determines the target trajectory information for the entire road by performing image conversion processing and trajectory tracking processing on the acquired vibration signals. The determined target trajectory information is then analyzed and processed. This technology can determine the traffic conditions along the entire road. By using bidirectional fiber optic and radar sensing equipment to detect vehicles on the road, it avoids the problems of false alarms, missed alarms, and susceptibility to interference that may exist with existing road monitoring cameras and infrared sensing equipment in adverse weather conditions. This meets the needs of modern traffic management and safety management. In addition, the use of bidirectional fiber optic radar sensing equipment can achieve full-range monitoring of the road with a wide coverage area, and can capture changes in the intensity of the original vibration signal in a timely manner, thereby effectively improving the level of transportation safety. Secondly, because light signals propagate quickly and are not affected by external interference, high-precision detection results can be obtained. Furthermore, fiber optic radar technology also has the advantages of small size, low power consumption, and low maintenance costs, making it widely applicable in practical applications.

[0106] The above embodiments mention that the radar sensing device can determine the initial trajectory image by performing image conversion processing on the original vibration signal. The following embodiments will describe in detail the specific process of the radar sensing device determining the initial trajectory image.

[0107] In another embodiment, a different vehicle trajectory analysis method is provided, based on the above embodiments, such as... Figure 5 As shown, the above S204 may include the following steps:

[0108] S302, preprocess each original vibration signal to determine the target vibration signal corresponding to each original vibration signal; the preprocessing includes filtering and / or equalization.

[0109] In this step, after acquiring the original vibration signal, since the acquired original vibration signal may contain other information besides vehicle vibration information, such as background noise and environmental noise, it can be filtered. Since the light signal intensity returned to the radar sensing device from different optical fibers is different, the original vibration signal needs to be equalized. That is, the value of the original vibration signal returned by the optical fiber closer to the radar sensing device is reduced, and the value of the original vibration signal returned by the optical fiber farther away from the radar sensing device is amplified, thereby determining the target vibration signal corresponding to the original vibration signal.

[0110] Additionally, see Figure 6 Filtering mainly includes mean filtering (i.e.,...) Figure 6 The methods used include mean-based noise reduction filtering and bandpass filtering. The specific process of mean-based filtering is as follows: using the value of a specific original vibration signal as a benchmark, the average value of other signals surrounding the original vibration signal (including the value of the original vibration signal) is calculated. This average value is then used as the filtered value of the original vibration signal, thus obtaining the processed vibration signal (i.e., the...). Figure 6 The preprocessed bidirectional sensing data); the specific process of the bandpass filtering method is as follows: Each original vibration signal undergoes a fast Fourier transform, transforming it from the time domain to the frequency domain. Then, a bandpass filter is used to filter the frequency domain data, retaining only the characteristic frequency bands containing vehicle vibration information. Next, an inverse fast Fourier transform is used to transform the acquired characteristic frequency bands from the frequency domain back to the time domain, thus obtaining the processed vibration signal (i.e., the preprocessed bidirectional sensing data). Figure 6 The process involves calculating coefficients column by column for the preprocessed bidirectional sensing data to balance the sensing response level. The specific process of balancing is as follows: sum the data on the left and right sides of the optical fiber column by column to obtain two values. Then, take the ratio of the relatively larger value to the sum of the values ​​in a certain column as the balancing coefficient of that column. Finally, update the data in that column by multiplying all the data in that column by the balancing coefficient.

[0111] S304 performs image conversion processing on the vibration signals of each target to determine the initial trajectory image.

[0112] In this step, after obtaining the target vibration signal, it is necessary to perform data image conversion processing on the target vibration signal to obtain the initial trajectory image; see continue. Figure 6Specifically, the target vibration signal can be standardized to the range of 0-255, and the digital signal can be converted into grayscale images on both sides of the bidirectional optical fiber. Here, 0-255 represents the brightness of each trajectory in the initial trajectory image, divided into 256 levels from 0 to 255. The numerical value represents the brightness of the color, with 0 indicating 0% brightness and 255 indicating 100% brightness. See also... Figure 3 The initial trajectory image shown is divided by the longitudinal axis of symmetry. The left image represents the grayscale image obtained after preprocessing and image conversion of the original vibration signal collected by the optical fiber on the left side of the road. The right image represents the grayscale image obtained after preprocessing and image conversion of the original vibration signal collected by the optical fiber on the right side of the road.

[0113] In the above embodiments, the radar sensing device first preprocesses the original vibration signal. After determining the target vibration signal corresponding to each original vibration signal, the target vibration signal is then processed by image conversion to determine the initial trajectory image. In this method, filtering the original vibration signal can suppress noise in the original vibration signal. Equalization processing of the original vibration signal can equalize the data at each point in the original vibration signal, making the processed data easier for subsequent processing. Image conversion processing of the target vibration signal can convert abstract floating-point data into intuitive image information, facilitating technicians to analyze and process the vehicle's trajectory.

[0114] The above embodiments mentioned that image conversion processing can be performed on the vibration signals of each target to determine the initial trajectory image. The following embodiments will describe in detail the specific process by which the radar sensing device determines the initial trajectory image by image conversion processing of the target vibration signals.

[0115] In another embodiment, a different vehicle trajectory analysis method is provided, based on the above embodiments, such as... Figure 7 As shown, the above S304 may include the following steps:

[0116] S402 performs image conversion processing on the vibration signals of each target to obtain grayscale images.

[0117] Among them, each grayscale image mainly refers to the left grayscale image and the right grayscale image. The left grayscale image represents the original vibration signal collected by the optical fiber laid on the left side of the road, and the right grayscale image represents the original vibration signal collected by the optical fiber laid on the right side of the road.

[0118] In this step, after obtaining the target vibration signal, it is necessary to perform data image conversion processing on the target vibration signal to obtain various grayscale images. Specifically, the target vibration signal can be normalized to the range of 0-255, converting the digital signal into grayscale images on the left and right sides of the bidirectional optical fiber. Here, 0-255 represents the brightness of each trajectory in the initial trajectory image, divided into 256 levels from 0 to 255. The numerical value represents the brightness of the color, with 0 indicating 0% brightness and 255 indicating 100% brightness. See also... Figure 3 The grayscale images shown on the left and right sides are divided by the vertical axis of symmetry. The left image represents the grayscale image obtained after preprocessing and image conversion of the original vibration signal collected by the optical fiber on the left side of the road. The right image represents the grayscale image obtained after preprocessing and image conversion of the original vibration signal collected by the optical fiber on the right side of the road.

[0119] S404 performs noise reduction processing on each grayscale image to determine the processed grayscale images.

[0120] In this step, after obtaining the grayscale images on the left and right sides, denoising processing needs to be performed on each grayscale image to obtain the processed grayscale images on the left and right sides; see continue. Figure 6 The denoising process mainly includes the horizontal averaging method and the minimum value filtering method. Specifically, the horizontal averaging method downsamples each grayscale image by N times using the horizontal averaging method. The purpose is to compress each grayscale image horizontally and suppress some noise. Here, the value of N can be 5, so as to obtain a better denoising effect. The minimum value filtering method performs vertical continuous minimum value filtering on each grayscale image by M points, so as to remove some background noise. Here, the value of M can be 6, so as to obtain a better denoising effect.

[0121] S406, perform a folding and merging process on each processed grayscale image to determine the initial trajectory image.

[0122] In this step, please continue to refer to Figure 6 After obtaining the processed grayscale images, it is necessary to fold and merge them to determine the initial trajectory image; specifically, in the process of... Figure 3 After denoising the grayscale images on both sides, continue to see... Figure 4 , Figure 4 To be Figure 3 The left and right grayscale images are folded along the vertical axis of symmetry to obtain a merged grayscale image, which allows the initial trajectory image to be determined.

[0123] In the above embodiments, by performing image conversion processing on each target vibration signal, abstract floating-point data can be converted into intuitive image information, which is convenient for technicians to analyze and process; by performing noise reduction processing on the grayscale image, noise unrelated to vehicle vibration information can be removed, thereby reducing the amount of computation; by performing folding and merging processing on the processed grayscale image, the left and right grayscale images can be merged into a single grayscale image initial trajectory for analysis, making the analysis results more accurate.

[0124] The above embodiments mention that trajectory tracking processing can be performed on the initial trajectory in the initial trajectory image to determine the target trajectory image. The following embodiments will describe in detail the specific process of determining the target trajectory image.

[0125] In another embodiment, a different vehicle trajectory analysis method is provided, based on the above embodiments, such as... Figure 7 As shown, the above S206 may include the following steps:

[0126] S502, Perform trajectory tracking processing on the initial trajectory in the initial trajectory image to determine the intermediate trajectory image; the intermediate trajectory image includes the intermediate trajectory corresponding to the vehicle.

[0127] In this step, after the radar sensing device obtains the initial trajectory image, since the initial trajectory in the initial trajectory image may be unclear, it is necessary to perform trajectory tracking processing on the initial trajectory in the initial trajectory image to determine the intermediate trajectory image, which includes the intermediate trajectory corresponding to the vehicle.

[0128] S504, perform trajectory post-processing on the intermediate trajectory in the intermediate trajectory image to determine the target trajectory image.

[0129] The trajectory post-processing mainly includes trajectory connection and / or trajectory extension; trajectory connection is to connect intermediate trajectories that have a break in the middle in the intermediate trajectory image to obtain a complete intermediate trajectory; trajectory extension is to extend the intermediate trajectory image to obtain a complete intermediate trajectory.

[0130] In this step, for broken intermediate trajectories in the intermediate trajectory image, trajectory connection processing is required to connect the broken intermediate trajectories into complete trajectories. Specifically, the trajectory connection process involves traversing adjacent and broken intermediate trajectory segments, determining whether the distance and slope between the intermediate trajectory and other intermediate trajectories meet preset thresholds. If the connection meets the conditions, a new trajectory segment is formed. That is, when the distance between the tail of one trajectory and the head of another trajectory is less than the distance threshold, and the absolute value of the slope of the two trajectories is less than the slope threshold, these two trajectories will be connected to form a new trajectory segment. Figure 8 As shown, it can be represented by a straight line segment. Figure 8 The intermediate trajectories 5.1 and 6.1 of the fracture are connected.

[0131] Additionally, for intermediate trajectories where the upper or lower half is lost in the intermediate trajectory image, trajectory extension processing is required. This extends the lost intermediate trajectory upwards or downwards along its original slope to form a complete trajectory. For example... Figure 9 As shown, it can be Figure 9 The missing middle trajectory in the upper half of the image is expanded in section 6.1. The expanded part is... Figure 9 5.1 in the middle.

[0132] It should be noted that when traversing the intermediate trajectories in the intermediate trajectory image, the intermediate trajectories may fall into four categories: complete intermediate trajectory, broken intermediate trajectory, loss of tracking of the upper or lower half of the intermediate trajectory, and broken intermediate trajectory with loss of tracking of the upper or lower half of the intermediate trajectory. Therefore, the corresponding trajectory post-processing methods for these four cases are: no intermediate trajectory processing, trajectory connection processing, trajectory expansion processing, and trajectory connection processing and trajectory expansion processing. After performing trajectory post-processing on the intermediate trajectories in the intermediate trajectory image, the target trajectory image can be determined, and the trajectory segments in the target trajectory image are all complete trajectory segments.

[0133] In the above embodiments, by performing trajectory tracking processing on the initial trajectory in the initial trajectory image, the intermediate trajectory corresponding to the vehicle can be clearly represented in the initial trajectory image, thereby obtaining the intermediate trajectory image; then, by performing trajectory post-processing on the intermediate trajectory in the intermediate trajectory image, not only can the breaks in the intermediate trajectory image be connected, but also the intermediate trajectory with lost upper or lower half tracking can be extended to determine the final target trajectory image.

[0134] The above embodiments mention that the radar sensing device can perform trajectory tracking processing on the initial trajectory in the initial trajectory image to determine the intermediate trajectory image. The following embodiments will describe in detail the specific process of the radar sensing device performing trajectory tracking processing on the initial trajectory to determine the intermediate trajectory image.

[0135] In another embodiment, a different vehicle trajectory analysis method is provided, based on the above embodiments, such as... Figure 12 As shown, the above S502 may include the following steps:

[0136] S602, a preset sliding window is used to perform threshold detection processing on the initial trajectory in the initial trajectory image to determine the starting point of the initial trajectory.

[0137] The preset sliding window can be a 3*4 sliding window, a 4*4 sliding window, or a sliding window of other sizes; no specific limitation is made here.

[0138] Furthermore, since the initial trajectory image can be either the initial trajectory image of the left lane or the initial trajectory image of the right lane, or it can be formed by merging the initial trajectory images of the left and right lanes, when using a preset sliding window to perform threshold detection processing on the initial trajectory in the initial trajectory image, the initial trajectory images of the left and right lanes can be detected separately, or the merged initial trajectory image can be detected directly. When using a preset sliding window to traverse the initial trajectory in the initial trajectory image, the initial trajectory images of the left and right lanes can be traversed separately, and the average gray level within the sliding window can be calculated separately. Then, the average of these two average gray levels is taken, and this average is the average gray level within the sliding window. When using a preset sliding window to traverse the initial trajectory in the initial trajectory image, the initial trajectory image formed by merging the initial trajectory images of the left and right lanes can also be traversed, and the average gray level within the sliding window can be calculated.

[0139] In this step, the radar sensing device uses a sliding window to perform threshold detection processing on the initial trajectory in the initial trajectory image, thereby determining the trajectory starting point in the initial trajectory image. Taking the traversal of the initial trajectory in the initial trajectory image using a preset sliding window as an example, the specific process can be as follows: Figure 11 As shown, a 3*4 sliding window is used to traverse the initial trajectory image. The traversal can start from the top left corner of the initial trajectory image. At this time, the average gray level within the sliding window is calculated and compared with a preset gray level threshold. When the average gray level is greater than the preset gray level threshold, the point within the sliding window is considered to be the starting point of the initial trajectory. When the average gray level is less than the preset gray level threshold, the point within the sliding window is not considered to be the starting point of the initial trajectory. At this time, the position of the sliding window is moved until the average gray level within the sliding window is greater than the preset gray level threshold.

[0140] It should be noted that the preset grayscale threshold can be a value set by the user or a value automatically set by the radar sensing device according to different road types, such as tunnels and highways.

[0141] S604, obtain the candidate direction of movement corresponding to the vehicle and the vehicle information corresponding to the starting point of the trajectory; the vehicle information includes at least one of vehicle speed, lane and vehicle type.

[0142] Among them, vehicle type refers to the size of the vehicle. Based on the size of the vehicle, vehicle types can be divided into large vehicles and small vehicles. When determining the initial vehicle type, it can be determined based on the width of the initial trajectory in the initial trajectory image. For a wider initial trajectory in the initial trajectory image, it can be preliminarily determined that the vehicle corresponding to the initial trajectory is a large vehicle; for a narrower initial trajectory in the initial trajectory image, it can be preliminarily determined that the vehicle corresponding to the initial trajectory is a small vehicle.

[0143] Vehicle speed refers to the initial vehicle speed in the initial trajectory image. It can be set according to the vehicle type. For example, when the vehicle type is a large vehicle, the initial vehicle speed can be set to 60 km / h or 70 km / h; when the vehicle type is a small vehicle, the initial vehicle speed can be set to 80 km / h.

[0144] Lanes can be left or right. The vehicle can be determined to be traveling in the left or right lane by examining the initial trajectory in the initial trajectory image. For example, the vehicle can be determined by comparing the average gray values ​​of the two initial trajectory images. If the average gray value of the initial trajectory image of the left lane is greater than that of the initial trajectory image of the right lane, the vehicle is considered to be traveling in the left lane; otherwise, the vehicle is considered to be traveling in the right lane.

[0145] The candidate movement direction is the direction in which the vehicle is most likely to travel. In fact, when the radar sensing device obtains the candidate movement direction corresponding to the vehicle in the initial trajectory, it moves along the direction with the largest average gray value in the initial trajectory image traversed by the sliding window.

[0146] In this step, please continue to refer to Figure 11 After determining the starting point of the initial trajectory in the initial trajectory image, the radar sensing device will acquire the candidate movement direction corresponding to the vehicle corresponding to the initial trajectory, as well as the vehicle information corresponding to the vehicle at the starting point of the trajectory.

[0147] S606, based on vehicle information, move the sliding window along the candidate movement direction on the initial trajectory by a preset step size to determine at least one intermediate trajectory point on the initial trajectory.

[0148] The preset step size can be automatically adjusted based on vehicle information; intermediate trajectory points refer to all points in the initial trajectory except for the trajectory starting point.

[0149] In this step, after acquiring the candidate movement direction corresponding to the vehicle on the initial trajectory and the vehicle information corresponding to the vehicle at the starting point of the trajectory, the radar sensing device will, based on the vehicle information, move the sliding window along the candidate movement direction on the initial trajectory with a preset step size, thereby determining at least one intermediate trajectory point on the initial trajectory. Specifically, based on the acquired vehicle information, the radar sensing device uses the sliding window to calculate the average gray level of adjacent positions along the candidate movement direction with a preset step size, and calculates whether the average gray level within the sliding window is greater than a preset threshold. When the average gray level within the sliding window is greater than the preset threshold, the point is considered to be a point on the initial trajectory, i.e., an intermediate trajectory point.

[0150] S608 determines the intermediate trajectory image based on the trajectory start point and intermediate trajectory points.

[0151] In this step, after acquiring the first intermediate trajectory point on the initial trajectory, the radar sensing device calculates the vehicle speed, lane, and vehicle type of the first intermediate trajectory point. Specifically, when calculating the vehicle speed, the slope between the trajectory start point and the first intermediate trajectory point is calculated, and this slope is set as the vehicle speed. This speed is more accurate than the initial speed corresponding to the vehicle information in S604 and can be approximated as the vehicle's actual speed. Then, the radar sensing device can acquire the vehicle's second trajectory point, third trajectory point, and so on, until all intermediate trajectory points in the initial trajectory are acquired. Finally, the intermediate trajectory image is determined based on the trajectory start point and intermediate trajectory points.

[0152] In the above embodiments, the starting point of the initial trajectory can be determined by performing threshold detection processing on the initial trajectory in the initial image through a preset sliding window. By obtaining the candidate movement direction corresponding to the vehicle and the vehicle information corresponding to the vehicle at the trajectory starting point, the sliding window can move along the candidate movement direction on the initial trajectory with a preset step size, thereby determining at least one intermediate trajectory point in the initial trajectory. Finally, the intermediate trajectory image is determined based on the obtained trajectory starting point and the intermediate points of each trajectory. This can make the unclear or ambiguous initial trajectory in the initial trajectory image clearer.

[0153] The above embodiments mention that the radar sensing device can determine the intermediate trajectory image based on the trajectory start point and intermediate trajectory points. The following embodiments will describe in detail the specific working process of the radar sensing device before determining the intermediate trajectory image based on the trajectory start point and intermediate trajectory points.

[0154] In another embodiment, another vehicle trajectory analysis method is provided. Based on the above embodiment, before step S608, the method may further include the following steps:

[0155] Step A involves weakening the other points between the starting point of the trajectory and adjacent intermediate trajectory points, and weakening the other points between every two adjacent intermediate trajectory points.

[0156] Among them, weakening processing refers to using grayscale reduction to reduce the brightness of some trajectory points. For example, 60%-70% of the brightness of the trajectory points can be erased.

[0157] In this step, the radar sensing device can record the trajectory starting point, each intermediate trajectory point, and updated information such as vehicle speed, lane, and vehicle type. Then, it weakens the other points between the trajectory starting point and adjacent intermediate trajectory points, and weakens the other points between every two adjacent intermediate trajectory points. Specifically, it weakens the points between the initial trajectory point and the first intermediate trajectory point, the first intermediate trajectory point and the second intermediate trajectory point, ... and the second-to-last intermediate trajectory point and the last intermediate trajectory point.

[0158] In the above embodiments, since the sliding window moves with a preset step size, it is necessary to weaken the other points between the trajectory starting point and the connected intermediate trajectory points, as well as the other points between every two adjacent intermediate trajectory points, so as to reduce the influence of other points when determining other initial trajectories in the subsequent initial trajectory image, thereby reducing the misjudgment of subsequent initial trajectories.

[0159] The above embodiments mention that the radar sensing device can determine the intermediate trajectory image based on the trajectory starting point and intermediate trajectory points. The following embodiments will describe in detail the specific process of the radar sensing device removing the intermediate trajectory image.

[0160] In another embodiment, a different vehicle trajectory analysis method is provided, based on the above embodiments, such as... Figure 13 As shown, the above S608 may further include the following steps:

[0161] S702, connect the starting point of the trajectory with the adjacent intermediate trajectory points to determine the first line segment.

[0162] S704, connect every two adjacent intermediate trajectory points to determine at least one second line segment.

[0163] S706, determine the intermediate trajectory based on the first line segment and the second line segment, and obtain the intermediate trajectory image.

[0164] In the above steps, after acquiring the trajectory starting point and each intermediate trajectory point, the radar sensing device first connects the trajectory starting point and the intermediate trajectory points adjacent to the trajectory starting point with straight lines to determine the first line segment; then, it connects every two adjacent intermediate trajectory points to determine at least one second line segment; finally, it determines the intermediate trajectory based on the first line segment and each of the second line segments, thereby obtaining an intermediate trajectory image. The intermediate trajectory is determined based on the first and second line segments, resulting in an intermediate trajectory image.

[0165] In the above embodiment, a first line segment is determined by connecting the trajectory starting point with adjacent intermediate trajectory points, and at least one second line segment is determined by connecting every two adjacent intermediate trajectory points. Finally, the intermediate trajectory is determined by the first line segment and the second line segment to obtain an intermediate trajectory image. In this way, isolated trajectory starting points and each intermediate trajectory point can be connected to determine the intermediate trajectory image.

[0166] Since the intermediate trajectory determined by the radar sensing device in the above embodiments may have a broken middle section or the upper or lower half of the intermediate trajectory may be lost, it is necessary to perform trajectory post-processing on the intermediate trajectory image obtained in the above embodiments in order to determine the target trajectory image. The following embodiments will describe in detail the specific process of the radar sensing device determining the target trajectory image.

[0167] In another embodiment, another vehicle trajectory analysis method is provided. Based on the above embodiment, S504 may further include the following steps:

[0168] Step B involves performing trajectory connection processing on the intermediate trajectories in the intermediate trajectory image, and / or performing trajectory expansion processing on the intermediate trajectories in the intermediate trajectory image to determine the target trajectory image.

[0169] The trajectory connection process connects intermediate trajectories that are broken in the middle of the intermediate trajectory image into a complete trajectory; the trajectory expansion process connects intermediate trajectories that are missing their upper or lower halves into a complete trajectory.

[0170] In this step, after acquiring the intermediate trajectory image, the radar sensing device needs to analyze and process the intermediate trajectory in the intermediate trajectory image, connecting the intermediate trajectories that have broken in the middle into complete trajectories, or connecting the intermediate trajectories in the intermediate trajectory image that have missing upper or lower parts into complete trajectories, thereby determining the target trajectory image.

[0171] In the above embodiments, by processing the intermediate trajectory images where the trajectory is broken or the upper or lower half is missing, a more complete and accurate target trajectory image can be determined, thus providing a data foundation for subsequent analysis of the target trajectory.

[0172] The above embodiments mention that the radar sensing device can perform trajectory connection processing on the intermediate trajectories in the intermediate trajectory image. In fact, the intermediate trajectory image includes multiple intermediate trajectories. The following embodiments will describe in detail the specific process of the radar sensing device performing trajectory connection processing on the intermediate trajectories in the intermediate trajectory image where trajectory breaks occur.

[0173] In another embodiment, a different vehicle trajectory analysis method is provided, based on the above embodiments, such as... Figure 14 As shown, step B above, which involves connecting the intermediate trajectories in the intermediate trajectory image, may further include the following steps:

[0174] S802, traverse each intermediate trajectory in the intermediate trajectory image and determine the subsequent intermediate trajectory corresponding to the current intermediate trajectory.

[0175] S804 connects the current intermediate trajectory with the corresponding subsequent intermediate trajectory according to their relative positional relationship.

[0176] Among them, the relative positional relationship mainly refers to the distance and slope relationship between the intermediate trajectory where the trajectory breaks and other intermediate trajectories.

[0177] In the above steps, the radar sensing device traverses each intermediate trajectory in the intermediate trajectory image to determine whether the distance and slope between an intermediate trajectory that may have a broken trajectory and other intermediate trajectories meet a preset threshold. If the connection meets the conditions, a new trajectory segment is formed. Specifically, when the distance between the tail of one intermediate trajectory and the head of another intermediate trajectory is less than a distance threshold, and the absolute value of the slope of the two trajectories is less than a slope threshold, the two intermediate trajectories will be connected to form a new trajectory segment. The preset threshold can be set manually.

[0178] See also Figure 8 It can be achieved by using straight line segments Figure 8 The intermediate trajectories 5.1 and 6.1 of the fracture are connected. The specific steps for connecting the trajectories are as follows:

[0179] (1) Traverse each intermediate trajectory in the intermediate trajectory image and determine whether the intermediate trajectory has a possible subsequent intermediate trajectory segment. If so, proceed to step (2).

[0180] (2) Calculate the distance and slope difference between the tail coordinates of the intermediate trajectory segment and the head coordinates of the subsequent intermediate trajectory segment. If the distance is less than the corresponding distance threshold and the slope difference is less than the corresponding slope threshold, then connect the two intermediate trajectory segments into a new trajectory segment, which is the target trajectory segment, and delete the old intermediate trajectory segment; otherwise, proceed to step (3).

[0181] (3) If the intermediate trajectory segment cannot be connected to the subsequent intermediate trajectory segment, then continue to determine whether the subsequent intermediate trajectory segment has a next intermediate trajectory segment. If it does, then proceed to step (2); if not, it means that the intermediate trajectory segment is already the last segment and does not need to be connected to any intermediate trajectory segment.

[0182] In the above embodiments, by traversing each intermediate trajectory in the intermediate trajectory image, the subsequent intermediate trajectory corresponding to the current intermediate trajectory can be determined, and the current intermediate trajectory and the corresponding subsequent intermediate trajectory can be connected according to their relative positional relationship. This method can connect intermediate trajectories in the intermediate trajectory image that have broken trajectories, which facilitates subsequent processing.

[0183] The above embodiments provide a method for radar sensing devices to handle situations where the intermediate trajectory in the intermediate trajectory image is broken. The following embodiments will provide a detailed explanation of the specific processing procedure of radar sensing devices when the upper or lower half of the intermediate trajectory in the intermediate trajectory image is lost.

[0184] In another embodiment, a different vehicle trajectory analysis method is provided, based on the above embodiments, such as... Figure 15 As shown, step B above, which involves trajectory expansion processing of the intermediate trajectory in the intermediate trajectory image, may further include the following steps:

[0185] S902 determines the extension direction corresponding to the current intermediate trajectory based on the preset road section.

[0186] The preset road section can be the entire road or a segment extracted from the entire road.

[0187] In this step, the radar sensing device can determine the expansion direction of the intermediate trajectory in the intermediate trajectory image that needs to be expanded upwards or downwards based on the preset road interval; specifically, when determining the expansion direction corresponding to the current trajectory, continue to refer to... Figure 9 When the intermediate trajectory is not reached Figure 9 The four sides (i.e.) Figure 9When the middle trajectory is located on the four sides (up, down, left, and right), it is considered that the middle trajectory needs to be extended. The possible directions of extension are upward or downward. For example, when one end of the middle trajectory reaches the four sides of the preset road section, but the other end does not reach the four sides of the preset road section, the end that does not reach the preset road section needs to be extended until it reaches the four sides of the preset road section.

[0188] S904, perform trajectory expansion processing on the current intermediate trajectory along the expansion direction.

[0189] The trajectory extension process involves extending the intermediate trajectory in the intermediate trajectory image where the upper and / or lower halves of the intermediate trajectory are missing.

[0190] In this step, after determining the expansion direction corresponding to the current intermediate trajectory, the radar sensing device can perform trajectory expansion processing along the expansion direction for the intermediate trajectory that needs to be expanded; continuing to refer to the figure, it can... Figure 9 The missing middle trajectory in the upper half of the image is expanded in section 6.1. The expanded part is... Figure 9 Section 5.1 details the specific steps for trajectory extension as follows:

[0191] (1) Determine the location and direction to be expanded, and decide whether to extend upwards or downwards;

[0192] (2) Find the nearest points on the trajectory and calculate their slopes;

[0193] (3) Obtain a suitable slope value based on the average of the above slopes;

[0194] (4) Calculate the coordinates of the points that need to be expanded based on the original trajectory points and the slope value obtained in step (3);

[0195] (5) Add extended points to the trajectory, and repeat steps (2)-(5) until the trajectory is extended to the required length or the next point cannot be calculated.

[0196] In the above embodiments, the expansion direction corresponding to the current intermediate trajectory can be determined by the preset road interval, and the current intermediate trajectory can be expanded along the expansion direction. This method can expand the intermediate trajectory in the intermediate trajectory image where the upper and / or lower half of the intermediate trajectory is lost, which facilitates subsequent processing.

[0197] In the above embodiments, by performing trajectory connection and trajectory expansion processing on the intermediate trajectories in the intermediate trajectory image, the final target trajectory image can be determined. The target trajectory in the target trajectory image is a clear and distinct target trajectory. The following embodiments will describe the process of analyzing and processing the target trajectory in the target trajectory image.

[0198] In another embodiment, a different vehicle trajectory analysis method is provided. Based on the above embodiments, the method may further include:

[0199] Step C: Calculate and process traffic flow information based on the target trajectory image to determine the traffic flow information; the traffic flow information includes at least one of traffic volume, average vehicle speed, and traffic density.

[0200] Traffic flow refers to the number of vehicles passing through a certain location or section of a road within a unit of time. Average vehicle speed includes time-averaged speed and spatial-averaged speed. Time-averaged speed refers to the average instantaneous speed of all vehicles passing through the section at any observation location within a certain unit of time at any observation location along the entire road. Spatial-averaged speed refers to the average speed of all vehicles within a specific spatial area on the observed road at a certain observation time. Traffic density refers to the density of vehicles traveling on the road, that is, the number of vehicles in a unit space (generally 1 km) of the road at a certain time.

[0201] In this step, the calculation process for traffic flow information is as follows: Figure 16 As shown, after acquiring the target trajectory image, the radar sensing device needs to perform traffic flow information calculation and processing on the acquired target trajectory image, so as to determine the traffic flow information of the entire road; wherein, the traffic flow information includes at least one of traffic volume, average vehicle speed and traffic density.

[0202] In the above embodiments, by performing traffic flow information calculation and processing on the target trajectory image, the traffic flow information of the road in the entire time domain and in all time and space can be calculated. By analyzing the traffic flow information, the entire road can be detected, thereby effectively improving the level of transportation safety.

[0203] The above embodiments mention that the radar sensing device can perform traffic flow information calculation and processing based on the target trajectory image to determine the traffic flow information of the entire road. The following embodiments will describe in detail the specific process of the radar sensing device determining the traffic flow information of the entire road.

[0204] In another embodiment, a different vehicle trajectory analysis method is provided, based on the above embodiments, such as... Figure 17 As shown, step C above may include the following steps:

[0205] S1002, obtain the position to be measured corresponding to the point to be measured, and obtain the start and end positions of the target trajectory in the target trajectory image.

[0206] S1004. Based on the relative positional relationship between the starting and ending positions of the same target trajectory and the position to be measured, the number of vehicles passing through the point to be measured is accumulated to determine the traffic flow corresponding to the point to be measured and obtain traffic flow information.

[0207] Traffic flow refers to the number of vehicles passing through a certain location or section of a road within a unit of time; relative positional relationship refers to the sequential relationship between the measured location and the starting location of the target trajectory, as well as between the measured location and the ending location.

[0208] In the above steps, the radar sensing device first traverses all target trajectories along the entire road within the measurement period. If the location to be measured is greater than the starting position of the target trajectory and less than the ending position of the target trajectory, the traffic flow count is incremented by 1. After the traversal is complete, the traffic flow at the measured point within this measurement period can be obtained. See also Figure 18 The traffic flow observation point is located 500m from the road. Target trajectories 1 through 9 all start at positions less than the observation point and end at positions greater than the observation point. Therefore... Figure 18 The traffic flow was 9.

[0209] In the above embodiments, by obtaining the location to be measured corresponding to the point to be measured and the starting and ending positions of the target trajectory in the target trajectory image, and based on the relative positional relationship between the starting and ending positions of the same target trajectory and the location to be measured, the number of vehicles passing through the point to be measured is accumulated, thereby determining the traffic flow corresponding to the point to be measured. Monitoring the traffic flow allows technicians to balance the traffic flow in advance. This can not only divert traffic flow on congested roads to the periphery or other roads, but also limit traffic volume during peak hours to divert it to other times. Furthermore, technical or economic means can be used to balance traffic flow, such as issuing dynamic traffic information for guidance and evacuation, or implementing congestion pricing to regulate and balance traffic distribution.

[0210] The traffic flow monitoring method described in the above embodiments is detailed. The traffic flow information also includes average vehicle speed, which includes time-averaged vehicle speed and / or spatial-averaged vehicle speed. The following embodiments will describe in detail the method for monitoring the average vehicle speed along the entire road.

[0211] In another embodiment, another vehicle trajectory analysis method is provided. Based on the above embodiments, step C may further include the following steps:

[0212] S1102, based on the relative positional relationship between the starting and ending positions of the same target trajectory and the position to be measured, the speeds of vehicles passing through the point to be measured are accumulated to determine the time-averaged vehicle speed at the point to be measured, thereby obtaining traffic flow information.

[0213] The time-average vehicle speed refers to the average instantaneous speed of all vehicles passing through the cross section at any observation point along the entire road within a certain unit of time.

[0214] In the above steps, when monitoring the time-averaged vehicle speed along the entire road, the radar sensing device can accumulate the speeds of vehicles passing through the target point based on the relative positional relationship between the starting and ending positions of the same target trajectory and the target location, thus determining the corresponding time-averaged vehicle speed at the target point and obtaining traffic flow information. Specifically, the radar sensing device first traverses all target trajectories along the entire road within the measurement period. If the target location is greater than the starting position of the target trajectory but less than the ending position of the target trajectory, the speed corresponding to that trajectory is accumulated, and the trajectory count value is incremented by 1. After the traversal is complete, the accumulated speeds are divided by the trajectory count value to obtain the average speed, which is the time-averaged vehicle speed at the target location within this measurement period. See also... Figure 18 The traffic flow to be measured is located 500m from the road. Target trajectories 1 to 9 all have starting positions less than the observation point and ending positions greater than the observation point. The average speed of vehicles at the point to be measured is obtained by summing the speeds of target trajectories 1 to 9 and then dividing by 9.

[0215] S1104, and / or, acquire the time to be measured and the start and end times of the target trajectory; based on the relative temporal relationship between the start and end times of the same target trajectory and the time to be measured, accumulate the speeds of vehicles passing through the point to be measured, determine the spatial average vehicle speed corresponding to the point to be measured, and obtain traffic flow information.

[0216] Among them, the spatial average vehicle speed refers to the average speed of all vehicles within a specific spatial area on the observed road at a certain observation time.

[0217] In this step, when monitoring the spatial average vehicle speed along the entire road, the radar sensing device can first acquire the time to be measured as well as the start and end times of the target trajectory; then, based on the relative temporal relationship between the start and end times of the same target trajectory and the time to be measured, the speeds of vehicles passing through the point to be measured are accumulated to determine the spatial average vehicle speed corresponding to the point to be measured.

[0218] Specifically, the radar sensing device first traverses all target trajectories within a specific spatial area (such as the entire length of a road). If the time to be measured is greater than the start time of the trajectory but less than the end time of the trajectory, the speed corresponding to that trajectory is accumulated, and the trajectory count is incremented by 1. After the traversal is complete, the accumulated speeds are divided by the trajectory count to obtain the average speed. This average speed is the spatial average vehicle speed within that specific spatial area at the time to be measured. See also Figure 19 The time to be measured is 5 minutes into the measurement cycle. Among them, the time to be measured for target trajectory 4, target trajectory 5, target trajectory 6 and target trajectory 8 is greater than the starting time of the trajectory and less than the ending time of the trajectory. Therefore, the speeds of target trajectory 4, target trajectory 5, target trajectory 6 and target trajectory 8 are summed and then divided by 4 to obtain the spatial average vehicle speed at the point to be measured.

[0219] In the above embodiments, by analyzing the starting and ending positions of the same target trajectory, as well as the relative positional relationship between the starting and ending positions and the position to be measured, and by accumulating the speeds of vehicles passing through the point to be measured, the time-averaged vehicle speed corresponding to the point to be measured can be determined. Analysis of the time-averaged vehicle speed can be used not only to measure the driving efficiency and speed of vehicles on the road, but also to assess traffic flow, road congestion, and the incidence of traffic accidents. Furthermore, by acquiring the time to be measured and the starting and ending times of the target trajectory, and based on the relative temporal relationship between the starting and ending times of the same target trajectory and the time to be measured, the speeds of vehicles passing through the point to be measured can be accumulated, thereby determining the spatial average vehicle speed corresponding to the point to be measured. Analysis of the spatial average vehicle speed can be used to measure road capacity and traffic congestion.

[0220] The above embodiments provide a detailed description of the traffic flow monitoring method. Traffic flow information also includes traffic density. The following embodiments will provide a detailed description of the method for monitoring traffic density along the entire road.

[0221] In another embodiment, another vehicle trajectory analysis method is provided, such as Figure 20 As shown, step C above may also include the following steps:

[0222] S1202, obtain the time to be tested and the start and end times of the target trajectory.

[0223] S1204: Based on the relative temporal relationship between the start and end times of the same target trajectory and the time to be measured, the number of vehicles passing through the point to be measured is accumulated to determine the traffic density corresponding to the point to be measured and obtain traffic flow information.

[0224] Traffic density refers to the density of vehicles traveling on a road, that is, the number of vehicles in a unit space (usually 1 km) of road at a certain moment.

[0225] In the above steps, when monitoring the traffic density of the entire road, the radar sensing device can first acquire the time to be measured as well as the start and end times of the target trajectory. Then, based on the relative temporal relationship between the start and end times of the same target trajectory and the time to be measured, the number of vehicles passing through the point to be measured is accumulated, thereby determining the traffic density corresponding to the point to be measured.

[0226] Specifically, the radar sensing device first traverses all target trajectories within a specific spatial area (such as the entire length of a road). If the time to be measured is greater than the start time of the trajectory but less than the end time of the trajectory, the trajectory count is incremented by 1. After the traversal is complete, the trajectory count is divided by the length of the spatial area (in kilometers) to obtain the traffic density. See also... Figure 19 The time to be measured is 5 minutes into the measurement cycle. Among them, the time to be measured for target trajectory 4, target trajectory 5, target trajectory 6 and target trajectory 8 is greater than the trajectory start time and less than the trajectory end time. Therefore, the traffic density corresponding to the point to be measured is obtained by summing the trajectory numbers of target trajectory 4, target trajectory 5, target trajectory 6 and target trajectory 8 and dividing by the spatial area length of 1km.

[0227] In the above embodiments, by acquiring the time to be measured and the start and end times of the target trajectory, and by accumulating the number of vehicles passing through the point to be measured based on the relative temporal relationship between the start and end times of the same target trajectory and the time to be measured, the traffic density corresponding to the point to be measured can be determined. By analyzing the traffic density, the density of vehicles in the road space can be evaluated.

[0228] The following detailed embodiment illustrates the process of the vehicle trajectory analysis method in this application. Based on the above embodiment, the implementation process of this method may include the following:

[0229] S1, the radar sensing device acquires the raw vibration signals collected by each of the two-way optical fibers; the raw vibration signals are the vibration signals generated when the vehicle passes over the road.

[0230] S2, preprocess each original vibration signal to determine the target vibration signal corresponding to each original vibration signal; preprocessing includes filtering and / or equalization; the initial trajectory image includes the initial trajectory formed by the vehicle on the road.

[0231] S3, perform image conversion processing on the vibration signals of each target to obtain grayscale images;

[0232] S4, Denoise each grayscale image and determine the processed grayscale image;

[0233] S5, fold and merge the processed grayscale images to determine the initial trajectory image;

[0234] S6, a preset sliding window is used to perform threshold detection processing on the initial trajectory in the initial trajectory image to determine the trajectory starting point in the initial trajectory;

[0235] S7, obtain the candidate direction of movement corresponding to the vehicle and the vehicle information corresponding to the starting point of the trajectory; the vehicle information includes at least one of vehicle speed, lane and vehicle type.

[0236] S8. Based on the vehicle information, move the sliding window along the candidate movement direction on the initial trajectory by a preset step size to determine at least one intermediate trajectory point on the initial trajectory.

[0237] S9 weakens the other points between the starting point of the trajectory and the adjacent intermediate trajectory points, and weakens the other points between every two adjacent intermediate trajectory points.

[0238] S10, connect the starting point of the trajectory with the adjacent intermediate trajectory points to determine the first line segment;

[0239] S11, connect every two adjacent intermediate trajectory points to determine at least one second line segment;

[0240] S12, determine the intermediate trajectory based on the first line segment and the second line segment, and obtain the intermediate trajectory image; the intermediate trajectory image includes the intermediate trajectory corresponding to the vehicle; the target trajectory image includes the target trajectory corresponding to the vehicle;

[0241] S14, perform trajectory connection processing on the intermediate trajectories in the intermediate trajectory image, and / or perform trajectory expansion processing on the intermediate trajectories in the intermediate trajectory image to determine the target trajectory image; perform trajectory connection processing on the intermediate trajectories in the intermediate trajectory image by executing S14-S15; perform trajectory expansion processing on the intermediate trajectories in the intermediate trajectory image by executing S16-S17.

[0242] S14, Traverse each intermediate trajectory in the intermediate trajectory image and determine the subsequent intermediate trajectory corresponding to the current intermediate trajectory;

[0243] S15, connect the current intermediate trajectory with the corresponding subsequent intermediate trajectory according to their relative positional relationship;

[0244] S16, determine the expansion direction corresponding to the current intermediate trajectory based on the preset road section;

[0245] S17, perform trajectory expansion processing on the current intermediate trajectory along the expansion direction;

[0246] S18, Perform traffic flow information calculation and processing based on the target trajectory image to determine traffic flow information; traffic flow information includes at least one of traffic flow, average vehicle speed, and traffic density; determine traffic flow by executing S19-S20; determine average vehicle speed by executing S21-S22; determine traffic density by executing S23-S24.

[0247] S19, obtain the position to be measured corresponding to the point to be measured, and obtain the start and end positions of the target trajectory in the target trajectory image;

[0248] S20: Based on the relative positional relationship between the starting and ending positions of the same target trajectory and the position to be measured, the number of vehicles passing through the point to be measured is accumulated to determine the traffic flow corresponding to the point to be measured and obtain traffic flow information.

[0249] S21, the average vehicle speed includes time-averaged vehicle speed and / or spatial-averaged vehicle speed. The process of determining the time-averaged vehicle speed is as follows: based on the relative positional relationship between the starting and ending positions of the same target trajectory and the position to be measured, the speeds of vehicles passing through the point to be measured are accumulated to determine the corresponding time-averaged vehicle speed at the point to be measured, thereby obtaining traffic flow information.

[0250] S22, and / or, the process of determining the spatial average vehicle speed is as follows: obtain the time to be measured and obtain the start time and end time of the target trajectory; based on the relative temporal relationship between the start time and end time of the same target trajectory and the time to be measured, accumulate the speeds of vehicles passing through the point to be measured, determine the spatial average vehicle speed corresponding to the point to be measured, and obtain traffic flow information.

[0251] S23, obtain the time to be measured and the start and end times of the target trajectory;

[0252] S24. Based on the relative temporal relationship between the start and end times of the same target trajectory and the time to be measured, the number of vehicles passing through the point to be measured is accumulated to determine the traffic density corresponding to the point to be measured and obtain traffic flow information.

[0253] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0254] Based on the same inventive concept, this application also provides a vehicle trajectory analysis device for implementing the vehicle trajectory analysis method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of the one or more vehicle trajectory analysis device embodiments provided below can be found in the limitations of the vehicle trajectory analysis method described above, and will not be repeated here.

[0255] In one embodiment, such as Figure 21 As shown, a vehicle trajectory analysis device is provided, including: an acquisition module 11, a determination module 12, and a processing module 13, wherein:

[0256] The acquisition module 11 is used to acquire the original vibration signals collected by each of the two-way optical fibers; the original vibration signals are the vibration signals generated when a vehicle passes over the road.

[0257] The determination module 12 is used to perform image conversion processing on each original vibration signal to determine the initial trajectory image; the initial trajectory image includes the initial trajectory formed by the vehicle on the road.

[0258] Processing module 13 is used to perform trajectory tracking processing on the initial trajectory in the initial trajectory image to determine the target trajectory image; the target trajectory image includes the target trajectory corresponding to the vehicle.

[0259] In another embodiment, another vehicle trajectory analysis device is provided. Based on the above embodiments, the determining module 12 may include:

[0260] The target vibration signal determination unit is used to preprocess each original vibration signal to determine the target vibration signal corresponding to each original vibration signal; the preprocessing includes filtering and / or equalization.

[0261] The initial trajectory image determination unit is used to perform image conversion processing on the vibration signals of each target to determine the initial trajectory image.

[0262] In another embodiment, another vehicle trajectory analysis device is provided. Based on the above embodiments, the initial trajectory image determination unit may include:

[0263] The grayscale image acquisition subunit is used to perform image conversion processing on the vibration signals of each target to obtain grayscale images.

[0264] The grayscale image processing subunit is used to perform noise reduction processing on each grayscale image and determine the processed grayscale images.

[0265] The initial trajectory image is determined by defining the sub-units and folding and merging the processed grayscale images to determine the initial trajectory image.

[0266] In another embodiment, another vehicle trajectory analysis device is provided. Based on the above embodiments, the processing module 13 may include:

[0267] The intermediate trajectory image determination unit is used to perform trajectory tracking processing on the initial trajectory in the initial trajectory image and determine the intermediate trajectory image; the intermediate trajectory image includes the intermediate trajectory corresponding to the vehicle.

[0268] The target trajectory image determination unit is used to perform trajectory post-processing on the intermediate trajectory in the intermediate trajectory image to determine the target trajectory image.

[0269] In another embodiment, another vehicle trajectory analysis device is provided. Based on the above embodiments, the intermediate trajectory image determination unit may include:

[0270] The trajectory start point determination subunit is used to perform threshold detection processing on the initial trajectory in the initial trajectory image using a preset sliding window to determine the trajectory start point in the initial trajectory.

[0271] The vehicle information acquisition subunit is used to acquire the candidate direction of movement corresponding to the vehicle and the vehicle information corresponding to the starting point of the trajectory; the vehicle information includes at least one of vehicle speed, lane and vehicle type.

[0272] The intermediate trajectory point determination subunit is used to move the sliding window along the candidate movement direction according to the vehicle information and a preset step size on the initial trajectory to determine at least one intermediate trajectory point on the initial trajectory.

[0273] The intermediate trajectory image determination subunit is used to determine the intermediate trajectory image based on the trajectory start point and intermediate trajectory points.

[0274] In another embodiment, another vehicle trajectory analysis device is provided. Based on the above embodiments, the intermediate trajectory image determination unit may further include:

[0275] The weakening processing subunit is used to weaken other points between the trajectory starting point and adjacent intermediate trajectory points, and to weaken other points between every two adjacent intermediate trajectory points.

[0276] In another embodiment, another vehicle trajectory analysis device is provided. Based on the above embodiment, the intermediate trajectory image determination subunit is used to connect the trajectory starting point with adjacent intermediate trajectory points to determine the first line segment.

[0277] The intermediate trajectory image determination subunit is also used to connect every two adjacent intermediate trajectory points to determine at least one second line segment.

[0278] The intermediate trajectory image determination subunit is also used to determine the intermediate trajectory based on the first line segment and the second line segment, and obtain the intermediate trajectory image.

[0279] In another embodiment, another vehicle trajectory analysis device is provided. Based on the above embodiment, the target trajectory image determination unit is used to perform trajectory connection processing on the intermediate trajectory in the intermediate trajectory image, and / or to perform trajectory expansion processing on the intermediate trajectory in the intermediate trajectory image to determine the target trajectory image.

[0280] In another embodiment, another vehicle trajectory analysis device is provided. Based on the above embodiments, the target trajectory image determination unit may include:

[0281] The intermediate trajectory determination sub-unit is used to traverse each intermediate trajectory in the intermediate trajectory image and determine the subsequent intermediate trajectory corresponding to the current intermediate trajectory.

[0282] The processing subunit is used to connect the current intermediate trajectory with the corresponding subsequent intermediate trajectory according to their relative positional relationship.

[0283] In another embodiment, another vehicle trajectory analysis device is provided. Based on the above embodiments, the target trajectory image determination unit may further include:

[0284] The extension direction determination sub-unit is used to determine the extension direction corresponding to the current intermediate trajectory based on the preset road interval.

[0285] The trajectory extension processing subunit is used to extend the current intermediate trajectory along the extension direction.

[0286] In another embodiment, a different vehicle trajectory analysis device is provided. Based on the above embodiments, the vehicle trajectory analysis device may further include:

[0287] The traffic flow information determination module is used to calculate and process traffic flow information based on the target trajectory image to determine the traffic flow information; the traffic flow information includes at least one of traffic flow, average vehicle speed, and traffic density.

[0288] In another embodiment, another vehicle trajectory analysis device is provided. Based on the above embodiments, the traffic flow information determination module may include:

[0289] The location information acquisition unit is used to acquire the location of the point to be measured and the start and end positions of the target trajectory in the target trajectory image.

[0290] The traffic flow information determination unit is used to accumulate the number of vehicles passing through the test point based on the relative positional relationship between the starting and ending positions of the same target trajectory and the test point, thereby determining the traffic flow corresponding to the test point and obtaining traffic flow information.

[0291] In another embodiment, a different vehicle trajectory analysis device is provided. Based on the above embodiments, the traffic flow information determination module may further include:

[0292] The time-average vehicle speed determination unit is used to accumulate the speeds of vehicles passing through the test point based on the relative positional relationship between the starting and ending positions of the same target trajectory and the test point, thereby determining the corresponding time-average vehicle speed at the test point and obtaining traffic flow information.

[0293] In this step,

[0294] And / or, a spatial average vehicle speed determination unit is used to acquire the time to be measured and the start and end times of the target trajectory; based on the relative temporal relationship between the start and end times of the same target trajectory and the time to be measured, the speeds of vehicles passing through the point to be measured are accumulated to determine the spatial average vehicle speed corresponding to the point to be measured, thereby obtaining traffic flow information.

[0295] In another embodiment, a different vehicle trajectory analysis device is provided. Based on the above embodiments, the traffic flow information determination module may further include:

[0296] The time information acquisition unit is used to acquire the time to be measured, as well as the start and end times of the target trajectory.

[0297] The traffic density determination unit is used to accumulate the number of vehicles passing through the test point based on the relative temporal relationship between the start and end times of the same target trajectory and the test time, thereby determining the traffic flow information corresponding to the test point.

[0298] Each module in the aforementioned vehicle trajectory analysis device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0299] In one embodiment, a computer device is provided, which may be a radar sensing device, and its internal structure diagram may be as follows: Figure 21 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a performance parameter prediction method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0300] Those skilled in the art will understand that Figure 21 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0301] In one embodiment, a radar sensing device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0302] The original vibration signals collected by each of the two-way optical fibers are obtained; the original vibration signals are the vibration signals generated when vehicles pass over the road.

[0303] Image conversion processing is performed on each original vibration signal to determine the initial trajectory image; the initial trajectory image includes the initial trajectory formed by the vehicle on the road.

[0304] The initial trajectory in the initial trajectory image is processed by trajectory tracking to determine the target trajectory image; the target trajectory image includes the target trajectory corresponding to the vehicle.

[0305] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0306] Each raw vibration signal is preprocessed to determine the target vibration signal corresponding to each raw vibration signal; the preprocessing includes filtering and / or equalization.

[0307] Image conversion processing is performed on the vibration signals of each target to determine the initial trajectory image.

[0308] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0309] Image conversion processing is performed on the vibration signals of each target to obtain grayscale images;

[0310] Denoising is performed on each grayscale image to determine the processed grayscale images;

[0311] The processed grayscale images are folded and merged to determine the initial trajectory image.

[0312] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0313] The initial trajectory in the initial trajectory image is processed for trajectory tracking to determine the intermediate trajectory image; the intermediate trajectory image includes the intermediate trajectory corresponding to the vehicle.

[0314] Perform trajectory post-processing on the intermediate trajectory in the intermediate trajectory image to determine the target trajectory image.

[0315] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0316] A preset sliding window is used to perform threshold detection processing on the initial trajectory in the initial trajectory image to determine the trajectory starting point in the initial trajectory;

[0317] Obtain the candidate direction of movement for the vehicle and the vehicle information at the starting point of the trajectory; the vehicle information includes at least one of vehicle speed, lane, and vehicle type.

[0318] Based on the vehicle information, the sliding window is moved along the candidate movement direction by a preset step size on the initial trajectory to determine at least one intermediate trajectory point on the initial trajectory.

[0319] The intermediate trajectory image is determined based on the starting point and intermediate trajectory points.

[0320] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0321] Weaken the other points between the starting point of the trajectory and the adjacent intermediate trajectory points, and weaken the other points between every two adjacent intermediate trajectory points.

[0322] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0323] Connect the starting point of the trajectory with the adjacent intermediate trajectory points to determine the first line segment;

[0324] Connect any two adjacent intermediate trajectory points to determine at least one second line segment;

[0325] The intermediate trajectory is determined based on the first and second line segments, and the intermediate trajectory image is obtained.

[0326] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0327] Perform trajectory connection processing on the intermediate trajectories in the intermediate trajectory image, and / or perform trajectory expansion processing on the intermediate trajectories in the intermediate trajectory image to determine the target trajectory image.

[0328] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0329] Traverse each intermediate trajectory in the intermediate trajectory image and determine the subsequent intermediate trajectory corresponding to the current intermediate trajectory.

[0330] Connect the current intermediate trajectory with the corresponding subsequent intermediate trajectory according to their relative positions.

[0331] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0332] Determine the expansion direction corresponding to the current intermediate trajectory based on the preset road section;

[0333] Extend the current intermediate trajectory along the extension direction.

[0334] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0335] Traffic flow information is calculated and processed based on the target trajectory image to determine the traffic flow information; the traffic flow information includes at least one of traffic volume, average vehicle speed, and traffic density.

[0336] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0337] Obtain the position of the point to be measured and the start and end positions of the target trajectory in the target trajectory image;

[0338] Based on the relative positional relationship between the starting and ending positions of the same target trajectory and the position to be measured, the number of vehicles passing through the point to be measured is accumulated to determine the traffic flow corresponding to the point to be measured and obtain traffic flow information.

[0339] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0340] Based on the relative positional relationship between the starting and ending positions of the same target trajectory and the position to be measured, the speeds of vehicles passing through the point to be measured are accumulated to determine the time-averaged vehicle speed at the point to be measured, thereby obtaining traffic flow information.

[0341] And / or, obtain the time to be measured and the start and end times of the target trajectory; based on the relative temporal relationship between the start and end times of the same target trajectory and the time to be measured, accumulate the speeds of vehicles passing through the point to be measured, determine the spatial average vehicle speed corresponding to the point to be measured, and obtain traffic flow information.

[0342] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0343] Obtain the time to be tested, as well as the start and end times of the target trajectory;

[0344] Based on the relative temporal relationship between the start and end times of the same target trajectory and the time to be measured, the number of vehicles passing through the point to be measured is accumulated to determine the traffic density corresponding to the point to be measured and obtain traffic flow information.

[0345] In one embodiment, a radar sensing system is provided, comprising a bidirectional optical fiber and a radar sensing device. The bidirectional optical fiber is connected to the radar sensing device and is respectively disposed on both sides of a road. The bidirectional optical fiber is used to collect raw vibration signals generated when a vehicle passes through the road and send each raw vibration signal to the radar sensing device. The radar sensing device is used to perform the steps of the above-described vehicle trajectory analysis method.

[0346] In one embodiment, a readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0347] The radar sensing equipment acquires the raw vibration signals collected by each of the two-way optical fibers; the raw vibration signals are the vibration signals generated when vehicles pass over the road.

[0348] Image conversion processing is performed on each original vibration signal to determine the initial trajectory image; the initial trajectory image includes the initial trajectory formed by the vehicle on the road.

[0349] The initial trajectory in the initial trajectory image is processed by trajectory tracking to determine the target trajectory image; the target trajectory image includes the target trajectory corresponding to the vehicle.

[0350] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0351] Each raw vibration signal is preprocessed to determine the target vibration signal corresponding to each raw vibration signal; the preprocessing includes filtering and / or equalization.

[0352] Image conversion processing is performed on the vibration signals of each target to determine the initial trajectory image.

[0353] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0354] Image conversion processing is performed on the vibration signals of each target to obtain grayscale images;

[0355] Denoising is performed on each grayscale image to determine the processed grayscale images;

[0356] The processed grayscale images are folded and merged to determine the initial trajectory image.

[0357] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0358] The initial trajectory in the initial trajectory image is processed for trajectory tracking to determine the intermediate trajectory image; the intermediate trajectory image includes the intermediate trajectory corresponding to the vehicle.

[0359] Perform trajectory post-processing on the intermediate trajectory in the intermediate trajectory image to determine the target trajectory image.

[0360] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0361] A preset sliding window is used to perform threshold detection processing on the initial trajectory in the initial trajectory image to determine the trajectory starting point in the initial trajectory;

[0362] Obtain the candidate direction of movement for the vehicle and the vehicle information at the starting point of the trajectory; the vehicle information includes at least one of vehicle speed, lane, and vehicle type.

[0363] Based on the vehicle information, the sliding window is moved along the candidate movement direction by a preset step size on the initial trajectory to determine at least one intermediate trajectory point on the initial trajectory.

[0364] The intermediate trajectory image is determined based on the starting point and intermediate trajectory points.

[0365] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0366] Weaken the other points between the starting point of the trajectory and the adjacent intermediate trajectory points, and weaken the other points between every two adjacent intermediate trajectory points.

[0367] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0368] Connect the starting point of the trajectory with the adjacent intermediate trajectory points to determine the first line segment;

[0369] Connect any two adjacent intermediate trajectory points to determine at least one second line segment;

[0370] The intermediate trajectory is determined based on the first and second line segments, and the intermediate trajectory image is obtained.

[0371] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0372] Perform trajectory connection processing on the intermediate trajectories in the intermediate trajectory image, and / or perform trajectory expansion processing on the intermediate trajectories in the intermediate trajectory image to determine the target trajectory image.

[0373] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0374] Traverse each intermediate trajectory in the intermediate trajectory image and determine the subsequent intermediate trajectory corresponding to the current intermediate trajectory.

[0375] Connect the current intermediate trajectory with the corresponding subsequent intermediate trajectory according to their relative positions.

[0376] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0377] Determine the expansion direction corresponding to the current intermediate trajectory based on the preset road section;

[0378] Extend the current intermediate trajectory along the extension direction.

[0379] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0380] Traffic flow information is calculated and processed based on the target trajectory image to determine the traffic flow information; the traffic flow information includes at least one of traffic volume, average vehicle speed, and traffic density.

[0381] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0382] Obtain the position of the point to be measured and the start and end positions of the target trajectory in the target trajectory image;

[0383] Based on the relative positional relationship between the starting and ending positions of the same target trajectory and the position to be measured, the number of vehicles passing through the point to be measured is accumulated to determine the traffic flow corresponding to the point to be measured and obtain traffic flow information.

[0384] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0385] Based on the relative positional relationship between the starting and ending positions of the same target trajectory and the position to be measured, the speeds of vehicles passing through the point to be measured are accumulated to determine the time-averaged vehicle speed at the point to be measured, thereby obtaining traffic flow information.

[0386] And / or, obtain the time to be measured and the start and end times of the target trajectory; based on the relative temporal relationship between the start and end times of the same target trajectory and the time to be measured, accumulate the speeds of vehicles passing through the point to be measured, determine the spatial average vehicle speed corresponding to the point to be measured, and obtain traffic flow information.

[0387] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0388] Obtain the time to be tested, as well as the start and end times of the target trajectory;

[0389] Based on the relative temporal relationship between the start and end times of the same target trajectory and the time to be measured, the number of vehicles passing through the point to be measured is accumulated to determine the traffic density corresponding to the point to be measured and obtain traffic flow information.

[0390] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0391] The radar sensing equipment acquires the raw vibration signals collected by each of the two-way optical fibers; the raw vibration signals are the vibration signals generated when vehicles pass over the road.

[0392] Image conversion processing is performed on each original vibration signal to determine the initial trajectory image; the initial trajectory image includes the initial trajectory formed by the vehicle on the road.

[0393] The initial trajectory in the initial trajectory image is processed by trajectory tracking to determine the target trajectory image; the target trajectory image includes the target trajectory corresponding to the vehicle.

[0394] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0395] Each raw vibration signal is preprocessed to determine the target vibration signal corresponding to each raw vibration signal; the preprocessing includes filtering and / or equalization.

[0396] Image conversion processing is performed on the vibration signals of each target to determine the initial trajectory image.

[0397] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0398] Image conversion processing is performed on the vibration signals of each target to obtain grayscale images;

[0399] Denoising is performed on each grayscale image to determine the processed grayscale images;

[0400] The processed grayscale images are folded and merged to determine the initial trajectory image.

[0401] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0402] The initial trajectory in the initial trajectory image is processed for trajectory tracking to determine the intermediate trajectory image; the intermediate trajectory image includes the intermediate trajectory corresponding to the vehicle.

[0403] Perform trajectory post-processing on the intermediate trajectory in the intermediate trajectory image to determine the target trajectory image.

[0404] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0405] A preset sliding window is used to perform threshold detection processing on the initial trajectory in the initial trajectory image to determine the trajectory starting point in the initial trajectory;

[0406] Obtain the candidate direction of movement for the vehicle and the vehicle information at the starting point of the trajectory; the vehicle information includes at least one of vehicle speed, lane, and vehicle type.

[0407] Based on the vehicle information, the sliding window is moved along the candidate movement direction by a preset step size on the initial trajectory to determine at least one intermediate trajectory point on the initial trajectory.

[0408] The intermediate trajectory image is determined based on the starting point and intermediate trajectory points.

[0409] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0410] Weaken the other points between the starting point of the trajectory and the adjacent intermediate trajectory points, and weaken the other points between every two adjacent intermediate trajectory points.

[0411] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0412] Connect the starting point of the trajectory with the adjacent intermediate trajectory points to determine the first line segment;

[0413] Connect any two adjacent intermediate trajectory points to determine at least one second line segment;

[0414] The intermediate trajectory is determined based on the first and second line segments, and the intermediate trajectory image is obtained.

[0415] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0416] Perform trajectory connection processing on the intermediate trajectories in the intermediate trajectory image, and / or perform trajectory expansion processing on the intermediate trajectories in the intermediate trajectory image to determine the target trajectory image.

[0417] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0418] Traverse each intermediate trajectory in the intermediate trajectory image and determine the subsequent intermediate trajectory corresponding to the current intermediate trajectory.

[0419] Connect the current intermediate trajectory with the corresponding subsequent intermediate trajectory according to their relative positions.

[0420] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0421] Determine the expansion direction corresponding to the current intermediate trajectory based on the preset road section;

[0422] Extend the current intermediate trajectory along the extension direction.

[0423] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0424] Traffic flow information is calculated and processed based on the target trajectory image to determine the traffic flow information; the traffic flow information includes at least one of traffic volume, average vehicle speed, and traffic density.

[0425] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0426] Obtain the position of the point to be measured and the start and end positions of the target trajectory in the target trajectory image;

[0427] Based on the relative positional relationship between the starting and ending positions of the same target trajectory and the position to be measured, the number of vehicles passing through the point to be measured is accumulated to determine the traffic flow corresponding to the point to be measured and obtain traffic flow information.

[0428] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0429] Based on the relative positional relationship between the starting and ending positions of the same target trajectory and the position to be measured, the speeds of vehicles passing through the point to be measured are accumulated to determine the time-averaged vehicle speed at the point to be measured, thereby obtaining traffic flow information.

[0430] And / or, obtain the time to be measured and the start and end times of the target trajectory; based on the relative temporal relationship between the start and end times of the same target trajectory and the time to be measured, accumulate the speeds of vehicles passing through the point to be measured, determine the spatial average vehicle speed corresponding to the point to be measured, and obtain traffic flow information.

[0431] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0432] Obtain the time to be tested, as well as the start and end times of the target trajectory;

[0433] Based on the relative temporal relationship between the start and end times of the same target trajectory and the time to be measured, the number of vehicles passing through the point to be measured is accumulated to determine the traffic density corresponding to the point to be measured and obtain traffic flow information.

[0434] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data shall comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0435] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0436] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0437] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A vehicle trajectory analysis method, characterized in that, The method is applied to a radar system, which includes interconnected bidirectional optical fibers and radar sensing devices, with the bidirectional optical fibers respectively installed on both sides of a road; the method includes: The radar sensing device acquires the raw vibration signals collected by each of the two-way optical fibers; the raw vibration signals are the vibration signals generated when a vehicle passes over the road. The original vibration signals are subjected to image conversion processing to determine an initial trajectory image; the initial trajectory image includes the initial trajectory formed by the vehicle on the road. The initial trajectory in the initial trajectory image is subjected to trajectory tracking processing to determine the target trajectory image; the target trajectory image includes the target trajectory corresponding to the vehicle. The step of performing image conversion processing on each of the original vibration signals to determine the initial trajectory image includes: The original vibration signals are preprocessed to determine the target vibration signal corresponding to each original vibration signal; the preprocessing includes filtering and / or equalization. Image conversion processing is performed on the vibration signals of each target to obtain grayscale images; Denoise the grayscale images and determine the processed grayscale images; The processed grayscale images are folded and merged to determine the initial trajectory image.

2. The method according to claim 1, characterized in that, The step of performing trajectory tracking processing on the initial trajectory in the initial trajectory image to determine the target trajectory image includes: The initial trajectory in the initial trajectory image is subjected to trajectory tracking processing to determine an intermediate trajectory image; the intermediate trajectory image includes the intermediate trajectory corresponding to the vehicle. The intermediate trajectory in the intermediate trajectory image is post-processed to determine the target trajectory image.

3. The method according to claim 2, characterized in that, The step of performing trajectory tracking processing on the initial trajectory in the initial trajectory image to determine the intermediate trajectory image includes: A preset sliding window is used to perform threshold detection processing on the initial trajectory in the initial trajectory image to determine the trajectory starting point in the initial trajectory; Obtain the candidate direction of movement corresponding to the vehicle and the vehicle information corresponding to the vehicle at the starting point of the trajectory; the vehicle information includes at least one of vehicle speed, lane, and vehicle type. Based on the vehicle information, the sliding window is moved along the candidate movement direction by a preset step size on the initial trajectory to determine at least one intermediate trajectory point on the initial trajectory; The intermediate trajectory image is determined based on the trajectory starting point and the intermediate trajectory points.

4. The method according to claim 3, characterized in that, Before determining the intermediate trajectory image based on the trajectory starting point and the intermediate trajectory points, the method further includes: The points between the starting point of the trajectory and the adjacent intermediate trajectory points are weakened, and the points between any two adjacent intermediate trajectory points are also weakened.

5. The method according to claim 3, characterized in that, Determining the intermediate trajectory image based on the trajectory starting point and the intermediate trajectory points includes: Connect the starting point of the trajectory with the adjacent intermediate trajectory points to determine the first line segment; Connect every two adjacent intermediate trajectory points to determine at least one second line segment; The intermediate trajectory is determined based on the first line segment and the second line segment, and the intermediate trajectory image is obtained.

6. The method according to any one of claims 2-5, characterized in that, The step of performing trajectory post-processing on the intermediate trajectory in the intermediate trajectory image to determine the target trajectory image includes: The intermediate trajectory in the intermediate trajectory image is subjected to trajectory connection processing, and / or the intermediate trajectory in the intermediate trajectory image is subjected to trajectory expansion processing to determine the target trajectory image.

7. The method according to claim 6, characterized in that, The intermediate trajectory image includes multiple intermediate trajectories, and the trajectory connection processing of the intermediate trajectories in the intermediate trajectory image includes: Traverse each intermediate trajectory in the intermediate trajectory image to determine the subsequent intermediate trajectory corresponding to the current intermediate trajectory; The current intermediate trajectory is connected to the corresponding subsequent intermediate trajectory according to their relative positional relationship.

8. The method according to claim 6, characterized in that, The process of extending the intermediate trajectory in the intermediate trajectory image includes: Determine the expansion direction corresponding to the current intermediate trajectory based on the preset road section; The current intermediate trajectory is extended along the extension direction.

9. The method according to claim 1, characterized in that, The method further includes: Traffic flow information is calculated and processed based on the target trajectory image to determine the traffic flow information; the traffic flow information includes at least one of traffic volume, average vehicle speed, and traffic density.

10. The method according to claim 9, characterized in that, The step of calculating and processing traffic flow information based on the target trajectory image to determine traffic flow information includes: Obtain the position to be measured corresponding to the point to be measured, and obtain the start and end positions of the target trajectory in the target trajectory image; Based on the relative positional relationship between the starting and ending positions of the same target trajectory and the position to be measured, the number of vehicles passing through the point to be measured is accumulated to determine the traffic flow corresponding to the point to be measured, and the traffic flow information is obtained.

11. The method according to claim 10, characterized in that, The average vehicle speed includes time-averaged vehicle speed and / or spatial-averaged vehicle speed. The step of calculating and processing traffic flow information based on the target trajectory image to determine the traffic flow information includes: Based on the relative positional relationship between the starting and ending positions of the same target trajectory and the position to be measured, the speeds of vehicles passing through the point to be measured are accumulated to determine the time-averaged vehicle speed at the point to be measured, thereby obtaining the traffic flow information. And / or, obtain the time to be measured and obtain the start time and end time of the target trajectory; based on the relative temporal relationship between the start time and end time of the same target trajectory and the time to be measured, accumulate the speeds of vehicles passing through the point to be measured, determine the spatial average vehicle speed corresponding to the point to be measured, and obtain the traffic flow information.

12. The method according to claim 9, characterized in that, The step of calculating and processing traffic flow information based on the target trajectory image to determine traffic flow information includes: Obtain the time to be measured, as well as the start and end times of the target trajectory; Based on the relative temporal relationship between the start time and the end time of the same target trajectory and the time to be measured, the number of vehicles passing through the point to be measured is accumulated to determine the traffic density corresponding to the point to be measured, and the traffic flow information is obtained.

13. A vehicle trajectory analysis device, characterized in that, Applied to a radar system, the radar system includes interconnected bidirectional optical fibers and radar sensing equipment, with the bidirectional optical fibers respectively installed on both sides of a road; the device includes: The acquisition module is used to acquire the original vibration signals collected by each of the bidirectional optical fibers; the original vibration signals are the vibration signals generated when a vehicle passes over the road; A determination module is used to perform image conversion processing on each of the original vibration signals to determine an initial trajectory image; the initial trajectory image includes the initial trajectory formed by the vehicle on the road. The processing module is used to perform trajectory tracking processing on the initial trajectory in the initial trajectory image to determine the target trajectory image; the target trajectory image includes the target trajectory corresponding to the vehicle. The determining module is specifically used to preprocess each of the original vibration signals to determine the target vibration signal corresponding to each of the original vibration signals; the preprocessing includes filtering and / or equalization. Image conversion processing is performed on the vibration signals of each target to obtain grayscale images; Denoise the grayscale images and determine the processed grayscale images; The processed grayscale images are folded and merged to determine the initial trajectory image.

14. A radar sensing device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the vehicle trajectory analysis method according to any one of claims 1 to 12.

15. A radar sensing system, characterized in that, It includes bidirectional optical fibers and the radar sensing device as described in claim 14, wherein the bidirectional optical fibers are respectively connected to the radar sensing device and are respectively installed on both sides of the road; The bidirectional optical fiber is used to collect the original vibration signals generated when the vehicle passes through the road and send each of the original vibration signals to the radar sensing device. The radar sensing device is used to perform the steps of the vehicle trajectory analysis method according to any one of claims 1-12.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the vehicle trajectory analysis method according to any one of claims 1 to 12.

17. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the vehicle trajectory analysis method according to any one of claims 1 to 12.

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