CN107315413B - Multi-vehicle cooperative positioning algorithm considering relative positions between vehicles in vehicle-vehicle communication environment - Google Patents

Multi-vehicle cooperative positioning algorithm considering relative positions between vehicles in vehicle-vehicle communication environment Download PDF

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CN107315413B
CN107315413B CN201710564723.8A CN201710564723A CN107315413B CN 107315413 B CN107315413 B CN 107315413B CN 201710564723 A CN201710564723 A CN 201710564723A CN 107315413 B CN107315413 B CN 107315413B
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CN107315413A (en
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鲁光泉
潘日佩
王云鹏
陈鹏
丁川
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Beihang University
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0257—Control of position or course in two dimensions specially adapted to land vehicles using a radar
    • 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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50—Systems of measurement based on relative movement of target
    • G01S13/52—Discriminating between fixed and moving objects or between objects moving at different speeds
    • 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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50—Systems of measurement based on relative movement of target
    • G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
    • 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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
    • 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
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
    • G05D1/0278—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using satellite positioning signals, e.g. GPS

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  • Computer Networks & Wireless Communication (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Automation & Control Theory (AREA)
  • Traffic Control Systems (AREA)
  • Navigation (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

A multi-vehicle cooperative positioning algorithm considering relative positions between vehicles in a vehicle-vehicle communication environment belongs to the technical field of vehicles. The method comprises the steps of acquiring, processing and storing motion state information, position information, relative position information of two vehicles and prediction information of the target vehicle on the vehicle through a vehicle-vehicle communication technology and sensors such as a vehicle-mounted GPS and a millimeter wave radar, carrying out data fusion and prediction on the acquired information to obtain positioning information with higher positioning precision and better positioning effect, and sending the positioning information to other communication vehicles as input values to form a closed-loop process. Under the existing technical level, the invention simply and effectively improves the positioning precision by utilizing the advanced vehicle-to-vehicle communication technology and the vehicle-mounted terminal under the condition of considering the relative position between the vehicles.

Description

Multi-vehicle cooperative positioning algorithm considering relative positions between vehicles in vehicle-vehicle communication environment
Technical Field
The invention relates to the technical field of automobiles, in particular to a multi-vehicle cooperative positioning algorithm considering relative positions among vehicles.
Background
With the development of the car networking technology, the safety and service applications of the car become more and more abundant, and meanwhile, accurate car position information becomes more and more important. The gps (global Positioning system) is widely used in the field of vehicle navigation, and becomes a main source of vehicle position information. However, in urban environments, satellite signals are blocked due to standing of high buildings, and multipath signals are seriously interfered, so that the positioning error of the GPS is high, sometimes reaching tens of meters to hundreds of meters, and even the positioning cannot be performed. Although when the positioning effect is good, the positioning error can reach 5 to 10 meters. The low positioning accuracy and the poor positioning stability limit the use of the GPS system in the life safety fields of vehicle safety, anti-collision detection and the like.
Most of the existing methods for improving positioning accuracy are based on the principle of a satellite navigation positioning system, including position estimation based on a carrier phase and position estimation based on a pseudo range, but positioning errors still exist after the methods are processed, and the problem of GPS signal loss cannot be solved. In general, a car-mounted terminal uses a combination navigation method to improve positioning accuracy by combining inertial navigation and GPS positioning, but an inertial navigation sensor having sufficient accuracy is expensive, and even an expensive sensor requires a long initialization time and causes errors to accumulate over a long period of time.
At present, people pay more attention to the functionality and safety of vehicles, so that the vehicle is inevitably provided with a plurality of sensors, the vehicle CAN acquire and store information such as the position, the speed, the acceleration and the course angle of the vehicle through a CAN (controller area network) bus, a GPS (global positioning system) antenna, an inertial navigation sensor and the like, and CAN acquire the relative position information of a target vehicle and the vehicle through a radar sensor and the like.
Meanwhile, with the increasing maturity of communication technology and vehicle-to-vehicle communication technology, all detected vehicle information can be interacted within a certain range between vehicles through wireless communication equipment, so that each vehicle can simultaneously sense all information of the vehicle and a target vehicle. This provides a new possibility for the multi-vehicle co-location to improve the location accuracy of the own vehicle.
Disclosure of Invention
The purpose of the invention is as follows: a multi-vehicle cooperative positioning algorithm considering relative positions between vehicles in a vehicle-vehicle communication environment is used for at least solving part of problems in the prior art, and under the condition of the prior art, more accurate relative position information and position, speed and acceleration information of a vehicle and a target vehicle are measured between the vehicles to further improve positioning accuracy and reliability.
The technical scheme is as follows: a multi-vehicle co-location algorithm that accounts for relative position between vehicles in a vehicle-to-vehicle communication environment, comprising the steps of:
a vehicle-vehicle communication information interaction device (such as DSRC, wifi and the like), a vehicle-mounted GPS device and a front millimeter wave radar are required to be arranged on a vehicle participating in the multi-vehicle cooperative positioning algorithm, and a vehicle information acquisition and storage module, a multi-vehicle cooperative positioning calculation module and an information sending module are required to be added into a vehicle-mounted control system.
The vehicle information acquisition and storage module comprises an information acquisition unit, a filtering unit and an information storage unit. The module collects, filters and stores the running information of the vehicle, the relative position information of the vehicle and the target vehicle, the information of the target vehicle and the positioning prediction information of the target vehicle on the vehicle.
In the multi-vehicle cooperative positioning calculation module, the acquired information is used for calculation so as to improve the self-vehicle positioning precision and predict the position information of the target vehicle. The module comprises a self-vehicle positioning calculation unit, an information complementing unit, a relative position calculation unit and a target vehicle positioning prediction unit. The self-vehicle positioning calculation unit extracts the measurement information matrix of the self-vehicle A at the time t
Figure BDA0001348035580000021
(including positioning information, speed information, and acceleration information of the own vehicle A) and the target vehicle BnPrediction information matrix for own vehicle A
Figure BDA0001348035580000022
(including the target vehicle B)nTarget vehicle B obtained by the calculation ofnPosition estimation value of own vehicle A and target vehicle BnSpeed information and acceleration information) of the vehicle A, then carrying out information fusion through a Federal Kalman filter to obtain an information matrix of the vehicle A at the time t
Figure BDA0001348035580000023
(including the position information calculated by the vehicle a, the speed information and the acceleration information of the vehicle a).
In the multi-vehicle cooperative positioning calculation module, an information complementing unit complements two types of information, wherein the first type is that the target vehicle information is not transmitted to the own vehicle at a certain moment due to equipment problems or signal interference of communication equipment of the target vehicle information. The second type is that the GPS signal of the own vehicle is lost at a certain moment, and the position information of the own vehicle at the moment cannot be obtained. When the two situations happen, the information complementing unit processes the lost data.
In the multi-vehicle co-location calculation module, the relative position calculation unit is used for calculating any target vehicle BnRelative position to the bicycle A
Figure BDA0001348035580000024
The input quantity of the module is the relative position information of each vehicle and the front vehicle obtained under the vehicle-vehicle communication
Figure BDA0001348035580000031
The relative position information obtained from the vehicle and any one of the target vehicles is obtained by superposition of the obtained required relative positions.
In the multi-vehicle cooperative positioning calculation module, because the time points of data acquisition of each vehicle cannot be completely the same, in order to improve the reliability and safety of positioning accuracy, further prediction of the position estimation of the target vehicle by the vehicle is required. Target vehicle positioning prediction unit utilizes vehicle information matrix obtained at time t
Figure BDA0001348035580000032
Relative position vector
Figure BDA0001348035580000033
And a target vehicle BnTo predict the target vehicle B based on the speed and acceleration information ofnInformation matrix at time t +1
Figure BDA0001348035580000034
(which comprisesFrom vehicle A to target vehicle BnThe position estimate at time t +1 and the speed information and acceleration information of the own vehicle a).
In the information sending module, three groups of information are sent to all other target vehicles capable of receiving the own information, and the target vehicle B is predicted for the own vehicle A at the moment of t +1nInformation matrix of
Figure BDA0001348035580000035
Information matrix of self vehicle A at time t
Figure BDA0001348035580000036
And the front vehicle B which can be measured by the radar at the moment t +1nRelative position information with respect to the own vehicle a
Figure BDA0001348035580000037
And the target vehicle positioning prediction information, the target vehicle information and the relative position information of the vehicle and the target vehicle which are respectively acquired by the corresponding information acquisition units form a closed-loop process.
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FIG. 1 is a structural composition diagram of the present invention
FIG. 2 is a flow chart of the present invention
FIG. 3 is the simulation results of the present invention: average error contrast map of Kalman filtering and multi-vehicle cooperative positioning algorithm
Detailed description of the preferred embodiments
The present invention will be described in detail below with reference to the accompanying drawings and embodiments.
As shown in FIG. 1, the invention is composed of a hardware part and a vehicle-mounted control system, wherein the hardware part comprises communication equipment, vehicle-mounted GPS equipment, a front millimeter wave radar and a CAN bus. The vehicle-mounted control system mainly comprises three modules, namely a vehicle information acquisition and storage module, a multi-vehicle cooperative positioning calculation module and an information sending module.
In the hardware part of the invention, communication equipment is used for completing information interaction between vehicles, vehicle-mounted GPS equipment is used for measuring vehicle position information, a front millimeter wave radar is used for measuring the relative position information of a vehicle and a front vehicle, and a CAN bus is used for measuring and reading the speed information of the vehicle.
As shown in the work flow chart of fig. 2, in the information collecting and storing module of the vehicle-mounted control system of the present invention, the information collecting unit collects the driving information of the vehicle by using the CAN bus and the vehicle-mounted GPS device, collects the relative position information of the vehicle and the target vehicle by using the millimeter wave radar and the vehicle-to-vehicle communication device, collects the target vehicle information and the target vehicle positioning prediction information by using the vehicle-to-vehicle communication device, then the filtering unit performs filtering processing on the obtained information to improve the information reliability, and finally the storing unit stores and packages the information.
In all the following formulae of the present invention, (x, y),
Figure BDA0001348035580000041
respectively representing the transverse and longitudinal position coordinates, transverse and longitudinal speed and transverse and longitudinal acceleration of the vehicle, setting the vehicle A as the self vehicle, and extracting the measurement information matrix of the self vehicle A at the time t from the vehicle information acquisition and storage module by the self vehicle positioning calculation unit
Figure BDA0001348035580000042
And a target vehicle BnTo the prediction information matrix of the self vehicle A
Figure BDA0001348035580000043
Then, information of the vehicle A and prediction information of other vehicles are transmitted to the vehicle A by using a federal Kalman filter and are respectively used as independent subsystems of the federal Kalman filter to carry out information fusion, and a relatively accurate information matrix of the vehicle A at the time t required by people is obtained
Figure BDA0001348035580000044
The information complementing unit in the multi-vehicle cooperative positioning calculation module starts to work under two working conditions: 1. when the target vehicle BnWhen the information at the time t is not transmitted to the vehicle A due to communication problems, the information complementing unit utilizes the target vehicle B acquired at the time t-1nInformation matrix of
Figure BDA0001348035580000045
To calculate the target vehicle B at time tnInformation matrix of
Figure BDA0001348035580000046
Wherein the acceleration at the time t-1 is set
Figure BDA0001348035580000047
The position and speed information can not be changed and still be used until t moment, and the rest position and speed information is calculated by using a kinematic formula to complement the missing information; 2. when the self vehicle A can not collect GPS information at the time t, the information complementing unit utilizes the vehicle information matrix calculated by the self vehicle positioning calculation unit at the time t-1
Figure BDA0001348035580000048
To calculate the information matrix of the vehicle A at time t
Figure BDA0001348035580000049
The acceleration at time t-1 is also set
Figure BDA00013480355800000410
And the position and speed information is not changed and still used until the time t, and the rest position and speed information is calculated by using a kinematic formula to complement the missing information.
In the multi-vehicle cooperative positioning calculation module, the relative position calculation unit is used for calculating any target vehicle BnRelative position to the bicycle A
Figure BDA00013480355800000411
If the radar can measure the relative position to the target vehicle, the measured data is directly used
Figure BDA0001348035580000051
If the target vehicle cannot directly measure the relative position, the target vehicle obtained by vehicle-vehicle communication can measure the relative position with the target vehicle to carry out vector superposition, and the relative position between the vehicle and the target vehicle is obtained by calculationLocation.
In the multi-vehicle cooperative positioning calculation module, the target vehicle positioning prediction unit extracts the vehicle information matrix obtained at the time t from the vehicle positioning calculation unit
Figure BDA0001348035580000052
Vector obtained by relative position calculating unit
Figure BDA0001348035580000053
And a target vehicle BnVelocity and acceleration information of (1), still setting the acceleration
Figure BDA0001348035580000054
The prediction target vehicle B of the self vehicle A is calculated by combining a kinematic formula until the time t +1nInformation matrix of
Figure BDA0001348035580000055
In the information sending module, three groups of information are sent, and the target vehicle B is predicted for the self vehicle A at the time t +1nInformation matrix of
Figure BDA0001348035580000056
Information matrix of self vehicle A at time t
Figure BDA0001348035580000057
And the front vehicle B which can be measured by the radar at the moment t +1nRelative position to the bicycle A
Figure BDA0001348035580000058
The information is sent to the target vehicles within all communication distances.
The algorithm of the invention is subjected to simulation analysis to generate 30 vehicle GPS positioning information, Gaussian noise with variance of 5m is added to 30 vehicles, each vehicle is communicated with 6 vehicles with the nearest distance around, a multi-vehicle cooperative positioning algorithm considering the relative positions between the vehicles under the vehicle-vehicle communication environment is executed, the average error of the 30 vehicles is calculated and compared with the average error only subjected to Kalman filtering.
Fig. 3 illustrates that the multi-vehicle cooperative positioning algorithm considering the relative positions between vehicles in the vehicle-vehicle communication environment has a good effect, and has the characteristics of high speed and great improvement of positioning accuracy.
According to the invention, after the motion state information, the position information and the relative position information of the two vehicles of the self vehicle and the target vehicle are acquired, processed and stored through the vehicle-vehicle communication technology and the vehicle-mounted sensor, the obtained data are fused and predicted and are sent to other communication vehicles, so that the positioning precision is improved on the vehicle-mounted terminal simply and effectively under the condition of considering the relative position between the vehicles, and the method is superior to the prior art method.
The embodiments of the present invention have been described in detail, but the present invention is not limited to the details of the above embodiments, and the details may be changed and replaced within the scope and the overall framework of the technical idea of the present invention, for example, the information collecting mode, the communication mode, the filtering mode and the information fusion mode of the present invention for the own vehicle and the target vehicle belong to the protection scope of the present invention.

Claims (4)

1. A multi-vehicle cooperative positioning method considering relative positions between vehicles in a vehicle-to-vehicle communication environment comprises the following steps:
s1, vehicles participating in the multi-vehicle cooperative positioning method are provided with vehicle-vehicle communication information interaction equipment, vehicle-mounted GPS equipment and a front millimeter wave radar, and a vehicle-mounted control system comprises a vehicle information acquisition and storage module, a multi-vehicle cooperative positioning calculation module and an information sending module;
s2, the vehicle information acquisition and storage module comprises an information acquisition unit, a filtering unit and an information storage unit; the module collects, filters and stores the running information of the vehicle, the relative position information of the vehicle and the target vehicle, the target vehicle information and the positioning prediction information of the target vehicle on the vehicle;
s3, in the multi-vehicle cooperative positioning calculation module, calculation is carried out by utilizing the acquired information so as to improve the self-vehicle positioning precision and predict the position information of the target vehicle; the moldThe block comprises a self-vehicle positioning calculation unit, an information complementing unit, a relative position calculation unit and a target vehicle positioning prediction unit; the self-vehicle positioning calculation unit extracts the measurement information matrix of the self-vehicle A at the time t
Figure FDA0002483308880000011
And a target vehicle BnPrediction information matrix for own vehicle A
Figure FDA0002483308880000012
Wherein
Figure FDA0002483308880000013
Including the positioning information, speed information and acceleration information of the own vehicle A,
Figure FDA0002483308880000014
including the target vehicle BnTarget vehicle B obtained by the calculation ofnPosition estimation value of own vehicle A and target vehicle BnThen carrying out information fusion through a Federal Kalman filter to obtain an information matrix of the vehicle A at the time t
Figure FDA0002483308880000015
The method comprises the steps of obtaining position information, speed information and acceleration information of a vehicle A through calculation of the vehicle A;
s4, in the multi-vehicle cooperative positioning calculation module, an information complementing unit complements two types of information, wherein the first type is that the target vehicle information is not transmitted to the own vehicle at a certain moment due to equipment problems or signal interference of communication equipment of the target vehicle information; the second type is that the GPS signal of the self-vehicle is lost at a certain moment, and the position information of the self-vehicle at the moment can not be obtained; when the two situations occur, the information complementing unit processes the lost data;
s5, in the multi-vehicle cooperative positioning calculation module, the relative position calculation unit is used for calculating any target vehicle B at the time tnRelative position to the bicycle A
Figure FDA0002483308880000016
The input quantity of the module is the relative position information of each vehicle and the front vehicle obtained under the vehicle-vehicle communication
Figure FDA0002483308880000017
Obtaining relative position information from the vehicle and any one of the target vehicles by superposition of the obtained required relative positions;
s6, in the multi-vehicle cooperative positioning calculation module, because the time points of data acquisition of each vehicle cannot be completely the same, further prediction is carried out on the position estimation of the vehicle to the target vehicle in order to improve the reliability and the safety of positioning accuracy; target vehicle positioning prediction unit utilizes vehicle information matrix obtained at time t
Figure FDA0002483308880000018
Relative position vector
Figure FDA0002483308880000021
And a target vehicle BnTo predict the target vehicle B based on the speed and acceleration information ofnInformation matrix at time t +1
Figure FDA0002483308880000022
Figure FDA0002483308880000023
Comprising a pair of target vehicles BnEstimating the position at the moment of t +1, and acquiring speed information and acceleration information of the vehicle A;
s7, in the information sending module, three groups of information are sent to all other target vehicles capable of receiving the own information, and the target vehicle B is predicted for the own vehicle A at the moment t +1nInformation matrix of
Figure FDA0002483308880000024
Information matrix of self vehicle A at time t
Figure FDA0002483308880000025
And the front vehicle B which can be measured by the radar at the moment t +1nRelative position information with respect to the own vehicle a
Figure FDA0002483308880000026
2. A multi-vehicle cooperative localization method considering a relative position between vehicles in a vehicle-to-vehicle communication environment according to claim 1, characterized in that: in step S3, the vehicle location calculation unit will extract the measurement information matrix of the vehicle a at time t in the vehicle information collection and storage module
Figure FDA0002483308880000027
And a target vehicle BnTo the prediction information matrix of the self vehicle A
Figure FDA0002483308880000028
Then, information of the vehicle A and prediction information of other vehicles are transmitted to the vehicle A by using a federal Kalman filter and are respectively used as independent subsystems of the federal Kalman filter for information fusion to obtain an information matrix of the vehicle A at the time t
Figure FDA0002483308880000029
3. A multi-vehicle cooperative localization method considering a relative position between vehicles in a vehicle-to-vehicle communication environment according to claim 1, characterized in that: in step S4, the information complementing unit in the multi-vehicle co-location calculating module starts to operate under two conditions: 1. when the target vehicle BnWhen the information at the time t is not transmitted to the vehicle A due to communication problems, the information complementing unit utilizes the vehicle B acquired at the time t-1nInformation matrix of
Figure FDA00024833088800000210
To calculate the target vehicle B at time tnInformation matrix of
Figure FDA00024833088800000211
Wherein the acceleration at the time t-1 is set
Figure FDA00024833088800000212
The position and speed information can not be changed and still be used until t moment, and the rest position and speed information is calculated by using a kinematic formula to complement the missing information; 2. when the self vehicle A can not collect GPS information at the time t, the information complementing unit utilizes the vehicle information matrix calculated by the self vehicle positioning calculation unit at the time t-1
Figure FDA00024833088800000213
To calculate the information matrix of the vehicle A at time t
Figure FDA00024833088800000214
The acceleration at time t-1 is also set
Figure FDA00024833088800000215
And the position and speed information is not changed and still used until the time t, and the rest position and speed information is calculated by using a kinematic formula to complement the missing information.
4. A multi-vehicle cooperative localization method considering a relative position between vehicles in a vehicle-to-vehicle communication environment according to claim 1, characterized in that: in step S7, the information transmission module transmits three sets of information, each of which predicts the target vehicle B for the own vehicle a at time t +1nInformation matrix of
Figure FDA00024833088800000216
Information matrix of self vehicle A at time t
Figure FDA00024833088800000217
And the front vehicle B which can be measured by the radar at the moment t +1nRelative position to the bicycle A
Figure FDA00024833088800000218
The three groups of data respectively correspond to the target vehicle positioning prediction information, the target vehicle information and the relative position information of the vehicle and the target vehicle acquired by the information acquisition unit, and the information is sent to the target vehicles in all communication distances to form a closed loop process.
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