CN113727434A - Vehicle-road cooperative auxiliary positioning system and method based on edge computing gateway - Google Patents
Vehicle-road cooperative auxiliary positioning system and method based on edge computing gateway Download PDFInfo
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- CN113727434A CN113727434A CN202111292171.2A CN202111292171A CN113727434A CN 113727434 A CN113727434 A CN 113727434A CN 202111292171 A CN202111292171 A CN 202111292171A CN 113727434 A CN113727434 A CN 113727434A
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- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
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Abstract
The invention provides a vehicle-road cooperative auxiliary positioning system and method based on an edge computing gateway, wherein the positioning system comprises a vehicle-mounted terminal, the edge computing gateway, a data acquisition unit and a cloud server; the data acquisition unit is used for acquiring road information of a road; the edge computing gateway is used for sending the road information to the cloud server; the cloud server is used for generating a road model according to the road information and sending the road model to each edge computing gateway; and the edge computing gateway is also used for determining the position information of the calibrated vehicle according to the signal intensity value of V2X communication between each edge computing gateway and the vehicle-mounted terminal in the detection interval and the gateway coordinate of each edge computing gateway in the detection interval when the calibrated vehicle runs in the detection interval of the road. In this way, even if the vehicle travels to a road with no satellite signals or poor GPS signals, the unknown vehicle on the road can still be accurately located.
Description
Technical Field
The invention relates to the technical field of intelligent networked automobiles, in particular to a vehicle-road cooperative auxiliary positioning system and method based on an edge computing gateway.
Background
At present, vehicle-road coordination has become an important component of intelligent traffic and intelligent cities, and has become a future development direction of intelligent roads, wherein high-precision positioning of vehicles is a necessary condition for realizing intelligent traffic and automatic driving.
In the prior art, a vehicle radio communication technology (namely V2X) is usually adopted to carry a global satellite positioning system (namely GNSS) to carry out high-precision positioning of a vehicle. The positioning system is a high-precision GNSS enhanced positioning technology based on real-time kinematic measurement (RTK), namely the positioning system needs to continuously receive satellite data and continuously optimize errors such as ionospheric errors, orbit errors, path effects and the like; meanwhile, the mobile vehicle can be accurately positioned only by continuously correcting the self-vehicle positioning according to the self-vehicle position and the reference station error information. However, since the satellite signals cannot be completely covered, when the satellite signals encounter a remote mountain area or a road in a place with a complicated environment such as a tall building stand, a tunnel, a viaduct and the like, the GPS signals are poor, and the positioning of the mobile vehicle is inaccurate.
Disclosure of Invention
The invention solves the problems that: in places where satellite signals cannot be covered or GPS signals are poor, how to realize accurate positioning of a moving vehicle on a road.
In order to solve the problems, the invention provides a vehicle-road cooperative auxiliary positioning system based on an edge computing gateway, which comprises a vehicle-mounted terminal arranged on a calibration vehicle, edge computing gateways respectively arranged on lamp posts at the side of a road where the calibration vehicle runs, and a data acquisition unit and a cloud server respectively connected with the edge computing gateways;
the data acquisition unit is used for acquiring road information of the road;
the edge computing gateway is used for sending the road information to the cloud server;
the cloud server is used for generating a road model according to the road information and sending the road model to each edge computing gateway;
the edge computing gateway is further configured to, when the calibrated vehicle runs in the detection interval of the road, compute vehicle coordinates of the calibrated vehicle according to signal intensity values of V2X communication between each edge computing gateway and the vehicle-mounted terminal in the detection interval and gateway coordinates of each edge computing gateway in the detection interval, select effective vehicle coordinates from each vehicle coordinate, and determine location information of the calibrated vehicle according to the effective vehicle coordinates.
Optionally, the edge computing gateway includes a data extraction module, a V2X wireless communication module, and an AI processing module;
the data extraction module is used for sending the road information acquired by the corresponding data acquisition unit to the cloud server;
the V2X wireless communication module is used for carrying out V2X communication with the vehicle-mounted terminal;
the AI processing module is configured to determine the location information of the calibrated vehicle.
Optionally, the AI processing module is specifically configured to:
marking the detection section in the road model;
when the calibrated vehicle enters the detection interval, acquiring the gateway coordinates of each edge computing gateway in the detection interval and the signal intensity value of V2X communication between each edge computing gateway and the vehicle-mounted terminal in the detection interval;
calculating the linear distance between each edge calculation gateway and the calibrated vehicle in the detection interval according to each signal intensity value;
calculating the vehicle coordinates of the calibration vehicle according to the gateway coordinates of each edge calculation gateway in the detection interval and the linear distance between each edge calculation gateway and the calibration vehicle;
selecting the effective vehicle coordinates from the vehicle coordinates;
and determining the position information of the calibrated vehicle according to the effective vehicle coordinates.
Optionally, the calculation formula of the signal strength value is as follows:
wherein, RSSI is the signal strength value, b is the signal strength value when the signal transmitting end and the signal receiving end are separated by 1 meter, L is the distance between the signal transmitting end and the signal receiving end, and n is the signal path attenuation factor.
Optionally, the selecting the effective vehicle coordinate from the vehicle coordinates includes:
randomly selecting one of the vehicle coordinates as a reference coordinate, and using the rest vehicle coordinates as undetermined coordinates;
comparing each undetermined coordinate with the reference coordinate respectively;
when the difference value between the undetermined coordinate and the reference coordinate is within a preset coordinate range, taking the undetermined coordinate and the reference coordinate as the effective vehicle coordinate;
and when the difference value between each undetermined coordinate and the reference coordinate exceeds the preset coordinate range, selecting one of the undetermined coordinates as the reference coordinate, and comparing each undetermined coordinate with the reference coordinate again until the effective vehicle coordinate is selected.
Optionally, the AI processing module is further configured to mark the calibration vehicle in the road model according to the position information of the calibration vehicle.
Optionally, the data acquisition unit includes a high-definition camera disposed on the lamp post at the road side, and the high-definition camera is configured to acquire a picture of the road;
the cloud server is used for synthesizing the images of the road collected by the high-definition camera into the road model through a synthesis algorithm.
Optionally, the edge computing gateway further includes a position sensor, and the position sensor is configured to record a position coordinate of the light pole on the road side, and use the position coordinate of the light pole as the gateway coordinate of the edge computing gateway.
Optionally, the data extraction module includes RS485, RS232, PWM, and a multi-path ethernet interface.
The invention also provides a vehicle-road cooperative auxiliary positioning method based on the edge computing gateway, and the vehicle-road cooperative auxiliary positioning system based on any edge computing gateway comprises the following steps:
acquiring road information of a road on which a calibrated vehicle runs;
the road information is sent to a cloud server, a road model is generated by the cloud server according to the road information, and the road model is sent to each edge computing gateway;
when the calibrated vehicle runs in the detection section of the road, calculating the vehicle coordinates of the calibrated vehicle according to the signal intensity value of V2X communication between each edge calculation gateway and a vehicle-mounted terminal in the detection section and the gateway coordinates of each edge calculation gateway in the detection section, selecting effective vehicle coordinates from each vehicle coordinate, and determining the position information of the calibrated vehicle according to the effective vehicle coordinates.
Compared with the prior art, the method has the advantages that the road information is acquired by calibrating each data acquisition unit beside the road where the vehicle runs, each edge computing gateway beside the road side sends the road information acquired by the corresponding data acquisition unit to the cloud server, and the cloud server generates a road model according to all the road information and sends the road model to each edge computing gateway; when a calibrated vehicle runs in a detection interval of a road, V2X communication is carried out between each edge calculation gateway in the detection interval and a vehicle-mounted terminal, a corresponding number of signal intensity values are generated, vehicle coordinates of the calibrated vehicle can be obtained through calculation after any edge calculation gateway in the detection interval obtains the corresponding number of signal intensity values and gateway coordinates of each edge calculation gateway in the detection interval, vehicle coordinates of the calibrated vehicle are obtained after the same operation is carried out on each edge calculation gateway in the detection interval, then the vehicle coordinates are analyzed and judged by any edge calculation gateway in the detection interval, effective vehicle coordinates are selected from the vehicle coordinates, and position information of the calibrated vehicle is determined according to the effective vehicle coordinates, so that accurate positioning of the vehicle is achieved. Therefore, even if the vehicle runs to a road without satellite signals or GPS signal differences, the vehicle can still be accurately positioned through the vehicle-road cooperative auxiliary positioning system based on the edge computing gateway, positioning is not required to be carried out through receiving satellite data, GPS positioning can be separated, vehicle-road cooperative auxiliary positioning and auxiliary driving behaviors in the road are realized, and therefore the position information of the vehicle can be collected in real time in vehicle networking application, and vehicle-road cooperative management can be better carried out. In addition, in the application of the internet of vehicles, compared with a high-precision RTK positioning system with higher hardware construction and software algorithm cost, the hardware construction cost for performing auxiliary positioning by adopting the edge computing gateway is lower, and the real-time dynamic position updating of the vehicle can be met by utilizing the low time delay of V2X communication and the edge computing capability of the edge computing gateway, so that safe and reliable practical experience is provided for the cooperation of the vehicle and the road.
Drawings
FIG. 1 is a schematic diagram of a vehicle-road cooperative auxiliary positioning system based on an edge computing gateway in an embodiment of the present invention;
FIG. 2 is a block diagram of an edge computing gateway in an embodiment of the invention;
FIG. 3 is a schematic diagram illustrating a method for calibrating a driving range of a vehicle according to an embodiment of the present invention;
fig. 4 is a flowchart of a vehicle-road cooperative auxiliary positioning method based on an edge computing gateway in the embodiment of the present invention.
Description of reference numerals:
10. a data acquisition unit; 20. a vehicle-mounted terminal; 30. an edge computing gateway; 31. a data extraction module; 32. a V2X wireless communication module; 33. an AI processing module; 40. and (4) a cloud server.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein.
Due to the fact that satellite signals cannot be fully covered, local road sections (such as high-rise forest road sections, remote mountain road sections, tunnels and the like) on the road where the vehicle runs do not have satellite signals or GPS signal differences. At present, V2X is usually adopted to carry GNSS to carry out high-precision positioning of a vehicle, when the vehicle runs on a road section with good satellite signals or GPS signals, the positioning mode can realize accurate positioning, but when the vehicle runs on a road section without satellite signals or poor GPS signals, the condition that satellite data cannot be received or the satellite data is received, and large delay exists, so that the vehicle positioning is inaccurate. The invention relates to a vehicle-road cooperative auxiliary positioning system based on an edge computing gateway, which can solve the problem of inaccurate positioning when a vehicle runs to a road section without satellite signals or GPS signals, namely, two positioning modes are simultaneously adopted during the running process of the vehicle on the road, wherein one mode is a main positioning mode for carrying GNSS to carry out high-precision positioning on the vehicle by V2X, and the other mode is an auxiliary positioning mode for carrying out high-precision positioning on the vehicle by adopting the vehicle-road cooperative auxiliary positioning system based on the edge computing gateway.
With reference to fig. 1, an embodiment of the present invention provides a vehicle-road cooperative auxiliary positioning system based on an edge computing gateway, including a vehicle-mounted terminal 20 disposed on a calibration vehicle, edge computing gateways 30 respectively disposed on lamp posts on the side of a road where the calibration vehicle travels, and a data acquisition unit 10 and a cloud server 40 respectively connected to the edge computing gateways 30;
the data acquisition unit 10 is used for acquiring road information of a road;
the edge computing gateway 30 is configured to send the road information to the cloud server 40;
the cloud server 40 is configured to generate a road model according to the road information, and send the road model to each edge computing gateway 30;
the edge calculation gateway 30 is further configured to, when the calibrated vehicle runs in the detection zone of the road, calculate vehicle coordinates of the calibrated vehicle according to the signal intensity value of V2X communication between each edge calculation gateway 30 and the vehicle-mounted terminal 20 in the detection zone and the gateway coordinates of each edge calculation gateway 30 in the detection zone, select effective vehicle coordinates from each vehicle coordinate, and determine location information of the calibrated vehicle according to the effective vehicle coordinates.
Specifically, the vehicle-mounted terminal 20 is disposed on a calibration vehicle, so that the calibration vehicle communicates with the edge computing gateway 30 via the vehicle-mounted terminal 20 via V2X, wherein the calibration vehicle refers to a selected one of a plurality of vehicles running on a road to be positioned, and the selected vehicle is the calibration vehicle. Edge computing gateways 30 are arranged on lamp poles beside a road where the calibration vehicle runs, each edge computing gateway 30 is connected with one data acquisition unit 10, and meanwhile, each edge computing gateway 30 is in communication connection with a cloud server 40. The road information acquired by each data acquisition unit 10 is local section information of a road on which a calibration vehicle runs, all the data acquisition units 10 can comprehensively acquire full section information of the road on which the calibration vehicle runs, the edge computing gateway 30 sends the road information acquired by the corresponding data acquisition unit 10 (namely, the data acquisition unit 10 connected with the edge computing gateway 30) to the cloud server 40, and the cloud server 40 generates a road model according to the road information sent by all the edge computing gateways 30 and sends the generated road model to each edge computing gateway 30. When the detection interval of the vehicle running on the road is calibrated, each edge computing gateway 30 in the detection interval performs V2X communication with the vehicle-mounted terminal 20 and generates a corresponding number of signal intensity values, and because the edge computing gateways 30 have the ring network characteristic, each edge computing gateway 30 in the detection interval can obtain the signal intensity values of V2X communication between other edge computing gateways 30 in the detection interval and the vehicle-mounted terminal 20 and the gateway coordinates of other edge computing gateways 30. Taking a certain edge calculation gateway 30 in the detection interval as an example for explanation, after the edge calculation gateway 30 obtains a corresponding number of signal intensity values and gateway coordinates of each edge calculation gateway 30 in the detection interval, vehicle coordinates of a calibrated vehicle can be obtained through calculation, after the same operation is performed on each edge calculation gateway 30 in the detection interval, a corresponding number of vehicle coordinates are obtained, then, a certain edge calculation gateway 30 in the detection interval analyzes and judges the vehicle coordinates, effective vehicle coordinates are selected from the vehicle coordinates, and position information of the calibrated vehicle is determined according to the effective vehicle coordinates, so that accurate positioning of the vehicle is realized.
Therefore, even if the vehicle runs to a road without satellite signals or GPS signal differences, the vehicle can still be accurately positioned through the vehicle-road cooperative auxiliary positioning system based on the edge computing gateway, positioning is not required to be carried out through receiving satellite data, GPS positioning can be separated, vehicle-road cooperative auxiliary positioning and auxiliary driving behaviors in the road are realized, and therefore the position information of the vehicle can be collected in real time in vehicle networking application, and vehicle-road cooperative management can be better carried out. In addition, in the application of the internet of vehicles, compared with a high-precision RTK positioning system with higher hardware construction and software algorithm cost, the hardware construction cost for auxiliary positioning by adopting the edge computing gateway 30 is lower, and by utilizing the low time delay of V2X communication and the edge computing capability of the edge computing gateway 30, the real-time dynamic position updating of vehicles can be met, and safe and reliable actual experience is provided for the cooperation of the vehicle and the road.
Further, the edge computing gateway 30 has a plurality of data access functions such as an optical fiber, an ethernet, an RS485, an RS232, a PWM, an I/O interface, and the like, and supports a wireless network such as 5G, V2X, and can interact with a vehicle provided with the in-vehicle terminal 20. The real-time information of the road can be acquired through the connection of the wired interface and the wireless signal, and then the data is subjected to algorithm processing such as AI (artificial intelligence) and the like through the strong edge computing capability of the vehicle, so that the position and state information of the vehicle in the road can be fed back in real time, and the real-time data can be provided for the vehicle.
Optionally, the data acquisition unit 10 includes a high-definition camera disposed on a lamp post at the road side, and the high-definition camera is used for acquiring images of the road; the cloud server 40 is configured to synthesize the images of the road collected by the high definition camera into the road model through a synthesis algorithm.
In this embodiment, the high definition camera acquires images of a road, and the cloud server 40 synthesizes the images of the road acquired by the high definition camera into a road model through a synthesis algorithm, so as to establish the road model.
Further, the high-definition camera in the data acquisition unit 10, the lamp post located beside the road side where the calibration vehicle runs, and the edge calculation gateway 30 arranged on the lamp post together form a smart lamp post, and the edge calculation gateway 30 serves as a core device of the smart lamp post to determine the position information of the calibration vehicle.
Further, when a road model is established, the intelligent lamp poles are uniformly distributed on two sides of a road where the calibrated vehicle runs, and the distance between the intelligent lamp poles on the same side of the road is usually 50 meters. Install the high definition digtal camera in edge calculation gateway 30 and the data acquisition unit 10 on the wisdom lamp pole to debug high definition digtal camera's shooting angle, can cover whole road with the picture that the high definition digtal camera on all wisdom lamp poles of assurance road both sides was shot.
Further, the data collecting unit 10 further includes a radar disposed on a lamp post of a road side where the calibration vehicle travels and a ground sensor disposed on a signboard of the road where the calibration vehicle travels.
Optionally, as shown in fig. 2, the edge computing gateway 30 includes a data extraction module 31, a V2X wireless communication module 32, and an AI processing module 33; the data extraction module 31 is configured to send the road information acquired by the corresponding data acquisition unit 10 to the cloud server 40; the V2X wireless communication module 32 is used for carrying out V2X communication with the vehicle-mounted terminal 20; the AI processing module 33 is used to determine location information for the calibration vehicle.
In this embodiment, the edge computing gateway 30 includes a plurality of modules, which are a data extraction module 31, a V2X wireless communication module 32, and an AI processing module 33, where the data extraction module 31 extracts road information acquired by the data acquisition unit 10 from the data acquisition unit 10 and sends the road information to the cloud server 40, so that the cloud server 40 establishes a road model according to the road information, and at the same time, performs V2X communication with the vehicle-mounted terminal 20 through the V2X wireless communication module 32 and generates a signal intensity value, and the AI processing module 33 calculates vehicle coordinates of a calibrated vehicle according to the signal intensity value and gateway coordinates of the edge computing gateway 30, selects effective vehicle coordinates from the vehicle coordinates, and determines position information of the calibrated vehicle according to the effective vehicle coordinates. In this way, the edge computing gateway 30 implements different operation processes by setting different modules, and the modules cooperate with each other to implement accurate positioning of the vehicle on the road.
Optionally, the data extraction module 31 includes RS485, RS232, PWM and multiple ethernet interfaces. So, the accessible ethernet draws the real-time picture that high definition digtal camera was shot, through internet of things interface connectable ground sensor (earth magnetism, traffic signal tablet etc.) such as RS485 to draw the positional information of traffic facilities such as road sign tablet, make cloud server 40 can generate the road model that is closer to actual road according to the real-time picture of road and the traffic facilities position on the road, provide more and more accurate data basis for the car networking is used.
Furthermore, a road information acquisition part consisting of a high-definition camera, a radar and a ground sensor is a static road model building process. The static road model is divided into four levels, namely an image perception layer, a road layer, a lane layer and a traffic facility layer, wherein the image perception layer is formed by a high-definition camera and a road synthesized by multiple paths of sections shot by the high-definition camera, the road layer comprises south and north lanes, intersections and the like, the road layer is formed by the geometric shape, the topological structure and the road grade of the road, the lane layer is formed by the geometric shape, the topological structure and lane marks of the lanes, and the traffic facility layer is formed by sign boards, road edges, isolation belts and the like on the road.
Optionally, the edge computing gateway 30 further includes a position sensor, and the position sensor is configured to record a position coordinate of a light pole on the road side, and use the position coordinate of the light pole as a gateway coordinate of the edge computing gateway 30.
Since the specific location information (i.e., location coordinates) of the lamp posts on both sides of the road is known and usually does not change, when the edge computing gateway 30 and the high-definition camera are installed on the lamp posts, they are in a static state, and at this time, the location coordinates of the corresponding lamp posts can be recorded by the location angle sensor in the edge computing gateway 30, and the location coordinates of the lamp posts are used as the gateway coordinates of the edge computing gateway 30, so as to obtain the gateway coordinates of the edge computing gateway 30, and there is no need to obtain the gateway coordinates of the edge computing gateway 30 through a series of additional computations.
Optionally, the AI processing module 33 is specifically configured to:
marking a detection section in the road model;
when the vehicle is calibrated to enter the detection interval, acquiring gateway coordinates of each edge calculation gateway 30 in the detection interval and signal intensity values of V2X communication between each edge calculation gateway 30 and the vehicle-mounted terminal 20 in the detection interval;
calculating the linear distance between each edge calculation gateway 30 and the calibrated vehicle in the detection interval according to each signal intensity value;
calculating the vehicle coordinates of the calibrated vehicle according to the gateway coordinates of each edge calculation gateway 30 in the detection interval and the straight-line distance between each edge calculation gateway 30 and the calibrated vehicle;
selecting effective vehicle coordinates from the vehicle coordinates;
and determining the position information of the calibrated vehicle according to the effective vehicle coordinates.
Specifically, when a calibration vehicle is positioned, a detection zone needs to be calibrated first, the detection zone is a local section area in a road where the calibration vehicle runs, and two lamp posts on two sides of the road are taken as an example for explanation. The road area synthesized by the road pictures shot by the high-definition cameras on the four lamp poles is taken as a detection section, and four edge calculation gateways 30 are arranged in the detection section and are respectively marked as a gateway S1, a gateway S2, a gateway N1 and a gateway N2. As shown in fig. 3, when the edge calculation gateway 30 in the detection zone detects that the calibrated vehicle enters the detection zone, the in-vehicle terminal 20 on the calibrated vehicle performs V2X communication with the gateways S1, S2, N1, and N2, respectively, the gateway S1 records the RSSI value of V2X communication between the gateway S1 and the in-vehicle terminal 20, and the gateways S2, N1, and N2 record the corresponding RSSI value of V2X communication, respectively, by using the ring network characteristics of the gateways, the gateway S1 can take the RSSI values and gateway coordinates of the gateways S2, N1, and N2, and the vehicle coordinates of the calibrated vehicle relative to the road can be calculated by using a difference algorithm. With reference to fig. 3, the following is specifically exemplified:
1. gateway coordinates S1 (x 1, y 1), S2 (x 2, y 2), N1 (x 3, y 3), N2 (x 4, y 4) of known gateways S1, S2, N1, N2;
2. calculating linear distances LS1, LS2, LN1 and LN2 between the gateways S1, S2, N1 and N2 and the vehicle-mounted terminal 20 respectively through a calculation formula of the signal strength value RSSI;
3. the coordinates (x 0, y 0) of the in-vehicle terminal 20, the gateway coordinates S1 (x 1, y 1), S2 (x 2, y 2), N1 (x 3, y 3), N2 (x 4, y 4), and the straight-line distances LS1, LS2, LN1, LN2 satisfy the following equations:
4. using the linearized equation solution, the coordinates (x 0, y 0) of the vehicle-mounted terminal 20 can be calculated as follows:
the vehicle coordinates of the calibrated vehicle are calculated in the four gateways S1, S2, N1 and N2 in the detection zone by adopting the method to obtain four groups of vehicle coordinates, the four groups of vehicle coordinates are analyzed and judged in any one of the four gateways S1, S2, N1 and N2, for example, the gateway closest to the vehicle-mounted terminal 20 is used for analyzing and judging, then effective vehicle coordinates are selected from the effective vehicle coordinates, and finally the position information of the calibrated vehicle is determined according to the effective vehicle coordinates. Thus, the positioning of the calibration vehicle is realized.
Optionally, the calculation formula of the signal strength value is:
wherein, RSSI is the signal strength value, b is the signal strength value when the signal transmitting end and the signal receiving end are separated by 1 meter, L is the distance between the signal transmitting end and the signal receiving end, and n is the signal path attenuation factor.
Specifically, the parameters b and n in the formula are known reference values, the vehicle-mounted terminal 20 serves as a signal transmitting end to transmit the V2X communication signal, and the edge computing gateway 30 serves as a signal receiving end to receive the V2X communication signal transmitted by the vehicle-mounted terminal 20. For each edge calculation gateway 30 in the detection interval, after acquiring the signal strength value RSSI communicated with V2X between the in-vehicle terminals 20, the straight-line distance L between the edge calculation gateway 30 and the in-vehicle terminals 20 can be calculated by substituting the signal strength value RSSI into the above calculation formula.
Optionally, the selecting effective vehicle coordinates from the vehicle coordinates specifically includes:
randomly selecting one of the vehicle coordinates as a reference coordinate, and using the other vehicle coordinates as undetermined coordinates;
comparing each coordinate to be determined with a reference coordinate;
when the difference value between the coordinates to be determined and the reference coordinates is within a preset coordinate range, taking the coordinates to be determined and the reference coordinates as effective vehicle coordinates;
and when the difference value between each coordinate to be determined and the reference coordinate exceeds the preset coordinate range, selecting one coordinate from the coordinates to be determined as the reference coordinate, and comparing each coordinate to be determined with the reference coordinate again until the effective vehicle coordinate is selected.
In this embodiment, the preset coordinate range is a preset coordinate value range, and may be set by a designer according to experience or through experiments. By analyzing and comparing the vehicle coordinates calculated by each edge calculation gateway 30 in the detection region, the vehicle coordinates with large coordinate value difference (such as the vehicle coordinates obtained due to calculation errors) can be eliminated, so that effective vehicle coordinates closer to the actual position coordinates of the vehicle are obtained, and the vehicle positioning accuracy is improved.
Optionally, the AI processing module 33 is further configured to mark the calibration vehicle in the road model according to the position information of the calibration vehicle.
In this embodiment, after the AI processing module 33 in the edge computing gateway 30 calculates the position information of the calibration vehicle relative to the road, the calibration vehicle may be marked in the road model. Therefore, by means of the strong edge computing capability of the edge computing gateway 30, the position of an unknown vehicle can be rapidly computed, and the position of the unknown vehicle can be continuously corrected according to the position of a known lamp post, so that accurate positioning is realized.
With reference to fig. 4, another embodiment of the present invention provides a vehicle-road cooperative auxiliary positioning method based on an edge computing gateway, where the positioning method is based on any one of the above vehicle-road cooperative auxiliary positioning systems based on an edge computing gateway, and the positioning method includes the following steps:
s100, acquiring road information of a road on which a calibration vehicle runs;
step S200, sending the road information to a cloud server 40, generating a road model by the cloud server 40 according to the road information, and sending the road model to each edge computing gateway 30;
step S300, when the detection section of the road where the vehicle runs is calibrated, calculating the vehicle coordinates of the calibrated vehicle according to the signal intensity value of V2X communication between each edge calculation gateway 30 and the vehicle-mounted terminal 20 in the detection section and the gateway coordinates of each edge calculation gateway 30 in the detection section, selecting effective vehicle coordinates from each vehicle coordinate, and determining the position information of the calibrated vehicle according to the effective vehicle coordinates.
In this embodiment, the road information of the road on which the calibrated vehicle runs is obtained, then the road information is sent to the cloud server 40, the cloud server 40 generates a road model according to the road information, and sends the road model to each edge computing gateway 30, when the calibrated vehicle runs in the detection interval of the road, the vehicle coordinates of the calibrated vehicle are computed according to the signal intensity value of V2X communication between each edge computing gateway 30 and the vehicle-mounted terminal 20 in the detection interval and the gateway coordinates of each edge computing gateway 30 in the detection interval, effective vehicle coordinates are selected from each vehicle coordinate, and the position information of the calibrated vehicle is determined according to the effective vehicle coordinates, so that the unknown vehicle on the road is accurately positioned. Therefore, even if the vehicle runs to a road without satellite signals or GPS signals, the vehicle can still be accurately positioned by the positioning method of the embodiment, so that the vehicle can be separated from GPS positioning, and the cooperative auxiliary positioning of the vehicle and the driving assisting behavior in the road are realized, so that the position information of the vehicle can be acquired in real time in the application of the internet of vehicles, and the cooperative management of the vehicle and the road can be better performed.
Although the present disclosure has been described above, the scope of the present disclosure is not limited thereto. Various changes and modifications may be effected therein by one of ordinary skill in the pertinent art without departing from the spirit and scope of the present disclosure, and these changes and modifications are intended to be within the scope of the present disclosure.
Claims (10)
1. The vehicle-road cooperative auxiliary positioning system based on the edge computing gateway is characterized by comprising a vehicle-mounted terminal (20) arranged on a calibration vehicle, the edge computing gateways (30) respectively arranged on lamp posts at the sides of roads on which the calibration vehicle runs, and a data acquisition unit (10) and a cloud server (40) respectively connected with the edge computing gateways (30);
the data acquisition unit (10) is used for acquiring road information of the road;
the edge computing gateway (30) is used for sending the road information to the cloud server (40);
the cloud server (40) is used for generating a road model according to the road information and sending the road model to each edge computing gateway (30);
the edge computing gateways (30) are further used for computing vehicle coordinates of the calibrated vehicle according to signal intensity values of V2X communication between each edge computing gateway (30) and the vehicle-mounted terminal (20) in the detection interval and gateway coordinates of each edge computing gateway (30) in the detection interval when the calibrated vehicle runs in the detection interval of the road, selecting effective vehicle coordinates from each vehicle coordinate, and determining position information of the calibrated vehicle according to the effective vehicle coordinates.
2. The edge computing gateway based vehicle-road cooperative auxiliary positioning system as claimed in claim 1, wherein the edge computing gateway (30) comprises a data extraction module (31), a V2X wireless communication module (32) and an AI processing module (33);
the data extraction module (31) is used for sending the road information acquired by the data acquisition unit (10) to the cloud server (40);
the V2X wireless communication module (32) is used for carrying out V2X communication with the vehicle-mounted terminal (20);
the AI processing module (33) is configured to determine the location information of the calibration vehicle.
3. The edge computing gateway-based vehicle access co-assisted positioning system of claim 2, wherein the AI processing module (33) is specifically configured to:
marking the detection section in the road model;
when the calibrated vehicle enters the detection interval, acquiring the gateway coordinates of each edge computing gateway (30) in the detection interval and the signal intensity value of V2X communication between each edge computing gateway (30) and the vehicle-mounted terminal (20) in the detection interval;
calculating the linear distance between each edge calculation gateway (30) and the calibrated vehicle in the detection interval according to each signal intensity value;
calculating the vehicle coordinates of the calibrated vehicle according to the gateway coordinates of each edge calculation gateway (30) in the detection interval and the straight-line distance between each edge calculation gateway (30) and the calibrated vehicle;
selecting the effective vehicle coordinates from the vehicle coordinates;
and determining the position information of the calibrated vehicle according to the effective vehicle coordinates.
4. The edge computing gateway-based vehicle access cooperative auxiliary positioning system according to claim 3, wherein the signal strength value is calculated by the following formula:
wherein, RSSI is the signal strength value, b is the signal strength value when the signal transmitting end and the signal receiving end are separated by 1 meter, L is the distance between the signal transmitting end and the signal receiving end, and n is the signal path attenuation factor.
5. The edge computing gateway-based roadway cooperative auxiliary positioning system of claim 3, wherein the selecting the valid vehicle coordinates from the vehicle coordinates comprises:
randomly selecting one of the vehicle coordinates as a reference coordinate, and using the rest vehicle coordinates as undetermined coordinates;
comparing each undetermined coordinate with the reference coordinate respectively;
when the difference value between the undetermined coordinate and the reference coordinate is within a preset coordinate range, taking the undetermined coordinate and the reference coordinate as the effective vehicle coordinate;
and when the difference value between each undetermined coordinate and the reference coordinate exceeds the preset coordinate range, selecting one of the undetermined coordinates as the reference coordinate, and comparing each undetermined coordinate with the reference coordinate again until the effective vehicle coordinate is selected.
6. The edge computing gateway-based vehicle road co-assisted positioning system of claim 2, wherein the AI processing module (33) is further configured to mark the calibration vehicle in the road model according to the location information of the calibration vehicle.
7. The edge computing gateway-based vehicle and road cooperative auxiliary positioning system as claimed in claim 1, wherein the data acquisition unit (10) comprises a high-definition camera arranged on a lamp post at the road side, and the high-definition camera is used for acquiring pictures of the road;
the cloud server (40) is used for synthesizing the images of the road collected by the high-definition camera into the road model through a synthesis algorithm.
8. An edge computing gateway based vehicle road cooperative auxiliary positioning system according to claim 2, wherein the edge computing gateway (30) further comprises a position sensor for recording position coordinates of a lamp post at the road side as the gateway coordinates of the edge computing gateway (30).
9. The edge computing gateway-based vehicle access co-location system according to claim 2, wherein the data extraction module (31) comprises RS485, RS232, PWM and multi-path ethernet interfaces.
10. An edge computing gateway-based vehicle-road cooperative auxiliary positioning method based on any one of claims 1 to 9, characterized by comprising the following steps:
acquiring road information of a road on which a calibrated vehicle runs;
sending the road information to a cloud server (40), generating a road model by the cloud server (40) according to the road information, and sending the road model to each edge computing gateway (30);
when the calibrated vehicle runs in the detection section of the road, calculating the vehicle coordinates of the calibrated vehicle according to the signal intensity value of V2X communication between each edge calculation gateway (30) and a vehicle-mounted terminal (20) in the detection section and the gateway coordinates of each edge calculation gateway (30) in the detection section, selecting effective vehicle coordinates from each vehicle coordinate, and determining the position information of the calibrated vehicle according to the effective vehicle coordinates.
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