Lane positioning methods, devices, vehicles, storage media, and software products

Abstract

This application relates to the field of vehicle technology, and in particular to a lane positioning method, device, vehicle, storage medium, and program product. The method includes: acquiring road edge data, lane line data, lane number data, and odometer data of the road where the vehicle is currently located; generating lane observations, lane change observations, and lane number change observations based on at least one of the road edge data, lane line data, lane number data, and odometer data; updating the state of a preset filter based on the lane observations, lane change observations, and lane number change observations until the state converges, thereby locating the lane where the vehicle is currently located. This application's technology does not rely on high-precision maps, thus solving the problems of high cost, large data volume, and insufficient map freshness associated with lane positioning using high-precision maps in related technologies.

Description

Technical Field

[0001] This application relates to the field of vehicle positioning technology, and in particular to a lane positioning method, device, vehicle, storage medium, and program product. Background Technology

[0002] In high-speed NOA (Navigate on Autopilot), a map is needed to provide information about the vehicle's current location and the target road (such as on / off ramps), so that the vehicle planning module can initiate corresponding planning (acceleration / deceleration / lane change) in advance to correctly drive to the destination.

[0003] High-precision maps and high-precision positioning are commonly used in related technologies to determine the current lane of a vehicle. However, high-precision maps are relatively expensive to produce and maintain, and due to the huge amount of data, the update speed is relatively slow, which may result in the map information not being up-to-date. In addition, in order to cooperate with high-precision maps, vehicles also need a high-precision positioning system, but the cost of the positioning system is also relatively high. Summary of the Invention

[0004] This application provides a lane positioning method, device, vehicle, storage medium, and program product to solve the problems of high cost, large data volume, and insufficient map freshness in related technologies for lane positioning using high-precision maps.

[0005] The first aspect of this application provides a lane positioning method, comprising the following steps: acquiring road edge data, lane line data, lane number data, and odometer data of the road where the vehicle is currently located; generating lane observation, lane change observation, and lane number change observation based on at least one of the road edge data, lane line data, lane number data, and odometer data; updating the state of a preset filter based on the lane observation, lane change observation, and lane number change observation until the state converges, thereby locating the lane where the vehicle is currently located.

[0006] Optionally, in one embodiment of this application, obtaining lane number data of the current road includes: obtaining a target map of the road where the vehicle is currently located; and obtaining lane number data of the road where the vehicle is currently located based on the target map, wherein the target map is an ADAS map or an SD map.

[0007] Optionally, in one embodiment of this application, lane observation, lane change observation, and lane number change observation are generated based on at least one of road edge data, lane line data, lane number data, and odometer data, including: generating lane observation based on road edge data and lane line data; generating lane change observation based on lane line data or odometer data; and generating lane number change observation based on lane number data and lane line data.

[0008] Optionally, in one embodiment of this application, the preset filter is a dual histogram filter, which represents the vehicle's position in the left lane of the current road and the vehicle's position in the right lane of the current road, respectively; the state converges when the probability that the vehicle is in either lane is greater than a preset threshold.

[0009] Optionally, in one embodiment of this application, locating the lane where the vehicle is currently located includes: identifying lanes with a probability greater than a preset threshold as the lane where the vehicle is currently located.

[0010] Optionally, in one embodiment of this application, generating lane observations based on road edge data and lane line data includes: generating single-sided lane observations if the road edge data is single-sided road edge data, generating double-sided lane observations if the road edge data is double-sided road edge data, and not generating lane observations otherwise.

[0011] A second aspect of this application provides a lane positioning device, comprising: an acquisition module for acquiring road edge data, lane line data, lane number data, and odometer data of the current road where the vehicle is located; a generation module for generating lane observation, lane change observation, and lane number change observation based on at least one of the road edge data, lane line data, lane number data, and odometer data; and a positioning module for updating the state of a preset filter based on the lane observation, lane change observation, and lane number change observation until the state converges, thereby positioning the lane where the vehicle is currently located.

[0012] Optionally, in one embodiment of this application, the acquisition module is further configured to: acquire a target map of the road where the vehicle is currently located; and acquire lane number data of the road where the vehicle is located based on the target map, wherein the target map is an ADAS map or an SD map.

[0013] Optionally, in one embodiment of this application, the generation module is further configured to: generate lane observations based on road edge data and lane line data; generate lane change observations based on lane line data or odometer data; and generate lane number change observations based on lane number data and lane line data.

[0014] Optionally, in one embodiment of this application, the preset filter is a dual histogram filter, which represents the vehicle's position in the left lane of the current road and the vehicle's position in the right lane of the current road, respectively; the state converges when the probability that the vehicle is in either lane is greater than a preset threshold.

[0015] Optionally, in one embodiment of this application, the positioning module is further configured to: identify lanes with a probability greater than a preset threshold as the lane in which the vehicle is currently located.

[0016] Optionally, in one embodiment of this application, the generation module is further configured to: generate a single-sided lane observation if the road edge data is single-sided road edge data, generate a double-sided lane observation if the road edge data is double-sided road edge data, and otherwise not generate a lane observation.

[0017] A third aspect of this application provides a vehicle including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform the lane positioning method as described above.

[0018] A fourth aspect of this application provides a computer-readable storage medium having a computer program or instructions stored thereon, which are executed by a processor to perform a lane positioning method, such as the one described above.

[0019] A fifth aspect of this application provides a computer program product, including a computer program or instructions, which, when executed, implement the lane positioning method as described in the above embodiments.

[0020] Therefore, this application has at least the following beneficial effects:

[0021] This application embodiment can generate lane observations, lane change observations, and lane number change observations based on road edge data, lane line data, lane number data, and odometer data of the vehicle's current road location. It then updates the state of a preset filter based on these changes, taking into account the impact of real-time observations on positioning, improving positioning accuracy. When the filter state converges, it achieves lane positioning without the need for high-precision maps, reducing positioning costs and the amount of data and computation required for subsequent positioning calculations. This allows the vehicle to plan its route based on lane positioning. Therefore, it solves the technical problems of high cost and large data volume associated with using high-precision maps for lane positioning in related technologies.

[0022] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0023] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0024] Figure 1 This is a flowchart of the lane positioning method provided according to an embodiment of this application;

[0025] Figure 2 A flowchart illustrating lane positioning according to an embodiment of this application;

[0026] Figure 3 This is a schematic diagram of lane observation provided according to an embodiment of this application;

[0027] Figure 4 This is a schematic diagram of lane observation at the edge of a road on one side according to an embodiment of this application;

[0028] Figure 5 This is a schematic diagram of lane change observation based on lane line data according to an embodiment of this application;

[0029] Figure 6 This is a schematic diagram of lane change observation based on odometer data provided in an embodiment of this application;

[0030] Figure 7 This is a schematic diagram illustrating the observation of lane number changes according to an embodiment of this application;

[0031] Figure 8 This is a schematic diagram of filter updating according to an embodiment of this application;

[0032] Figure 9 This is an example diagram of a lane positioning device provided according to an embodiment of this application;

[0033] Figure 10 This is a structural schematic diagram of a vehicle provided according to an embodiment of this application. Detailed Implementation

[0034] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0035] The following description, with reference to the accompanying drawings, describes a lane positioning method, apparatus, vehicle, storage medium, and program product according to embodiments of this application. This application provides a lane positioning method in which lane observation, lane change observation, and lane number change observation are generated based on road edge data, lane line data, lane number data, and odometer data of the road where the vehicle is currently located. The state of a preset filter is then updated based on the change observation. This method takes into account the impact of real-time observation on positioning, improves positioning accuracy, and achieves lane positioning when the filter state converges. Lane positioning can be achieved without the use of high-precision maps, reducing positioning costs and the amount of data and computation required for subsequent positioning calculations, so that the vehicle can plan its route based on lane positioning.

[0036] Specifically, Figure 1 This is a schematic flowchart of the lane positioning method provided in the embodiments of this application.

[0037] like Figure 1 As shown, the lane positioning method includes the following steps:

[0038] In step S101, the road edge data, lane line data, lane number data, and odometer data of the road where the vehicle is currently located are obtained.

[0039] Among them, road edge data is the road edge of the vehicle's current road, which can be obtained through sensors (LiDAR or cameras); lane line data includes lane line IDs; lane number data includes the number of lanes; and odometer data includes the vehicle's odometer trajectory.

[0040] In this embodiment of the application, obtaining the number of lanes on the current road includes: obtaining a target map of the road where the vehicle is currently located; and obtaining the number of lanes on the road where the vehicle is currently located based on the target map.

[0041] The target map can be an ADAS or SD map.

[0042] It is understood that the embodiments of this application can obtain a map of the road where the vehicle is currently located, and obtain lane number data of the road where the vehicle is currently located based on the map.

[0043] In step S102, lane observation, lane change observation and lane number change observation are generated based on at least one of road edge data, lane line data, lane number data and odometer data, respectively.

[0044] It is understood that the embodiments of this application can generate lane observation, lane change observation and lane number change observation based on at least one of road edge data, lane line data, lane number data and odometer data, as detailed below.

[0045] In this embodiment of the application, lane observation, lane change observation and lane number change observation are generated based on at least one of road edge data, lane line data, lane number data and odometer data, respectively, including: generating lane observation based on road edge data and lane line data; generating lane change observation based on lane line data or odometer data; and generating lane number change observation based on lane number data and lane line data.

[0046] It is understood that the embodiments of this application can generate lookup observations based on road edge data and lane line data, generate lane change observations based on lane line data or odometer data, and generate lane number change observations based on lane number data and lane line data, so as to locate the lane where the vehicle is currently located based on the observations.

[0047] In this embodiment of the application, generating lane observations based on road edge data and lane line data includes: generating single-sided lane observations if the road edge data is single-sided road edge data, generating double-sided lane observations if the road edge data is double-sided road edge data, and not generating lane observations otherwise.

[0048] Since in real-world scenarios, there may be situations where the road edge cannot be perceived or only one side of the road edge can be perceived, this application embodiment can generate single-sided lane observation when the road edge data is single-sided road edge data, generate double-sided lane observation when the road edge data is double-sided road edge data, and not generate lane observation when the road edge cannot be perceived.

[0049] In step S103, the state of the preset filter is updated based on lane observation, lane change observation and lane number change observation until the state converges, and the lane where the vehicle is currently located is located.

[0050] The preset filter is a dual histogram filter, which represents the vehicle's position in the left lane of the current road and the vehicle's position in the right lane of the current road, respectively. State convergence occurs when the probability of the vehicle being in either lane is greater than a preset threshold. The preset threshold can be set according to specific circumstances and is not specifically limited.

[0051] It is understood that the embodiments of this application can update the state of the filter based on lane observation, lane change observation and lane number change observation until the state of the filter converges, and locate the lane where the vehicle is currently located. The lane where the vehicle is currently located can be determined without using a high-precision map, which reduces the amount of data. Furthermore, since it does not use a high-precision map, the maintenance cost of the high-precision map can be reduced, thereby reducing the cost of lane positioning.

[0052] It should be noted that, in this embodiment of the application, the vehicle's position in the lane from the left of the current road is determined by counting from the road edge to the left of the vehicle, and the vehicle's position in the lane from the right of the current road is determined by counting from the road edge to the right of the vehicle.

[0053] In this embodiment of the application, locating the lane where the vehicle is currently located includes: identifying lanes with a probability greater than a preset threshold as the lane where the vehicle is currently located.

[0054] It is understood that, in the embodiments of this application, lanes with a probability greater than a preset threshold can be used as the lane in which the vehicle is currently located.

[0055] The lane positioning method of this application is illustrated below with a specific embodiment. It generates corresponding observations by sensing lane lines, odometer readings, and navigation information provided by ADAS maps. These observations are then input into a histogram filter for fusion. When the filter converges, the position with the highest filter probability is the current vehicle's lane location. Figure 2 As shown, the specific process is as follows:

[0056] I. Current lane observation generation.

[0057] The relationship between lane lines and road edges, perceived by sensors (laser point clouds or cameras), can generate an observation of the current lane. For example... Figure 3 As shown, the road edges on both sides can be clearly observed, so a complete current lane observation can be generated based on the lane lines and edges, which can be used to generate the current lane observation of the second lane from the left / the third lane from the right.

[0058] However, due to various reasons (such as excessive road width / obstruction), only one side of the road edge is observed. In such cases, a single-sided observation can be generated, such as... Figure 4 As shown, the current lane observation of the first lane from the left is generated.

[0059] If the road edge cannot be observed, no corresponding current lane observation is generated.

[0060] II. Lane change observation generation.

[0061] By tracking the lane line IDs provided by the perceived lane lines, corresponding lane change observations can also be obtained, such as... Figure 5 As shown, lane line number 1003 changes from the right side of the vehicle to the left side, which generates an observation of a right lane change.

[0062] Using vehicle odometer information, such as Figure 6 As shown, the lane change pattern of the odometer's trajectory can also be used to obtain corresponding lane change observations.

[0063] III. Generation of Lane Number Change Observation.

[0064] By using NumOfLaneDrvDir (used to obtain lane number data) in the ADAS map, the lane number change of the current driving road map can be obtained. Combined with perception information or LaneExtendedProfileConectivity (used to describe lane-level connectivity and topology information), observations of lane number changes can be generated.

[0065] by Figure 7For example, if a vehicle is currently in lane A-2 and travels from road segment A to road segment B, the number of lanes changes from 3 to 2. Based on the connection information of different road segments, it can be known that A-1 merges into B-1, and a corresponding lane number change observation can be generated (the number of lanes on the right decreases by 1).

[0066] IV. Histogram Filter.

[0067] This application uses a dual histogram filter, where the two filters represent the current vehicle's position in the left / right lane of the current road. The state of each bin in the filter is updated based on different observations. The value of each bin represents the probability that the vehicle belongs to that lane. When the state converges (the probability of a bin is greater than a certain threshold), it can be deduced that the vehicle is in the corresponding lane. The filter updates based on different observations are as follows: Figure 8 As shown.

[0068] This application requires onboard sensors to provide relevant inputs, requires LiDAR or cameras to generate lane lines and lane edges and related information (such as lane line IDs) through a perception system, requires vehicle IMU (Inertial Measurement Unit) and wheel speed sensor to provide vehicle odometer information, requires vehicle navigation system to provide ADAS map information, and requires onboard storage unit to store algorithm software and computing unit to run the algorithm.

[0069] In summary, this application does not use high-precision maps. Instead, it generates corresponding observations by sensing lane lines, odometer readings, and ADAS map information. These observations are then fused using a histogram filter to provide the current lane's location. This allows the current lane information to be used in conjunction with navigation information, enabling planning in highway scenarios (such as on / off ramps) without relying on high-precision maps. Consequently, the amount of data and computation is significantly reduced.

[0070] The lane positioning method proposed in this application can generate lane observation, lane change observation, and lane number change observation based on the road edge data, lane line data, lane number data, and odometer data of the road where the vehicle is currently located. Then, the state of the preset filter is updated according to the change observation. It can take into account the impact of real-time observation on positioning, improve positioning accuracy, and realize the positioning of the lane where the vehicle is located when the filter state converges. Lane positioning can be achieved without the use of high-precision maps, reducing positioning costs and reducing the amount of data and computation in subsequent positioning calculations, so that the vehicle can plan its path based on lane positioning.

[0071] Next, the lane positioning device proposed according to the embodiments of this application is described with reference to the accompanying drawings.

[0072] Figure 9 This is a block diagram of a lane positioning device according to an embodiment of this application.

[0073] like Figure 9 As shown, the lane positioning device 10 includes: an acquisition module 100, a generation module 200, and a positioning module 300.

[0074] The acquisition module 100 is used to acquire road edge data, lane line data, lane number data and odometer data of the current road where the vehicle is located; the generation module 200 is used to generate lane observation, lane change observation and lane number change observation based on at least one of the road edge data, lane line data, lane number data and odometer data; the positioning module 300 is used to update the state of the preset filter based on the lane observation, lane change observation and lane number change observation until the state converges, and locate the lane where the vehicle is currently located.

[0075] In this embodiment of the application, the acquisition module 100 is further configured to: acquire a target map of the road where the vehicle is currently located; and acquire lane number data of the road where the vehicle is located based on the target map, wherein the target map is an ADAS map or an SD map.

[0076] In this embodiment, the generation module 200 is further configured to: generate lane observations based on road edge data and lane line data; generate lane change observations based on lane line data or odometer data; and generate lane number change observations based on lane number data and lane line data.

[0077] In this embodiment, the preset filter is a dual histogram filter, which represents the vehicle's position in the left lane of the current road and the vehicle's position in the right lane of the current road, respectively; the state converges when the probability that the vehicle is in either lane is greater than a preset threshold.

[0078] In this embodiment of the application, the positioning module 300 is further used to: identify lanes with a probability greater than a preset threshold as the lane where the vehicle is currently located.

[0079] In this embodiment of the application, the generation module 300 is further configured to: generate a single-sided lane observation if the road edge data is single-sided road edge data, generate a double-sided lane observation if the road edge data is double-sided road edge data, and otherwise not generate a lane observation.

[0080] It should be noted that the foregoing explanation of the lane positioning method embodiment also applies to the lane positioning device of this embodiment, and will not be repeated here.

[0081] According to the lane positioning device proposed in this application, lane observation, lane change observation, and lane number change observation can be generated based on the road edge data, lane line data, lane number data, and odometer data of the road where the vehicle is currently located. Then, the state of the preset filter is updated according to the change observation. It can take into account the impact of real-time observation on positioning, improve positioning accuracy, and realize the positioning of the lane where the vehicle is located when the filter state converges. Lane positioning can be achieved without the use of high-precision maps, reducing positioning costs and reducing the amount of data and computation for subsequent positioning calculations, so that the vehicle can plan its route based on lane positioning.

[0082] Figure 10 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include:

[0083] The memory 1001, the processor 1002, and the computer program stored on the memory 1001 and capable of running on the processor 1002.

[0084] When the processor 1002 executes the program, it implements the lane positioning method provided in the above embodiments.

[0085] Furthermore, the vehicle also includes:

[0086] Communication interface 1003 is used for communication between memory 1001 and processor 1002.

[0087] The memory 1001 is used to store computer programs that can run on the processor 1002.

[0088] The memory 1001 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0089] If the memory 1001, processor 1002, and communication interface 1003 are implemented independently, then the communication interface 1003, memory 1001, and processor 1002 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 10 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0090] Optionally, in a specific implementation, if the memory 1001, processor 1002, and communication interface 1003 are integrated on a single chip, then the memory 1001, processor 1002, and communication interface 1003 can communicate with each other through an internal interface.

[0091] The processor 1002 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0092] This application also provides a computer-readable storage medium storing a computer program or instructions thereon, which, when executed by a processor, implements the lane positioning method described above.

[0093] This application also provides a computer program product, including a computer program or instructions, which, when executed, implement the lane positioning method described above.

[0094] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0095] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0096] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0097] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0098] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

Claims

1. A lane positioning method characterized by, The method comprises the following steps: obtaining road edge data, lane line data, lane number data and odometer data of a road where a vehicle is currently located; generating lane observation, lane change observation and lane number change observation based on at least one of the road edge data, the lane line data, the lane number data and the odometer data, respectively; updating a state of a preset filter based on the lane observation, the lane change observation and the lane number change observation until the state converges, and positioning a lane where the vehicle is currently located, wherein the preset filter is a double histogram filter, and the double histogram filter is respectively a lane position of the vehicle from left on a current road and a lane position of the vehicle from right on the current road; the state convergence is that a probability of the vehicle on any lane is greater than a preset threshold; the generating the lane observation, the lane change observation and the lane number change observation based on at least one of the road edge data, the lane line data, the lane number data and the odometer data comprises: generating the lane observation based on the road edge data and the lane line data; generating the lane change observation based on the lane line data or the odometer data; and generating the lane number change observation based on the lane number data and the lane line data; the generating the lane observation based on the road edge data and the lane line data comprises: if the road edge data is single-side road edge data, generating single-side lane observation, if the road edge data is double-side road edge data, generating double-side lane observation, and otherwise, not generating lane observation; and the positioning the lane where the vehicle is currently located comprises: taking the lane with the probability greater than the preset threshold as the lane where the vehicle is currently located.

2. The lane positioning method according to claim 1, characterized by, The obtaining the lane number data of the road where the vehicle is currently located comprises: obtaining a target map of the road where the vehicle is currently located; obtaining the lane number data of the road where the vehicle is currently located based on the target map, wherein the target map is an ADAS map or an SD map.

3. A lane positioning device, characterized by, The method comprises the following steps: an obtaining module, configured to obtain road edge data, lane line data, lane number data and odometer data of a road where a vehicle is currently located; a generating module, configured to generate lane observation, lane change observation and lane number change observation based on at least one of the road edge data, the lane line data, the lane number data and the odometer data, respectively; The positioning module is configured to update a state of a preset filter based on the lane observation, the lane change observation and the lane number change observation until the state converges, and to position a lane in which the vehicle currently locates, wherein the preset filter is a double histogram filter, and the double histogram filter is respectively a lane position of the vehicle on a left lane of a current road and a lane position of the vehicle on a right lane of the current road; the state converges when a probability of the vehicle on any lane is greater than a preset threshold; and the lane observation, the lane change observation and the lane number change observation are respectively generated based on at least one of the road edge data, the lane line data, the lane number data and the odometer data, including: generating the lane observation based on the road edge data and the lane line data; generating the lane change observation based on the lane line data or based on the odometer data; and generating the lane number change observation based on the lane number data and the lane line data; the lane observation is generated based on the road edge data and the lane line data, including: generating a single-side lane observation if the road edge data is single-side road edge data, generating a double-side lane observation if the road edge data is double-side road edge data, and not generating the lane observation otherwise; and the lane in which the vehicle currently locates is positioned, including: taking the lane with the probability greater than the preset threshold as the lane in which the vehicle currently locates.

4. A vehicle characterized by comprising: The lane positioning method comprises: A memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the lane positioning method according to any one of claims 1-2.

5. A computer readable storage medium having stored thereon a computer program or instructions, characterized in that, The computer program or instructions are executed by the processor to implement the lane positioning method according to any one of claims 1-2.

6. A computer program product comprising computer programs or instructions, characterized in that, The computer program or instructions are executed to implement the lane positioning method according to any one of claims 1-2. The computer program or instructions are executed to implement the lane positioning method according to any one of claims 1-2.

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