CN111314857A - Vehicle real-time travel track acquisition method based on vehicle passing video data - Google Patents
Vehicle real-time travel track acquisition method based on vehicle passing video data Download PDFInfo
- Publication number
- CN111314857A CN111314857A CN202010091310.4A CN202010091310A CN111314857A CN 111314857 A CN111314857 A CN 111314857A CN 202010091310 A CN202010091310 A CN 202010091310A CN 111314857 A CN111314857 A CN 111314857A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- time
- information
- intersection
- real
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/029—Location-based management or tracking services
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
- H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Traffic Control Systems (AREA)
Abstract
The application relates to the technical field of intelligent traffic control, in particular to a vehicle real-time travel track acquisition method based on vehicle passing video data. The problem that due to the lack of GPS equipment, a vehicle cannot acquire a travel track, dynamic updating of a vehicle travel chain cannot be achieved, and system resources consumed by the travel chain are too large in calculation can be solved to a certain extent. The method comprises the following steps: constructing an optimal adjacency matrix of the road network according to the topological information of the road network of the city; analyzing vehicle passing video data from the detection point location to obtain vehicle running information, wherein the vehicle running information comprises a snapshot time, a snapshot point location and a license plate number; and comparing and judging the vehicle running information and the historical trip chain of the vehicle based on the optimal adjacency matrix, and constructing a real-time trip track of the vehicle.
Description
Technical Field
The application relates to the technical field of intelligent traffic control, in particular to a vehicle real-time travel track acquisition method based on vehicle passing video data.
Background
The real-time travel track of the vehicle is a big data product derived along with the popularization and application of electronic police and gate equipment in a traffic system. When the vehicle passes through the detection equipment arranged at the urban intersection, the vehicle passing data can be recorded. The vehicle passing data contains rich space-time information, the characteristics of a user trip mode and the research of microscopic traffic flow message service can be mined by analyzing the vehicle passing data, the urban traffic planning and management level can be improved, the congestion degree of urban roads is reduced, and the operation efficiency of a traffic system is improved.
In some implementations of obtaining the vehicle travel track, the vehicle travel track is collected through a GPS system configured for the vehicle, and then a spectral clustering method is adopted to realize automatic splitting of a vehicle travel chain from the perspective of the time-space characteristics of bayonet data, so that a starting point, a passing point position and a terminal point of the single travel track of the vehicle are identified. Firstly, reading vehicle passing data of all-day intersection detection equipment to generate a complete travel chain of a vehicle in one day; then, establishing a similar matrix by adopting a Gaussian kernel function (RBF), calculating an adjacency matrix and a degree matrix, and further determining a characteristic matrix by constructing a Laplace matrix; and finally, obtaining a cluster division result by adopting a DBSCASN cluster analysis method, thereby obtaining a starting point, an end point and an approach point position of a single trip track of the vehicle.
However, if the vehicle is not equipped with a GPS device, the trip chain information of the vehicle cannot be acquired; in addition, the method needs to split the trip chain of the vehicle after the data is collected all day long, the trip chain of the vehicle cannot be generated immediately, and the trip chain of the vehicle cannot be updated dynamically.
Disclosure of Invention
The application provides a real-time vehicle travel track acquisition method based on vehicle passing video data, which analyzes and processes vehicle passing video data by constructing an optimal adjacency matrix of a city road network and compares and judges vehicle travel information and a historical travel chain, and can solve the problems that due to the lack of GPS equipment, a vehicle cannot acquire a travel track, the dynamic update of the vehicle travel chain cannot be realized, and the travel chain consumes too large system resources for calculation to a certain extent.
The embodiment of the application is realized as follows:
the embodiment of the application provides a real-time vehicle travel track obtaining method based on vehicle passing video data, which comprises the following steps:
constructing an optimal adjacency matrix of the road network according to the topological information of the road network of the city;
analyzing vehicle passing video data from the detection point location to obtain vehicle running information, wherein the vehicle running information comprises a snapshot time, a snapshot point location and a license plate number;
and comparing and judging the vehicle running information and the historical trip chain of the vehicle based on the optimal adjacency matrix, and constructing a real-time trip track of the vehicle.
The beneficial effect of this application lies in: the method has the advantages that the optimal adjacency matrix of the urban road network is constructed, so that the missing paths of the vehicles among different detection point positions can be quickly supplemented; further, by analyzing the vehicle video data, the travel track of the vehicle can be acquired without depending on GPS equipment to a certain extent; further, by comparing and judging the vehicle driving information and the historical trip chain, the dynamic updating of the vehicle trip chain can be realized, the real-time trip track of the vehicle is constructed, and the system resources consumed by calculating the vehicle trip chain can be reduced.
Drawings
Specifically, in order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments are briefly described below, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without any creative effort.
Fig. 1 is a schematic diagram of a real-time vehicle travel track acquisition system 100 based on vehicle-passing video data according to some embodiments of the present application;
FIG. 2 illustrates a schematic diagram of an exemplary computing device 200 in an embodiment of the present application;
fig. 3 is a schematic flow chart illustrating a method for acquiring a real-time travel track of a vehicle based on vehicle-passing video data according to an embodiment of the present application;
fig. 4 shows a schematic flow chart of acquiring an optimal adjacency matrix of a road network in a vehicle real-time travel track acquisition method based on vehicle-passing video data according to the embodiment of the present application;
FIG. 5 shows a schematic view of an urban road junction according to an embodiment of the present application;
fig. 6 shows a schematic flow chart of path completion processing in a vehicle real-time travel track acquisition method based on vehicle passing video data according to an embodiment of the present application.
Detailed Description
Certain exemplary embodiments will now be described to provide an overall understanding of the principles of the structure, function, manufacture, and use of the devices and methods disclosed herein. One or more examples of these embodiments are illustrated in the accompanying drawings. Those of ordinary skill in the art will understand that the devices and methods specifically described herein and illustrated in the accompanying drawings are non-limiting exemplary embodiments and that the scope of the various embodiments of the present invention is defined solely by the claims. Features illustrated or described in connection with one exemplary embodiment may be combined with features of other embodiments. Such modifications and variations are intended to be included within the scope of the present invention.
Reference throughout this specification to "embodiments," "some embodiments," "one embodiment," or "an embodiment," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases "in various embodiments," "in some embodiments," "in at least one other embodiment," or "in an embodiment" or the like throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Thus, the particular features, structures, or characteristics shown or described in connection with one embodiment may be combined, in whole or in part, with the features, structures, or characteristics of one or more other embodiments, without limitation. Such modifications and variations are intended to be included within the scope of the present invention.
Fig. 1 is a schematic diagram of a real-time vehicle travel track acquisition system 100 based on vehicle-passing video data according to some embodiments of the present application. The real-time vehicle travel track acquiring system 100 based on vehicle-passing video data is a platform capable of automatically acquiring a real-time vehicle travel track. The vehicle real-time travel trajectory acquisition system 100 based on the passing vehicle video data may include a server 110, at least one storage device 120, at least one network 130, one or more vehicle information acquisition apparatuses 150-1, 150-2. The server 110 may include a processing engine 112.
In some embodiments, the server 110 may be a single server or a group of servers. The server farm can be centralized or distributed (e.g., server 110 can be a distributed system). In some embodiments, the server 110 may be local or remote. For example, server 110 may access data stored in storage device 120 via network 130. Server 110 may be directly connected to storage device 120 to access the stored data. In some embodiments, the server 110 may be implemented on a cloud platform. The cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, multiple clouds, the like, or any combination of the above. In some embodiments, server 110 may be implemented on a computing device as illustrated in FIG. 2 herein, including one or more components of computing device 200.
In some embodiments, the server 110 may include a processing engine 112. Processing engine 112 may process information and/or data related to the service request to perform one or more of the functions described herein. For example, the processing engine 112 may be configured to obtain passing video data transmitted by the vehicle information collection device 150 and send the passing video data to the storage device 120 via the network 130 for updating the data stored therein. In some embodiments, processing engine 112 may include one or more processors. The processing engine 112 may include one or more hardware processors, such as a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an application specific instruction set processor (ASIP), an image processor (GPU), a physical arithmetic processor (PPU), a Digital Signal Processor (DSP), a field-programmable gate array (FPGA), a Programmable Logic Device (PLD), a controller, a micro-controller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination of the above.
In some embodiments, the storage device 120 may be connected to the network 130 to enable communication with one or more components of the real-time vehicle travel trajectory acquisition system 100 based on the passing video data. One or more components of the vehicle real-time travel trajectory acquisition system 100 based on the passing video data may access data or instructions stored in the storage device 120 via the network 130. In some embodiments, the storage device 120 may be directly connected to or in communication with one or more components of the vehicle real-time travel trajectory acquisition system 100 based on the passing video data. In some embodiments, storage device 120 may be part of server 110.
The network 130 may facilitate the exchange of information and/or data. In some embodiments, one or more components of the real-time vehicle travel track acquisition system 100 based on the vehicle passing video data may send information and/or data to other components of the real-time vehicle travel track acquisition system 100 based on the vehicle passing video data via the network 130. For example, the server 110 may acquire/obtain the passing vehicle video data from the vehicle information collecting device 150 through the network 130. In some embodiments, the network 130 may be any one of a wired network or a wireless network, or a combination thereof. In some embodiments, the network 130 may include one or more network access points. For example, the network 130 may include wired or wireless network access points, such as base stations and/or Internet switching points 130-1, 130-2, and so forth. Through the access point, one or more components of the vehicle real-time travel trajectory acquisition system 100 based on the passing video data may be connected to the network 130 to exchange data and/or information.
The vehicle information collection device 150 may include an electronic police, a video detector, and the like. In some embodiments, the vehicle information collecting device 150 may transmit the collected passing video data to one or more devices in the vehicle real-time travel track acquiring system 100 based on the passing video data. For example, the vehicle information collection device 150 may send the passing vehicle video data to the server 110 for processing, or store in the storage device 120.
FIG. 2 is a schematic diagram of an exemplary computing device 200 shown in accordance with some embodiments of the present application. The server 110, the storage device 120, and the traffic information collection apparatus 150 may be implemented on the computing device 200. For example, the processing engine 112 may be implemented on the computing device 200 and configured to implement the functionality disclosed herein.
For ease of understanding, only one processor is exemplarily depicted in fig. 2. However, it should be noted that the computing device 200 in the present application may include multiple processors, and thus the operations and/or methods described in the present application that are implemented by one processor may also be implemented by multiple processors, collectively or independently. For example, if in the present application a processor of computing device 200 performs steps 1 and 2, it should be understood that steps 1 and 2 may also be performed by two different processors of computing device 200, either collectively or independently.
The vehicles described herein may include taxis, private cars, pick-up cars, buses, unmanned vehicles, and the like, or any combination thereof. The system or method applications of the present application may include web pages, plug-ins to browsers, client terminals, customization systems, internal analysis systems, artificial intelligence robots, etc., or any combination thereof.
Fig. 3 shows a schematic flow chart of a method for acquiring a real-time travel track of a vehicle based on vehicle-passing video data according to an embodiment of the application.
In step 301, an optimal adjacency matrix of the road network is constructed according to the road network topology information of the city.
The method comprises the steps of obtaining information such as an urban road network topological structure and intersection detection points, required to be completed, of a real-time travel track of a vehicle, and finally displaying the track information of the vehicle on a map.
In an actual traffic scene, the change of traffic data mainly depends on the topological structure of the urban road network, so that the topological information of the road network can be fully utilized. For example, intersections in urban roads, also called road intersections, may construct an optimal adjacency matrix of a road network based on urban road network topology information of the intersections, and then obtain shortest paths and distances between different detection points by using a computer algorithm in the optimal adjacency matrix.
The intersection is the intersection of two or more roads in the urban road network and is the junction for collecting, steering and evacuating various vehicles. The intersections are divided into plane intersections, ring intersections and three-dimensional intersections.
The plane intersection is an intersection formed by intersecting roads on the same plane and generally has the forms of T shape, Y shape, cross shape, X shape, dislocation, ring shape and the like. At the intersection position without traffic control, vehicles pass through the intersection to form conflict points due to different driving directions. When a vehicle passes through a three-way intersection, 3 conflict points exist, when the vehicle passes through a four-way intersection, 16 conflict points exist, when the vehicle passes through a five-way intersection, 50 conflict points exist, and each conflict point is actually a potential traffic accident point.
The roundabout is that a roundabout with a large area is arranged in the middle of the intersection, and vehicles are interwoven into the roundabout and drive in one direction around the roundabout. The conflict points are eliminated by the vehicles in an interlaced operation mode, and street scenery can be beautified by the roundabout greening. The intersection is a circular intersection suitable for intersections with more than four branches, the terrain needs to be wide and flat, the traffic volume of the intersected roads is uniform, the left-turning traffic volume is large, and the total traffic volume of motor vehicles at the intersection is not more than 3000 vehicles per hour. The roundabout has the defects of large occupied area, vehicles needing to detour, easy blockage when the traffic flow is increased and inconvenience for pedestrian traffic. Generally, the traffic capacity can be improved by reducing the size of the roundabout, so that in some cities, the diameter of the roundabout is reduced to about one third of the diameter of an inscribed circle of the outer edge of the roundabout, and meanwhile, a guide island which is deviated to an outlet is arranged to widen an entrance lane and provide rules that roundabout vehicles have priority and the like, so that a novel roundabout-miniature roundabout intersection is formed.
The three-dimensional intersection is formed by intersecting roads on different planes. The traffic flow control system arranges the traffic flows which conflict with each other on roads with different elevations respectively, thereby ensuring the smooth traffic and the traffic safety. The three-dimensional intersection mainly comprises three parts, namely an overpass, an approach and a ramp. The overpass is a road bridge crossing roads or a tunnel bridge passing through roads. The approach is a bridge head road connected with the overpass. The ramp is a road section connecting the road and the road surface under the overpass. The three-dimensional intersection comprises a ramp connecting an upper intersection road and a lower intersection road.
The method and the device for acquiring the real-time travel track of the vehicle based on the vehicle passing video data extract the track information of the vehicle between intersections of the urban road network in real time and display the track information on the map, so that abundant traffic running condition information can be obtained, effective data support is provided for traffic managers, and the problem of urban traffic jam is solved. The following describes the acquisition of the optimal adjacency matrix of the urban road network in detail.
Fig. 4 shows a schematic flow chart for acquiring an optimal adjacency matrix of a road network in a vehicle real-time travel track acquisition method based on vehicle-passing video data according to the embodiment of the present application.
In step 401, an original adjacency matrix capable of reflecting a road network structure is constructed based on road network topology information of a city.
In some embodiments, an original adjacency matrix L capable of reflecting a road network structure may be constructed based on intersection and road section critical information in the urban road network topology information.
For example, based on the interconnection relationship among a large number of adjacent intersections in an urban road network, the original topological information of the road network can be extracted in a matrix manner, so that an original adjacent matrix L of the road network is obtained, and the construction of a real-time vehicle travel chain is facilitated through the adjacent matrix.
In some embodiments, the original adjacency matrix storage structure includes a plurality of vertices, each vertex stores the edge information in a one-dimensional array, and then combines the information of all the vertices to represent the adjacency relationship between the vertices in the matrix diagram, and the original adjacency matrix is particularly represented in a two-dimensional array in some embodiments.
In some embodiments, the road network topology includes intersection information and road segment criticality information.
The intersection information comprises intersection point location information, intersection turning and canalization information.
The intersection point location information comprises an intersection number and longitude and latitude. By numbering all intersections in the road network, the intersections can be quickly referenced in the construction of the original adjacency matrix. The longitude and latitude information of the intersection is unique to the intersection, the data of the longitude and latitude information comes from the provision of an open map, and the longitude and latitude information can be acquired through information acquisition devices arranged at different intersections of a city. For example, according to the vehicle information acquisition device in the embodiment of the application, the vehicle information acquisition device can acquire the passing data of the intersection, and can acquire the longitude and latitude information of the intersection through the built-in GPS device.
The intersection turning and canalization information comprises intersection entrance lane information, traffic direction information, turning information and turning downstream intersection information.
For example, the number of entrance roads, the traffic direction, the turn information, and the downstream intersection information of the entrance road for each intersection in the road network. In some embodiments, the intersections are numbered according to a certain rule, so that quick reference is facilitated when a traffic track is constructed. The steering and channeling information is information such as diversion islands, road markings (such as double yellow lines), indication signs (such as stop lines to be steered by vehicles), isolation fences (or isolation green belts) and the like arranged at intersections to separate and control possibly conflicting traffic flows to enter a certain preset route, as shown in fig. 5.
The channeling of the intersection is to arrange various traffic islands on a traffic lane of the intersection to close part of the intersection, and to divide the traffic-forbidden areas by marked lines, so as to disperse the conflict points of traffic flow intersection as much as possible, realize the reduction of traffic flow conflict through the intersection turning and channeling design to control the traffic flow, adjust the conflict angle and improve the vehicle passing efficiency of the intersection.
If the lane of the intersection has no facility and marking line for controlling the direction, the definition of the vehicle channel can be damaged, and the driving safety is reduced. Therefore, in order to enable a driver to drive at the intersection according to a correct route and reduce the randomness, the method of intersection turning and canalization is necessary.
Generally speaking, intersection turning and canalization are designed with the following principles: the canalized running route is simple and clear, and the too complicated design is easy to cause the vehicle to run by mistake, but reduces the use effect; the phenomenon that the flow separation and flow combination of the traffic flow are concentrated at one point is avoided; the width of the diversion lane is proper, and if the diversion lane is too wide, the vehicles can run in parallel, so that collision accidents are easy to happen; the driver should be able to notice the presence of the air guiding device before approaching the air guiding device.
The section critical information may specifically include: road section direction, road section length, road section lane number, road section longitude and latitude, road section grade, road section speed limit, road section upstream intersection and road section downstream intersection. The direction of the road section is consistent with the direction information of the inlet road in the intersection point location information, and the channelized traffic flow direction of the intersection is reflected; the road section length information reflects the length of a path between adjacent intersections and can be used as a data basis for calculating the shortest distance between the intersections; the number of lanes on the road section is consistent with the entrance lane information in the intersection point location information; the road section longitude and latitude comprise the longitude and latitude of an initial intersection and the longitude and latitude of an end intersection where the road section is located; the road section grade information is configured into four grades of an express way, a main road, a secondary road and a branch road. The road section upstream intersection and the road section downstream intersection are the most adjacent intersection point information of the road section in different traffic directions.
In step 402, an optimal adjacency matrix of the road network is constructed by using a Floyd algorithm based on the original adjacency matrix, and the optimal adjacency matrix is used for obtaining the shortest path between road intersections.
In some embodiments, the Floyd algorithm may be used to construct the optimal adjacency matrix M, and the shortest paths between different detection point locations are found and calculated in the optimal adjacency matrix, that is, the shortest paths between different detection point locations are calculated by using intersections of adjacent road networks in the optimal adjacency matrix as the shortest paths. .
The Floyd (Floyd-Warshall: Floyed) algorithm, also known as an interpolation method, uses dynamic programming to find the shortest path between multiple sources in a given weighted graph. The Floyd algorithm is an algorithm that finds the shortest path in a weighted graph with positive or negative edge weights, and a single execution of the algorithm will find the length of the shortest path between all pairs of vertices, although it does not return details of the path itself, but can reconstruct the path by simple modifications to the algorithm.
The path finding process is described as follows: starting from any one unilateral path, the distance between all two points is the weight of the edge, and if no edge is connected between the two points, the weight is infinite; for each pair of vertices u and v, it is retrieved whether there is one vertex w, so that the path from u to w to v is shorter than the known path, and if there is a shorter path, the shortest path is updated. For example, the shortest path from any node i to any node j includes two possibilities, where one scenario is directly from node i to node j; another scenario is to go from node i through several nodes k to reach node j. The algorithm assumes that Dis (i, j) is the distance between the shortest path from the node i to the node j, for each node k, the algorithm checks whether Dis (i, k) + Dis (k, j) < Dis (i, j) is true, if true, the algorithm proves that the path from the node i to the node k to the node j is shorter than the path from the node i to the node j directly, and updates Dis (i, j) ═ Dis (i, k) + Dis (k, j), and when the algorithm finishes traversing and calculating all the nodes k, the shortest path from the node i to the node j is recorded in the final Dis (i, j).
With reference to fig. 3, in step 302, the vehicle passing video data from the detection point location is analyzed to obtain vehicle driving information, where the vehicle driving information includes a snapshot time, a snapshot point location, and a license plate number.
In some embodiments, the detection points, i.e. each intersection, are provided with a vehicle information acquisition device. The vehicle information acquisition device is used for acquiring the driving information of vehicles passing through the intersection, wherein the vehicles comprise small private cars, motorcycles, large buses and the like passing through all directions of the intersection.
For example, an electronic police detector (abbreviated as an electric police) is arranged at an entrance road of each intersection of a city road network, so that a slap-on-demand function can be realized. Therefore, the travel track of the vehicle is dynamically updated based on the electric alarm vehicle-passing data received in real time.
The vehicle driving information further comprises snapshot time, snapshot point positions and license plate numbers, and in some embodiments, the vehicle driving information further comprises information such as vehicle driving direction, lane information where the vehicle is located, vehicle type and the like.
The license plate number is the unique identification of the vehicle, the historical trip chain of the specific vehicle can be searched in a plurality of historical trip chains according to the license plate number, so that whether the vehicle has trip records or not is judged, and the travel characteristics of the vehicle are researched according to the last record information of the historical trip chain obtained through searching.
The snapshot point locations correspond to the intersection point locations in the map information, and the relationship between the positions of the vehicles at the snapshot time and the last recorded point location of the historical trip chain can be compared according to the snapshot point location information of the vehicles, so that whether the vehicles are at the same intersection or adjacent intersections is obtained.
The snapshot time is the record of the time when the vehicle passes through the detection point, namely a certain intersection. By comparing the snapshot time of the vehicle with the last recorded time of the historical trip chain, whether the vehicle belongs to a new trip can be judged, and the judgment can also be used as a basis for updating the historical trip chain.
The information of the vehicle driving direction and the lane where the vehicle is located can be used for calculating the next intersection or the coat intersection of the vehicle path; in the optimal adjacency matrix, the shortest path of the vehicle between different detection point positions can be calculated according to the driving direction of the vehicle and the information of the lane where the vehicle is located, and the shortest path can complement the missing path of the vehicle.
In step 303, based on the optimal adjacency matrix, the vehicle travel information and the historical travel chain of the vehicle are compared and determined, and a real-time travel track of the vehicle is constructed.
Based on the vehicle running information, the current time-space information of the vehicle can be obtained, and the time-space information can be compared with the last recorded time-space information of the historical trip chain of the vehicle for judgment so as to determine the relationship between the current trip and the historical trip. The updating of the historical travel chain and the new travel chain of the vehicle will be explained below.
In some embodiments, when the snapshot point position of the vehicle is the same as the last record point position of its historical trip chain, it may be considered that the intersection where the vehicle is currently located is the same as the intersection of the last record point position in its historical trip chain. In this case, the vehicle travel information is compared with the historical trip chain of the vehicle, and the snapshot time interval is needed to be studied, so that the following determination is made:
if the difference value between the current snapshot time of the vehicle and the last recorded time of the historical trip chain of the vehicle is larger than a first threshold value, adding a new trip chain for the vehicle; otherwise, updating the historical trip chain.
The first threshold may be set according to an actual situation, and the application is not particularly limited, and in this embodiment, the first threshold is set to 10 minutes. The intersection where the vehicle is located at present is the same as the last recorded point of the historical trip chain, the interval time of the snapshot time of the intersection is less than 10 minutes, the current trip of the vehicle and the historical trip of the vehicle are considered to be the same trip, the historical trip chain of the vehicle is updated, and the current snapshot point and the snapshot time of the vehicle are updated to the historical trip chain; and if the intersection where the vehicle is located at present is the same as the last recorded point of the historical trip chain, but the interval time of the snapshot time is more than 10 minutes, and the current trip and the historical trip of the vehicle are not the same, adding a new trip chain for the vehicle.
In some embodiments, when the current snapshot point of the vehicle is adjacent to the last record point of the historical trip chain, it may be considered that the intersection where the vehicle is currently located, the last record point of the historical trip chain, and the position of the intersection are also located at two adjacent intersections on the optimal adjacency matrix. In this case, the comparison between the vehicle travel information and the historical trip chain of the vehicle is determined, and the interval time between the snapshot times needs to be studied, so that the following determination is made:
if the difference value between the current snapshot time of the vehicle and the last recorded time of the historical trip chain of the vehicle is larger than a second threshold value, adding a new trip chain for the vehicle; otherwise, updating the historical trip chain.
The second threshold is the time required for the vehicle to pass through the adjacent snapshot points at the minimum speed limit of the road. When the intersection where the vehicle is currently located and the last recorded point of the historical trip chain of the vehicle are located and the intersection position is located at an adjacent position in the optimal adjacency matrix, the time interval of the snapshot time of the vehicle needs to be considered, and if the time interval is larger than the maximum passing time of the vehicle passing through the adjacent intersection, the vehicle is considered to be in a new journey, and a trip chain is newly added to the vehicle; and if the interval time is less than the maximum passing time of the adjacent intersection, and the vehicle is considered to be in the previous trip, updating the historical trip chain of the vehicle.
In some embodiments, when the snapshot point location where the vehicle is currently located is not adjacent to and different from the last record point location of the historical trip chain, it may be considered that the intersection where the vehicle is currently located is not directly linked with the last record point location of the historical trip chain, and the intersection is not directly linked in the optimal adjacency matrix. In this case, the comparison between the vehicle travel information and the historical trip chain of the vehicle is determined, and the interval time between the snapshot times needs to be studied, so that the following determination is made:
if the difference value between the current snapshot time of the vehicle and the last recorded time of the historical trip chain of the vehicle is larger than a third threshold value, adding a new trip chain for the vehicle; otherwise, performing path completion processing on the historical trip chain to obtain an updated historical trip chain.
The third threshold may be set according to actual conditions, and the application is not particularly limited, and in this embodiment, the third threshold is set to 60 minutes. If the time interval is larger than a preset third threshold value, the vehicle is considered to be in a new journey, and a trip chain is added for the vehicle; and if the time interval is smaller than a preset third threshold value, the vehicle is considered to be in the previous trip, and a historical trip chain of the vehicle is updated.
However, since the intersection where the vehicle is currently located is not directly and necessarily connected with the last recorded point of the historical travel chain, the intersection needs to be subjected to completion processing to obtain a complete travel track.
Fig. 6 shows a schematic flow chart of path completion processing in a vehicle real-time travel track acquisition method based on vehicle passing video data according to an embodiment of the present application.
In step 601, a first intersection adjacent to the intersection is obtained according to the snapshot point location and the vehicle driving direction.
And if the difference value between the current snapshot time of the vehicle and the last recorded time of the historical trip chain of the vehicle is smaller than a preset third threshold value, the vehicle is considered to be in the previous trip. Due to the fact that an electric alarm is in a snapshot fault in a specific path or a vehicle information acquisition device is not arranged at some intersections of the path, complete path information is lacked among the nearest 2 snapshot points.
For the situation, according to the intersection where the current snapshot time of the vehicle is located, the current driving direction of the vehicle and the information of the lane where the vehicle is located, the previous intersection of the route can be calculated in the optimal adjacency matrix, and the previous intersection is called as a first intersection. The first intersection may also be considered the penultimate point of detection of the vehicle approach, which is relatively accurate in location.
In step 602, a next adjacent second intersection of the last recorded point is obtained by complementing the last recorded point of the historical trip chain and the last recorded vehicle driving direction.
And after the intersection of the vehicle at the previous path of the current snapshot point position is obtained, the path of the vehicle missing path is continuously supplemented. And according to the last recorded point position of the vehicle historical trip chain, the vehicle driving direction and the information of the lane where the vehicle is located, calculating and determining the next intersection of the last recorded point position in the optimal adjacency matrix, wherein the intersection is called as a second intersection. The second intersection is also relatively accurate in its position.
In step 603, the shortest path between the first intersection and the second intersection is obtained by querying the optimal adjacency matrix.
And if the missing path of the vehicle is still incomplete, the missing path between the first intersection and the second intersection needs to be completed continuously.
And calculating and completing the path between the two intersections by using a path finding algorithm in the optimal adjacency matrix. The optimal adjacency matrix is constructed based on the Floyd algorithm. The algorithm is to find the shortest path between two detection points. There are two general situations when finding the shortest path between two detection point locations, one is directly from point i to point j; the other is from point i through several nodes K, K ∈ K, and then to point j, where K is the set of all intermediate nodes.
Assuming that D (i, j) is the distance of the shortest path from point i to point j, for each node k, the following relation is set:
D(i，k)+D(k，j)＜D(i，j)
verifying whether the relation is established or not for each node k; if yes, proving that the path from i to k to j is shorter than the path from i to j, the correction relation is:
D(i，j)＝D(i，k)+D(k，j)
it can be found that the numerical values recorded in all nodes k, D (i, j) after traversing in the optimal adjacency matrix are updated to the distance of the shortest path from i to j, so that the missing path between the first intersection and the second intersection can be completed.
In some embodiments, the license plate number information of the vehicle does not exist in the historical trip chain in the database, that is, the vehicle may be considered to be a first trip within a certain period of time, in which case a new trip chain is added for the vehicle.
In some embodiments, after the real-time travel track of the vehicle is successfully constructed, according to a certain frequency, if the historical travel chain of the vehicle is not updated continuously within a preset fourth threshold time, the historical travel chain of the vehicle is stored in the database.
And monitoring the travel track of each vehicle at a set frequency, and storing the historical travel chain of the vehicle into a database if the travel track of a certain license plate is not updated within a set fourth threshold time. Generally, the fourth threshold may be set according to practical situations, and the application is not limited specifically. The vehicle passing track data stored in the database can provide a basis for calculating the evaluation index of the traffic state and can also provide efficient decision information support for improving the urban traffic control.
The method has the advantages that the optimal adjacency matrix of the urban road network is constructed, so that the missing paths of the vehicles among different detection points can be quickly supplemented; further, by analyzing the vehicle video data, the travel track of the vehicle can be acquired without depending on GPS equipment to a certain extent; further, by comparing and judging the vehicle driving information and the historical trip chain, the dynamic updating of the vehicle trip chain can be realized, the real-time trip track of the vehicle is constructed, and the system resources consumed by calculating the vehicle trip chain can be reduced.
Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data blocks," modules, "" engines, "" units, "" components, "or" systems. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.
Claims (11)
1. A real-time vehicle travel track obtaining method based on vehicle passing video data is characterized by comprising the following steps:
constructing an optimal adjacency matrix of the road network according to the topological information of the road network of the city;
analyzing vehicle passing video data from the detection point location to obtain vehicle running information, wherein the vehicle running information comprises a snapshot time, a snapshot point location and a license plate number;
and comparing and judging the vehicle running information and the historical trip chain of the vehicle based on the optimal adjacency matrix, and constructing a real-time trip track of the vehicle.
2. The method for acquiring the real-time travel track of the vehicle based on the vehicle passing video data as claimed in claim 1, wherein the comparing and determining of the vehicle travel information and the historical travel chain of the vehicle specifically comprises:
the snapshot point location is the same as the last record point location of the historical trip chain, and if the difference value between the snapshot time and the last record time of the historical trip chain is greater than a first threshold value, a new trip chain is added for the vehicle; otherwise, updating the historical trip chain.
3. The method for acquiring the real-time travel track of the vehicle based on the vehicle passing video data as claimed in claim 1, wherein the comparing and determining of the vehicle travel information and the historical travel chain of the vehicle specifically comprises:
the snapshot point location is adjacent to the last record point location of the historical trip chain, and if the difference value between the snapshot time and the last record time of the historical trip chain is greater than a second threshold value, a new trip chain is added for the vehicle; otherwise, updating the historical trip chain.
4. The method for acquiring the real-time travel track of the vehicle based on the vehicle-passing video data as claimed in claim 3, wherein the second threshold is set as the time required for the vehicle to pass through between the adjacent snapshot points at the minimum speed limit of the road.
5. The method for acquiring the real-time travel track of the vehicle based on the vehicle passing video data as claimed in claim 1, wherein the comparing and determining of the vehicle travel information and the historical travel chain of the vehicle specifically comprises:
the snapshot point location is not adjacent to and different from the last record point location of the historical trip chain, and if the difference value between the snapshot time and the last record time of the historical trip chain is greater than a third threshold value, a new trip chain is added for the vehicle; otherwise, performing path completion processing on the historical trip chain to obtain an updated historical trip chain.
6. The method for acquiring the real-time travel track of the vehicle based on the vehicle passing video data according to claim 5, wherein the path completion processing is performed on the historical travel chain, and specifically comprises the following steps:
according to the snapshot point position and the vehicle running direction, completing to obtain an adjacent intersection, namely a first intersection, of the previous path of the intersection where the vehicle is located;
according to the last recorded point position of the historical trip chain, the last recorded vehicle driving direction and the last recorded lane information, completing to obtain an adjacent intersection, namely a second intersection, of the next path of the vehicle at the last recorded point position;
and obtaining the shortest path between the first intersection and the second intersection by inquiring the optimal adjacency matrix.
7. The method for acquiring the real-time travel track of the vehicle based on the vehicle passing video data as claimed in claim 1, wherein the comparing and determining of the vehicle travel information and the historical travel chain of the vehicle specifically comprises:
and if the historical trip chain does not comprise the license plate number, adding a trip chain for the vehicle.
8. The method for acquiring real-time travel tracks of vehicles based on vehicle-passing video data according to claim 1, wherein the step of constructing an optimal adjacency matrix of a road network according to road network topology information of a city comprises the following steps:
constructing an original adjacency matrix capable of reflecting a road network structure based on road network topological information of a city;
and constructing an optimal adjacency matrix of the road network by using a Floyd algorithm based on the original adjacency matrix, wherein the optimal adjacency matrix is used for acquiring the shortest path between intersections.
9. The method for acquiring real-time travel tracks of vehicles according to claim 8, wherein the road network topology information comprises: intersection point location information, intersection turning and channelizing information and road section critical information.
10. The method for acquiring the real-time travel track of the vehicle based on the vehicle passing video data as claimed in claim 1, wherein the vehicle driving information further comprises: the driving direction of the vehicle, the information of the lane where the vehicle is located and the type of the vehicle.
11. The method for acquiring the real-time travel track of the vehicle based on the vehicle passing video data as claimed in claim 1, wherein after the real-time travel track of the vehicle is constructed, the method comprises the following steps:
and storing the historical trip chain which is not continuously updated to the database within a fourth threshold time at a certain frequency.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010091310.4A CN111314857B (en) | 2020-02-13 | 2020-02-13 | Vehicle real-time travel track acquisition method based on vehicle passing video data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010091310.4A CN111314857B (en) | 2020-02-13 | 2020-02-13 | Vehicle real-time travel track acquisition method based on vehicle passing video data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111314857A true CN111314857A (en) | 2020-06-19 |
| CN111314857B CN111314857B (en) | 2022-02-08 |
Family
ID=71159916
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010091310.4A Active CN111314857B (en) | 2020-02-13 | 2020-02-13 | Vehicle real-time travel track acquisition method based on vehicle passing video data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111314857B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112309118A (en) * | 2020-11-03 | 2021-02-02 | 广州市交通规划研究院 | Vehicle trajectory calculation method based on space-time similarity |
| CN113140114A (en) * | 2021-03-09 | 2021-07-20 | 中山大学 | Vehicle travel path reconstruction method based on travel time estimation |
| CN113255088A (en) * | 2021-05-21 | 2021-08-13 | 上海天壤智能科技有限公司 | Data completion method and system for bayonet vehicle-passing record |
| CN117423237A (en) * | 2023-12-18 | 2024-01-19 | 湖南力唯中天科技发展有限公司 | Method and system for detecting and generating multiple special service route conflicts |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105225494A (en) * | 2015-11-03 | 2016-01-06 | 中兴软创科技股份有限公司 | Based on the Vehicle tracing method and apparatus of electronic police data |
| CN105225476A (en) * | 2014-06-10 | 2016-01-06 | 浙江宇视科技有限公司 | A kind of generation of track of vehicle, polymerization and device |
| CN105528894A (en) * | 2015-11-28 | 2016-04-27 | 浙江宇视科技有限公司 | Operation detection method and device for front-end monitoring devices based on track information |
| CN107195180A (en) * | 2017-06-08 | 2017-09-22 | 青岛海信网络科技股份有限公司 | A kind of traffic trip track extraction method and device based on the alert data of electricity |
| CN109034448A (en) * | 2018-06-14 | 2018-12-18 | 重庆邮电大学 | Trajectory predictions method based on track of vehicle semantic analysis and deepness belief network |
| CN109190056A (en) * | 2018-08-22 | 2019-01-11 | 深圳先进技术研究院 | A kind of track of vehicle method for reconstructing, system and electronic equipment |
| CN109448370A (en) * | 2018-10-29 | 2019-03-08 | 东南大学 | A kind of traffic control sub-area division method based on track of vehicle data |
| CN109949351A (en) * | 2019-03-19 | 2019-06-28 | 郑州信大先进技术研究院 | Target vehicle driving trace extraction element and method based on video structural monitoring |
| US10462610B1 (en) * | 2018-11-07 | 2019-10-29 | Uber Technologies, Inc. | Low latency client location estimation with dynamic integration of server output |
| CN110634289A (en) * | 2019-09-04 | 2019-12-31 | 南京洛普股份有限公司 | Urban road traffic optimal path online planning method based on electric police data |
-
2020
- 2020-02-13 CN CN202010091310.4A patent/CN111314857B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105225476A (en) * | 2014-06-10 | 2016-01-06 | 浙江宇视科技有限公司 | A kind of generation of track of vehicle, polymerization and device |
| CN105225494A (en) * | 2015-11-03 | 2016-01-06 | 中兴软创科技股份有限公司 | Based on the Vehicle tracing method and apparatus of electronic police data |
| CN105528894A (en) * | 2015-11-28 | 2016-04-27 | 浙江宇视科技有限公司 | Operation detection method and device for front-end monitoring devices based on track information |
| CN107195180A (en) * | 2017-06-08 | 2017-09-22 | 青岛海信网络科技股份有限公司 | A kind of traffic trip track extraction method and device based on the alert data of electricity |
| CN109034448A (en) * | 2018-06-14 | 2018-12-18 | 重庆邮电大学 | Trajectory predictions method based on track of vehicle semantic analysis and deepness belief network |
| CN109190056A (en) * | 2018-08-22 | 2019-01-11 | 深圳先进技术研究院 | A kind of track of vehicle method for reconstructing, system and electronic equipment |
| CN109448370A (en) * | 2018-10-29 | 2019-03-08 | 东南大学 | A kind of traffic control sub-area division method based on track of vehicle data |
| US10462610B1 (en) * | 2018-11-07 | 2019-10-29 | Uber Technologies, Inc. | Low latency client location estimation with dynamic integration of server output |
| CN109949351A (en) * | 2019-03-19 | 2019-06-28 | 郑州信大先进技术研究院 | Target vehicle driving trace extraction element and method based on video structural monitoring |
| CN110634289A (en) * | 2019-09-04 | 2019-12-31 | 南京洛普股份有限公司 | Urban road traffic optimal path online planning method based on electric police data |
Non-Patent Citations (2)
| Title |
|---|
| JIANBO TANG ET.AL: "A Novel Method for Road Intersection Construction From Vehicle Trajectory Data", 《IEEE ACCESS》 * |
| 阮树斌 等: "基于车牌识别数据的机动车出行轨迹提取算法", 《浙江大学学报(工学版)》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112309118A (en) * | 2020-11-03 | 2021-02-02 | 广州市交通规划研究院 | Vehicle trajectory calculation method based on space-time similarity |
| CN113140114A (en) * | 2021-03-09 | 2021-07-20 | 中山大学 | Vehicle travel path reconstruction method based on travel time estimation |
| CN113140114B (en) * | 2021-03-09 | 2022-07-29 | 中山大学 | Vehicle travel path reconstruction method based on travel time estimation |
| CN113255088A (en) * | 2021-05-21 | 2021-08-13 | 上海天壤智能科技有限公司 | Data completion method and system for bayonet vehicle-passing record |
| CN117423237A (en) * | 2023-12-18 | 2024-01-19 | 湖南力唯中天科技发展有限公司 | Method and system for detecting and generating multiple special service route conflicts |
| CN117423237B (en) * | 2023-12-18 | 2024-04-05 | 湖南力唯中天科技发展有限公司 | Method and system for detecting and generating multiple special service route conflicts |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111314857B (en) | 2022-02-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111314857B (en) | Vehicle real-time travel track acquisition method based on vehicle passing video data | |
| US10916127B2 (en) | Intelligent telematics system for defining vehicle ways | |
| CN108428338B (en) | Traffic road condition analysis method and device and electronic equipment | |
| CN100580735C (en) | Real-time dynamic traffic information processing method based on car detecting technique | |
| US11699100B2 (en) | System for determining traffic metrics of a road network | |
| CN110749329B (en) | Lane level topology construction method and device based on structured road | |
| CN111063208A (en) | Lane-level traffic guidance method and system based on Internet of vehicles | |
| CN112991743B (en) | Real-time traffic risk AI prediction method based on driving path and system thereof | |
| US20200320866A1 (en) | Method for defining road networks | |
| CN113779430B (en) | Road network data generation method and device, computing equipment and storage medium | |
| CN107490384A (en) | A kind of optimal static path system of selection based on city road network | |
| CN108332761B (en) | Method and equipment for using and creating road network map information | |
| CN112033420A (en) | Lane map construction method and device | |
| CN109523781B (en) | Intersection prediction method based on satellite positioning | |
| Boubakri et al. | High definition map update for autonomous and connected vehicles: A survey | |
| US20230137263A1 (en) | Method and apparatus for generating structured trajectories from geospatial observations | |
| CN113724497A (en) | Method and device for predicting real-time traffic flow of target road | |
| CN111325979B (en) | Method and device for dividing traffic control multistage subareas | |
| CN116295336A (en) | Construction method, device, equipment and storage medium of map hierarchical structure | |
| CN115525943A (en) | Method and system for constructing three-dimensional road model based on bus line topological relation | |
| CN114116854A (en) | Track data processing method, device, equipment and storage medium | |
| CN111337043A (en) | Path planning method and device, storage medium and electronic equipment | |
| JP7154006B2 (en) | Vehicle control device and vehicle control system | |
| US20230132499A1 (en) | Method and apparatus for generating structured trajectories from geospatial observations | |
| CN117268412A (en) | Path planning method, device, equipment, storage medium and vehicle |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |