CN118172938B - Vehicle trajectory full-course tracking method based on distributed optical fiber and laser radar - Google Patents

Abstract

The invention provides a vehicle track whole course tracking method based on distributed optical fibers and a laser radar, and belongs to the technical field of traffic engineering; the distributed optical fiber monitoring equipment is arranged on a road section without an intersection, wherein the road section is long in distance and free of vehicle intersection, the laser radar is arranged on a road intersection with complex vehicle intersection conditions and limited monitoring range, the advantages of two vehicle track detection technologies are fully exerted, and the whole-course tracking of the vehicle track is realized.

Description

Vehicle trajectory full-course tracking method based on distributed optical fiber and laser radar

Technical Field

The present invention relates to the technical field of traffic engineering, and in particular to a vehicle trajectory full-course tracking method based on distributed optical fiber and laser radar.

Background technique

The statements in this section merely provide background art related to the present invention and do not necessarily constitute prior art.

The intelligent transportation system is committed to using advanced sensor technology, communication technology, information technology, control technology, etc. to integrate and apply them to transportation construction and operation, and establish a real-time, accurate, and efficient ground transportation system, so as to better ensure traffic safety and improve the efficiency of transportation resources. In the construction of smart highways, the information coordination of pedestrians, vehicles, roads, and traffic environment is a key content; among them, the perception, detection, and tracking of vehicle trajectories play a fundamental role in the construction of the entire system and all stages of smart highways: on the one hand, it can provide data support for macro traffic information such as traffic flow monitoring, traffic density monitoring, and road capacity monitoring; on the other hand, precise and accurate vehicle trajectory tracking can provide a basis for micro traffic services such as traffic conflict recognition and warning, personalized navigation, traffic behavior analysis, and driving behavior prediction.

Among the current vehicle trajectory tracking and recognition technologies, the technology based on Laser Radar (LR) can establish a 3D point cloud image within the recognition range and clearly distinguish the vehicle's position, shape, movement and other information, but its cost is relatively high and it is difficult to achieve long-distance continuous vehicle trajectory detection; the technology based on Distributed Acoustic Sensing (DAS) can achieve long-distance continuous vehicle trajectory detection at a relatively low cost, but its recognition accuracy is poor for areas with high vehicle density such as road intersections and merging areas; there are solutions in the existing technology that use the fusion of millimeter-wave radar and LiDAR to monitor vehicle trajectories, but most of them are only for vehicles at intersections and cannot achieve full-process tracking of target vehicles on the way within a large range.

Summary of the invention

In order to address the deficiencies in the prior art, the present invention provides a method for full-course tracking of vehicle trajectories based on distributed optical fiber and laser radar, which fully utilizes the advantages of distributed optical fiber monitoring equipment and laser radar to achieve full-course tracking of on-the-way target vehicle trajectories within a large range.

In order to achieve the above object, the present invention adopts the following technical solution:

In a first aspect, the present invention provides a method for full-course tracking of vehicle trajectories based on distributed optical fiber and laser radar.

A vehicle trajectory tracking method based on distributed optical fiber and laser radar, wherein the distributed optical fiber monitoring equipment is installed on a road section without intersections, and the laser radar is installed at a road intersection, including the following processes:

Obtain the status data of the in-transit targets identified by the distributed optical fiber monitoring equipment and the laser radar, establish a real-time status database of the in-transit targets, and assign identification numbers to the in-transit targets;

In the identification intersection area of the distributed optical fiber monitoring device and the laser radar, a first target position time series identified by the distributed optical fiber monitoring device and a second target position time series identified by the laser radar are obtained;

After the coordinate system conversion, the similarity between the first target position time series and the second target position time series is determined. If the similarity is greater than a set threshold, it is considered that the two target position time series are from the same en-route target.

The monitoring data of the in-transit target is merged to update the real-time status database of the in-transit target, and the full trajectory of the in-transit target is obtained by combining the vehicle trajectories identified separately by the distributed optical fiber and the lidar.

As a further limitation of the first aspect of the present invention, the fields of the identification number generated according to the in-transit target state data identified by the distributed optical fiber monitoring device include: the identification bit of the distributed optical fiber monitoring device, the target type, the relative position, the real-time vehicle speed and the absolute position;

According to the status data of the on-road target identified by the laser radar, the fields of the generated identification number include: laser radar identification bit, target type, relative position, real-time vehicle speed and absolute position;

The fields in the identification number include: the distributed optical fiber monitoring identification position, the lidar monitoring identification position, the target type, the relative position, the real-time vehicle speed and the absolute position.

As a further limitation of the first aspect of the present invention, when the in-transit target is being monitored by a distributed optical fiber monitoring device, the bit value of the distributed optical fiber monitoring device identification bit is the serial number of the distributed optical fiber monitoring device currently being monitored; otherwise, the bit value is 0;

When the on-the-way target is being monitored by the laser radar, the bit value of the laser radar identification bit is the number of the laser radar currently being monitored; otherwise, the bit value is 0.

As a further limitation of the first aspect of the present invention, acquiring the first target position time series includes:

When the target in transit is under the monitoring of the laser radar, the laser radar installation position is taken as the coordinate origin, and the longitude and latitude coordinates of the coordinate origin are obtained. The laser radar coordinate system The axis is aligned with true north, The axis is aligned with due east;

The absolute position of the in-transit target is obtained according to the relative position of the in-transit target and the longitude and latitude coordinates of the coordinate origin, and the first target position time series is generated according to the absolute position within a set time period.

As a further limitation of the first aspect of the present invention, acquiring the second target position time series includes:

Get fiber optic The latitude and longitude coordinates of the marker points are , and the relative positions of these marking points in this section of optical fiber are expressed in the coordinate system of the distributed optical fiber monitoring device as ;

At a certain moment, the relative position of the on-the-way target is , if any , then the absolute position of the en-route target can be determined as If any , then the absolute position of the in-transit target is determined by the linear interpolation method.

As a further limitation of the first aspect of the present invention, determining the similarity between the first target position time series and the second target position time series includes:

Create a distance matrix based on the distance or similarity between each data point in the two time series;

Create a dynamic programming table of the same size as the distance matrix, where each table element represents the distance of the best alignment path from the starting point to the current point;

Starting from the starting point, the distance of the best alignment path to each point is calculated by gradually updating the elements in the dynamic programming table. The updating method is to select the one with the smallest distance among the three adjacent points around the current point and add its distance to the current point.

According to the last element in the dynamic programming table, backtrack to find the best alignment path, where the best alignment path represents the best matching method between the two time series;

The similarity or distance between two time series is calculated based on the points on the best alignment path, and the sum or average distance of all points on the path is used as the similarity measure.

As a further limitation of the first aspect of the present invention, updating the real-time status database of in-transit targets includes:

The unique number of the target in transit is retained as the value during lidar monitoring, and the real-time vehicle speed and absolute position are determined by the arithmetic mean of the monitoring values of the distributed fiber optic monitoring equipment and the lidar.

In a second aspect, the present invention provides a vehicle trajectory full-range tracking system based on distributed optical fiber and laser radar.

A vehicle trajectory tracking system based on distributed optical fiber and laser radar, where the distributed optical fiber monitoring equipment is installed on the road section without intersections and the laser radar is installed at the road intersection, including:

The trajectory data acquisition module is configured to: acquire the state data of the in-transit targets identified by the distributed optical fiber monitoring equipment and the laser radar, establish a real-time state database of the in-transit targets, and assign identification numbers to the in-transit targets;

The time series generation module is configured to: obtain a first target position time series identified by the distributed optical fiber monitoring device and a second target position time series identified by the laser radar in an identification intersection area of the distributed optical fiber monitoring device and the laser radar;

The similarity judgment module is configured to: judge the similarity between the first target position time series and the second target position time series after the coordinate system conversion, and if the similarity is greater than a set threshold, it is considered that the two target position time series are from the same en-route target;

The full-course trajectory generation module is configured to merge the monitoring data of the in-course target and update the real-time status database of the in-course target, and combine the vehicle trajectories independently identified by the distributed optical fiber and the laser radar to obtain the full-course trajectory of the in-course target.

In a third aspect, the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the method for full-course vehicle trajectory tracking based on distributed optical fiber and laser radar as described in the first aspect of the present invention.

In a fourth aspect, the present invention provides an electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the method for full-course tracking of vehicle trajectories based on distributed optical fiber and laser radar as described in the first aspect of the present invention are implemented.

Compared with the prior art, the present invention has the following beneficial effects:

The present invention innovatively proposes a method for full-course tracking of vehicle trajectories based on distributed optical fiber and laser radar. On roads where non-intersection sections and intersections appear alternately, distributed optical fiber monitoring equipment is installed on non-intersection sections with long distances and no vehicle intersections, and laser radars are installed on road intersections where vehicle intersections are complex but the monitoring range is limited. This method gives full play to the advantages of distributed optical fiber monitoring equipment and laser radars, and realizes full-course tracking of the trajectories of target vehicles on the road within a large range.

Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

FIG1 is a schematic flow chart of a method for tracking vehicle trajectories throughout the entire process based on distributed optical fiber and laser radar provided in Example 1 of the present invention;

FIG2 is a schematic diagram of coordinate system conversion provided by Embodiment 1 of the present invention;

FIG3 is a schematic diagram of a vehicle trajectory full-course tracking system based on distributed optical fiber and laser radar provided in Example 2 of the present invention;

Among them, 101-laser radar; 102-end optical fiber.

Detailed ways

The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

Embodiment 1:

As shown in FIG1 , Embodiment 1 of the present invention provides a schematic flow chart of a method for tracking vehicle trajectories throughout the entire process based on distributed optical fiber and laser radar, including the following processes:

S1: Identify all en-route targets through distributed optical fiber and laser radar, establish a real-time status database of en-route targets, and assign a unique identification number to each target;

S2: Matching of distributed optical fiber and lidar coordinate systems;

S3: Fusion identification of in-transit targets using distributed fiber and lidar tracking;

S4: Update the in-transit target status database.

In S1, a database of in-transit target status is established, including:

Build a real-time database to assign a unique identification number to the in-transit target detected by distributed optical fiber or lidar in the database as a piece of data. Other fields of the data include:

(1) DAS monitoring flag: This field indicates whether the current in-transit target is under DAS system monitoring. If it is under monitoring, the value of this data bit is the number of the DAS device currently being monitored; otherwise, the value of this data bit is 0.

(2) Laser radar monitoring flag: This field indicates whether the target is currently under laser radar monitoring. If it is, the value of this data bit is the number of the laser radar currently being monitored; otherwise, the value of this data bit is 0.

(3) Target type: This field indicates the estimation of the target vehicle type based on the existing algorithm. The DAS system can obtain the target vehicle type classification through data denoising, feature extraction, and pattern recognition. The LiDAR system can obtain the target vehicle type classification through background filtering, point cloud clustering, and pattern recognition. The optional values of this field include: 1-small car, 2-SUV, 3-large car, 4-other.

(4) Relative position: This field indicates the coordinates of the target in the coordinate system of the sensor currently being monitored.

(4-1) After the laser radar tracks the target, it can give the displacement vector of the geometric center point of the target in the 3D point cloud in the orthogonal coordinate system with the laser radar as the origin: :

(1);

in, Along the The unit vector of the axis is , the relative position of the target is , , and The coordinates of the geometric center point of the target.

(4-2) After the DAS system tracks the target, it can give the displacement vector of the target in the one-dimensional coordinate system: (2);

in, For along The unit vector of the axis.

At the time , the relative position of the target is .

(5) Real-time vehicle speed: This field indicates the target’s real-time vehicle speed.

(5-1) In laser radar, the velocity vector of the target is:

(3);

The real-time vehicle speed (i.e. instantaneous speed) is the modulus of the above formula: (4).

(5-2) In the DAS system, the velocity vector of the target is: (5);

The real-time vehicle speed (i.e. instantaneous speed) is the modulus of the above formula: (6).

(6) Absolute position: This field indicates the actual position of the target on the ground, expressed in longitude and latitude coordinates. The method for determining this field is given below.

(6-1) When the target is under the monitoring of the laser radar, let the laser radar currently monitoring the target be numbered LR-1, and the longitude and latitude coordinates of its installation position (i.e., the origin of the coordinates) measured in advance are , and make its coordinate system The axis is aligned with true north, The axis is aligned with due east.

At a certain moment, the relative position of the monitoring target is , the unit length is 1 meter.

The latitude of the target can be obtained by the following formula: (7);

The longitude is obtained by the following formula: (8).

(6-2) When the target is under the monitoring of the DAS system, let the fiber segment currently monitoring the target be numbered DAS-1. The latitude and longitude coordinates of the marker points are At the same time, the relative positions of these marking points in this section of optical fiber (along the direction of optical fiber travel) need to be measured, expressed as the coordinates in the DAS coordinate system: ,The method of selecting marking points is : sparse in straight sections and dense in curved sections, so that under the design accuracy, the distance between two adjacent marking points can be approximately regarded as a straight line.

At a certain moment, the relative position of the monitoring target is ;

If yes , is the kth coordinate in the DAS coordinate system, then the absolute position of the target can be determined as , is the latitude coordinate of the monitoring target, is the longitude coordinate of the monitoring target;

If yes , is the k+ 1th coordinate in the DAS coordinate system, then the absolute position of the target can be determined by the linear interpolation method:

latitude: (9);

longitude: (10);

in, To monitor the latitude of the target, To monitor the longitude of the target, for The latitude of the location, for The latitude of the location, for The longitude of the location, for The longitude of the location, The calculation method is: (11);

The above conversion relationship between longitude and latitude and distance is made under the premise of ignoring the curvature of the earth. Since the distance range in the application scenario of the present invention is at a lower order of magnitude than the equatorial length, this simplification is reasonable.

In S2, the distributed optical fiber matches the laser radar coordinate system, including:

Since the monitoring range of the laser radar is small and the monitoring range of the DAS system is large, there is an area where the monitoring ranges of the two devices overlap near each laser radar installation location. This area is the key to transferring the tracking target from the DAS system to the laser radar (or vice versa). The first thing to do is to match the coordinate systems of the two systems in the overlapping area. As shown in Figure 2, taking a laser radar 101 installed at a crossroads and the terminal optical fiber 102 near it as an example, in the three-dimensional coordinate system of the laser radar 101, the terminal optical fiber 102 is reflected as a line segment.

The coordinate system is the coordinate system of the laser radar 101, is the vertical projection point of the laser radar 101 installation position on the ground. The installation height of the laser radar 101 from the ground is measured in advance. , the angle between the direction of the end optical fiber 102 and the north direction is , The vertical distance from the end optical fiber 102 is , the origin of the coordinate system of the laser radar 101 Located at the center of LiDAR 101, The axis is aligned with true north, The axis is aligned with due east, The axis is aligned with the vertical upward direction.

Then, on the last section of optical fiber 102, the coordinates are The point is expressed in the coordinate system of the laser radar 101 as: (12);

In S3, the fusion identification of in-transit targets tracked by distributed optical fiber and lidar includes:

Existing DAS systems and LiDAR in time The vehicle trajectory data obtained and , whose data form is a time series, and whose data value is the target position coordinate after the coordinate conversion in the previous step; by judging the similarity of the two time series, if it exceeds a certain threshold, it is considered that the data of the two vehicle trajectories come from the same target, and the fusion identification is completed, as follows:

Since the operating frequencies of lidar and DAS equipment are usually different, the lengths of time series obtained in the same time are different. The present invention adopts a dynamic time warping (DTW) algorithm to eliminate the frequency difference between the two sensors and effectively distinguish the similarities of the two signals from time series of different lengths.

S3.1: Create a distance matrix. Create a distance matrix based on the distance or similarity between each data point in the two time series. Usually, a metric such as Euclidean distance, Manhattan distance, or correlation coefficient is used to calculate the distance between data points.

S3.2: Initialize the dynamic programming table and create a dynamic programming table of the same size as the distance matrix, where each table element represents the distance of the best alignment path from the starting point to the current point;

S3.3: Dynamic programming calculation, starting from the starting point, by gradually updating the elements in the dynamic programming table, calculate the distance of the best alignment path to each point. The updating method is to select the one with the smallest distance among the three adjacent points around the current point and add its distance to the current point;

S3.4: Backtrack the best path, and find the best alignment path according to the last element in the dynamic programming table. The best alignment path represents the best matching method between the two time series;

S3.5: Calculate similarity. Calculate the similarity or distance between the two time series based on the points on the optimal alignment path. The sum of the distances of all points on the path or the average distance can be used as the similarity measure.

In S4, the in-transit target status database is updated, specifically including:

When the time series from the two sensors are fused and identified in S3, and the vehicle types of the two data are judged to be the same, the two data are merged into one in the in-transit target status database.

In this embodiment, preferably, the unique number is retained as the value during laser radar monitoring.

In this embodiment, preferably, the "real-time vehicle speed" and the "absolute position" are determined by the arithmetic average of the values of the two sensors.

Embodiment 2:

Embodiment 2 of the present invention provides a vehicle trajectory full-course tracking system based on distributed optical fiber and laser radar, wherein the distributed optical fiber monitoring equipment is installed on a road section without intersections, and the laser radar is installed at a road intersection, as shown in FIG3 , including:

The trajectory data acquisition module is configured to: acquire the state data of the in-transit targets identified by the distributed optical fiber monitoring equipment and the laser radar, establish a real-time state database of the in-transit targets, and assign identification numbers to the in-transit targets;

The time series generation module is configured to: obtain a first target position time series identified by the distributed optical fiber monitoring device and a second target position time series identified by the laser radar in an identification intersection area of the distributed optical fiber monitoring device and the laser radar;

The similarity judgment module is configured to: judge the similarity between the first target position time series and the second target position time series after the coordinate system conversion, and if the similarity is greater than a set threshold, it is considered that the two target position time series are from the same en-route target;

The full-course trajectory generation module is configured to merge the monitoring data of the in-course target and update the real-time status database of the in-course target, and combine the vehicle trajectories independently identified by the distributed optical fiber and the laser radar to obtain the full-course trajectory of the in-course target.

The working methods of each module of the system are the same as the processes S1-S4 provided in Example 1, and will not be repeated here.

Embodiment 3:

Embodiment 3 of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the method for full-course tracking of vehicle trajectories based on distributed optical fiber and laser radar as described in Embodiment 1 of the present invention.

Embodiment 4:

Embodiment 4 of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for full-course tracking of vehicle trajectories based on distributed optical fiber and laser radar as described in Embodiment 1 of the present invention are implemented.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims (9)

1. A vehicle trajectory tracking method based on distributed optical fiber and laser radar, characterized in that the distributed optical fiber monitoring equipment is installed on the road section without intersections, and the laser radar is installed at the road intersection, including the following process: Obtain the status data of the in-transit targets identified by the distributed optical fiber monitoring equipment and the laser radar, establish a real-time status database of the in-transit targets, and assign identification numbers to the in-transit targets; In the identification intersection area of the distributed optical fiber monitoring device and the laser radar, a first target position time series identified by the distributed optical fiber monitoring device and a second target position time series identified by the laser radar are obtained; After the coordinate system conversion, the similarity between the first target position time series and the second target position time series is determined, including: Create a distance matrix based on the distance or similarity between each data point in the two time series; Create a dynamic programming table of the same size as the distance matrix, where each table element represents the distance of the best alignment path from the starting point to the current point; Starting from the starting point, the distance of the best alignment path to each point is calculated by gradually updating the elements in the dynamic programming table. The updating method is to select the one with the smallest distance among the three adjacent points around the current point and add its distance to the current point. According to the last element in the dynamic programming table, backtrack to find the best alignment path, where the best alignment path represents the best matching method between the two time series; Calculate the similarity or distance between two time series based on the points on the best alignment path, and use the sum of the distances of all points on the path or the average distance as the similarity measure; If the similarity is greater than the set threshold, the two target position time series are considered to come from the same en-route target; The monitoring data of the in-transit target is merged to update the real-time status database of the in-transit target, and the full trajectory of the in-transit target is obtained by combining the vehicle trajectories identified separately by the distributed optical fiber and the lidar. 2. The vehicle trajectory full-course tracking method based on distributed optical fiber and laser radar as claimed in claim 1, characterized in that: According to the state data of the in-transit target identified by the distributed optical fiber monitoring device, the fields of the generated identification number include: the identification bit of the distributed optical fiber monitoring device, the target type, the relative position, the real-time vehicle speed and the absolute position; According to the status data of the on-road target identified by the laser radar, the fields of the generated identification number include: laser radar identification bit, target type, relative position, real-time vehicle speed and absolute position; The fields in the identification number include: the distributed optical fiber monitoring identification position, the lidar monitoring identification position, the target type, the relative position, the real-time vehicle speed and the absolute position. 3. The vehicle trajectory full-course tracking method based on distributed optical fiber and laser radar as claimed in claim 2, characterized in that: When the in-transit target is being monitored by a distributed optical fiber monitoring device, the bit value of the distributed optical fiber monitoring device identification bit is the number of the distributed optical fiber monitoring device currently being monitored; otherwise, the bit value is 0; When the on-the-way target is being monitored by the laser radar, the bit value of the laser radar identification bit is the number of the laser radar currently being monitored; otherwise, the bit value is 0. 4. The vehicle trajectory full-course tracking method based on distributed optical fiber and laser radar as claimed in claim 1, characterized in that: The acquisition of the first target position time series includes: When the target in transit is under the monitoring of the laser radar, the laser radar installation position is taken as the coordinate origin, and the longitude and latitude coordinates of the coordinate origin are obtained. The laser radar coordinate system The axis is aligned with true north, The axis is aligned with due east; The absolute position of the in-transit target is obtained according to the relative position of the in-transit target and the longitude and latitude coordinates of the coordinate origin, and the first target position time series is generated according to the absolute position within a set time period. 5. The vehicle trajectory full-course tracking method based on distributed optical fiber and laser radar as claimed in claim 1, characterized in that: The acquisition of the second target position time series includes: Get fiber optic The latitude and longitude coordinates of the marker points are , and the relative positions of these marking points in this section of optical fiber are expressed in the coordinate system of the distributed optical fiber monitoring device as ; At a certain moment, the relative position of the on-the-way target is , if any , then the absolute position of the en-route target can be determined as If any , then the absolute position of the in-transit target is determined by the linear interpolation method. 6. The vehicle trajectory full-course tracking method based on distributed optical fiber and laser radar as claimed in claim 1, characterized in that: Update the real-time status database of in-transit targets, including: The unique number of the target in transit is retained as the value during lidar monitoring, and the real-time vehicle speed and absolute position are determined by the arithmetic mean of the monitoring values of the distributed fiber optic monitoring equipment and the lidar. 7. A vehicle trajectory tracking system based on distributed optical fiber and laser radar, characterized in that the distributed optical fiber monitoring equipment is installed on the road section without intersections, and the laser radar is installed at the road intersection, including: The trajectory data acquisition module is configured to: acquire the state data of the in-transit targets identified by the distributed optical fiber monitoring equipment and the laser radar, establish a real-time state database of the in-transit targets, and assign identification numbers to the in-transit targets; The time series generation module is configured to: obtain a first target position time series identified by the distributed optical fiber monitoring device and a second target position time series identified by the laser radar in an identification intersection area of the distributed optical fiber monitoring device and the laser radar; The similarity judgment module is configured to judge the similarity between the first target position time series and the second target position time series after the coordinate system conversion, including: Create a distance matrix based on the distance or similarity between each data point in the two time series; Create a dynamic programming table of the same size as the distance matrix, where each table element represents the distance of the best alignment path from the starting point to the current point; Starting from the starting point, the distance of the best alignment path to each point is calculated by gradually updating the elements in the dynamic programming table. The updating method is to select the one with the smallest distance among the three adjacent points around the current point and add its distance to the current point. According to the last element in the dynamic programming table, backtrack to find the best alignment path, where the best alignment path represents the best matching method between the two time series; Calculate the similarity or distance between two time series based on the points on the best alignment path, and use the sum of the distances of all points on the path or the average distance as the similarity measure; If the similarity is greater than the set threshold, the two target position time series are considered to come from the same en-route target; The full-course trajectory generation module is configured to merge the monitoring data of the in-course target and update the real-time status database of the in-course target, and combine the vehicle trajectories independently identified by the distributed optical fiber and the laser radar to obtain the full-course trajectory of the in-course target. 8. A computer-readable storage medium having a program stored thereon, characterized in that when the program is executed by a processor, the steps in the vehicle trajectory full-course tracking method based on distributed optical fiber and laser radar as described in any one of claims 1 to 6 are implemented. 9. An electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the vehicle trajectory full-course tracking method based on distributed optical fiber and laser radar as described in any one of claims 1 to 6 are implemented.