CN114910083A - Positioning method, positioning device, electronic apparatus, and storage medium - Google Patents
Positioning method, positioning device, electronic apparatus, and storage medium Download PDFInfo
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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
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational 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
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—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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Abstract
The application discloses a positioning method, a positioning device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring high-precision map data in a preset area of the current position of a vehicle and IPM (intelligent power management) map data of a vehicle travelable area; predicting target position information of the vehicle according to a matching result of the high-precision map data and the IPM map data of the vehicle travelable area; and positioning the vehicle according to the target position information of the vehicle. The positioning stability under a special scene is realized through the method and the device, and the influence of the reduction of the positioning precision caused by insufficient information is reduced. The present application may be used in an autonomous vehicle.
Description
Technical Field
The present disclosure relates to the field of automatic driving technologies, and in particular, to a positioning method, a positioning device, an electronic device, and a storage medium.
Background
The positioning technology of the automatic driving vehicle mainly adopts integrated navigation, fuses low-frequency GNSS/RTK signals and high-frequency IMU information through a Kalman filter, and outputs high-frequency and high-precision positioning information.
In the related technology, in order to prevent the positioning accuracy of the GNSS signal from being reduced under the conditions of multipath interference and the like, the vehicle body odometer information, the camera vision semantic SLAM and the laser radar SLAM are gradually combined and applied, and are added into the whole positioning filter as auxiliary observation information, so that a set of integral multi-sensor fusion positioning scheme is formed.
However, in difficult scenes such as viaducts and tunnels, the stability of the lidar SLAM cannot be guaranteed. The accuracy and stability of the vision SLAM are based on vision detection results, and effective positioning results cannot be calculated under the conditions of influences of vehicle shielding, road surface abrasion, accumulated water and accumulated snow on the road surface or illumination and the like.
Disclosure of Invention
The embodiment of the application provides a positioning method, a positioning device, electronic equipment and a storage medium, so that positioning stability is improved while positioning accuracy is guaranteed.
The embodiment of the application adopts the following technical scheme:
in a first aspect, an embodiment of the present application provides a positioning method, where the method is used for an autonomous vehicle, and the method includes: acquiring high-precision map data in a preset area of the current position of a vehicle and IPM (intelligent power management) map data of a vehicle travelable area; predicting target position information of the vehicle according to a matching result of the high-precision map data and the IPM map data of the vehicle driving area; and positioning the vehicle according to the target position information of the vehicle.
In a second aspect, an embodiment of the present application further provides a positioning device, where the positioning device is used for an autonomous vehicle, the device includes: the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring high-precision map data in a preset area of the current position of a vehicle and IPM map data of a vehicle driving area; the matching module is used for predicting the target position information of the vehicle according to the matching result of the high-precision map data and the IPM map data of the vehicle driving area; and the positioning module is used for positioning the vehicle according to the target position information of the vehicle.
In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the above method.
In a fourth aspect, embodiments of the present application further provide a computer-readable storage medium storing one or more programs that, when executed by an electronic device that includes a plurality of application programs, cause the electronic device to perform the above-described method.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects:
the method comprises the steps of obtaining high-precision map data in a preset area of the current position of a vehicle and IPM map data of a vehicle driving area, matching the high-precision map data and the IPM map data, inputting the obtained candidate position serving as an observation value into a filter, and correcting the predicted current position. Therefore, positioning stability in a special scene is realized, and the influence of the reduction of the positioning accuracy caused by insufficient information is reduced. Through the method and the device, the positioning stability and accuracy of the vehicle are guaranteed.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic flow chart of a positioning method in an embodiment of the present application;
FIG. 2 is a schematic structural diagram of a positioning device according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the 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 application.
In order to make the present application better understood by those skilled in the art, some technical terms appearing in the embodiments of the present application are explained below:
GNSS: global Navigation Satellite System, Global Navigation Satellite System.
GPS: global Positioning System, Global Positioning System.
High-precision maps: different from the traditional navigation map, the high-precision map contains a large amount of driving assistance information, and the most important information depends on the accurate three-dimensional representation of a road network, such as intersection layout, road sign positions and the like. One of the most important features of high-precision maps is precision, which enables a vehicle to reach a centimeter-level precision, which is important to ensure the safety of an autonomous vehicle.
The inventor finds that in order to prevent the problem of positioning accuracy reduction caused by multipath interference and the like of a GNSS signal, the related art has some defects when fusion positioning is carried out by using vehicle body odometer information (information such as vehicle body speed and steering wheel angle), a camera vision SLAM and a laser radar SLAM.
1. The traditional SLAM method based on feature points has too large limitation, such as: dynamic objects, shading, illumination and the like, and stability cannot be guaranteed.
2. Lidar is expensive, cannot adapt to the requirements of mass production vehicles, and simultaneously, in some difficult scenes, such as: viaducts, tunnels and the like cannot obtain a good mapping effect, and the positioning precision is further influenced.
3. The positioning scheme matching the semantic elements such as lane lines and the like with the high-precision map has low cost, is also the main flow direction of the current visual positioning, but can be influenced by visual detection results, such as: the matching with the high-precision map fails due to the abrasion of lane lines, accumulated water and accumulated snow on the road surface, the shielding of vehicles and the like, and an effective positioning result cannot be returned.
In order to solve the above problems, in the positioning method provided in the embodiment of the present application, the feasible region in the image is identified, and the location accuracy and stability of the autonomous vehicle in a difficult scene are ensured by locating the autonomous vehicle according to the shape of the feasible region, the road surface elements in the feasible region, and the edge information of the feasible region in the high-precision map.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
An embodiment of the present application provides a positioning method, and as shown in fig. 1, a schematic flow chart of the positioning method in the embodiment of the present application is provided, where the method at least includes the following steps S110 to S130:
in step S110, high-precision map data in a preset area of the current position of the vehicle and IPM map data of a vehicle travelable area are acquired.
The method comprises the steps of firstly obtaining the current position of a vehicle in a prediction mode, and then further obtaining high-precision map data in a preset area of the current position of the vehicle.
The high-precision map data refers to high-precision map data in a current position setting area of the own vehicle. The high-precision map data is acquired in advance, manufactured and read in an off-line mode.
And the high-precision map data in the preset area comprises high-precision map data which takes the self-vehicle as the origin and A meters as the radius if the set area is circular or the radius range is within A meters.
It should be noted that the prediction result is obtained by a kalman filter.
Further, IPM map data of the vehicle travelable region needs to be obtained. It can be understood that after the image data after the recognition of the autonomous vehicle sensing module is acquired, IPM transformation is performed on the result of the image data to obtain IPM graph data as an inverse perspective transformation result of the operable area.
And a step S120 of predicting target position information of the vehicle according to a matching result of the high-precision map data and the IPM map data of the vehicle travelable region.
After the high-precision map data set and the IPM map data of the vehicle travelable area are obtained through the above steps, the target position information of the vehicle can be predicted based on the matching result of the high-precision map data and the IPM map data of the vehicle travelable area.
It should be noted that, since the autonomous driving vehicle may be in a special scene, such as an overpass in a city, a junction between a main road and a secondary road, a road section with a severely worn road surface, and the like, there may be a plurality of candidate position information, and if data matching is not adopted, accurate positioning information cannot be obtained well.
And step S130, positioning the vehicle according to the target position information of the vehicle.
After the target position information of the vehicle is input into the Kalman filter as an observation value, the vehicle can be accurately positioned through initial predicted position data corrected by the observation information.
By providing additional observation information, the stability of the positioning algorithm in a special scene can be improved, and the times of manual takeover when the positioning accuracy is reduced due to insufficient information are reduced.
For example, the autonomous vehicle may extract a travelable region in the image through at least one image capturing unit to generate IPM map data. The drivable area is not completely equal to the road surface element, the road surface element is only used as one factor in a loss function, and if the conditions of shielding, abrasion and the like are met on part of the road surface element, the candidate positioning points can still be guaranteed to be provided.
In one embodiment of the present application, the predicting target location information of a vehicle based on a result of matching the high-precision map data and the IPM map data of the vehicle travelable region includes: predicting target position information of the vehicle according to a matching result of a drivable area shape in the high-precision map data in the IPM map data of the drivable area of the vehicle; and/or predicting the target position information of the vehicle according to the matching result of the road surface element information in the IPM map data of the vehicle driving area in the high-precision map data.
In a specific implementation, the target position information of the vehicle is predicted according to a matching result of the shape of the drivable region in the IPM map data of the drivable region of the vehicle in the high-precision map data. It is noted that the shape of the drivable region includes, but is not limited to, straight lanes, curves, lateral distances, and the like.
The drivable region shape may include a straight road or an arc road.
In some embodiments, the determination of the target location information may be made by calculating weights of different shapes.
The target position information of the vehicle may be predicted based on a result of matching the road surface element information in the IPM map data of the vehicle travelable region with the high-accuracy map data. It is noted that the road surface information in the travelable area includes, but is not limited to, lane lines, road surface arrows, sidewalks, stop lines, road surface letters, and the like.
In some embodiments, the determination of the target position information may be performed by calculating weights of different road surface information.
In one embodiment of the present application, the target position information of the predicted vehicle includes at least one of candidate position information of: the method comprises the following steps of position information of different heights but the same longitude and latitude, different position information of main and auxiliary roads in a preset error range interval, predicted position information after tunnel exit and observation position information based on GNSS.
The candidate position information is candidate position information in high-precision map data in a preset area, and due to the fact that GNSS signals have influence factors of multipath interference, a plurality of candidate positions of the automatic driving vehicles exist in the high-precision map data. The method can be used for the same longitude and latitude position with different heights, different positions of a main road and a secondary road in an error range interval, a predicted position after the main road and the secondary road are out of a tunnel, a GNSS observation position and the like. The method is used for solving the problem that the automatic driving vehicle runs in the difficult scenes such as elevated bridges in urban areas, main and auxiliary road junctions, bridge bottoms/tunnel entrances and exits, road sections with serious road surface abrasion and the like.
In one embodiment of the present application, the predicting target location information of a vehicle based on a matching result of the high-precision map data and the IPM map data of the vehicle travelable region includes: the shape of the travelable region in the IPM map data includes: the method comprises the steps of calculating a road curvature parameter of a current road according to a first shape feature, calculating a road width parameter of the current road according to a second shape feature, and calculating candidate position information of target position information of the predicted vehicle in the current road according to a first weight preset by the first shape feature and a second weight of the second shape feature.
In specific implementation, the probability of different candidate positions in the high-precision map is calculated according to the first shape feature, the second shape feature and the corresponding weight. That is, the road curvature parameter of the current road is calculated according to the first shape feature, the road width parameter of the current road is calculated according to the second shape feature, and the candidate position information of the target position information of the predicted vehicle in the current road is calculated according to the first weight preset by the first shape feature and the second weight of the second shape feature.
For example, the curvature of the current road is calculated from the shape characteristic S (first shape feature), that is, from the shape of the feasible region. And calculating the width of the current road from the road width characteristic W (second shape feature), that is, from the width of the feasible region. Then, according to the corresponding weights of the shape characteristic S and the road width characteristic W, the probability of the candidate position can be calculated.
Preferably, for the road width characteristic W1, the width of the current road is calculated according to the width of the feasible region, and the determination of the positions of the main road, the auxiliary road and the ramp can be assisted according to the matching result in the high-precision map data.
Preferably, for the road width characteristic W2, the width of the current road is calculated according to the width of the feasible region, and the position judgment during incomplete road surface information identification can be assisted according to the matching result in the high-precision map data;
preferably, the road width characteristic W3 is a characteristic that calculates the width of the current road from the width of the feasible region, and the feasible region shape characteristic is combined with the matching result in the high-precision map data to assist the determination of the positions of the overpass and the disk bridge.
In an embodiment of the present application, the predicting target location information of a vehicle based on a matching result of the high-precision map data and the IPM map data of the vehicle travelable region further includes: and calculating candidate position information of the target position information of the predicted vehicle in the current road according to a third weight preset by the first road characteristics of the road surface element information of the drivable area in the IPM map data, the first weight and the second weight.
In specific implementation, if the first road characteristic can be identified, the candidate position information of the target position information of the predicted vehicle on the current road can be calculated according to a third weight preset by the first road characteristic, the first weight and the second weight.
It is noted that the first road surface characteristic includes, but is not limited to, a road surface information characteristic, i.e., road surface information in a travelable area. The matching algorithm in the related art is usually based on the road surface information characteristic, but the characteristic can cause low confidence of the calculation result due to road surface abrasion, road vehicle shielding, accuracy of the identification model and the like, thereby influencing the determination of the candidate position.
It should be noted that the road information characteristics include, but are not limited to, lane lines, road arrows, sidewalks, stop lines, road characters, etc., and are not limited thereto.
For example, first, the curvature of the current road is calculated from the shape characteristic S (first shape feature), that is, from the shape of the feasible region. And calculating the width of the current road from the road width characteristic W (second shape characteristic), that is, from the width of the feasible region, the road surface information characteristic I (first road surface characteristic), that is, the road surface information in the travelable region.
Then, according to preset weights with different characteristics, calculating the probability of each candidate position:
P i ＝ω 1 {S，S map }+ω 2 {W，W map }+ω 3 {I，I map }
wherein ω is 1 ，ω 2 ，ω 3 Is a predetermined weight. i is the number of candidate positions.
{S，S map And the shape characteristic matching result is obtained, and the shape characteristic matching result can be calculated according to the curvature similarity.
{W，W map And the road width characteristic matching result is obtained, and the road width characteristic matching result can be calculated according to the road width similarity.
{I，I map And the result is the road surface information characteristic matching result, and can be calculated according to the road surface information similarity.
In addition, the similarity calculation method may be arbitrarily selected, for example, the road width characteristic similarity may be set to be a ratio of the road width of the feasible region to the current road width in the map, which is not specifically limited in the embodiment of the present application and may be selected by a person skilled in the art according to an actual scene.
And finally, obtaining the candidate position with the maximum probability.
In an embodiment of the present application, the acquiring high-precision map data within a preset area of a current location of a vehicle and IPM map data of a vehicle travelable area includes: acquiring high-precision map data in a preset area of the current position of the vehicle according to the prediction result of the current position of the vehicle of a preset filter; and carrying out IPM transformation according to the recognition result of the image in front of the vehicle to obtain IPM graph data of the vehicle driving area.
And in specific implementation, according to the prediction result of the current position of the vehicle of the preset Kalman filter, acquiring high-precision map data in a preset area of the current position of the vehicle. Then, IPM conversion is performed based on the recognition result of the vehicle front image, and IPM map data of the vehicle travelable region is obtained.
For example, when the autonomous vehicle enters the disk bridge environment, the GNSS is influenced by the multipath effect, and the autonomous vehicle may fix the position under the bridge at the same time (for example, the autonomous vehicle is actually on the bridge but has the same longitude and latitude and different heights), but the road surface element cannot be obtained at this time, the weight value of the candidate position may be calculated according to the road curvature in the IPM map data of the travelable region and the shape of the travelable region, so as to obtain an accurate position.
In one embodiment of the present application, the locating a vehicle according to target position information of the vehicle includes: determining target position information of the vehicle according to the predicted probability values of the position information of the vehicles; and inputting the target position information of the vehicle as an observed value into a preset filter to correct the current position of the vehicle.
In specific implementation, the position obtained by the candidate position is used as the target position information of the vehicle, and the target position information of the vehicle is used as an observation value and is input to a preset filter to correct the current position of the vehicle. It should be noted that the current position of the vehicle was previously predicted from a predicted value, and is here corrected by introducing a new observed value.
The embodiment of the present application further provides a positioning apparatus 200, as shown in fig. 2, which provides a schematic structural diagram of the positioning apparatus in the embodiment of the present application, where the positioning apparatus 200 at least includes: an obtaining module 210, a matching module 220, and a positioning module 230, wherein:
the obtaining module 210 is configured to obtain high-precision map data in a preset area of a current position of a vehicle and IPM map data of a vehicle driving-capable area;
a matching module 220, configured to predict target location information of a vehicle according to a matching result of the high-precision map data and the IPM map data of the vehicle drivable area;
and a positioning module 230, configured to position the vehicle according to the target position information of the vehicle.
In an embodiment of the present application, the obtaining module 210 is specifically configured to: the method comprises the steps of firstly obtaining the current position of a vehicle in a prediction mode, and then further obtaining high-precision map data in a preset area of the current position of the vehicle.
The high-precision map data refers to high-precision map data in a current position setting area of the own vehicle. The high-precision map data is acquired in advance, manufactured and read in an off-line mode.
And the high-precision map data in the preset area comprises high-precision map data which takes the self-vehicle as the origin and A meters as the radius if the set area is circular or the radius range is within A meters.
It should be noted that the prediction result is obtained by a kalman filter.
Further, IPM map data of the vehicle travelable region needs to be obtained. It can be understood that after the image data after the recognition of the autonomous vehicle sensing module is acquired, IPM transformation is performed on the result of the image data to obtain IPM graph data as an inverse perspective transformation result of the operable area.
In an embodiment of the present application, the matching module 220 is specifically configured to: after the high-precision map data set and the IPM map data of the vehicle travelable region are obtained through the above steps, the target position information of the vehicle can be predicted based on the matching result of the high-precision map data and the IPM map data of the vehicle travelable region.
It should be noted that, since the autonomous driving vehicle may be in a special scene, such as an overpass in a city, a junction between a main road and a secondary road, a road section with a severely worn road surface, and the like, there may be a plurality of candidate position information, and if data matching is not adopted, accurate positioning information cannot be obtained well.
In an embodiment of the present application, the positioning module 230 is specifically configured to: after the target position information of the vehicle is input into the Kalman filter as an observation value, the vehicle can be accurately positioned through initial predicted position data corrected by the observation information.
By providing additional observation information, the stability of the positioning algorithm in a special scene can be improved, and the times of manual takeover when the positioning accuracy is reduced due to insufficient information are reduced.
For example, the autonomous vehicle may extract a travelable region in the image through at least one image capturing unit to generate IPM map data. The drivable area is not completely equal to the road surface element, the road surface element is only used as one factor in a loss function, and if the conditions of shielding, abrasion and the like are met on part of the road surface element, the candidate positioning points can still be guaranteed to be provided.
It can be understood that the positioning device can implement each step of the positioning method provided in the foregoing embodiments, and the explanations regarding the positioning method are applicable to the positioning device, and are not repeated here.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Referring to fig. 3, at a hardware level, the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other by an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 3, but this does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
The processor reads the corresponding computer program from the non-volatile memory into the memory and runs the computer program to form the positioning device on a logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:
acquiring high-precision map data in a preset area of the current position of a vehicle and IPM (intelligent power management) map data of a vehicle travelable area;
predicting target position information of the vehicle according to a matching result of the high-precision map data and the IPM map data of the vehicle driving area;
and positioning the vehicle according to the target position information of the vehicle.
The method performed by the positioning apparatus according to the embodiment shown in fig. 1 of the present application may be implemented in or by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, etc. as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The electronic device may further execute the method executed by the positioning apparatus in fig. 1, and implement the functions of the positioning apparatus in the embodiment shown in fig. 1, which are not described herein again in this embodiment of the application.
An embodiment of the present application further provides a computer-readable storage medium storing one or more programs, where the one or more programs include instructions, which, when executed by an electronic device including a plurality of application programs, enable the electronic device to perform the method performed by the positioning apparatus in the embodiment shown in fig. 1, and are specifically configured to perform:
acquiring high-precision map data in a preset area of the current position of a vehicle and IPM (intelligent power management) map data of a vehicle travelable area;
predicting target position information of the vehicle according to a matching result of the high-precision map data and the IPM map data of the vehicle travelable area;
and positioning the vehicle according to the target position information of the vehicle.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A positioning method, wherein for an autonomous vehicle, the method comprises:
acquiring high-precision map data in a preset area of the current position of a vehicle and IPM (intelligent power management) map data in a drivable area of the vehicle;
predicting target position information of the vehicle according to a matching result of the high-precision map data and the IPM map data of the vehicle driving area;
and positioning the vehicle according to the target position information of the vehicle.
2. The method according to claim 1, wherein the predicting target location information of the vehicle based on the matching result of the high-precision map data and the IPM map data of the vehicle travelable region comprises:
predicting target position information of the vehicle according to a matching result of a drivable area shape in the high-precision map data in the IPM map data of the drivable area of the vehicle;
and/or the presence of a gas in the gas,
and predicting the target position information of the vehicle according to the matching result of the road surface element information in the IPM map data of the vehicle driving area in the high-precision map data.
3. The method of claim 2, wherein the target location information of the predicted vehicle includes at least candidate location information of one of: the method comprises the following steps of position information of different heights but the same longitude and latitude, different position information of main and auxiliary roads in a preset error range interval, predicted position information after tunnel exit and observation position information based on GNSS.
4. The method according to claim 2, wherein the predicting the target location information of the vehicle based on the matching result of the high-precision map data and the IPM map data of the vehicle travelable region comprises:
the shape of the travelable region in the IPM map data includes: a first shape feature, a second shape feature, a road curvature parameter of the current road being calculated from the first shape feature, a road width parameter of the current road being calculated from the second shape feature,
and calculating candidate position information of the target position information of the predicted vehicle in the current road according to a first weight preset by the first shape feature and a second weight of the second shape feature.
5. The method according to claim 4, wherein the predicting of the target location information of the vehicle based on the matching result of the high-precision map data and the IPM map data of the vehicle travelable region, further comprises:
and calculating candidate position information of the target position information of the predicted vehicle in the current road according to a third weight preset by the first road characteristics of the road surface element information of the drivable area in the IPM map data, the first weight and the second weight.
6. The method according to claim 1, wherein the acquiring of the high-precision map data within the preset area of the current position of the vehicle and the IPM map data of the vehicle travelable area comprises:
acquiring high-precision map data in a preset area of the current position of the vehicle according to the prediction result of the current position of the vehicle of a preset filter;
and carrying out IPM transformation according to the recognition result of the image in front of the vehicle to obtain IPM graph data of the vehicle driving area.
7. The method of claim 1, wherein said locating a vehicle based on target location information of the vehicle comprises:
determining target position information of the vehicle according to the predicted probability values of the position information of the vehicles;
and inputting the target position information of the vehicle as an observed value into a preset filter to correct the current position of the vehicle.
8. A positioning device for use in an autonomous vehicle, the device comprising:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring high-precision map data in a preset area of the current position of a vehicle and IPM map data of a vehicle driving area;
the matching module is used for predicting the target position information of the vehicle according to the matching result of the high-precision map data and the IPM map data of the vehicle driving area;
and the positioning module is used for positioning the vehicle according to the target position information of the vehicle.
9. An electronic device, comprising:
a processor; and
a memory arranged to store computer executable instructions which, when executed, cause the processor to perform the method of any of claims 1 to 7.
10. A computer readable storage medium storing one or more programs which, when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform the method of any of claims 1-7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116202538A (en) * | 2023-05-05 | 2023-06-02 | 广州小鹏自动驾驶科技有限公司 | Map matching fusion method, device, equipment and storage medium |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008009966A2 (en) * | 2006-07-21 | 2008-01-24 | Trw Limited | Determining the location of a vehicle on a map |
| CN107339996A (en) * | 2017-06-30 | 2017-11-10 | 百度在线网络技术（北京）有限公司 | Vehicle method for self-locating, device, equipment and storage medium |
| CN110031012A (en) * | 2019-05-27 | 2019-07-19 | 爱驰汽车有限公司 | Method, system, equipment and the storage medium of the high-precision map of automobile real-time matching |
| JP2020026985A (en) * | 2018-08-09 | 2020-02-20 | 株式会社豊田中央研究所 | Vehicle position estimation device and program |
| CN111307163A (en) * | 2018-12-11 | 2020-06-19 | 北京地平线机器人技术研发有限公司 | Positioning method and positioning device of movable equipment and electronic equipment |
| CN113804214A (en) * | 2021-11-19 | 2021-12-17 | 智道网联科技(北京)有限公司 | Vehicle positioning method and device, electronic equipment and computer readable storage medium |
| CN113834492A (en) * | 2021-09-22 | 2021-12-24 | 广州小鹏自动驾驶科技有限公司 | Map matching method, system, device and readable storage medium |
| CN113899375A (en) * | 2021-12-10 | 2022-01-07 | 腾讯科技（深圳）有限公司 | Vehicle positioning method and device, storage medium and electronic equipment |
| CN114034307A (en) * | 2021-11-19 | 2022-02-11 | 智道网联科技(北京)有限公司 | Lane line-based vehicle pose calibration method and device and electronic equipment |
| CN114228741A (en) * | 2021-11-16 | 2022-03-25 | 智道网联科技(北京)有限公司 | Lane keeping method and apparatus for autonomous vehicle |
-
2022
- 2022-04-22 CN CN202210430656.1A patent/CN114910083A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008009966A2 (en) * | 2006-07-21 | 2008-01-24 | Trw Limited | Determining the location of a vehicle on a map |
| CN107339996A (en) * | 2017-06-30 | 2017-11-10 | 百度在线网络技术（北京）有限公司 | Vehicle method for self-locating, device, equipment and storage medium |
| JP2020026985A (en) * | 2018-08-09 | 2020-02-20 | 株式会社豊田中央研究所 | Vehicle position estimation device and program |
| CN111307163A (en) * | 2018-12-11 | 2020-06-19 | 北京地平线机器人技术研发有限公司 | Positioning method and positioning device of movable equipment and electronic equipment |
| CN110031012A (en) * | 2019-05-27 | 2019-07-19 | 爱驰汽车有限公司 | Method, system, equipment and the storage medium of the high-precision map of automobile real-time matching |
| CN113834492A (en) * | 2021-09-22 | 2021-12-24 | 广州小鹏自动驾驶科技有限公司 | Map matching method, system, device and readable storage medium |
| CN114228741A (en) * | 2021-11-16 | 2022-03-25 | 智道网联科技(北京)有限公司 | Lane keeping method and apparatus for autonomous vehicle |
| CN113804214A (en) * | 2021-11-19 | 2021-12-17 | 智道网联科技(北京)有限公司 | Vehicle positioning method and device, electronic equipment and computer readable storage medium |
| CN114034307A (en) * | 2021-11-19 | 2022-02-11 | 智道网联科技(北京)有限公司 | Lane line-based vehicle pose calibration method and device and electronic equipment |
| CN113899375A (en) * | 2021-12-10 | 2022-01-07 | 腾讯科技（深圳）有限公司 | Vehicle positioning method and device, storage medium and electronic equipment |
Non-Patent Citations (3)
| Title |
|---|
| KANG JEONG MIN ET AL.: "Lane-Level Map-Matching Method for Vehicle Localization Using GPS and Camera on a High-Definition Map", 《SENSORS》, vol. 20, no. 8, 2 June 2020 (2020-06-02), pages 1 - 22 * |
| 刘亚群: "基于视觉的车道级定位算法研究", 《中国优秀硕士学位论文全文数据库 (信息科技辑)》, 15 August 2016 (2016-08-15), pages 138 - 926 * |
| 潘尧;吴聪;张颖江;刘伶俐;辛梓;: "智能车辆双目视觉与地图组合定位研究", 湖北工业大学学报, no. 04, 15 August 2017 (2017-08-15), pages 57 - 61 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116202538A (en) * | 2023-05-05 | 2023-06-02 | 广州小鹏自动驾驶科技有限公司 | Map matching fusion method, device, equipment and storage medium |
| CN116202538B (en) * | 2023-05-05 | 2023-08-29 | 广州小鹏自动驾驶科技有限公司 | Map matching fusion method, device, equipment and storage medium |
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