Methods, vehicles, storage media, and computer program products for generating a recommended lane

Abstract

The present disclosure provides a method, a vehicle, a storage medium and a computer program product for generating a recommended lane. The method comprises: identifying a target road structure scene, and determining a target road range; obtaining local lane information in the target road range from crowdsourcing data, wherein the local lane information is used to prompt a lane traffic direction and / or a road structure change; constructing a target lane-level topology relationship based on the local lane information; and generating a recommended lane for a target vehicle by using the target lane-level topology relationship and a navigation trajectory of the target vehicle. The present disclosure solves the technical problems of redundancy of crowdsourcing data and complexity of crowdsourcing data processing in the related art.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle data processing, and more specifically, to a method for generating recommended lanes, a vehicle, a storage medium, and a computer program product. Background Technology

[0002] In intelligent transportation systems and autonomous driving technologies, crowdsourced data is primarily used to build high-precision maps. This data contains detailed road geometry features and lane-level topological relationships to support accurate vehicle localization and route planning. This process typically involves extracting lane line information from large amounts of crowdsourced data, performing data fusion, alignment, and clustering, and finally fitting lane centerlines to construct a complete lane-level road topology model. However, existing lane-level information generation technologies based on crowdsourced data have the following main drawbacks:

[0003] (1) Crowdsourced data processing is complex: Existing technologies require the use of lane-level topology relationships across the entire road segment to generate recommended lanes. Therefore, the processing of crowdsourced data requires precise spatial alignment and clustering.

[0004] (2) Redundancy of crowdsourced data: Existing technologies focus on the spatial geometric position of lane lines, i.e., absolute position information, which increases the complexity of map data and the use of storage space.

[0005] There is currently no effective solution to the above problems. Summary of the Invention

[0006] This disclosure provides a method, vehicle, storage medium, and computer program product for generating recommended lanes, to at least solve the technical problems of crowdsourced data redundancy and complex crowdsourced data processing in related technologies for generating recommended lanes.

[0007] According to one embodiment of this disclosure, a method for generating recommended lanes is provided, comprising: identifying a target road structure scene and determining the target road range; obtaining local lane information within the target road range from crowdsourced data, wherein the local lane information is used to indicate lane travel direction and / or road structure changes; constructing a target lane-level topology relationship based on the local lane information; and generating recommended lanes for the target vehicle using the target lane-level topology relationship and the navigation trajectory of the target vehicle.

[0008] Optionally, the target road structure scenario includes one of the following: a road structure scenario with varying number of lanes; a road structure scenario with bifurcating and merging roads; or a road structure scenario at a road intersection.

[0009] Optionally, the crowdsourced data includes multiple data packets. Obtaining local lane information within the target road area from the crowdsourced data includes: extracting initial road data within the target road area from the multiple data packets; extracting local identification information from the initial road data and performing spatial location clustering on the local identification information to obtain the clustering results corresponding to each of the multiple data packets; performing data alignment on the clustering results corresponding to each of the multiple data packets to obtain data alignment results; and extracting local lane information from the data alignment results. The local lane information includes: category turning information of multiple sign groups within the target road area, where each sign group contains multiple directional signs along the road travel direction.

[0010] Optionally, constructing a target lane-level topology based on local lane information includes: establishing connections between multiple identifier groups contained in the local lane information to obtain an initial lane-level topology; filtering the initial lane-level topology based on preset filtering conditions to obtain a target lane-level topology, wherein the preset filtering conditions are determined based on road traffic conditions and / or lane crossing driving experience within the target road area.

[0011] Optionally, establishing connections between multiple sign groups contained in the local lane information to obtain the initial lane-level topology includes: using a fully connected approach, establishing connections between every two adjacent sign groups in the multiple sign groups contained in the local lane information to obtain the initial lane-level topology.

[0012] Optionally, generating recommended lanes for the target vehicle using the target lane-level topology and the target vehicle's navigation trajectory includes: selecting candidate signs from multiple indicator signs contained in multiple sign groups using the target lane-level topology and the target vehicle's navigation trajectory; determining candidate lanes based on the candidate signs; and generating recommended lanes for the target vehicle from the candidate lanes according to preset lane recommendation criteria, wherein the preset lane recommendation criteria are determined based on the smoothness of traffic flow and / or the flexibility of lane changing in the candidate lanes.

[0013] Optionally, the method for generating recommended lanes further includes: displaying candidate signs and recommended signs corresponding to recommended lanes using preset interface elements, wherein the preset interface elements are arranged in a grid, and multiple rows of grids in the grid arrangement correspond to multiple sign groups, each row of grids contains multiple cells, and the multiple cells contained in the same row of grids correspond to multiple indicator signs contained in the same sign group.

[0014] Optionally, displaying candidate icons and recommended lane icons using preset interface elements includes: displaying candidate icons using a first cell display method and displaying recommended icons using a second cell display method in the preset interface elements, wherein the first cell display method and the second cell display method use different special effects to distinguish different icon objects.

[0015] According to one embodiment of this disclosure, an apparatus for generating recommended lanes is also provided, comprising: an identification module for identifying a target road structure scene and determining the target road range; an acquisition module for acquiring local lane information within the target road range from crowdsourced data, wherein the local lane information is used to indicate lane travel direction and / or road structure changes; a construction module for constructing a target lane-level topology relationship based on the local lane information; and a generation module for generating recommended lanes for the target vehicle using the target lane-level topology relationship and the navigation trajectory of the target vehicle.

[0016] Optionally, the target road structure scenario includes one of the following: a road structure scenario with varying number of lanes; a road structure scenario with bifurcating and merging roads; or a road structure scenario at a road intersection.

[0017] Optionally, the acquisition module is further configured to: extract initial road data within the target road range from multiple data packets respectively; extract local identification information from the initial road data and perform spatial location clustering on the local identification information to obtain the clustering results corresponding to each of the multiple data packets; perform data alignment on the clustering results corresponding to each of the multiple data packets to obtain data alignment results; and extract local lane information from the data alignment results, wherein the local lane information includes: category turning information of multiple sign groups within the target road range, and each sign group contains multiple indicator signs along the road travel direction.

[0018] Optionally, the construction module is also used to: build connections between multiple identifier groups contained in the local lane information to obtain an initial lane-level topology; filter the initial lane-level topology based on preset filtering conditions to obtain a target lane-level topology, wherein the preset filtering conditions are determined based on the road traffic conditions and / or lane crossing driving experience within the target road area.

[0019] Optionally, the building module is also used to: construct connections between every two adjacent sign groups in the multiple sign groups contained in the local lane information using a fully connected approach, to obtain the initial lane-level topology.

[0020] Optionally, the generation module is further configured to: select candidate signs from multiple indicator signs contained in multiple sign groups using the target lane-level topology relationship and the navigation trajectory of the target vehicle; determine candidate lanes based on the candidate signs; and generate recommended lanes for the target vehicle from the candidate lanes according to preset lane recommendation criteria, wherein the preset lane recommendation criteria are determined based on the smoothness of traffic flow and / or the flexibility of lane changing in the candidate lanes.

[0021] Optionally, the device for generating recommended lanes further includes a display module for displaying candidate signs and recommended signs corresponding to recommended lanes using preset interface elements. The preset interface elements are arranged in a grid, where multiple rows of grids correspond to multiple sign groups, each row of grids contains multiple cells, and the multiple cells in the same row of grids correspond to multiple indicator signs in the same sign group.

[0022] Optionally, the display module is also used to: display candidate icons in a first cell display mode and recommend icons in a second cell display mode among preset interface elements, wherein the first cell display mode and the second cell display mode use different special effects to distinguish different icon objects.

[0023] According to one embodiment of this disclosure, an electronic device is also provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this disclosure when it runs.

[0024] According to another aspect of the embodiments of the present disclosure, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to perform the methods of the various embodiments of the present disclosure.

[0025] According to another aspect of the embodiments of this disclosure, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of this disclosure.

[0026] According to another aspect of the embodiments of this disclosure, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods in various embodiments of this disclosure.

[0027] In this embodiment, the method involves identifying the target road structure scene, determining the target road range, and obtaining local lane information within the target road range from crowdsourced data. Based on the local lane information, a target lane-level topology relationship is constructed. Then, using the target lane-level topology relationship and the navigation trajectory of the target vehicle, a recommended lane is generated for the target vehicle. By utilizing the local sparse features and local lane information in the crowdsourced data to generate recommended lanes for the target vehicle, the goal of generating recommended lanes quickly and efficiently is achieved. This simplifies lane line information and reduces the complexity of the lane line processing process, thereby solving the technical problems of crowdsourced data redundancy and complex crowdsourced data processing in related technologies for generating recommended lanes. Attached Figure Description

[0028] The accompanying drawings, which are included to provide a further understanding of this disclosure and form part of this application, illustrate exemplary embodiments of this disclosure and are used to explain this disclosure, but do not constitute an undue limitation of this disclosure. In the drawings:

[0029] Figure 1 This is a hardware structure diagram of a data processing method according to one embodiment of the present disclosure;

[0030] Figure 2 This is a flowchart of a method for generating recommended lanes according to one embodiment of the present disclosure;

[0031] Figure 3 This is a schematic diagram of a method for generating recommended lanes according to one embodiment of the present disclosure;

[0032] Figure 4A This is a schematic diagram of another method for generating recommended lanes according to one embodiment of the present disclosure;

[0033] Figure 4B This is a schematic diagram of another method for generating recommended lanes according to one embodiment of the present disclosure;

[0034] Figure 4C This is a schematic diagram of another method for generating recommended lanes according to one embodiment of the present disclosure;

[0035] Figure 5A This is a schematic diagram of another method for generating recommended lanes according to one embodiment of the present disclosure;

[0036] Figure 5B This is a schematic diagram of another method for generating recommended lanes according to one embodiment of the present disclosure;

[0037] Figure 6A This is a schematic diagram of another method for generating recommended lanes according to one embodiment of the present disclosure;

[0038] Figure 6B This is a schematic diagram of another method for generating recommended lanes according to one embodiment of the present disclosure;

[0039] Figure 7A This is a schematic diagram of another method for generating recommended lanes according to one embodiment of the present disclosure;

[0040] Figure 7B This is a schematic diagram of another method for generating recommended lanes according to one embodiment of the present disclosure;

[0041] Figure 8 This is a structural block diagram of a recommended lane generation device according to one embodiment of the present disclosure. Detailed Implementation

[0042] To enable those skilled in the art to better understand the present disclosure, the technical solutions of the present disclosure will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present disclosure, and not all embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present disclosure.

[0043] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0044] According to an embodiment of this disclosure, a method embodiment for generating recommended lanes is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0045] This method embodiment can be executed in an electronic device or similar computing device that includes memory and a processor. Taking a computer terminal as an example, the computer terminal may include one or more processors (processors may include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), digital signal processing (DSP) chips, microcontroller units (MCUs), field-programmable gate arrays (FPGAs), neural network processors (NPUs), tensor processors (TPUs), artificial intelligence (AI) type processors, etc.) and memory for storing data. Optionally, the computer terminal may also include transmission devices, input / output devices, and display devices for communication functions. Those skilled in the art will understand that the above structural description is merely illustrative and does not limit the structure of the computer terminal. For example, the computer terminal may include more or fewer components than described above, or have a different configuration than described above.

[0046] The memory can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the method for generating recommended lanes in this embodiment of the disclosure. The processor executes various functional applications and data processing by running the computer program stored in the memory, thereby implementing the aforementioned method for generating recommended lanes. The memory may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0047] The transmission device is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the mobile terminal's communication provider. In one example, the transmission device includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0048] Display devices can be, for example, touchscreen liquid crystal displays (LCDs) and touch displays (also referred to as "touchscreens" or "touch displays"). The LCD allows users to interact with the user interface of the mobile terminal. In some embodiments, the mobile terminal has a graphical user interface (GUI), which allows users to interact with the GUI through finger contact and / or gestures on a touch-sensitive surface. Optional human-computer interaction functions include: creating web pages, drawing, word processing, creating electronic documents, playing games, video conferencing, instant messaging, sending and receiving emails, call interfaces, playing digital video, playing digital music, and / or web browsing, etc. Executable instructions for performing the above human-computer interaction functions are configured / stored in one or more processor-executable computer program products or readable storage media.

[0049] This application provides a method for generating recommended lanes. This method can be used to provide lane recommendation functionality for preset application scenarios. These preset application scenarios may include the following scenarios in the vehicle field: autonomous driving scenarios for commuting, AI-powered driver assistance scenarios for family cars, automatic parking assistance (APA) scenarios (such as memory parking for self-owned parking spaces in garages, intelligent parking for designated parking spaces in parking lots, etc.), and navigation-guided pilot (NGP) scenarios in urban or highway areas. Furthermore, these preset application scenarios may also include, but are not limited to: driving scenarios for intelligent driving trucks or unmanned trucks in the logistics and transportation field, driving scenarios for autonomous agricultural vehicles in the agricultural machinery field, path planning scenarios for drones, and path planning scenarios for intelligent robots (such as cleaning robots, service robots, delivery robots, etc.).

[0050] When the aforementioned preset application scenario is a scenario in a field other than the vehicle field, those skilled in the art should understand that the vehicles in the above method for generating recommended lanes can be replaced with other objects (such as agricultural machinery, drones, robots, etc.). Based on this, this application embodiment takes the field of vehicle technology as an example to illustrate the specific implementation of the above method for generating recommended lanes.

[0051] Figure 1 This is a hardware structure diagram of a data processing method according to one embodiment of the present disclosure, such as... Figure 1 As shown, the hardware structure includes: terminal equipment, vehicle, and roadside equipment.

[0052] For example, terminal devices typically refer to intelligent devices involved in communication, including but not limited to the target vehicle itself, mobile positioning terminals (such as smartphones, smart wearable devices, and in-vehicle communication systems of other vehicles), etc. These devices collect environmental data through built-in sensors or through user interaction, and then transmit the collected data to a cloud server or the target vehicle's in-vehicle control unit via a network for analysis and processing. The network includes, but is not limited to, cellular networks (such as 4G, 5G), Wi-Fi, Bluetooth, and Vehicle-to-Everything (V2X) technologies, responsible for real-time data transmission to ensure that the vehicle can promptly receive location information and status data shared by other vehicles, pedestrians, roadside equipment, etc. Roadside equipment refers to devices installed on the side of the road for monitoring and sensing the road environment, including but not limited to radar, cameras, infrared sensors, weather sensors, traffic light controllers, etc. Data collected by roadside equipment is transmitted to the vehicle or cloud server via a network for vehicle control and traffic management.

[0053] Furthermore, a driving scenario switching command can be issued via the terminal device and sent to the vehicle via the network. Upon receiving the driving scenario switching command from the terminal device, the vehicle sends an information subscription request to the roadside equipment. The roadside equipment receives the information subscription request from the vehicle and transmits the continuously collected roadside perception information back to the vehicle.

[0054] Figure 2 This is a flowchart of a method for generating recommended lanes according to one embodiment of the present disclosure, such as... Figure 2 As shown, the method includes the following steps:

[0055] Step S20: Identify the target road structure scene and determine the target road range;

[0056] Step S22: Obtain local lane information within the target road area from crowdsourced data, wherein the local lane information is used to indicate lane travel direction and / or changes in road structure;

[0057] The aforementioned crowdsourced data is used to characterize road information collected by numerous drivers or vehicles during driving using onboard sensors, smartphones, and other devices. This crowdsourced data includes, but is not limited to: vehicle location (real-time vehicle location obtained via GPS or other positioning technologies); vehicle trajectory (data recording vehicle driving paths, which can be used to analyze common routes and driving patterns); lane line information (lane line image data captured by onboard cameras or LiDAR sensors, used to identify lane structure and boundaries); traffic signs and signals (including speed limit signs, no-entry signs, directional arrows, etc.); and road conditions (such as road surface quality, traffic congestion, and construction areas).

[0058] Step S24: Construct the target lane-level topology based on local lane information;

[0059] The aforementioned local lane information is used to characterize the category of turning information indicated by the arrows within the target road area of ​​the lane. The category of turning information indicated by the arrows is used to indicate changes in the lane's travel direction and / or road structure.

[0060] For example, Figure 3 This is a schematic diagram of a method for generating recommended lanes according to one embodiment of the present disclosure, such as... Figure 3 As shown, according to the driving direction from right to left, there are three sets of arrows, namely 301, 302 and 303, which are local lane information. The arrows are sorted from bottom to top, and the turning information of each set of arrows is as follows: (1) 301: left turn, straight, straight, straight right turn, right turn; (2) 302: left turn, left turn, straight, straight, straight, right turn; (3) 303: left turn, left turn, straight, straight, straight, right turn.

[0061] Step S26: Using the target lane-level topology and the target vehicle's navigation trajectory, generate a recommended lane for the target vehicle.

[0062] The navigation trajectory of the target vehicle mentioned above is used to characterize the driving trajectory provided to the target vehicle by the navigation system.

[0063] For example, firstly, the target road structure scene for which recommended lanes need to be generated is identified from the entire road network, and the target road range is determined based on the target road structure scene. The target road structure scene can be identified using techniques such as pattern recognition, image analysis, or location-based event detection. Then, local lane information for that road segment is extracted from crowdsourced data. Based on this local lane information, marker groups within the lanes are identified, and the spatial relationships between these marker groups are determined to construct the target lane-level topology. Finally, recommended lanes are generated by combining the target lane-level topology and the navigation trajectory of the target vehicle. Lanes that align with the target vehicle's direction of travel, have good traffic conditions, and offer flexible lane-changing are selected as candidate lanes, and the optimal lane is chosen as the recommended lane based on preset lane recommendation criteria.

[0064] Based on steps S20 to S26 above, the method involves identifying the target road structure scene, determining the target road range, and obtaining local lane information within the target road range from crowdsourced data. A target lane-level topology is constructed based on this local lane information. Then, using the target lane-level topology and the target vehicle's navigation trajectory, a recommended lane is generated for the target vehicle. By utilizing the local sparse features and local lane information in the crowdsourced data to generate recommended lanes, the method achieves the goal of generating recommended lanes quickly and efficiently. This simplifies lane line information and reduces the complexity of lane line processing, thereby solving the technical problems of crowdsourced data redundancy and complex crowdsourced data processing in related technologies for generating recommended lanes.

[0065] Optionally, the target road structure scenario includes one of the following:

[0066] Road structure scenarios with varying lane numbers;

[0067] The road structure scenario described above, which involves changes in the number of lanes, is used to characterize how the number of lanes on certain road segments in a road network can increase or decrease due to changes in design requirements or road functions.

[0068] For example, increases in the number of lanes typically occur at transitions from low-density to high-density areas, or at locations where one-way roads become two-way roads. Adding lanes aims to increase road capacity, disperse traffic flow, and reduce congestion, especially on roads leading into city centers or near transportation hubs such as train stations and airports.

[0069] For example, lane reductions are commonly seen when roads transition from wide to narrow sections, or when lanes are reduced to make way for specific facilities such as bus stops or bike lanes. Lane reductions can lead to traffic merging and increase the need for drivers to change lanes.

[0070] For example, Figure 4AThis is a schematic diagram of another method for generating recommended lanes according to one embodiment of the present disclosure, illustrating a road structure scenario with varying lane numbers. Figure 4A As shown, from Figure 4A As can be seen from the direction of the arrows, the number of lanes decreases in the direction of the arrows.

[0071] Road structure scenarios where roads branch and merge;

[0072] The road structure scenario described above, where roads branch and merge, is used to characterize locations in a road network where vehicle travel paths separate or merge. Road branching is used to characterize a road splitting into two or more roads at a certain point, while road merging is used to characterize two or more roads merging into one road at a certain point.

[0073] For example, Figure 4B This is a schematic diagram of another method for generating recommended lanes according to one embodiment of the present disclosure, illustrating a road structure scenario where roads branch and merge. Figure 4B As shown, following the direction of the solid arrow, it can be seen that the forked roads converge; following the direction of the dashed arrow, it can be seen that the forked roads branch off.

[0074] Road structure scene at a road intersection.

[0075] The aforementioned road intersections are used to represent locations where two or more roads meet. Road structure scenarios at road intersections include, but are not limited to, the following types:

[0076] (1) Crossroads: Four roads intersect at right angles, usually with lanes in four directions, each of which may have a straight lane, a left-turn lane and a right-turn lane.

[0077] (2) T-junction: Two roads form a "T" shape intersection. Vehicles in one direction have the option to go straight or turn, while vehicles in the other direction usually can only go straight.

[0078] (3) Five-way intersection: Five roads intersect, forming a more complex traffic structure than a crossroads. Drivers need to choose the correct direction of travel based on road signs and traffic light signals.

[0079] (4) Roundabout: In this type of intersection design, vehicles travel around the central island and leave the roundabout through the exit lane to enter other roads. The lanes inside the roundabout are usually more complex and need to follow specific rules.

[0080] For example, Figure 4C This is a schematic diagram of another method for generating recommended lanes according to one embodiment of the present disclosure, illustrating a crossroads in a road structure scenario. For example... Figure 4C As shown, the four roads intersect at right angles and typically have lanes in four directions, each of which may have a straight lane, a left-turn lane, and a right-turn lane.

[0081] Optionally, in step S22, the crowdsourced data includes: multiple data packets, and obtaining local lane information within the target road area from the crowdsourced data includes:

[0082] Step S221: Extract initial road data within the target road range from multiple data packets;

[0083] The aforementioned data packets contain individual crowdsourced data provided by the target vehicle within the target road area and other crowdsourced data provided by surrounding vehicles.

[0084] Step S222: Extract local identification information from the initial road data, and perform spatial location clustering on the local identification information to obtain the clustering results corresponding to each of the multiple data packets;

[0085] The aforementioned local marking information is used to characterize the arrow marking information in the lane, wherein the arrow marking information in the lane can indicate the direction of travel and / or changes in the road structure.

[0086] For example, firstly, initial road data within the target road area is extracted from each crowdsourced data set. This initial road data includes, but is not limited to, information such as arrow markers, traffic signs, and lane lines. Then, arrow marker information is extracted from the initial road data. The arrow markers in each data set are identified and extracted, including their direction (left turn, straight, right turn, etc.) and their location in the image. Next, spatial location clustering is performed on the local marker information. The extracted arrow markers are clustered according to their actual spatial location on the map. Even if the image positions or angles of arrow markers differ slightly in different data sets, as long as their locations on the map are close, they will be grouped into the same group, thereby identifying the main arrow markers on each lane, i.e., the clustering results corresponding to each of the multiple data sets.

[0087] Step S223: Perform data alignment on the clustering results corresponding to each of the multiple data packets to obtain the data alignment result;

[0088] For example, lane line-assisted alignment can be used to ensure that the position and direction information of all arrow markers correspond to each other, reducing inconsistencies caused by factors such as vehicle angle, speed, or sensor accuracy. This involves aligning the lane lines within the arrow range with the clustering results of multiple data packets to obtain the data alignment result. Alternatively, the coordinates of arrow markers in different data packets can be transformed into a common reference coordinate system for comparison and matching. By comparing arrow markers in the same lane across different data packets and matching their spatial location and steering information, the positions of the arrow markers are calibrated to ensure consistent representation on the map. For instance, if a data packet is missing some arrows, it can be supplemented by comparing and adding arrow information from adjacent data packets, thus achieving data calibration.

[0089] Step S224: Extract local lane information from the data alignment result. The local lane information includes: category turning information of multiple sign groups within the target road area. Each sign group contains multiple indicator signs along the road travel direction.

[0090] For example, the arrow markers in the data alignment results are parsed to extract the lane's travel direction information. The sequence of arrow markers on each lane is examined to identify consecutive arrow groups, which are then arranged along the road's travel direction to indicate the lane's travel direction. For each identified arrow group, category turning information is extracted, namely the specific type of arrow marker (left turn, straight, right turn, etc.) and the turning direction.

[0091] For example, with Figure 3 Taking the arrow group information within box 301 as an example, if the perception is complete, it would be left turn, straight, straight, straight right turn, right turn. However, due to occlusion or other reasons, the result of a single perception may be incomplete, inevitably leading to missing arrows. One perception result might be left turn, straight, straight, straight right turn; another perception result might be straight, straight right turn, right turn. Then, by using the position and type, we can still obtain left turn, straight, straight, straight right turn, right turn. If lane lines are needed for alignment assistance, the lane lines within the arrow range can be cropped, such as... Figure 3 Lane lines enclosed by boxes 301, 302, and 303. After crowdsourced data alignment, a set of arrow information is directly extracted, that is, the category and turning information of each arrow from top to bottom along the direction of travel on the road are identified.

[0092] Based on the above optional embodiments, initial road data within the target road range is extracted from multiple data packets; local identification information is extracted from the initial road data, and spatial location clustering is performed on the local identification information to obtain the clustering results corresponding to each of the multiple data packets; data alignment is performed on the clustering results corresponding to each of the multiple data packets to obtain data alignment results; local lane information is extracted from the data alignment results. This method can comprehensively utilize crowdsourced data from different vehicles, increasing the diversity and coverage of the data, improving the accuracy and completeness of information extraction, and performing spatial location clustering on the extracted local identification information can identify arrow markings on the lanes and distinguish changes in different lanes or road structures, reducing information confusion caused by vehicle position, angle, or sensor errors, and improving data reliability.

[0093] Optionally, in step S24, constructing the target lane-level topology based on local lane information includes:

[0094] Step S241: Establish connections between multiple identifier groups contained in the local lane information to obtain the initial lane-level topology.

[0095] For example, the connection relationships between the extracted multiple groups of identifiers (including multiple arrow identifiers along the road travel direction, such as left turn, straight, and right turn arrows) are transformed into a topology structure to form an initial lane-level topology relationship.

[0096] Step S242: Based on preset filtering conditions, the initial lane-level topology is filtered to obtain the target lane-level topology. The preset filtering conditions are determined based on the road traffic conditions and / or lane crossing driving experience within the target road area.

[0097] The preset filtering conditions mentioned above include, but are not limited to:

[0098] (1) Road traffic rules: Consider the solid and dashed nature of lane lines, such as not being able to change lanes by crossing solid lines.

[0099] (2) Driving experience: Avoid changing lanes across multiple lanes in a row, as this not only increases the difficulty of driving but also reduces driving safety.

[0100] For example, since the initially constructed lane-level topology may contain connections that do not conform to actual road conditions or driving experience, these connections need to be filtered to ensure that the final lane-level topology is reasonable and practical. Examples include lane changes across solid lines and consecutive lane changes across multiple lanes. By applying preset filtering conditions, unreasonable connections will be deleted or corrected, and the remaining connections will constitute the target lane-level topology, which is closer to real road conditions.

[0101] Based on the above optional embodiments, connections are constructed between multiple identifier groups contained in the local lane information to obtain an initial lane-level topology. Based on preset filtering conditions, the initial lane-level topology is filtered to obtain a target lane-level topology. By constructing the target lane-level topology, the connection and turning relationships between lanes can be represented more intuitively, thereby simplifying the processing of crowdsourced data. Furthermore, by applying preset filtering conditions to filter the initial lane-level topology, it is ensured that the final target lane-level topology is more consistent with actual road traffic conditions and driving experience, reducing the problem of inaccurate lane connections caused by crowdsourced data redundancy or errors, and improving the accuracy of recommended lanes.

[0102] Optionally, in step S241, establishing connections between multiple identifier groups included in the local lane information to obtain the initial lane-level topology includes:

[0103] Step S401: Using a fully connected approach, a connection is constructed between every two adjacent sign groups in the multiple sign groups contained in the local lane information to obtain the initial lane-level topology.

[0104] For example, along the direction of travel on the road, based on two adjacent groups of markers (containing multiple arrow markers along the direction of travel, such as left turn, straight, and right turn arrows), the connection relationship between the arrows is generated, i.e., the initial lane-level topology relationship. In this process, a fully connected approach is used, that is, one arrow from the preceding group can be connected to every arrow in the following group to obtain the initial lane-level topology relationship.

[0105] For example, Figure 5A This is a schematic diagram of another method for generating recommended lanes according to one embodiment of the present disclosure, as shown below. Figure 5A As shown, in Figure 3The three groups of identifiers 301, 302, and 303 are connected to form the initial lane-level topology. A fully connected approach is used, establishing connections between 301 and 302, and between 302 and 303. Taking the straight arrow 102 in 301 as an example, this arrow is connected to each arrow in 302, with different colors representing the possible connections between the arrows. For example, green represents the most likely, yellow represents feasible but with user experience issues, red represents continuous lane crossings prohibited, and black represents completely prohibited. The corresponding connection relationships between the straight arrow in 301 and each arrow in 302 are as follows: 102 to 201: continuous lane crossing prohibited; 102 to 202: feasible but with user experience issues; 102 to 203: most likely; 102 to 204: feasible but with user experience issues; 102 to 205: continuous lane crossing prohibited; 102 to 206: completely prohibited. Using the same method, connections are established between 302 and 303. Taking the straight arrow 203 in 302 as an example, this arrow is connected to each arrow in 303, and different colors are used to indicate the possible connection relationships between the arrows. The connection relationships between the straight arrow in 302 and each arrow in 303 are as follows: 203 and 401: continuous lane crossing is prohibited; 203 and 402: feasible but with user experience issues; 203 and 403: most likely; 203 and 404: feasible but with user experience issues; 203 and 405: continuous lane crossing is prohibited; 203 and 406: completely prohibited.

[0106] For example, since the initially constructed lane-level topology may contain some connections that do not conform to actual road traffic conditions or driving experience, these connections need to be filtered to ensure that the final lane-level topology is reasonable and practical. Figure 5B This is a schematic diagram of another method for generating recommended lanes according to one embodiment of the present disclosure, as shown below. Figure 5B As shown, in Figure 5A Based on the initial lane-level topology, the initial lane-level topology is filtered using preset filtering conditions to obtain the target lane-level topology. Figure 5B The arrows shown indicate the most likely connections between the arrows in the three groups of identifiers 301, 302, and 303. Specifically: 101-201-401; 101-202-402; 102-203-403; 103-204-404; 104-205-305; 104-206-306; 105-206-306.

[0107] Based on the above optional embodiments, a fully connected approach is adopted to establish a connection between every two adjacent sign groups in the multiple sign groups contained in the local lane information, thereby obtaining the initial lane-level topology. The fully connected approach can quickly establish a connection between adjacent sign groups without complex logical judgments or additional data processing steps, which speeds up the construction of the initial lane-level topology and makes the entire processing flow more efficient.

[0108] Optionally, in step S26, generating a recommended lane for the target vehicle using the target lane-level topology and the target vehicle's navigation trajectory includes:

[0109] Step S261: Using the target lane-level topology and the navigation trajectory of the target vehicle, candidate signs are selected from multiple indicator signs contained in multiple sign groups respectively.

[0110] For example, the navigation trajectory of the target vehicle is analyzed, i.e., the direction the target vehicle is about to travel (such as turning left, going straight, or turning right). Then, from the target lane-level topology, all indicator signs (arrow signs) that match the direction of the navigation trajectory are found, and these signs are used as candidate signs for subsequent lane selection.

[0111] Step S262: Determine candidate lanes based on candidate identifiers;

[0112] The candidate lanes mentioned above are used to represent one or more lanes that the navigation system provides for the target vehicle to use.

[0113] For example, once candidate lane identifiers are identified, lanes containing these candidate lane identifiers are identified based on lane-level topology. These lanes are then designated as candidate lanes. During this process, all possible candidate lanes are considered to prepare for the next step of lane recommendation.

[0114] Step S263: Generate a recommended lane for the target vehicle from the candidate lanes according to the preset lane recommendation criteria, wherein the preset lane recommendation criteria are determined based on the smoothness of traffic flow and / or the flexibility of lane changing in the candidate lanes.

[0115] The aforementioned preset lane recommendation criteria include, but are not limited to: recommending a centrally located lane to increase the flexibility of the target vehicle, and recommending a smooth-flowing road to improve the driving experience.

[0116] For example, a candidate lane is selected from the candidate lanes as the recommended lane for the target vehicle based on preset lane recommendation criteria. These criteria consider lane flow smoothness and lane-changing flexibility, aiming to provide the best driving experience. For instance, a centrally located lane is prioritized, as this typically increases vehicle maneuverability at intersections. Furthermore, lanes where vehicles can pass smoothly may be prioritized, avoiding lanes that may experience traffic congestion or poor driving conditions.

[0117] Based on the above optional embodiments, candidate signs are selected from multiple indicator signs included in multiple sign groups by utilizing the target lane-level topology relationship and the navigation trajectory of the target vehicle; candidate lanes are determined based on the candidate signs; and recommended lanes are generated for the target vehicle from the candidate lanes according to the preset lane recommendation criteria. This not only makes full use of the local lane information in the crowdsourced data, but also combines the actual driving needs of the vehicle, making the selection of recommended lanes more accurate and efficient.

[0118] Alternatively, the method for generating recommended lanes also includes:

[0119] Step S27: Display candidate signs and recommended lane signs corresponding to recommended lanes using preset interface elements. The preset interface elements are arranged in a grid. Multiple rows of grids in the grid arrangement correspond to multiple sign groups. Each row of grids contains multiple cells. The multiple cells in the same row of grids correspond to multiple indicator signs in the same sign group.

[0120] Specifically, this refers to the current situation or the availability of lanes on the road at a certain distance, determined by the navigation direction. Figure 6A This is a schematic diagram of another method for generating recommended lanes according to one embodiment of the present disclosure. Figure 5B Taking the arrow information in 301 and 302 as an example, since the arrow information in 302 and 303 is the same, we will use the arrows in 301 and 302 as examples. For the arrow information in 301, a grid arrangement is used to display the arrow information in 301. Multiple rows of grids in this arrangement correspond to the arrow information in 301, and each row contains multiple cells. The multiple cells in the same row correspond to multiple indicator signs contained in 301. The arrow information in 301, along the road travel direction, from left to right (101-105), is: left turn - straight - straight - straight and right turn - right turn. For the left-turn navigation trajectory, the corresponding candidate indicator is the leftmost left-turn arrow indicator 101; for the straight navigation trajectory, the corresponding candidate indicators are the three middle straight arrow indicators 102-104; for the right-turn navigation trajectory, the corresponding candidate indicator is the rightmost right-turn arrow indicator 105. Therefore, the candidate indicators generated for the corresponding left-turn, straight, and right-turn navigation trajectories are as follows: Figure 6A As shown, arrows indicate multiple indicator icons contained in the same icon group, and hollow arrows indicate candidate icon information.

[0121] For example, Figure 6B This is a schematic diagram of another method for generating recommended lanes according to one embodiment of the present disclosure. Figure 6BCandidate identifiers generated from the arrows in time 302. The arrow information in time 302 is displayed using a grid arrangement. Multiple rows of grids correspond to the arrow information in time 302, and each row contains multiple cells. The multiple cells within the same row correspond to multiple indicator identifiers within time 302. Along the road travel direction, from left to right, 201-206 are: left turn - left turn - straight - straight - straight - right turn. For the left-turn navigation trajectory, the corresponding candidate identifiers are the left-turn arrows 201-202; for the straight navigation trajectory, the corresponding candidate identifiers are the three middle straight arrows 203-205; for the right-turn navigation trajectory, the corresponding candidate identifier is the rightmost right-turn arrow 206. Therefore, the candidate identifiers generated for the left-turn, straight, and right-turn navigation trajectories are as follows: Figure 6B As shown, arrows indicate multiple indicator icons contained in the same icon group, and hollow arrows indicate candidate icon information.

[0122] For example, the recommendation identifier is determined based on the candidate identifier, and the recommendation identifier is highlighted.

[0123] Based on the above optional embodiments, preset interface elements are used to display the candidate signs and the recommended signs corresponding to the recommended lanes, so that users can see the candidate signs for each lane and turn at a glance. This intuitive display method helps users understand and make decisions more quickly.

[0124] Optionally, in step S27, displaying the candidate identifier and the recommended identifier corresponding to the recommended lane using preset interface elements includes:

[0125] Step S271: In the preset interface elements, candidate icons are displayed using the first cell display method, and recommended icons are displayed using the second cell display method. The first cell display method and the second cell display method use different special effects to distinguish different icon objects.

[0126] For example, different effects are used in preset interface elements to distinguish between candidate icons and recommended icons, allowing users to intuitively see which icons are recommended and which are only candidate icons. For instance, effects such as color, borders, and backgrounds can be used to differentiate between candidate and recommended icons.

[0127] For example, the recommended lane is derived from candidate lanes based on crowdsourced data statistics and real-world driving experience. For a given navigation direction, if there is only one candidate lane, that lane is necessarily the only recommended lane. If there are multiple candidate lanes for a given navigation direction, a candidate lane is selected as the recommended lane for the target vehicle based on preset lane recommendation criteria. Based on these preset lane recommendation criteria... Figure 6A The recommended lanes for each navigation direction are as follows: Figure 7A As shown, Figure 6B The recommended lanes for each navigation direction are as follows: Figure 7B As shown, recommended lane markings and candidate lane markings are distinguished by different effects to remind drivers.

[0128] For example, Figure 7A This is a schematic diagram of another method for generating recommended lanes according to one embodiment of the present disclosure. Figure 6A Taking the arrow icon information in the diagram as an example, when the navigation direction is left turn, it will... Figure 7A The first cell in the first row will be highlighted; when the navigation direction is straight, it will... Figure 7A The third cell in the second row will be highlighted; when the navigation direction is right, it will... Figure 7A The fifth cell in the third row is highlighted. Figure 7A The Chinese and Israeli systems use a black background to indicate a highlight.

[0129] For example, Figure 7B This is a schematic diagram of another method for generating recommended lanes according to one embodiment of the present disclosure. Figure 6B Taking the arrow icon information in the diagram as an example, when the navigation direction is left turn, it will... Figure 7B The second cell in the first row will be highlighted; when the navigation direction is straight, it will... Figure 7B The fourth cell in the second row will be highlighted; when the navigation direction is right, it will... Figure 7B The sixth cell in the third row is highlighted. Figure 7B The Chinese and Israeli systems use a black background to indicate a highlight.

[0130] Based on the above optional embodiments, in the preset interface elements, candidate icons are displayed using a first cell display method, and recommendation icons are displayed using a second cell display method. By using different special effects to distinguish between candidate icons and recommendation icons, users can intuitively see the recommendation icons and candidate representations, which helps users make decisions quickly.

[0131] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0132] According to an embodiment of this disclosure, an apparatus for generating recommended lanes is provided. It should be noted that the apparatus can be used to execute the method for generating recommended lanes described above.

[0133] Figure 8 This is a structural block diagram of a recommended lane generation device according to one embodiment of the present disclosure, such as... Figure 8 As shown, the device includes:

[0134] The recognition module 801 is used to recognize the target road structure scene and determine the target road range;

[0135] The acquisition module 802 is used to acquire local lane information within the target road area from crowdsourced data, wherein the local lane information is used to indicate lane travel direction and / or changes in road structure;

[0136] Module 803 is used to construct the target lane-level topology based on local lane information;

[0137] The generation module 804 is used to generate recommended lanes for the target vehicle by utilizing the target lane-level topology relationship and the navigation trajectory of the target vehicle.

[0138] Optionally, the target road structure scenario includes one of the following: a road structure scenario with varying number of lanes; a road structure scenario with bifurcating and merging roads; or a road structure scenario at a road intersection.

[0139] Optionally, the acquisition module 802 is further configured to: extract initial road data within the target road range from multiple data packets respectively; extract local identification information from the initial road data and perform spatial location clustering on the local identification information to obtain the clustering results corresponding to each of the multiple data packets; perform data alignment on the clustering results corresponding to each of the multiple data packets to obtain data alignment results; and extract local lane information from the data alignment results, wherein the local lane information includes: category turning information of multiple sign groups within the target road range, and each of the multiple sign groups contains multiple indicator signs along the road travel direction.

[0140] Optionally, the construction module 803 is further configured to: construct connections between multiple identifier groups contained in the local lane information to obtain an initial lane-level topology; and filter the initial lane-level topology based on preset filtering conditions to obtain a target lane-level topology, wherein the preset filtering conditions are determined based on the road traffic conditions and / or lane crossing driving experience within the target road area.

[0141] Optionally, the construction module 803 is also used to: construct a connection between every two adjacent sign groups in the multiple sign groups contained in the local lane information using a fully connected method to obtain an initial lane-level topology.

[0142] Optionally, the generation module 804 is further configured to: select candidate signs from multiple indicator signs included in multiple sign groups using the target lane-level topology relationship and the navigation trajectory of the target vehicle; determine candidate lanes based on the candidate signs; and generate recommended lanes for the target vehicle from the candidate lanes according to preset lane recommendation criteria, wherein the preset lane recommendation criteria are determined based on the smoothness of traffic flow and / or the flexibility of lane changing in the candidate lanes.

[0143] Optionally, the device for generating recommended lanes further includes a display module 805, which displays candidate signs and recommended signs corresponding to recommended lanes using preset interface elements. The preset interface elements are arranged in a grid, and multiple rows of grids in the grid arrangement correspond to multiple sign groups. Each row of grids contains multiple cells, and the multiple cells contained in the same row of grids correspond to multiple indicator signs contained in the same sign group.

[0144] Optionally, the display module 805 is also used to display candidate icons in a first cell display mode and recommended icons in a second cell display mode in preset interface elements, wherein the first cell display mode and the second cell display mode use different special effects to distinguish different icon objects.

[0145] Embodiments of this application also provide a vehicle, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods described in various embodiments of this disclosure when it runs.

[0146] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0147] Step S1: Identify the target road structure scene and determine the target road range;

[0148] Step S2: Obtain local lane information within the target road area from crowdsourced data, wherein the local lane information is used to indicate lane travel direction and / or changes in road structure;

[0149] Step S3: Construct the target lane-level topology based on local lane information;

[0150] Step S4: Using the target lane-level topology and the target vehicle's navigation trajectory, generate a recommended lane for the target vehicle.

[0151] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of the various embodiments of this disclosure.

[0152] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0153] Step S1: Identify the target road structure scene and determine the target road range;

[0154] Step S2: Obtain local lane information within the target road area from crowdsourced data, wherein the local lane information is used to indicate lane travel direction and / or changes in road structure;

[0155] Step S3: Construct the target lane-level topology based on local lane information;

[0156] Step S4: Using the target lane-level topology and the target vehicle's navigation trajectory, generate a recommended lane for the target vehicle.

[0157] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0158] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of this disclosure.

[0159] Optionally, in this embodiment, the above-mentioned computer program product can be configured as a computer program that performs the following steps:

[0160] Step S1: Identify the target road structure scene and determine the target road range;

[0161] Step S2: Obtain local lane information within the target road area from crowdsourced data, wherein the local lane information is used to indicate lane travel direction and / or changes in road structure;

[0162] Step S3: Construct the target lane-level topology based on local lane information;

[0163] Step S4: Using the target lane-level topology and the target vehicle's navigation trajectory, generate a recommended lane for the target vehicle.

[0164] In the above embodiments of this disclosure, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0165] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0166] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0167] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0168] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0169] The above description is only a preferred embodiment of this disclosure. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principles of this disclosure, and these improvements and modifications should also be considered within the scope of protection of this disclosure.

Claims

1. A method for generating recommended lanes, characterized in that, include: Identify the target road structure scene and determine the target road range; Initial road data within the target road area is extracted from multiple data packets contained in the crowdsourced data. Local identification information is extracted from the initial road data, and spatial location clustering is performed on the local identification information to obtain the clustering results corresponding to each of the multiple data packets; Lane lines within the local identification information range are extracted from the multiple data packets, and the clustering results corresponding to the multiple data packets are aligned using the lane lines to obtain data alignment results. Local lane information is extracted from the data alignment results. The local lane information is used to indicate lane travel direction and / or changes in road structure. The local lane information includes: category turning information of multiple sign groups within the target road area and travel direction of multiple lanes within the target road area. Each of the multiple sign groups contains multiple indicator signs along the road travel direction. The travel direction of the lane is obtained by arranging the multiple indicator signs along the road travel direction. Construct the target lane-level topology based on the local lane information; Using the target lane-level topology and the target vehicle's navigation trajectory, a recommended lane is generated for the target vehicle.

2. The method according to claim 1, characterized in that, The target road structure scenario includes one of the following: Road structure scenarios with varying lane numbers; Road structure scenarios where roads branch and merge; Road structure scene at a road intersection.

3. The method according to claim 1, characterized in that, Constructing the target lane-level topology based on the local lane information includes: A connection is established between the multiple identifier groups included in the local lane information to obtain an initial lane-level topology; The initial lane-level topology is filtered based on preset filtering conditions to obtain the target lane-level topology. The preset filtering conditions are determined based on the road traffic conditions and / or lane crossing driving experience within the target road area.

4. The method according to claim 3, characterized in that, Establishing connections among the multiple identifier groups included in the local lane information to obtain the initial lane-level topology includes: Using a fully connected approach, connections are established between every two adjacent sign groups in the plurality of sign groups included in the local lane information to obtain the initial lane-level topology.

5. The method according to claim 1, characterized in that, Generating the recommended lane for the target vehicle by utilizing the target lane-level topology and the navigation trajectory of the target vehicle includes: Using the target lane-level topology and the navigation trajectory of the target vehicle, candidate icons are selected from the multiple indicator icons included in the multiple icon groups respectively; Candidate lanes are determined based on the candidate identifiers; According to preset lane recommendation criteria, a recommended lane is generated for the target vehicle from the candidate lanes, wherein the preset lane recommendation criteria are determined based on the smoothness of traffic flow and / or the flexibility of lane changing in the candidate lanes.

6. The method according to claim 5, characterized in that, The method further includes: The candidate identifiers and the recommended identifiers corresponding to the recommended lanes are displayed using preset interface elements. The preset interface elements are arranged in a grid, and the multiple rows of grids in the grid arrangement correspond to the multiple identifier groups. Each row of grids contains multiple cells, and the multiple cells contained in the same row of grids correspond to the multiple indicator identifiers contained in the same identifier group.

7. The method according to claim 6, characterized in that, The recommended identifiers, which are displayed using the preset interface elements, include: In the preset interface elements, the candidate identifier is displayed using a first cell display method, and the recommended identifier is displayed using a second cell display method. The first cell display method and the second cell display method use different special effects to distinguish different identifier objects.

8. A vehicle, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method for generating recommended lanes as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the method for generating recommended lanes as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method for generating recommended lanes according to any one of claims 1 to 7.

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