CN113625323B - Vehicle real-time positioning system, method and medium based on vehicle-road cooperation and vehicle - Google Patents
Vehicle real-time positioning system, method and medium based on vehicle-road cooperation and vehicle Download PDFInfo
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- 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
- G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
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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
- G01S19/40—Correcting position, velocity or attitude
- G01S19/41—Differential correction, e.g. DGPS [differential GPS]
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Abstract
The invention discloses a vehicle real-time positioning system based on vehicle-road cooperation, which comprises: the system comprises an on-board unit, a road side unit and a cloud computing center; the road side unit is used for periodically sending a differential data service request to the cloud computing center; the cloud computing center is used for verifying the differential data service request of each road side unit, forwarding the differential data service request passing the verification to the differential data service center, acquiring differential data corresponding to the differential data service request, and sending each differential data to the road side unit corresponding to the differential data service request; the road side unit is used for sending the received differential data to a vehicle-mounted unit in a preset service range; the vehicle-mounted unit is used for acquiring inertial navigation data of the inertial navigation module and satellite observation data of the GNSS module, and performing fusion calculation based on the differential data, the inertial navigation data and the satellite observation data to perform real-time high-precision positioning. Therefore, by adopting the embodiment of the application, the positioning accuracy of the vehicle can be improved under the condition that the satellite signal of the vehicle is weak.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a vehicle real-time positioning system and method based on vehicle-road cooperation.
Background
Vehicle-road cooperation is a hot word of intelligent traffic in recent years and in the future, is one of the core technologies of the next generation of vehicle networking, and enables vehicles and surrounding environments to cooperate and cooperate through information interaction and sharing of vehicles and vehicles (V2V), vehicles and roads (V2I), vehicles and people (V2P), vehicles and clouds (V2N) and the like, so that an integrated network of intelligent traffic management control, vehicle intelligent control and intelligent dynamic information service is realized, and high-precision real-time positioning of vehicles is a key link for improving vehicle-road cooperation service quality.
At present, the vehicle-road cooperative positioning is generally carried out on the basis of a vehicle-end GNSS positioning antenna and a chip in a cooperative manner, meter-level positioning accuracy can only be realized, and the requirement for the vehicle-road cooperative development with higher and higher accuracy requirements cannot be met. The method has the advantages that high-precision positioning of a vehicle end can be realized by purchasing Real Time Kinematic (RTK) service in a partial scene, low-frequency (mainly 1 HZ) positioning with sub-decimeter-level precision can be realized, the vehicle can travel a distance of about 20 meters in 1 second according to calculation of the vehicle speed of 70 kilometers per hour, turning, braking, acceleration and other conditions are met, sub-hierarchical positioning can not be realized for the vehicle position within 20 meters, and meter-level positioning can be realized when satellite signals are weak or the satellite signals are unlocked, so that the precision of vehicle positioning is reduced.
Disclosure of Invention
The embodiment of the application provides a vehicle real-time positioning system and method based on vehicle-road cooperation. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In a first aspect, an embodiment of the application provides a vehicle real-time positioning system based on vehicle-road cooperation, and the system comprises a vehicle-mounted unit, a road side unit and a cloud computing center;
the system comprises a road side unit and a cloud computing center, wherein the road side unit is used for periodically sending differential data service requests to the cloud computing center, and the differential data service requests comprise port numbers or approximate coordinates of the road side unit;
the cloud computing center is used for verifying the differential data service request of each road side unit, forwarding the verified differential data service request to the differential data service center, acquiring differential data corresponding to the differential data service request, and sending each differential data to the corresponding road side unit;
the road side unit is used for sending the received differential data to the vehicle-mounted unit within a preset service range;
the vehicle-mounted unit is used for acquiring inertial navigation data of the inertial navigation module and satellite observation data of the GNSS module, and performing fusion calculation based on the differential data, the inertial navigation data and the satellite observation data to perform real-time high-precision positioning.
Optionally, the vehicle-mounted unit is preset with a network delay verification unit, and is configured to determine target differential data by judging whether a network delay exists according to the differential data;
the network delay verification unit comprises a differential data prediction module, a delay unit and a delay unit, wherein the differential data prediction module is used for comparing the differential data received in real time with the differential data at the moment predicted by the prediction module;
when the difference value between the differential data received in real time and the predicted differential data at the moment is larger than a preset threshold value, executing a preset ping value to generate an execution result;
when the data loss occurs in the execution result, determining that network delay occurs, and taking the differential data obtained by the prediction module as the target differential data at the moment;
when the difference value between the differential data received in real time and the predicted differential data at the moment is smaller than a preset threshold value, determining that no network delay exists, and taking the differential data received in real time as target differential data at the moment;
and the vehicle-mounted unit is used for performing fusion calculation on the target differential data, the inertial navigation data and the satellite observation data so as to perform real-time high-precision positioning.
Optionally, the on-board unit is configured to perform fusion calculation on the target differential data, the inertial navigation data, and the satellite observation data to perform real-time high-precision positioning, and includes:
the vehicle-mounted unit is used for carrying out differential solution on satellite observation data of the GNSS module and target differential data acquired by the vehicle end through the steady Kalman filter to generate low-frequency sub-decimeter positioning data of the vehicle end;
the vehicle-mounted unit is also used for filtering the low-frequency sub-decimeter positioning data and the inertial navigation data of the vehicle end through a Kalman filter to generate high-frequency sub-decimeter positioning data.
Optionally, after vehicle-end low-frequency sub-decimeter-level positioning data and inertial navigation data are filtered by a kalman filter, generating high-frequency sub-decimeter-level positioning data includes:
according to the obtained satellite observation data signal intensity, resolving the low-frequency sub-decimeter positioning data and the inertial navigation data of the vehicle end by adopting a loose coupling or tight coupling resolving mode to generate high-frequency sub-decimeter positioning data;
wherein, when the signal intensity of the satellite observation data is larger than a preset threshold value, a preset loose coupling resolving mode is adopted to remove the noise of the navigation parameters output by the inertial navigation subsystem and the respective noise of the satellite observation data of the GNSS module obtained by the vehicle end through a Kalman filter, then errors of the position, the speed and the attitude are output, and a positioning result is generated after the low-frequency sub-decimeter positioning result of the vehicle end is corrected according to the errors of the position, the speed and the attitude,
when the satellite observation data signal intensity is smaller than a preset threshold value, a preset close coupling resolving mode is adopted to remove noise of navigation parameters output by the inertial navigation subsystem and data such as pseudo range, pseudo range rate and the like of satellite observation data obtained by a vehicle end through a steady Kalman filter, errors of position, speed and attitude are output, finally, the position, speed and attitude parameters of the inertial navigation subsystem are corrected according to the errors of the position, speed and attitude, and finally, a high-frequency high-precision positioning result is generated.
Optionally, the cloud computing center is provided with a roadside unit verification module, configured to determine whether the service request passes verification according to whether a roadside device sending the location service request is in a service registration white list;
the service registration white list comprises RSU service registration information and differential data service center information, the RSU service registration information comprises a user name, a password, a port number and an IP address of the RSU, and the differential data service center information comprises at least one of the port number and the IP address of the differential data service center;
optionally, a plurality of adjacent rsus may share one user name and one password.
Optionally, the vehicle-mounted unit is provided with a difference database for storing historical difference data within a certain period from the current moment;
the on-board unit is further configured to construct a difference data prediction model for predicting difference data from the historical difference data, and predict a plurality of difference data within a difference age after the current time based on the difference data prediction model.
In a second aspect, an embodiment of the present application provides a vehicle real-time positioning method based on vehicle-road coordination, where the method includes:
determining a road side unit in the area range of the current moment of the vehicle to be positioned;
the method comprises the steps that a road side unit sends a differential data service request to a cloud computing center, wherein the service request comprises a road side unit port number or approximate coordinates;
the cloud computing center verifies the differential service requests of the road side units according to the road side equipment information, forwards the verified differential data service requests to the differential data service center, obtains differential data corresponding to each differential data service request, and sends each differential data to the corresponding road side unit;
the road side unit sends the differential data to the vehicle-mounted unit in the service range of the road side unit;
the vehicle-mounted unit acquires inertial navigation positioning data and satellite observation positioning data, and fusion calculation is carried out on the inertial navigation positioning data and the satellite observation positioning data based on differential data so as to realize real-time high-precision positioning.
Optionally, before performing fusion calculation on the differential data, the inertial navigation positioning data and the satellite observation positioning data to perform real-time high-precision positioning, the method further includes:
performing network delay verification on the differential data acquired by the road side unit by adopting a differential prediction model;
comparing the differential data received in real time with the differential data at the moment predicted by the differential prediction model;
executing a preset ping value to generate an execution result;
when the data loss occurs in the execution result, determining that network delay occurs, and taking the differential data obtained by the prediction module as the target differential data at the moment;
when the difference value between the differential data received in real time and the predicted differential data at the moment is larger than a preset threshold value, determining that network delay exists, and taking the differential data obtained by the prediction module as target differential data at the moment;
when the difference value between the differential data received in real time and the predicted differential data at the moment is smaller than a preset threshold value, determining that no network delay exists, and taking the differential data received in real time as target differential data at the moment;
and the vehicle-mounted unit is used for carrying out fusion calculation on the basis of the target differential data, the inertial navigation data and the satellite observation data so as to carry out real-time high-precision positioning.
In a third aspect, an embodiment of the present application provides a computer storage medium, on which a computer program is stored, where the computer program is used to implement a method as any one of the above when executed by a processor
In a fourth aspect, embodiments of the present application provide a vehicle comprising one or more processors;
storage means for storing one or more programs;
the vehicle-mounted GNSS module, the vehicle-mounted inertial navigation module and the vehicle-mounted V2X communication module;
when the one or more programs are executed by the one or more processors, the one or more processors implement the method for vehicle real-time positioning based on vehicle-road coordination.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
in the embodiment of the application, firstly, a road side unit in an area range where a vehicle to be positioned is located at the current moment is determined, then the road side unit sends a differential data service request to a cloud computing center, the service request comprises a road side unit port number or approximate coordinates, then the cloud computing center verifies the differential service request of the road side unit according to road side equipment information, the verified differential data service request is forwarded to the differential data service center, differential data corresponding to each differential data service request are obtained, each differential data is sent to the corresponding road side unit, the road side unit sends the differential data to an on-board unit in the service range, finally, the on-board unit obtains inertial navigation positioning data and satellite observation positioning data, and fusion calculation is carried out on the inertial navigation positioning data and the satellite observation positioning data based on the differential data so as to carry out real-time high-precision positioning. Because the differential data, the inertial navigation data and the satellite observation data collected by the vehicle-mounted unit are subjected to fusion calculation to realize real-time high-precision positioning, the precision of vehicle positioning is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a schematic system structure diagram of a vehicle real-time positioning system based on vehicle-road coordination according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a method for positioning a vehicle in real time based on vehicle-road coordination according to an embodiment of the present application;
fig. 3 is a schematic diagram of a high-precision integrated navigation positioning according to an embodiment of the present application.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of systems and methods consistent with certain aspects of the invention, as detailed in the appended claims.
In the description of the present invention, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
The following detailed description will be made using exemplary embodiments.
Referring to fig. 1, fig. 1 is a schematic diagram of a vehicle real-time positioning system based on vehicle-road coordination according to an embodiment of the present application, where the system includes: the system comprises an on-board unit, a road side unit and a cloud computing center; the system comprises a road side unit and a cloud computing center, wherein the road side unit is used for periodically sending a differential data service request to the cloud computing center, the differential data service request comprises a port number or approximate coordinates of the road side unit, and it needs to be noted that the port number or the approximate coordinates of the differential service request can only correspond to the road side unit; the cloud computing center is used for verifying the differential data service request of each road side unit, forwarding the verified differential data service request to the differential data service center, acquiring differential data corresponding to the differential data service request, and sending each differential data to the corresponding road side unit; the road side unit is used for sending the received differential data to the vehicle-mounted unit within a preset service range; the vehicle-mounted unit is used for acquiring inertial navigation data of the inertial navigation module and satellite observation data of the GNSS module, and performing fusion calculation based on the differential data, the inertial navigation data and the satellite observation data to perform real-time high-precision positioning.
Specifically, the cloud computing center establishes connection with the differential data service center through a port number, an IP, a user name and a password of a differential service request, sends rough coordinates of each road side unit requesting the differential data service to the differential data service center, the differential data service center automatically selects a group of best nearby fixed reference stations respectively through a computer according to each rough coordinate, integrally corrects orbit errors of GNSS, errors caused by ionosphere, troposphere and atmospheric refraction according to information sent by the base stations, and sends high-precision differential signals to the cloud computing center.
In this embodiment, the rsus are rsus configured with GNSS antennas or rsus not configured with GNSS antennas; the road side unit is used for generating rough coordinates and sending the rough coordinates to the cloud computing center, and comprises: a road side unit configured with the GNSS antenna is used for acquiring the current rough coordinate and sending the rough coordinate to a cloud computing center; or the road side unit without the GNSS antenna is used for connecting the cloud computing center, and the rough coordinates are configured in the cloud computing center according to the number of the road side unit.
In a possible implementation manner, when the road side unit sends the rough coordinates to the cloud computing center, the road side unit equipped with the GNSS antenna may send the obtained coordinates to the cloud computing center. The roadside unit without the GNSS antenna may directly configure its approximate coordinates according to the roadside unit number in the cloud computing center.
In this embodiment, the on-board unit is preset with a network delay verification unit, configured to determine target differential data by determining whether a network delay exists according to the differential data; the network delay verification unit comprises a differential data prediction module, a delay unit and a delay unit, wherein the differential data prediction module is used for comparing the differential data received in real time with the differential data at the moment predicted by the prediction module; when the difference value between the differential data received in real time and the predicted differential data at the moment is larger than a preset threshold value, executing a preset ping value to generate an execution result; when the data loss occurs in the execution result, determining that network delay occurs, and taking the differential data obtained by the prediction module as the target differential data at the moment; when the difference value between the differential data received in real time and the predicted differential data at the moment is smaller than a preset threshold value, determining that no network delay exists, and taking the differential data received in real time as target differential data at the moment; and the vehicle-mounted unit is used for performing fusion calculation on the target differential data, the inertial navigation data and the satellite observation data so as to perform real-time high-precision positioning.
Specifically, the vehicle-mounted unit is used for performing fusion calculation on target differential data, inertial navigation data and satellite observation data so as to perform real-time high-precision positioning, and comprises the following steps: the vehicle-mounted unit is used for carrying out differential solution on satellite observation data and target differential data of the GNSS module acquired by the vehicle end through the steady Kalman filter to generate low-frequency sub-decimeter positioning data of the vehicle end; the vehicle-mounted unit is also used for filtering the low-frequency sub-decimeter positioning data and the inertial navigation data of the vehicle end through a Kalman filter to generate high-frequency sub-decimeter positioning data.
In this embodiment, with car end low frequency sub decimeter level location data and inertial navigation data after kalman filter filters, generate the sub decimeter level location data of high frequency and include: according to the obtained satellite observation data signal intensity, resolving the low-frequency sub-decimeter positioning data and the inertial navigation data of the vehicle end by adopting a loose coupling or tight coupling resolving mode to generate high-frequency sub-decimeter positioning data; when the signal intensity of the satellite observation data is greater than a preset threshold value, a preset loose coupling calculation mode is adopted to remove noise of navigation parameters output by an inertial navigation subsystem and respective noise of satellite observation data of a GNSS module acquired by a vehicle end through a Kalman filter, errors of position, speed and attitude are output, a positioning result is generated after a low-frequency sub-decimeter positioning result of the vehicle end is corrected according to the errors of the position, the speed and the attitude, when the signal intensity of the satellite observation data is less than the preset threshold value, a preset tight coupling calculation mode is adopted to remove noise of the navigation parameters output by the inertial navigation subsystem and respective noise of data such as pseudo range, pseudo range rate and the like of the satellite observation data acquired by the vehicle end through a Kalman robust filter, errors of the position, the speed and the attitude are output, and finally the position, the speed and the attitude parameters of the inertial navigation subsystem are corrected according to the errors of the position, the speed and the attitude, and the high-precision positioning result of high frequency is generated.
In this embodiment, the cloud computing center is provided with a roadside unit verification module, configured to determine whether a service request passes verification according to whether a roadside device that sends a location service request is in a service registration white list; the service registration white list comprises road side unit service registration information and differential data service center information, the road side unit service registration information comprises a user name, a password, a port number and an IP address of road side equipment, the differential data service center information comprises at least one of the port number and the IP address of a differential data service center, the road side unit service registration information and the differential data service center information in the service registration white list are associated, and each group of road side unit service registration information corresponds to one group of differential data service center information;
in a preferred embodiment, the differentiated data service center information further includes user name and password information, and after receiving the differentiated service request forwarded by the cloud service center, the differentiated data service center further verifies the user name and the password of the differentiated service request related to the road side unit, which are stored by the differentiated data service center, so as to improve security.
In a preferred embodiment, a plurality of adjacent road side units may share one user name and one password, that is, a plurality of adjacent road side units are grouped and share one user name and one password, so as to save the service cost of the road side unit. In the embodiment, the vehicle-mounted unit is provided with a difference database for storing historical difference data within a certain period from the current moment; the on-board unit is further configured to construct a difference data prediction model for predicting difference data from the historical difference data, and predict a plurality of difference data within a difference age after the current time based on the difference data prediction model.
Specifically, the differential data prediction model is constructed in advance, and the differential prediction model may be an autoregressive moving average model (ARMA); the difference age may refer to a prediction period, and may be 10s, and difference data at a plurality of times within 10s is predicted once.
In the embodiment of the application, firstly, a road side unit in an area range where a vehicle to be positioned is located at the current moment is determined, then the road side unit sends a differential data service request to a cloud computing center, the service request comprises a road side unit port number or approximate coordinates, then the cloud computing center verifies the differential service request of the road side unit according to road side equipment information, the verified differential data service request is forwarded to the differential data service center, differential data corresponding to each differential data service request are obtained, each differential data is sent to the corresponding road side unit, the road side unit sends the differential data to an on-board unit in the service range, finally, the on-board unit obtains inertial navigation positioning data and satellite observation data, and based on the differential data, the inertial navigation positioning data and the satellite observation positioning data are subjected to fusion calculation to achieve real-time high-precision positioning. Because the differential data, the inertial navigation data and the satellite observation data collected by the vehicle-mounted unit are subjected to fusion calculation to realize real-time high-precision positioning, the precision of vehicle positioning is improved.
The method for locating a vehicle in real time based on vehicle-road coordination according to the embodiment of the present application will be described in detail below with reference to fig. 2 to 3. The method can be implemented by means of a computer program and can be run on a Von Neumann-based vehicle-road coordination real-time positioning device. The computer program may be integrated into the application or may run as a separate tool-like application.
Fig. 2 is a schematic flow chart of a vehicle real-time positioning method based on vehicle-road cooperation, which is applied to a vehicle-mounted unit according to an embodiment of the present application. As shown in fig. 2, the method of the embodiment of the present application may include the following steps:
s101, determining a road side unit in the area range of the current moment of the vehicle to be positioned;
in the embodiment of the application, before performing fusion calculation on differential data, inertial navigation positioning data and satellite observation positioning data to perform real-time high-precision positioning, firstly, a differential prediction model is adopted to perform network delay verification on the differential data acquired by a road side unit, then, the differential data received in real time and the differential data predicted by the differential prediction model at the moment are compared, a preset ping value is executed, an execution result is generated, then, after data loss occurs in the execution result, network delay is determined to occur, the differential data obtained by a prediction module is used as target differential data at the moment, secondly, when the difference value between the differential data received in real time and the differential data predicted at the moment is larger than a preset threshold value, network delay is determined to exist, the differential data obtained by the prediction module is used as the target differential data at the moment, when the difference value between the differential data received in real time and the differential data predicted at the moment is smaller than the preset threshold value, the differential data received in real time is used as the target differential data at the moment, and finally, the vehicle-mounted unit is used for performing fusion calculation on the navigation data and the satellite observation data to perform real-time high-precision positioning based on the target differential data.
Typically, the difference data is simulated GNSS observation data and is calculated from ionosphere and troposphere models calculated in real time from raw observations from several CORS stations near the location.
In the embodiment of the application, after the obtained differential data are decoded, a differential data prediction model can be established on the basis of an existing mature autoregressive moving average model (ARMA) by combining the characteristics of the observed quantity of each GNSS data, and the data can be predicted in a differential age, specifically, a vehicle-mounted unit is provided with a differential database for storing historical differential data in a certain period from the current moment; the on-board unit is further configured to construct a difference data prediction model for predicting difference data from the historical difference data, and predict a plurality of difference data within a difference age after the current time based on the difference data prediction model. The difference age may refer to a prediction period, and may be 10s, and difference data at a plurality of times within 10s is predicted once.
S102, the road side unit sends a differential data service request to a cloud computing center, wherein the service request comprises a road side unit port number or approximate coordinates;
s103, the cloud computing center verifies the differential service requests of the road side units according to the road side equipment information, forwards the verified differential data service requests to the differential data service center, obtains differential data corresponding to each differential data service request, and sends each differential data to the corresponding road side unit;
it should be noted that, in the process of using the differentiated service by the terminal, the differential information predicted in the differential age may be compared with the differential information actually received at the predicted time, so as to establish a perfect verification mechanism and ensure the reliability in the use process, thereby achieving a certain positive effect when the network is delayed for a short time or the terminal.
S104, the road side unit sends the differential data to the vehicle-mounted unit in the service range of the road side unit;
and S105, the vehicle-mounted unit acquires inertial navigation positioning data and satellite observation positioning data, and fusion calculation is carried out on the inertial navigation positioning data and the satellite observation positioning data based on the differential data so as to carry out real-time high-precision positioning.
In a possible implementation mode, when the vehicle-mounted unit carries out fusion calculation on target differential data, inertial navigation data and satellite observation data so as to carry out real-time high-precision positioning, differential solution is carried out on the satellite observation data and the differential data acquired by the vehicle-mounted unit through a steady Kalman filter so as to generate vehicle-end low-frequency sub-decimeter positioning data; and filtering the low-frequency sub-decimeter positioning data and the inertial navigation data of the vehicle end by a Kalman filter to generate high-frequency sub-decimeter positioning data.
Specifically, when the low-frequency sub-decimeter-level positioning data and the inertial navigation data of the vehicle end are filtered by a Kalman filter to generate high-frequency sub-decimeter-level positioning data, firstly, a loose coupling or tight coupling resolving mode is determined to be adopted to resolve the low-frequency sub-decimeter-level positioning data and the inertial navigation data of the vehicle end according to the signal intensity of the acquired satellite observation data to generate high-frequency sub-decimeter-level positioning data; when the signal intensity of the satellite observation data is greater than a preset threshold value, a preset loose coupling calculation mode is adopted to remove noise of the navigation parameter output by the inertial navigation subsystem and noise of the satellite observation data of the GNSS module obtained by the vehicle end through a Kalman filter, then errors of the position, the speed and the attitude are output, a positioning result is generated after the low-frequency sub-decimeter positioning result of the vehicle end is corrected according to the errors of the position, the speed and the attitude, when the signal intensity of the satellite observation data is less than the preset threshold value, a preset tight coupling calculation mode is adopted to remove noise of the navigation parameter output by the inertial navigation subsystem and the noise of data such as pseudo range and pseudo range rate of the satellite observation data obtained by the vehicle end through a Kalman robust filter, then errors of the position, the speed and the attitude are output, finally the position, the speed and the attitude of the inertial navigation subsystem are corrected according to the errors of the position, the speed and the attitude, and a high-frequency high-precision positioning result is finally generated.
In some embodiments, the satellite observation data signal strength may be quantified by observed satellite signal strength, and since the satellites are in constant motion and are simultaneously shielded by buildings and the like, the number of the received satellite signals at different times may be different, when the number of the satellites of the received satellite signals is greater than a certain threshold (the number of the satellites), the satellite signal strength is determined to be strong, a preset loose coupling calculation mode is adopted to remove noise of the navigation parameters output by the inertial navigation subsystem and noise of the satellite observation data of the GNSS module obtained by the vehicle end through a kalman filter, and then position, speed and attitude errors are output, and a positioning result is generated after a low-frequency sub-decimeter positioning result of the vehicle end is corrected according to the errors of the position, speed and attitude, and when the number of the satellites of the received satellite signals is less than a certain threshold (the number of the satellites is determined to be weak, the satellite signal strength is determined, a preset tight coupling calculation mode is adopted to remove noise of the navigation parameters output by the inertial navigation subsystem and noise of data such as pseudo-range and pseudo-range rate of the satellite observation data obtained by the vehicle end through a robust filter, and finally a high-frequency navigation result is generated according to the high-positioning precision of the high-frequency navigation subsystem.
As shown in fig. 3, fig. 3 is the robust kalman filter high-precision integrated navigation computation: and selecting a loose coupling and tight coupling resolving mode based on a scene by using positioning data and differential data output by a vehicle-end GNSS antenna and an inertial navigation subsystem, and obtaining the co-precision integrated navigation positioning information through steady Kalman filtering. The scene with strong satellite signals selects a loosely-coupled resolving mode, and the scene with weak satellite signals selects a tightly-coupled resolving mode. The specific principle and steps are as follows:
B1. inertial navigation solution
The inertial navigation resolving deduces navigation information at the moment according to the navigation information at the previous moment, wherein the navigation information comprises attitude, speed and position, and the inertial navigation resolving mainly relates to the following contents:
and (3) posture updating: according to the data form output by the gyroscope, an updating method based on the angular increment or the angular velocity is carried out, and the updating based on the angular velocity also comprises the steps of firstly calculating the angular increment by using the angular velocity and then updating by using the angular increment method.
And (3) updating the speed: and removing the gravity acceleration to obtain the acceleration under an inertial coordinate system, and obtaining the speed through integration.
And (3) updating the position: the velocity is integrated to obtain the position.
B2. And (3) resolving by using loose coupling Kalman filtering: the inertial navigation subsystem and the GNSS receiver output position and speed information, then the Kalman filter carries out optimization processing, errors of the output position, speed and attitude are used for correcting the MEMS inertial navigation module, and the mode is called integrated navigation loose coupling resolving.
B3. And (3) tightly-coupled Kalman filtering resolving: the combined filter is adopted to filter the pseudo range and pseudo range rate of the MEMS inertial navigation and the GNSS, meanwhile, the MEMS inertial navigation is used for assisting the GNSS in speed, the satellite tracking capability of the receiver is improved, and therefore the dynamic characteristic and the anti-interference capability of the receiver are improved.
B4. Robust estimation principle:
kalman filtering is essentially a special case of least squares recursion, i.e. finding the optimum by minimizing the variance, but kalman filtering is not robust against gross errors. Robust Estimation (Robust Estimation) is also called Robust Estimation in measurement, and is mainly proposed to address the defect of poor anti-gross error capability of least squares, so as to apply Estimation method to generate certain resistance to measurement gross error. The principle of mining effective information in the measurement data and filtering useless or unfavorable information is to be followed when estimating the parameters. The M estimation theory in the steady estimation has a plurality of parameter estimation methods, and the method is widely applied to measurement adjustment because the calculation is simple and clear and is similar to the least square thought. Different from the least square method, the M estimation can resist gross error, and the weight selection iterative method in the parameter estimation method is widely applied and is easy to realize by a program, so that the weight selection iterative method is used for carrying out robust estimation, and the main method steps are as follows.
C1. And (4) setting up an error equation. Make the initial values of the weights all 1, even if ω is 1 ＝ω 2 ＝…＝ω n =1,w = i, then
P is a weight matrix.
C2. Calculation of the normal equation
First estimating residual V and parameters
C3. By passing
Updating the value of the weight factor according to
Updating equivalence weights
Then, the normal equation is recalculated, and the residual error V and the parameters are estimated for the second time
C4. And (3) repeatedly calculating an equivalent weight sum equation, terminating iteration when the difference value of the two solutions meets the tolerance requirement, and calculating at the moment:
due to the fact that
And then
The form of the weight function varies with the choice of the different p functions. The weight function varies with the correction number in the adjustment process, omega i And V i In inverse ratio, V i Smaller, ω i 、
The larger the weight function is, the more the measurement values containing gross errors will tend to approach zero or even equal zero after a number of iterations, resulting in it not being affected in adjustment, the gross errors being reflected to a large extent by the residual of the measurement values. This method of estimating parameters by varying weights until they approach zero during the adjustment process is called the weight selection iterative method.
A weight selection iteration method is applied to a measurement adjustment model, an equivalent weight is introduced, an estimation criterion considering a measurement value weight matrix is used, the adjustment model can effectively resist the influence of gross errors, and the method is called an robust least square method. The Huber function method, the residual absolute sum minimum method, the danish method, the IGG scheme, etc. are several of the robust least squares methods commonly used.
In a standard Kalman filtering recursion formula, a state parameter to be estimated of a system at the moment k and a system dynamic noise variance matrix are influenced by gross errors, an equal weighting principle is introduced into Kalman filtering, and a proper weight function can be selected to replace an observation noise covariance matrix, so that the influence of the gross errors on an estimation result is reduced or eliminated.
In the invention, the robust calculation is carried out by adopting a residual absolute sum minimum method, and the algorithm process is as follows:
in the method, the rho function is as follows:
p(u)＝|u|
the weight factors are:
V i the weighting problem can occur in the iterative computation process when the value is =0, and at this momentThe weight factor may be written as:
in the formula, k is a small value.
According to the adjustment criterion in the adjustment process:
incorporating equivalent weight factors
The normal equation and its solution can be calculated as:
in the formula, the equivalent weight factor
Form a diagonal matrix
By the method, the vehicle high-precision real-time positioning technology method, the device and the system based on vehicle-road cooperation are obtained, low-cost, all-weather, high-precision real-time positioning without vehicle-end maintenance can be realized, and vehicle-road cooperative development and travel experience service upgrading are accelerated.
It should be noted that, in the following description,
1. problem of centralized positioning with respect to differential data transmitted using RSUs
The real-time differential positioning utilizes a virtual reference station consisting of a plurality of base stations with the coverage radius of nearly 30km near the differential service request position (RSU position) to carry out positioning, and the radius is far larger than the distance between the RSUs, so that a group of differential correction information can be shared by all the RSUs within a small range at the same time, and the request amount of the RSUs for the differential information can be greatly reduced.
2. Problems relating to time delay
The current 3G/4G network can meet the real-time difference requirement, and the network delay is smaller due to the addition of 5G in the future. If network interruption is prevented, the reliability of the communication system can be improved by enhancing the redundancy construction of the infrastructure. Meanwhile, the prediction of the difference data in the difference age can play a certain positive role in short-time network interruption.
In the embodiment of the application, firstly, a road side unit in an area range where a vehicle to be positioned is located at the current moment is determined, then the road side unit sends a differential data service request to a cloud computing center, the service request comprises a road side unit port number or approximate coordinates, then the cloud computing center verifies the differential service request of the road side unit according to road side equipment information, the verified differential data service request is forwarded to the differential data service center, differential data corresponding to each differential data service request are obtained, each differential data is sent to the corresponding road side unit, the road side unit sends the differential data to an on-board unit in the service range, finally, the on-board unit obtains inertial navigation positioning data and satellite observation data, and based on the differential data, the inertial navigation positioning data and the satellite observation positioning data are subjected to fusion calculation to achieve real-time high-precision positioning. According to the method and the device, the differential data, the inertial navigation data and the satellite observation data collected by the vehicle-mounted unit are subjected to fusion calculation to realize real-time high-precision positioning, so that the precision of vehicle positioning is improved.
The invention also provides a computer readable medium, on which program instructions are stored, and the program instructions, when executed by a processor, implement the vehicle-road coordination-based real-time positioning method provided by the above-mentioned method embodiments.
The present invention also provides a computer program product containing instructions which, when run on a computer, cause the computer to execute the method for real-time vehicle positioning based on vehicle-road coordination of the above-mentioned method embodiments.
The present application further provides a vehicle, comprising: one or more processors; storage means for storing one or more programs; the vehicle-mounted GNSS module, the vehicle-mounted inertial navigation module and the vehicle-mounted V2X communication module; when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method as in steps S101-S105.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware associated with computer program instructions, and the program may be stored in a computer readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. The storage medium of the program may be a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present application and is not to be construed as limiting the scope of the present application, so that the present application is not limited thereto, and all equivalent variations and modifications can be made to the present application.
Claims (9)
1. A vehicle real-time positioning system based on vehicle-road cooperation is characterized by comprising a vehicle-mounted unit, a road side unit and a cloud computing center;
the road side unit is used for periodically sending a differential data service request to the cloud computing center, wherein the differential data service request comprises a port number or approximate coordinates of the road side unit;
the cloud computing center is used for verifying the differential data service request of each road side unit, forwarding the verified differential data service request to the differential data service center, acquiring differential data corresponding to the differential data service request, and sending each differential data to the corresponding road side unit;
the road side unit is used for sending the received differential data to a vehicle-mounted unit within a preset service range;
the vehicle-mounted unit is used for acquiring inertial navigation data of an inertial navigation module and satellite observation data of a GNSS module, and performing fusion calculation based on the differential data, the inertial navigation data and the satellite observation data to perform real-time high-precision positioning; wherein,
the vehicle-mounted unit is provided with a difference database and is used for storing historical difference data within a certain period from the current moment;
the on-board unit is further used for constructing a difference data prediction model used for predicting difference data according to the historical difference data, and predicting a plurality of difference data in difference age after the current time based on the difference data prediction model.
2. The system of claim 1,
the vehicle-mounted unit is preset with a network delay verification unit and is used for judging whether network delay exists according to the differential data to determine target differential data;
the network delay verification unit comprises a differential data prediction module, a delay unit and a delay unit, wherein the differential data prediction module is used for comparing the differential data received in real time with the differential data at the moment predicted by the prediction module;
when the difference between the difference data received in real time and the difference data predicted at the moment is greater than the preset threshold value,
executing a preset ping value to generate an execution result;
when the execution result has data loss, determining that network delay occurs, and taking the differential data obtained by the prediction module as the target differential data at the moment;
when the difference value between the differential data received in real time and the predicted differential data at the moment is smaller than a preset threshold value, determining that no network delay exists, and taking the differential data received in real time as target differential data at the moment;
and the vehicle-mounted unit is used for performing fusion calculation on the target differential data, the inertial navigation data and the satellite observation data so as to perform real-time high-precision positioning.
3. The system of claim 2,
the vehicle-mounted unit is used for performing fusion calculation on the target differential data, the inertial navigation data and the satellite observation data so as to perform real-time high-precision positioning, and comprises the following steps:
the vehicle-mounted unit is used for carrying out differential solution on satellite observation data of the GNSS module acquired by the vehicle end and the target differential data through a steady Kalman filter to generate low-frequency sub-decimeter positioning data of the vehicle end;
and the vehicle-mounted unit is also used for generating high-frequency sub-decimeter positioning data after the low-frequency sub-decimeter positioning data of the vehicle end and the inertial navigation data are filtered by a Kalman filter.
4. The system of claim 3, wherein the generating high frequency sub-decimeter positioning data after filtering the vehicle-end low frequency sub-decimeter positioning data and the inertial navigation data through a Kalman filter comprises:
determining to adopt a loose coupling or tight coupling resolving mode to resolve the vehicle-end low-frequency sub-decimeter-level positioning data and the inertial navigation data according to the acquired satellite observation data signal intensity to generate high-frequency sub-decimeter-level positioning data;
wherein, when the satellite observation data signal intensity is larger than a preset threshold value, a preset loose coupling calculation mode is adopted to remove the noise of the navigation parameters output by the inertial navigation subsystem and the respective noise of the satellite observation data of the GNSS module obtained by the vehicle end through a Kalman filter, then errors of the position, the speed and the attitude are output, and a positioning result is generated after the low-frequency sub-decimeter positioning result of the vehicle end is corrected according to the errors of the position, the speed and the attitude,
when the satellite observation data signal intensity is smaller than a preset threshold value, a preset close coupling resolving mode is adopted to remove the noise of the navigation parameters output by the inertial navigation subsystem and the noise of the pseudo range and pseudo range rate data of the satellite observation data obtained by the vehicle end through a steady Kalman filter, then errors of the position, the speed and the attitude are output, finally, the position, the speed and the attitude parameters of the inertial navigation subsystem are corrected according to the errors of the position, the speed and the attitude, and finally, a high-frequency high-precision positioning result is generated.
5. The system of claim 1,
the cloud computing center is provided with a road side unit verification module for determining whether the service request passes the verification according to whether the road side unit sending the positioning service request is in a service registration white list;
the service registration white list comprises RSU service registration information and differential data service center information, the RSU service registration information comprises a user name, a password, a port number and an IP address of a RSU, and the differential data service center information comprises at least one of the port number and the IP address of a differential data service center; wherein,
multiple adjacent road side units can share one user name and one password.
6. A vehicle positioning method based on vehicle-road coordination is characterized by comprising the following steps:
determining a road side unit in the area range of the current moment of the vehicle to be positioned;
the road side unit sends a differential data service request to a cloud computing center, wherein the service request comprises a road side unit port number or approximate coordinates;
the cloud computing center verifies the differential service requests of the road side units according to the road side unit information, forwards the verified differential data service requests to a differential data service center, acquires differential data corresponding to each differential data service request, and sends each differential data to the road side unit corresponding to the differential data service request;
the road side unit sends the differential data to an on-board unit within the service range of the road side unit;
the vehicle-mounted unit acquires inertial navigation positioning data and satellite observation positioning data, and performs fusion calculation on the inertial navigation positioning data and the satellite observation positioning data based on the differential data so as to perform real-time high-precision positioning; the vehicle-mounted unit is provided with a difference database and is used for storing historical difference data within a certain period from the current moment;
the on-board unit is further used for constructing a difference data prediction model used for predicting difference data according to the historical difference data, and predicting a plurality of difference data in difference age after the current time based on the difference data prediction model.
7. The method according to claim 6, further comprising, before performing the fusion calculation on the differential data, the inertial navigation positioning data and the satellite observation positioning data to perform real-time high-precision positioning, the following steps:
performing network delay verification on the differential data acquired by the road side unit by adopting a differential prediction model;
comparing the differential data received in real time with the differential data at the moment predicted by the differential prediction model;
executing a preset ping value to generate an execution result;
when the execution result has data loss, determining that network delay occurs, and taking the differential data obtained by the prediction module as the target differential data at the moment;
when the difference value between the difference data received in real time and the predicted difference data at the moment is larger than a preset threshold value, determining that network delay exists, and taking the difference data obtained by the prediction module as target difference data at the moment;
when the difference value between the differential data received in real time and the predicted differential data at the moment is smaller than a preset threshold value, determining that no network delay exists, and taking the differential data received in real time as target differential data at the moment;
and the vehicle-mounted unit is used for carrying out fusion calculation based on the target differential data, the inertial navigation data and the satellite observation data so as to carry out real-time high-precision positioning.
8. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 6-7.
9. A vehicle, comprising:
one or more processors;
storage means for storing one or more programs;
the vehicle-mounted GNSS module, the vehicle-mounted inertial navigation module and the vehicle-mounted V2X communication module;
when executed by the one or more processors, cause the one or more processors to implement the method of claims 6-7.
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