CN112099040A - Whole-course continuous track vehicle tracking system and method based on laser radar network - Google Patents
Whole-course continuous track vehicle tracking system and method based on laser radar network Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
- G01S17/50—Systems of measurement based on relative movement of target
- G01S17/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
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- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
- G08G1/054—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed photographing overspeeding vehicles
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Abstract
The invention belongs to the technical field of traffic image processing and traffic information, and discloses a system and a method for tracking vehicles with a whole continuous track based on a laser radar network, wherein the system for tracking vehicles with a whole continuous track based on the laser radar network comprises the following components: the system comprises a laser radar network, other roadside sensors, a roadside processor, a fusion server and network communication equipment; the method for tracking the vehicle with the whole continuous track based on the laser radar network comprises the following steps: synchronizing time; scanning a monitoring range; detecting multiple targets of the vehicle; tracking a track; carrying out snapshot and license plate recognition; analyzing an event; converting coordinates; information coding; reporting detection point information; information fusion in the whole process; and displaying the whole-course track of the vehicle. The invention can track the vehicles on the highway in real time and in the whole course, accurately capture the video images of the event occurrence time period by combining the camera, realize the timely processing of the vehicles causing the event, and realize small time delay, low calculation load and small network transmission load.
Description
Technical Field
The invention belongs to the technical field of traffic image processing and traffic information, and particularly relates to a system and a method for tracking vehicles in a whole-course continuous track based on a laser radar network.
Background
Currently, monitoring and management of passing vehicles is an important work content for highway management. In order to guarantee road safety and effectively reduce the probability of traffic accidents, the method has important value on real-time whole-course continuous trajectory tracking of illegal vehicles. The existing track tracking technology is mainly realized by adopting video equipment, points are distributed and monitored along a highway, and illegal vehicles are tracked in a manual inspection or image recognition mode. Although the application of the deep learning technology greatly improves the recognition rate, the method can still be seriously influenced in a scene with poor ambient illumination, and a great obstacle is caused to the whole-course continuous track tracking.
The application of radar technology to the expressway provides a solution for solving the problems. Especially, the application of the high-resolution laser radar on the road side provides a more ideal technology for tracking multiple lanes and multiple vehicle targets. In the existing literature, there are some vehicle tracking methods based on vehicle-mounted lidar. The Chinese patent application with the application number of 201910948851.1 provides a three-dimensional multi-target tracking method fusing images and laser point clouds; a method for detecting and tracking a vehicle based on a 3D laser radar is proposed in chinese patent application No. 202010029355.9. There are also a few methods of vehicle tracking based on roadside lidar. A method for arranging multiple radars at the road side to improve the accuracy is provided in Chinese patent application with the application number of 201911020615. X. The existing literature is mainly a vehicle tracking method for a roadside radar at a single detection point. The vehicle tracking method of the whole-course continuous track of the expressway is not reported.
Therefore, it is necessary to provide a method for tracking vehicles on a highway along a full-range continuous track based on a laser radar network to implement a high-accuracy and high-real-time vehicle tracking technology along a full-range continuous track.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) the existing track tracking technology is mainly realized by adopting video equipment, points are distributed and monitored along a highway, illegal vehicles are tracked in a manual inspection or image recognition mode, however, in a scene with poor environmental illumination, the illegal vehicles are still seriously influenced, and a great obstacle is caused to the whole-course continuous track tracking.
(2) The existing literature is mainly a vehicle tracking method for a roadside radar at a single detection point. The vehicle tracking method of the whole-course continuous track of the expressway is not reported.
The difficulty in solving the above problems and defects is: the whole-course continuous track tracking needs to track the vehicle in real time in all weather and in the whole course without dead angles, and the video technology is limited by the environmental illumination condition and cannot be realized; the roadside radar with a single monitoring point has limited coverage, and the requirement of full-process continuous track tracking is difficult to meet the real-time connection of multiple radar boundaries.
The significance of solving the problems and the defects is as follows: the realization of the whole-course continuous track tracking has the following significance: on one hand, real-time road condition information can be accurately provided for the vehicle, road condition progress can be more accurately predicted, and high-quality navigation service is provided; on the other hand, a real-time and accurate road information platform is provided for road operation service, and a foundation is provided for improvement of services such as road toll, road rescue, emergency management and the like.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a full-process continuous track vehicle tracking system and method based on a laser radar network.
The invention is realized in this way, a laser radar network-based whole continuous track vehicle tracking system, which adopts a multilayer hierarchical architecture and comprises: the system comprises a laser radar network, other roadside sensors, a roadside processor, a fusion server and network communication equipment.
The laser radar network comprises laser radars of all detection points in the whole process of the highway and is used for collecting three-dimensional space information of road driving;
the other roadside sensors comprise cameras for acquiring video images;
the roadside processor is used for analyzing point cloud information transmitted by the laser radar at the detection point, carrying out vehicle multi-target detection and tracking in the monitoring range of the detection point, triggering camera snapshot and license plate recognition, and reporting vehicle detection information to the fusion server;
the fusion server comprises a calculation processor, a data memory, a display terminal and the like and is used for carrying out fusion processing on the vehicle tracking track reported by each detection point in the whole course of high speed to obtain the vehicle tracking track information of the whole course of the highway and displaying the vehicle tracking track information;
the network communication equipment comprises a router, a switch and the like, and is used for connecting the equipment to realize information interaction among the equipment.
Furthermore, the laser radar monitoring range of the laser radar network covers the whole course of the expressway, no dead angle exists, and the monitoring ranges of the radars at adjacent detection points are slightly overlapped.
Another object of the present invention is to provide a method for tracking a vehicle with a laser radar network-based vehicle with a full continuous track using the system for tracking a vehicle with a full continuous track based on a laser radar network, which includes the following steps:
synchronizing the time of a laser radar network, other roadside sensors and a roadside processor;
and time synchronization is carried out on all radars, other road side sensors and the road side processor in the laser radar network through a time synchronization (PTP) server.
Step two, scanning a monitoring range in real time by a detection point radar;
the laser radar scans the road of the monitored area in real time at a certain frequency to acquire three-dimensional point cloud data.
Step three, carrying out vehicle multi-target detection on the detection points in a monitoring range;
and analyzing the acquired three-dimensional point cloud data, removing the background, segmenting the point cloud in the road space, and identifying the vehicle.
Fourthly, tracking the vehicle track in the monitoring range by the detection point;
the vehicle track is tracked by analyzing the continuous multi-frame vehicle point clouds, predicting the positions of moving vehicles in a monitoring range and matching on a time sequence.
Fifthly, the camera at the detection point performs snapshot and license plate recognition;
once the radar detects that the vehicle enters the camera snapshot area, a signal is sent to trigger the camera to snapshot, license plate recognition is carried out, and the license plate is bound with the tracked vehicle.
Step six, detecting point event analysis;
by analyzing the radar tracks of the monitoring points and combining with the road traffic line, whether illegal lane changing, reverse driving, illegal parking and the like occur is judged.
Step seven, coordinate conversion is carried out on the detection points;
in order to unify the information to the world coordinate system, coordinate conversion is carried out before the report server, and the local coordinate information is converted into the information under the world coordinate system.
Step eight, information encoding is carried out on the detection points;
in order to facilitate the integration of the servers, the monitoring point uniformly encodes the information to be reported according to a certain rule, for example: and adding a monitoring point code into the vehicle number prefix.
Step nine, reporting detection point information;
and uploading the vehicle information in the detection range of the detection point to the fusion server.
Step ten, information fusion in the whole process;
and after receiving the vehicle dynamic information and the event information of each monitoring point, the fusion server splices the areas which are alternately covered by each radar to realize the tracking transition processing of the vehicles between the adjacent radars.
And step eleven, displaying the whole-course track of the vehicle.
After the information is fused, the vehicle can be continuously shown on the whole track, and the vehicle participating in the event can be shown
Further, in the first step, laser radars are distributed on a road end needing to be monitored according to a distance of 250 meters, a single radar scanning range is 270 meters, and radar scanning areas face the same direction. And a license plate recognition system is arranged at a first installation point where the vehicle enters a monitoring area and is used for matching license plates with radar data.
Further, in the third step, the method for detecting multiple targets of the vehicle by the detection point in the monitoring range comprises the following steps: through analysis and processing of the radar three-dimensional point cloud information, vehicles in a monitoring range are detected, and information of vehicle speed, vehicle position coordinates, vehicle size, vehicle postures and lanes where the vehicles are located is detected.
Further, in step three, the target detection analysis includes: three-dimensional point cloud background separation, point cloud segmentation and point cloud classification.
Further, in step four, the method for tracking the vehicle trajectory by the detection point in the monitoring range includes: and continuously positioning and tracking each vehicle in the monitoring range by analyzing the position information of each vehicle in the front frame and the rear frame of the radar point cloud and combining the motion trend prediction.
Further, in the fifth step, the camera is used for capturing and identifying the license plate according to specific requirements, for example, a traffic violation event occurs at a detection point or a vehicle just enters a highway.
Further, in step six, the analysis of the detection point event includes: congestion, emergency lane occupancy, illegal parking, retrograde motion, illegal lane change, pedestrians, and obstacles.
Further, in the seventh step, the coordinate conversion is that the detection point converts the detected vehicle information and the local coordinates in the event into a world coordinate system of the whole expressway.
Further, in the step eight, the information coding is that the detection points number the detected vehicles and is performed according to the uniform rule of the whole expressway.
Further, in the ninth step, the information reporting is that the detection point reports the vehicle detection information and the event to the fusion server in real time and is matched with a timestamp; and the appearance point cloud data of the vehicle is reported according to the requirement, and is not reported in real time, so that the network load is reduced.
Further, in the step ten, the whole-process information fusion is to splice the vehicle detection information reported by each detection point in real time according to the timestamp, and the key point is information integration of the connection part of the adjacent monitoring points; and indicating the detection points to report the outline point cloud information of the vehicle as required, and recording the real-time information into a database so as to generate vehicle track information.
Further, in the eleventh step, the displaying of the whole course track is to display the continuous track of the vehicle by combining the reported real-time vehicle information, road condition information, relevant event information, and stored historical data with the three-dimensional image information or model of the whole course highway.
Further, the method for tracking the vehicle on the whole continuous track based on the laser radar network further comprises the following steps:
firstly, selecting the installation positions and the fixed angles of a radar and a license plate recognition camera according to the scanning range and the light-emitting angle of the radar;
secondly, a time synchronization (PTP) server is deployed in the fusion server to finish time calibration of all equipment, so that time synchronization of the whole system is realized;
and thirdly, measuring the distance of the license plate recognition snapshot range, placing a calibration object at the optimal recognition position, and completing the position calibration of the license plate recognition equipment and the current radar scanning area. Calculating the synchronous interval range of the license plate identification data and the radar point cloud data according to the vehicle speed and time delay;
fourthly, acquiring specific longitude and latitude information of all radar installation positions through a GPS, acquiring 8-10 longitude and latitude information in a radar area by non-linear walking, and synchronizing the working areas and positions of all equipment to a geodetic coordinate system in a unified manner;
and fifthly, randomly selecting a plurality of test vehicles to enter the current area for measuring actual data, and adjusting the radar linkage time according to the information such as the morphological characteristics, the physical size and the like of the vehicles to realize the whole-process track tracking of the vehicles in the whole area range.
It is another object of the present invention to provide a computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface to implement the method for lidar network-based vehicle tracking of a globally continuous trajectory.
It is another object of the present invention to provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method for laser radar network-based vehicle tracking with a global continuous trajectory.
By combining all the technical schemes, the invention has the advantages and positive effects that: the system and the method for tracking vehicles with the whole-course continuous track based on the laser radar network can track the vehicles on the expressway in real time and in the whole-course continuous track, and can accurately capture video images of the time periods of occurrence of events by combining the camera, thereby realizing the timely treatment of vehicles causing the events. The invention optimizes the network transmission load and the calculation load, and can realize small time delay, low calculation load and small network transmission load.
Drawings
Fig. 1 is a schematic structural diagram of a full-range continuous-track vehicle tracking system based on a lidar network according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for tracking a vehicle along a continuous track in the whole course based on a lidar network according to an embodiment of the present invention.
Fig. 3 is a schematic layout diagram of a radar network according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problems in the prior art, the invention provides a full-range continuous track vehicle tracking system and a full-range continuous track vehicle tracking method based on a laser radar network, and the invention is described in detail below by combining the attached drawings.
As shown in fig. 1, a laser radar network-based full-range continuous track vehicle tracking system provided by an embodiment of the present invention adopts a multi-layer hierarchical architecture, including: the system comprises a laser radar network, other roadside sensors, a roadside processor, a fusion server and network communication equipment.
The laser radar network comprises laser radars of all detection points in the whole process of the highway and is used for collecting three-dimensional space information of road driving;
the other roadside sensors comprise cameras for acquiring video images;
the roadside processor is used for analyzing point cloud information transmitted by the laser radar at the detection point, carrying out vehicle multi-target detection and tracking in the monitoring range of the detection point, triggering camera snapshot and license plate recognition, and reporting vehicle detection information to the fusion server;
the fusion server comprises a calculation processor, a data memory, a display terminal and the like and is used for carrying out fusion processing on the vehicle tracking track reported by each detection point in the whole course of high speed to obtain the vehicle tracking track information of the whole course of the highway and displaying the vehicle tracking track information;
the network communication equipment comprises a router, a switch and the like, and is used for connecting the equipment to realize information interaction among the equipment.
Furthermore, the laser radar monitoring range of the laser radar network covers the whole course of the expressway, no dead angle exists, and the monitoring ranges of the radars at adjacent detection points are slightly overlapped.
As shown in fig. 2, the method for tracking a vehicle based on a laser radar network along a continuous track in a whole course according to an embodiment of the present invention includes the following steps:
s101, synchronizing the time of a laser radar network, other roadside sensors and a roadside processor;
s102, detecting point radar scans a monitoring range in real time;
s103, carrying out vehicle multi-target detection on the detection points in a monitoring range;
s104, tracking the vehicle track in the monitoring range by the detection point;
s105, the camera at the detection point performs snapshot and license plate recognition;
s106, detecting point event analysis;
s107, carrying out coordinate conversion on the detection points;
s108, information encoding is carried out on the detection points;
s109, reporting detection point information;
s110, information fusion in the whole process;
and S111, displaying the whole-course track of the vehicle.
In step S101 provided in the embodiment of the present invention, the laser radars are arranged at a road end to be monitored according to a distance of 250 meters, a single radar has a scanning range of 270 meters, and radar scanning areas face the same direction. And a license plate recognition system is arranged at a first installation point where the vehicle enters a monitoring area and is used for matching license plates with radar data.
In step S103 provided in the embodiment of the present invention, the method for detecting multiple targets of a vehicle at a detection point in a monitoring range includes: through analysis and processing of the radar three-dimensional point cloud information, vehicles in a monitoring range are detected, and information of vehicle speed, vehicle position coordinates, vehicle size, vehicle postures and lanes where the vehicles are located is detected.
In step S103 provided in the embodiment of the present invention, the target detection and analysis includes: three-dimensional point cloud background separation, point cloud segmentation and target classification.
In step S104 provided in the embodiment of the present invention, the method for tracking the vehicle trajectory by using the detection point in the monitoring range includes: and continuously positioning and tracking each vehicle in the monitoring range by analyzing the position information of each vehicle in the front frame and the rear frame of the radar point cloud and combining the motion trend prediction.
In step S105 provided in the embodiment of the present invention, the capturing and the license plate recognition of the camera are performed according to specific requirements, for example, when a traffic violation occurs at a detection point or a vehicle just enters a highway.
In step S106 provided in the embodiment of the present invention, the detecting point event analysis includes: congestion, emergency lane occupancy, illegal parking, retrograde motion, illegal lane change, pedestrians, and obstacles.
In step S107 provided in the embodiment of the present invention, the coordinate conversion is to convert the detected vehicle information and the local coordinates in the event into a world coordinate system of the whole expressway by the detection point.
In step S108 provided in the embodiment of the present invention, the information encoding is performed by numbering the detected vehicles according to the detection point and according to the uniform rule of the whole expressway.
In step S109 provided by the embodiment of the present invention, the information reporting is that the detection point reports the vehicle detection information and the event to the fusion server in real time, and a timestamp is assigned; and the appearance point cloud data of the vehicle is reported according to the requirement, and is not reported in real time, so that the network load is reduced.
In step S110 provided in the embodiment of the present invention, the whole process information fusion is to splice the vehicle detection information reported by each detection point in real time according to the timestamp, and the key point is information integration of the connection part of adjacent monitoring points; and indicating the detection points to report the outline point cloud information of the vehicle as required, and recording the real-time information into a database so as to generate vehicle track information.
In step S111 provided in the embodiment of the present invention, the whole course track display is to display a continuous track of the vehicle by using the reported real-time vehicle information, road condition information, related event information, and stored historical data, in combination with the three-dimensional image information or model of the whole course highway.
The technical solution of the present invention will be further described with reference to the following examples.
As shown in fig. 3, in the layout of the radar provided in the embodiment of the present invention, laser radars (single radar scanning range 270 meters) are arranged at a road end to be monitored at a distance of 250 meters, and radar scanning areas face the same direction. And a license plate recognition system is arranged at a first installation point where the vehicle enters a monitoring area and is used for matching license plates with radar data.
Firstly, selecting the installation positions and the fixed angles of a radar and a license plate recognition camera according to the scanning range and the light-emitting angle of the radar;
secondly, a time synchronization (PTP) server is deployed in the fusion server to finish time calibration of all equipment, so that time synchronization of the whole system is realized;
and thirdly, measuring the distance of the license plate recognition snapshot range, placing a calibration object at the optimal recognition position, and completing the position calibration of the license plate recognition equipment and the current radar scanning area. Calculating the synchronous interval range of the license plate identification data and the radar point cloud data according to the vehicle speed and time delay;
fourthly, acquiring specific longitude and latitude information of all radar installation positions through a GPS, acquiring 8-10 longitude and latitude information in a radar area by non-linear walking, and synchronizing the working areas and positions of all equipment to a geodetic coordinate system in a unified manner;
and fifthly, randomly selecting a plurality of test vehicles to enter the current area for measuring actual data, and adjusting the radar linkage time according to the information such as the morphological characteristics, the physical size and the like of the vehicles to realize the whole-process track tracking of the vehicles in the whole area range.
The method is implemented in 2 kilometers of the whole journey of the Hanshao expressway experimental section, can realize all-weather continuous track tracking of vehicles, is combined with camera shooting and shooting, and realizes snapshot of license plates of over-limit vehicles, and the accuracy rate reaches 99%.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A laser radar network-based whole continuous track vehicle tracking system is characterized in that the laser radar network-based whole continuous track vehicle tracking system adopts a multilayer hierarchical architecture and comprises: the system comprises a laser radar network, other roadside sensors, a roadside processor, a fusion server and network communication equipment;
the laser radar network comprises laser radars of all detection points in the whole process of the highway and is used for collecting three-dimensional space information of road driving;
the other roadside sensors comprise cameras for acquiring video images;
the roadside processor is used for analyzing point cloud information transmitted by the laser radar at the detection point, carrying out vehicle multi-target detection and tracking in the monitoring range of the detection point, triggering camera snapshot and license plate recognition, and reporting vehicle detection information to the fusion server;
the fusion server comprises a calculation processor, a data memory, a display terminal and the like and is used for carrying out fusion processing on the vehicle tracking track reported by each detection point in the whole course of high speed to obtain the vehicle tracking track information of the whole course of the highway and displaying the vehicle tracking track information;
the network communication equipment comprises a router, a switch and the like, and is used for connecting the equipment to realize information interaction among the equipment.
2. The lidar network based continuous track vehicle tracking system of claim 1, wherein the lidar network has a lidar monitoring range that covers the entire length of the highway, no dead space, and a small amount of overlap between adjacent checkpoint radar monitoring ranges.
3. The method for tracking the vehicle with the laser radar network based whole continuous track is characterized by comprising the following steps of:
synchronizing the time of a laser radar network, other roadside sensors and a roadside processor;
step two, scanning a monitoring range in real time by a detection point radar;
step three, carrying out vehicle multi-target detection on the detection points in a monitoring range;
fourthly, tracking the vehicle track in the monitoring range by the detection point;
fifthly, the camera at the detection point performs snapshot and license plate recognition;
step six, detecting point event analysis;
step seven, coordinate conversion is carried out on the detection points;
step eight, information encoding is carried out on the detection points;
step nine, reporting detection point information;
step ten, information fusion in the whole process;
and step eleven, displaying the whole-course track of the vehicle.
4. The method for tracking the vehicle with the whole continuous track based on the laser radar network as claimed in claim 3, wherein in the step one, the laser radars are distributed at the road end to be monitored according to the distance of 250 meters, the single radar scanning range is 270 meters, and the radar scanning areas face to the same direction; installing a license plate recognition system at a first installation point where a vehicle enters a monitoring area, wherein the license plate recognition system is used for matching license plates with radar data;
in the third step, the method for detecting multiple targets of the vehicle by the detection points in the monitoring range comprises the following steps: through analysis and processing of the radar three-dimensional point cloud information, vehicles in a monitoring range are detected, and information of vehicle speed, vehicle position coordinates, vehicle size, vehicle postures and lanes where the vehicles are located is detected.
5. The lidar network based vehicle tracking method for the whole continuous track of the vehicle as claimed in claim 3, wherein in step three, the target detection analysis comprises: three-dimensional point cloud background separation, point cloud segmentation and point cloud classification;
in the fourth step, the method for tracking the vehicle track by the detection point in the monitoring range includes: and continuously positioning and tracking each vehicle in the monitoring range by analyzing the position information of each vehicle in the front frame and the rear frame of the radar point cloud and combining the motion trend prediction.
6. The laser radar network-based whole-course continuous track vehicle tracking method according to claim 3, wherein in the fifth step, the camera snapshot and the license plate recognition are performed according to specific requirements, such as a traffic violation event occurring at a detection point or a vehicle just entering a highway;
in step six, the analysis of the detection point events comprises: congestion, emergency lane occupancy, illegal parking, retrograde motion, illegal lane change, pedestrians, and obstacles.
7. The lidar network based vehicle tracking method having a global continuous track of claim 3, wherein in the seventh step, the coordinate transformation is that the detection point transforms the local coordinates in the detected vehicle information and event into the world coordinate system of the global highway;
in the step eight, the information coding is that the detected vehicles are numbered by the detection point and are carried out according to the uniform rule of the whole highway;
in the step nine, the information reporting is that the detection point reports the vehicle detection information and the event to the fusion server in real time and is matched with a timestamp; reporting the appearance point cloud data of the vehicle in a non-real time manner according to the requirement;
step ten, splicing the vehicle detection information reported by each detection point in real time according to the timestamp in the whole process information fusion, wherein the key point is information integration of the connection part of the adjacent monitoring points; indicating a detection point to report the appearance point cloud information of the vehicle according to needs, and recording the real-time information into a database so as to generate vehicle track information;
in the eleventh step, the whole-course track display is to display the continuous track of the vehicle by combining the reported real-time vehicle information, road condition information, relevant event information and stored historical data with the three-dimensional image information or model of the whole-course expressway.
8. The lidar network based global continuous track vehicle tracking method of claim 3, further comprising:
firstly, selecting the installation positions and the fixed angles of a radar and a license plate recognition camera according to the scanning range and the light-emitting angle of the radar;
secondly, a time synchronization (PTP) server is deployed in the fusion server to finish time calibration of all equipment, so that time synchronization of the whole system is realized;
thirdly, measuring the distance of the license plate recognition snapshot range, placing a calibration object at the optimal recognition position, and completing the position calibration of the license plate recognition equipment and the current radar scanning area; calculating the synchronous interval range of the license plate identification data and the radar point cloud data according to the vehicle speed and time delay;
fourthly, acquiring specific longitude and latitude information of all radar installation positions through a GPS, acquiring 8-10 longitude and latitude information in a radar area by non-linear walking, and synchronizing the working areas and positions of all equipment to a geodetic coordinate system in a unified manner;
and fifthly, randomly selecting a plurality of test vehicles to enter the current area for measuring actual data, and adjusting the radar linkage time according to the information such as the morphological characteristics, the physical size and the like of the vehicles to realize the whole-process track tracking of the vehicles in the whole area range.
9. A computer program product stored on a computer readable medium, comprising a computer readable program that, when executed on an electronic device, provides a user input interface to implement the lidar network-based method for full range continuous trajectory vehicle tracking.
10. A computer readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method for laser radar network based vehicle tracking of globally continuous trajectories according to claim 3.
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| CN113129592A (en) * | 2021-04-16 | 2021-07-16 | 江西方兴科技有限公司 | Holographic sensing system and method for traffic state of highway tunnel |
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| CN113420805B (en) * | 2021-06-21 | 2022-11-29 | 车路通科技(成都)有限公司 | Dynamic track image fusion method, device, equipment and medium for video and radar |
| CN113420805A (en) * | 2021-06-21 | 2021-09-21 | 车路通科技(成都)有限公司 | Dynamic track image fusion method, device, equipment and medium for video and radar |
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| CN113936465A (en) * | 2021-10-26 | 2022-01-14 | 公安部道路交通安全研究中心 | Traffic incident detection method and device |
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| CN114910928A (en) * | 2022-05-20 | 2022-08-16 | 山东高速建设管理集团有限公司 | Method and device for positioning vehicles in tunnel and storage medium |
| CN115410370A (en) * | 2022-08-31 | 2022-11-29 | 南京慧尔视智能科技有限公司 | Abnormal parking detection method and device, electronic equipment and storage medium |
| CN115424442A (en) * | 2022-08-31 | 2022-12-02 | 南京慧尔视智能科技有限公司 | Radar map-based vehicle driving event detection method, device, equipment and medium |
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