CN110967023B - Automobile positioning method and automobile positioning device - Google Patents
Automobile positioning method and automobile positioning device Download PDFInfo
- Publication number
- CN110967023B CN110967023B CN201811166409.5A CN201811166409A CN110967023B CN 110967023 B CN110967023 B CN 110967023B CN 201811166409 A CN201811166409 A CN 201811166409A CN 110967023 B CN110967023 B CN 110967023B
- Authority
- CN
- China
- Prior art keywords
- automobile
- road
- feature
- sub
- state information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 82
- 230000003746 surface roughness Effects 0.000 claims abstract description 13
- 230000001133 acceleration Effects 0.000 claims description 26
- 238000004590 computer program Methods 0.000 claims description 8
- 238000013507 mapping Methods 0.000 claims description 8
- 238000000926 separation method Methods 0.000 claims description 7
- 230000006870 function Effects 0.000 claims description 6
- 238000012876 topography Methods 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 12
- 230000003068 static effect Effects 0.000 description 6
- 230000003287 optical effect Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 230000000007 visual effect Effects 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 3
- 238000013500 data storage Methods 0.000 description 3
- 230000007613 environmental effect Effects 0.000 description 3
- 238000012937 correction Methods 0.000 description 2
- 230000001174 ascending effect Effects 0.000 description 1
- 239000010426 asphalt Substances 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000000725 suspension Substances 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- 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
Abstract
The application provides an automobile positioning method and an automobile positioning device, wherein the automobile positioning method comprises the following steps: acquiring running state information of an automobile, wherein the running state information is used for representing the state information of the automobile in the running process; determining a surface feature of a road on which the automobile has traveled based on the operating state information of the automobile, the surface feature of the road on which the automobile has traveled being indicative of one or more of a topography and a surface roughness of the road on which the automobile has traveled; and determining the current position of the automobile according to the surface characteristics of the road on which the automobile is driven and the preset surface characteristics. Correspondingly, a corresponding device is also provided. By adopting the application, the accuracy of automobile positioning can be effectively improved.
Description
Technical Field
The application relates to the technical field of automatic driving, in particular to an automobile positioning method and an automobile positioning device.
Background
Advanced driving assistance systems and automatic driving systems have developed rapidly in recent years and become the dominant trend in future traffic. Context awareness, map positioning, planning decisions and execution control are essential key technologies for an automatic driving system. Wherein map positioning provides a precondition for implementation of automatic driving from point a to point B for an automatic driving system, i.e. map positioning solves the problem of "where me is" for automatic driving. The automatic driving system can plan a route to a destination correctly only based on the current position information of the automobile, so that the automatic driving of the automobile cannot be realized without a map positioning module. For the positioning problem of automatic driving, the following solutions can be generally adopted, which are respectively: global positioning system (global positioning system, GPS) based positioning, GPS and inertial navigation unit (inertial measurement unit, IMU) based positioning, vision or laser based synchronized positioning and map building (simultaneous localization and mapping, SLAM) positioning.
However, the above positioning method is greatly affected by external environment, for example, in the environment where GPS signals are weak, such as in residential communities or underground garages, it is difficult to implement vehicle positioning by using GPS. As another example, for SLAM positioning, such as a map constructed on a sunny day, the characteristics change after raining, which may cause positioning failure.
Therefore, how to improve the accuracy of positioning is in need of solving.
Disclosure of Invention
The application provides an automobile positioning method and an automobile positioning device, which can effectively improve the positioning accuracy.
In a first aspect, an embodiment of the present application provides an automobile positioning method, including:
acquiring running state information of an automobile, wherein the running state information is used for representing the state information of the automobile in the running process; determining a surface feature of a road on which the automobile has traveled according to the running state information of the automobile, the surface feature of the road on which the automobile has traveled being used to represent one or more of a surface relief condition and a surface roughness of the road on which the automobile has traveled; and determining the current position of the automobile according to the surface characteristics of the road on which the automobile is driven and preset surface characteristics, wherein the preset surface characteristics comprise the mapping relation between the road and the surface characteristics.
By implementing the embodiment of the application, in the environments such as parking lots or residential communities with obstacle shielding, the automobiles can be effectively positioned according to the surface characteristics of roads, and the situation that the positioning accuracy is low due to environmental influence is avoided, so that the positioning accuracy is effectively improved.
In one possible implementation manner, the determining the surface feature of the road on which the automobile has traveled according to the running state information of the automobile includes: inputting the running state information into a vibration model of the automobile, and outputting the road surface height of a road on which the automobile runs; determining the change of the road surface height of the road on which the automobile runs along with the distance according to the road surface height of the road on which the automobile runs and the running state information; and carrying out pavement characteristic matching on the road on which the automobile runs according to the change of the pavement height of the road on which the automobile runs along with the change of the distance, so as to obtain the surface characteristics of the road on which the automobile runs.
According to the embodiment of the application, the surface characteristics of the road on which the automobile runs are estimated according to the running state information by modeling the vibration of the automobile, on one hand, the running state information such as the speed and the acceleration can be obtained according to the vehicle-mounted sensor, and the cost in the automobile positioning process is reduced; on the other hand, the road surface features are stable and are not influenced by obstacles and the like, and the positioning accuracy can be effectively improved.
In one possible implementation manner, the matching the road surface characteristics of the road on which the automobile is driven according to the change of the road surface height of the road on which the automobile is driven along with the change of the distance, to obtain the surface characteristics of the road on which the automobile is driven, includes: carrying out top-layer feature matching on the road on which the automobile runs according to the change of the road surface height of the road on which the automobile runs along with the change of the distance, so as to obtain the top-layer road surface feature of the road on which the automobile runs; and performing sub-feature matching on the road on which the automobile runs according to the top road surface feature of the road on which the automobile runs, so as to obtain the sub-road surface feature of the road on which the automobile runs.
In one possible implementation, the top road surface features include one or more of a number of sub-features, a top feature frequency distribution, a top feature maximum amplitude, a top feature average amplitude, and a top feature variance; the sub-road surface features include one or more of sub-feature separation distance, sub-feature duration distance, sub-feature frequency distribution, sub-feature maximum amplitude, sub-feature average amplitude, and sub-feature variance.
In one possible implementation manner, the inputting the running state information into the vibration model of the automobile, and outputting the road surface height of the road on which the automobile has run, includes: inputting the running state information a (t) into a vibration model of the automobile, and outputting the road surface height h (t) of a road on which the automobile has run, wherein the vibration model is as follows: h (t) =f -1 (a(t))。
In one possible implementation, the running state information includes one or more of acceleration information and speed information.
In one possible implementation, the locations where the car is currently located include one or more of a heave location and a location where the roughness is greater than a roughness threshold.
In a second aspect, an embodiment of the present application provides an automobile positioning device, including:
the system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring running state information of an automobile, and the running state information is used for representing the state information of the automobile in the running process; a first determining unit configured to determine a surface feature of a road on which the automobile has traveled, based on running state information of the automobile, the surface feature of the road on which the automobile has traveled being used to represent one or more of a surface relief condition and a surface roughness of the road on which the automobile has traveled; and the second determining unit is used for determining the current position of the automobile according to the surface characteristics of the road on which the automobile is driven and preset surface characteristics, wherein the preset surface characteristics comprise the mapping relation between the road and the surface characteristics.
In one possible implementation manner, the first determining unit includes: the input/output subunit is used for inputting the running state information into the vibration model of the automobile and outputting the road surface height of the road on which the automobile runs; a determining subunit, configured to determine a change of a road surface height of a road on which the automobile has traveled along with a distance according to the road surface height of the road on which the automobile has traveled and the running state information; and the characteristic matching subunit is used for matching the road surface characteristics of the road on which the automobile runs according to the change of the road surface height of the road on which the automobile runs along with the change of the distance, so as to obtain the surface characteristics of the road on which the automobile runs.
In one possible implementation manner, the feature matching subunit is specifically configured to perform top-layer feature matching on the road on which the automobile has travelled according to the change of the road surface height of the road on which the automobile has travelled along with the change of the distance, so as to obtain the top-layer road surface feature of the road on which the automobile has travelled; and performing sub-feature matching on the road on which the automobile runs according to the top road surface feature of the road on which the automobile runs, so as to obtain the sub-road surface feature of the road on which the automobile runs.
In one possible implementation, the top road surface features include one or more of a number of sub-features, a top feature frequency distribution, a top feature maximum amplitude, a top feature average amplitude, and a top feature variance; the sub-road surface features include one or more of sub-feature separation distance, sub-feature duration distance, sub-feature frequency distribution, sub-feature maximum amplitude, sub-feature average amplitude, and sub-feature variance.
In one possible implementation manner, the input/output subunit is configured to input the running state information into the vibration model of the automobile, and output the road surface height of the road on which the automobile has run, where the road surface height is specifically:
Inputting the running state information a (t) into a vibration model of the automobile, and outputting the road surface height h (t) of a road on which the automobile has run, wherein the vibration model is as follows: h (t) =f -1 (a(t))。
In one possible implementation, the running state information includes one or more of acceleration information and speed information.
In one possible implementation, the locations where the car is currently located include one or more of a heave location and a location where the roughness is greater than a roughness threshold.
In a third aspect, an embodiment of the present application further provides an automobile positioning device, which can implement the automobile positioning method according to any one of the first aspect; the automobile positioning device can be a chip, equipment and the like; the automobile positioning device can realize the method through software, hardware or executing corresponding software through hardware.
When part or all of the above-mentioned car positioning methods are implemented by software, the car positioning device includes: a memory and a processor; the memory is used for storing program instructions; the processor is configured to execute program instructions stored in the memory, which when executed, enable the vehicle locating device to implement a method provided in at least one of the first aspects described above.
In one possible implementation, the memory may be a physically separate unit or may be integrated with the processor.
In a fourth aspect, an embodiment of the present application provides an automobile positioning system, including an automobile positioning device and an on-vehicle sensor, where the on-vehicle sensor is used to measure running state information of an automobile; the automobile positioning device is used for acquiring running state information measured by the vehicle-mounted sensor, determining the surface characteristics of the road on which the automobile runs according to the running state information, and determining the current position of the automobile according to the surface characteristics of the road on which the automobile runs.
In one possible implementation manner, the automobile positioning device may be a chip, a separate device or the like, and the embodiment of the application is not limited.
In a fifth aspect, embodiments of the present application provide a computer readable storage medium having stored therein program instructions which, when executed on a computer or processor, cause the computer or processor to perform the method of at least one of the first aspects.
In a sixth aspect, embodiments of the present application provide a computer program product comprising program instructions which, when executed on a computer or processor, cause the computer or processor to perform the method of at least one of the first aspects.
Drawings
In order to more clearly describe the embodiments of the present application or the technical solutions in the background art, the following description will describe the drawings that are required to be used in the embodiments of the present application or the background art.
Fig. 1 is a schematic diagram of an architecture of an autopilot system according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an automobile positioning device according to an embodiment of the present application;
FIG. 3 is a schematic diagram of an automobile positioning system according to an embodiment of the present application;
FIG. 4 is a schematic flow chart of an automobile positioning method according to an embodiment of the present application;
FIG. 5 is a schematic diagram of an automobile vibration model according to an embodiment of the present application;
FIG. 6 is a schematic illustration of a process for determining road surface characteristics according to an embodiment of the present application;
FIG. 7 is a schematic view of a hierarchical feature modeling scenario provided by an embodiment of the present application;
fig. 8 is a schematic view of a scenario of an automobile positioning method according to an embodiment of the present application;
fig. 9 is a schematic view of a road feature matching method according to an embodiment of the present application;
FIG. 10 is a schematic view of a scenario for vehicle positioning according to an embodiment of the present application;
FIG. 11 is a schematic view of a scenario for automatic driving of an automobile in a parking garage under the ground according to an embodiment of the present application;
FIG. 12 is a schematic view of another vehicle positioning device according to an embodiment of the present application;
fig. 13 is a schematic structural diagram of a first determining unit according to an embodiment of the present application.
Detailed Description
Embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application.
The terms first and second and the like in the description, in the claims and in the drawings are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
It should be understood that, in the present application, "at least one (item)" means one or more, "a plurality" means two or more, "at least two (items)" means two or three and three or more, "and/or" for describing an association relationship of an association object, three kinds of relationships may exist, for example, "a and/or B" may mean: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
Referring to fig. 1, fig. 1 is a schematic architecture diagram of an autopilot system according to an embodiment of the present application, as shown in fig. 1, the autopilot system includes: the system comprises a map positioning module 101, an environment sensing module 2, a planning decision module 3 and an execution control module 104; the map positioning module, the environment sensing module, the planning decision module and the execution control module can be respectively in an independent form, can also be in an integrated form and the like, and the specific forms of the four modules are not limited in the embodiment of the application. The map positioning module, the environment sensing module, the planning decision module and the execution control module can be connected with each other through connectors, or can be connected through wireless communication, and the like.
The map positioning module can estimate the position, the posture and the like of the automobile according to the prior map information, the vehicle-mounted sensor, the GPS, the IMU and the like to obtain the positioning information of the automobile.
The environment sensing module can collect driving environment information around the automobile through a plurality of sensors. Among them, the sensors may include various visual sensors such as monocular sensors, binocular sensors, and multi-ocular sensors, and the like, and radar sensors; the radar sensor may be, for example, an ultrasonic radar sensor, a millimeter wave radar sensor, a lidar sensor, or the like.
And the planning decision module can carry out planning decision on the motion trail of the automobile according to the driving environment information around the automobile collected by the environment sensing module and the positioning information of the map positioning module.
And the execution control module can realize the automatic running control function of the automobile according to the steering instruction, the accelerating instruction, the braking instruction and the like output by the planning decision module.
From the above, it can be seen that the map positioning module provides a realization basis for an unmanned system or an automatic driving system, and in a general car navigation system, the map positioning module also provides a realization basis for car navigation. However, in an environment where GPS signals are weak, such as a residential area or a basement, it is difficult to locate an automobile using GPS. When the SLAM method is adopted to realize positioning, the SLAM is greatly influenced by external environment, such as a map constructed on sunny days, and the positioning failure is caused by characteristic change caused by remorse in rainy days; and positioning difficulties can also be caused when the light changes greatly; also, as background features vary greatly, positioning difficulties can result. For example, when a parking lot is parked, a map is constructed by adopting SLAM, and then positioning is performed when the parking lot is empty, and positioning failure can be caused by the fact that features cannot be matched. Meanwhile, SLAM is a feature-based map, and thus the memory amount of SLAM maps is large.
Therefore, the embodiment of the application provides a method which has low cost and can stably position even when the external environment changes. Specifically, the automobile positioning method provided by the embodiment of the application can be also applied to road sections where automobiles frequently pass, such as commute routes or cell garages and the like. The method provided by the embodiment of the application will be specifically described below.
It can be understood that before describing the method provided by the embodiment of the present application, referring to fig. 2, fig. 2 is a schematic structural diagram of an automobile positioning device provided by the embodiment of the present application, where the automobile positioning device may be used to perform the automobile positioning method provided by the embodiment of the present application.
As shown in fig. 2, the vehicle positioning device may include a processor 201 and an in-vehicle sensor 202, which may be coupled by a connector, which may include various interfaces, transmission lines, buses, etc., and the embodiment of the application is not limited.
The vehicle-mounted sensor may be used to read running state information of the automobile, for example, the vehicle-mounted sensor may be used to read acceleration information, speed information, etc. of the automobile, and the embodiment of the application is not limited. Optionally, the vehicle-mounted sensor may also include other names, and the names of the vehicle-mounted sensor are not limited in the embodiments of the present application.
The processor may be one or more central processing units (central processing unit, CPU), and in the case where the processor is a CPU, the CPU may be a single-core CPU or a multi-core CPU. Alternatively, the processor may be a processor group of multiple processors coupled to each other by one or more buses. In the alternative, the processor may include other types of processors, and the like, and embodiments of the present application are not limited.
It will be appreciated that in one possible implementation, the vehicle positioning device may further include a memory (not shown in the figure) that may be used to store computer program instructions, including an Operating System (OS), and various types of computer program code for executing embodiments of the present application, and optionally, a memory that includes, but is not limited to, a non-power-down volatile memory, such as an embedded multimedia card (embedded multi media card, EMMC), a universal flash memory (universal flash storage, UFS) or a read-only memory (ROM), or other types of static storage devices that may store static information and instructions, a power-down volatile memory (volatile memory), such as a random access memory (random access memory, RAM) or other types of dynamic storage devices that may store information and instructions, and an electrically erasable programmable read-only memory (EEPROM), a read-only memory (compact disc read-only memory), or a CD-ROM that may have data storage, a compact disc, a CD-ROM, or any other type of magnetic storage device that may have data storage, a data storage device, a compact disc, a disk, a CD-ROM, or any other type of storage device that may be used to store data, a computer-readable storage medium, or any other form of storage medium that may be capable of storing instructions. In the embodiment of the application, the memory can be used for storing preset surface characteristics and the like.
In particular, the processor may be used to determine the surface characteristics of the current road of the vehicle, determine the current location of the vehicle, and so on, based on the operating state information of the vehicle.
It will be appreciated that in a specific implementation, the automobile positioning device may be in the form of a chip, or may be in the form of a stand-alone device, or the like, and embodiments of the present application are not limited thereto.
It will be appreciated that the foregoing is merely a schematic structural diagram of an automobile positioning device provided by an embodiment of the present application, and in a specific implementation, the automobile positioning device may have more or less components than those shown, may combine two or more components, may have different configuration implementations of different components, and so on.
It may be understood that, fig. 2 shows an example of an automobile positioning device, in a specific implementation, a system for implementing a method provided by an embodiment of the present application may also be shown in fig. 3, and fig. 3 is a schematic structural diagram of an automobile positioning system provided by an embodiment of the present application, where the automobile positioning system includes: an automobile positioning device 301 and an in-vehicle sensor 302. The vehicle positioning device may be connected to the vehicle-mounted sensor through a connector, or the vehicle positioning device may also be connected to the vehicle-mounted sensor through a wireless manner, etc., which is not limited by the embodiment of the application.
The automobile positioning device 301 may include: a processor 3011 and a memory 3012. The processor and the memory may be coupled by connectors, which may include various interfaces, transmission lines, buses, and the like, as the embodiments of the application are not limited in this respect. It should be appreciated that in various embodiments of the application, coupled is intended to mean interconnected by a particular means, including directly or indirectly through other devices, e.g., through various interfaces, transmission lines, buses, etc.
Memory, which may be used to store computer program instructions, including Operating System (OS), and various types of computer program code for performing embodiments of the present application, may alternatively be used, including but not limited to, non-power-down volatile memory, such as embedded multimedia cards (embedded multi media card, EMMC), universal flash storage (universal flash storage, UFS) or read-only memory (ROM), or other types of static storage devices that may store static information and instructions, power-down volatile memory (volatile memory), such as random access memory (random access memory, RAM), or other types of dynamic storage devices that may store information and instructions, and may also be electrically erasable programmable read-only memory (electrically erasable programmable read-only memory, EEPROM), compact disk read-only memory (compact disc read-only memory, CD-ROM) or other optical disk storage, optical disk storage (including compact discs, laser discs, digital versatile discs, blu-ray discs, etc.), magnetic disk storage media, or other types of static storage devices that may be used to store static information and instructions, or other types of dynamic storage devices that may be used to store information and instructions, and may also be used in the form of electrically erasable programmable read-only memory (electrically erasable programmable read-only memory, EEPROM), compact disc storage (compact disc read-only memory) or other optical disk storage media that may be used to store data in the form of computer-readable storage media that may be used to store data and the computer-readable storage media. In an embodiment of the present application, the memory may be used to store a predetermined surface characteristic.
The processor may be one or more central processing units (central processing unit, CPU), and in the case where the processor is a CPU, the CPU may be a single-core CPU or a multi-core CPU. Alternatively, the processor may be a processor group of multiple processors coupled to each other by one or more buses. In the alternative, the processor may include other types of processors, and the like, and embodiments of the present application are not limited. In particular, the processor may be used to determine the surface characteristics of the current road of the vehicle, determine the current location of the vehicle, and so on, based on the operating state information of the vehicle.
Where the vehicle locating means is in the form of a stand-alone device, the vehicle locating means may further comprise a transceiver (not shown) operable to receive and transmit data, for example, the transceiver may be operable to receive operating state information of the vehicle from an on-board sensor, etc., the embodiments of the present application are not limited.
The vehicle-mounted sensor can be used for reading running state information of the automobile, for example, the vehicle-mounted sensor can be used for reading acceleration data, speed data and the like of the automobile, and the embodiment of the application is not limited. Optionally, the vehicle-mounted sensor may also include other names, and the names of the vehicle-mounted sensor are not limited in the embodiments of the present application.
It will be appreciated that fig. 3 and 2 respectively show different structures for performing the method provided by the embodiment of the present application, and in a specific implementation, the embodiment of the present application is not limited to the above structures uniquely.
Referring to fig. 4, fig. 4 is a schematic flow chart of an automobile positioning method according to an embodiment of the present application, and the automobile positioning method can be applied to the automobile positioning device shown in fig. 2 and the automobile positioning system shown in fig. 3. As shown in fig. 4, the automobile positioning method includes:
401. the automobile positioning device acquires running state information of an automobile, wherein the running state information is used for representing the state information of the automobile in the running process.
In the embodiment of the application, the acquired running state information of the automobile can be acquired by the vehicle-mounted sensor in the automobile positioning device; or may be obtained by an on-board sensor connected to the vehicle positioning device, and then the vehicle positioning device obtains the running state information from the on-board sensor, which is not limited to the above two implementation manners in the embodiment of the present application.
In particular, the running state information of the automobile may include acceleration data (i.e., acceleration information) and speed data (i.e., speed information) of the automobile. The acceleration data may include vertical acceleration data of the automobile, or may include horizontal acceleration data of the automobile, which is not limited in the embodiment of the present application.
402. The vehicle positioning device determines a surface feature of a road on which the vehicle has traveled based on the running state information of the vehicle, the surface feature of the road on which the vehicle has traveled being used to represent one or more of a surface relief condition and a surface roughness of the road on which the vehicle has traveled.
In embodiments of the present application, the surface characteristics of the road on which the automobile has traveled may be used to represent one or more of the surface relief and surface roughness of the road on which the automobile has traveled. In particular, the surface characteristics of the road on which the vehicle has traveled may be used to represent the topography of the road on which the vehicle has traveled; alternatively, the surface characteristics of the road on which the automobile has traveled may be used to represent the surface roughness of the road on which the automobile has traveled; alternatively, the surface characteristics of the road on which the automobile has traveled may be used to represent the surface relief state and surface roughness of the road on which the automobile has traveled. The surface relief condition can be used for representing that pits, bulges, speed-reducing zones and the like on a road can cause the automobile to jolt; the surface roughness may indicate that microscopic undulations of the road surface and road surfaces of different materials (such as asphalt road surfaces, concrete road surfaces, gravel road surfaces, etc.) may cause high vibration of the vehicle.
Specifically, the determining, according to the running state information of the automobile, the surface feature of the road on which the automobile has run includes:
41 Inputting the running state information into a vibration model of the automobile, and outputting the road surface height of the road on which the automobile has run;
42 Determining the change of the road surface height of the road on which the automobile has run along with the distance according to the road surface height of the road on which the automobile has run and the running state information;
43 And (3) carrying out pavement characteristic matching on the road on which the automobile runs according to the change of the pavement height of the road on which the automobile runs along with the change of the distance, so as to obtain the surface characteristics of the road on which the automobile runs.
The vibration model of the automobile can comprise a low-pass filter model of the automobile.
For a more visual understanding of the surface features of the road on which the vehicle has been driven according to the embodiment of the present application, refer to fig. 5, and fig. 5 is a schematic diagram of an automobile vibration model according to the embodiment of the present application. Among them, the main function of the automobile tire and suspension is to alleviate the vibration of the road surface, so the vibration model of the automobile can be simplified as a low-pass filter.
Abstracting a vibration model of an automobile into a low-pass filter f (x), taking a road surface height h (t) as an input signal, and taking acceleration data a (t) of the automobile as output of the filter, namely:
a(t)＝f(h(t)) (1)
From the inverse function of equation (1), it is possible to obtain:
h(t)＝f -1 (a(t)) (2)
for example, the specific form of the filter (x) may be a laplace transform, where h(s) is assumed to be h (t), i.e., h(s) =l (h (t)); a(s) is assumed to be the laplace transform of a (t), i.e., a(s) =l (a (t)).
The form of writing equation f (x) as a transfer function may be as follows:
the inverse function may be as follows:
the road surface height can be obtained according to the formula (2) and the acceleration data of the automobile, and then the change h(s) of the road surface height along with the distance can be obtained through the speed data v of the automobile, as follows:
h(s)＝h(t*v) (3)
referring to fig. 6, fig. 6 is a schematic diagram of a process for determining a road surface feature according to an embodiment of the present application, and as shown in fig. 6, the surface feature of the road on which the vehicle has traveled can be obtained by using acceleration data (which may also be referred to as an acceleration signal), and the above formula (2) and formula (3). It can be appreciated that the above formula (1), formula (2) and formula (3) are a method provided in the embodiments of the present application, and in a specific implementation, h(s) may be determined according to other operation status information, which is not limited in the embodiments of the present application.
More specifically, the pavement features have rich information, so that hierarchical feature modeling can be further performed on the pavement features, and therefore, the embodiment of the application further provides a method for performing hierarchical feature modeling, as follows:
The road surface feature matching of the road on which the automobile is running according to the change of the road surface height of the road on which the automobile is running along with the change of the distance is carried out to obtain the surface feature of the road on which the automobile is running, and the road surface feature matching method comprises the following steps:
431 Carrying out top-layer feature matching on the road on which the automobile runs according to the change of the road surface height of the road on which the automobile runs along with the change of the distance to obtain the top-layer road surface feature of the road on which the automobile runs;
432 And performing sub-feature matching on the road on which the automobile is running according to the top road surface feature of the road on which the automobile is running, so as to obtain the sub-road surface feature of the road on which the automobile is running.
For visual understanding, referring to fig. 7, fig. 7 is a schematic view of a scenario of hierarchical feature modeling according to an embodiment of the present application. As shown in fig. 7, the top road surface features may include feature 1 (i.e., S1), feature 2 (S2), and feature 3 (S3) shown in fig. 7; the sub-road features may include sub-features under the top-level features shown in fig. 7, such as sub-feature 1.1, sub-feature 1.2, …, sub-feature 1.N, as well as sub-features 3.1, …, sub-feature 3.N, etc. under feature 3. It will be appreciated that the number of top layer pavement features shown above, as well as the number of sub-features of each top layer pavement feature, are merely one example.
In order to more vividly distinguish the top road surface features and the sub road surface features, the embodiment of the application also provides a method for quantitatively distinguishing the top road surface features and the sub road surface features, which is as follows:
the top road surface features comprise one or more of sub-feature quantity, top feature frequency distribution, top feature maximum amplitude, top feature average amplitude and top feature variance;
the sub-road surface features include one or more of sub-feature separation distance, sub-feature duration distance, sub-feature frequency distribution, sub-feature maximum amplitude, sub-feature average amplitude, and sub-feature variance.
The number of sub-features refers to the number of sub-features corresponding to the top road surface features, as shown in fig. 7, the number of sub-features of feature 1 is 4 (the number shown in the figure), the number of sub-features of feature 2 is 3, and the number of sub-features of feature 3 is 3. The top-level characteristic frequency distribution refers to the distance interval distribution situation among all sub-characteristics, and particularly refers to the distribution characteristic situation that the amplitude of the top-level characteristic time history curve changes with frequency through Fourier. As shown in fig. 7, the distance spacing between the individual sub-features in feature 1 is greater than the distance spacing between the individual sub-features in feature 2, but less than the spacing between the individual sub-features in feature 3. The top road characteristic maximum amplitude refers to the top road characteristic corresponding to the maximum road height that the vehicle passes when driving on each top road characteristic, as shown in fig. 7, and as can be seen from the figure, the top road maximum amplitude may be the amplitude of the characteristic 3. The top-layer characteristic average amplitude refers to the average of the amplitudes of the sub-characteristics corresponding to the respective top-layer road surface characteristics, as shown in fig. 7, the average amplitude of the characteristic 1 may be the average of the amplitudes of the 4 sub-characteristics in the figure, and the average amplitude of the characteristic 2 may be the average of the amplitudes of the 3 sub-characteristics in the figure. Top level feature variance refers to the variance between the amplitude of each top level road feature, as shown in fig. 7, between the amplitude of feature 1, the amplitude of feature 2, and the amplitude of feature 3.
Where the sub-feature separation distance may refer to the distance between individual sub-features. The sub-feature duration distance refers to the distance that the vehicle travels over the sub-feature. The sub-characteristic frequency distribution may refer to the characteristic of the distribution of amplitude with frequency of the sub-characteristic time history curve after fourier transformation. The maximum sub-feature amplitude refers to the sub-feature corresponding to the maximum road surface height that the vehicle passes by when the vehicle is traveling over each sub-feature. The sub-feature average amplitude refers to the average of the amplitudes of the individual sub-features, and in particular, may refer to the average of the amplitudes between the sub-features of a sub-feature, as shown in fig. 7, for which sub-feature 1.1 the car is running, and thus the average amplitude of the sub-feature may refer to the average of the amplitudes of the individual small amplitudes in the sub-feature 1.1. The sub-feature variance refers to the amplitude variance between sub-features.
By further distinguishing the top layer road surface characteristics and the sub road surface characteristics, the layering modeling can be carried out on the road on which the automobile runs, so that the accuracy and precision of road surface characteristic determination are improved, and the accuracy of positioning of the automobile positioning device is further improved.
403. And the automobile positioning device determines the current position of the automobile according to the surface characteristics of the road on which the automobile is driven and preset surface characteristics, wherein the preset surface characteristics comprise the mapping relation between the road and the surface characteristics.
In the embodiment of the application, the preset surface features include a mapping relation between a road and the surface features, that is, the current position can be obtained according to the surface features of the road which has already been driven and the preset surface features by storing the preset surface features in the automobile positioning device.
It will be appreciated that the current location of the vehicle may include a rough location on the road and a location where the surface roughness of the road is greater than a roughness threshold, whereby the vehicle locating means may accurately locate the current location based on the surface characteristics. The roughness threshold is used to measure the surface roughness of the road, so the roughness threshold is not limited in the embodiments of the present application.
It can be understood that the method provided by the embodiment of the application can also be used for correcting the navigation system of the automobile, for example, after the automobile positioning device determines the current position of the automobile, the automobile positioning device can compare whether the current position of the automobile is consistent with the position in the navigation system installed in the automobile positioning device, and if not, the position displayed in the navigation system can be modified.
By implementing the embodiment of the application, in the environments such as parking lots or residential communities with obstacle shielding, the automobiles can be effectively positioned according to the surface characteristics of roads, and the situation that the positioning accuracy is low due to environmental influence is avoided, so that the positioning accuracy is effectively improved.
For visual understanding of the method shown in fig. 4, the following will be described with reference to specific examples.
First, referring to fig. 8, fig. 8 is a schematic view of a scenario illustrating an automobile positioning method according to an embodiment of the present application, where the method may be applied to the automobile positioning device shown in fig. 2 or to the automobile positioning system shown in fig. 3. As shown in fig. 8, the automobile positioning method includes:
801. the method comprises the steps of acquiring acceleration data and speed data of an automobile when the automobile runs on a preset road through a vehicle-mounted sensor, carrying out feature modeling on the preset road according to the acceleration data and the speed data, and storing the road features of the preset road after the road features are acquired.
In the embodiment of the application, the preset road can be a road through which the user frequently passes, a road of a city where the user is located, and the like.
It will be appreciated that reference may also be made to the method shown in fig. 4 for a specific implementation of step 801, which will not be described in detail here.
802. And acquiring acceleration data and speed data when the automobile runs, and performing feature matching on the current running road of the automobile according to the acceleration data and the speed data.
Specifically, referring to fig. 9, fig. 9 is a schematic view of a road feature matching method according to an embodiment of the present application, as shown in fig. 9, where the road feature matching method includes:
91 Based on the automobile vibration model, extracting the road surface characteristics of the current road to obtain the change h(s) of the road surface height along with the distance;
92 Performing top layer feature matching; for example, N-th top-level feature matching is performed, where N is an integer greater than or equal to 1, and is used to distinguish between different objects, and does not represent ordering.
93 Judging whether the top road surface characteristics of the current road are matched with the Nth top road surface characteristics, and if so, performing sub-characteristic matching; if not, carrying out the n+1th top layer feature matching;
94 For example, an mth sub-feature match is performed, where m is an integer greater than or equal to 1;
95 Judging whether the sub-feature of the current road is matched with the m-th sub-feature of the Nth top-layer feature, if so, determining that the top-layer road surface feature and the sub-road surface feature of the current road are sequentially the m-th sub-feature of the Nth layer; if not, the m+1 sub-feature matching is performed.
803. And (5) carrying out position prediction on the automobile.
After the surface features of the road on which the automobile is currently running are obtained, the road on which the automobile is currently running can be determined according to the road features of the preset road, and therefore the position prediction of the automobile is achieved.
It can be understood that the embodiment of the present application may also implement correction of the vehicle position, and specifically, for a specific example of the correction method, reference may be made to fig. 10, where fig. 10 is a schematic view of a vehicle positioning scenario provided by the embodiment of the present application. As shown in fig. 10, during the running process of the automobile, the automobile sequentially passes through a position 1 (1#), a position 2 (2#) and a position 3 (3#) shown in fig. 10, specifically, the position 1 is a manhole cover, and the position 2 is a deceleration strip. The manhole cover is first hit, the speed of the vehicle is 27.2, then the deceleration strip is hit, and the speed of the vehicle is 19.1. Wherein the first waveform change point 1# is at 15m of the initial position, the second waveform change point 2# is at 80m of the initial position, and the third waveform change point 3# is at 200m of the initial position. In the feature matching stage, the matched waveform is 2#, the position can be determined to be 80m in sequence, the predicted position of the automobile is 70m, and the position can be corrected to be 80m based on the road feature matching result.
In the embodiment of the application, the positioning method depends on the road surface characteristics, so that the method is not influenced by the strength of GPS signals, and the vehicle positioning is realized through the road surface characteristics in environments with weak GPS signals such as residential communities, underground garages and the like. The positioning is realized based on the road surface characteristics, such as characteristics of a ground deceleration strip, an ascending slope, a descending slope, a pothole and the like, cannot change or change less in a longer time range, and are not easily influenced by external light rays and weather. Meanwhile, the road surface features have sparse characteristics, namely the distribution of the road surface features in space is scattered, so that the stored data size of the road surface features is smaller. The extraction of the road surface features depends on an on-vehicle acceleration sensor, and most of the vehicles are provided with the acceleration sensor currently, so that the acceleration sensor does not need to be added, and the cost of the acceleration sensor is low even if the acceleration sensor needs to be added.
Referring to fig. 11, fig. 11 is a schematic view of a scenario of automatic driving of an automobile in an underground parking garage according to an embodiment of the present application. As shown in the figure 11 of the drawings,
the application is mainly applied to road sections (commute routes, cell garages and the like) where vehicles frequently pass. For example, in a typical scenario, when an autopilot car enters a cell and needs to stop the car to an underground garage, the car enters the underground garage and then faces the problems of weak GPS signals and difficult positioning.
The automobile needs to reach the point B of the underground garage from the point A outside the underground garage. In the process, the automatic driving method can be used for positioning the automobile based on the gradient and the deceleration strip on the road surface of the underground garage, so that the automatic driving from the point A to the point B outside the garage is realized.
The foregoing details of the method according to the embodiments of the present application and the apparatus according to the embodiments of the present application are provided below.
Referring to fig. 12, fig. 12 is a schematic structural diagram of an automobile positioning device according to an embodiment of the present application, where the automobile positioning device may be used to perform the methods described in fig. 4 to 11, and as shown in fig. 12, the automobile positioning device includes:
an acquiring unit 1210, configured to acquire running state information of an automobile, where the running state information is used to represent state information of the automobile in a running process;
a first determining unit 1220 for determining a surface feature of a road on which the automobile has traveled, based on the running state information of the automobile, the surface feature of the road on which the automobile has traveled being indicative of one or more of a surface relief condition and a surface roughness of the road on which the automobile has traveled;
a second determining unit 1230, configured to determine the current location of the vehicle according to the surface feature of the road on which the vehicle has traveled and a preset surface feature, where the preset surface feature includes a mapping relationship between the road and the surface feature.
By implementing the embodiment of the application, in the environments such as parking lots or residential communities with obstacle shielding, the automobiles can be effectively positioned according to the surface characteristics of roads, and the situation that the positioning accuracy is low due to environmental influence is avoided, so that the positioning accuracy is effectively improved.
Specifically, as shown in fig. 13, the first determining unit 1220 includes:
an input/output sub-unit 1221 for inputting the operation state information into a vibration model of the automobile and outputting a road surface height of a road on which the automobile has traveled;
a determining subunit 1222 for determining a change in road surface height of the road on which the automobile has traveled with the course according to the road surface height of the road on which the automobile has traveled and the running state information;
and the feature matching subunit 1223 is configured to match the road surface features of the road on which the automobile has traveled according to the road surface height of the road on which the automobile has traveled along with the change of the distance, so as to obtain the surface features of the road on which the automobile has traveled.
Specifically, the feature matching subunit 1223 is specifically configured to perform top-level feature matching on the road on which the automobile has traveled according to a change of a road surface height of the road on which the automobile has traveled along with a change of a distance, to obtain a top-level road surface feature of the road on which the automobile has traveled; and performing sub-feature matching on the road on which the automobile is running according to the top road surface feature of the road on which the automobile is running, so as to obtain the sub-road surface feature of the road on which the automobile is running.
Specifically, the top road surface features include one or more of a sub-feature number, a top feature frequency distribution, a top feature maximum amplitude, a top feature average amplitude, and a top feature variance;
the sub-road surface features include one or more of sub-feature separation distance, sub-feature duration distance, sub-feature frequency distribution, sub-feature maximum amplitude, sub-feature average amplitude, and sub-feature variance.
Specifically, the vibration model of the automobile includes a low-pass filter model of the automobile.
Specifically, the running state information includes one or more of acceleration information and speed information.
Specifically, the current position of the automobile includes one or more of a rough position and a position with a roughness greater than a roughness threshold.
It will be appreciated that reference is made to the method shown in fig. 4 to 11 for a specific implementation of the car positioning device of fig. 12, and this will not be described in detail here.
It will be appreciated that the processor shown in fig. 2 may be used to perform the implementation shown in steps 401 to 403 in fig. 4, and may also be used to perform the implementation shown in the acquisition unit 1210, the first determination unit 1220 and the second determination unit 1230, which are not described in detail here. Alternatively, in a possible implementation manner, the method shown in step 401 may also be performed by a transceiver (not shown in the drawing) in the automobile positioning device shown in fig. 2, or the method shown in the acquisition unit may also be performed by a transceiver in the automobile positioning device shown in fig. 2, or the like, which is not limited uniquely by the embodiment of the present application.
It will be appreciated that the processor shown in fig. 3 may also be used to perform the implementation shown in steps 401 to 403 in fig. 4, and may also be used to perform the implementation shown in the acquisition unit 1210, the first determination unit 1220 and the second determination unit 1230, which will not be described in detail here. Alternatively, in a possible implementation manner, the method shown in step 401 may also be performed by a transceiver (not shown in the drawing) in the automobile positioning device shown in fig. 3, or the method shown in the acquisition unit may also be performed by a transceiver in the automobile positioning device shown in fig. 3, or the like, which is not limited uniquely by the embodiment of the present application.
Embodiments of the present application also provide a computer readable storage medium having instructions stored therein that, when executed on an automobile positioning device, implement the method flows shown in fig. 4, 8, 9, etc.
Embodiments of the present application also provide a computer program product that, when run on an automobile positioning device, implements the method flows shown in fig. 4, 8, 9, etc.
Those of ordinary skill in the art will appreciate that implementing all or part of the above-described method embodiments may be accomplished by a computer program to instruct related hardware, the program may be stored in a computer readable storage medium, and the program may include the above-described method embodiments when executed. And the aforementioned storage medium includes: ROM or random access memory RAM, magnetic or optical disk, etc.
Claims (17)
1. A method of locating an automobile, comprising:
acquiring running state information of an automobile, wherein the running state information is used for representing the state information of the automobile in the running process;
determining a surface feature of a road on which the automobile has traveled according to the running state information of the automobile, the surface feature of the road on which the automobile has traveled being used to represent one or more of a surface relief condition and a surface roughness of the road on which the automobile has traveled;
and determining the current position of the automobile according to the surface characteristics of the road on which the automobile is driven and preset surface characteristics, wherein the preset surface characteristics comprise the mapping relation between the road and the surface characteristics.
2. The method of claim 1, wherein the determining the surface characteristics of the road on which the vehicle has traveled based on the operating state information of the vehicle comprises:
inputting the running state information into a vibration model of the automobile, and outputting the road surface height of a road on which the automobile runs;
determining the change of the road surface height of the road on which the automobile runs along with the distance according to the road surface height of the road on which the automobile runs and the running state information;
And carrying out pavement characteristic matching on the road on which the automobile runs according to the change of the pavement height of the road on which the automobile runs along with the change of the distance, so as to obtain the surface characteristics of the road on which the automobile runs.
3. The method according to claim 2, wherein the step of performing the road surface feature matching on the road on which the automobile has traveled according to the change of the road surface height of the road on which the automobile has traveled along with the change of the distance, to obtain the surface feature of the road on which the automobile has traveled, comprises:
carrying out top-layer feature matching on the road on which the automobile runs according to the change of the road surface height of the road on which the automobile runs along with the change of the distance, so as to obtain the top-layer road surface feature of the road on which the automobile runs;
and performing sub-feature matching on the road on which the automobile runs according to the top road surface feature of the road on which the automobile runs, so as to obtain the sub-road surface feature of the road on which the automobile runs.
4. The method of claim 3, wherein the top level pavement features include one or more of a number of sub-features, a top level feature frequency distribution, a top level feature maximum amplitude, a top level feature average amplitude, and a top level feature variance;
The sub-road surface features include one or more of sub-feature separation distance, sub-feature duration distance, sub-feature frequency distribution, sub-feature maximum amplitude, sub-feature average amplitude, and sub-feature variance.
5. The method according to any one of claims 2 to 4, wherein the inputting the operation state information into the vibration model of the automobile, outputting the road surface height of the road on which the automobile has traveled, comprises:
inputting the operation state information a (t) to the deviceIn a vibration model of an automobile, outputting a road surface height h (t) of a road on which the automobile has traveled, wherein the vibration model is: h (t) =f -1 (a(t))。
6. The method according to any one of claims 1 to 5, wherein the operation state information includes one or more of acceleration information and speed information.
7. The method of any one of claims 1 to 6, wherein the current location of the car comprises one or more of a heave location and a location with a roughness greater than a roughness threshold.
8. An automobile positioning device, comprising:
the system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring running state information of an automobile, and the running state information is used for representing the state information of the automobile in the running process;
A first determining unit configured to determine a surface feature of a road on which the automobile has traveled, based on running state information of the automobile, the surface feature of the road on which the automobile has traveled being used to represent one or more of a surface relief condition and a surface roughness of the road on which the automobile has traveled;
and the second determining unit is used for determining the current position of the automobile according to the surface characteristics of the road on which the automobile is driven and preset surface characteristics, wherein the preset surface characteristics comprise the mapping relation between the road and the surface characteristics.
9. The apparatus of claim 8, wherein the first determining unit comprises:
the input/output subunit is used for inputting the running state information into the vibration model of the automobile and outputting the road surface height of the road on which the automobile runs;
a determining subunit, configured to determine a change of a road surface height of a road on which the automobile has traveled along with a distance according to the road surface height of the road on which the automobile has traveled and the running state information;
and the characteristic matching subunit is used for matching the road surface characteristics of the road on which the automobile runs according to the change of the road surface height of the road on which the automobile runs along with the change of the distance, so as to obtain the surface characteristics of the road on which the automobile runs.
10. The apparatus of claim 9, wherein the device comprises a plurality of sensors,
the characteristic matching sub-unit is specifically configured to perform top-layer characteristic matching on the road on which the automobile has traveled according to the change of the road surface height of the road on which the automobile has traveled along with the change of the distance, so as to obtain the top-layer road surface characteristic of the road on which the automobile has traveled; and performing sub-feature matching on the road on which the automobile runs according to the top road surface feature of the road on which the automobile runs, so as to obtain the sub-road surface feature of the road on which the automobile runs.
11. The apparatus of claim 10, wherein the top level road surface features include one or more of a number of sub-features, a top level feature frequency distribution, a top level feature maximum amplitude, a top level feature average amplitude, and a top level feature variance;
the sub-road surface features include one or more of sub-feature separation distance, sub-feature duration distance, sub-feature frequency distribution, sub-feature maximum amplitude, sub-feature average amplitude, and sub-feature variance.
12. The device according to any one of claims 9-11, characterized by an input output subunit for inputting the running state information into a vibration model of the car, outputting the road surface height of the road on which the car has run, in particular:
Inputting the running state information a (t) into a vibration model of the automobile, and outputting the road surface height h (t) of a road on which the automobile has run, wherein the vibration model is as follows: h (t) =f -1 (a(t))。
13. The apparatus according to any one of claims 8 to 12, wherein the operation state information includes one or more of acceleration information and velocity information.
14. The apparatus of any one of claims 8 to 13, wherein the current location of the vehicle comprises one or more of a heave location and a location with a roughness greater than a roughness threshold.
15. An automobile positioning device, comprising: a processor; the processor being operative to invoke program instructions in the memory to perform corresponding functions in the method as claimed in any of claims 1 to 7.
16. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein program instructions, which when run on a computer or a processor, cause the computer or the memory to perform the method of any of claims 1 to 7.
17. A computer program product comprising instructions which, when run on a computer or processor, cause the computer or processor to perform the method of any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811166409.5A CN110967023B (en) | 2018-09-30 | 2018-09-30 | Automobile positioning method and automobile positioning device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811166409.5A CN110967023B (en) | 2018-09-30 | 2018-09-30 | Automobile positioning method and automobile positioning device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110967023A CN110967023A (en) | 2020-04-07 |
| CN110967023B true CN110967023B (en) | 2023-11-10 |
Family
ID=70029442
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811166409.5A Active CN110967023B (en) | 2018-09-30 | 2018-09-30 | Automobile positioning method and automobile positioning device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110967023B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111521186A (en) * | 2020-05-27 | 2020-08-11 | 广州小鹏车联网科技有限公司 | Vehicle positioning method and device, vehicle and storage medium |
| CN111649740B (en) * | 2020-06-08 | 2022-09-20 | 武汉中海庭数据技术有限公司 | Method and system for high-precision positioning of vehicle based on IMU |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09159473A (en) * | 1995-12-08 | 1997-06-20 | Matsushita Electric Ind Co Ltd | Navigation apparatus and map matching method |
| JP2001296132A (en) * | 2000-04-13 | 2001-10-26 | Alpine Electronics Inc | Navigation apparatus |
| JP2004184089A (en) * | 2002-11-29 | 2004-07-02 | Matsushita Electric Ind Co Ltd | Present position specifying device and method |
| CN108120449A (en) * | 2017-12-21 | 2018-06-05 | 深圳市沃特沃德股份有限公司 | The method and device of car assisted positioning |
| CN108340922A (en) * | 2018-02-12 | 2018-07-31 | 北京搜狐新媒体信息技术有限公司 | The method and apparatus for determining road surface road conditions |
-
2018
- 2018-09-30 CN CN201811166409.5A patent/CN110967023B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09159473A (en) * | 1995-12-08 | 1997-06-20 | Matsushita Electric Ind Co Ltd | Navigation apparatus and map matching method |
| JP2001296132A (en) * | 2000-04-13 | 2001-10-26 | Alpine Electronics Inc | Navigation apparatus |
| JP2004184089A (en) * | 2002-11-29 | 2004-07-02 | Matsushita Electric Ind Co Ltd | Present position specifying device and method |
| CN108120449A (en) * | 2017-12-21 | 2018-06-05 | 深圳市沃特沃德股份有限公司 | The method and device of car assisted positioning |
| CN108340922A (en) * | 2018-02-12 | 2018-07-31 | 北京搜狐新媒体信息技术有限公司 | The method and apparatus for determining road surface road conditions |
Non-Patent Citations (1)
| Title |
|---|
| 蒋如意.视觉辅助驾驶系统多维度道路建模及车辆定位优化.2015, * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110967023A (en) | 2020-04-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106352867B (en) | Method and device for determining the position of a vehicle | |
| CN101765757B (en) | Wrong-way travel warning device and wrong-way travel warning method | |
| CN102809379B (en) | For the system and method for sensor-based environmental model structure | |
| CN103109160B (en) | For determining the method and apparatus of the position of vehicle on road surface and there is the automobile of this equipment | |
| CN101246010B (en) | Lane determining device, method, and program | |
| CN104061899B (en) | A kind of vehicle side inclination angle based on Kalman filtering and angle of pitch method of estimation | |
| CN108162974B (en) | Vehicle control using road angle data | |
| US20150314663A1 (en) | Road roughness preview with drive history | |
| CN105488243A (en) | Joint probabilistic modeling and inference of intersection structure | |
| CN106289275A (en) | For improving unit and the method for positioning precision | |
| CN111311902B (en) | Data processing method, device, equipment and machine readable medium | |
| CN101159014A (en) | Method for recognition an object in an image and image recognition device | |
| CN104395944B (en) | The identification in orientation track | |
| Anderson et al. | Estimation of slip angles using a model based estimator and GPS | |
| US9911332B1 (en) | Method, apparatus, and computer program product for parking likelihood estimation based on probe data collection | |
| CN104296756A (en) | Method for operating a motor vehicle and motor vehicle | |
| EP4026049A1 (en) | Vehicular sensor system calibration | |
| CN110967023B (en) | Automobile positioning method and automobile positioning device | |
| CN108466621A (en) | effective rolling radius | |
| Bellone et al. | Autonomous driving in the real-world: The weather challenge in the Sohjoa Baltic project | |
| CN108981723A (en) | System and method for verifying road curvature map datum | |
| CN112767740B (en) | Parking lot selection method and device | |
| CN110869869A (en) | Method for operating a highly automated vehicle (HAF), in particular a highly automated vehicle | |
| EP3961153A1 (en) | Navigation system with high definition mapping mechanism and method of operation thereof | |
| CN114639263B (en) | Vehicle parking position identification method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |