CN110769373A - Community discovery-based vehicle positioning method - Google Patents
Community discovery-based vehicle positioning method Download PDFInfo
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- CN110769373A CN110769373A CN201910911903.8A CN201910911903A CN110769373A CN 110769373 A CN110769373 A CN 110769373A CN 201910911903 A CN201910911903 A CN 201910911903A CN 110769373 A CN110769373 A CN 110769373A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/51—Relative positioning
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- 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
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- 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]
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- 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]
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- 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]
- H04W4/46—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for vehicle-to-vehicle communication [V2V]
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Abstract
The invention discloses a vehicle position determination algorithm. The method comprises the steps of utilizing the community relation in the Internet of vehicles, using the real-time position of the vehicle with strong GPS signals, utilizing the relation among the communities, utilizing the communication capacity and the neighbor position relation among vehicle nodes, and carrying out position calculation on the vehicles which are located in a certain distance range of the vehicle with strong GPS signals, have the same or similar driving destinations and have weak GPS signals. Compared with the existing urban Internet of vehicles positioning method, the method has lower positioning error, is particularly excellent in performance of GPS signal weak positions of viaducts, underground garages and the like, and has higher positioning result stability under different traffic conditions.
Description
Technical Field
The invention relates to a positioning method of vehicle nodes in a vehicle self-organizing network, in particular to a vehicle position determining method based on community discovery.
Background
The Ad-hoc Networks (hereinafter referred to as "vehicle networking") mainly comprises vehicle-to-vehicle (V2V/IVC), vehicle-to-road side units (V2I/VRC) and direct communication and multi-hop communication between the vehicle-to-vehicle (V2P), and an Ad-hoc, distributed control vehicle-specific and short-distance communication network can be dynamically and quickly constructed in the existing road network. The car networking has the main characteristics of large network scale, fast change of network density along with time and space, special movement of vehicle nodes, frequent change of network topology, frequent network division, no obvious hardware constraint of the nodes, rich external auxiliary information of the nodes and different service quality requirements of different vehicles.
Community discovery intuitively means that particularly dense groups of network nodes appear in the network. The connection relations among the nodes are not disordered, and the connection relations are characterized by containing a certain rule, the connection relations inside each community are relatively close, and the connection relations among the communities are relatively sparse. Through discovering communities contained in networks such as biological networks, social networks, scientific and technical networks, information networks and the like, hidden internal structures in the networks can be understood more deeply and visually.
At present, the method for positioning the vehicle mainly comprises the following steps:
compared with the traditional measuring method, the GPS global positioning system has the characteristics of high positioning precision, short observation time, simple and convenient operation, capability of rapidly providing three-dimensional coordinates and all-weather operation, and capability of continuously providing three-dimensional position and speed information with higher precision. The technology of combining other navigation systems by using GPS is also mature, and the GPS/INS, the GPS/DR, the GPS/MM, the GPS/DR/MM and the like are common.
The vehicle-mounted sensor positioning system is used for making up for quick drift errors of the low-cost IMU, assisting the MSINS to greatly reduce system speed and position errors, and positioning the vehicle with higher precision in navigation. In addition, under different driving conditions, a positioning method for combining the in-vehicle sensor with satellite positioning and fusing sensor information and satellite positioning information by using a Kalman filter is available.
The positioning system of the internet of vehicles is an information interaction network formed by the position, the driving speed, the driving route and the like of the vehicles. The method mainly comprises a positioning method based on a ranging technology and a positioning method based on a non-ranging technology. The former includes an RSSI method and a TDOA method, and the latter utilizes information such as self network topology, connectivity, Hop count and the like to carry out position estimation, including a centroid method, a DV-Hop method, an Amorphous method, an APIT method, a convex optimization positioning method and the like.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a vehicle positioning method based on community discovery. The method comprises the steps of utilizing the community relation in the Internet of vehicles, using the real-time position of the vehicle with strong GPS signals, utilizing the relation among the communities, utilizing the communication capacity and the neighbor position relation among vehicle nodes, and carrying out position calculation on the vehicles which are located in a certain distance range of the vehicle with strong GPS signals, have the same or similar driving destinations and have weak GPS signals. Compared with the existing urban Internet of vehicles positioning method, the method has lower positioning error, is particularly excellent in performance of GPS signal weak positions of viaducts, underground garages and the like, and has higher positioning result stability under different traffic conditions.
The invention discloses a vehicle positioning method based on community discovery, which comprises the following steps:
step 1: vehicle nodes which have the same driving destination and are in the same community use the characteristic that the vehicle nodes can communicate with each other in the internet of vehicles to enable the vehicle nodes with the available GPS to carry out position calculation on the vehicle nodes with the unavailable GPS. Calculating the current position according to two conditions that whether the node vector of the vehicle to be positioned in the community contains the existing historical destination information or not at any time period T;
step 2: if no relevant historical destination information exists in the node vector of the vehicle to be positioned in the time period T, the calculation is carried out by dividing the node vector into whether the previous time period, namely the time period T-1, contains the position information: 1) if the time period (T-1) has no position information, the vehicle to be positioned sends a request packet to a neighbor vehicle within a certain distance range around at a certain frequency to obtain position feedback, after receiving the request packet, the neighbor vehicle adds a data packet to the position of the time period T and returns the data packet to the vehicle to be positioned, and the vehicle to be positioned receives the position information and stores the position information into a temporary position set. Meanwhile, the signal strength from the neighbor node when receiving feedback is stored in the temporary signal set. Calculating the position information of the vehicle to be positioned at the moment according to the temporary position set and the temporary signal set; 2) if the T-1 time period has position information, firstly, acquiring the average speed and the running direction of the vehicle to be positioned in the T-1 time period, jointly calculating the total average speed of the two time periods according to the average speed of the time period T, and then calculating the position information of the vehicle to be positioned at the moment according to the included angle between the running direction and the earth rotation direction;
and 3, step 3: if the node vector of the vehicle to be positioned in the time period T has relevant historical destination information, the calculation is carried out by dividing whether the current vehicle has one destination or not: 1) if the current vehicle has only one destination, vehicle identifiers (vehicle node IDs) of all the same destinations are stored in the temporary vehicle set, and the position information of the vehicle to be positioned is calculated according to the position information of the neighbor vehicles of the same destination. Firstly, sending a detection request message to a neighbor vehicle, storing a vehicle identifier in a feedback message into a temporary vehicle set, then sending a position request message and all elements of the temporary vehicle set to a neighbor node, judging whether the identifier of the current vehicle is in the temporary vehicle set after the neighbor vehicle receives the position request message, if so, indicating that the current vehicle is a neighbor vehicle which can be detected around the vehicle to be positioned and has the same destination with the vehicle to be positioned, feeding back the position of the current vehicle to the vehicle to be positioned, and calculating the position information of the current vehicle by utilizing the step 2 after the vehicle to be positioned receives the feedback; 2) if the vehicle to be located has more than one destination, the most suitable destination needs to be selected. Firstly, calculating a linear distance between the vehicle to be positioned and the neighbor vehicle according to the signal intensity when the neighbor vehicle sends feedback, then calculating a driving direction included angle between the two vehicles according to a T-1 time period and a signal transmission direction when the neighbor vehicle sends feedback in the time period T, then calculating the time required by receiving the neighbor feedback to positioning, finally calculating the number of the neighbor vehicles capable of communicating, finally calculating the most suitable destination according to the destination overlapping degree, and calculating the position information of the vehicle to be positioned at the moment according to the method in the step 3) and the step 1).
And 4, step 4: and correcting the positions calculated in the step 2 and the step 3.
Preferably, the position correction in the fourth step is specifically: and (3) calculating a GPS positioning position D1 of the vehicle available for the GPS, comparing the position D2 obtained by the calculation in the step 2 and the step 3, and obtaining the distance Len between the two positions by a norm solving method, wherein Len | | | D1-D2| |. And storing all destination information of the vehicle to be positioned in the time period T into the temporary position set, iteratively deleting the destination and calculating a Len value until the Len value is smaller than a threshold value error, and finishing the position correction. Wherein error can be obtained by inquiring the relation between the strength of the GPS signal and the position error.
The method utilizes a community network structure in the Internet of vehicles to determine the real-time position of the vehicle, and the community network structure is divided according to the driving destination of the current journey of the vehicle, so that the method is suitable for the Internet of vehicles in rapid change; the positioning mode is based on communication inside the vehicle community, and is suitable for the situation with high requirement on the real-time performance of the position; the correction of the positioning result ensures the reliability of the positioning result and reduces the error rate of the positioning.
Drawings
FIG. 1 is a flow chart of vehicle position calculation
FIG. 2 is a diagram illustrating a process of determining whether or not there is position information in a time zone (T-1)
FIG. 3 is a diagram illustrating a determination of whether there is a history of destinations in time T
FIG. 4 is a flow chart of positioning result correction
Detailed Description
The invention will be further explained with reference to the drawings.
Clustering and finding a community relation in the Internet of vehicles by taking the destination as a characteristic value through the destination where the vehicles travel; and calculating the position of the vehicle with weak GPS signal or lost vehicle in real time by using the vehicle association closeness in the community relation and taking the vehicle with normal GPS signal as a reference. (after the result is calculated, whether the result calculated by the method for the vehicle with the abnormal GPS signal needs to be corrected or not is determined according to the comparison between the result calculated by the method for the vehicle with the normal GPS signal and the real-time GPS result)
Based on the facts, the method can utilize most vehicles with accurate positions in the Internet of vehicles to calculate the positions of a small part of vehicles which cannot be positioned by themselves due to GPS signal loss. Because the number of vehicles in the internet of vehicles is large, and because most of the vehicles are in high proximity to temporary destinations in a short time period, the invention adopts a vehicle positioning method based on community discovery to calculate the position of the vehicle, and because most of the vehicles with normal GPS signals occupy, the method can be further optimized through the accurate data, and the accuracy of the positioning result is improved.
As shown in fig. 1, a vehicle positioning method based on community discovery:
firstly, in the time period T for calculating the position of the vehicle, the vehicle needing to calculate the position in the time period T is firstly judged whether to have historical destination information, because if the vehicle does not have the historical destination information, the vehicle needing to calculate the position is just added into the internet of vehicles, historical data is not generated, or the vehicle is suddenly lost in the time period and purposeful information in the last time period is obtained, and whether the time period (T-1) has the position information is further judged.
And secondly, if the vehicle has historical destination information, judging whether the destination information is the same historical destination or not, and if not, acquiring the direction, strength and transmission time of the transmitted signal of the neighbor vehicle to optimize the destination to be the most appropriate destination.
And thirdly, when the vehicles have historical destination information in a time period T, if the number of the historical destinations is one, storing the identifiers of the vehicles at present in a temporary set, continuously detecting the neighbor nodes according to a certain frequency, then broadcasting request position information to the neighbor nodes, if the identifiers of the neighbor nodes are also in the temporary set, directly positioning, and otherwise, packaging the position information of the neighbor nodes and returning to continue detection.
And fourthly, if the destination is not only a historical destination, calculating the linear distance according to the signal intensity and the attenuation degree of the signal, solving the message transmission direction and the transmission time and counting the number of the neighbor vehicles in the calculation process, and finally calculating the most suitable destination to be transferred into the calculation method of only one historical destination to continue the calculation.
And fifthly, when detecting that the position information does not exist in the time period (T-1), requesting position feedback by a neighbor vehicle at a certain frequency, extracting information after receiving the position feedback, analyzing the signal intensity when the feedback is received, and then calculating the real-time position according to the intensity.
Sixthly, if there is position information, acquiring the average speed of the time period T and the time period (T-1) and the driving direction in the time period (T-1), and then calculating the position.
And seventhly, after the position of the vehicle is calculated, comparing the GPS positioning position of the vehicle which can be used by the GPS and is normal with the position of the vehicle obtained by the method, if the distance difference between the GPS positioning position and the position of the vehicle is larger than a certain threshold value, the result of the method needs to be adjusted, the position needs to be corrected, and the method can be considered to be accurate until the difference between the GPS positioning position and the position of the vehicle is within a certain threshold value range.
Eighth, if the GPS is available and the difference between the normal vehicle's location and the calculated position is within a certain threshold, then the method is accurate and a final community-discovery-based vehicle position calculation can be made. The first to fourth steps described above describe calculation steps for determining whether the vehicle has history destination information during the time period T,
fig. 2 shows a flow chart of the method in the case of a destination in the time period T. The specific calculation process is as follows:
detecting neighbor nodes and solving identifier intersection
Sinter＝Slabel-l∩Stemp-neighbor＝{cinter-1,cinter-2,…,cinter-N}
Storing not only one destination, but also a set of linear distances
Store angles to sets
Storing transmission times to sets
Calculating the most appropriate location destination
The fifth to sixth steps describe the calculation steps for determining whether or not the vehicle position is determined in the time slot (T-1) when the vehicle has no history destination in the time slot T, and fig. 3 shows a flowchart of the method in the case where the vehicle position is determined in the time slot (T-1). The specific calculation process is as follows:
depositing temporary collections
Detection signal strength
Calculating position
Averaging speed
Calculating position (T)
The seventh to eighth steps describe determining whether to perform positioning result correction according to the difference between the solved position and the GPS position after the vehicle position is solved, and fig. 4 shows a flowchart of a method after determining whether to perform position correction.
The foregoing is illustrative of the present invention and is not to be construed as limiting thereof. One of ordinary skill in the art would recognize that any variations or modifications would come within the scope of the present invention.
Claims (2)
1. The vehicle positioning method based on community discovery is characterized by comprising the following steps: the method comprises the following steps:
step 1: vehicle nodes which have the same driving destination and are in the same community use the characteristic that the vehicle nodes can communicate with each other in the Internet of vehicles to enable the vehicle nodes with the available GPS to carry out position calculation on the vehicle nodes with the unavailable GPS; calculating the current position according to two conditions that whether the node vector of the vehicle to be positioned in the community contains the existing historical destination information or not at any time period T;
step 2: if no relevant historical destination information exists in the node vector of the vehicle to be positioned in the time period T, the calculation is carried out by dividing the node vector into whether the previous time period, namely the time period T-1, contains the position information: 1) if the time period (T-1) has no position information, the vehicle to be positioned sends a request packet to a neighbor vehicle within a certain distance range around at a certain frequency to obtain position feedback, after receiving the request packet, the neighbor vehicle adds a data packet to the position of the time period T and returns the data packet to the vehicle to be positioned, and the vehicle to be positioned receives the position information and stores the position information into a temporary position set; meanwhile, storing the signal strength from the neighbor node when receiving feedback into a temporary signal set; calculating the position information of the vehicle to be positioned at the moment according to the temporary position set and the temporary signal set; 2) if the T-1 time period has position information, firstly, acquiring the average speed and the running direction of the vehicle to be positioned in the T-1 time period, jointly calculating the total average speed of the two time periods according to the average speed of the time period T, and then calculating the position information of the vehicle to be positioned at the moment according to the included angle between the running direction and the earth rotation direction;
and 3, step 3: if the node vector of the vehicle to be positioned in the time period T has relevant historical destination information, the calculation is carried out by dividing whether the current vehicle has one destination or not: 1) if the current vehicle has only one destination, storing the vehicle identifiers of all the same destinations into a temporary vehicle set, and calculating the position information of the vehicle to be positioned according to the position information of the neighbor vehicles of the same destination; firstly, sending a detection request message to a neighbor vehicle, storing a vehicle identifier in a feedback message into a temporary vehicle set, then sending a position request message and all elements of the temporary vehicle set to a neighbor node, judging whether the identifier of the current vehicle is in the temporary vehicle set after the neighbor vehicle receives the position request message, if so, indicating that the current vehicle is a neighbor vehicle which can be detected around the vehicle to be positioned and has the same destination with the vehicle to be positioned, feeding back the position of the current vehicle to the vehicle to be positioned, and calculating the position information of the current vehicle by utilizing the step 2 after the vehicle to be positioned receives the feedback; 2) if the vehicle to be positioned has more than one destination, the most suitable destination needs to be selected; firstly, calculating a linear distance between the vehicle to be positioned and the neighbor vehicle according to the signal intensity when the neighbor vehicle sends feedback, then calculating a driving direction included angle between the two vehicles according to a T-1 time period and a signal transmission direction when the neighbor vehicle sends feedback in the time period T, then calculating the time required by receiving the neighbor feedback to positioning, finally calculating the number of the neighbor vehicles capable of communicating, finally calculating the most suitable destination according to the destination overlapping degree, and calculating the position information of the vehicle to be positioned at the moment according to the method in the step 3) and the step 1);
and 4, step 4: and correcting the positions calculated in the step 2 and the step 3.
2. The community discovery-based vehicle positioning method according to claim 1, wherein: the position correction in the fourth step is specifically: calculating a GPS positioning position D1 of the vehicle available for the GPS, comparing the position D2 obtained by the calculation in the steps 2 and 3, and obtaining a distance Len between the two positions by a norm solving method, wherein Len | | | D1-D2| |; and storing all destination information of the vehicle to be positioned in the time period T into the temporary position set, iteratively deleting the destination and calculating a Len value until the Len value is smaller than a threshold value error, and finishing the position correction.
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