Method for determining mapping range and mapping system

Abstract

The invention discloses a method for determining a mapping range and a mapping system, and is applied to the technical field of automatic driving. The method comprises the steps of obtaining map information, wherein the map information comprises at least one piece of lane information including a current lane where a vehicle is located; determining lane information of the current lane based on the map information and a first position coordinate, the first position coordinate being determined based on positioning information generated by a positioning system of the vehicle; determining the offset of the vehicle relative to the current lane; determining a second position coordinate of the vehicle based on the lane information of the current lane and the offset; under the condition that the first position coordinate is matched with the second position coordinate, determining the type of the current lane based on the lane information of the current lane; and under the condition that the type of the current lane is the target type road section, determining that the current lane is located in the mapping range. Whether the current lane is the target type road section or not can be accurately recognized, then whether the current lane needs mapping or not is determined, and the mapping accuracy and effectiveness are improved.

Description

Technical Field

[0001] This application belongs to the field of autonomous driving technology, and in particular relates to a method for determining the mapping range and a mapping system. Background Technology

[0002] In the field of autonomous driving, internal roads refer to transitional roads connecting closed areas (such as parking lots) with public roads, and are a type of target road segment. Internal roads typically lack unified planning and design, making real-time perception quite difficult. Due to the limited resources (such as computing power and time costs) available for map building in the cloud or on-device, accurately defining boundaries is crucial.

[0003] If the boundary is too large, a large number of non-core areas will be included in the mapping scope, resulting in ineffective consumption of resources and reduced mapping efficiency. If the boundary is too small, key road sections or potential risk areas may be missed, resulting in incomplete map coverage, which will make it difficult to meet the positioning and navigation needs of autonomous vehicles driving on internal roads, and ultimately reduce the user's autonomous driving experience. Summary of the Invention

[0004] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a method and system for determining the mapping range, which can accurately identify whether the current lane is located on a target type road segment, thereby determining whether the current lane needs mapping, improving the accuracy and effectiveness of mapping, and thus enhancing the user's autonomous driving experience.

[0005] Firstly, this application provides a method for determining the mapping scope, including: Obtain map information, which includes at least one lane information, including the current lane in which the vehicle is located; Based on the map information and the first location coordinates, the lane information of the current lane is determined, wherein the first location coordinates are determined based on the positioning information generated by the vehicle's own positioning system; Determine the offset of the vehicle relative to the current lane, wherein the offset represents the ratio of the distance the vehicle has traveled in the current lane to the length of the lane centerline of the current lane; Based on the lane information of the current lane and the offset, the second position coordinates of the vehicle are determined; If the first position coordinates match the second position coordinates, the type of the current lane is determined based on the lane information of the current lane. The type of the current lane includes a first type of road and a second type of road with different types. If the current lane is a target type road segment, it is determined that the current lane is located within the mapping range, and the target type road segment is either the first type road or the second type road.

[0006] Secondly, this application provides a mapping system including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned method for determining the mapping range.

[0007] This application provides a method and system for determining the mapping range. It acquires map information including at least one lane, such as the current lane of a vehicle, and uses the vehicle's own positioning system to generate positioning information to obtain first position coordinates. From this, the current lane information is obtained. Since the offset represents the ratio of the vehicle's travel distance in the current lane to the length of the lane's centerline, the vehicle's relative position in the current lane is determined. Combined with the known lane information, the vehicle's specific position in the map information, i.e., second position coordinates, can be determined. Then, by comparing the vehicle's first and second position coordinates, it is determined whether they match, thereby confirming the accuracy of the lane information in the map.

[0008] If the first and second location coordinates match, it indicates that the lane information for the current lane in the map is accurate. Therefore, the lane type contained in the current lane information is taken as the current lane type. Based on the current lane type, it is then used to accurately identify whether the current lane is located on a target type road segment and whether detailed mapping is needed. The current lane type includes different types of roads: Type 1 and Type 2. If the current lane type is a target type road segment (Type 1 or Type 2), it is determined that the current lane is within the mapping area and detailed mapping is required to meet the driving needs of autonomous driving on the target type road segment, thereby improving the safety, reliability, and user experience of autonomous driving.

[0009] Additional aspects and advantages of embodiments 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 embodiments of this application. Attached Figure Description

[0010] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is an application scenario diagram of the method for determining the mapping range provided in the embodiments of this application; Figure 2 This is a schematic diagram of the first process of the method for determining the mapping range provided in the embodiments of this application; Figure 3 This is a schematic diagram of the second process of the method for determining the mapping range provided in the embodiments of this application; Figure 4 This is a schematic diagram of the third process of the method for determining the mapping range provided in the embodiments of this application; Figure 5 This is a schematic diagram illustrating the determination of the second position coordinates in the method for determining the mapping range provided in this application embodiment; Figure 6 This is a schematic diagram illustrating a scenario of the method for determining the mapping range provided in an embodiment of this application; Figure 7 This is a schematic diagram of the module of the mapping range determination device provided in the embodiments of this application; Figure 8 This is a first structural schematic diagram of the mapping system provided in the embodiments of this application; Figure 9 This is a second structural schematic diagram of the mapping system provided in the embodiments of this application. Detailed Implementation

[0011] The embodiments of this application are described in detail below. Examples of the embodiments of this application 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 are only used to explain this application, and should not be construed as limiting this application.

[0012] In the description of this application, it should be understood that 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. Therefore, features defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0013] In view of the problems existing in the background art, this application provides a method and system for determining the mapping range.

[0014] Please see Figure 1 , Figure 1 This diagram illustrates an application scenario of the mapping range determination method provided in this application. The application scenario includes a mapping system 100, which comprises a vehicle 10, a server 20, the current lane, and a network. The mapping range determination method provided in this application can be executed collaboratively by the vehicle 10 and the server 20.

[0015] In this embodiment, vehicle 10 is located in the current lane (driving or parked), and vehicle 10 can establish a communication connection with server 20 via a network. Vehicle 10 can upload real-time data (such as location, speed, energy consumption, etc.) to server 20, and can also obtain service data (such as high-precision maps, real-time traffic conditions, etc.) issued by server 20. The network can include, but is not limited to, wireless local area networks (WLAN), local area networks (LAN), cellular networks, 4G networks, 5G networks, satellite communication networks, etc., and this embodiment does not limit the types of networks used.

[0016] Vehicle 10 includes a positioning system and an antenna. The positioning system is an intelligent system that can obtain the real-time geographical location of vehicle 10. Through the positioning system, vehicle 10 can obtain specific geographical location information of its location in real time. Vehicle 10 receives and transmits wireless signals through the antenna to communicate and interact with server 20.

[0017] Optionally, the positioning system may include, but is not limited to: satellite positioning systems (such as BeiDou Navigation Satellite System, Global Positioning System GPS), inertial positioning systems, integrated positioning systems, etc., and the antenna may include, but is not limited to: radio frequency antennas, cellular communication antennas, global navigation satellite system navigation antennas (i.e., GNSS navigation antennas), etc., and the embodiments of this application do not limit this.

[0018] Among them, server 20 is a remote server cluster and supporting service system supported by cloud computing technology. Server 20 communicates and interacts with vehicle 10 through the network, and can store a large amount of data generated by vehicle 10 (such as real-time data from sensors in vehicle 10, driving trajectory, fault logs, etc.), avoiding insufficient storage space in vehicle 10; server 20 can also optimize the energy consumption of vehicle 10 and provide early warning of potential faults of vehicle 10 through big data analysis.

[0019] The server 20 can communicate and interact with the vehicle 10 in real time. The server 20 can provide the vehicle 10 (such as an autonomous vehicle 10) with beyond-line-of-sight information (such as real-time road conditions ahead, traffic light status, etc.) to make up for the perception limitations of the vehicle 10's sensors and improve driving safety and stability.

[0020] The server 20 can be a server. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms. This application embodiment does not limit this.

[0021] Based on the above description of the relevant scenarios, this application provides a method for determining the mapping range. The method for determining the mapping range is described in detail below: Please see Figure 2 The method for determining the mapping range provided in this application embodiment is implemented by steps 011, 012, 013, 014, 015 and 016, which are described in detail below.

[0022] Step 011: Obtain map information, which includes at least one lane information, including the current lane where the vehicle is located; Step 012: Based on map information and the first location coordinates, determine the lane information of the current lane. The first location coordinates are determined based on the positioning information generated by the vehicle's own positioning system. Among these, map information is a digital collection of roads and their surrounding environment; the current lane is the lane the vehicle is in; and lane information is a collection of data directly related to the lane. Lane information includes at least the geographical location of the lane and the type of lane.

[0023] Specifically, map information can be stored on the server and / or in the vehicle. It can be obtained by communicating and interacting with the server, or it can be read from the map information stored locally in the vehicle. Both the server and / or the vehicle can determine the vehicle's first position coordinates based on the vehicle's own positioning information, and then combine the first position coordinates with the map information to determine the lane in the map information, and use that lane as the current lane.

[0024] In one alternative embodiment, please refer to Figure 3 Step 012 includes: Step 0121: Obtain the lane identifier of the current lane where the vehicle is located from the map information based on the first location coordinates; Step 0122: Construct road network data based on map information; Step 0123: Obtain lane information that matches the lane sign from the road network data to serve as the lane information for the current lane.

[0025] Lane markings are unique identifiers assigned to individual lanes on a road within the map information, serving as the core index. Location information is data representing the real-time spatial position of a vehicle, generated by the vehicle's positioning system. Road network data is structured data describing road infrastructure within a defined area, including lane representations and corresponding lane information for each lane.

[0026] Specifically, a vehicle can obtain positioning information through its built-in positioning system, or by communicating with nearby communication base stations to determine its relative position and distance to these base stations (whose specific location coordinates are known). Alternatively, it can obtain positioning information by sensing the vehicle's motion state through its built-in sensors (gyroscope, inertial measurement unit, accelerometer, etc.). The coordinate information from this positioning information is used as the primary position coordinates.

[0027] The vehicle transmits its first location coordinates to the server. The server combines the first location coordinates with map information (such as pre-stored on the server or obtained in real time from a relevant database), and uses a positioning matching algorithm (such as distance matching, track matching, etc.) to determine the lane that matches the first location coordinates in the map information and designates it as the current lane. Then, the server extracts the lane identifier of the current lane and sends it back to the vehicle. Optionally, the vehicle can also retrieve the lane identifier of the current lane from the map information based on the first location coordinates.

[0028] Meanwhile, the vehicle-side system will structure the map information, transform it into searchable road network data, and combine it with the vehicle's location information to define the surrounding area and extract the road network data within that area.

[0029] In one alternative embodiment, please refer to Figure 4 Step 0123 includes: Step 01231: Based on location information and road network data, construct R-tree data near the vehicle; Step 01232: Traverse the R-tree data to obtain the lane information that matches the lane identifier, and use it as the lane information for the current lane.

[0030] Among them, R-tree is a spatial index data structure designed specifically for querying multidimensional spatial data (such as map coordinates, graphic boundaries, etc.).

[0031] Specifically, R-trees are used to transform the road network data generated by the vehicle into R-tree data to achieve fast retrieval and adapt to real-time requirements. The construction process of R-tree data is as follows: Based on the road network data near the vehicle, all lanes are extracted, and each lane is bounded by a minimum bounding box (e.g., set as the first bounding box); then every N (at least 2, such as 3) of the nearest first bounding boxes are reconstructed into a larger bounding box (e.g., set as the second bounding box), and bounding boxes at the same level do not contain each other, while second bounding boxes can intersect; and any first bounding box is located within at least one second bounding box.

[0032] Then, the process is repeated, enclosing every N (at least 2, such as 3) nearest bounding boxes of the same level (e.g., all being second bounding boxes) with a larger bounding box; until finally, there are N or fewer maximum bounding boxes remaining, which completes the construction of the R-tree data. From the first bounding box to the maximum bounding box, the level of the bounding boxes increases sequentially.

[0033] Because R-tree data supports range queries (retrieving target data within a specified query range) and nearest neighbor queries (retrieving one or more data entities closest to a target point), a nearest neighbor query can be performed using the lane ID to determine which maximum bounding box the lane ID is located in. Then, the query proceeds downwards through the lower-level bounding boxes until the first bounding box is reached. The lane within that bounding box is the current lane, and thus, the lane information for the current lane can be obtained.

[0034] Thus, using an R-tree to determine the lane information of the current lane can reduce invalid queries and improve query efficiency.

[0035] Step 013: Determine the vehicle's offset relative to the current lane; Offset refers to the ratio of the distance a vehicle has traveled in the current lane to the length of the lane's centerline. For example, if the current lane is 500 meters long and the vehicle has traveled 200 meters from its starting point to its current position, the offset is 0.4. When the current lane is curved and / or sloped, the straight-line distance alone cannot accurately determine the vehicle's exact position. The offset, however, can determine the vehicle's percentage of travel progress within the current lane, thus allowing for a more accurate determination of its position.

[0036] Specifically, vehicle sensors (such as cameras and radar) can identify changes in lane markings and determine whether a vehicle has changed lanes. After a vehicle changes lanes, the distance the vehicle travels in the current lane at each moment can be recorded in real time based on the vehicle's travel time and speed. Based on the ratio of the travel distance to the length of the current lane, the vehicle's offset relative to the current lane can be calculated.

[0037] Step 014: Determine the vehicle's second position coordinates based on the lane information and offset of the current lane; The first location coordinate is the vehicle's precise location in the map information.

[0038] Specifically, the offset can determine the proportion of the vehicle's travel progress in the current lane, and thus accurately determine the vehicle's specific position in the current lane. Since the lane information of the current lane in the map information includes the specific position information of each position in the current lane, the corresponding position information of the vehicle, namely the first position coordinates, can be determined.

[0039] For example, by using a preset coordinate system or establishing a custom coordinate system, the relevant information of the current lane and the vehicle can be substituted into the coordinate system. The specific position information of each location in the current lane information corresponds to specific coordinate values. Based on the offset, the specific position of the vehicle in the current lane is known and determined, so the specific coordinates of the vehicle in this coordinate system are also determined. Then, by transforming the coordinates according to the correspondence between this coordinate system and the geographical location, the first position coordinates of the vehicle can be calculated.

[0040] In one alternative embodiment, please continue to refer to Figure 4 Step 014 includes: Step 0141: Map the offset to the center line of the current lane to obtain the mapped offset; Step 0142: Traverse from the starting point to the ending point of the lane centerline according to the preset step size to determine the first offset point that matches the mapped offset, and the second offset point before the first offset point. The distance between the first offset point and the starting point is greater than the mapped offset, and the distance between the second offset point and the starting point is less than the mapped offset. Step 0143: Based on the local coordinates of the second offset point and the offset vector formed by the first and second offset points, generate the second position coordinates of the vehicle, which include latitude and longitude coordinates.

[0041] The lane information includes the lane centerline of the current lane, which is a virtual curve representing the lane's geometry. The mapping offset is a precise value converted from a preset part of the vehicle (such as the antenna) to the arc length along the lane centerline. The preset step size is based on experience or user-defined length values, such as 5 meters, 2 meters, 1 meter, etc.

[0042] Specifically, the vehicle's offset relative to the current lane is converted into a mapped offset to adapt to the shape of the current lane's centerline, whether it is a straight line or a curve (such as a slope or a turn). Traversing from the starting point to the ending point of the lane centerline with a preset step size yields multiple traversal points. The distance between each traversal point and the starting point (i.e., the distance to be traveled) is known. Based on the distances between each traversal point and the starting point, as well as the mapped offset, the first offset point and the second offset point can be determined.

[0043] The specific local coordinates of the first and second offset points are known. The offset vector formed by the first and second offset points (e.g., the vector pointing from the second offset point to the first offset point) can be determined. Based on the distance between the first offset point and the starting point, the ratio of the magnitude of the vector formed by the second offset point and the vehicle to the magnitude of the offset vector can be determined (their directions may be the same or opposite). This allows the determination of the vehicle's second position coordinates. Considering that the current lane may include curves, inclines, and declines, the exact position of the vehicle can be accurately determined using the offset vector formed by the first and second offset points.

[0044] Please see Figure 5 Where OM is the lane centerline, point O is the starting point, and point M is the ending point; point A is the second offset point, point B is the first offset point, and point C is the vehicle's position. The offset vector is the vector starting from point A and ending at point B; the vector formed by the second offset point and the vehicle is the vector starting from point A and ending at point C. Vectors AB and AC have the same direction, and the ratio of their magnitudes can be calculated from the vehicle's offset relative to the current lane. Since the local coordinates of points A and B are known, the specific coordinates of each point on vector AB can be determined based on vector AB, and the coordinates of point C can be obtained from the ratio of the magnitudes of the two vectors. Combining the coordinates of point C and the lane information of the current lane, the vehicle's second position coordinates can be obtained.

[0045] Optionally, the specific coordinates of each traversed point in the current lane can be converted into coordinates in a preset coordinate system. Based on the relative relationship between the preset coordinate system and the coordinate system corresponding to the second position coordinates (such as a latitude and longitude coordinate system), a coordinate transformation formula is determined. By mapping the offset, the first offset point, and the second offset point, the coordinate values ​​of the vehicle in the preset coordinate system are determined. Finally, the transformation is performed according to the coordinate transformation formula to obtain the second position coordinates of the vehicle.

[0046] Optionally, the second position coordinates may also include, but are not limited to, spatial rectangular coordinates based on the Earth's centroid ellipsoid, Gauss-Kruger coordinates, Universal Transverse Mercator coordinates (UTM coordinates), etc. The second position coordinates are obtained according to the coordinate transformation formula between the preset coordinate system and the coordinate system corresponding to the first position coordinates.

[0047] Step 015: If the first position coordinates match the second position coordinates, determine the type of the current lane based on the lane information of the current lane; The current lane type includes different types of roads: Type 1 and Type 2.

[0048] Specifically, if the vehicle's first position coordinates match its second position coordinates (coordinate values ​​are equal or within an acceptable error range), it indicates that the lane information for the current lane is accurate, and the type of the current lane contained in the lane information is also considered accurate. Subsequent judgments are then made based on the type of the current lane.

[0049] In one optional embodiment, the first type of road is an internal road (such as roads within residential areas, schools, factories, etc.), and the second type of road is an external road (such as urban arterial roads, highways, etc.). Internal roads are areas in the map information where vehicle passage is restricted; external roads are areas in the map information where vehicle passage is not restricted; and the target type road segment is an internal road.

[0050] It's understandable that external roads are public roads, and their basic data has been collected in a standardized manner, with corresponding publicly available detailed maps. Therefore, there's no need for the server and / or vehicle to perform repeated detailed mapping; the data can be directly obtained to meet driving needs. Internal roads, on the other hand, are non-public roads, generally lack publicly available detailed maps, and have complex scenarios (such as narrow lanes for turning around, and mixed pedestrian and vehicle traffic). Therefore, the server and / or vehicle need to perform detailed mapping to provide an accurate baseline for autonomous driving.

[0051] In one optional embodiment, the internal road includes parking areas and non-parking areas, with the target type road segment being the non-parking area. Parking areas (such as parking lots or roadside parking spaces) provide parking space for vehicles; non-parking areas refer to passable road segments within the target type road segment, excluding parking areas. It is understood that when a vehicle is located in a parking area, it is parked and not moving, so road mapping is not required. When a vehicle is located in a non-parking area, it still needs to continue driving to enter a parking area or a non-target type road segment, therefore mapping is required to assist in the autonomous driving or driver assistance process.

[0052] In one alternative embodiment, please refer to Figure 3 or Figure 4 Step 015 includes: Step 0151: If the difference between the first and second position coordinates of the vehicle is less than a preset difference threshold, determine the type of the current lane based on the lane information.

[0053] The preset difference threshold is a value set based on experience.

[0054] Specifically, the vehicle's first and second position coordinates are unified into a single coordinate system (either the coordinate system corresponding to the first or second position coordinates). The difference between the two (such as Euclidean distance, geodetic distance, or coordinate differential) is the coordinate difference. If the coordinate difference is less than a preset difference threshold, the vehicle's first and second position coordinates are confirmed to match; if the coordinate difference is greater than or equal to the preset difference threshold, the vehicle's first and second position coordinates are confirmed to be mismatched.

[0055] Step 016: If the current lane type is the target type road segment, determine that the current lane is within the mapping range.

[0056] Among them, the target type road segment is the road segment that meets the driving needs of vehicles. The target type road segment is either a first type road or a second type road segment. For example, the first type road is an external road, and the second type road is an internal road.

[0057] Specifically, if the vehicle's first and second position coordinates match, it indicates that the lane information for the current lane is accurate. Therefore, the lane type contained in the lane information is taken as the current lane type. This allows for precise identification of whether the current lane is located on the target type road segment and whether detailed mapping is required. If the current lane type is the target type road segment, it is determined that the current lane is within the mapping area and detailed mapping is necessary.

[0058] Optionally, if the vehicle's first and second position coordinates do not match, indicating that the lane information is inaccurate, the current lane type is marked as unknown.

[0059] For example, please see Figure 6 When the target type road segment is an internal road, the road corresponding to the dark gray area is an external road, and the large black box is a specific area that is not publicly accessible (such as a residential area, school, etc.) that does not include external roads. The current lane is located in a specific area, and the black solid arrow in the current lane is the path of the vehicle. Figure 6 The vehicle shown in the diagram obtains lane information of the current lane, the vehicle's offset relative to the current lane, and the second position coordinates generated by the positioning system. Finally, it is determined that the current lane is an internal road, which is within the mapping range and requires high-precision mapping.

[0060] In this way, the mapping area can be accurately identified, effectively reducing the amount of unnecessary work in the mapping process, improving mapping efficiency, and thus improving the safety and reliability of autonomous driving.

[0061] In one alternative embodiment, please refer to Figure 3 or Figure 4 The method for determining the mapping range also includes step 017, which will be explained in detail below.

[0062] Step 017: Generate the target map within the mapping area based on the mapping data collected within the mapping area.

[0063] Mapping data is the core foundational data used to construct maps, including static information such as lane topology and lane parameters, as well as information about the surrounding environment of vehicles, such as moving objects and temporary road conditions. The target map is a high-precision map.

[0064] Specifically, once it is determined that the current lane is within the mapping range, the server can receive mapping data from the vehicle within the mapping range. Then, after preprocessing the mapping data (such as correcting errors), effective features (such as lane boundaries) are extracted. Based on the static information framework, information from the surrounding scene is overlaid, and a target map is generated using a preset algorithm (such as the NDT-ICP algorithm).

[0065] Optionally, a trained large model can be used to analyze images and other information about road conditions collected by the vehicle to determine whether the current lane is within the mapping area. If it is, a dynamic mapping process for the target map can then be implemented. The target map generated by the server can also be compared with historical maps within the mapping area to update details in the target map in real time, and the updated target map can be fed back to the vehicle.

[0066] In this way, by leveraging the computing power of the server, we can improve mapping efficiency and the accuracy of the target map, while reducing the computing load on the vehicle.

[0067] Based on the method described in the above embodiments, this application also provides a mapping range determination apparatus for performing the steps in the above mapping range determination method. Please refer to... Figure 7 , Figure 7 This is a schematic diagram of a mapping range determination device 200 provided in an embodiment of this application. The mapping range determination device 200 includes: The acquisition module 201 is used to acquire map information, which includes at least one lane information, including the current lane where the vehicle is located. The first determining module 202 is used to determine the lane information of the current lane based on map information and first position coordinates, wherein the first position coordinates are determined based on positioning information generated by the vehicle's own positioning system. The second determining module 203 is used to determine the offset of the vehicle relative to the current lane, wherein the offset represents the ratio of the distance the vehicle travels in the current lane to the length of the lane centerline of the current lane. The third determining module 204 is used to determine the first position coordinates of the vehicle based on the lane information and offset of the current lane; The fourth determining module 205, when determining that the first position coordinates and the second position coordinates match, determines the type of the current lane based on the lane information, wherein the second position coordinates are the positioning information generated by the vehicle's own positioning system, and the type of the current lane includes external road and internal road; The fifth determination module 206 is used to determine whether the current lane is located within the mapping range and whether the target type road segment is a first type road or a second type road when the current lane type is a target type road segment.

[0068] It should be noted that the specific details of each module unit in the above-mentioned mapping range determination device have been described in detail in the embodiments of the above-mentioned mapping range determination method, and will not be repeated here.

[0069] In the embodiments of this application, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0070] In one optional embodiment, the mapping range determination device in this application embodiment can be implemented in hardware, such as a mapping system or a component in the mapping system, such as an integrated circuit or a chip; the mapping range determination device can also be implemented in software, such as as an application installed in the mapping system.

[0071] This application also provides a mapping system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various processes of the above-described method for determining the mapping range and achieves the same technical effect. To avoid repetition, these will not be described again here.

[0072] In one alternative embodiment, please refer to Figure 8 , Figure 8 This is a schematic diagram of the structure of the mapping system provided in the embodiments of this application. The mapping system 300 includes a processor 301 and a memory 302. The memory 302 stores a computer program 303 that can run on the processor 301. When the computer program 303 is executed by the processor 301, it implements the various processes of the embodiments of the above-described method for determining the mapping range and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0073] In one alternative embodiment, the mapping system includes vehicles and servers. The vehicles and servers communicate and interact via a network to collaboratively determine the type of road segment and create high-precision maps.

[0074] The vehicle has communication and interaction capabilities and includes a positioning system, through which it can obtain its location information in real time. Optionally, the type of vehicle is not limited, and the positioning system may include, but is not limited to: satellite positioning systems (such as BeiDou Navigation Satellite System, Global Positioning System GPS), inertial positioning systems, and integrated positioning systems, etc., which are not limited in this application embodiment.

[0075] The server is a remote server supported by cloud computing technology, possessing powerful computing capabilities. The server communicates and interacts with the vehicle, providing it with real-time and diverse perception and map information to assist in driving and improve driving safety and stability.

[0076] Optionally, the server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms. This application embodiment does not limit this.

[0077] All of the above technical solutions can be combined in any way to form optional embodiments of this application, and will not be described in detail here.

[0078] In one alternative embodiment, please refer to Figure 9 , Figure 9 This is a schematic diagram of the mapping system provided in an embodiment of this application. The mapping system 300 includes a vehicle 310 and a server 320. The vehicle 310 and the server 320 work together to implement the various processes of the embodiments of the above-described method for determining the mapping range, and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0079] This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described method for determining the mapping range and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0080] The processor can be the processor in the mapping system described in the above embodiments. The computer-readable storage medium can be a computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, etc.

[0081] Computer-readable media can include computer storage media and communication media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented by any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media include RAM, ROM, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other solid-state storage technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape cassettes, magnetic tape, disk storage, or other magnetic storage devices. Of course, those skilled in the art will recognize that computer storage media are not limited to the above-mentioned types.

[0082] This application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the above-described method for determining the mapping range. The processor may be the processor in the mapping system described above. The various processes of the embodiments of the method for determining the mapping range, when executed by the processor, implement the above-described method and achieve the same technical effects; therefore, to avoid repetition, they will not be described again here.

[0083] It is understood that in the specific implementation of this application, data related to user identity or characteristics is involved. When the above embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0084] In the description of this specification, the references to terms such as "certain embodiments," "an alternative embodiment," and "exemplarily" indicate that a specific feature, structure, material, or characteristic described in connection with an 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.

[0085] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, 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 according to the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0086] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for determining the mapping range, characterized in that, include: Obtain map information, which includes at least one lane information, including the current lane in which the vehicle is located; Based on the map information and the first location coordinates, the lane information of the current lane is determined, wherein the first location coordinates are determined based on the positioning information generated by the vehicle's own positioning system; Determine the offset of the vehicle relative to the current lane, wherein the offset represents the ratio of the distance the vehicle has traveled in the current lane to the length of the lane centerline of the current lane; Based on the lane information of the current lane and the offset, the second position coordinates of the vehicle are determined; If the first position coordinates match the second position coordinates, the type of the current lane is determined based on the lane information of the current lane. The type of the current lane includes a first type of road and a second type of road with different types. If the current lane is a target type road segment, it is determined that the current lane is located within the mapping range, and the target type road segment is either the first type road or the second type road.

2. The determination method according to claim 1, characterized in that, The first type of road is an internal road, and the second type of road is an external road; the internal road is an area in the map information where vehicle passage is restricted; the external road is an area in the map information where vehicle passage is not restricted; the target type road segment is the internal road.

3. The determination method according to claim 2, characterized in that, The internal road includes parking areas and non-parking areas, and the target type road segment is the non-parking area.

4. The determination method according to claim 1, characterized in that, Determining the lane information of the current lane based on the map information and the first location coordinates includes: Based on the first location coordinates, the lane identifier of the current lane where the vehicle is located is obtained from the map information; Based on the map information, road network data is constructed; Lane information matching the lane identifier is obtained from the road network data and used as the lane information for the current lane.

5. The determination method according to claim 4, characterized in that, The step of obtaining lane information matching the lane identifier from the road network data as the lane information of the current lane includes: Based on the location information and the road network data, construct the R-tree data near the vehicle; Traverse the R-tree data to obtain the lane information that matches the lane identifier, and use it as the lane information for the current lane.

6. The determination method according to claim 1, characterized in that, The lane information includes the center line of the current lane. Determining the second position coordinates of the vehicle based on the lane information and the offset includes: The offset is mapped to the lane centerline of the current lane to obtain the mapped offset; Traverse from the starting point to the ending point of the lane centerline according to a preset step size to determine the first offset point that matches the mapping offset, and the second offset point preceding the first offset point. The distance between the first offset point and the starting point is greater than the mapping offset, and the distance between the second offset point and the starting point is less than the mapping offset. The second position coordinates are generated based on the local coordinates of the second offset point and the offset vector formed by the first offset point and the second offset point. The second position coordinates include latitude and longitude coordinates.

7. The determining method according to claim 1 or 6, characterized in that, When the first position coordinates match the second position coordinates generated by the vehicle, determining the type of the current lane based on the lane information includes: If the difference between the first position coordinates and the second position coordinates of the vehicle is less than a preset difference threshold, the type of the current lane is determined based on the lane information.

8. The determination method according to claim 1, characterized in that, Also includes: Based on the mapping data collected within the mapping area, a target map within the mapping area is generated.

9. A mapping system, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for determining the mapping range as described in any one of claims 1-8.

10. The mapping system according to claim 9, characterized in that, The mapping system includes vehicles and servers.

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