CN110211388A

CN110211388A - Multilane free-flow vehicle matching process and system based on 3D laser radar

Info

Publication number: CN110211388A
Application number: CN201910447632.5A
Authority: CN
Inventors: 胡攀攀; 杨勇刚; 李康
Original assignee: Wuhan Wanji Information Technology Co Ltd
Current assignee: Wuhan Wanji Information Technology Co Ltd
Priority date: 2019-05-27
Filing date: 2019-05-27
Publication date: 2019-09-06

Abstract

The present invention provides a kind of multilane free-flow vehicle matching process and system based on 3D laser radar, wherein, the above method includes: the point cloud data that the vehicle to be matched in the detection zone of multilane free flow system is acquired by the 3D laser radar vehicle detection module being arranged in multilane free flow system；The trace information of the vehicle to be matched is determined according to the point cloud data, and the trace information is uploaded to the comprehensive control module in the multilane free flow system, wherein, the comprehensive control module according at least one of determine the trace information corresponding to vehicle, vehicle corresponding to the antenna Transaction Information, and the vehicle to be matched is carried out to capture whether vehicle corresponding to obtained candid photograph information of vehicles is same vehicle: the trace information of acquisition, the antenna Transaction Information of the vehicle to be matched, the candid photograph information of vehicles.

Description

3D laser radar-based multi-lane free flow vehicle matching method and system

Technical Field

The invention relates to the field of laser radars, in particular to a multilane free flow vehicle matching method and system based on a 3D laser radar.

Background

The multilane free flow technology is an information interaction system based on vehicle-road communication, aims to solve the effective technical means of road traffic jam, and provides a non-stop charging technology without speed limitation, lane limitation and speed. However, one key problem to be solved by the multilane free flow technology is the matching of the transaction information and the snapshot information of the road test unit RSU antenna and the vehicle-mounted OBU, and the correct matching of the transaction information and the snapshot information can provide the charging information normally completed by the vehicle-mounted OBU on one hand and can provide the condition that the vehicle-mounted OBU has abnormal transaction or no vehicle-mounted OBU escapes. There are some technical means for solving the problem of vehicle positioning and matching in the prior art related to the multilane free flow technology.

The related technology provides a system for positioning the vehicle position by a plurality of single-line lasers or one multi-line laser, and also provides a method for solving matching of vehicle positioning and snapshot information. In the related technology, the laser is adopted to acquire the position of the vehicle and match the position of the vehicle-mounted OBU acquired by the RSU antenna, the method only provides one light curtain to detect the vehicle, the accuracy of the position of the vehicle or the change of the position of the vehicle cannot be accurately captured, and the logic matching process is complex. The related technology also provides a method for capturing a picture of a vehicle by using a laser trigger capture camera in multi-lane free flow, mainly describes the method for capturing the picture, and adopts laser which is also single line or light curtain laser in essence, so that the position of the vehicle cannot be accurately tracked and positioned in real time.

Aiming at the problems that the vehicle transaction information and the vehicle snapshot information cannot be accurately and efficiently matched in the related technology, and the like, an effective technical scheme is not provided.

Disclosure of Invention

The embodiment of the invention provides a 3D laser radar-based multi-lane free flow vehicle matching method and system, which are used for at least solving the problems that data measured by a laser radar is wrong due to the fact that a laser radar coordinate system and a reference plane coordinate system deviate in the related technology.

According to an embodiment of the invention, a 3D laser radar-based multilane free flow vehicle matching method is provided, which includes: collecting point cloud data of a vehicle to be matched in a detection area of a multi-lane free flow system through a 3D laser radar vehicle detection module arranged in the multi-lane free flow system; determining track information of the vehicle to be matched according to the point cloud data, and uploading the track information to a comprehensive control module in the multi-lane free flow system, wherein the comprehensive control module determines the vehicle corresponding to the track information, the vehicle corresponding to the antenna transaction information and whether the vehicle corresponding to the snapshot vehicle information obtained by snapshot of the vehicle to be matched is the same vehicle according to at least one of the following: the track information, the antenna transaction information of the vehicle to be matched and the snapshot vehicle information are obtained.

According to another embodiment of the invention, there is also provided a 3D lidar-based multilane free flow vehicle matching method, including: acquiring track information of a vehicle to be matched, which is determined by a point cloud data set of the vehicle to be matched, wherein the point cloud data set is acquired in a detection area of a multilane free flow system by a 3D laser radar vehicle detection module arranged in the multilane free flow system; acquiring antenna transaction information of the vehicle to be matched and snapshot vehicle information obtained by snapshot of the vehicle to be matched by a snapshot module of the multi-lane free flow system; and comparing the track information, the antenna transaction information and the snapshot vehicle information to determine whether the vehicle corresponding to the track information, the vehicle corresponding to the antenna transaction information and the vehicle corresponding to the snapshot vehicle information are the same vehicle.

According to another embodiment of the present invention, there is also provided a 3D lidar-based multilane free flow vehicle matching apparatus, applied to a multilane free flow system, including: the system comprises a 3D laser radar vehicle detection module, a data acquisition module and a data processing module, wherein the 3D laser radar vehicle detection module is used for acquiring point cloud data of vehicles to be matched in a detection area of a multi-lane free flow system; determining track information of the vehicle to be matched according to the point cloud data, and uploading the track information to a comprehensive control module in the multi-lane free flow system, wherein the comprehensive control module determines the vehicle corresponding to the track information, the vehicle corresponding to the antenna transaction information and whether the vehicle corresponding to the snapshot vehicle information obtained by snapshot of the vehicle to be matched is the same vehicle according to at least one of the following: the track information, the antenna transaction information of the vehicle to be matched and the snapshot vehicle information are obtained.

According to another embodiment of the present invention, there is also provided a 3D lidar-based multilane free flow vehicle matching apparatus, applied to a multilane free flow system, including: the comprehensive control module is used for acquiring track information of the vehicle to be matched, which is determined by a point cloud data set of the vehicle to be matched, wherein the point cloud data set is acquired in a detection area of a multi-lane free flow system by a 3D laser radar vehicle detection module arranged in the multi-lane free flow system; acquiring antenna transaction information of the vehicle to be matched and snapshot vehicle information obtained by snapshot of the vehicle to be matched by a snapshot module of the multi-lane free flow system; and comparing the track information, the antenna transaction information and the snapshot vehicle information to determine whether the vehicle corresponding to the track information, the vehicle corresponding to the antenna transaction information and the vehicle corresponding to the snapshot vehicle information are the same vehicle.

According to another embodiment of the present invention, there is also provided a 3D lidar based multilane free flow vehicle matching system, including: the system comprises a 3D laser radar vehicle detection module, a comprehensive control module and a data processing module, wherein the 3D laser radar vehicle detection module is arranged in a multi-lane free flow system and is used for collecting point cloud data of a vehicle to be matched in a detection area of the multi-lane free flow system, determining track information of the vehicle to be matched according to the point cloud data and uploading the track information to the comprehensive control module in the multi-lane free flow system; and the comprehensive control module is used for determining the vehicle corresponding to the track information, the vehicle corresponding to the antenna transaction information and whether the vehicle corresponding to the snapshot vehicle information is the same vehicle according to the acquired track information, the antenna transaction information of the vehicle to be matched and the snapshot vehicle information obtained by snapshot of the vehicle to be matched by the snapshot module of the multi-lane free flow system.

According to another embodiment of the present invention, there is also provided a storage medium having a computer program stored therein, wherein the computer program is arranged to execute any of the above 3D lidar based multilane free-flow vehicle matching methods when executed.

According to the invention, the 3D laser radar vehicle detection module arranged in the multi-lane free flow system firstly collects the point cloud data of the vehicle to be matched in the detection area of the multi-lane free flow system, determines the track information of the vehicle to be matched according to the point cloud data, further uploads the track information to the comprehensive control module in the multi-lane free flow system, then the comprehensive control module determines the vehicle corresponding to the track information according to the acquired track information, the vehicle corresponding to the antenna transaction information and the vehicle corresponding to the snapshot vehicle information whether are the same vehicle or not according to the antenna transaction information and the snapshot vehicle information which are acquired by the snapshot module of the multi-lane free flow system of the vehicle to be matched in advance, the method comprises the steps of collecting point cloud data of vehicles to be matched in a detection area of a multi-lane free flow system through a 3D laser radar vehicle detection module, generating vehicle track information according to the point cloud data, introducing the vehicle track information, and further efficiently and accurately determining the vehicles corresponding to the track information, the vehicles corresponding to antenna transaction information and whether the vehicles corresponding to snapshot vehicle information are the same vehicle.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:

fig. 1 is a block diagram of a hardware structure of a detection device of a 3D lidar-based multi-lane free-flow vehicle matching method according to an embodiment of the present invention;

FIG. 2 is a flow chart of a 3D lidar based multi-lane free-flow vehicle matching method according to an embodiment of the present disclosure;

FIG. 3 is another flow chart of a 3D lidar based multi-lane free-flow vehicle matching method according to an embodiment of the present disclosure;

FIG. 4 is yet another flow chart of a 3D lidar based multi-lane free-flow vehicle matching method according to an embodiment of the present disclosure;

FIG. 5 is yet another flow chart of a 3D lidar based multi-lane free-flow vehicle matching method according to an embodiment of the present disclosure;

fig. 6 is a block diagram of a structure of a 3D lidar-based multi-lane free-flow vehicle matching apparatus according to an embodiment of the present invention;

fig. 7 is another structural block diagram of a 3D lidar based multi-lane free-flow vehicle matching apparatus according to an embodiment of the present invention;

FIG. 8 is a schematic structural diagram of a 3D lidar based multi-lane free-flow vehicle matching system according to a preferred embodiment of the present invention;

FIG. 9 is another schematic diagram of the structure of a 3D lidar based multi-lane free-flow vehicle matching system according to the preferred embodiment of the invention;

FIG. 10 is a schematic diagram of another structure of a 3D lidar based multi-lane free-flow vehicle matching system according to a preferred embodiment of the invention;

fig. 11 is a further structural diagram of a 3D lidar based multi-lane free-flow vehicle matching system according to a preferred embodiment of the invention.

Detailed Description

The invention will be described in detail hereinafter with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.

Example 1

The method provided by embodiment 1 of the present application may be implemented in a detection device or a similar computing device. Taking the operation on the detection device as an example, fig. 1 is a hardware structure block diagram of the detection device of the 3D lidar-based multi-lane free-flow vehicle matching method according to the embodiment of the present invention. As shown in fig. 1, the detection device 10 may include one or more (only one shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, and optionally, a transmission device 106 for communication functions and an input-output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is merely illustrative and is not intended to limit the structure of the above-described detection apparatus. For example, the detection device 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration with equivalent functionality to that shown in FIG. 1 or with more functionality than that shown in FIG. 1.

The memory 104 may be used to store a computer program, for example, a software program and a module of an application software, such as a computer program corresponding to the multilane free flow vehicle matching method in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, so as to implement the method described above. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the detection device 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of such networks may include wireless networks provided by the communication provider of the detection device 10. In one example, the transmission device 106 includes a Network adapter (NIC), which can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.

In the present embodiment, a multi-lane free flow vehicle matching method operable on the detection device is provided, and fig. 2 is a flowchart of a 3D lidar based multi-lane free flow vehicle matching method according to an embodiment of the present invention, as shown in fig. 2, the flowchart includes the following steps:

step S202, collecting point cloud data of a vehicle to be matched in a detection area of a multi-lane free flow system through a 3D laser radar vehicle detection module arranged in the multi-lane free flow system;

step S204, determining the track information of the vehicle to be matched according to the point cloud data, and uploading the track information to a comprehensive control module in the multi-lane free flow system, wherein the comprehensive control module determines whether the vehicle corresponding to the track information, the vehicle corresponding to the antenna transaction information and the vehicle corresponding to the snapshot vehicle information obtained by snapshotting the vehicle to be matched are the same vehicle according to at least one of the following items: the obtained track information, the antenna transaction information of the vehicle to be matched and the vehicle information are captured.

In the process of determining the vehicle track information according to the point cloud data, the following technical scheme can be further executed: extracting the three-dimensional contour information of the vehicle to be matched from the collected point cloud data through the 3D laser radar vehicle detection module to form a transverse coordinate set, a longitudinal coordinate set and a height value set of the vehicle; after the vehicle to be matched passes through the detection area, acquiring a point cloud data set of the vehicle to be matched according to the transverse coordinate set, the longitudinal coordinate set and the height value set, wherein the point cloud data set is used for determining the track information.

Specifically, the 3D lidar vehicle detection module extracts the three-dimensional contour information of the vehicle to be matched from the collected point cloud data to form a transverse coordinate set, a longitudinal coordinate set and a height value set of the vehicle, and the method can be implemented by the following scheme:

the 3D laser radar vehicle detection module collects point cloud data of the vehicle to be matched in a scanning period, and after coordinate conversion, the point cloud data are converted into three-dimensional contour data of the vehicle to be matchedWhereinRepresenting that the 3D laser radar vehicle detection module scans the transverse coordinate ordered set of the vehicle to be matched by the ith laser line at the time t in the scanning period,representing that the 3D laser radar vehicle detection module scans the longitudinal coordinate ordered set of the vehicle to be matched by the ith laser ray at the time t in the scanning period,indicating that the 3D laser radar vehicle detection module scans the vehicle to be matched with the ith laser ray at the t moment in the scanning periodA corresponding set of height values.

Optionally, the determined trajectory information of the vehicle to be matched may include:

a set of vehicle trajectory information (N, T, L) of the vehicle to be matched detected in the detection area by the 3D lidar vehicle detection module_T，XL_T，XR_T，Y_T) N represents a vehicle number detected by a 3D laser radar vehicle detection module, T represents an ordered time T set and a lane set L for detecting the position information of the vehicle to be matched in the time when the vehicle to be matched passes through the detection area_TLeft boundary lateral coordinate set XL_TRight border lateral coordinate set XR_TSet of longitudinal coordinates Y_TRespectively represent L_t、XL_t、XR_t、Y_tWherein the position information of the matching vehicle at time t is (N, t, L)_t，XL_t，XR_t，Y_t) Wherein N represents the vehicle number detected by the 3D laser radar vehicle detection module, and L_tRepresents the lane, XL, in which the vehicle to be matched is located at time t_tLeft-side boundary lateral coordinates, XR, representing the position information of the vehicle to be matched at time t_tRight boundary lateral coordinate, Y, representing position information of vehicle to be matched at time t_tThe method comprises the following steps of representing a longitudinal coordinate of position information of a vehicle to be matched at the moment t, wherein after a 3D laser radar vehicle detection module arranged in a multi-lane free flow system collects point cloud data of the vehicle to be matched in a detection area of the multi-lane free flow system, the method further comprises the following steps: the 3D laser radar vehicle detection module triggers the snapshot module to snapshot the vehicle to be matched to obtain a set (N, L) of snapshot vehicle information_t′，P_TP), wherein L_t′Is L_TPT represents the license plate set of the vehicle to be matched, and P represents the picture set of the vehicle to be matched.

In an optional embodiment, the 3D lidar vehicle detection module triggers the snapshot module to snapshot the vehicle to be matched, where the snapshot module includes at least one of: triggering when the 3D laser radar vehicle detection module detects that the vehicle to be matched starts to pass through a detection area; triggering when the 3D laser radar vehicle detection module detects that the vehicle to be matched is in the middle position of a detection area; triggering when the 3D laser radar vehicle detection module detects that the vehicle to be matched changes lanes; and triggering when the 3D laser radar vehicle detection module detects that the head of the vehicle to be matched is about to leave a detection area.

In an optional embodiment, after the point cloud data of the vehicle to be matched in the detection area of the multi-lane free flow system is collected by a 3D lidar vehicle detection module arranged in the multi-lane free flow system, the method comprises the following steps: determining the characteristic information and the vehicle detection information of the vehicle to be matched according to the point cloud data, wherein the characteristic information at least comprises one of the following information: the length of the vehicle to be matched, the width of the vehicle to be matched, the height of the vehicle to be matched, the speed of the vehicle to be matched, the type of the vehicle to be matched, and the vehicle detection information include: the vehicle number of the vehicle to be matched, the vehicle type of the vehicle to be matched, the lane, the speed of the vehicle to be matched, the length of the vehicle to be matched, the width of the vehicle to be matched, the height of the vehicle to be matched, the time for the head of the vehicle to be matched to enter a detection area, and the time for the parking space of the vehicle to be matched to leave the detection area; and uploading the characteristic information and the vehicle detection information to a background or a data center.

The 3D laser radar vehicle detection module at least comprises a 3D laser radar unit, and the 3D laser radar unit is used for forming at least two scanning planes with included angles.

In an optional embodiment, when the 3D lidar vehicle detection module includes at least two 3D lidar units, a first preset angle exists between a scanning plane formed by a laser line emitted by one of the two 3D lidar units and the vehicle to be matched, so that the one 3D lidar unit can cover the farthest range of the detection area, and a second preset angle exists between a scanning plane formed by a laser line emitted by the other of the two 3D lidar units and the vehicle to be matched, so that the one 3D lidar unit can cover the closest range of the detection area, where the 3D lidar unit includes: the multi-line laser radar based on Mechanical scanning is a quasi-solid laser radar based on Micro-Electro Mechanical System (MEMS for short) and is a solid laser radar based on FLASH.

The above embodiment is a description of a multilane free flow vehicle matching process from the 3D lidar vehicle detection module as an execution subject, and the following description of a multilane free flow vehicle matching process from the integrated control module as an execution subject is also provided.

Fig. 3 is another flowchart of a 3D lidar based multi-lane free-flow vehicle matching method according to an embodiment of the present invention, as shown in fig. 3, the flowchart includes the following steps:

step S302, track information of a vehicle to be matched, which is determined by a point cloud data set of the vehicle to be matched, is obtained, wherein the point cloud data set is acquired by a 3D laser radar vehicle detection module arranged in a multi-lane free flow system in a detection area of the multi-lane free flow system;

step S304, acquiring antenna transaction information of the vehicle to be matched and snapshot vehicle information obtained by a snapshot module of the multi-lane free flow system for snapshot of the vehicle to be matched;

step S306, the track information, the antenna transaction information and the snapshot vehicle information are compared to determine whether the vehicle corresponding to the track information, the vehicle corresponding to the antenna transaction information and the vehicle corresponding to the snapshot vehicle information are the same vehicle.

By the invention, the track information of the vehicle to be matched determined by the point cloud data set of the vehicle to be matched is obtained, the antenna transaction information of the vehicle to be matched and the snapshot vehicle information obtained by the snapshot module of the multilane free flow system are compared to determine the vehicle corresponding to the track information, the vehicle corresponding to the antenna transaction information and the snapshot vehicle information, and whether the vehicle corresponding to the snapshot vehicle information is the same vehicle, wherein the point cloud data set is acquired by the 3D laser radar vehicle detection module arranged in the multilane free flow system in the detection area of the multilane free flow system, and the technical scheme is adopted to solve the problems that the vehicle transaction information and the snapshot information of the vehicle cannot be accurately and efficiently matched in the related technology and the like, the method comprises the steps of collecting point cloud data of vehicles to be matched in a detection area of a multi-lane free flow system through a 3D laser radar vehicle detection module, generating vehicle track information according to the point cloud data, introducing the vehicle track information, and further efficiently and accurately determining the vehicles corresponding to the track information, the vehicles corresponding to antenna transaction information and whether the vehicles corresponding to snapshot vehicle information are the same vehicle.

Optionally, the 3D lidar vehicle detection module collects point cloud data of the vehicle in adjacent scanning periods, converts the point cloud data into the three-dimensional profile data, matches the point cloud data through an ordered set of lateral coordinates of the vehicle, the ordered set of lateral coordinates of the current scanning periodLeft and right boundary coordinates ofOrdered set of range and the lateral coordinates of a previous scan cycleLeft and right boundary coordinates ofIf the ranges overlap, the two are judgedThe data of the scanning period is the same vehicle data; otherwise, different vehicle data is determined.

Further, an embodiment of the present invention further provides a method for detecting vehicle merging and shunting through a 3D lidar vehicle detection module, including:

the three-dimensional profile data of the vehicle has a part or all of the transverse coordinate ordered setLeft and right boundary coordinates ofAbsolute difference of (2)If the vehicle width is larger than the preset vehicle width threshold value, judging that the parallel operation exists;

performing parallel operation and vehicle separation according to the comparison and analysis of the non-parallel-operation abscissa ordered set and the parallel-operation abscissa ordered set in the three-dimensional contour data, and performing vehicle separation from the parallel-operation abscissa ordered set; or dividing the vehicle according to the width change in the abscissa ordered set of the parallel operation and/or the height change comparative analysis in the vehicle height value set.

Specifically, according to the orderly set of abscissa branch car, realize through the following technical scheme: the 3D laser radar unit scans the parallel operation vehicle, in a scanning period or an adjacent scanning period, a part of light beams in scanning light beams of the 3D laser radar unit scan the cross section of the parallel operation vehicle, and a part of light beams scan the cross section of one vehicle in the parallel operation vehicle, so that a larger difference value exists between the left and right boundary coordinates of the transverse ordered coordinate of the scanning parallel operation vehicle and the left and right boundary coordinates of the transverse ordered coordinate of the one vehicle, and a boundary point of the transverse ordered coordinate in the parallel operation vehicle is calculated according to the left and right boundary coordinate values of the transverse ordered coordinate after the cross section of the scanning one vehicle is converted;

according to the height value set, the separation can be realized through the following technical scheme: when the height of the scanned parallel operation vehicle changes greatly, the 3D laser radar scans the parallel operation vehicle, the height value in the converted height value set changes along with the height change of the vehicle in the same scanning section, and a point with large height change is found out to be used as a demarcation point.

The following example specifically provides a multi-lane free flow vehicle detection process based on a 3D laser radar, which improves the matching success rate and accuracy of the transaction between an RSU antenna and a vehicle-mounted OBU and the license plate snapshot in the multi-lane free flow system in the prior art, and can capture the problem that the transaction of the vehicle-mounted OBU is abnormal, or there is no OBU vehicle, or the information of the vehicle-mounted OBU does not conform to the actual information of the vehicle.

Optionally, an embodiment of the present invention provides a method for detecting a multi-lane free flow vehicle based on a 3D lidar, and fig. 4 is another flowchart of a method for matching a multi-lane free flow vehicle based on a 3D lidar according to an embodiment of the present invention, as shown in fig. 4, including:

s402: the method comprises the following steps that point cloud data acquired in real time by a 3D laser radar vehicle detection module (which can be arranged on a gantry or a vertical rod of a multilane free flow system) are preprocessed;

s404: extracting vehicle point cloud data from the acquired point cloud data to form a complete vehicle point cloud data set, and/or triggering a snapshot module to snapshot vehicle information and acquire vehicle track information of a vehicle passing through a detection area;

s406: and acquiring the transaction information of the antenna transaction module, matching the transaction information, the vehicle track information and the vehicle snapshot information, and uploading the transaction information, the vehicle track information and the vehicle snapshot information to a background server or a data center.

Further, the step S402 further includes:

the point cloud data received by the 3D laser radar vehicle detection module at one time is part of or all point cloud data acquired by the 3D laser radar in one scanning period;

the 3D laser radar vehicle detection module receives the collected point cloud data, and the point cloud data in a complete scanning period is formed according to the beam sequence of the 3D laser radar and then is processed;

or the 3D laser radar vehicle detection module processes the acquired point cloud data after receiving the point cloud data, and when the point cloud data in a scanning period are received and processed, the point cloud data in the scanning period and the processed point cloud data are formed into a complete point cloud data in the scanning period according to the beam sequence of the 3D laser radar;

in addition, processing the abnormal point value of the collected point cloud data; the coordinate transformation can also be carried out on the collected point cloud data to transform the point cloud data into a transverse coordinate value vertical to the driving direction, a longitudinal coordinate value parallel to the driving direction and a coordinate value in the height direction.

Further, extracting the vehicle point cloud data may be achieved by: the 3D laser radar vehicle detection module extracts three-dimensional contour information of the vehicle from the collected point cloud data according to the height change to form a transverse coordinate set, a longitudinal coordinate set and a height value set of the vehicle;

and matching vehicle data acquired by two adjacent complete scanning periods of the 3D laser radar through transverse coordinate values, and acquiring a point cloud data set of the vehicle after the vehicle passes through a detection area of the 3D laser radar module.

Further, fig. 5 is still another flowchart of a 3D lidar-based multilane free flow vehicle matching method according to an embodiment of the present invention, and as shown in fig. 5, acquiring vehicle trajectory information and matching may be implemented by the following technical solutions:

s502: the 3D laser radar vehicle detection module detects vehicle running track information in real time, and the position information of the vehicle at the time t is (N, t, L)_t，XL_t，XR_t，Y_t) WhereinN represents the vehicle number detected by the 3D laser radar vehicle detection module, L_tXL, which indicates the lane in which the vehicle is located at time t_tLateral left-side boundary coordinates, XR, representing the vehicle position at or at time t_tRight boundary lateral coordinate, Y, representing the vehicle position at time t_tA longitudinal coordinate representing the position of the vehicle at the time T, and a vehicle track information set (N, T, L) of the vehicle passing through the detection area of the 3D laser radar vehicle detection module_T，XL_T，XR_T，Y_T) Wherein the time set T represents the ordered time T set for detecting the vehicle position in the time when the vehicle passes through the detection area, and the lane set L_TLeft boundary lateral coordinate set XL_TRight border lateral coordinate set XR_TSet of longitudinal coordinates Y_TRespectively represent L_t、XL_t、XR_t、Y_tAn ordered set of;

s504: the 3D laser radar vehicle detection module triggers the snapshot module to snapshot the vehicle to obtain the vehicle picture and the license plate, and a vehicle snapshot information set (N, L)_t′PT, P), wherein L_t′Is L_TPT represents a license plate set of the vehicle, P represents a vehicle picture set, and vehicle information acquired by the snapshot module is accurately matched with vehicle information detected by the 3D laser radar vehicle detection module through a vehicle number N;

s506: the antenna transaction module acquires a position information set and transaction information of a vehicle-mounted OBU passing through a transaction area, the position information set of the OBU is calculated and matched with a vehicle track information set, and/or license plate information in the transaction information is matched with license plate information in vehicle snapshot information, and it is judged that a vehicle transacted by the antenna transaction module and a vehicle detected by the 3D laser radar vehicle detection module are the same vehicle.

Further, the method for acquiring the vehicle point cloud data by the 3D laser radar vehicle detection module further comprises the following steps:

at the moment t, point cloud data of the vehicle is adopted in one scanning period of the 3D laser radar, and coordinate conversion is carried out on the point cloud dataConverted into three-dimensional profile data of the vehicleWhereinIndicating that the 3D laser radar scans the ordered set of the transverse coordinates of the vehicle by the ith light beam at the moment t in the scanning period,represents that the 3D laser radar scans the longitudinal coordinate ordered set of the vehicle by the ith light ray in the scanning period at the time t,indicating that the ith beam of light scans the vehicle and is associated with the 3D lidar at time t during the scanning periodA corresponding set of height values;

collecting point cloud data of the vehicle in adjacent scanning periods of the 3D laser radar, converting the point cloud data into three-dimensional contour data, matching the point cloud data through the sequential set of transverse coordinates of the vehicle, and collecting the sequential set of transverse coordinates of the current scanning periodLeft and right boundary coordinates ofOrdered set of range and lateral coordinates of previous scan cycleLeft and right boundary coordinates ofIf the ranges are overlapped, the data of the two scanning periods are judged to be the same vehicleVehicle data; otherwise, judging that the vehicle data are different;

after the vehicle passes through the 3D laser radar detection area, all three-dimensional contour data of the vehicle can be obtained, and data are extracted from the data to form a vehicle track information set.

Further, the method for detecting vehicle merging and shunting by the 3D laser radar vehicle detection module further comprises the following steps:

performing parallel operation and vehicle separation according to the comparison and analysis of the non-parallel-operation abscissa ordered set and the parallel-operation abscissa ordered set in the three-dimensional contour data, and performing vehicle separation from the parallel-operation abscissa ordered set; or the vehicle is divided by comparing and analyzing the width change in the abscissa ordered set of the parallel operation and/or the height change in the vehicle height value set.

The 3D laser radar vehicle detection module triggers the snapshot module to snapshot the vehicle, and comprises one and/or more of the following processes:

triggering when the 3D laser radar vehicle detection module detects that the vehicle starts to pass through the detection area;

the method comprises the steps that when a 3D laser radar vehicle detection module detects that a vehicle is located in the middle of a detection area, triggering is carried out;

triggering when the 3D laser radar vehicle detection module detects that the vehicle changes lanes;

triggering when the 3D laser radar vehicle detection module detects that the vehicle head is about to leave the detection area;

when the snapshot module comprises a plurality of cameras, the 3D laser radar vehicle detection module triggers the snapshot cameras of the lanes where the vehicles are located to snapshot according to the positions of the vehicles, and vehicle snapshot information snapshot by different cameras is combined into a vehicle snapshot information set through vehicle numbers;

when the 3D laser radar vehicle detection module comprises a plurality of 3D laser radar units, when the vehicle passes through the detection areas of different 3D laser radar units, the capturing modules are triggered to capture the vehicle image information, and the captured vehicle image information is combined into a vehicle capturing information set through time position or vehicle number matching.

The 3D laser radar vehicle detection module collects point cloud data of vehicles passing through a detection area, calculates vehicle characteristic information at least comprising length, width, height and speed, identifies vehicle types, forms vehicle detection information and at least comprises information (vehicle number, vehicle type identification, lane, speed, length, width, height, vehicle head entering time and vehicle head leaving time);

the snapshot module is used for snapshot of a vehicle picture and recognition of a license plate to form vehicle snapshot information, and the vehicle snapshot information at least comprises (vehicle number, license plate set, picture set and snapshot time set) information;

the antenna transaction module and the vehicle-mounted OBU perform transaction to meet the international transaction protocol and at least comprise OBU transaction information consisting of (OBUID, transaction vehicle type, transaction time, transaction license plate and successful transaction identifier);

the comprehensive control module performs logic processing and matching on the received vehicle detection information, vehicle snapshot information and OBU transaction information to form vehicle information which at least comprises (vehicle number, OBU ID, transaction vehicle type, transaction time, transaction license plate, successful transaction identification, vehicle type identification, lane, speed, length, width, height, vehicle head entering time, vehicle head leaving time, snapshot license plate, picture set and snapshot time set) information.

Optionally, an embodiment of the present invention provides a 3D lidar-based multilane free flow vehicle positioning matching system, including a 3D lidar vehicle detection module, an antenna transaction module, a snapshot module, and a comprehensive control module, where the 3D lidar vehicle detection module includes at least one 3D lidar unit and a data processing unit; the detection area of the 3D laser radar vehicle detection module covers all lanes on a road, the detection area is projected onto the road in the driving direction of the vehicle, the minimum distance range of a scanning surface formed by a scanning beam of the 3D laser radar vehicle from the position right below the 3D laser radar vehicle detection module is 0-6 meters, and the maximum distance range is 20-30 meters;

when the field angle of the 3D laser radar unit cannot cover the detection area, a plurality of 3D laser radar units are needed, and a certain preset angle is formed between the 3D laser radar units and the vehicle running direction;

the 3D laser radar vehicle detection module is used for acquiring point cloud data of vehicles on a road, is connected with the snapshot module, and is used for triggering the snapshot module for multiple times to snapshot and acquire track information and vehicle characteristic information of the vehicles passing through a detection area;

the snapshot area of the snapshot module is not smaller than the detection area of the 3D laser radar vehicle detection module, the snapshot area covers the detection area of the vehicle, and the snapshot module is used for snapshot of the vehicle and transmission of vehicle pictures and license plate information;

the antenna transaction module consists of one or more RSU units and is used for performing transaction with a vehicle-mounted OBU and acquiring position information of the vehicle OBU, the transaction area of the antenna transaction module is not smaller than the snapshot area, and the transaction area covers the snapshot area;

the comprehensive control module is respectively connected with the 3D laser radar vehicle detection module, the snapshot module and the antenna transaction module, and is used for acquiring a track information set, vehicle detection information, vehicle snapshot information, vehicle transaction information and OBU position information of a vehicle, matching and combining all the information to form vehicle information, and uploading the vehicle information to a background or a service center.

Furthermore, the scanning beam of the 3D laser radar unit is not less than 2 lines, and the 3D laser radar vehicle detection module at least comprises one 3D laser radar unit;

when the field angle of the 3D laser radar unit in the vertical direction cannot cover the vehicle detection range, at least two 3D laser radar units need to be installed, a scanning surface formed by scanning the light beam of one 3D laser radar unit has a preset angle with the driving direction, the 3D laser radar unit can cover the farthest range of the detection area, and a scanning surface formed by scanning the light beam of the other 3D laser radar unit has another preset angle with the driving direction, so that the 3D laser radar unit can cover the nearest range of the detection area;

the 3D laser radar unit is connected with the snapshot module, and/or the data processing unit is connected with the snapshot module;

the snapshot module at least comprises a head snapshot camera and/or a tail snapshot camera.

Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.

In this embodiment, a multilane free flow vehicle matching device is further provided, and the device is used to implement the foregoing embodiments and preferred embodiments, which have already been described and are not described again. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.

Fig. 6 is a block diagram showing the structure of a 3D lidar based multi-lane free-flow vehicle matching apparatus according to an embodiment of the present invention, as shown in fig. 6, the apparatus including:

a 3D lidar vehicle detection module 60, wherein the 3D lidar vehicle detection module is configured to collect point cloud data of a vehicle to be matched in a detection area of a multilane free flow system; and determining track information of the vehicle to be matched according to the point cloud data, and uploading the track information to a comprehensive control module in the multi-lane free flow system, wherein the comprehensive control module determines the vehicle corresponding to the track information, the vehicle corresponding to the antenna transaction information and the vehicle corresponding to the snapshot vehicle information whether the vehicle corresponding to the snapshot vehicle information is the same vehicle according to the acquired track information, the antenna transaction information of the vehicle to be matched and the snapshot vehicle information obtained by the snapshot module of the multi-lane free flow system.

Optionally, the 3D lidar vehicle detection module is further configured to extract three-dimensional contour information of the vehicle to be matched from the collected point cloud data, and form a transverse coordinate set, a longitudinal coordinate set, and a height value set of the vehicle; after the vehicle to be matched passes through the detection area, acquiring a point cloud data set of the vehicle to be matched according to the transverse coordinate set, the longitudinal coordinate set and the height value set, wherein the point cloud data set is used for determining the track information.

In an optional embodiment, the 3D lidar vehicle detection module is further configured to acquire point cloud data of the vehicle to be matched in one scanning period, and after coordinate conversion, transform the point cloud data into three-dimensional contour data of the vehicle to be matchedWhereinRepresenting that the 3D laser radar vehicle detection module scans the transverse coordinate ordered set of the vehicle to be matched by the ith laser line at the time t in the scanning period,representing that the 3D laser radar vehicle detection module scans the longitudinal coordinate ordered set of the vehicle to be matched by the ith laser ray at the time t in the scanning period,indicating that the 3D laser radar vehicle detection module scans the vehicle to be matched with the ith laser ray at the t moment in the scanning periodA corresponding set of height values.

In an optional embodiment, the 3D lidar vehicle detection module is further configured to trigger the snapshot module to snapshot the vehicle to be matched, where the snapshot module includes at least one of: triggering when the 3D laser radar vehicle detection module detects that the vehicle to be matched starts to pass through a detection area; triggering when the 3D laser radar vehicle detection module detects that the vehicle to be matched is in the middle position of a detection area; triggering when the 3D laser radar vehicle detection module detects that the vehicle to be matched changes lanes; and triggering when the 3D laser radar vehicle detection module detects that the head of the vehicle to be matched is about to leave a detection area.

The determined track information of the vehicle to be matched may include: a set of vehicle trajectory information (N, T, L) of the vehicle to be matched detected in the detection area by the 3D lidar vehicle detection module_T，XL_T，XR_T，Y_T) N represents a vehicle number detected by a 3D laser radar vehicle detection module, T represents an ordered time T set and a lane set L for detecting the position information of the vehicle to be matched in the time when the vehicle to be matched passes through the detection area_TLeft boundary lateral coordinate set XL_TRight border lateral coordinate set XR_TSet of longitudinal coordinates Y_TRespectively represent L_t、XL_t、XR_t、Y_tWherein the position information of the matching vehicle at time t is (N, t, L)_t，XL_t，XR_t，Y_t) Wherein N represents the vehicle number detected by the 3D laser radar vehicle detection module, and L_tIndicating the location of the vehicle to be matched at time tLane, XL_tLeft-side boundary lateral coordinates, XR, representing the position information of the vehicle to be matched at time t_tRight boundary lateral coordinate, Y, representing position information of vehicle to be matched at time t_tThe method comprises the following steps of representing a longitudinal coordinate of position information of a vehicle to be matched at the moment t, wherein after a 3D laser radar vehicle detection module arranged in a multi-lane free flow system collects point cloud data of the vehicle to be matched in a detection area of the multi-lane free flow system, the method further comprises the following steps: the 3D laser radar vehicle detection module triggers the snapshot module to snapshot the vehicle to be matched to obtain a set (N, L) of snapshot vehicle information_t′PT, P), wherein L_t′Is L_TA subset of (1), P_TAnd representing the license plate set of the vehicle to be matched, and P represents the picture set of the vehicle to be matched.

Determining the characteristic information and the vehicle detection information of the vehicle to be matched according to the point cloud data, wherein the characteristic information at least comprises one of the following information: the length of the vehicle to be matched, the width of the vehicle to be matched, the height of the vehicle to be matched, the speed of the vehicle to be matched, the type of the vehicle to be matched, and the vehicle detection information include: the vehicle number of the vehicle to be matched, the vehicle type of the vehicle to be matched, the lane, the speed of the vehicle to be matched, the length of the vehicle to be matched, the width of the vehicle to be matched, the height of the vehicle to be matched, the time for the head of the vehicle to be matched to enter a detection area, and the time for the parking space of the vehicle to be matched to leave the detection area; and uploading the characteristic information and the vehicle detection information to a background or a data center.

Wherein, 3D lidar vehicle detection module includes a 3D lidar unit at least, 3D lidar unit includes at least two scanning planes that exist the contained angle.

In an optional embodiment, when the 3D lidar vehicle detection module includes at least two 3D lidar units, a first preset angle exists between a scanning surface formed by a laser line emitted by one of the two 3D lidar units and the vehicle to be matched, so that the one 3D lidar unit can cover the farthest range of the detection area, and a second preset angle exists between a scanning surface formed by a laser line emitted by the other of the two 3D lidar units and the vehicle to be matched, so that the one 3D lidar unit can cover the closest range of the detection area.

Fig. 7 is another structural block diagram of a 3D lidar based multi-lane free-flow vehicle matching apparatus according to an embodiment of the present invention, as shown in fig. 7, the apparatus including:

the comprehensive control module 70 is used for acquiring track information of the vehicle to be matched, which is determined by a point cloud data set of the vehicle to be matched, wherein the point cloud data set is acquired by a 3D laser radar vehicle detection module 60 arranged in a multi-lane free flow system in a detection area of the multi-lane free flow system; acquiring antenna transaction information of the vehicle to be matched and snapshot vehicle information obtained by snapshot of the vehicle to be matched by a snapshot module of the multi-lane free flow system; and comparing the track information, the antenna transaction information and the snapshot vehicle information to determine whether the vehicle corresponding to the track information, the vehicle corresponding to the antenna transaction information and the vehicle corresponding to the snapshot vehicle information are the same vehicle.

It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.

According to another embodiment of the present invention, there is also provided a 3D lidar based multilane free flow vehicle matching system, including: the system comprises a 3D laser radar vehicle detection module, a data acquisition module and a data processing module, wherein the 3D laser radar vehicle detection module is used for acquiring point cloud data of vehicles to be matched in a detection area of a multi-lane free flow system; and determining track information of the vehicle to be matched according to the point cloud data, and uploading the track information to a comprehensive control module in the multi-lane free flow system, wherein the comprehensive control module determines whether the vehicle corresponding to the track information, the vehicle corresponding to the antenna transaction information and the vehicle corresponding to the snapshot vehicle information are the same vehicle according to the acquired track information, the antenna transaction information of the vehicle to be matched and the snapshot vehicle information obtained by the snapshot module of the multi-lane free flow system.

Optionally, the 3D lidar vehicle detection module comprises at least one 3D lidar unit, the 3D lidar unit comprises at least two scanning planes with an included angle, wherein, when the 3D lidar vehicle detection module has at least two 3D lidar units, a first preset angle is formed between a scanning surface formed by laser lines emitted by one of the two 3D laser radar units and the vehicle to be matched, so that said one 3D lidar unit can cover the furthest extent of said detection area, a second preset angle is formed between a scanning surface formed by the laser line emitted by the other 3D laser radar unit of the two 3D laser radar units and the vehicle to be matched, so that said one 3D lidar unit can cover the closest range of said detection area.

In an embodiment of the present invention, the system further includes: the snapshot module is connected with the 3D laser radar vehicle detection module, and the snapshot module at least comprises one of the following components: the automobile tail capturing device comprises a head capturing camera and a tail capturing camera, wherein the head capturing camera comprises one or more capturing cameras, and the tail capturing camera comprises one or more capturing cameras.

The following describes the above-mentioned multilane free flow vehicle matching process with reference to a plurality of preferred embodiments, as shown in fig. 8, fig. 4 already shows the multilane free flow vehicle detection process based on the 3D lidar vehicle detection module, as shown in fig. 4, and the multilane free flow vehicle detection method based on the 3D lidar vehicle detection module according to the preferred embodiments of the present invention is as follows.

S402, preprocessing point cloud data acquired by the 3D laser radar vehicle detection module in real time.

It should be noted that, all the light beams of the 3D lidar scan the detection area on the road at a certain moment or within a very short time, which is uniformly described as a moment for convenience of description; the scanning period of the 3D lidar refers to the time required for all beams of the 3D lidar to pass from the start scanning angle to the end scanning angle at a time.

The pretreatment of the point cloud data collected in real time comprises the following steps:

specifically, the 3D lidar vehicle detection module receives point cloud data acquired by the 3D lidar at one time, or point cloud data acquired by scanning one or more or all beams of the 3D lidar, or point cloud data acquired by scanning part of angles of multiple or all beams of the 3D lidar, so that the 3D lidar vehicle detection module needs to receive the point cloud data acquired by the 3D lidar at one time or multiple times, and completes the reception of the point cloud data acquired by scanning all beams of the 3D lidar within one scanning period.

Further, in a complete scanning period of the 3D laser radar, according to the beam sequence of the 3D laser radar, the 3D laser radar vehicle detection module forms received point cloud data collected in the scanning period into complete ground point cloud data, namely the point cloud data collected by the 3D laser radar in the scanning period, and then carries out point cloud data processing.

Or the 3D laser radar vehicle detection module receives the collected point cloud data once and processes the point cloud data, when the point cloud data in a scanning period are received, the point cloud data are processed, and the 3D laser radar vehicle detection module forms the point cloud data collected in a complete scanning period and the processed point cloud data according to the line beam sequence of the 3D laser radar.

Processing abnormal point values of the collected point cloud data, such as zero values, mutation values, unreasonable values and the like of some points, wherein the processing mode adopts an average value of adjacent points;

and carrying out coordinate transformation on the collected point cloud data to convert the point cloud data into a transverse coordinate value vertical to the driving direction, a longitudinal coordinate value parallel to the driving direction and a coordinate value in the height direction.

It should be noted that the lateral coordinate (expressed by X) in the present embodiment refers to the coordinate perpendicular to the road running direction, the longitudinal coordinate (expressed by Y) refers to the coordinate along the road running direction, and the height coordinate (expressed by Z) refers to the coordinate perpendicular to the road surface upward.

It can be understood that the 3D lidar vehicle detection module receives a part of the point cloud data or all of the point cloud data in one scanning period of the 3D lidar, and can perform exception handling and/or coordinate transformation handling.

S404: and extracting vehicle point cloud data from the acquired point cloud data to form a complete vehicle point cloud data set, and/or triggering a snapshot module to snapshot vehicle information and acquire vehicle track information of the vehicle passing through the detection area.

Specifically, the 3D laser radar vehicle detection module extracts three-dimensional contour information of the vehicle meeting the height from data after coordinate transformation according to height changes of the 3D laser radar scanned on a road and the vehicle, and a transverse coordinate set, a longitudinal coordinate set and a height value set of the vehicle are formed;

it can be understood that, in a scanning period of the 3D lidar, one or more or all light beams of the 3D lidar are scanned onto the vehicle, each light beam scanned onto the vehicle is point cloud data for collecting an outline of the vehicle, the points collected by each light beam form a transverse coordinate set, a longitudinal coordinate set and a height value set of the vehicle, and one or more transverse coordinate sets, longitudinal coordinate sets and height value sets exist in one 3D lidar scanning period.

And matching vehicle data acquired by two adjacent complete scanning periods of the 3D laser radar through transverse coordinate values, and acquiring a point cloud data set of the vehicle after the vehicle passes through the detection area of the 3D laser radar module.

After the vehicle passes through the 3D laser radar detection area, the 3D laser radar vehicle detection module acquires all three-dimensional contour data of the vehicle, and data are extracted from the data to form the vehicle track information set.

The 3D laser radar vehicle detection module triggers the snapshot module to snapshot vehicle information, and at least comprises one and/or a plurality of the following processes:

and triggering when the 3D laser radar vehicle detection module detects that the vehicle head is about to leave the detection area.

Further, the 3D lidar vehicle detection module acquires vehicle point cloud data that the vehicle passes through the detection area completely as follows.

At the moment t, point cloud data of the vehicle is adopted in one scanning period of the 3D laser radar, and is converted into three-dimensional profile data of the vehicle after coordinate conversionWhereinIndicating that the 3D laser radar scans the ordered set of the transverse coordinates of the vehicle by the ith light beam at the moment t in the scanning period,representing 3D laserAt time t, during the scanning period, the ith light beam scans the ordered set of longitudinal coordinates of the vehicle,indicating that the ith beam of light scans the vehicle and is associated with the 3D lidar at time t during the scanning periodA corresponding set of height values;

collecting point cloud data of the vehicle in the adjacent scanning period of the 3D laser radar, converting the point cloud data into three-dimensional contour data, matching the point cloud data through the sequential set of transverse coordinates of the vehicle, and collecting the sequential set of transverse coordinates in the current scanning periodLeft and right boundary coordinates ofOrdered set of range and the lateral coordinates of a previous scan cycleLeft and right boundary coordinates ofIf the ranges are overlapped, the data of the two scanning periods are judged to be the same vehicle data; otherwise, different vehicle data is determined.

Further, when the three-dimensional profile data of the vehicle has a part or all of the transverse coordinate ordered setLeft and right boundary coordinates ofAbsolute difference of (2)If the vehicle width is larger than the preset vehicle width threshold value, judging that the parallel operation exists;

when merging occurs, the vehicle needs to enter the vehicle and be separated, the vehicle is separated from the parallel vehicle abscissa ordered set according to the comparison and analysis of the parallel vehicle abscissa ordered set and the parallel vehicle abscissa ordered set in the three-dimensional contour data; or the vehicle is divided by comparing and analyzing the width change in the abscissa ordered set of the parallel operation and/or the height change in the vehicle height value set.

S406, acquiring transaction information of the antenna transaction module, matching the transaction information, the vehicle track information and the vehicle snapshot information, and uploading the matched information to a background server or a data center.

Specifically, the antenna transaction module is communicated with the vehicle-mounted OBU to acquire position information and transaction information of the vehicle-mounted OBU and transmit the position information and the transaction information of the OBU to the comprehensive control module, and the comprehensive control module acquires the position information, the transaction information, a vehicle track information set, vehicle detection information and vehicle snapshot information of the OBU, logically matches and processes the information to form vehicle information, and uploads the vehicle information to a background server or a data center.

Further, fig. 5 shows another process of 3D lidar-based multi-lane free-flow vehicle positioning matching according to a preferred embodiment of the present invention, and as shown in fig. 5, the process includes:

s502, the 3D laser radar vehicle detection module detects vehicle track information in real time, and obtains a vehicle track information set and vehicle detection information of a vehicle passing through a detection area.

Specifically, the 3D laser radar vehicle detection module detects vehicle running track information in real time, and the position information of the vehicle at the time t is (N, t, L)_t，XL_t，XR_t，Y_t) Where N represents the vehicle number detected by the 3D lidar vehicle detection module, L_tIndicating the lane in which the vehicle is located at time t,XL_tlateral left-side boundary coordinates, XR, representing the vehicle position at or at time t_tRight boundary lateral coordinate, Y, representing the vehicle position at time t_tA longitudinal coordinate representing the position of the vehicle at the time T, and a vehicle track information set (N, T, L) of the vehicle passing through the detection area of the 3D laser radar vehicle detection module_T，XL_T，XR_T，Y_T) Wherein the time set T represents the ordered time T set for detecting the vehicle position in the time when the vehicle passes through the detection area, and the lane set L_TLeft boundary lateral coordinate set XL_TRight border lateral coordinate set XR_TSet of longitudinal coordinates Y_TRespectively represent L_t、XL_t、XR_t、Y_tAn ordered set of (a).

Understandably, the position information of the vehicle at the time t is track information that the vehicle passes by when the vehicle enters the 3D laser radar detection area at the time, the transverse coordinates of the left and right boundaries are obtained from the transverse coordinates in the three-dimensional profile data of the vehicle, and the longitudinal coordinates are obtained from the longitudinal coordinates in the three-dimensional profile data of the vehicle;

it should be noted that the position information (N, t, L) of the vehicle_t，XL_t，XR_t，Y_t) Is the vehicle position information scanned by one or more or all beams of the 3D lidar.

Preferably, the vehicle position information longitudinal coordinate selects a center position coordinate value of the longitudinal coordinate in the three-dimensional profile data of the vehicle.

Set of vehicle trajectory information (N, T, L)_T，XL_T，XR_T，Y_T) Then the vehicle is from entering the 3D lidar detection area to leaving the 3D lidar detection area, in which the 3D lidar scans an ordered set of positional information of the vehicle over time.

The 3D laser radar vehicle detection module identifies vehicle types according to the three-dimensional contour information, the length, the width, the height and other characteristic information of the vehicle to form vehicle detection information, and transmits the vehicle track information set and the vehicle detection information to the comprehensive control module.

S504, the 3D laser radar vehicle detection module triggers the snapshot module to snapshot the vehicle to form a vehicle snapshot information set, and the vehicle snapshot information set is matched with the vehicle detection information.

Specifically, the 3D laser radar vehicle detection module triggers the snapshot module to snapshot the vehicle to obtain a vehicle picture and a license plate, and a vehicle snapshot information set (N, L)_t′PT, P), where N represents a vehicle number, provided by a 3D lidar vehicle detection module, L_t′Indicating a vehicle lane is L_TThe PT represents a license plate set of the vehicle, the P represents a vehicle picture set, and vehicle information acquired by the snapshot module is accurately matched with vehicle information detected by the 3D laser radar vehicle detection module through the vehicle number N.

It can be understood that the vehicle snapshot information set represents an information set for multiple snapshot recognition of the same vehicle, and the multiple snapshots are one snapshot camera for multiple vehicle snapshots or one or multiple snapshots of different snapshot cameras;

further, license plate numbers in the license plate set which are captured and identified for multiple times may be the same or different, and license plates may not be identified;

further, when the snapshot module comprises a plurality of cameras, the 3D laser radar vehicle detection module triggers the snapshot cameras of the lanes where the vehicles are located to snapshot according to the positions of the vehicles, and vehicle snapshot information that is snapshot by different cameras is merged into a vehicle snapshot information set through vehicle numbers;

when 3D laser radar vehicle detection module includes a plurality of 3D laser radar units, when the vehicle passes through the detection area of different 3D laser radar units, triggers respectively the snapshot module takes a candid photograph, and the vehicle picture information of taking a candid photograph passes through time position or vehicle serial number matching, merges into a vehicle snapshot information set.

Further, when the vehicle changes lanes and the snapshot module has a plurality of snapshot cameras, the 3D laser radar vehicle detection module triggers the snapshot cameras of the lanes where the vehicle is located to snapshot, and vehicle snapshot information snapshot by different cameras is combined into a vehicle snapshot information set through the vehicle serial number.

The snapshot module transmits the snapshot vehicle information set to the comprehensive processing module, or the snapshot module takes place a snapshot, just transmits the snapshot vehicle information to the comprehensive processing module, constitutes the vehicle snapshot information set by the comprehensive processing module with all the snapshot information of this vehicle, and the comprehensive control module matches vehicle detection information and vehicle snapshot information set according to the vehicle number.

S506, the antenna transaction module acquires the position information set and the transaction information of the vehicle-mounted OBU and matches the position information set and the transaction information with vehicle track information or snapshot information.

Specifically, the antenna transaction module acquires a position information set and transaction information of a vehicle-mounted OBU passing through a transaction area, the position information set of the OBU is matched with the vehicle track information set in a calculating mode, and/or license plate information in the transaction information is matched with license plate information in vehicle snapshot information, and it is judged that a vehicle transacted by the antenna transaction module and a vehicle detected by the 3D laser radar vehicle detection module are the same vehicle.

Further, the antenna transaction module interacts with the vehicle-mounted OBU, and each frame of interaction has one OBU position information (t', OBUID, X)_t′，Y_t′) OBUID represents on-board OBUID, X_t′Representing the lateral position coordinate, Y, of the OBU at time t' or instant_t′Representing the longitudinal position coordinates of the OBU at time T 'or instant, forming a set of OBU position information (T', OBUID, X) throughout the transaction_T′，Y_T′)，T′、X_T′、Y_T′Respectively represent t' and X_t′、Y_t′The antenna transaction module sends the position information set and the transaction information of the vehicle-mounted OBU to the comprehensive control module,

integrated control module said OBU location information set (T', OBUID, X)_T′，Y_T′) And the set of vehicle trajectory information (N, T, L)_T，XL_T，XR_T，Y_T) Calculating and matching, and judging that the vehicle transacted by the antenna transaction module and the vehicle detected by the 3D laser radar vehicle detection module are the same vehicle; or the comprehensive control module judges that the vehicle in the current transaction is the same as the vehicle captured by the capturing module according to the matching of the license plate information in the transaction information and the license plate set in the vehicle capturing information set.

Further, a vehicle track information set acquired by the 3D laser radar vehicle detection module, a vehicle snapshot information set snapshot by the snapshot module, and a vehicle-mounted OBU position information set and transaction information acquired by the antenna transaction module are all transmitted to the comprehensive control module, and the comprehensive control module performs matching combination on the information.

Further, the OBU location information set (T', OBUID, X)_T′，Y_T′) And a vehicle track information set (N, T, L)_T，XL_T，XR_T，Y_T) Calculating matching:

the time set T' in the OBU position information set and the time set T in the vehicle track information set have intersection of time or time periods, and in the same time or time period, the longitudinal position coordinate set Y in the OBU position information set_T′And a vehicle track information longitudinal coordinate set Y_TThe transverse position coordinate set X in the OBU position information set has intersection or the longitudinal coordinate section has intersection_T′The coordinate point in (1) is a transverse coordinate set XL positioned at the left side boundary of the vehicle track information_TAnd right side boundary lateral coordinate set XR_TComposed set of sequences of transverse coordinate segments (XL)_T，XR_T) If so, judging that the vehicle transacted by the antenna transaction module and the vehicle detected by the 3D laser radar vehicle detection module are the same vehicle, and judging that the vehicle transacted by the antenna transaction module and the vehicle snapshot by the snapshot module are the same vehicle;

the license plate number that antenna transaction module obtained from on-vehicle OBU is the same with the license plate number of snapshot module discernment, judges that the vehicle of antenna transaction module transaction and the vehicle of snapshot module snapshot are the same vehicle to it is the same vehicle to judge the vehicle of antenna transaction module transaction and the vehicle that 3D laser radar vehicle detection module detected.

Further, the 3D laser radar vehicle detection module collects point cloud data of vehicles passing through the detection area, calculates vehicle characteristic information at least comprising length, width, height and speed, identifies vehicle types, forms vehicle detection information and at least comprises (vehicle number, identified vehicle type, lane, speed, length, width, height, vehicle head entering time and vehicle head leaving time) information;

the snapshot module takes a snapshot of a vehicle picture and identifies a license plate to form a vehicle snapshot information set, and the vehicle snapshot information set at least comprises (a vehicle number, a license plate set, a picture set and a snapshot time set) information;

the antenna module and the vehicle-mounted OBU trade meet the international trade protocol and at least comprise OBU trade information consisting of (OBUID, trade vehicle type, trade time, trade license plate and trade success identification);

the comprehensive control module carries out logic processing on the received vehicle detection information, the vehicle snapshot information and the OBU transaction information to form vehicle information after matching, at least contains (vehicle number, OBUID, transaction vehicle type, transaction time, transaction license plate, successful transaction identification, vehicle type identification, lane, speed, length, width, height, vehicle head entering time, vehicle head leaving time, snapshot license plate, picture set and snapshot time set) information, and uploads the vehicle information to a background or a service center.

It can be understood that vehicle detection information, and/or vehicle snapshot information, and/or OBU transaction information is abnormal and/or inconsistent, and/or has no partial information, and the vehicle information formed is also uploaded to a background or a service center; the uploaded vehicle information protocol meets the requirements of the relevant national standard protocol.

Fig. 8 is a schematic structural diagram of a 3D lidar-based multi-lane free-flow vehicle matching system according to a preferred embodiment of the invention.

Fig. 9 is another schematic structural diagram of a 3D lidar based multi-lane free-flow vehicle matching system according to a preferred embodiment of the present invention, the 3D lidar vehicle detection module of which includes 2 3D lidar units.

Fig. 10 is a schematic diagram of another structure of a 3D lidar based multi-lane free-flow vehicle matching system according to a preferred embodiment of the invention, wherein a 3D lidar vehicle detection module of the system comprises a 3D lidar unit and a snapshot module comprises a snapshot camera.

Fig. 11 is a schematic structural view of a further structure of the 3D lidar-based multilane free flow vehicle matching system according to the preferred embodiment of the present invention, in which a 3D lidar vehicle detection module and an antenna transaction module of the system are on the same portal, and a snapshot module is on another portal, and along the vehicle traveling direction, passes through the 3D lidar vehicle detection module first and then passes through the snapshot module.

Taking the contents shown in fig. 8 and 9 as an example, the similar technical solutions in fig. 10 and 11 are similar to those in fig. 8 and 9, and as shown in fig. 8, the 3D lidar-based multilane free flow vehicle matching system is composed of a 3D lidar vehicle detection module, an antenna transaction module, a snapshot module, and a comprehensive control module; the 3D laser radar vehicle detection module is arranged on a portal or a vertical rod above a road and connected with the snapshot module to trigger the snapshot module to snapshot vehicle information, the snapshot module is arranged on the portal or the vertical rod, a snapshot camera unit is arranged above each lane, and each snapshot camera unit is connected with the comprehensive control module; the antenna transaction module is arranged on a portal frame or a vertical rod and consists of one or more RSU units, one RSU unit is arranged above each vehicle, and each RSU unit is connected with the comprehensive control module; the comprehensive control module is connected with the 3D laser radar vehicle detection module, acquires a vehicle track information set and vehicle detection information, is connected with the snapshot module, acquires vehicle snapshot information, is connected with the antenna module, acquires vehicle-mounted OBU track information and transaction information, logically matches and combines the information to form vehicle information meeting national standards, and uploads the vehicle information to a background or a service center.

Specifically, the 3D laser radar vehicle detection module is installed on the upper surface of the portal frame or the upright rod, the 3D laser radar vehicle detection module can be selectively installed on the center of the road or on the side of the road, and preferably, the 3D laser radar vehicle detection module is located above the center of the road; the 3D laser radar vehicle detection module at least comprises a 3D laser radar unit and a data processing unit, wherein the 3D laser radar unit is used for collecting point cloud data on a road in real time and sending the data to the data processing unit for processing.

The detection area of the 3D laser radar vehicle detection module covers all lanes on a road, the detection area is projected onto the road in the driving direction of the vehicle, the minimum distance range of a scanning surface formed by a scanning beam of the 3D laser radar unit from the position right below the 3D laser radar vehicle detection module is 0-6 meters, and the maximum distance range is 20-30 meters.

As shown in fig. 9, when the field angle of the 3D lidar unit cannot cover the detection area, a plurality of 3D lidar units need to be used, and the 3D lidar units have a certain preset angle with the vehicle traveling direction; specifically, when the field angle of the 3D lidar units in the vertical direction cannot cover the vehicle detection range, at least two 3D lidar units need to be installed, a scanning surface formed by scanning a light beam of one 3D lidar unit has a preset angle with the driving direction, so that the 3D lidar unit can cover the farthest range of the detection area, and a scanning surface formed by scanning a light beam of the other 3D lidar unit has another preset angle with the driving direction, so that the 3D lidar unit can cover the nearest range of the detection area; when a 3D laser radar unit can satisfy detection area scope requirement, then according to actual detection range, the angle of the scanning face of good 3D laser radar unit and driving direction can be adjusted.

The 3D laser radar vehicle detection module is connected with the snapshot module, and is substantially characterized in that the 3D laser radar unit is connected with a snapshot camera in the snapshot module, or the data processing unit is connected with the snapshot camera in the snapshot module and is used for triggering the snapshot module to snapshot vehicle information; the 3D laser radar vehicle detection module is connected with the comprehensive control module and used for transmitting the vehicle track information set and the vehicle detection information to the comprehensive control module.

Furthermore, the snapshot module at least comprises a snapshot camera unit and a light supplement lamp, the snapshot area of the snapshot module is not smaller than the detection area of the 3D laser radar vehicle detection module, the snapshot area covers the detection area of the vehicle, and the snapshot module is used for snapshot the vehicle and transmitting the vehicle picture and the license plate information to the comprehensive control module;

it should be understood that the snapshot module either waits for all the image information of the detected vehicle to be snapshot and then transmits the snapshot information to the integrated control module, or transmits the snapshot data to the integrated control module once.

When will take a candid photograph locomotive and rear of a vehicle, then take a candid photograph the module and contain two sets of candid photograph cameras and light filling lamp at least, be used for taking a candid photograph locomotive and rear of a vehicle respectively, the rear of a vehicle is taken a candid photograph or is adopted the trigger mode, perhaps adopts the time delay and takes a candid photograph the mode.

The antenna transaction module is used for performing transaction with the vehicle-mounted OBU to acquire position information and transaction information of the vehicle-mounted OBU, the transaction area of the antenna transaction module is not smaller than the snapshot area, and the transaction area covers the snapshot area.

The comprehensive control module is connected with the 3D laser radar vehicle detection module, acquires a vehicle track information set and vehicle detection information, is connected with the snapshot module, acquires vehicle snapshot information, is connected with the antenna module, acquires vehicle-mounted OBU track information and transaction information, logically matches and combines the information to form vehicle information meeting national standards, and uploads the vehicle information to a background or a service center.

Embodiments of the present invention also provide a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.

Alternatively, in the present embodiment, the storage medium may be configured to store a computer program for executing the steps of:

s1, collecting point cloud data of a vehicle to be matched in a detection area of the multi-lane free flow system through a 3D laser radar vehicle detection module arranged in the multi-lane free flow system;

s2, determining track information of the vehicle to be matched according to the point cloud data, and uploading the track information to a comprehensive control module in the multi-lane free flow system, wherein the comprehensive control module determines whether the vehicle corresponding to the track information, the vehicle corresponding to the antenna transaction information, and the vehicle corresponding to the snapshot vehicle information obtained by snapshotting the vehicle to be matched are the same vehicle according to at least one of the following: the track information, the antenna transaction information of the vehicle to be matched and the snapshot vehicle information are obtained.

Optionally, the storage medium is further arranged to store a computer program for performing the steps of:

s3, extracting the three-dimensional contour information of the vehicle to be matched from the collected point cloud data through the 3D laser radar vehicle detection module to form a transverse coordinate set, a longitudinal coordinate set and a height value set of the vehicle;

and S4, after the vehicle to be matched passes through the detection area, acquiring a point cloud data set of the vehicle to be matched according to the transverse coordinate set, the longitudinal coordinate set and the height value set, wherein the point cloud data set is used for determining the track information.

Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.

Embodiments of the present invention also provide an electronic device, which includes a memory (which may be the same as or different from the memory 104 in fig. 1) and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to perform the steps in any of the above method embodiments.

Optionally, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.

Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:

s5, collecting point cloud data of a vehicle to be matched in a detection area of the multi-lane free flow system through a 3D laser radar vehicle detection module arranged in the multi-lane free flow system;

s6, determining track information of the vehicle to be matched according to the point cloud data, and uploading the track information to a comprehensive control module in the multi-lane free flow system, wherein the comprehensive control module determines whether the vehicle corresponding to the track information, the vehicle corresponding to the antenna transaction information, and the vehicle corresponding to the snapshot vehicle information obtained by snapshotting the vehicle to be matched are the same vehicle according to at least one of the following: the track information, the antenna transaction information of the vehicle to be matched and the snapshot vehicle information are obtained.

s1, acquiring track information of the vehicle to be matched, which is determined by a point cloud data set of the vehicle to be matched, wherein the point cloud data set is acquired by a 3D laser radar vehicle detection module arranged in a multi-lane free flow system in a detection area of the multi-lane free flow system;

s2, acquiring antenna transaction information of the vehicle to be matched and snapshot vehicle information obtained by snapshot of the vehicle to be matched by a snapshot module of the multi-lane free flow system;

s3, determining whether the vehicle corresponding to the track information, the vehicle corresponding to the antenna transaction information and the vehicle corresponding to the snapshot vehicle information are the same vehicle or not by comparing the track information, the antenna transaction information and the snapshot vehicle information.

s4, acquiring track information of the vehicle to be matched, which is determined by a point cloud data set of the vehicle to be matched, wherein the point cloud data set is acquired by a 3D laser radar vehicle detection module arranged in a multi-lane free flow system in a detection area of the multi-lane free flow system;

s5, acquiring antenna transaction information of the vehicle to be matched and snapshot vehicle information obtained by snapshot of the vehicle to be matched by a snapshot module of the multi-lane free flow system;

s6, determining whether the vehicle corresponding to the track information, the vehicle corresponding to the antenna transaction information and the vehicle corresponding to the snapshot vehicle information are the same vehicle or not by comparing the track information, the antenna transaction information and the snapshot vehicle information.

Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation manners, and this embodiment is not described herein again.

Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing program codes, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.

It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A3D laser radar-based multilane free flow vehicle matching method is characterized by comprising the following steps:

collecting point cloud data of a vehicle to be matched in a detection area of a multi-lane free flow system through a 3D laser radar vehicle detection module arranged in the multi-lane free flow system;

determining track information of the vehicle to be matched according to the point cloud data, and uploading the track information to a comprehensive control module in the multi-lane free flow system, wherein the comprehensive control module determines the vehicle corresponding to the track information, the vehicle corresponding to the antenna transaction information and whether the vehicle corresponding to the snapshot vehicle information obtained by snapshot of the vehicle to be matched is the same vehicle according to at least one of the following: the track information, the antenna transaction information of the vehicle to be matched and the snapshot vehicle information are obtained.

2. The method according to claim 1, wherein after collecting point cloud data of a vehicle to be matched within a detection area of a multilane free flow system by a 3D lidar vehicle detection module disposed in the multilane free flow system, the method comprises:

extracting the three-dimensional contour information of the vehicle to be matched from the collected point cloud data through the 3D laser radar vehicle detection module to form a transverse coordinate set, a longitudinal coordinate set and a height value set of the vehicle;

after the vehicle to be matched passes through the detection area, acquiring a point cloud data set of the vehicle to be matched according to the transverse coordinate set, the longitudinal coordinate set and the height value set, wherein the point cloud data set is used for determining the track information.

3. The method according to claim 2, wherein the extracting three-dimensional contour information of the vehicle to be matched from the collected point cloud data by the 3D lidar vehicle detection module forms a lateral coordinate set, a longitudinal coordinate set and a height value set of the vehicle, and comprises:

the 3D laser radar vehicle detection module collects point cloud data of the vehicle to be matched in a scanning period, and after coordinate conversion, the point cloud data are converted into three-dimensional contour data of the vehicle to be matchedWhereinRepresenting that the 3D laser radar vehicle detection module scans the transverse coordinate ordered set of the vehicle to be matched by the ith laser line at the time t in the scanning period,representing that the 3D laser radar vehicle detection module scans the longitudinal coordinate ordered set of the vehicle to be matched by the ith laser ray at the time t in the scanning period,indicating that the 3D laser radar vehicle detection module scans the vehicle to be matched with the ith laser ray at the t moment in the scanning periodA corresponding set of height values;

the method further comprises the following steps:

the ordered set of lateral coordinates at a current scan cycle of the 3D lidar vehicle detection moduleLeft and right boundary coordinates ofOrdered set of range and the lateral coordinates of a previous scan cycleLeft and right boundary coordinates ofIf the ranges are overlapped, judging that the data of the current scanning period and the data of the previous scanning period are the same vehicle data;

the ordered set of lateral coordinates at a current scan cycle of the 3D lidar vehicle detection moduleLeft and right boundary coordinates ofOrdered set of range and the lateral coordinates of a previous scan cycleLeft and right boundary coordinates ofAnd if the ranges do not overlap, determining that the data of the current scanning period and the data of the previous scanning period are vehicle data of different vehicles.

4. The method according to claim 1, wherein the determined trajectory information of the vehicle to be matched comprises:

a set of vehicle trajectory information (N, T, L) of the vehicle to be matched detected in the detection area by the 3D lidar vehicle detection module_T，XL_T，XR_T，Y_T) N represents a vehicle number detected by a 3D laser radar vehicle detection module, T represents an ordered time T set and a lane set L for detecting the position information of the vehicle to be matched in the time when the vehicle to be matched passes through the detection area_TLeft boundary lateral coordinate set XL_TRight border lateral coordinate set XR_TSet of longitudinal coordinates Y_TRespectively represent L_t、XL_t、XR_t、Y_tWherein the position information of the matching vehicle at time t is (N, t, L)_t，XL_t，XR_t，Y_t) Wherein N represents a 3D lidar vehicleVehicle number, L, detected by the detection module_tRepresents the lane, XL, in which the vehicle to be matched is located at time t_tLeft-side boundary lateral coordinates, XR, representing the position information of the vehicle to be matched at time t_tRight boundary lateral coordinate, Y, representing position information of vehicle to be matched at time t_tThe method comprises the following steps that longitudinal coordinates of position information of a vehicle to be matched at the moment t are represented, and after point cloud data of the vehicle to be matched in a detection area of the multi-lane free flow system are collected through a 3D laser radar vehicle detection module arranged in the multi-lane free flow system, the method further comprises the following steps:

the 3D laser radar vehicle detection module triggers the snapshot module to snapshot the vehicle to be matched to obtain a set (N, L) of snapshot vehicle information_tP), wherein L is_t′Is L_TPT represents the license plate set of the vehicle to be matched, and P represents the picture set of the vehicle to be matched.

5. The method of claim 4, wherein triggering the snapshot module to snapshot the vehicle to be matched by the 3D lidar vehicle detection module comprises at least one of:

triggering when the 3D laser radar vehicle detection module detects that the vehicle to be matched starts to pass through a detection area;

triggering when the 3D laser radar vehicle detection module detects that the vehicle to be matched is in the middle position of a detection area;

triggering when the 3D laser radar vehicle detection module detects that the vehicle to be matched changes lanes;

triggering when the 3D laser radar vehicle detection module detects that the head of the vehicle to be matched is about to leave a detection area; wherein,

when the snapshot module comprises a plurality of cameras, triggering the snapshot camera of the lane where the vehicle is located to snapshot through the 3D laser radar vehicle detection module according to the vehicle position of the vehicle to be matched, and merging vehicle snapshot information snapshot by different cameras into a vehicle snapshot information set through vehicle numbers;

when the 3D laser radar vehicle detection module comprises a plurality of 3D laser radar units, when vehicles to be matched pass through detection areas of different 3D laser radar units, the capturing modules are triggered to capture images respectively, and captured vehicle image information is combined into a vehicle capturing information set through time position or vehicle number matching.

6. The method according to claim 1, wherein after collecting point cloud data of a vehicle to be matched within a detection area of a multilane free flow system by a 3D lidar vehicle detection module disposed in the multilane free flow system, the method comprises:

determining the characteristic information and the vehicle detection information of the vehicle to be matched according to the point cloud data, wherein the characteristic information at least comprises one of the following information: the length of the vehicle to be matched, the width of the vehicle to be matched, the height of the vehicle to be matched, the speed of the vehicle to be matched, the type of the vehicle to be matched, and the vehicle detection information include: the vehicle number of the vehicle to be matched, the vehicle type of the vehicle to be matched, the lane, the speed of the vehicle to be matched, the length of the vehicle to be matched, the width of the vehicle to be matched, the height of the vehicle to be matched, the time for the head of the vehicle to be matched to enter a detection area, and the time for the parking space of the vehicle to be matched to leave the detection area;

and uploading the characteristic information and the vehicle detection information to a background or a data center.

7. A3D laser radar-based multilane free flow vehicle matching method is characterized by comprising the following steps:

acquiring track information of a vehicle to be matched, which is determined by a point cloud data set of the vehicle to be matched, wherein the point cloud data set is acquired in a detection area of a multilane free flow system by a 3D laser radar vehicle detection module arranged in the multilane free flow system;

acquiring antenna transaction information of the vehicle to be matched and snapshot vehicle information obtained by snapshot of the vehicle to be matched by a snapshot module of the multi-lane free flow system;

and comparing the track information, the antenna transaction information and the snapshot vehicle information to determine whether the vehicle corresponding to the track information, the vehicle corresponding to the antenna transaction information and the vehicle corresponding to the snapshot vehicle information are the same vehicle.

8. A3D lidar based multilane free flow vehicle matching system, comprising:

the system comprises a 3D laser radar vehicle detection module, a comprehensive control module and a data processing module, wherein the 3D laser radar vehicle detection module is arranged in a multi-lane free flow system and is used for collecting point cloud data of a vehicle to be matched in a detection area of the multi-lane free flow system, determining track information of the vehicle to be matched according to the point cloud data and uploading the track information to the comprehensive control module in the multi-lane free flow system;

the comprehensive control module is used for determining the vehicle corresponding to the track information, the vehicle corresponding to the antenna transaction information and whether the vehicle corresponding to the snapshot vehicle information obtained by snapshot of the vehicle to be matched is the same vehicle according to at least one of the following conditions: the track information, the antenna transaction information of the vehicle to be matched and the snapshot vehicle information are obtained.

9. The system of claim 8, wherein the 3D lidar vehicle detection module comprises at least one 3D lidar unit, the 3D lidar unit is configured to form at least two scanning planes with included angles, wherein, when the 3D lidar vehicle detection module comprises at least two 3D lidar units, a first preset angle exists between a scanning plane formed by a laser line emitted by one 3D lidar unit of the two 3D lidar units and a driving direction so that the one 3D lidar unit can cover a farthest range of the detection area, and a second preset angle exists between a scanning plane formed by a laser line emitted by the other 3D lidar unit of the two 3D lidar units and the vehicle to be matched so that the one 3D lidar unit can cover a nearest range of the detection area, the 3D lidar unit includes: the multi-line laser radar based on mechanical scanning is a quasi-solid laser radar based on a Micro Electro Mechanical System (MEMS) and a solid laser radar based on FLASH.

10. The system of claim 8, further comprising: the snapshot module is connected with the 3D laser radar vehicle detection module, and the snapshot module at least comprises one of the following components: the automobile tail capturing device comprises a head capturing camera and a tail capturing camera, wherein the head capturing camera comprises one or more capturing cameras, and the tail capturing camera comprises one or more capturing cameras.