CN117872416A - Positioning calibration method, positioning calibration device, computer equipment and storage medium - Google Patents

Positioning calibration method, positioning calibration device, computer equipment and storage medium Download PDF

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Publication number
CN117872416A
CN117872416A CN202311662376.4A CN202311662376A CN117872416A CN 117872416 A CN117872416 A CN 117872416A CN 202311662376 A CN202311662376 A CN 202311662376A CN 117872416 A CN117872416 A CN 117872416A
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vehicle
positioning data
data
satellite positioning
state
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陈亮
姜博
刘智城
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Beijing Wanji Technology Co Ltd
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Beijing Wanji Technology Co Ltd
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Priority to CN202311662376.4A priority Critical patent/CN117872416A/en
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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
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/23—Testing, monitoring, correcting or calibrating of receiver elements
    • G01S19/235—Calibration of receiver components

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
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Abstract

The application relates to a positioning calibration method, a positioning calibration device, computer equipment and a storage medium. The method comprises the following steps: determining the shielding state of the vehicle at the current moment according to the satellite positioning data and the road map data of the vehicle at the current moment, determining the current predicted positioning data of the vehicle at the current moment according to the historical track data of the vehicle and the predicted positioning data of the previous moment under the condition that the shielding state of the vehicle at the current moment is shielding, and calibrating the satellite positioning data of the vehicle at the current moment according to the current predicted positioning data. By adopting the method, the actual positioning data (namely satellite positioning data) of the vehicle can be calibrated under the condition that the vehicle is shielded, so that the positioning error is reduced, and the accuracy of the obtained positioning result is improved.

Description

Positioning calibration method, positioning calibration device, computer equipment and storage medium
Technical Field
The present disclosure relates to the field of traffic technologies, and in particular, to a positioning calibration method, a positioning calibration device, a computer device, and a storage medium.
Background
Along with the development of automatic driving technology, the requirements on vehicle positioning are also higher and higher, and the positioning modes adopted by the vehicles are also various at present.
In the related art, positioning is generally performed using a global navigation satellite system installed in an autonomous vehicle. However, in areas without satellite positioning data such as tunnels, urban canyons, viaducts, and the like, the problem of lower vehicle positioning accuracy exists due to unlocking, shielding, or loss of satellite positioning data by adopting the related technology.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a positioning calibration method, a positioning calibration device, a computer device, and a storage medium, which can improve vehicle positioning accuracy in the event of loss of lock, shielding, or loss of satellite positioning data.
In a first aspect, an embodiment of the present application provides a positioning calibration method, including:
determining the state of satellite positioning data according to the satellite positioning data and road map data of the vehicle at the current moment;
under the condition that the state of the satellite positioning data is abnormal, determining the current predicted positioning data of the vehicle at the current moment according to the historical track data of the vehicle and the historical satellite positioning data when the last positioning state of the vehicle before the current moment is in a fixed solution state;
and calibrating satellite positioning data of the vehicle at the current moment according to the current predicted positioning data.
In one embodiment, determining the state of the satellite positioning data according to the satellite positioning data and road map data of the vehicle at the current moment comprises:
determining the interval distance between the center point of the vehicle and a target lane line on the vehicle driving road according to the satellite positioning data and the road map data at the current moment;
if the interval distance is greater than or equal to a preset distance threshold, determining that the state of the satellite positioning data is abnormal;
if the interval distance is smaller than the distance threshold value, determining that the state of the satellite positioning data is normal.
In one embodiment, the road map information includes position coordinates of a center line of each lane on the road, and the satellite positioning data includes vehicle position coordinates; according to satellite positioning data and road map data at the current moment, determining the interval distance between the central point of the vehicle and the target lane line on the running road of the vehicle comprises the following steps:
determining a target lane where a vehicle is located according to the position coordinates of the central line of each lane and the vehicle position coordinates of the current moment;
acquiring target position coordinates of two positions closest to the vehicle on the central line of the target lane according to the position coordinates of the central line of the target lane;
And determining the interval distance according to the target position coordinates and the vehicle position coordinates at the current moment.
In one embodiment, before determining the state of the satellite positioning data according to the satellite positioning data and the road map data of the vehicle at the current moment, the method further comprises:
determining the positioning state of the vehicle at the current moment according to the satellite positioning data at the current moment;
when the positioning state at the current time is the non-stationary solution state, a step of determining the state of the satellite positioning data from the satellite positioning data and the road map data of the vehicle at the current time is performed.
In one embodiment, determining a positioning state of the vehicle at the current time according to satellite positioning data at the current time includes:
under the condition that satellite positioning data at the current moment is high-precision positioning data, determining that the positioning state of the vehicle at the current moment is a fixed solution state;
and under the condition that the satellite positioning data at the current moment is non-high-precision positioning data, determining that the positioning state of the vehicle at the current moment is a non-fixed solution state.
In one embodiment, determining current predicted positioning data of the vehicle at the current time according to historical track data of the vehicle and historical satellite positioning data when a last positioning state of the vehicle before the current time is a fixed solution state includes:
According to the historical track data of the vehicle and satellite positioning data when the last positioning state of the vehicle before the current moment is a fixed solution state, external interference data of the vehicle at the current moment is determined;
and determining current predicted positioning data according to the historical satellite positioning data and the external interference data.
In one embodiment, the method further comprises:
after the satellite positioning data at the current moment are calibrated, the control display end calibrates the vehicle from the deviated lane to the current lane.
In a second aspect, embodiments of the present application provide a positioning calibration device, including:
the first determining module is used for determining the state of satellite positioning data according to the satellite positioning data and road map data of the vehicle at the current moment;
the second determining module is used for determining current predicted positioning data of the vehicle at the current moment according to the historical track data of the vehicle and the historical satellite positioning data when the last positioning state of the vehicle before the current moment is a fixed solution state under the condition that the state of the satellite positioning data is abnormal;
and the calibration module is used for calibrating satellite positioning data of the vehicle at the current moment according to the current predicted positioning data.
In a third aspect, embodiments of the present application provide a computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of the method of any of the embodiments of the first aspect described above when the computer program is executed.
In a fourth aspect, embodiments of the present application provide a readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of any of the embodiments of the first aspect described above.
In a fifth aspect, embodiments of the present application also provide a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method of any of the embodiments of the first aspect described above.
The positioning calibration method, device, computer equipment and storage medium provided by the embodiment of the application comprise the following steps: determining the state of satellite positioning data according to the satellite positioning data of the vehicle at the current moment and road map data, determining the current predicted positioning data of the vehicle at the current moment according to the historical track data of the vehicle and the historical satellite positioning data when the last positioning state of the vehicle before the current moment is in a fixed solution state under the condition that the state of the satellite positioning data is abnormal, and calibrating the satellite positioning data of the vehicle at the current moment according to the current predicted positioning data. By adopting the method, under the condition that the state of the satellite positioning data is abnormal, the actual positioning data (namely the satellite positioning data) of the vehicle can be calibrated, the positioning error is reduced, and the accuracy of the obtained positioning result is improved; meanwhile, the calibration process in the method does not need manual participation, so that the error of positioning calibration can be greatly reduced, the accuracy of positioning calibration is improved, and the speed of positioning calibration is improved; in addition, the method is realized without a complex deep learning algorithm, and the positioning calibration process is simpler; furthermore, the method can be applied to the area without satellite positioning data, does not limit the area environment without satellite positioning data, and has wide applicability compared with the prior art; in addition, the method does not need to add additional entities, so that the operation process is simple, convenient and efficient.
Drawings
FIG. 1 is a flow chart of a positioning calibration method according to an embodiment;
FIG. 2 is a flow chart of a positioning calibration method according to another embodiment;
FIG. 3 is a flow chart of a positioning calibration method according to another embodiment;
FIG. 4 is a flow chart of a positioning calibration method according to another embodiment;
FIG. 5 is a flow chart of a positioning calibration method according to another embodiment;
FIG. 6 is a flow chart of a positioning calibration method according to another embodiment;
FIG. 7 is a flow chart of a positioning calibration method according to another embodiment;
FIG. 8 is a flow chart of a positioning calibration method according to another embodiment;
FIG. 9 is a block diagram of a positioning calibration device in one embodiment;
fig. 10 is an internal structural view of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
In the field of automatic driving, vehicle positioning technology is widely applied, and positioning modes adopted by automatic driving vehicles are also various. In the related art, positioning is generally performed using a global navigation satellite system installed in an autonomous vehicle. However, in areas without satellite positioning data such as tunnels, urban canyons, viaducts and the like, due to the phenomena of unlocking, shielding or losing satellite positioning data and the like, the problem that the vehicle positioning accuracy is not accurate enough or positioning is not available in the related technology, and the running of an automatic driving vehicle is seriously influenced exists. Based on the above, the embodiment of the application provides a positioning calibration method, which can improve the positioning accuracy of a vehicle under the condition that satellite positioning data is unlocked, shielded or lost.
The positioning calibration method provided by the embodiment of the application can be applied to an automatic driving Vehicle, and the automatic driving Vehicle can comprise a plurality of hardware modules with different functions, such as a Vehicle-mounted module, a Vehicle-to-outside information exchange (V2X) communication module, a global navigation satellite system, a display module, a positioning calculation module and the like. Alternatively, the above-mentioned autonomous vehicle may be a fully-autonomous vehicle or a semi-autonomous vehicle, and the type of the autonomous vehicle is not limited in the embodiments of the present application. The positioning calibration method is suitable for the process that the automatic driving vehicle enters automatic driving.
Alternatively, the above-described positioning calculation module may not be limited to be a variety of personal computers, notebook computers, smartphones, tablet computers, servers, or portable wearable devices. The positioning calculation module is respectively in communication connection with the global navigation satellite system, the display module and the vehicle-mounted module, and the communication modes can be Bluetooth, wi-Fi, mobile network connection and the like. The following embodiments will specifically describe a specific procedure of the positioning calibration method, and use an execution body as a positioning calculation module to describe a specific procedure of the positioning calibration method.
Fig. 1 is a schematic flow chart of a positioning calibration method according to an embodiment of the present application, where the method may include the following steps:
s100, determining the state of satellite positioning data according to the satellite positioning data of the vehicle at the current moment and road map data.
Alternatively, the satellite positioning data of the vehicle may include information of position coordinates, speed, and heading angle. Alternatively, the road map data may include map data of each road in the target area; the map data can comprise information such as area identification of each road, upstream and downstream intersection identification of each intersection on each road, longitude and latitude coordinates of the central line of each lane on each road, and a lane number; the longitude and latitude coordinates of the center line can be understood as the longitude and latitude coordinates of each spacing point on the center line.
For example, if the traffic direction of a road is from left to right, the intersection on the left side of the road is referred to as the upstream intersection, and the intersection on the right side is referred to as the downstream intersection.
Meanwhile, the state of the satellite positioning data can be a normal state or an abnormal state, wherein when the state of the satellite positioning data is abnormal, the satellite positioning data can be represented as losing lock, shielding or losing. Optionally, the loss of lock of the satellite positioning data may be understood as that the global navigation satellite system disposed on the vehicle can collect the satellite positioning data, but the number of the searched satellites is less; the satellite positioning data shielding can be understood as a shielding state that the vehicle is subjected to surrounding high buildings, trees, viaducts, tunnels, urban canyons and other environmental objects at the current moment; the loss of satellite positioning data may be understood as the inability of the global navigation satellite system to acquire satellite positioning data.
The positioning calculation module can acquire satellite positioning data of the current moment acquired by the global navigation satellite system, acquire Road map data from a Road Side Unit (RSU) of a Road Side through the V2X communication module in the automatic driving vehicle, and then determine the state of the satellite positioning data according to the satellite positioning data of the vehicle at the current moment and the Road map data.
For example, the mode of determining the state of the satellite positioning data according to the satellite positioning data and the road map data of the vehicle at the current time may be to train an algorithm model in advance, and then input the satellite positioning data and the road map data of the vehicle at the current time into the algorithm model, and the algorithm model outputs the state of the satellite positioning data.
For example, the state of the satellite positioning data may be determined according to the satellite positioning data of the vehicle at the current time and the road map data, or the specific position of the corresponding lane on the corresponding road at the current time may be determined according to the satellite positioning data of the vehicle at the current time and the road map data, and then the state of the satellite positioning data may be determined according to the actual environmental matters around the specific position of the corresponding lane.
And S200, when the state of the satellite positioning data is abnormal, determining the current predicted positioning data of the vehicle at the current moment according to the historical track data of the vehicle and the historical satellite positioning data when the last positioning state of the vehicle before the current moment is in a fixed solution state.
In practical application, the positioning calculation module can adopt a prediction analysis algorithm to perform prediction processing according to historical track data of the vehicle and historical satellite positioning data when the last positioning state of the vehicle before the current moment is a fixed solution state, so as to obtain current predicted positioning data of the vehicle at the current moment.
Alternatively, the predictive analysis algorithm may be a simple average method, a moving average method, an exponential smoothing method, a linear regression method, and the like, which is not limited to the embodiment of the present application.
In this embodiment of the present application, the historical track data may be data such as a speed, a longitude and latitude, and a heading angle of the vehicle in a preset time period before a time when the last positioning state is the fixed solution state.
And S300, calibrating satellite positioning data of the vehicle at the current moment according to the current predicted positioning data.
Specifically, the positioning calculation module may calibrate satellite positioning data of the vehicle at the current moment by using a positioning data calibration algorithm according to the current predicted positioning data. Alternatively, the positioning data calibration algorithm may be a map matching-based positioning data fuzzy correction algorithm, a fuzzy logic-based positioning data correction algorithm, a reference value-based positioning data correction algorithm, or the like, which is not limited in this embodiment of the present application.
In an embodiment, before performing the step in S100, as shown in fig. 2, the method may further include the following steps:
s400, determining the positioning state of the vehicle at the current moment according to the satellite positioning data at the current moment.
Specifically, satellite positioning data of the vehicle at the current moment can be searched in the mapping relation table, and then the positioning state corresponding to the satellite positioning data searched in the mapping relation table is determined as the positioning state of the vehicle at the current moment. Optionally, the mapping relationship table may include different satellite positioning data, different positioning states and a corresponding relationship between the two.
The positioning state may be a fixed solution state or a non-fixed solution state. In practical applications, the non-stationary solution state may be a floating solution state or a pseudo-range differential state.
In this case, the accuracy of the satellite positioning data can be stabilized in the centimeter level in the fixed solution state, so that it can be determined that the state of the satellite positioning data is normal in this state. When the state of the satellite positioning data is temporarily abnormal, the positioning state of the vehicle can be changed from the fixed solution state to the floating solution state immediately, and the state of the satellite positioning data is indicated to be abnormal, wherein after the state of the satellite positioning data is abnormal, the positioning calculation module cannot determine that the satellite positioning data is high-precision positioning data, and further, after a period of time, the positioning calculation module still cannot determine that the satellite positioning data is high-precision positioning data, and the positioning state of the vehicle can be withdrawn from the floating solution state to the pseudo-range differential state.
In practical application, in the running process of the automatic driving vehicle, the display module in the automatic driving vehicle can acquire satellite positioning data of the vehicle at different moments acquired by the global navigation satellite system, display the satellite positioning data at different moments in real time, and display the positioning state of the vehicle at different moments, whether the satellite positioning data are identification of high-precision positioning data or not and the satellite positioning data after calibration at different moments.
In one embodiment, the step of determining the positioning state of the vehicle at the current time according to the satellite positioning data at the current time in S400 may include: under the condition that satellite positioning data at the current moment is high-precision positioning data, determining that the positioning state of the vehicle at the current moment is a fixed solution state; and under the condition that the satellite positioning data at the current moment is non-high-precision positioning data, determining that the positioning state of the vehicle at the current moment is a non-fixed solution state.
Specifically, a real-time carrier phase difference technology may be used to analyze satellite positioning data at the current time of the vehicle, determine that the satellite positioning data at the current time is high-precision positioning data or non-high-precision positioning data, and determine that the positioning state of the vehicle at the current time is a fixed solution state if the satellite positioning data at the current time is high-precision positioning data, or determine that the positioning state of the vehicle at the current time is a non-fixed solution state if the satellite positioning data at the current time is non-high-precision positioning data.
S500, when the positioning state at the current moment is a non-fixed solution state, executing a step of determining the state of satellite positioning data according to the satellite positioning data and road map data of the vehicle at the current moment.
Further, in the case where it is determined that the positioning state at the current time of the vehicle is the non-stationary solution state, the execution of the step in S100 described above may be started.
Here, it should be noted that, when the positioning state of the vehicle at the current time is a non-stationary solution state, it may be determined that the state of the satellite positioning data is abnormal, but this result may be caused by a data acquisition error of the global navigation satellite system, so it is necessary to further determine whether the vehicle deviates from the lane, and then determine the state of the satellite positioning data accurately according to whether the vehicle deviates from the lane.
In this embodiment of the present application, when the positioning state at the current time is the fixed solution state, the satellite positioning data at the current time of the vehicle may be directly determined as the calibrated satellite positioning data.
According to the technical scheme, the state of satellite positioning data is determined according to the satellite positioning data of the vehicle at the current moment and road map data, when the state of the satellite positioning data is abnormal, current prediction positioning data of the vehicle at the current moment is determined according to historical track data of the vehicle and historical satellite positioning data when the last positioning state of the vehicle before the current moment is a fixed solution state, and the satellite positioning data of the vehicle at the current moment is calibrated according to the current prediction positioning data; according to the method, under the condition that the state of satellite positioning data is abnormal, the actual positioning data (namely satellite positioning data) of the vehicle can be calibrated, positioning errors are reduced, and the accuracy of an obtained positioning result is improved; meanwhile, the calibration process in the method does not need manual participation, so that the error of positioning calibration can be greatly reduced, the accuracy of positioning calibration is improved, and the speed of positioning calibration is improved; in addition, the method is realized without a complex deep learning algorithm, and the positioning calibration process is simpler; furthermore, the method can be applied to the area without satellite positioning data, does not limit the area environment without satellite positioning data, and has wide applicability compared with the prior art; in addition, the method does not need to add additional entities, so that the operation process is simple, convenient and efficient.
The following describes the above-described process of determining the state of satellite positioning data from satellite positioning data of a vehicle at a current time and road map data. In an embodiment, as shown in fig. 3, the step of determining the state of the satellite positioning data according to the satellite positioning data and the road map data of the vehicle at the current time in S100 may be implemented as follows:
s110, determining the interval distance between the center point of the vehicle and the target lane line on the running road of the vehicle according to the satellite positioning data and the road map data at the current moment.
Alternatively, the target lane line may be a lane line corresponding to a lane closest to the vehicle on a road on which the vehicle travels; the lane line may be the center line of the lane or the line between two points on the center line of the lane. Alternatively, the lane line may be a straight line or a curved line, and may be specifically determined according to the shape of the lane.
Specifically, the positioning calculation module may train an algorithm model in advance, and then input both satellite positioning data and road map data of the vehicle at the current moment into the algorithm model, where the algorithm model outputs a separation distance between a center point of the vehicle and a target lane line on a road on which the vehicle travels.
S120, if the interval distance is greater than or equal to a preset distance threshold, determining that the state of the satellite positioning data is abnormal.
The distance threshold may be determined by user-definition, or may be determined according to a historical empirical value, but in the embodiment of the present application, the distance threshold may be equal to a sum of the lane width and the vehicle width.
In practical application, the positioning calculation module can determine whether the distance between the center point of the vehicle and the target lane line on the vehicle driving road is greater than or equal to a preset distance threshold, if so, the positioning calculation module indicates that the vehicle has deviated from the driving lane at the current moment, and the positioning drift is caused by shielding at the moment, the shielding state of the vehicle at the current moment is that shielding exists, and the state of satellite positioning data is determined to be abnormal.
And S130, if the interval distance is smaller than the distance threshold value, determining that the state of the satellite positioning data is normal.
Meanwhile, under the condition that the distance between the center point of the vehicle and the target lane line on the vehicle driving road is smaller than the distance threshold value, the condition that the current moment of the vehicle does not deviate from the driving lane is indicated, the shielding state of the current moment of the vehicle is that shielding does not exist, and at the moment, the state of satellite positioning data can be determined to be normal.
According to the technical scheme, according to satellite positioning data and road map data at the current moment, the interval distance between the center point of the vehicle and a target lane line on a vehicle driving road is determined, when the interval distance is greater than or equal to a preset distance threshold value, the state of the satellite positioning data is determined to be abnormal, and when the interval distance is less than the distance threshold value, the state of the satellite positioning data is determined to be normal; the method can firstly determine the interval distance between the center point of the vehicle and the nearest lane line, then determine the state of satellite positioning data according to the interval distance, and the process can be realized without complex algorithm, thereby reducing the complexity of state determination of the satellite positioning data and improving the speed of state determination of the satellite positioning data.
A procedure for determining a separation distance between a center point of a vehicle and a target lane line on a vehicle traveling road based on satellite positioning data and road map data at the present time will be described. In one embodiment, the road map information includes position coordinates of a center line of each lane on the road, and the satellite positioning data includes vehicle position coordinates; as shown in fig. 4, the step in S110 may be implemented as follows:
S111, determining a target lane where the vehicle is located according to the position coordinates of the center line of each lane and the vehicle position coordinates of the current moment.
In the embodiment of the application, the road map information includes position coordinates of the center line of each lane on the road. Alternatively, the position coordinates of the center line of the lane may include position coordinates of each of the spaced points on the center line of the lane. In the embodiment of the present application, the position coordinates are two-dimensional plane coordinates, that is, an x-axis coordinate and a y-axis coordinate. Here, the vehicle position coordinates may be center position coordinates of the vehicle.
In practical application, taking the direction that the x axis is the width of the vehicle and the width of the lane line as an example, the positioning calculation module can acquire the x coordinate of the vehicle at the current moment from the vehicle position coordinate of the vehicle at the current moment, acquire the x coordinate of the central line of each lane from the position coordinates of the central line of each lane, then compare the x coordinate of the current moment of the vehicle with the x coordinate of the central line of every two adjacent lanes on the vehicle driving road, determine two target adjacent lanes where the x coordinate of the current moment of the vehicle is located, further calculate the distance between the x coordinate of the current moment of the vehicle and the x coordinate of the central line of the two target adjacent lanes respectively by adopting a distance calculation method, and determine the corresponding lane with the smallest distance as the target lane where the vehicle is located.
Alternatively, the distance calculation method may be a euclidean distance method, a manhattan distance method, a chebyshev distance method, a mahalanobis distance method, or the like, which is not limited in this embodiment of the present application.
S112, acquiring target position coordinates of two positions closest to the vehicle on the central line of the target lane according to the position coordinates of the central line of the target lane.
The method comprises the steps of obtaining position coordinates of a central line of a target lane from position coordinates of the central line of each lane, calculating the distance between each interval point on the central line of the target lane and a vehicle by adopting a distance calculation method according to the position coordinates of each interval point on the central line of the target lane and the vehicle position coordinates of the current moment of the vehicle, determining the interval point corresponding to the front nearest distance of the vehicle and the interval point corresponding to the rear nearest distance of the vehicle as two positions closest to the vehicle on the central line of the target lane, and obtaining the target position coordinates of the two positions closest to the vehicle on the central line of the target lane according to the position coordinates of the central line of the target lane.
S113, determining the interval distance according to the target position coordinate and the vehicle position coordinate at the current moment.
In the embodiment of the application, a linear equation between two positions closest to the vehicle can be constructed according to the target position coordinates of the two positions closest to the vehicle on the center line of the target lane, and then the distance between the center point of the vehicle and the connecting line between the two positions closest to the vehicle is calculated by adopting a point-to-linear distance calculation method according to the linear equation and the vehicle position coordinates (namely, the position coordinates of the center point of the vehicle) at the current moment of the vehicle.
For example, if the vehicle position coordinate at the current time of the vehicle is (x 0 ，y 0 ) The coordinates of the target positions at the two positions closest to the vehicle on the center line of the target lane corresponding to the vehicle are (x) 1 ,y 1 ) And (x) 2 ,y 2 ) A linear equation can be constructed from the target position coordinates of the two positions using the following equation (1), and correspondingly, the constructed linear equation is represented by the following equation (2).
y＝kx+b (1)
Further, the distance d between the center point of the vehicle and the closest two positions of the vehicle, which is calculated by using the point-to-straight distance calculation method, can be expressed by the following formula (3).
Wherein,
according to the technical scheme, a target lane where a vehicle is located is determined according to the position coordinates of the center line of each lane and the vehicle position coordinates of the current moment, the target position coordinates of two positions closest to the vehicle on the center line of the target lane are obtained according to the position coordinates of the center line of the target lane, and the interval distance between the center point of the vehicle and the target lane line on the vehicle driving road is determined according to the target position coordinates and the vehicle position coordinates of the current moment; the method can determine the interval distance of the connecting line between the vehicle and the two nearest positions on the central line of the target lane to prepare for determining the state of satellite positioning data, and the process of determining the interval distance is not realized by a complex algorithm, so that the complexity of determining the interval distance can be reduced, and the speed of determining the interval distance can be improved.
The process of determining the current predicted positioning data of the vehicle at the current time according to the historical track data of the vehicle and the historical satellite positioning data when the last positioning state of the vehicle before the current time is the fixed solution state is described below. In an embodiment, as shown in fig. 5, the step in S200 may be implemented as follows:
s210, according to the historical track data of the vehicle and the historical satellite positioning data when the last positioning state of the vehicle before the current moment is the fixed solution state, external interference data of the vehicle at the current moment are determined.
Specifically, the acceleration a of the vehicle during running may be determined according to the speed in the historical track data of the vehicle and the speed in the historical satellite positioning data when the last positioning state of the vehicle before the current time is the fixed solution state, and then the external disturbance data Q of the vehicle at the current time may be determined according to the time difference Δt between the current time and the corresponding time when the last positioning state of the vehicle before the current time is the fixed solution state k 。
Wherein,B k can represent a control matrix (i.e.)>)，The control vector (i.e., a) may be represented.
S220, determining current predicted positioning data according to the historical satellite positioning data and the external interference data.
In practical application, the current predicted positioning data can be determined by taking the historical satellite positioning data when the last positioning state of the vehicle before the current moment is the fixed solution state as the reference data.
For example, the historical satellite positioning data for the vehicle when the last positioning state before the current time was the fixed solution state (time k-1) includes the position p k-1 And velocity v k-1 The satellite positioning data of the current moment (moment k) of the vehicle comprise the position p k And velocity v k The time difference between the time k-1 and the time k is Δt, and the position p at the time k corresponds to k And velocity v k Can be represented by formula (4).
Further, the predicted positioning data at the time of k-1 and the formula (4) are utilizedObtaining predicted positioning data of the vehicle at the moment k>Can be represented by formula (5). Wherein the predicted positioning data +.>
However, during the running of the automatic driving vehicle, there may be some change in the control of the automatic driving vehicle due to external factors, such as the fact that the automatic driving vehicle may be accelerated or decelerated under human control, and the acceleration a generated by the human control of the acceleration or deceleration of the vehicle is taken into consideration for the position p in the formula (4) k And velocity v k The result obtained by the correction can be expressed by the formula (6).
In the embodiment of the present application, the position p after correction k And velocity v k On the basis of the above, the predicted positioning data at the k moment can be corrected by the external interference data of the vehicle at the current moment to obtain a result
Alternatively, the correction processing described above may be combined by a plurality of kinds of addition, subtraction, multiplication, division, logarithmic, and the like. In the embodiment of the present application, however, the procedure of the correction process can be expressed by the formula (7).
According to the technical scheme, external interference data of the vehicle at the current moment is determined according to historical track data of the vehicle and historical satellite positioning data when the last positioning state of the vehicle before the current moment is a fixed solution state, and current prediction positioning data is determined according to the historical satellite positioning data and the external interference data; according to the method, the external interference data can be taken into consideration to determine the current predicted track data of the vehicle at the current moment, so that the accuracy of the determined current predicted track data of the vehicle at the current moment is higher.
In one embodiment, as shown in fig. 6, the step of calibrating satellite positioning data of the vehicle at the current moment according to the current predicted positioning data in S300 may be implemented by:
S310, transforming the current predicted positioning data to obtain the current transformed predicted positioning data.
In practical application, the current predicted positioning data of the current moment and the current moment of the vehicleThe dimensions of the actual positioning data of the scale are different, and therefore, the matrix H can be transformed according to the dimensions k And transforming the current predicted positioning data to obtain the current transformed predicted positioning data. For example, latitude and longitude data expressed in a "degree" format in the current predicted positioning data may be converted into latitude and longitude data expressed in a "degree score" format in the actual positioning data.
Alternatively, the above-described dimensional transformation matrix may represent a dimensional transformation relationship between the current predicted positioning data and the actual positioning data. Wherein, the dimension of the predicted positioning data after the current transformation is the same as the dimension of the actual positioning data.
Continuing with the previous example, current predicted positioning dataPerforming transformation treatment to obtain->
S320, determining actual positioning data of the vehicle at the current moment according to the satellite positioning data at the current moment and the actual correlation between the speed and the position at the current moment.
In the embodiment of the application, the satellite positioning data of the vehicle at the current moment and the actual correlation between the speed and the position of the vehicle at the current moment can be simply combined to obtain the actual positioning data of the vehicle at the current moment.
Wherein the actual correlation between the speed and the position at the current moment can be determined by R k The satellite positioning data of the current moment can be represented byAnd (3) representing.
S330, calibrating satellite positioning data of the vehicle at the current moment according to the current transformed predicted positioning data and the actual positioning data.
In practical application, the positioning calculation module can average the current transformed predicted positioning data and the actual positioning data to obtain average positioning data, and then calibrate satellite positioning data of the vehicle at the current moment by adopting a positioning data calibration algorithm according to the average positioning data.
In addition, the positioning calculation module can also pre-train a positioning data calibration model, then input the predicted positioning data after current transformation, the actual positioning data and the satellite positioning data of the vehicle at the current moment into the positioning data calibration model, and the positioning data calibration model can output the calibrated satellite positioning data.
Optionally, the positioning data calibration model may be formed by combining at least one of a convolutional neural network model, a spatial pyramid pooling network model, a fully connected neural network model, a long-term and short-term memory neural network model, a cyclic neural network model, an antagonistic neural network model, and the like.
In one embodiment, as shown in fig. 7, the step of calibrating the satellite positioning data of the vehicle at the current moment according to the current transformed predicted positioning data and the actual positioning data in S330 may include:
s331, determining a positioning data fusion coefficient according to the predicted positioning data and the actual positioning data after current transformation.
In practical application, a filtering algorithm can be adopted to process the predicted positioning data and the actual positioning data after the current transformation to obtain a positioning data fusion coefficient.
Optionally, the filtering algorithm may be a median filtering method, an arithmetic average filtering method, a first-order lag filtering method, a weighted recursive average filtering method, a moving average filtering method, or the like.
In one embodiment, as shown in fig. 8, the step of determining the positioning data fusion coefficient according to the current transformed predicted positioning data and the actual positioning data in S331 may be implemented by the following steps:
s3311, acquiring a first variable distribution of the predicted positioning data after the current transformation and a second variable distribution of the actual positioning data.
Alternatively, the types of the first variable distribution and the second variable distribution may be the same or different; the first variable distribution and the second variable distribution may be a uniform distribution, an exponential distribution, a normal distribution, a binomial distribution, a poisson distribution, or the like, but in the embodiment of the present application, the first variable distribution and the second variable distribution may be gaussian distributions.
S3312, determining a positioning data fusion coefficient according to the first variable distribution and the second variable distribution.
Further, an arithmetic operation can be performed according to the first variable distribution and the second variable distribution to obtain a positioning data fusion coefficient. In the present embodiment, the arithmetic operation may be a multiplication operation.
Taking two different Gaussian distributions as examples of the first variable distribution and the second variable distribution, the mean and variance of the first variable distribution are u 0 Andthe mean and variance of the second variable distribution are u 1 And->The mean and variance of the new gaussian distribution obtained after multiplication of the first variable distribution and the second variable distribution are u ' and σ ', respectively ' 2 The mean and variance of the new gaussian distribution can be expressed by equation (8). />
Order theThen u 'and sigma' 2 Can be represented by formula (9).
Writing formula (9) into a matrix form, expressing the covariance of the new gaussian distribution by sigma,representing the mean value of each dimension, equation (9) may be written as equation (10).
K＝∑ 0 *(∑ 0 +∑ 1 ) -1
∑′＝∑ 0 -K*∑ 0 (10)
Wherein the subscript "0" indicates the covariance corresponding to the first variable distribution, the subscript "1" indicates the covariance corresponding to the second variable distribution, the superscript' "indicates the covariance corresponding to the new variable distribution,representing the mean value of each dimension corresponding to the first variable distribution, +.>Representing the mean value of each dimension corresponding to the second variable distribution, +.>The matrix K may be referred to as a localization data fusion coefficient, i.e., a kalman gain, representing the mean value of each dimension to which the new variable distribution corresponds.
In this embodiment of the present application, the covariance corresponding to the first variable distribution is equal to the variance corresponding to the first variable distribution, the covariance corresponding to the second variable distribution is equal to the variance corresponding to the second variable distribution, and the positioning data fusion coefficient obtained through transformation
S332, determining positioning overlapping data between the current predicted positioning data at the current moment and the satellite positioning data at the current moment according to the current transformed predicted positioning data, the satellite positioning data in the actual positioning data and the positioning data fusion coefficient.
It should be noted that, the arithmetic operation may be performed on the current transformed predicted positioning data, the satellite positioning data in the actual positioning data, and the positioning data fusion coefficient, so as to obtain positioning overlapping data between the current predicted positioning data at the current time and the satellite positioning data at the current time.
In practical applications, the current transformed predicted positioning data may be represented as (u) 0 ,∑ 0 ) Wherein, the method comprises the steps of, wherein,the actual positioning data may be expressed as (u) 1 ,∑ 1 ) Wherein, the method comprises the steps of, wherein,further, based on the current transformed predicted positioning data (u 0 ,∑ 0 ) Actual positioning data (u) 1 ,∑ 1 ) Satellite positioning data->And a positioning data fusion coefficient K, wherein the obtained positioning overlapping data can be represented by a formula (11).
S333, performing inverse transformation processing on the positioning overlapping data to obtain a calibration result.
Specifically, the positioning overlapping data can be subjected to inverse transformation according to the dimensional transformation matrix, so as to obtain a calibration result.
Correspondingly, the left side of equation (11) is multiplied by the transformation matrix H k To transpose the transform matrix H k The cancellation can be specifically expressed by the formula (12).
Wherein in formula (12) Representing the current transformed correlation between the speed and position of the vehicle at the current time. Here, the correlation after the current transformation may be obtained by transforming the current correlation between the speed and the position of the vehicle at the current time.
In practical application, the current correlation between the speed and the position of the vehicle at the current moment can be determined according to the historical correlation and the external interference data when the last positioning state of the vehicle before the current moment is the fixed solution state, and then the current correlation between the speed and the position of the vehicle at the current moment is transformed, so that the current transformed correlation is obtained.
Specifically, the historical correlation P between position and speed can be determined based on the last time the vehicle was positioned before the current time (time k-1) when the last positioning state was a fixed solution k-1 Then for history correlation P k-1 Arithmetically operating with external disturbance data to obtain current correlation P between speed and position of vehicle at current moment (k moment) k . In the embodiment of the present application, the above arithmetic operation is an addition operation.
With continued reference to the previous example, in general, the position p of the vehicle at time k k And velocity v k Current correlation P between k Can be expressed as by formula (13)
In practice, however, there is unknown disturbance due to the autonomous vehicleSo can be in the current correlation P k Adding some external interference data to improve the current correlation P between the speed and the position at the current moment k Wherein the external interference data may be represented by covariance Q k Is expressed by the noise of (a) and correspondingly, the external interference data is taken into consideration, the current correlation P k Correcting to obtain the current correlation P k Can be represented by formula (14).
Wherein T in equation (14) represents transpose.
Meanwhile, in practical application, the dimensions of the current predicted positioning data at the current moment and the actual positioning data of the vehicle at the current moment are different, so the matrix H can be transformed according to the dimensions k And transforming the current predicted positioning data to obtain the current transformed predicted positioning data. For example, latitude and longitude data expressed in a "degree" format in the current predicted positioning data may be converted into latitude and longitude data expressed in a "degree score" format in the actual positioning data.
Alternatively, the above-described dimensional transformation matrix may represent a dimensional transformation relationship between the current predicted positioning data and the actual positioning data. Wherein, the dimension of the predicted positioning data after the current transformation is the same as the dimension of the actual positioning data.
Continuing with the previous example, current predicted positioning dataPerforming transformation treatment to obtain->For the current correlation P k Performing transformation processing to obtain correlation after current transformation >
In one embodiment, after performing the calibration, the method may further include: and after the satellite positioning data at the current moment are calibrated, the display end is controlled to calibrate the vehicle from the deviated lane to the current lane.
According to the technical scheme, the current predicted positioning data is transformed to obtain the current transformed predicted positioning data, the actual positioning data of the vehicle at the current moment is determined according to the satellite positioning data at the current moment and the actual correlation between the speed and the position at the current moment, and the satellite positioning data of the vehicle at the current moment is calibrated according to the current transformed predicted positioning data and the actual positioning data; according to the method, the current predicted positioning data of the vehicle at the current moment can be subjected to transformation processing, so that the current predicted positioning data are converted into the current transformed predicted positioning data with the same dimension as the actual positioning data, and therefore the accuracy of a final calibration result is higher.
For the convenience of understanding of those skilled in the art, the positioning calibration method provided in the present application will be described by taking an execution body as an example of a positioning calculation module, and specifically, the method includes:
(1) Under the condition that satellite positioning data of the vehicle at the current moment is high-precision positioning data, determining that the positioning state of the vehicle at the current moment is a fixed solution state; or when the satellite positioning data of the vehicle at the current moment is non-high-precision positioning data, determining that the positioning state of the vehicle at the current moment is a non-fixed solution state.
(2) When the positioning state at the current moment is a non-fixed solution state, determining a target lane where the vehicle is located according to the position coordinates of the center line of each lane and the vehicle position coordinates at the current moment.
(3) And acquiring target position coordinates of two positions closest to the vehicle on the central line of the target lane according to the position coordinates of the central line of the target lane.
(4) And determining the interval distance between the central point of the vehicle and the target lane line on the vehicle driving road according to the target position coordinate and the vehicle position coordinate at the current moment.
(5) If the interval distance is greater than or equal to a preset distance threshold, determining that the state of the satellite positioning data is abnormal; or if the interval distance is smaller than the distance threshold value, determining that the state of the satellite positioning data is normal.
(6) When the satellite positioning data is abnormal, external interference data of the vehicle at the current moment is determined according to the historical track data of the vehicle and the satellite positioning data when the last positioning state of the vehicle before the current moment is in a fixed solution state.
(7) And determining current predicted positioning data according to the historical satellite positioning data and the external interference data.
(8) And calibrating satellite positioning data of the vehicle at the current moment according to the current predicted positioning data.
(9) After the satellite positioning data at the current moment are calibrated, the control display end calibrates the vehicle from the deviated lane to the current lane.
The implementation process in the above (1) to (9) may be specifically referred to the description of the above embodiment, and its implementation principle and technical effect are similar, and will not be repeated herein.
It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a positioning calibration device for realizing the positioning calibration method. The implementation of the solution provided by the device is similar to that described in the above method, so the specific limitation in one or more embodiments of the positioning calibration device provided below may be referred to the limitation of the positioning calibration method hereinabove, and will not be repeated here.
In one embodiment, as shown in fig. 9, there is provided a positioning calibration device comprising: a first determination module 11, a second determination module 12 and a calibration module 13, wherein:
a first determining module 11, configured to determine a state of satellite positioning data according to satellite positioning data and road map data of a vehicle at a current moment;
the second determining module 12 is configured to determine, when the state of the satellite positioning data is abnormal, current predicted positioning data of the vehicle at the current time according to historical track data of the vehicle and historical satellite positioning data when a last positioning state of the vehicle before the current time is a fixed solution state;
and the calibration module 13 is used for calibrating satellite positioning data of the vehicle at the current moment according to the current predicted positioning data.
The positioning calibration device provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar, and will not be described herein.
In one embodiment, the first determining module 11 includes: a separation distance determining unit, a first determining unit, and a second determining unit, wherein:
the interval distance determining unit is used for determining the interval distance between the central point of the vehicle and a target lane line on the vehicle driving road according to the satellite positioning data and the road map data at the current moment;
a first determining unit configured to determine that the state of the satellite positioning data is abnormal when the separation distance is greater than or equal to a preset distance threshold;
and the second determining unit is used for determining that the state of the satellite positioning data is normal when the interval distance is smaller than the distance threshold value.
The positioning calibration device provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar, and will not be described herein.
In one embodiment, the road map information includes position coordinates of a center line of each lane on the road, and the satellite positioning data includes vehicle position coordinates; the interval distance determining unit is specifically configured to:
Determining a target lane where a vehicle is located according to the position coordinates of the central line of each lane and the vehicle position coordinates of the current moment;
acquiring target position coordinates of two positions closest to the vehicle on the central line of the target lane according to the position coordinates of the central line of the target lane;
and determining the interval distance according to the target position coordinates and the vehicle position coordinates at the current moment.
The positioning calibration device provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar, and will not be described herein.
In one embodiment, the positioning calibration device further comprises: a positioning state determining module and a determining executing module, wherein:
the positioning state determining module is used for determining the positioning state of the vehicle at the current moment according to the satellite positioning data at the current moment;
and the determining execution module is used for executing the step of determining the state of the satellite positioning data according to the satellite positioning data and the road map data of the vehicle at the current moment when the positioning state at the current moment is the non-fixed solution state.
The positioning calibration device provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar, and will not be described herein.
In one embodiment, the positioning state determining module is specifically configured to:
under the condition that satellite positioning data at the current moment is high-precision positioning data, determining that the positioning state of the vehicle at the current moment is a fixed solution state;
and under the condition that the satellite positioning data at the current moment is non-high-precision positioning data, determining that the positioning state of the vehicle at the current moment is a non-fixed solution state.
The positioning calibration device provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar, and will not be described herein.
In one embodiment, the second determination module 12 includes: a third determination unit and a fourth determination unit, wherein:
the third determining unit is used for determining external interference data of the vehicle at the current moment according to the historical track data of the vehicle and satellite positioning data when the last positioning state of the vehicle before the current moment is a fixed solution state;
and the fourth determining unit is used for determining current predicted positioning data according to the historical satellite positioning data and the external interference data.
The positioning calibration device provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar, and will not be described herein.
For specific limitations of the positioning calibration device, reference may be made to the above limitations of the positioning calibration method, and no further description is given here. The various modules in the positioning calibration device described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, the internal structure of which may be as shown in FIG. 10. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing satellite positioning data and road map data of the vehicle at different moments. The network interface of the computer device is for communicating with an external endpoint via a network connection. The computer program is executed by a processor to implement a positioning calibration method.
It will be appreciated by those skilled in the art that the structure shown in fig. 10 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, there is also provided a computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
determining the state of satellite positioning data according to the satellite positioning data and road map data of the vehicle at the current moment;
under the condition that the state of the satellite positioning data is abnormal, determining the current predicted positioning data of the vehicle at the current moment according to the historical track data of the vehicle and the historical satellite positioning data when the last positioning state of the vehicle before the current moment is in a fixed solution state;
and calibrating satellite positioning data of the vehicle at the current moment according to the current predicted positioning data.
In one embodiment, there is also provided a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
Determining the state of satellite positioning data according to the satellite positioning data and road map data of the vehicle at the current moment;
under the condition that the state of the satellite positioning data is abnormal, determining the current predicted positioning data of the vehicle at the current moment according to the historical track data of the vehicle and the historical satellite positioning data when the last positioning state of the vehicle before the current moment is in a fixed solution state;
and calibrating satellite positioning data of the vehicle at the current moment according to the current predicted positioning data.
In one embodiment, there is also provided a computer program product comprising a computer program which, when executed by a processor, performs the steps of:
determining the state of satellite positioning data according to the satellite positioning data and road map data of the vehicle at the current moment;
under the condition that the state of the satellite positioning data is abnormal, determining the current predicted positioning data of the vehicle at the current moment according to the historical track data of the vehicle and the historical satellite positioning data when the last positioning state of the vehicle before the current moment is in a fixed solution state;
and calibrating satellite positioning data of the vehicle at the current moment according to the current predicted positioning data.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.

Claims (10)

1. A positioning calibration method, the method comprising:
determining the state of satellite positioning data according to satellite positioning data and road map data of a vehicle at the current moment;
under the condition that the state of the satellite positioning data is abnormal, determining current predicted positioning data of the vehicle at the current moment according to the historical track data of the vehicle and the historical satellite positioning data when the last positioning state of the vehicle before the current moment is a fixed solution state;
and calibrating satellite positioning data of the vehicle at the current moment according to the current predicted positioning data.
2. The method of claim 1, wherein determining the state of the satellite positioning data based on the satellite positioning data and the road map data of the vehicle at the current time comprises:
Determining the interval distance between the central point of the vehicle and a target lane line on the vehicle driving road according to the satellite positioning data at the current moment and the road map data;
if the interval distance is greater than or equal to a preset distance threshold, determining that the state of the satellite positioning data is abnormal;
and if the interval distance is smaller than the distance threshold value, determining that the state of the satellite positioning data is normal.
3. The method of claim 2, wherein the road map information includes position coordinates of a center line of each lane on the road, and the satellite positioning data includes vehicle position coordinates; the determining, according to the satellite positioning data at the current time and the road map data, a separation distance between a center point of the vehicle and a target lane line on the vehicle driving road includes:
determining a target lane where the vehicle is located according to the position coordinates of the center line of each lane and the vehicle position coordinates of the current moment;
acquiring target position coordinates of two positions closest to the vehicle on the central line of the target lane according to the position coordinates of the central line of the target lane;
And determining the interval distance according to the target position coordinate and the vehicle position coordinate at the current moment.
4. A method according to any one of claims 1-3, characterized in that before said determining the state of the satellite positioning data from the satellite positioning data of the vehicle at the current moment and road map data, the method further comprises:
determining the positioning state of the vehicle at the current moment according to the satellite positioning data at the current moment;
and executing the state step of determining satellite positioning data according to the satellite positioning data and road map data of the vehicle at the current moment when the positioning state at the current moment is a non-fixed solution state.
5. The method of claim 4, wherein determining the positioning state of the vehicle at the current time based on the satellite positioning data at the current time comprises:
under the condition that the satellite positioning data at the current moment is high-precision positioning data, determining the positioning state of the vehicle at the current moment as the fixed solution state;
and under the condition that the satellite positioning data at the current moment is non-high-precision positioning data, determining the positioning state of the vehicle at the current moment to be the non-fixed solution state.
6. A method according to any one of claims 1-3, wherein said determining current predicted positioning data of said vehicle at a current time based on historical track data of said vehicle and historical satellite positioning data of said vehicle when a last positioning state of said vehicle before said current time was a stationary solution state comprises:
according to the historical track data of the vehicle and the historical satellite positioning data when the last positioning state of the vehicle before the current moment is a fixed solution state, external interference data of the vehicle at the current moment is determined;
and determining the current predicted positioning data according to the historical satellite positioning data and the external interference data.
7. A method according to any one of claims 1-3, characterized in that the method further comprises:
and after the satellite positioning data at the current moment are calibrated, the display end is controlled to calibrate the vehicle from the deviated lane to the current lane.
8. A positioning calibration device, the device comprising:
the first determining module is used for determining the state of satellite positioning data according to the satellite positioning data and road map data of the vehicle at the current moment;
The second determining module is used for determining current predicted positioning data of the vehicle at the current moment according to the historical track data of the vehicle and the historical satellite positioning data when the last positioning state of the vehicle before the current moment is a fixed solution state under the condition that the state of the satellite positioning data is abnormal;
and the calibration module is used for calibrating satellite positioning data of the vehicle at the current moment according to the current predicted positioning data.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1-7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1-7.
CN202311662376.4A 2023-12-06 2023-12-06 Positioning calibration method, positioning calibration device, computer equipment and storage medium Pending CN117872416A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118259316A (en) * 2024-05-31 2024-06-28 国家海洋环境监测中心 Sea area ship track prediction method and system based on Beidou system

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118259316A (en) * 2024-05-31 2024-06-28 国家海洋环境监测中心 Sea area ship track prediction method and system based on Beidou system
CN118259316B (en) * 2024-05-31 2024-07-30 国家海洋环境监测中心 Sea area ship track prediction method and system based on Beidou system

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